karths/binary-10IQR-process · Datasets at Hugging Face

Unnamed: 0 int64 0 832k	id float64 2.49B 32.1B	type stringclasses 1 value	created_at stringlengths 19 19	repo stringlengths 7 112	repo_url stringlengths 36 141	action stringclasses 3 values	title stringlengths 1 744	labels stringlengths 4 574	body stringlengths 9 211k	index stringclasses 10 values	text_combine stringlengths 96 211k	label stringclasses 2 values	text stringlengths 96 188k	binary_label int64 0 1
684,040	23,404,251,696	IssuesEvent	2022-08-12 11:09:13	webcompat/web-bugs	https://api.github.com/repos/webcompat/web-bugs	closed	www.ancestry.com - design is broken	browser-firefox priority-normal engine-gecko	<!-- @browser: Firefox 103.0 --> <!-- @ua_header: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:103.0) Gecko/20100101 Firefox/103.0 --> <!-- @reported_with: unknown --> <!-- @public_url: https://github.com/webcompat/web-bugs/issues/109001 --> URL: https://www.ancestry.com/dna/insights Browser / Version: Firefox 103.0 Operating System: Windows 10 Tested Another Browser: Yes Edge Problem type: Design is broken Description: Images not loaded Steps to Reproduce: Pics dont load after Ffox upgrade <details> <summary>View the screenshot</summary> <img alt="Screenshot" src="https://webcompat.com/uploads/2022/8/2a62d5ff-4384-479f-a15b-53a3e147ebaf.jpeg"> </details> <details> <summary>Browser Configuration</summary> <ul> <li>None</li> </ul> </details> _From [webcompat.com](https://webcompat.com/) with ❤️_	1.0	www.ancestry.com - design is broken - <!-- @browser: Firefox 103.0 --> <!-- @ua_header: Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:103.0) Gecko/20100101 Firefox/103.0 --> <!-- @reported_with: unknown --> <!-- @public_url: https://github.com/webcompat/web-bugs/issues/109001 --> URL: https://www.ancestry.com/dna/insights Browser / Version: Firefox 103.0 Operating System: Windows 10 Tested Another Browser: Yes Edge Problem type: Design is broken Description: Images not loaded Steps to Reproduce: Pics dont load after Ffox upgrade <details> <summary>View the screenshot</summary> <img alt="Screenshot" src="https://webcompat.com/uploads/2022/8/2a62d5ff-4384-479f-a15b-53a3e147ebaf.jpeg"> </details> <details> <summary>Browser Configuration</summary> <ul> <li>None</li> </ul> </details> _From [webcompat.com](https://webcompat.com/) with ❤️_	non_process	design is broken url browser version firefox operating system windows tested another browser yes edge problem type design is broken description images not loaded steps to reproduce pics dont load after ffox upgrade view the screenshot img alt screenshot src browser configuration none from with ❤️	0
10,244	13,099,477,472	IssuesEvent	2020-08-03 21:41:52	GoogleCloudPlatform/python-docs-samples	https://api.github.com/repos/GoogleCloudPlatform/python-docs-samples	closed	Restrict noxfile	type: process	Currently we allow importing of a pytest.ini: https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/noxfile-template.py#L148-L159 we should restrict that to only let users ignore certain folders instead	1.0	Restrict noxfile - Currently we allow importing of a pytest.ini: https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/noxfile-template.py#L148-L159 we should restrict that to only let users ignore certain folders instead	process	restrict noxfile currently we allow importing of a pytest ini we should restrict that to only let users ignore certain folders instead	1
14,439	17,496,527,830	IssuesEvent	2021-08-10 01:36:33	emily-writes-poems/emily-writes-poems-scripts	https://api.github.com/repos/emily-writes-poems/emily-writes-poems-scripts	closed	don't process details unless poem exists in mongo	processing	check if poem exists in mongo before trying to update details	1.0	don't process details unless poem exists in mongo - check if poem exists in mongo before trying to update details	process	don t process details unless poem exists in mongo check if poem exists in mongo before trying to update details	1
9,900	12,906,360,476	IssuesEvent	2020-07-15 01:23:44	allinurl/goaccess	https://api.github.com/repos/allinurl/goaccess	closed	Filtering referrers out	command-line options log-processing question	How do hide-referer and ignore-referer work exactly? Are they matching against referrer domain or the whole URL? Does * match dots? Does ignore-referer exclude log line from all stats or just from referrer ones? I have this defined in config: ``` hide-referer .domain.com hide-referer domain.com ``` but I can still see domain.com, www.domain.com* and DOMAIN.COM (without protocol) in Referrers URLs pane. Domain is partially removed from Referring Sites pane, but domains like these are still there: ``` www.sub.domain.com sub2.domain2.tld.domain.com domain.com. ```	1.0	Filtering referrers out - How do hide-referer and ignore-referer work exactly? Are they matching against referrer domain or the whole URL? Does * match dots? Does ignore-referer exclude log line from all stats or just from referrer ones? I have this defined in config: ``` hide-referer .domain.com hide-referer domain.com ``` but I can still see domain.com, www.domain.com* and DOMAIN.COM (without protocol) in Referrers URLs pane. Domain is partially removed from Referring Sites pane, but domains like these are still there: ``` www.sub.domain.com sub2.domain2.tld.domain.com domain.com. ```	process	filtering referrers out how do hide referer and ignore referer work exactly are they matching against referrer domain or the whole url does match dots does ignore referer exclude log line from all stats or just from referrer ones i have this defined in config hide referer domain com hide referer domain com but i can still see domain com and domain com without protocol in referrers urls pane domain is partially removed from referring sites pane but domains like these are still there tld domain com domain com	1
333,640	24,383,900,757	IssuesEvent	2022-10-04 10:05:11	oleksandrblazhko/ai201-tsigankova	https://api.github.com/repos/oleksandrblazhko/ai201-tsigankova	opened	CW2	documentation	Завдання 1 1) Користувач проводить експерименти та формує прогноз погоди. 2) Офісний працівник працює. 3) Користувач пише технічні запитання. Завдання 2 1) Показник «Розподілена система» - значення 3. Мій ПП має середню завантаженість різними функціями та процесами. Лише дії, які можуть виконувати користувачі з певними даними, або офісні робітники з інформацією бази даних. 2) Показник «Спеціальні вимоги до навчання користувачів» - значення 3. Мій ПП вимагає від кожного типу користувачів певний рівень освіти, а саме середній та вище для науковців (з додатковим досвідом роботи), або середній для офісних робітників. Тобто більшість робітників повинні мати хоча б середній рівень освіти.	1.0	CW2 - Завдання 1 1) Користувач проводить експерименти та формує прогноз погоди. 2) Офісний працівник працює. 3) Користувач пише технічні запитання. Завдання 2 1) Показник «Розподілена система» - значення 3. Мій ПП має середню завантаженість різними функціями та процесами. Лише дії, які можуть виконувати користувачі з певними даними, або офісні робітники з інформацією бази даних. 2) Показник «Спеціальні вимоги до навчання користувачів» - значення 3. Мій ПП вимагає від кожного типу користувачів певний рівень освіти, а саме середній та вище для науковців (з додатковим досвідом роботи), або середній для офісних робітників. Тобто більшість робітників повинні мати хоча б середній рівень освіти.	non_process	завдання користувач проводить експерименти та формує прогноз погоди офісний працівник працює користувач пише технічні запитання завдання показник «розподілена система» значення мій пп має середню завантаженість різними функціями та процесами лише дії які можуть виконувати користувачі з певними даними або офісні робітники з інформацією бази даних показник «спеціальні вимоги до навчання користувачів» значення мій пп вимагає від кожного типу користувачів певний рівень освіти а саме середній та вище для науковців з додатковим досвідом роботи або середній для офісних робітників тобто більшість робітників повинні мати хоча б середній рівень освіти	0
21,270	28,441,964,718	IssuesEvent	2023-04-16 02:00:07	lizhihao6/get-daily-arxiv-noti	https://api.github.com/repos/lizhihao6/get-daily-arxiv-noti	opened	New submissions for Fri, 14 Apr 23	event camera white balance isp compression image signal processing image signal process raw raw image events camera color contrast events AWB	## Keyword: events ### Event-based tracking of human hands - Authors: Laura Duarte, Mohammad Safeea, Pedro Neto - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06534 - Pdf link: https://arxiv.org/pdf/2304.06534 - Abstract This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: event camera ### Neuromorphic Event-based Facial Expression Recognition - Authors: Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti, Andrea Leonardo, Sara Picchioni, Federico Becattini, Alberto Del Bimbo - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06351 - Pdf link: https://arxiv.org/pdf/2304.06351 - Abstract Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal. ### Event-based tracking of human hands - Authors: Laura Duarte, Mohammad Safeea, Pedro Neto - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06534 - Pdf link: https://arxiv.org/pdf/2304.06534 - Abstract This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - Authors: Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - Arxiv link: https://arxiv.org/abs/2304.06531 - Pdf link: https://arxiv.org/pdf/2304.06531 - Abstract 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ## Keyword: ISP ### CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input - Authors: Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Yurong Chen, Shunli Zhang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06454 - Pdf link: https://arxiv.org/pdf/2304.06454 - Abstract With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Learning-based Spatial and Angular Information Separation for Light Field Compression - Authors: Jinglei Shi, Yihong Xu, Christine Guillemot - Subjects: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV) - Arxiv link: https://arxiv.org/abs/2304.06322 - Pdf link: https://arxiv.org/pdf/2304.06322 - Abstract Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations. In this context, spatial information is defined as features that remain static regardless of perspectives, while angular information refers to features that vary between viewpoints. We propose a novel neural network that, by design, can separate angular and spatial information of a light field. The network represents spatial information using spatial kernels shared among all Sub-Aperture Images (SAIs), and angular information using sets of angular kernels for each SAI. To further improve the representation capability of the network without increasing parameter number, we also introduce angular kernel allocation and kernel tensor decomposition mechanisms. Extensive experiments demonstrate the benefits of information separation: when applied to the compression task, our network outperforms other state-of-the-art methods by a large margin. And angular information can be easily transferred to other scenes for rendering dense views, showing the successful separation and the potential use case for the view synthesis task. We plan to release the code upon acceptance of the paper to encourage further research on this topic. ### Learning Accurate Performance Predictors for Ultrafast Automated Model Compression - Authors: Ziwei Wang, Jiwen Lu, Han Xiao, Shengyu Liu, Jie Zhou - Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - Arxiv link: https://arxiv.org/abs/2304.06393 - Pdf link: https://arxiv.org/pdf/2304.06393 - Abstract In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost. ### DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos - Authors: Qi Zhao, M. Salman Asif, Zhan Ma - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06544 - Pdf link: https://arxiv.org/pdf/2304.06544 - Abstract Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for $960 \times 1920$ videos. ## Keyword: RAW ### TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval - Authors: Trung-Nghia Le, Tam V. Nguyen c, Minh-Quan Le, Trong-Thuan Nguyen, Viet-Tham Huynh, Trong-Le Do, Khanh-Duy Le, Mai-Khiem Tran, Nhat Hoang-Xuan, Thang-Long Nguyen-Ho, Vinh-Tiep Nguyen, Tuong-Nghiem Diep, Khanh-Duy Ho, Xuan-Hieu Nguyen, Thien-Phuc Tran, Tuan-Anh Yang, Kim-Phat Tran, Nhu-Vinh Hoang, Minh-Quang Nguyen, E-Ro Nguyen, Minh-Khoi Nguyen-Nhat, Tuan-An To, Trung-Truc Huynh-Le, Nham-Tan Nguyen, Hoang-Chau Luong, Truong Hoai Phong, Nhat-Quynh Le-Pham, Huu-Phuc Pham, Trong-Vu Hoang, Quang-Binh Nguyen, Hai-Dang Nguyen, Akihiro Sugimoto, Minh-Triet Tran - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06053 - Pdf link: https://arxiv.org/pdf/2304.06053 - Abstract 3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies. ### UniverSeg: Universal Medical Image Segmentation - Authors: Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John Guttag, Adrian V. Dalca - Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - Arxiv link: https://arxiv.org/abs/2304.06131 - Pdf link: https://arxiv.org/pdf/2304.06131 - Abstract While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu ### TransHP: Image Classification with Hierarchical Prompting - Authors: Wenhao Wang, Yifan Sun, Wei Li, Yi Yang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06385 - Pdf link: https://arxiv.org/pdf/2304.06385 - Abstract This paper explores a hierarchical prompting mechanism for the hierarchical image classification (HIC) task. Different from prior HIC methods, our hierarchical prompting is the first to explicitly inject ancestor-class information as a tokenized hint that benefits the descendant-class discrimination. We think it well imitates human visual recognition, i.e., humans may use the ancestor class as a prompt to draw focus on the subtle differences among descendant classes. We model this prompting mechanism into a Transformer with Hierarchical Prompting (TransHP). TransHP consists of three steps: 1) learning a set of prompt tokens to represent the coarse (ancestor) classes, 2) on-the-fly predicting the coarse class of the input image at an intermediate block, and 3) injecting the prompt token of the predicted coarse class into the intermediate feature. Though the parameters of TransHP maintain the same for all input images, the injected coarse-class prompt conditions (modifies) the subsequent feature extraction and encourages a dynamic focus on relatively subtle differences among the descendant classes. Extensive experiments show that TransHP improves image classification on accuracy (e.g., improving ViT-B/16 by +2.83% ImageNet classification accuracy), training data efficiency (e.g., +12.69% improvement under 10% ImageNet training data), and model explainability. Moreover, TransHP also performs favorably against prior HIC methods, showing that TransHP well exploits the hierarchical information. ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - Authors: Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - Arxiv link: https://arxiv.org/abs/2304.06531 - Pdf link: https://arxiv.org/pdf/2304.06531 - Abstract 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - Authors: Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06703 - Pdf link: https://arxiv.org/pdf/2304.06703 - Abstract Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. ### What does CLIP know about a red circle? Visual prompt engineering for VLMs - Authors: Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06712 - Pdf link: https://arxiv.org/pdf/2304.06712 - Abstract Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation. Despite that, their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models, such as GPT-3. Here we explore the idea of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text. In particular, we discover an emergent ability of CLIP, where, by simply drawing a red circle around an object, we can direct the model's attention to that region, while also maintaining global information. We show the power of this simple approach by achieving state-of-the-art in zero-shot referring expressions comprehension and strong performance in keypoint localization tasks. Finally, we draw attention to some potential ethical concerns of large language-vision models. ## Keyword: raw image ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - Authors: Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06703 - Pdf link: https://arxiv.org/pdf/2304.06703 - Abstract Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.	2.0	New submissions for Fri, 14 Apr 23 - ## Keyword: events ### Event-based tracking of human hands - Authors: Laura Duarte, Mohammad Safeea, Pedro Neto - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06534 - Pdf link: https://arxiv.org/pdf/2304.06534 - Abstract This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: event camera ### Neuromorphic Event-based Facial Expression Recognition - Authors: Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti, Andrea Leonardo, Sara Picchioni, Federico Becattini, Alberto Del Bimbo - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06351 - Pdf link: https://arxiv.org/pdf/2304.06351 - Abstract Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal. ### Event-based tracking of human hands - Authors: Laura Duarte, Mohammad Safeea, Pedro Neto - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06534 - Pdf link: https://arxiv.org/pdf/2304.06534 - Abstract This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - Authors: Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - Arxiv link: https://arxiv.org/abs/2304.06531 - Pdf link: https://arxiv.org/pdf/2304.06531 - Abstract 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ## Keyword: ISP ### CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input - Authors: Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Yurong Chen, Shunli Zhang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06454 - Pdf link: https://arxiv.org/pdf/2304.06454 - Abstract With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Learning-based Spatial and Angular Information Separation for Light Field Compression - Authors: Jinglei Shi, Yihong Xu, Christine Guillemot - Subjects: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV) - Arxiv link: https://arxiv.org/abs/2304.06322 - Pdf link: https://arxiv.org/pdf/2304.06322 - Abstract Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations. In this context, spatial information is defined as features that remain static regardless of perspectives, while angular information refers to features that vary between viewpoints. We propose a novel neural network that, by design, can separate angular and spatial information of a light field. The network represents spatial information using spatial kernels shared among all Sub-Aperture Images (SAIs), and angular information using sets of angular kernels for each SAI. To further improve the representation capability of the network without increasing parameter number, we also introduce angular kernel allocation and kernel tensor decomposition mechanisms. Extensive experiments demonstrate the benefits of information separation: when applied to the compression task, our network outperforms other state-of-the-art methods by a large margin. And angular information can be easily transferred to other scenes for rendering dense views, showing the successful separation and the potential use case for the view synthesis task. We plan to release the code upon acceptance of the paper to encourage further research on this topic. ### Learning Accurate Performance Predictors for Ultrafast Automated Model Compression - Authors: Ziwei Wang, Jiwen Lu, Han Xiao, Shengyu Liu, Jie Zhou - Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - Arxiv link: https://arxiv.org/abs/2304.06393 - Pdf link: https://arxiv.org/pdf/2304.06393 - Abstract In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost. ### DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos - Authors: Qi Zhao, M. Salman Asif, Zhan Ma - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06544 - Pdf link: https://arxiv.org/pdf/2304.06544 - Abstract Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for $960 \times 1920$ videos. ## Keyword: RAW ### TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval - Authors: Trung-Nghia Le, Tam V. Nguyen c, Minh-Quan Le, Trong-Thuan Nguyen, Viet-Tham Huynh, Trong-Le Do, Khanh-Duy Le, Mai-Khiem Tran, Nhat Hoang-Xuan, Thang-Long Nguyen-Ho, Vinh-Tiep Nguyen, Tuong-Nghiem Diep, Khanh-Duy Ho, Xuan-Hieu Nguyen, Thien-Phuc Tran, Tuan-Anh Yang, Kim-Phat Tran, Nhu-Vinh Hoang, Minh-Quang Nguyen, E-Ro Nguyen, Minh-Khoi Nguyen-Nhat, Tuan-An To, Trung-Truc Huynh-Le, Nham-Tan Nguyen, Hoang-Chau Luong, Truong Hoai Phong, Nhat-Quynh Le-Pham, Huu-Phuc Pham, Trong-Vu Hoang, Quang-Binh Nguyen, Hai-Dang Nguyen, Akihiro Sugimoto, Minh-Triet Tran - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06053 - Pdf link: https://arxiv.org/pdf/2304.06053 - Abstract 3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies. ### UniverSeg: Universal Medical Image Segmentation - Authors: Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John Guttag, Adrian V. Dalca - Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - Arxiv link: https://arxiv.org/abs/2304.06131 - Pdf link: https://arxiv.org/pdf/2304.06131 - Abstract While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu ### TransHP: Image Classification with Hierarchical Prompting - Authors: Wenhao Wang, Yifan Sun, Wei Li, Yi Yang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06385 - Pdf link: https://arxiv.org/pdf/2304.06385 - Abstract This paper explores a hierarchical prompting mechanism for the hierarchical image classification (HIC) task. Different from prior HIC methods, our hierarchical prompting is the first to explicitly inject ancestor-class information as a tokenized hint that benefits the descendant-class discrimination. We think it well imitates human visual recognition, i.e., humans may use the ancestor class as a prompt to draw focus on the subtle differences among descendant classes. We model this prompting mechanism into a Transformer with Hierarchical Prompting (TransHP). TransHP consists of three steps: 1) learning a set of prompt tokens to represent the coarse (ancestor) classes, 2) on-the-fly predicting the coarse class of the input image at an intermediate block, and 3) injecting the prompt token of the predicted coarse class into the intermediate feature. Though the parameters of TransHP maintain the same for all input images, the injected coarse-class prompt conditions (modifies) the subsequent feature extraction and encourages a dynamic focus on relatively subtle differences among the descendant classes. Extensive experiments show that TransHP improves image classification on accuracy (e.g., improving ViT-B/16 by +2.83% ImageNet classification accuracy), training data efficiency (e.g., +12.69% improvement under 10% ImageNet training data), and model explainability. Moreover, TransHP also performs favorably against prior HIC methods, showing that TransHP well exploits the hierarchical information. ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - Authors: Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - Arxiv link: https://arxiv.org/abs/2304.06531 - Pdf link: https://arxiv.org/pdf/2304.06531 - Abstract 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - Authors: Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06703 - Pdf link: https://arxiv.org/pdf/2304.06703 - Abstract Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. ### What does CLIP know about a red circle? Visual prompt engineering for VLMs - Authors: Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06712 - Pdf link: https://arxiv.org/pdf/2304.06712 - Abstract Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation. Despite that, their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models, such as GPT-3. Here we explore the idea of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text. In particular, we discover an emergent ability of CLIP, where, by simply drawing a red circle around an object, we can direct the model's attention to that region, while also maintaining global information. We show the power of this simple approach by achieving state-of-the-art in zero-shot referring expressions comprehension and strong performance in keypoint localization tasks. Finally, we draw attention to some potential ethical concerns of large language-vision models. ## Keyword: raw image ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - Authors: Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2304.06703 - Pdf link: https://arxiv.org/pdf/2304.06703 - Abstract Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.	process	new submissions for fri apr keyword events event based tracking of human hands authors laura duarte mohammad safeea pedro neto subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this paper proposes a novel method for human hands tracking using data from an event camera the event camera detects changes in brightness measuring motion with low latency no motion blur low power consumption and high dynamic range captured frames are analysed using lightweight algorithms reporting hand position data the chosen pick and place scenario serves as an example input for collaborative human robot interactions and in obstacle avoidance for human robot safety applications events data are pre processed into intensity frames the regions of interest roi are defined through object edge event activity reducing noise roi features are extracted for use in depth perception event based tracking of human hand demonstrated feasible in real time and at a low computational cost the proposed roi finding method reduces noise from intensity images achieving up to of data reduction in relation to the original while preserving the features the depth estimation error in relation to ground truth measured with wearables measured using dynamic time warping and using a single event camera is from to millimetres depending on the plane it is measured tracking of human hands in space using a single event camera data and lightweight algorithms to define roi features hands tracking in space keyword event camera neuromorphic event based facial expression recognition authors lorenzo berlincioni luca cultrera chiara albisani lisa cresti andrea leonardo sara picchioni federico becattini alberto del bimbo subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract recently event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution in this work we investigate the usage of such kind of data for emotion recognition by presenting nefer a dataset for neuromorphic event based facial expression recognition nefer is composed of paired rgb and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks we detail the data acquisition process as well as providing a baseline method for rgb and event data the collected data captures subtle micro expressions which are hard to spot with rgb data yet emerge in the event domain we report a double recognition accuracy for the event based approach proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal event based tracking of human hands authors laura duarte mohammad safeea pedro neto subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this paper proposes a novel method for human hands tracking using data from an event camera the event camera detects changes in brightness measuring motion with low latency no motion blur low power consumption and high dynamic range captured frames are analysed using lightweight algorithms reporting hand position data the chosen pick and place scenario serves as an example input for collaborative human robot interactions and in obstacle avoidance for human robot safety applications events data are pre processed into intensity frames the regions of interest roi are defined through object edge event activity reducing noise roi features are extracted for use in depth perception event based tracking of human hand demonstrated feasible in real time and at a low computational cost the proposed roi finding method reduces noise from intensity images achieving up to of data reduction in relation to the original while preserving the features the depth estimation error in relation to ground truth measured with wearables measured using dynamic time warping and using a single event camera is from to millimetres depending on the plane it is measured tracking of human hands in space using a single event camera data and lightweight algorithms to define roi features hands tracking in space keyword events camera there is no result keyword white balance there is no result keyword color contrast there is no result keyword awb sepicnet sharp edges recovery by parametric inference of curves in shapes authors kseniya cherenkova elona dupont anis kacem ilya arzhannikov gleb gusev djamila aouada subjects computer vision and pattern recognition cs cv graphics cs gr arxiv link pdf link abstract scanning as a technique to digitize objects in reality and create their models is used in many fields and areas though the quality of scans depends on the technical characteristics of the scanner the common drawback is the smoothing of fine details or the edges of an object we introduce sepicnet a novel deep network for the detection and parametrization of sharp edges in shapes as primitive curves to make the network end to end trainable we formulate the curve fitting in a differentiable manner we develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling the experiments were conducted on a newly introduced large scale dataset of scans where the sharp edge annotations were extracted from their parametric cad models and demonstrate significant improvement over state of the art methods keyword isp cabm content aware bit mapping for single image super resolution network with large input authors senmao tian ming lu jiaming liu yandong guo yurong chen shunli zhang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract with the development of high definition display devices the practical scenario of super resolution sr usually needs to super resolve large input like to higher resolution to reduce the computational and memory cost current methods first split the large input into local patches and then merge the sr patches into the output these methods adaptively allocate a subnet for each patch quantization is a very important technique for network acceleration and has been used to design the subnets current methods train an mlp bit selector to determine the propoer bit for each layer however they uniformly sample subnets for training making simple subnets overfitted and complicated subnets underfitted therefore the trained bit selector fails to determine the optimal bit apart from this the introduced bit selector brings additional cost to each layer of the sr network in this paper we propose a novel method named content aware bit mapping cabm which can remove the bit selector without any performance loss cabm also learns a bit selector for each layer during training after training we analyze the relation between the edge information of an input patch and the bit of each layer we observe that the edge information can be an effective metric for the selected bit therefore we design a strategy to build an edge to bit lookup table that maps the edge score of a patch to the bit of each layer during inference the bit configuration of sr network can be determined by the lookup tables of all layers our strategy can find better bit configuration resulting in more efficient mixed precision networks we conduct detailed experiments to demonstrate the generalization ability of our method the code will be released keyword image signal processing there is no result keyword image signal process there is no result keyword compression learning based spatial and angular information separation for light field compression authors jinglei shi yihong xu christine guillemot subjects computer vision and pattern recognition cs cv image and video processing eess iv arxiv link pdf link abstract light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations in this context spatial information is defined as features that remain static regardless of perspectives while angular information refers to features that vary between viewpoints we propose a novel neural network that by design can separate angular and spatial information of a light field the network represents spatial information using spatial kernels shared among all sub aperture images sais and angular information using sets of angular kernels for each sai to further improve the representation capability of the network without increasing parameter number we also introduce angular kernel allocation and kernel tensor decomposition mechanisms extensive experiments demonstrate the benefits of information separation when applied to the compression task our network outperforms other state of the art methods by a large margin and angular information can be easily transferred to other scenes for rendering dense views showing the successful separation and the potential use case for the view synthesis task we plan to release the code upon acceptance of the paper to encourage further research on this topic learning accurate performance predictors for ultrafast automated model compression authors ziwei wang jiwen lu han xiao shengyu liu jie zhou subjects computer vision and pattern recognition cs cv machine learning cs lg arxiv link pdf link abstract in this paper we propose an ultrafast automated model compression framework called seernet for flexible network deployment conventional non differen tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment they both cause heavy computational cost due to the complex compression policy search and evaluation process on the contrary we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation specifically we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search so that informative supervision is provided to learn the accurate performance predictor with acceptable cost then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy compared with the state of the art automated model compression methods experimental results on image classification and object detection show that our method achieves competitive accuracy complexity trade offs with significant reduction of the search cost dnerv modeling inherent dynamics via difference neural representation for videos authors qi zhao m salman asif zhan ma subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract existing implicit neural representation inr methods do not fully exploit spatiotemporal redundancies in videos index based inrs ignore the content specific spatial features and hybrid inrs ignore the contextual dependency on adjacent frames leading to poor modeling capability for scenes with large motion or dynamics we analyze this limitation from the perspective of function fitting and reveal the importance of frame difference to use explicit motion information we propose difference neural representation for videos dnerv which consists of two streams for content and frame difference we also introduce a collaborative content unit for effective feature fusion we test dnerv for video compression inpainting and interpolation dnerv achieves competitive results against the state of the art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for times videos keyword raw textanimar text based animal fine grained retrieval authors trung nghia le tam v nguyen c minh quan le trong thuan nguyen viet tham huynh trong le do khanh duy le mai khiem tran nhat hoang xuan thang long nguyen ho vinh tiep nguyen tuong nghiem diep khanh duy ho xuan hieu nguyen thien phuc tran tuan anh yang kim phat tran nhu vinh hoang minh quang nguyen e ro nguyen minh khoi nguyen nhat tuan an to trung truc huynh le nham tan nguyen hoang chau luong truong hoai phong nhat quynh le pham huu phuc pham trong vu hoang quang binh nguyen hai dang nguyen akihiro sugimoto minh triet tran subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract object retrieval is an important yet challenging task which has drawn more and more attention in recent years while existing approaches have made strides in addressing this issue they are often limited to restricted settings such as image and sketch queries which are often unfriendly interactions for common users in order to overcome these limitations this paper presents a novel shrec challenge track focusing on text based fine grained retrieval of animal models unlike previous shrec challenge tracks the proposed task is considerably more challenging requiring participants to develop innovative approaches to tackle the problem of text based retrieval despite the increased difficulty we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with objects five groups participated in our competition submitting a total of runs while the results obtained in our competition are satisfactory we note that the challenges presented by this task are far from being fully solved as such we provide insights into potential areas for future research and improvements we believe that we can help push the boundaries of object retrieval and facilitate more user friendly interactions via vision language technologies universeg universal medical image segmentation authors victor ion butoi jose javier gonzalez ortiz tianyu ma mert r sabuncu john guttag adrian v dalca subjects computer vision and pattern recognition cs cv machine learning cs lg arxiv link pdf link abstract while deep learning models have become the predominant method for medical image segmentation they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies image modalities or labels given a new segmentation task researchers generally have to train or fine tune models which is time consuming and poses a substantial barrier for clinical researchers who often lack the resources and expertise to train neural networks we present universeg a method for solving unseen medical segmentation tasks without additional training given a query image and example set of image label pairs that define a new segmentation task universeg employs a new cross block mechanism to produce accurate segmentation maps without the need for additional training to achieve generalization to new tasks we have gathered and standardized a collection of open access medical segmentation datasets with over scans which we refer to as megamedical we used this collection to train universeg on a diverse set of anatomies and imaging modalities we demonstrate that universeg substantially outperforms several related methods on unseen tasks and thoroughly analyze and draw insights about important aspects of the proposed system the universeg source code and model weights are freely available at transhp image classification with hierarchical prompting authors wenhao wang yifan sun wei li yi yang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this paper explores a hierarchical prompting mechanism for the hierarchical image classification hic task different from prior hic methods our hierarchical prompting is the first to explicitly inject ancestor class information as a tokenized hint that benefits the descendant class discrimination we think it well imitates human visual recognition i e humans may use the ancestor class as a prompt to draw focus on the subtle differences among descendant classes we model this prompting mechanism into a transformer with hierarchical prompting transhp transhp consists of three steps learning a set of prompt tokens to represent the coarse ancestor classes on the fly predicting the coarse class of the input image at an intermediate block and injecting the prompt token of the predicted coarse class into the intermediate feature though the parameters of transhp maintain the same for all input images the injected coarse class prompt conditions modifies the subsequent feature extraction and encourages a dynamic focus on relatively subtle differences among the descendant classes extensive experiments show that transhp improves image classification on accuracy e g improving vit b by imagenet classification accuracy training data efficiency e g improvement under imagenet training data and model explainability moreover transhp also performs favorably against prior hic methods showing that transhp well exploits the hierarchical information sepicnet sharp edges recovery by parametric inference of curves in shapes authors kseniya cherenkova elona dupont anis kacem ilya arzhannikov gleb gusev djamila aouada subjects computer vision and pattern recognition cs cv graphics cs gr arxiv link pdf link abstract scanning as a technique to digitize objects in reality and create their models is used in many fields and areas though the quality of scans depends on the technical characteristics of the scanner the common drawback is the smoothing of fine details or the edges of an object we introduce sepicnet a novel deep network for the detection and parametrization of sharp edges in shapes as primitive curves to make the network end to end trainable we formulate the curve fitting in a differentiable manner we develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling the experiments were conducted on a newly introduced large scale dataset of scans where the sharp edge annotations were extracted from their parametric cad models and demonstrate significant improvement over state of the art methods gated multi resolution transfer network for burst restoration and enhancement authors nancy mehta akshay dudhane subrahmanyam murala syed waqas zamir salman khan fahad shahbaz khan subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract burst image processing is becoming increasingly popular in recent years however it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts existing burst restoration methods usually do not consider the mutual correlation and non local contextual information among burst frames which tends to limit these approaches in challenging cases another key challenge lies in the robust up sampling of burst frames the existing up sampling methods cannot effectively utilize the advantages of single stage and progressive up sampling strategies with conventional and or recent up samplers at the same time to address these challenges we propose a novel gated multi resolution transfer network gmtnet to reconstruct a spatially precise high quality image from a burst of low quality raw images gmtnet consists of three modules optimized for burst processing tasks multi scale burst feature alignment mbfa for feature denoising and alignment transposed attention feature merging tafm for multi frame feature aggregation and resolution transfer feature up sampler rtfu to up scale merged features and construct a high quality output image detailed experimental analysis on five datasets validates our approach and sets a state of the art for burst super resolution burst denoising and low light burst enhancement what does clip know about a red circle visual prompt engineering for vlms authors aleksandar shtedritski christian rupprecht andrea vedaldi subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract large scale vision language models such as clip learn powerful image text representations that have found numerous applications from zero shot classification to text to image generation despite that their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models such as gpt here we explore the idea of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text in particular we discover an emergent ability of clip where by simply drawing a red circle around an object we can direct the model s attention to that region while also maintaining global information we show the power of this simple approach by achieving state of the art in zero shot referring expressions comprehension and strong performance in keypoint localization tasks finally we draw attention to some potential ethical concerns of large language vision models keyword raw image gated multi resolution transfer network for burst restoration and enhancement authors nancy mehta akshay dudhane subrahmanyam murala syed waqas zamir salman khan fahad shahbaz khan subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract burst image processing is becoming increasingly popular in recent years however it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts existing burst restoration methods usually do not consider the mutual correlation and non local contextual information among burst frames which tends to limit these approaches in challenging cases another key challenge lies in the robust up sampling of burst frames the existing up sampling methods cannot effectively utilize the advantages of single stage and progressive up sampling strategies with conventional and or recent up samplers at the same time to address these challenges we propose a novel gated multi resolution transfer network gmtnet to reconstruct a spatially precise high quality image from a burst of low quality raw images gmtnet consists of three modules optimized for burst processing tasks multi scale burst feature alignment mbfa for feature denoising and alignment transposed attention feature merging tafm for multi frame feature aggregation and resolution transfer feature up sampler rtfu to up scale merged features and construct a high quality output image detailed experimental analysis on five datasets validates our approach and sets a state of the art for burst super resolution burst denoising and low light burst enhancement	1
14,944	18,420,144,265	IssuesEvent	2021-10-13 15:17:49	python-trio/trio	https://api.github.com/repos/python-trio/trio	closed	Should we have a conventional way to "spawn a process into a nursery"?	subprocesses	We have `run_process`, which is a convenient way to run a process like it's a regular subroutine, with error checking (`check=`), cancellation support, automatic waiting, etc. And we have `trio.Process`, which is a fully general way to spawn a process and then do whatever with it. In the discussion in #872, @Badg [brought up](https://github.com/python-trio/trio/pull/872#discussion_r261434230) some dissatisfaction with both of these options. I think I mostly get the issue now, so I'll try to rephrase in my own words: In some ways, when a task spawns a subprocess it's very similar to spawning a new task: the subprocess is a chunk of code that runs simultaneously with the Trio task, and they can potentially interact. Trio has some strong and convenient conventions for spawning tasks: - trio always makes sure that both tasks are finished before exiting the nursery block - if the new task crashes, the exception propagates until caught, and automatically cancels sibling tasks - if the original task crashes, the new task is automatically cancelled too - if the surrounding context is cancelled, then this automatically cancels both tasks It would be nice to be able to get this combination of features for subprocesses too. But we don't currently have any simple way to do that. The closest thing we have is to use `async with` on a `Process` object. But that's stuck between two conflicting sets of conventions. The `Process` API mostly treats processes as independent objects, and our `async with process` mostly follows the general conventions for `async with <resource>`: it never cancels the body of the `with` (even if the process crashes). It doesn't care whether the body of the `with` block completed with an exception or not. And it doesn't do any particular checks on the subprocess's return code. This is also reasonable and what you'd expect for like, `with file_obj`. OTOH, the conventions above are specific to how Trio handles code, and really only show up with nurseries and nursery-like objects. Which a `Process`... kind of is, and kind of isn't. I don't think it makes sense to try to wedge all those nursery semantics into `async with process_obj`. In particular, it would be very weird for `async with process_obj` to suddenly enable the equivalent of `run_process`'s `check=True`. And then we'd need some way to allow it to be overridden with `check=False` on a case-by-case basis, and there's no obvious way to spell that. And if we use `check=True` and the subprocess fails and the body fails, do we raise a `MultiError`? In multierror v2, does that mean we need to wrap all outgoing exceptions in `MultiError`? It would involve a lot of weirdness. Another idea that's been proposed is to extend `run_process` so that it can be used like: ```python process_obj = await nursery.start(run_process, ...) ``` This has several attractive aspects: `run_process`'s job is to "domesticate" a subprocess so it acts like a subroutine – for example, the `check=` argument translates error-reporting from subprocess-style to subroutine-style. So here we're reusing that, plus a nursery's general ability to take any subroutine and push it off into a parallel task. You get nursery semantics because you used a nursery. Elegant! It's also kinda weird. It's effectively a totally different 'mode' of using `run_process`: normally `run_process` treats the `process_obj` as hidden, and the return value as important; this flips that around completely, so you can mess with the `process_obj`, and never see the return value. Normally, the `capture_*` arguments are a major part of why people use `run_process`; here, they're completely useless, and would probably need to be forbidden. The use of `start` is sorta gratuitous: ~~the actual process startup is synchronous, so you could just as well have a synchronous version. The `start` is just a trick to start a task and get a return value at the same time.~~ (never mind, see #1109) Are there other options to consider? Brainstorming: ```python async with trio.open_process(...) as process_obj: # creates a nursery ... process_obj = nursery.process_start_soon(...) # or start_process_soon or start_soon_process? async with trio.open_nursery() as nursery: await nursery.start(trio.process_as_task, ...) async with trio.open_nursery() as nursery: process_obj = trio.start_process(nursery, ...) ```	1.0	Should we have a conventional way to "spawn a process into a nursery"? - We have `run_process`, which is a convenient way to run a process like it's a regular subroutine, with error checking (`check=`), cancellation support, automatic waiting, etc. And we have `trio.Process`, which is a fully general way to spawn a process and then do whatever with it. In the discussion in #872, @Badg [brought up](https://github.com/python-trio/trio/pull/872#discussion_r261434230) some dissatisfaction with both of these options. I think I mostly get the issue now, so I'll try to rephrase in my own words: In some ways, when a task spawns a subprocess it's very similar to spawning a new task: the subprocess is a chunk of code that runs simultaneously with the Trio task, and they can potentially interact. Trio has some strong and convenient conventions for spawning tasks: - trio always makes sure that both tasks are finished before exiting the nursery block - if the new task crashes, the exception propagates until caught, and automatically cancels sibling tasks - if the original task crashes, the new task is automatically cancelled too - if the surrounding context is cancelled, then this automatically cancels both tasks It would be nice to be able to get this combination of features for subprocesses too. But we don't currently have any simple way to do that. The closest thing we have is to use `async with` on a `Process` object. But that's stuck between two conflicting sets of conventions. The `Process` API mostly treats processes as independent objects, and our `async with process` mostly follows the general conventions for `async with <resource>`: it never cancels the body of the `with` (even if the process crashes). It doesn't care whether the body of the `with` block completed with an exception or not. And it doesn't do any particular checks on the subprocess's return code. This is also reasonable and what you'd expect for like, `with file_obj`. OTOH, the conventions above are specific to how Trio handles code, and really only show up with nurseries and nursery-like objects. Which a `Process`... kind of is, and kind of isn't. I don't think it makes sense to try to wedge all those nursery semantics into `async with process_obj`. In particular, it would be very weird for `async with process_obj` to suddenly enable the equivalent of `run_process`'s `check=True`. And then we'd need some way to allow it to be overridden with `check=False` on a case-by-case basis, and there's no obvious way to spell that. And if we use `check=True` and the subprocess fails and the body fails, do we raise a `MultiError`? In multierror v2, does that mean we need to wrap all outgoing exceptions in `MultiError`? It would involve a lot of weirdness. Another idea that's been proposed is to extend `run_process` so that it can be used like: ```python process_obj = await nursery.start(run_process, ...) ``` This has several attractive aspects: `run_process`'s job is to "domesticate" a subprocess so it acts like a subroutine – for example, the `check=` argument translates error-reporting from subprocess-style to subroutine-style. So here we're reusing that, plus a nursery's general ability to take any subroutine and push it off into a parallel task. You get nursery semantics because you used a nursery. Elegant! It's also kinda weird. It's effectively a totally different 'mode' of using `run_process`: normally `run_process` treats the `process_obj` as hidden, and the return value as important; this flips that around completely, so you can mess with the `process_obj`, and never see the return value. Normally, the `capture_*` arguments are a major part of why people use `run_process`; here, they're completely useless, and would probably need to be forbidden. The use of `start` is sorta gratuitous: ~~the actual process startup is synchronous, so you could just as well have a synchronous version. The `start` is just a trick to start a task and get a return value at the same time.~~ (never mind, see #1109) Are there other options to consider? Brainstorming: ```python async with trio.open_process(...) as process_obj: # creates a nursery ... process_obj = nursery.process_start_soon(...) # or start_process_soon or start_soon_process? async with trio.open_nursery() as nursery: await nursery.start(trio.process_as_task, ...) async with trio.open_nursery() as nursery: process_obj = trio.start_process(nursery, ...) ```	process	should we have a conventional way to spawn a process into a nursery we have run process which is a convenient way to run a process like it s a regular subroutine with error checking check cancellation support automatic waiting etc and we have trio process which is a fully general way to spawn a process and then do whatever with it in the discussion in badg some dissatisfaction with both of these options i think i mostly get the issue now so i ll try to rephrase in my own words in some ways when a task spawns a subprocess it s very similar to spawning a new task the subprocess is a chunk of code that runs simultaneously with the trio task and they can potentially interact trio has some strong and convenient conventions for spawning tasks trio always makes sure that both tasks are finished before exiting the nursery block if the new task crashes the exception propagates until caught and automatically cancels sibling tasks if the original task crashes the new task is automatically cancelled too if the surrounding context is cancelled then this automatically cancels both tasks it would be nice to be able to get this combination of features for subprocesses too but we don t currently have any simple way to do that the closest thing we have is to use async with on a process object but that s stuck between two conflicting sets of conventions the process api mostly treats processes as independent objects and our async with process mostly follows the general conventions for async with it never cancels the body of the with even if the process crashes it doesn t care whether the body of the with block completed with an exception or not and it doesn t do any particular checks on the subprocess s return code this is also reasonable and what you d expect for like with file obj otoh the conventions above are specific to how trio handles code and really only show up with nurseries and nursery like objects which a process kind of is and kind of isn t i don t think it makes sense to try to wedge all those nursery semantics into async with process obj in particular it would be very weird for async with process obj to suddenly enable the equivalent of run process s check true and then we d need some way to allow it to be overridden with check false on a case by case basis and there s no obvious way to spell that and if we use check true and the subprocess fails and the body fails do we raise a multierror in multierror does that mean we need to wrap all outgoing exceptions in multierror it would involve a lot of weirdness another idea that s been proposed is to extend run process so that it can be used like python process obj await nursery start run process this has several attractive aspects run process s job is to domesticate a subprocess so it acts like a subroutine – for example the check argument translates error reporting from subprocess style to subroutine style so here we re reusing that plus a nursery s general ability to take any subroutine and push it off into a parallel task you get nursery semantics because you used a nursery elegant it s also kinda weird it s effectively a totally different mode of using run process normally run process treats the process obj as hidden and the return value as important this flips that around completely so you can mess with the process obj and never see the return value normally the capture arguments are a major part of why people use run process here they re completely useless and would probably need to be forbidden the use of start is sorta gratuitous the actual process startup is synchronous so you could just as well have a synchronous version the start is just a trick to start a task and get a return value at the same time never mind see are there other options to consider brainstorming python async with trio open process as process obj creates a nursery process obj nursery process start soon or start process soon or start soon process async with trio open nursery as nursery await nursery start trio process as task async with trio open nursery as nursery process obj trio start process nursery	1
3,394	6,516,741,198	IssuesEvent	2017-08-27 13:47:06	gaocegege/Processing.R	https://api.github.com/repos/gaocegege/Processing.R	closed	Support CI in macOS via Travis CI	community/processing for-new-contributors priority/p3 size/small status/to-be-claimed type/enhancement	Now we have Windows CI(AppVeyor) and Linux CI(Travis CI), macOS CI should be supported.	1.0	Support CI in macOS via Travis CI - Now we have Windows CI(AppVeyor) and Linux CI(Travis CI), macOS CI should be supported.	process	support ci in macos via travis ci now we have windows ci appveyor and linux ci travis ci macos ci should be supported	1
10,753	13,543,129,986	IssuesEvent	2020-09-16 18:27:50	googleapis/nodejs-assured-workloads	https://api.github.com/repos/googleapis/nodejs-assured-workloads	opened	GA Release of @google-cloud/assured-workloads	type: process	Package name: FIXME Current release: beta Proposed release: GA ## Instructions Check the lists below, adding tests / documentation as required. Once all the "required" boxes are ticked, please create a release and close this issue. ## Required - [ ] 28 days elapsed since last beta release with new API surface - [ ] Server API is GA - [ ] Package API is stable, and we can commit to backward compatibility - [x] All dependencies are GA ## Optional - [ ] Most common / important scenarios have descriptive samples - [ ] Public manual methods have at least one usage sample each (excluding overloads) - [ ] Per-API README includes a full description of the API - [ ] Per-API README contains at least one “getting started” sample using the most common API scenario - [ ] Manual code has been reviewed by API producer - [ ] Manual code has been reviewed by a DPE responsible for samples - [ ] 'Client Libraries' page is added to the product documentation in 'APIs & Reference' section of the product's documentation on Cloud Site	1.0	GA Release of @google-cloud/assured-workloads - Package name: FIXME Current release: beta Proposed release: GA ## Instructions Check the lists below, adding tests / documentation as required. Once all the "required" boxes are ticked, please create a release and close this issue. ## Required - [ ] 28 days elapsed since last beta release with new API surface - [ ] Server API is GA - [ ] Package API is stable, and we can commit to backward compatibility - [x] All dependencies are GA ## Optional - [ ] Most common / important scenarios have descriptive samples - [ ] Public manual methods have at least one usage sample each (excluding overloads) - [ ] Per-API README includes a full description of the API - [ ] Per-API README contains at least one “getting started” sample using the most common API scenario - [ ] Manual code has been reviewed by API producer - [ ] Manual code has been reviewed by a DPE responsible for samples - [ ] 'Client Libraries' page is added to the product documentation in 'APIs & Reference' section of the product's documentation on Cloud Site	process	ga release of google cloud assured workloads package name fixme current release beta proposed release ga instructions check the lists below adding tests documentation as required once all the required boxes are ticked please create a release and close this issue required days elapsed since last beta release with new api surface server api is ga package api is stable and we can commit to backward compatibility all dependencies are ga optional most common important scenarios have descriptive samples public manual methods have at least one usage sample each excluding overloads per api readme includes a full description of the api per api readme contains at least one “getting started” sample using the most common api scenario manual code has been reviewed by api producer manual code has been reviewed by a dpe responsible for samples client libraries page is added to the product documentation in apis reference section of the product s documentation on cloud site	1
10,119	13,044,162,246	IssuesEvent	2020-07-29 03:47:31	tikv/tikv	https://api.github.com/repos/tikv/tikv	closed	UCP: Migrate scalar function `TimeTimeTimeDiff` from TiDB	challenge-program-2 component/coprocessor difficulty/easy sig/coprocessor	## Description Port the scalar function `TimeTimeTimeDiff` from TiDB to coprocessor. ## Score * 50 ## Mentor(s) * @breeswish ## Recommended Skills * Rust programming ## Learning Materials Already implemented expressions ported from TiDB - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/rpn_expr) - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/expr)	2.0	UCP: Migrate scalar function `TimeTimeTimeDiff` from TiDB - ## Description Port the scalar function `TimeTimeTimeDiff` from TiDB to coprocessor. ## Score * 50 ## Mentor(s) * @breeswish ## Recommended Skills * Rust programming ## Learning Materials Already implemented expressions ported from TiDB - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/rpn_expr) - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/expr)	process	ucp migrate scalar function timetimetimediff from tidb description port the scalar function timetimetimediff from tidb to coprocessor score mentor s breeswish recommended skills rust programming learning materials already implemented expressions ported from tidb	1
4,295	7,192,445,960	IssuesEvent	2018-02-03 03:39:15	Great-Hill-Corporation/quickBlocks	https://api.github.com/repos/Great-Hill-Corporation/quickBlocks	closed	Add --count to isContract (or getAccounts) to show account nonce.	status-inprocess tools-getAccounts tools-isContract type-enhancement	eth_getTransactionCount is simple to implement. It returns the account's nonce, so maybe not so interesting for my work, but it's easy to add this option.	1.0	Add --count to isContract (or getAccounts) to show account nonce. - eth_getTransactionCount is simple to implement. It returns the account's nonce, so maybe not so interesting for my work, but it's easy to add this option.	process	add count to iscontract or getaccounts to show account nonce eth gettransactioncount is simple to implement it returns the account s nonce so maybe not so interesting for my work but it s easy to add this option	1
15,585	19,706,806,846	IssuesEvent	2022-01-12 23:10:08	googleapis/java-containeranalysis	https://api.github.com/repos/googleapis/java-containeranalysis	closed	Your .repo-metadata.json file has a problem 🤒	type: process api: containeranalysis repo-metadata: lint	You have a problem with your .repo-metadata.json file: Result of scan 📈: * release_level must be equal to one of the allowed values in .repo-metadata.json ☝️ Once you correct these problems, you can close this issue. Reach out to go/github-automation if you have any questions.	1.0	Your .repo-metadata.json file has a problem 🤒 - You have a problem with your .repo-metadata.json file: Result of scan 📈: * release_level must be equal to one of the allowed values in .repo-metadata.json ☝️ Once you correct these problems, you can close this issue. Reach out to go/github-automation if you have any questions.	process	your repo metadata json file has a problem 🤒 you have a problem with your repo metadata json file result of scan 📈 release level must be equal to one of the allowed values in repo metadata json ☝️ once you correct these problems you can close this issue reach out to go github automation if you have any questions	1
203,610	7,067,688,676	IssuesEvent	2018-01-08 02:05:40	xcat2/xcat-core	https://api.github.com/repos/xcat2/xcat-core	closed	Should goconserver logs be in /var/log/consoles ?	component:console priority:low	@chenglch Should goconserver logs be in `/var/log/consoles`? It seems like they do not conflict since the logs are under a `nodes` directory AND have `.log` in their filename.... ``` [root@briggs01 log]# ls -ltr goconserver/ total 92948 drwx------ 2 root root 4096 Dec 21 15:47 nodes -rw------- 1 root root 95169175 Dec 21 15:47 server.log [root@briggs01 log]# ls goconserver/nodes/ dummytest.log mid05tor12cn02.log mid05tor12cn05.log mid05tor12cn13.log mid05tor12cn15.log mid05tor12cn16.log mid05tor12cn18.log node-8335-gtc-1318c4a.log sn01.log sn02.log [root@briggs01 log]# ``` And `/var/log/consoles`.... ``` [root@briggs01 log]# ls -ltr consoles/ total 2024728 -rw-r--r-- 1 root root 239270 Nov 15 10:23 mid05tor12cn01 -rw-r--r-- 1 root root 185269 Nov 15 10:23 mid05tor12cn06 -rw-r--r-- 1 root root 189775 Nov 15 10:23 mid05tor12cn07 -rw-r--r-- 1 root root 275528624 Nov 15 10:23 mid05tor12cn08 ``` I thought that console was not working when looking at the logs but then realized it was in a different location. So having it like this would trip up some people if we eventually want to move to using gocons...	1.0	Should goconserver logs be in /var/log/consoles ? - @chenglch Should goconserver logs be in `/var/log/consoles`? It seems like they do not conflict since the logs are under a `nodes` directory AND have `.log` in their filename.... ``` [root@briggs01 log]# ls -ltr goconserver/ total 92948 drwx------ 2 root root 4096 Dec 21 15:47 nodes -rw------- 1 root root 95169175 Dec 21 15:47 server.log [root@briggs01 log]# ls goconserver/nodes/ dummytest.log mid05tor12cn02.log mid05tor12cn05.log mid05tor12cn13.log mid05tor12cn15.log mid05tor12cn16.log mid05tor12cn18.log node-8335-gtc-1318c4a.log sn01.log sn02.log [root@briggs01 log]# ``` And `/var/log/consoles`.... ``` [root@briggs01 log]# ls -ltr consoles/ total 2024728 -rw-r--r-- 1 root root 239270 Nov 15 10:23 mid05tor12cn01 -rw-r--r-- 1 root root 185269 Nov 15 10:23 mid05tor12cn06 -rw-r--r-- 1 root root 189775 Nov 15 10:23 mid05tor12cn07 -rw-r--r-- 1 root root 275528624 Nov 15 10:23 mid05tor12cn08 ``` I thought that console was not working when looking at the logs but then realized it was in a different location. So having it like this would trip up some people if we eventually want to move to using gocons...	non_process	should goconserver logs be in var log consoles chenglch should goconserver logs be in var log consoles it seems like they do not conflict since the logs are under a nodes directory and have log in their filename ls ltr goconserver total drwx root root dec nodes rw root root dec server log ls goconserver nodes dummytest log log log log log log log node gtc log log log and var log consoles ls ltr consoles total rw r r root root nov rw r r root root nov rw r r root root nov rw r r root root nov i thought that console was not working when looking at the logs but then realized it was in a different location so having it like this would trip up some people if we eventually want to move to using gocons	0
85,392	24,590,835,023	IssuesEvent	2022-10-14 01:56:52	sktime/BaseObject	https://api.github.com/repos/sktime/BaseObject	opened	Update Github Action Versions	ci / build good first issue	Describe Maintenance Item. Several of the Github actions used in our CI workflow are not the current versions. We should update them. Describe the solution you'd like We should make the following updates to our Github Actions: - `actions/checkout@v2` to `actions/checkout@v3` - `actions/setup-python@v2` to `actions/setup-python@v4` - `pre-commit/action@v2.0.0` to `pre-commit/action@v3.0.0` - `codecov/codecov-action@v2` to `codecov/codecov-action@v3` Additional context These changes did not appear to cause any problems on main `sktime` repository, so this will hopefully be an easy update.	1.0	Update Github Action Versions - Describe Maintenance Item. Several of the Github actions used in our CI workflow are not the current versions. We should update them. Describe the solution you'd like We should make the following updates to our Github Actions: - `actions/checkout@v2` to `actions/checkout@v3` - `actions/setup-python@v2` to `actions/setup-python@v4` - `pre-commit/action@v2.0.0` to `pre-commit/action@v3.0.0` - `codecov/codecov-action@v2` to `codecov/codecov-action@v3` Additional context These changes did not appear to cause any problems on main `sktime` repository, so this will hopefully be an easy update.	non_process	update github action versions describe maintenance item several of the github actions used in our ci workflow are not the current versions we should update them describe the solution you d like we should make the following updates to our github actions actions checkout to actions checkout actions setup python to actions setup python pre commit action to pre commit action codecov codecov action to codecov codecov action additional context these changes did not appear to cause any problems on main sktime repository so this will hopefully be an easy update	0
19,439	25,707,103,012	IssuesEvent	2022-12-07 02:00:09	lizhihao6/get-daily-arxiv-noti	https://api.github.com/repos/lizhihao6/get-daily-arxiv-noti	opened	New submissions for Wed, 7 Dec 22	event camera white balance isp compression image signal processing image signal process raw raw image events camera color contrast events AWB	## Keyword: events ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - Authors: Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.02791 - Pdf link: https://arxiv.org/pdf/2212.02791 - Abstract Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ## Keyword: event camera ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - Authors: Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.02791 - Pdf link: https://arxiv.org/pdf/2212.02791 - Abstract Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ### Privacy-Preserving Visual Localization with Event Cameras - Authors: Junho Kim, Young Min Kim, Yicheng Wu, Ramzi Zahreddine, Weston A. Welge, Gurunandan Krishnan, Sizhuo Ma, Jian Wang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.03177 - Pdf link: https://arxiv.org/pdf/2212.03177 - Abstract We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difficult to directly apply conventional image-based localization algorithms. To mitigate the gap, we propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras, and thus can naturally hide sensitive visual details. To further enhance the privacy protection in our event-based pipeline, we introduce privacy protection at two levels, namely sensor and network level. Sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user's view in private scene applications using a novel neural network inference pipeline. Both levels of protection involve light-weight computation and incur only a small performance loss. We thus project our method to serve as a building block for practical location-based services using event cameras. The code and dataset will be made public through the following link: https://github.com/82magnolia/event_localization. ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB There is no result ## Keyword: ISP ### SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud - Authors: Yan Wang, Junbo Yin, Wei Li, Pascal Frossard, Ruigang Yang, Jianbing Shen - Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) - Arxiv link: https://arxiv.org/abs/2212.02845 - Pdf link: https://arxiv.org/pdf/2212.02845 - Abstract LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D. ### Towards Energy Efficient Mobile Eye Tracking for AR Glasses through Optical Sensor Technology - Authors: Johannes Meyer - Subjects: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Image and Video Processing (eess.IV) - Arxiv link: https://arxiv.org/abs/2212.03189 - Pdf link: https://arxiv.org/pdf/2212.03189 - Abstract After the introduction of smartphones and smartwatches, AR glasses are considered the next breakthrough in the field of wearables. While the transition from smartphones to smartwatches was based mainly on established display technologies, the display technology of AR glasses presents a technological challenge. Many display technologies, such as retina projectors, are based on continuous adaptive control of the display based on the user's pupil position. Furthermore, head-mounted systems require an adaptation and extension of established interaction concepts to provide the user with an immersive experience. Eye-tracking is a crucial technology to help AR glasses achieve a breakthrough through optimized display technology and gaze-based interaction concepts. Available eye-tracking technologies, such as VOG, do not meet the requirements of AR glasses, especially regarding power consumption, robustness, and integrability. To further overcome these limitations and push mobile eye-tracking for AR glasses forward, novel laser-based eye-tracking sensor technologies are researched in this thesis. The thesis contributes to a significant scientific advancement towards energy-efficient mobile eye-tracking for AR glasses. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Leveraging Different Learning Styles for Improved Knowledge Distillation - Authors: Usma Niyaz, Deepti R. Bathula - Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - Arxiv link: https://arxiv.org/abs/2212.02931 - Pdf link: https://arxiv.org/pdf/2212.02931 - Abstract Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences like visual, auditory, etc., for acquiring and effectively processing information. Inspired by this concept, our work explores the idea of mixed information sharing with model compression in the context of Knowledge Distillation (KD) and Mutual Learning (ML). Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process. We formulate a combined KD and ML framework with one teacher and two student networks that share or exchange information in the form of predictions and feature maps. Our comprehensive experiments with benchmark classification and segmentation datasets demonstrate that with 15% compression, the ensemble performance of networks trained with diverse forms of knowledge outperforms the conventional techniques both quantitatively and qualitatively. ### Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis - Authors: Yuchao Gu, Xintao Wang, Yixiao Ge, Ying Shan, Xiaohu Qie, Mike Zheng Shou - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.03185 - Pdf link: https://arxiv.org/pdf/2212.03185 - Abstract Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper, we surprisingly find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead, learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability to capture textures and structures. We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation. Different from previous work that only pursues better details preservation, we propose Semantic-Quantized GAN (SeQ-GAN) with two learning phases to balance the two objectives. In the first phase, we propose a semantic-enhanced perceptual loss for better semantic compression. In the second phase, we fix the encoder and codebook, but enhance and finetune the decoder to achieve better details preservation. The proposed SeQ-GAN greatly improves VQ-based generative models and surpasses the GAN and Diffusion Models on both unconditional and conditional image generation. Our SeQ-GAN (364M) achieves Frechet Inception Distance (FID) of 6.25 and Inception Score (IS) of 140.9 on 256x256 ImageNet generation, a remarkable improvement over VIT-VQGAN (714M), which obtains 11.2 FID and 97.2 IS. ## Keyword: RAW ### Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances - Authors: Ajay Jaiswal, Tianlong Chen, Justin F. Rousseau, Yifan Peng, Ying Ding, Zhangyang Wang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.02675 - Pdf link: https://arxiv.org/pdf/2212.02675 - Abstract Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify \textit{hard-to-learn} (HTL) training samples, and improve pathology localization by attending them explicitly, during training in \textit{supervised, semi-supervised, and weakly-supervised} settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning \cite{hooker2019compressed} - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by $\sim$2-3\%. ## Keyword: raw image There is no result	2.0	New submissions for Wed, 7 Dec 22 - ## Keyword: events ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - Authors: Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.02791 - Pdf link: https://arxiv.org/pdf/2212.02791 - Abstract Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ## Keyword: event camera ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - Authors: Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.02791 - Pdf link: https://arxiv.org/pdf/2212.02791 - Abstract Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ### Privacy-Preserving Visual Localization with Event Cameras - Authors: Junho Kim, Young Min Kim, Yicheng Wu, Ramzi Zahreddine, Weston A. Welge, Gurunandan Krishnan, Sizhuo Ma, Jian Wang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.03177 - Pdf link: https://arxiv.org/pdf/2212.03177 - Abstract We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difficult to directly apply conventional image-based localization algorithms. To mitigate the gap, we propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras, and thus can naturally hide sensitive visual details. To further enhance the privacy protection in our event-based pipeline, we introduce privacy protection at two levels, namely sensor and network level. Sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user's view in private scene applications using a novel neural network inference pipeline. Both levels of protection involve light-weight computation and incur only a small performance loss. We thus project our method to serve as a building block for practical location-based services using event cameras. The code and dataset will be made public through the following link: https://github.com/82magnolia/event_localization. ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB There is no result ## Keyword: ISP ### SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud - Authors: Yan Wang, Junbo Yin, Wei Li, Pascal Frossard, Ruigang Yang, Jianbing Shen - Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) - Arxiv link: https://arxiv.org/abs/2212.02845 - Pdf link: https://arxiv.org/pdf/2212.02845 - Abstract LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D. ### Towards Energy Efficient Mobile Eye Tracking for AR Glasses through Optical Sensor Technology - Authors: Johannes Meyer - Subjects: Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Image and Video Processing (eess.IV) - Arxiv link: https://arxiv.org/abs/2212.03189 - Pdf link: https://arxiv.org/pdf/2212.03189 - Abstract After the introduction of smartphones and smartwatches, AR glasses are considered the next breakthrough in the field of wearables. While the transition from smartphones to smartwatches was based mainly on established display technologies, the display technology of AR glasses presents a technological challenge. Many display technologies, such as retina projectors, are based on continuous adaptive control of the display based on the user's pupil position. Furthermore, head-mounted systems require an adaptation and extension of established interaction concepts to provide the user with an immersive experience. Eye-tracking is a crucial technology to help AR glasses achieve a breakthrough through optimized display technology and gaze-based interaction concepts. Available eye-tracking technologies, such as VOG, do not meet the requirements of AR glasses, especially regarding power consumption, robustness, and integrability. To further overcome these limitations and push mobile eye-tracking for AR glasses forward, novel laser-based eye-tracking sensor technologies are researched in this thesis. The thesis contributes to a significant scientific advancement towards energy-efficient mobile eye-tracking for AR glasses. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Leveraging Different Learning Styles for Improved Knowledge Distillation - Authors: Usma Niyaz, Deepti R. Bathula - Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - Arxiv link: https://arxiv.org/abs/2212.02931 - Pdf link: https://arxiv.org/pdf/2212.02931 - Abstract Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences like visual, auditory, etc., for acquiring and effectively processing information. Inspired by this concept, our work explores the idea of mixed information sharing with model compression in the context of Knowledge Distillation (KD) and Mutual Learning (ML). Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process. We formulate a combined KD and ML framework with one teacher and two student networks that share or exchange information in the form of predictions and feature maps. Our comprehensive experiments with benchmark classification and segmentation datasets demonstrate that with 15% compression, the ensemble performance of networks trained with diverse forms of knowledge outperforms the conventional techniques both quantitatively and qualitatively. ### Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis - Authors: Yuchao Gu, Xintao Wang, Yixiao Ge, Ying Shan, Xiaohu Qie, Mike Zheng Shou - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.03185 - Pdf link: https://arxiv.org/pdf/2212.03185 - Abstract Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper, we surprisingly find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead, learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability to capture textures and structures. We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation. Different from previous work that only pursues better details preservation, we propose Semantic-Quantized GAN (SeQ-GAN) with two learning phases to balance the two objectives. In the first phase, we propose a semantic-enhanced perceptual loss for better semantic compression. In the second phase, we fix the encoder and codebook, but enhance and finetune the decoder to achieve better details preservation. The proposed SeQ-GAN greatly improves VQ-based generative models and surpasses the GAN and Diffusion Models on both unconditional and conditional image generation. Our SeQ-GAN (364M) achieves Frechet Inception Distance (FID) of 6.25 and Inception Score (IS) of 140.9 on 256x256 ImageNet generation, a remarkable improvement over VIT-VQGAN (714M), which obtains 11.2 FID and 97.2 IS. ## Keyword: RAW ### Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances - Authors: Ajay Jaiswal, Tianlong Chen, Justin F. Rousseau, Yifan Peng, Ying Ding, Zhangyang Wang - Subjects: Computer Vision and Pattern Recognition (cs.CV) - Arxiv link: https://arxiv.org/abs/2212.02675 - Pdf link: https://arxiv.org/pdf/2212.02675 - Abstract Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify \textit{hard-to-learn} (HTL) training samples, and improve pathology localization by attending them explicitly, during training in \textit{supervised, semi-supervised, and weakly-supervised} settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning \cite{hooker2019compressed} - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by $\sim$2-3\%. ## Keyword: raw image There is no result	process	new submissions for wed dec keyword events event based monocular dense depth estimation with recurrent transformers authors xu liu jianing li xiaopeng fan yonghong tian subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract event cameras offering high temporal resolutions and high dynamic ranges have brought a new perspective to address common challenges e g motion blur and low light in monocular depth estimation however how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor to this end we propose a novel event based monocular depth estimator with recurrent transformers namely ereformer which is the first pure transformer with a recursive mechanism to process continuous event streams technically for spatial modeling a novel transformer based encoder decoder with a spatial transformer fusion module is presented having better global context information modeling capabilities than cnn based methods for temporal modeling we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers improving temporal modeling capabilities while alleviating the expensive gpu memory cost the experimental results show that our ereformer outperforms state of the art methods by a margin on both synthetic and real world datasets we hope that our work will attract further research to develop stunning transformers in the event based vision community our open source code can be found in the supplemental material keyword event camera event based monocular dense depth estimation with recurrent transformers authors xu liu jianing li xiaopeng fan yonghong tian subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract event cameras offering high temporal resolutions and high dynamic ranges have brought a new perspective to address common challenges e g motion blur and low light in monocular depth estimation however how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor to this end we propose a novel event based monocular depth estimator with recurrent transformers namely ereformer which is the first pure transformer with a recursive mechanism to process continuous event streams technically for spatial modeling a novel transformer based encoder decoder with a spatial transformer fusion module is presented having better global context information modeling capabilities than cnn based methods for temporal modeling we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers improving temporal modeling capabilities while alleviating the expensive gpu memory cost the experimental results show that our ereformer outperforms state of the art methods by a margin on both synthetic and real world datasets we hope that our work will attract further research to develop stunning transformers in the event based vision community our open source code can be found in the supplemental material privacy preserving visual localization with event cameras authors junho kim young min kim yicheng wu ramzi zahreddine weston a welge gurunandan krishnan sizhuo ma jian wang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract we present a robust privacy preserving visual localization algorithm using event cameras while event cameras can potentially make robust localization due to high dynamic range and small motion blur the sensors exhibit large domain gaps making it difficult to directly apply conventional image based localization algorithms to mitigate the gap we propose applying event to image conversion prior to localization which leads to stable localization in the privacy perspective event cameras capture only a fraction of visual information compared to normal cameras and thus can naturally hide sensitive visual details to further enhance the privacy protection in our event based pipeline we introduce privacy protection at two levels namely sensor and network level sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user s view in private scene applications using a novel neural network inference pipeline both levels of protection involve light weight computation and incur only a small performance loss we thus project our method to serve as a building block for practical location based services using event cameras the code and dataset will be made public through the following link keyword events camera there is no result keyword white balance there is no result keyword color contrast there is no result keyword awb there is no result keyword isp semi supervised domain adaptation for object detection from point cloud authors yan wang junbo yin wei li pascal frossard ruigang yang jianbing shen subjects computer vision and pattern recognition cs cv artificial intelligence cs ai arxiv link pdf link abstract lidar based object detection is an indispensable task in advanced autonomous driving systems though impressive detection results have been achieved by superior detectors they suffer from significant performance degeneration when facing unseen domains such as different lidar configurations different cities and weather conditions the mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation uda techniques however these uda solutions just yield unsatisfactory detection results when there is a severe domain shift e g from waymo beam to nuscenes beam to address this we present a novel semi supervised domain adaptation method for object detection where only a few labeled target data is available yet can significantly improve the adaptation performance in particular our includes an inter domain adaptation stage and an intra domain generalization stage in the first stage an inter domain point cutmix module is presented to efficiently align the point cloud distribution across domains the point cutmix generates mixed samples of an intermediate domain thus encouraging to learn domain invariant knowledge then in the second stage we further enhance the model for better generalization on the unlabeled target set this is achieved by exploring intra domain point mixup in semi supervised learning which essentially regularizes the pseudo label distribution experiments from waymo to nuscenes show that with only labeled target data our can surpass the fully supervised oracle model with target label our code is available at towards energy efficient mobile eye tracking for ar glasses through optical sensor technology authors johannes meyer subjects computer vision and pattern recognition cs cv human computer interaction cs hc image and video processing eess iv arxiv link pdf link abstract after the introduction of smartphones and smartwatches ar glasses are considered the next breakthrough in the field of wearables while the transition from smartphones to smartwatches was based mainly on established display technologies the display technology of ar glasses presents a technological challenge many display technologies such as retina projectors are based on continuous adaptive control of the display based on the user s pupil position furthermore head mounted systems require an adaptation and extension of established interaction concepts to provide the user with an immersive experience eye tracking is a crucial technology to help ar glasses achieve a breakthrough through optimized display technology and gaze based interaction concepts available eye tracking technologies such as vog do not meet the requirements of ar glasses especially regarding power consumption robustness and integrability to further overcome these limitations and push mobile eye tracking for ar glasses forward novel laser based eye tracking sensor technologies are researched in this thesis the thesis contributes to a significant scientific advancement towards energy efficient mobile eye tracking for ar glasses keyword image signal processing there is no result keyword image signal process there is no result keyword compression leveraging different learning styles for improved knowledge distillation authors usma niyaz deepti r bathula subjects computer vision and pattern recognition cs cv machine learning cs lg arxiv link pdf link abstract learning style refers to a type of training mechanism adopted by an individual to gain new knowledge as suggested by the vark model humans have different learning preferences like visual auditory etc for acquiring and effectively processing information inspired by this concept our work explores the idea of mixed information sharing with model compression in the context of knowledge distillation kd and mutual learning ml unlike conventional techniques that share the same type of knowledge with all networks we propose to train individual networks with different forms of information to enhance the learning process we formulate a combined kd and ml framework with one teacher and two student networks that share or exchange information in the form of predictions and feature maps our comprehensive experiments with benchmark classification and segmentation datasets demonstrate that with compression the ensemble performance of networks trained with diverse forms of knowledge outperforms the conventional techniques both quantitatively and qualitatively rethinking the objectives of vector quantized tokenizers for image synthesis authors yuchao gu xintao wang yixiao ge ying shan xiaohu qie mike zheng shou subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract vector quantized vq based generative models usually consist of two basic components i e vq tokenizers and generative transformers prior research focuses on improving the reconstruction fidelity of vq tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers in this paper we surprisingly find that improving the reconstruction fidelity of vq tokenizers does not necessarily improve the generation instead learning to compress semantic features within vq tokenizers significantly improves generative transformers ability to capture textures and structures we thus highlight two competing objectives of vq tokenizers for image synthesis semantic compression and details preservation different from previous work that only pursues better details preservation we propose semantic quantized gan seq gan with two learning phases to balance the two objectives in the first phase we propose a semantic enhanced perceptual loss for better semantic compression in the second phase we fix the encoder and codebook but enhance and finetune the decoder to achieve better details preservation the proposed seq gan greatly improves vq based generative models and surpasses the gan and diffusion models on both unconditional and conditional image generation our seq gan achieves frechet inception distance fid of and inception score is of on imagenet generation a remarkable improvement over vit vqgan which obtains fid and is keyword raw attend who is weak pruning assisted medical image localization under sophisticated and implicit imbalances authors ajay jaiswal tianlong chen justin f rousseau yifan peng ying ding zhangyang wang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract deep neural networks dnns have rapidly become a textit de facto choice for medical image understanding tasks however dnns are notoriously fragile to the class imbalance in image classification we further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization as imbalances in such problems can have highly complex and often implicit forms of presence for example different pathology can have different sizes or colors w r t the background different underlying demographic distributions and in general different difficulty levels to recognize even in a meticulously curated balanced distribution of training data in this paper we propose to use pruning to automatically and adaptively identify textit hard to learn htl training samples and improve pathology localization by attending them explicitly during training in textit supervised semi supervised and weakly supervised settings our main inspiration is drawn from the recent finding that deep classification models have difficult to memorize samples and those may be effectively exposed through network pruning cite and we extend such observation beyond classification for the first time we also present an interesting demographic analysis which illustrates htls ability to capture complex demographic imbalances our extensive experiments on the skin lesion localization task in multiple training settings by paying additional attention to htls show significant improvement of localization performance by sim keyword raw image there is no result	1
882	3,348,216,394	IssuesEvent	2015-11-17 00:23:59	beesmart-it/trend-hrm	https://api.github.com/repos/beesmart-it/trend-hrm	opened	Can't remove client if have active processes associated	client company requirement selection process	Can't remove client if have active processes associated. One option is to inform user and confirm if agreed to cancell all processes.	1.0	Can't remove client if have active processes associated - Can't remove client if have active processes associated. One option is to inform user and confirm if agreed to cancell all processes.	process	can t remove client if have active processes associated can t remove client if have active processes associated one option is to inform user and confirm if agreed to cancell all processes	1
139	2,575,496,616	IssuesEvent	2015-02-11 23:32:13	tinkerpop/tinkerpop3	https://api.github.com/repos/tinkerpop/tinkerpop3	closed	Make Traversal serializable and work towards the .submit() model for OLAP and Server.	enhancement io process server	Many many moons ago `Traversal` implemented `Serializable`. Unfortunately, in Java8 (and Groovy), you can not serialize lambdas across JVM boundaries. With recent work around anonymous traversals, the need for lambdas is quickly fading to 0. With that, traversals (presumably) can now be serialized and sent over the wire. There are two situations in which traversal serialization is good: * `g.V.out.name.submit(g.compute())` // GraphComputer (OLAP) * `g.V.out.name.submit(server)` // Gremlin Server (OLTP or OLAP) With this, the `:>` model goes away in favor of `.submit()`. Why is this good? Over the wire communication is not "fat string", but native language embedding (for JVM-based languages). We should still keep `:>` around for non-JVM languages (e.g. Gremlin-Go -> GremlinServer) and for computations that will use lambdas (e.g. `g.V.filter{complex}` -> OLAP).	1.0	Make Traversal serializable and work towards the .submit() model for OLAP and Server. - Many many moons ago `Traversal` implemented `Serializable`. Unfortunately, in Java8 (and Groovy), you can not serialize lambdas across JVM boundaries. With recent work around anonymous traversals, the need for lambdas is quickly fading to 0. With that, traversals (presumably) can now be serialized and sent over the wire. There are two situations in which traversal serialization is good: * `g.V.out.name.submit(g.compute())` // GraphComputer (OLAP) * `g.V.out.name.submit(server)` // Gremlin Server (OLTP or OLAP) With this, the `:>` model goes away in favor of `.submit()`. Why is this good? Over the wire communication is not "fat string", but native language embedding (for JVM-based languages). We should still keep `:>` around for non-JVM languages (e.g. Gremlin-Go -> GremlinServer) and for computations that will use lambdas (e.g. `g.V.filter{complex}` -> OLAP).	process	make traversal serializable and work towards the submit model for olap and server many many moons ago traversal implemented serializable unfortunately in and groovy you can not serialize lambdas across jvm boundaries with recent work around anonymous traversals the need for lambdas is quickly fading to with that traversals presumably can now be serialized and sent over the wire there are two situations in which traversal serialization is good g v out name submit g compute graphcomputer olap g v out name submit server gremlin server oltp or olap with this the model goes away in favor of submit why is this good over the wire communication is not fat string but native language embedding for jvm based languages we should still keep around for non jvm languages e g gremlin go gremlinserver and for computations that will use lambdas e g g v filter complex olap	1
12,659	15,031,232,256	IssuesEvent	2021-02-02 08:39:43	GoogleCloudPlatform/fda-mystudies	https://api.github.com/repos/GoogleCloudPlatform/fda-mystudies	closed	[Mobile Apps] Responses are not saving from mobile apps	Blocker Bug P0 Process: Fixed Process: Tested dev Response datastore	Responses are not saving from mobile apps for any of the study activities Issue observed in aut-deployment environment and not observed in Dev environment	2.0	[Mobile Apps] Responses are not saving from mobile apps - Responses are not saving from mobile apps for any of the study activities Issue observed in aut-deployment environment and not observed in Dev environment	process	responses are not saving from mobile apps responses are not saving from mobile apps for any of the study activities issue observed in aut deployment environment and not observed in dev environment	1
6,005	8,809,528,341	IssuesEvent	2018-12-27 20:12:01	shirou/gopsutil	https://api.github.com/repos/shirou/gopsutil	closed	Many undefined functions in process on OpenBSD/amd64	os:openbsd package:process	``` src/github.com/shirou/gopsutil/process/process.go:135:15: undefined: Pids src/github.com/shirou/gopsutil/process/process.go:156:20: p.Times undefined (type Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:166:20: p.Times undefined (type Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:209:25: p.MemoryInfo undefined (type Process has no field or method MemoryInfo) src/github.com/shirou/gopsutil/process/process.go:224:20: p.CreateTime undefined (type Process has no field or method CreateTime) src/github.com/shirou/gopsutil/process/process.go:229:16: p.Times undefined (type Process has no field or method Times) src/github.com/shirou/gopsutil/process/process_posix.go:128:16: p.Uids undefined (type Process has no field or method Uids, but does have uids) *** Error code 2 Stop. make: stopped in /z/go/bhyve-vm-goagent ``` _Originally posted by @araujobsd in https://github.com/shirou/gopsutil/issues/349#issuecomment-359325468_	1.0	Many undefined functions in process on OpenBSD/amd64 - ``` src/github.com/shirou/gopsutil/process/process.go:135:15: undefined: Pids src/github.com/shirou/gopsutil/process/process.go:156:20: p.Times undefined (type Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:166:20: p.Times undefined (type Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:209:25: p.MemoryInfo undefined (type Process has no field or method MemoryInfo) src/github.com/shirou/gopsutil/process/process.go:224:20: p.CreateTime undefined (type Process has no field or method CreateTime) src/github.com/shirou/gopsutil/process/process.go:229:16: p.Times undefined (type Process has no field or method Times) src/github.com/shirou/gopsutil/process/process_posix.go:128:16: p.Uids undefined (type Process has no field or method Uids, but does have uids) *** Error code 2 Stop. make: stopped in /z/go/bhyve-vm-goagent ``` _Originally posted by @araujobsd in https://github.com/shirou/gopsutil/issues/349#issuecomment-359325468_	process	many undefined functions in process on openbsd src github com shirou gopsutil process process go undefined pids src github com shirou gopsutil process process go p times undefined type process has no field or method times src github com shirou gopsutil process process go p times undefined type process has no field or method times src github com shirou gopsutil process process go p memoryinfo undefined type process has no field or method memoryinfo src github com shirou gopsutil process process go p createtime undefined type process has no field or method createtime src github com shirou gopsutil process process go p times undefined type process has no field or method times src github com shirou gopsutil process process posix go p uids undefined type process has no field or method uids but does have uids error code stop make stopped in z go bhyve vm goagent originally posted by araujobsd in	1
2,649	5,427,981,038	IssuesEvent	2017-03-03 14:50:36	openvstorage/volumedriver	https://api.github.com/repos/openvstorage/volumedriver	closed	cannot create vdisk use qemu	process_wontfix state_question	_From @hoanhdo on December 12, 2016 11:16_ I running openvstorage in ubuntu 16.04. I create disk use `qemu-img convert CentOS-6-x86_64-GenericCloud.qcow2 openvstorage+tcp:172.16.86.132:26203/vdiskdemo` Error log `qemu-img: openvstorage+tcp:172.16.86.132:26203/vdiskdemo: error while converting raw: cannot create volume: Input/output error` _Copied from original issue: openvstorage/framework#1262_	1.0	cannot create vdisk use qemu - _From @hoanhdo on December 12, 2016 11:16_ I running openvstorage in ubuntu 16.04. I create disk use `qemu-img convert CentOS-6-x86_64-GenericCloud.qcow2 openvstorage+tcp:172.16.86.132:26203/vdiskdemo` Error log `qemu-img: openvstorage+tcp:172.16.86.132:26203/vdiskdemo: error while converting raw: cannot create volume: Input/output error` _Copied from original issue: openvstorage/framework#1262_	process	cannot create vdisk use qemu from hoanhdo on december i running openvstorage in ubuntu i create disk use qemu img convert centos genericcloud openvstorage tcp vdiskdemo error log qemu img openvstorage tcp vdiskdemo error while converting raw cannot create volume input output error copied from original issue openvstorage framework	1
9,392	12,394,947,880	IssuesEvent	2020-05-20 17:46:46	hashicorp/packer	https://api.github.com/repos/hashicorp/packer	closed	Vagrant post processor using artifice ova file	post-processor/artifice post-processor/vagrant question	It is possible to use an ova file created by virtualbo-ovf to vagrant post-processor using artifice post-processor ? ``` sample code: "builders":[ { "name": "vagrant-box", "type": "null", "communicator": "none" } ], "post-processors": [ [ { "type": "artifice", "files": [ "sample.ova" ] }, { "name": "vbox_ova_vagrant_post", "type": "vagrant", "output": "sample.box" } ] ] ``` I get this error: > `"Post-processor` failed: Unknown artifact type, can't build box: packer.post-processor.artifice" what's the difference from vagrant point of view if artifact come from virtualbox-ovf builder o from "null builder + artifice post-processor" ?	2.0	Vagrant post processor using artifice ova file - It is possible to use an ova file created by virtualbo-ovf to vagrant post-processor using artifice post-processor ? ``` sample code: "builders":[ { "name": "vagrant-box", "type": "null", "communicator": "none" } ], "post-processors": [ [ { "type": "artifice", "files": [ "sample.ova" ] }, { "name": "vbox_ova_vagrant_post", "type": "vagrant", "output": "sample.box" } ] ] ``` I get this error: > `"Post-processor` failed: Unknown artifact type, can't build box: packer.post-processor.artifice" what's the difference from vagrant point of view if artifact come from virtualbox-ovf builder o from "null builder + artifice post-processor" ?	process	vagrant post processor using artifice ova file it is possible to use an ova file created by virtualbo ovf to vagrant post processor using artifice post processor sample code builders name vagrant box type null communicator none post processors type artifice files name vbox ova vagrant post type vagrant output sample box i get this error post processor failed unknown artifact type can t build box packer post processor artifice what s the difference from vagrant point of view if artifact come from virtualbox ovf builder o from null builder artifice post processor	1
7,871	11,044,763,900	IssuesEvent	2019-12-09 13:56:56	prisma/lift	https://api.github.com/repos/prisma/lift	closed	Lift is trying to recreate type aliases	bug/2-confirmed kind/bug process/candidate	Right now lift is trying to recreate type aliases that already exists. ![image](https://user-images.githubusercontent.com/22195362/69784432-c511a780-11db-11ea-9f1f-639703e16092.png) In the above screenshot, type CUID already exists but lift is trying to recreate it and it is failing to do so. To Reproduce: 1. Create a new Prisma 2 project using the CLI 2. Add a type alias, for example `type CUID = String @id @default(cuid())` and use it in one place 3. Migrate the database 4. Add another field of any type to any model. 5. Try to migrate the database again, you will see the error mentioned in the screenshot above. Prisma2 version : `prisma2@2.0.0-preview017.1, binary version: 6159bf3a263921c3c28ee68e2c9e130b5a69c293`	1.0	Lift is trying to recreate type aliases - Right now lift is trying to recreate type aliases that already exists. ![image](https://user-images.githubusercontent.com/22195362/69784432-c511a780-11db-11ea-9f1f-639703e16092.png) In the above screenshot, type CUID already exists but lift is trying to recreate it and it is failing to do so. To Reproduce: 1. Create a new Prisma 2 project using the CLI 2. Add a type alias, for example `type CUID = String @id @default(cuid())` and use it in one place 3. Migrate the database 4. Add another field of any type to any model. 5. Try to migrate the database again, you will see the error mentioned in the screenshot above. Prisma2 version : `prisma2@2.0.0-preview017.1, binary version: 6159bf3a263921c3c28ee68e2c9e130b5a69c293`	process	lift is trying to recreate type aliases right now lift is trying to recreate type aliases that already exists in the above screenshot type cuid already exists but lift is trying to recreate it and it is failing to do so to reproduce create a new prisma project using the cli add a type alias for example type cuid string id default cuid and use it in one place migrate the database add another field of any type to any model try to migrate the database again you will see the error mentioned in the screenshot above version binary version	1
21,716	30,217,419,182	IssuesEvent	2023-07-05 16:33:56	q191201771/lal	https://api.github.com/repos/q191201771/lal	closed	Cannot play HLS stream on browser	#Question *In process	I think the problem is not directly related to LAL, but anyway I'm asking for some help... I'm using LAL to capture live streaming from camera and mic and pushing and serving the stream to/from my internet server. The streaming is captured and shown without any issues using VLC or browser HLS plugins. But when I try hls.js on my HTML page (without plugins) to show the streaming I got a message from hls.js saying "ManifestParsingError". Am I missing some detail to make this work? Are there some HTML sample page that I can examine and could play my stream? Can you recommend some another HTML HLS player? By the way, LAL is an extraordinary piece of software! Congratulations!	1.0	Cannot play HLS stream on browser - I think the problem is not directly related to LAL, but anyway I'm asking for some help... I'm using LAL to capture live streaming from camera and mic and pushing and serving the stream to/from my internet server. The streaming is captured and shown without any issues using VLC or browser HLS plugins. But when I try hls.js on my HTML page (without plugins) to show the streaming I got a message from hls.js saying "ManifestParsingError". Am I missing some detail to make this work? Are there some HTML sample page that I can examine and could play my stream? Can you recommend some another HTML HLS player? By the way, LAL is an extraordinary piece of software! Congratulations!	process	cannot play hls stream on browser i think the problem is not directly related to lal but anyway i m asking for some help i m using lal to capture live streaming from camera and mic and pushing and serving the stream to from my internet server the streaming is captured and shown without any issues using vlc or browser hls plugins but when i try hls js on my html page without plugins to show the streaming i got a message from hls js saying manifestparsingerror am i missing some detail to make this work are there some html sample page that i can examine and could play my stream can you recommend some another html hls player by the way lal is an extraordinary piece of software congratulations	1
396	2,845,497,180	IssuesEvent	2015-05-29 03:42:08	cyanisaac/openterminalos	https://api.github.com/repos/cyanisaac/openterminalos	closed	Filesystem Shield	Background Process	# The Filesystem Shield This feature will allow OTOS to protect itself from malicious scripts and similar. It will work to backup directories and verify that they are intact. They will also remove certain files that should not be there (at my choosing. A good example is _safeBootLock, which is my friend's special implemented secure bootlocker in his server's rom.) It will actively look for faults in the OTOS directory and replace any faulty files with a hidden backup copy. It will not prevent modification, it will only replace files that are deleted, so it is not a perfect system. ## Howto submit malicious script deletions If you would like to delete a specific malicious script, please comment it as a request here in the issues and I will take a look at the script in question. I need both a link to the program/pastebin id/text, and I need the name of the file.	1.0	Filesystem Shield - # The Filesystem Shield This feature will allow OTOS to protect itself from malicious scripts and similar. It will work to backup directories and verify that they are intact. They will also remove certain files that should not be there (at my choosing. A good example is _safeBootLock, which is my friend's special implemented secure bootlocker in his server's rom.) It will actively look for faults in the OTOS directory and replace any faulty files with a hidden backup copy. It will not prevent modification, it will only replace files that are deleted, so it is not a perfect system. ## Howto submit malicious script deletions If you would like to delete a specific malicious script, please comment it as a request here in the issues and I will take a look at the script in question. I need both a link to the program/pastebin id/text, and I need the name of the file.	process	filesystem shield the filesystem shield this feature will allow otos to protect itself from malicious scripts and similar it will work to backup directories and verify that they are intact they will also remove certain files that should not be there at my choosing a good example is safebootlock which is my friend s special implemented secure bootlocker in his server s rom it will actively look for faults in the otos directory and replace any faulty files with a hidden backup copy it will not prevent modification it will only replace files that are deleted so it is not a perfect system howto submit malicious script deletions if you would like to delete a specific malicious script please comment it as a request here in the issues and i will take a look at the script in question i need both a link to the program pastebin id text and i need the name of the file	1
13,103	15,496,494,983	IssuesEvent	2021-03-11 02:47:09	dluiscosta/weather_api	https://api.github.com/repos/dluiscosta/weather_api	opened	test_get_weather_cache_expiration takes to long to run	development process enhancement question	The ```test_get_weather_cache_expiration``` test currently is taking too long to run. Even though this test reduces the configured cache expiration time, it still needs to wait at least 60 seconds in order to reliably assert if a given saved cache expired, since that is the duration of the cycle in which MongoDB checks for documents with expired TTL. Is there a workaround this?	1.0	test_get_weather_cache_expiration takes to long to run - The ```test_get_weather_cache_expiration``` test currently is taking too long to run. Even though this test reduces the configured cache expiration time, it still needs to wait at least 60 seconds in order to reliably assert if a given saved cache expired, since that is the duration of the cycle in which MongoDB checks for documents with expired TTL. Is there a workaround this?	process	test get weather cache expiration takes to long to run the test get weather cache expiration test currently is taking too long to run even though this test reduces the configured cache expiration time it still needs to wait at least seconds in order to reliably assert if a given saved cache expired since that is the duration of the cycle in which mongodb checks for documents with expired ttl is there a workaround this	1
6,922	10,082,804,969	IssuesEvent	2019-07-25 12:13:54	linnovate/root	https://api.github.com/repos/linnovate/root	closed	document / item it is not deleted	2.0.7 Fixed Process bug	Documents - When you delete a specific document / item it is not deleted in the Details pane	1.0	document / item it is not deleted - Documents - When you delete a specific document / item it is not deleted in the Details pane	process	document item it is not deleted documents when you delete a specific document item it is not deleted in the details pane	1
2,671	5,475,622,933	IssuesEvent	2017-03-11 13:10:46	rubberduck-vba/Rubberduck	https://api.github.com/repos/rubberduck-vba/Rubberduck	opened	Usages of _HiddenModule's Width procedure don't resolve	bug parse-tree-processing	The RD context bar doesn't recognize the `Width` procedure in a usage like ```vb Width #i, j ``` I suspect there's a parser bug because `Width`'s first argument is a file number that must be prefixed with a `#`. Linking #1487 and #2834	1.0	Usages of _HiddenModule's Width procedure don't resolve - The RD context bar doesn't recognize the `Width` procedure in a usage like ```vb Width #i, j ``` I suspect there's a parser bug because `Width`'s first argument is a file number that must be prefixed with a `#`. Linking #1487 and #2834	process	usages of hiddenmodule s width procedure don t resolve the rd context bar doesn t recognize the width procedure in a usage like vb width i j i suspect there s a parser bug because width s first argument is a file number that must be prefixed with a linking and	1
17,751	23,665,767,415	IssuesEvent	2022-08-26 20:43:14	googleapis/python-api-common-protos	https://api.github.com/repos/googleapis/python-api-common-protos	closed	Dependency Dashboard	type: process	This issue lists Renovate updates and detected dependencies. Read the [Dependency Dashboard](https://docs.renovatebot.com/key-concepts/dashboard/) docs to learn more. ## Edited/Blocked These updates have been manually edited so Renovate will no longer make changes. To discard all commits and start over, click on a checkbox. - [ ] <!-- rebase-branch=renovate/distlib-0.x -->[chore(deps): update dependency distlib to v0.3.6](../pull/126) ## Ignored or Blocked These are blocked by an existing closed PR and will not be recreated unless you click a checkbox below. - [ ] <!-- recreate-branch=renovate/click-8.x -->[chore(deps): update dependency click to v8.1.3](../pull/124) - [ ] <!-- recreate-branch=renovate/setuptools-65.x -->[chore(deps): update dependency setuptools to v65.3.0](../pull/125) - [ ] <!-- recreate-branch=renovate/protobuf-4.x -->[chore(deps): update dependency protobuf to v4](../pull/107) ## Detected dependencies <details><summary>dockerfile</summary> <blockquote> <details><summary>.kokoro/docker/docs/Dockerfile</summary> - `ubuntu 22.04` </details> </blockquote> </details> <details><summary>pip_requirements</summary> <blockquote> <details><summary>.kokoro/requirements.txt</summary> - `argcomplete ==2.0.0` - `attrs ==22.1.0` - `bleach ==5.0.1` - `cachetools ==5.2.0` - `certifi ==2022.6.15` - `cffi ==1.15.1` - `charset-normalizer ==2.1.1` - `click ==8.0.4` - `colorlog ==6.6.0` - `commonmark ==0.9.1` - `cryptography ==37.0.4` - `distlib ==0.3.5` - `docutils ==0.19` - `filelock ==3.8.0` - `gcp-docuploader ==0.6.3` - `gcp-releasetool ==1.8.6` - `google-api-core ==2.8.2` - `google-auth ==2.11.0` - `google-cloud-core ==2.3.2` - `google-cloud-storage ==2.5.0` - `google-crc32c ==1.3.0` - `google-resumable-media ==2.3.3` - `googleapis-common-protos ==1.56.4` - `idna ==3.3` - `importlib-metadata ==4.12.0` - `jeepney ==0.8.0` - `jinja2 ==3.1.2` - `keyring ==23.8.2` - `markupsafe ==2.1.1` - `nox ==2022.8.7` - `packaging ==21.3` - `pkginfo ==1.8.3` - `platformdirs ==2.5.2` - `protobuf ==3.20.1` - `py ==1.11.0` - `pyasn1 ==0.4.8` - `pyasn1-modules ==0.2.8` - `pycparser ==2.21` - `pygments ==2.13.0` - `pyjwt ==2.4.0` - `pyparsing ==3.0.9` - `pyperclip ==1.8.2` - `python-dateutil ==2.8.2` - `readme-renderer ==37.0` - `requests ==2.28.1` - `requests-toolbelt ==0.9.1` - `rfc3986 ==2.0.0` - `rich ==12.5.1` - `rsa ==4.9` - `secretstorage ==3.3.3` - `six ==1.16.0` - `twine ==4.0.1` - `typing-extensions ==4.3.0` - `urllib3 ==1.26.12` - `virtualenv ==20.16.3` - `webencodings ==0.5.1` - `wheel ==0.37.1` - `zipp ==3.8.1` - `setuptools ==65.2.0` </details> </blockquote> </details> <details><summary>pip_setup</summary> <blockquote> <details><summary>setup.py</summary> - `protobuf >= 3.15.0, <5.0.0dev` - `grpcio >= 1.0.0, <2.0.0dev` </details> </blockquote> </details> --- - [ ] <!-- manual job -->Check this box to trigger a request for Renovate to run again on this repository	1.0	Dependency Dashboard - This issue lists Renovate updates and detected dependencies. Read the [Dependency Dashboard](https://docs.renovatebot.com/key-concepts/dashboard/) docs to learn more. ## Edited/Blocked These updates have been manually edited so Renovate will no longer make changes. To discard all commits and start over, click on a checkbox. - [ ] <!-- rebase-branch=renovate/distlib-0.x -->[chore(deps): update dependency distlib to v0.3.6](../pull/126) ## Ignored or Blocked These are blocked by an existing closed PR and will not be recreated unless you click a checkbox below. - [ ] <!-- recreate-branch=renovate/click-8.x -->[chore(deps): update dependency click to v8.1.3](../pull/124) - [ ] <!-- recreate-branch=renovate/setuptools-65.x -->[chore(deps): update dependency setuptools to v65.3.0](../pull/125) - [ ] <!-- recreate-branch=renovate/protobuf-4.x -->[chore(deps): update dependency protobuf to v4](../pull/107) ## Detected dependencies <details><summary>dockerfile</summary> <blockquote> <details><summary>.kokoro/docker/docs/Dockerfile</summary> - `ubuntu 22.04` </details> </blockquote> </details> <details><summary>pip_requirements</summary> <blockquote> <details><summary>.kokoro/requirements.txt</summary> - `argcomplete ==2.0.0` - `attrs ==22.1.0` - `bleach ==5.0.1` - `cachetools ==5.2.0` - `certifi ==2022.6.15` - `cffi ==1.15.1` - `charset-normalizer ==2.1.1` - `click ==8.0.4` - `colorlog ==6.6.0` - `commonmark ==0.9.1` - `cryptography ==37.0.4` - `distlib ==0.3.5` - `docutils ==0.19` - `filelock ==3.8.0` - `gcp-docuploader ==0.6.3` - `gcp-releasetool ==1.8.6` - `google-api-core ==2.8.2` - `google-auth ==2.11.0` - `google-cloud-core ==2.3.2` - `google-cloud-storage ==2.5.0` - `google-crc32c ==1.3.0` - `google-resumable-media ==2.3.3` - `googleapis-common-protos ==1.56.4` - `idna ==3.3` - `importlib-metadata ==4.12.0` - `jeepney ==0.8.0` - `jinja2 ==3.1.2` - `keyring ==23.8.2` - `markupsafe ==2.1.1` - `nox ==2022.8.7` - `packaging ==21.3` - `pkginfo ==1.8.3` - `platformdirs ==2.5.2` - `protobuf ==3.20.1` - `py ==1.11.0` - `pyasn1 ==0.4.8` - `pyasn1-modules ==0.2.8` - `pycparser ==2.21` - `pygments ==2.13.0` - `pyjwt ==2.4.0` - `pyparsing ==3.0.9` - `pyperclip ==1.8.2` - `python-dateutil ==2.8.2` - `readme-renderer ==37.0` - `requests ==2.28.1` - `requests-toolbelt ==0.9.1` - `rfc3986 ==2.0.0` - `rich ==12.5.1` - `rsa ==4.9` - `secretstorage ==3.3.3` - `six ==1.16.0` - `twine ==4.0.1` - `typing-extensions ==4.3.0` - `urllib3 ==1.26.12` - `virtualenv ==20.16.3` - `webencodings ==0.5.1` - `wheel ==0.37.1` - `zipp ==3.8.1` - `setuptools ==65.2.0` </details> </blockquote> </details> <details><summary>pip_setup</summary> <blockquote> <details><summary>setup.py</summary> - `protobuf >= 3.15.0, <5.0.0dev` - `grpcio >= 1.0.0, <2.0.0dev` </details> </blockquote> </details> --- - [ ] <!-- manual job -->Check this box to trigger a request for Renovate to run again on this repository	process	dependency dashboard this issue lists renovate updates and detected dependencies read the docs to learn more edited blocked these updates have been manually edited so renovate will no longer make changes to discard all commits and start over click on a checkbox pull ignored or blocked these are blocked by an existing closed pr and will not be recreated unless you click a checkbox below pull pull pull detected dependencies dockerfile kokoro docker docs dockerfile ubuntu pip requirements kokoro requirements txt argcomplete attrs bleach cachetools certifi cffi charset normalizer click colorlog commonmark cryptography distlib docutils filelock gcp docuploader gcp releasetool google api core google auth google cloud core google cloud storage google google resumable media googleapis common protos idna importlib metadata jeepney keyring markupsafe nox packaging pkginfo platformdirs protobuf py modules pycparser pygments pyjwt pyparsing pyperclip python dateutil readme renderer requests requests toolbelt rich rsa secretstorage six twine typing extensions virtualenv webencodings wheel zipp setuptools pip setup setup py protobuf grpcio check this box to trigger a request for renovate to run again on this repository	1
16,573	21,581,211,694	IssuesEvent	2022-05-02 18:56:43	bazelbuild/bazel	https://api.github.com/repos/bazelbuild/bazel	closed	Does bazel support visual studio vs2019 debug mode?	type: support / not a bug (process) untriaged team-OSS	When tensorflow .lilb and tensorflow.dll are generated by ` bazel build --config=v1 --config=opt //tensorflow ` they can be loaded and works well in visual studio 2019 Release mode, BUT failed in debug mode So if I want to build tensorlfow.lib which can be used by vs2019's debug mode, how to choose bazel options ? in other words, selecting bazel build options equals to cmake's Debug option	1.0	Does bazel support visual studio vs2019 debug mode? - When tensorflow .lilb and tensorflow.dll are generated by ` bazel build --config=v1 --config=opt //tensorflow ` they can be loaded and works well in visual studio 2019 Release mode, BUT failed in debug mode So if I want to build tensorlfow.lib which can be used by vs2019's debug mode, how to choose bazel options ? in other words, selecting bazel build options equals to cmake's Debug option	process	does bazel support visual studio debug mode when tensorflow lilb and tensorflow dll are generated by bazel build config config opt tensorflow they can be loaded and works well in visual studio release mode but failed in debug mode so if i want to build tensorlfow lib which can be used by s debug mode how to choose bazel options in other words selecting bazel build options equals to cmake s debug option	1
34,845	7,872,344,478	IssuesEvent	2018-06-25 10:53:46	mozilla/addons-server	https://api.github.com/repos/mozilla/addons-server	closed	Remove ipython from packages on travis	component: code quality priority: p4 triaged	We install IPython and ipdb by default in `dev.txt` which is fine, and should potentially be run by CircleCI UI Tests (since that's the only place where we install everything from scratch already). But let's maybe try to simplify our travis installation a bit and don't install ipython / ipdb there.	1.0	Remove ipython from packages on travis - We install IPython and ipdb by default in `dev.txt` which is fine, and should potentially be run by CircleCI UI Tests (since that's the only place where we install everything from scratch already). But let's maybe try to simplify our travis installation a bit and don't install ipython / ipdb there.	non_process	remove ipython from packages on travis we install ipython and ipdb by default in dev txt which is fine and should potentially be run by circleci ui tests since that s the only place where we install everything from scratch already but let s maybe try to simplify our travis installation a bit and don t install ipython ipdb there	0
3,840	2,540,840,681	IssuesEvent	2015-01-28 00:58:23	CienProject2014/OneLevelHero_ProposalTeam	https://api.github.com/repos/CienProject2014/OneLevelHero_ProposalTeam	opened	엔딩 보상 시스템	priority:Middle	1. 엔딩에 따른 보상 종류 정하기 a. 아이템 b. 스테이터스 c. 스킬 d. 기타 2. 엔딩 보상 구체적으로 정하기 a. 어떤 방식으로 ? b. 얼마나? c. 기타 등등... 3. 엔딩 보상에 고려해야할 것들 a. 성취감을 충분히 느끼게 하느냐 b. 두번 이상 플레이시, 난이도, 흥미, 동기 를 해치지 않을 것 c. 플레이 중 획득되는 것과 차별을 둘 것 d. 설정 상 엔딩 보상은 "용사를 돕기위해 신이 주신 선물"	1.0	엔딩 보상 시스템 - 1. 엔딩에 따른 보상 종류 정하기 a. 아이템 b. 스테이터스 c. 스킬 d. 기타 2. 엔딩 보상 구체적으로 정하기 a. 어떤 방식으로 ? b. 얼마나? c. 기타 등등... 3. 엔딩 보상에 고려해야할 것들 a. 성취감을 충분히 느끼게 하느냐 b. 두번 이상 플레이시, 난이도, 흥미, 동기 를 해치지 않을 것 c. 플레이 중 획득되는 것과 차별을 둘 것 d. 설정 상 엔딩 보상은 "용사를 돕기위해 신이 주신 선물"	non_process	엔딩 보상 시스템 엔딩에 따른 보상 종류 정하기 a 아이템 b 스테이터스 c 스킬 d 기타 엔딩 보상 구체적으로 정하기 a 어떤 방식으로 b 얼마나 c 기타 등등 엔딩 보상에 고려해야할 것들 a 성취감을 충분히 느끼게 하느냐 b 두번 이상 플레이시 난이도 흥미 동기 를 해치지 않을 것 c 플레이 중 획득되는 것과 차별을 둘 것 d 설정 상 엔딩 보상은 용사를 돕기위해 신이 주신 선물	0
15,890	20,075,037,203	IssuesEvent	2022-02-04 11:43:37	climatepolicyradar/navigator	https://api.github.com/repos/climatepolicyradar/navigator	opened	Detect and store language for passage	Document processing	When processing a Document, the language of each passage which is extracted from that document should be detected. The language detected should be stored in the database.	1.0	Detect and store language for passage - When processing a Document, the language of each passage which is extracted from that document should be detected. The language detected should be stored in the database.	process	detect and store language for passage when processing a document the language of each passage which is extracted from that document should be detected the language detected should be stored in the database	1
18,372	24,498,948,210	IssuesEvent	2022-10-10 11:08:47	deepset-ai/haystack	https://api.github.com/repos/deepset-ai/haystack	closed	Pipeline validation fails for PreProcessor initialised with strings containing spaces for the remove_substrings param	type:bug topic:preprocessing journey:intermediate	Describe the bug When using the `PreProcessor` with the `remove_substrings` parameter with a configuration where the PreProcessor should remove substrings with a space in the string, pipeline validation fails if using `load_from_config` or `load_from_yaml`. Error message ``` raise PipelineConfigError( haystack.errors.PipelineConfigError: 'with space' is not a valid variable name or value. Use alphanumeric characters or dash, underscore and colon only.) ``` This is also true for any parameter value in custom nodes which is very limiting regarding what you can implement. Expected behavior Pipeline validation does not care about the formatting of the values that are passed in. Checking variable names is fine but values shouldn't be checked. Additional context To Reproduce ```python from haystack.pipelines import Pipeline if __name__ == '__main__': pipeline_cfg = { 'version': '1.8', 'components': [ { 'name': 'preprocessor', 'type': 'PreProcessor', 'params': { 'remove_substrings': ['with space'] } } ], 'pipelines': [ { 'name': 'indexing', 'nodes': [ {'name': 'preprocessor', 'inputs': ['File']} ] } ] } pp = Pipeline.load_from_config(pipeline_cfg, pipeline_name='indexing') ``` FAQ Check - [x ] Have you had a look at [our new FAQ page](https://haystack.deepset.ai/overview/faq)? System: - OS: - GPU/CPU: - Haystack version (commit or version number): 1.8 - DocumentStore: - Reader: - Retriever:	1.0	Pipeline validation fails for PreProcessor initialised with strings containing spaces for the remove_substrings param - Describe the bug When using the `PreProcessor` with the `remove_substrings` parameter with a configuration where the PreProcessor should remove substrings with a space in the string, pipeline validation fails if using `load_from_config` or `load_from_yaml`. Error message ``` raise PipelineConfigError( haystack.errors.PipelineConfigError: 'with space' is not a valid variable name or value. Use alphanumeric characters or dash, underscore and colon only.) ``` This is also true for any parameter value in custom nodes which is very limiting regarding what you can implement. Expected behavior Pipeline validation does not care about the formatting of the values that are passed in. Checking variable names is fine but values shouldn't be checked. Additional context To Reproduce ```python from haystack.pipelines import Pipeline if __name__ == '__main__': pipeline_cfg = { 'version': '1.8', 'components': [ { 'name': 'preprocessor', 'type': 'PreProcessor', 'params': { 'remove_substrings': ['with space'] } } ], 'pipelines': [ { 'name': 'indexing', 'nodes': [ {'name': 'preprocessor', 'inputs': ['File']} ] } ] } pp = Pipeline.load_from_config(pipeline_cfg, pipeline_name='indexing') ``` FAQ Check - [x ] Have you had a look at [our new FAQ page](https://haystack.deepset.ai/overview/faq)? System: - OS: - GPU/CPU: - Haystack version (commit or version number): 1.8 - DocumentStore: - Reader: - Retriever:	process	pipeline validation fails for preprocessor initialised with strings containing spaces for the remove substrings param describe the bug when using the preprocessor with the remove substrings parameter with a configuration where the preprocessor should remove substrings with a space in the string pipeline validation fails if using load from config or load from yaml error message raise pipelineconfigerror haystack errors pipelineconfigerror with space is not a valid variable name or value use alphanumeric characters or dash underscore and colon only this is also true for any parameter value in custom nodes which is very limiting regarding what you can implement expected behavior pipeline validation does not care about the formatting of the values that are passed in checking variable names is fine but values shouldn t be checked additional context to reproduce python from haystack pipelines import pipeline if name main pipeline cfg version components name preprocessor type preprocessor params remove substrings pipelines name indexing nodes name preprocessor inputs pp pipeline load from config pipeline cfg pipeline name indexing faq check have you had a look at system os gpu cpu haystack version commit or version number documentstore reader retriever	1
260,919	19,688,987,366	IssuesEvent	2022-01-12 03:20:02	chaos-mesh/chaos-mesh	https://api.github.com/repos/chaos-mesh/chaos-mesh	closed	does test environment dashboard could add user token permission?	documentation	when use k3s environment, installing chaos-mesh, i can't find ```add user token``` option in settings on dashboard；how can i set ```user token``` in k3s environment？ version: ``` v2.1.0 ```	1.0	does test environment dashboard could add user token permission? - when use k3s environment, installing chaos-mesh, i can't find ```add user token``` option in settings on dashboard；how can i set ```user token``` in k3s environment？ version: ``` v2.1.0 ```	non_process	does test environment dashboard could add user token permission when use environment installing chaos mesh i can t find add user token option in settings on dashboard；how can i set user token in environment？ version	0
42,164	5,428,801,102	IssuesEvent	2017-03-03 16:45:55	phetsims/circuit-construction-kit-common	https://api.github.com/repos/phetsims/circuit-construction-kit-common	opened	Create artwork for the objects	design:artwork	@arouinfar can you please work with our artist to create artwork for the objects to be used in the 2nd screen?	1.0	Create artwork for the objects - @arouinfar can you please work with our artist to create artwork for the objects to be used in the 2nd screen?	non_process	create artwork for the objects arouinfar can you please work with our artist to create artwork for the objects to be used in the screen	0
8,343	11,497,832,785	IssuesEvent	2020-02-12 10:45:53	18F/tts-tech-portfolio	https://api.github.com/repos/18F/tts-tech-portfolio	closed	create `prep step` issues from icebox/backlog issues	Jan2020-inperson epic: customer service epic: internal workflow/procedures prep step workflow: process	As a team performing sprint planning and grooming; we should ensure we dedicate a portion of the time to break out preparation tasks to ensure issues are ready to be worked when appropriate. E.g. a proactive issue to perform a small/short engagement and communication (email/meeting/outline/etc) to obtain guidance and understanding around an issue / to get a head of a long lead issue and direction. #### Acceptance Criteria: - [x] create an established label - https://github.com/18F/tts-tech-portfolio/labels/prep%20step - [ ] Update sprint/grooming documentation - [ ] Complete https://github.com/18F/tts-tech-portfolio/issues/222	1.0	create `prep step` issues from icebox/backlog issues - As a team performing sprint planning and grooming; we should ensure we dedicate a portion of the time to break out preparation tasks to ensure issues are ready to be worked when appropriate. E.g. a proactive issue to perform a small/short engagement and communication (email/meeting/outline/etc) to obtain guidance and understanding around an issue / to get a head of a long lead issue and direction. #### Acceptance Criteria: - [x] create an established label - https://github.com/18F/tts-tech-portfolio/labels/prep%20step - [ ] Update sprint/grooming documentation - [ ] Complete https://github.com/18F/tts-tech-portfolio/issues/222	process	create prep step issues from icebox backlog issues as a team performing sprint planning and grooming we should ensure we dedicate a portion of the time to break out preparation tasks to ensure issues are ready to be worked when appropriate e g a proactive issue to perform a small short engagement and communication email meeting outline etc to obtain guidance and understanding around an issue to get a head of a long lead issue and direction acceptance criteria create an established label update sprint grooming documentation complete	1
29,733	5,846,049,814	IssuesEvent	2017-05-10 15:24:41	jOOQ/jOOQ	https://api.github.com/repos/jOOQ/jOOQ	closed	INSERT INTO .. SET Record statement should not take defaulted null values into consideration	C: Functionality P: Medium R: Invalid T: Defect	Similar to other API elements, the `INSERT INTO .. SET record` API should not explicitly set values that are: - `null` (in Java) - `NOT NULL` (in the database) --- See also - #2700 - #4161 - https://groups.google.com/forum/#!topic/jooq-user/8hwhDanETYs	1.0	INSERT INTO .. SET Record statement should not take defaulted null values into consideration - Similar to other API elements, the `INSERT INTO .. SET record` API should not explicitly set values that are: - `null` (in Java) - `NOT NULL` (in the database) --- See also - #2700 - #4161 - https://groups.google.com/forum/#!topic/jooq-user/8hwhDanETYs	non_process	insert into set record statement should not take defaulted null values into consideration similar to other api elements the insert into set record api should not explicitly set values that are null in java not null in the database see also	0
35,855	8,016,937,622	IssuesEvent	2018-07-25 14:40:15	MicrosoftDocs/live-share	https://api.github.com/repos/MicrosoftDocs/live-share	closed	Support VS Code's task running features for guests	feature-request vscode	> Note that developers starting the collaboration session (hosts) do not have this limitation. It is specific to participants joining a session. Visual Studio Code supports the concept of [running tasks via the command palette](https://code.visualstudio.com/Docs/editor/tasks). While developers starting the collaboration session (hosts) can use this feature freely, participants that join the session are not able to take advantage of the capability as VS Code attempts to run these tasks locally. We should enable remote execution of these tasks for the guest so they are also able to take advantage of the feature.	1.0	Support VS Code's task running features for guests - > Note that developers starting the collaboration session (hosts) do not have this limitation. It is specific to participants joining a session. Visual Studio Code supports the concept of [running tasks via the command palette](https://code.visualstudio.com/Docs/editor/tasks). While developers starting the collaboration session (hosts) can use this feature freely, participants that join the session are not able to take advantage of the capability as VS Code attempts to run these tasks locally. We should enable remote execution of these tasks for the guest so they are also able to take advantage of the feature.	non_process	support vs code s task running features for guests note that developers starting the collaboration session hosts do not have this limitation it is specific to participants joining a session visual studio code supports the concept of while developers starting the collaboration session hosts can use this feature freely participants that join the session are not able to take advantage of the capability as vs code attempts to run these tasks locally we should enable remote execution of these tasks for the guest so they are also able to take advantage of the feature	0
272,140	29,794,985,385	IssuesEvent	2023-06-16 01:02:03	billmcchesney1/hadoop	https://api.github.com/repos/billmcchesney1/hadoop	closed	CVE-2020-11111 (High) detected in jackson-databind-2.9.10.1.jar - autoclosed	Mend: dependency security vulnerability	## CVE-2020-11111 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>jackson-databind-2.9.10.1.jar</b></p></summary> <p>General data-binding functionality for Jackson: works on core streaming API</p> <p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p> <p>Path to vulnerable library: /hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-documentstore/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-common/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-client/target/lib/jackson-databind-2.9.10.1.jar</p> <p> Dependency Hierarchy: - :x: jackson-databind-2.9.10.1.jar (Vulnerable Library) <p>Found in HEAD commit: <a href="https://github.com/billmcchesney1/hadoop/commit/6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a">6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a</a></p> <p>Found in base branch: <b>trunk</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png?' width=19 height=20> Vulnerability Details</summary> <p> FasterXML jackson-databind 2.x before 2.9.10.4 mishandles the interaction between serialization gadgets and typing, related to org.apache.activemq.* (aka activemq-jms, activemq-core, activemq-pool, and activemq-pool-jms). <p>Publish Date: 2020-03-31 <p>URL: <a href=https://www.mend.io/vulnerability-database/CVE-2020-11111>CVE-2020-11111</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>8.8</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Origin: <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113">https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113</a></p> <p>Release Date: 2020-03-31</p> <p>Fix Resolution: 2.9.10.4</p> </p> </details> <p></p>	True	CVE-2020-11111 (High) detected in jackson-databind-2.9.10.1.jar - autoclosed - ## CVE-2020-11111 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>jackson-databind-2.9.10.1.jar</b></p></summary> <p>General data-binding functionality for Jackson: works on core streaming API</p> <p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p> <p>Path to vulnerable library: /hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-documentstore/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-common/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-client/target/lib/jackson-databind-2.9.10.1.jar</p> <p> Dependency Hierarchy: - :x: jackson-databind-2.9.10.1.jar (Vulnerable Library) <p>Found in HEAD commit: <a href="https://github.com/billmcchesney1/hadoop/commit/6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a">6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a</a></p> <p>Found in base branch: <b>trunk</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png?' width=19 height=20> Vulnerability Details</summary> <p> FasterXML jackson-databind 2.x before 2.9.10.4 mishandles the interaction between serialization gadgets and typing, related to org.apache.activemq.* (aka activemq-jms, activemq-core, activemq-pool, and activemq-pool-jms). <p>Publish Date: 2020-03-31 <p>URL: <a href=https://www.mend.io/vulnerability-database/CVE-2020-11111>CVE-2020-11111</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>8.8</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Origin: <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113">https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113</a></p> <p>Release Date: 2020-03-31</p> <p>Fix Resolution: 2.9.10.4</p> </p> </details> <p></p>	non_process	cve high detected in jackson databind jar autoclosed cve high severity vulnerability vulnerable library jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to vulnerable library hadoop yarn project hadoop yarn hadoop yarn server hadoop yarn server timelineservice documentstore target lib jackson databind jar hadoop yarn project hadoop yarn hadoop yarn server hadoop yarn server timelineservice hbase hadoop yarn server timelineservice hbase common target lib jackson databind jar hadoop yarn project hadoop yarn hadoop yarn server hadoop yarn server timelineservice hbase hadoop yarn server timelineservice hbase client target lib jackson databind jar dependency hierarchy x jackson databind jar vulnerable library found in head commit a href found in base branch trunk vulnerability details fasterxml jackson databind x before mishandles the interaction between serialization gadgets and typing related to org apache activemq aka activemq jms activemq core activemq pool and activemq pool jms publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction required scope unchanged impact metrics confidentiality impact high integrity impact high availability impact high for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution	0
7,415	10,540,552,982	IssuesEvent	2019-10-02 08:41:32	threefoldtech/jumpscaleX_threebot	https://api.github.com/repos/threefoldtech/jumpscaleX_threebot	closed	Threebot: can not start directory package	process_wontfix type_bug	The package started the first time, but when I stopped it and tried to start it again. I keep getting this same error: `can not release un-acquired lock` ![image](https://user-images.githubusercontent.com/10920323/65828102-7f963400-e297-11e9-8d02-7ef1e66d7b29.png)	1.0	Threebot: can not start directory package - The package started the first time, but when I stopped it and tried to start it again. I keep getting this same error: `can not release un-acquired lock` ![image](https://user-images.githubusercontent.com/10920323/65828102-7f963400-e297-11e9-8d02-7ef1e66d7b29.png)	process	threebot can not start directory package the package started the first time but when i stopped it and tried to start it again i keep getting this same error can not release un acquired lock	1
171,133	27,065,571,052	IssuesEvent	2023-02-14 00:01:03	department-of-veterans-affairs/vets-design-system-documentation	https://api.github.com/repos/department-of-veterans-affairs/vets-design-system-documentation	closed	Update Sketch with Radio button tile variation	vsp-design-system-team va-radio	## Description There are teams interested in using the Tile version of Radio button. It's only in Storybook right now. Update Sketch library with the Radio button with tile and description text. ## Details Storybook link https://design.va.gov/storybook/?path=/docs/components-va-radio--default#with-description-text ## Tasks - [ ] Update Sketch library to include Radio button with tile and description text - [ ] Get review and sign off ## Acceptance Criteria - [ ] Sketch library has been updated - [ ] Update to Radio button in Sketch has been announced to designers	1.0	Update Sketch with Radio button tile variation - ## Description There are teams interested in using the Tile version of Radio button. It's only in Storybook right now. Update Sketch library with the Radio button with tile and description text. ## Details Storybook link https://design.va.gov/storybook/?path=/docs/components-va-radio--default#with-description-text ## Tasks - [ ] Update Sketch library to include Radio button with tile and description text - [ ] Get review and sign off ## Acceptance Criteria - [ ] Sketch library has been updated - [ ] Update to Radio button in Sketch has been announced to designers	non_process	update sketch with radio button tile variation description there are teams interested in using the tile version of radio button it s only in storybook right now update sketch library with the radio button with tile and description text details storybook link tasks update sketch library to include radio button with tile and description text get review and sign off acceptance criteria sketch library has been updated update to radio button in sketch has been announced to designers	0
18,328	24,445,771,477	IssuesEvent	2022-10-06 17:49:42	microsoft/vscode	https://api.github.com/repos/microsoft/vscode	closed	Detaching a terminal editor doesn't fire vscode.window.onDidCloseTerminal event	bug api terminal-process	- VS Code Version: stable and from sources Steps to Reproduce: 1. Create a terminal editor 2. Run `workbench.action.terminal.detachSession` command 3. :bug: `vscode.window.onDidCloseTerminal` isn't fired	1.0	Detaching a terminal editor doesn't fire vscode.window.onDidCloseTerminal event - - VS Code Version: stable and from sources Steps to Reproduce: 1. Create a terminal editor 2. Run `workbench.action.terminal.detachSession` command 3. :bug: `vscode.window.onDidCloseTerminal` isn't fired	process	detaching a terminal editor doesn t fire vscode window ondidcloseterminal event vs code version stable and from sources steps to reproduce create a terminal editor run workbench action terminal detachsession command bug vscode window ondidcloseterminal isn t fired	1
7,099	10,252,919,377	IssuesEvent	2019-08-21 10:01:23	dotnet/corefx	https://api.github.com/repos/dotnet/corefx	closed	Properties of System.Diagnostics.Process should be null rather than throwing exceptions	area-System.Diagnostics.Process	The following code throws `System.InvalidOperationException: No process is associated with this object.` when trying to access the ProcessName property although it has a null check. I mean for some reason accessing all properties of the Process object throws an InvalidOperationException. Wouldn't it make more sense that they are just null if the underlying process is disposed? Otherwise something like this: `public string ProcessName => proc?.ProcessName ?? "<empty>";` is not possible and always requires a try catch method body like this: ``` public string ProcessName { get { try { return proc.ProcessName; } catch (InvalidOperationException) { return "<empty>"; } } } ``` Repro: ``` public MainWindow() { InitializeComponent(); proc = Process.GetCurrentProcess(); } private Process proc; public string ProcessName => proc?.ProcessName ?? "<empty>"; private void Button_Click(object sender, RoutedEventArgs e) { MessageBox.Show(ProcessName); proc?.Close(); proc?.Dispose(); MessageBox.Show(ProcessName); //Throws System.InvalidOperationException: No process is associated with this object. } ```	1.0	Properties of System.Diagnostics.Process should be null rather than throwing exceptions - The following code throws `System.InvalidOperationException: No process is associated with this object.` when trying to access the ProcessName property although it has a null check. I mean for some reason accessing all properties of the Process object throws an InvalidOperationException. Wouldn't it make more sense that they are just null if the underlying process is disposed? Otherwise something like this: `public string ProcessName => proc?.ProcessName ?? "<empty>";` is not possible and always requires a try catch method body like this: ``` public string ProcessName { get { try { return proc.ProcessName; } catch (InvalidOperationException) { return "<empty>"; } } } ``` Repro: ``` public MainWindow() { InitializeComponent(); proc = Process.GetCurrentProcess(); } private Process proc; public string ProcessName => proc?.ProcessName ?? "<empty>"; private void Button_Click(object sender, RoutedEventArgs e) { MessageBox.Show(ProcessName); proc?.Close(); proc?.Dispose(); MessageBox.Show(ProcessName); //Throws System.InvalidOperationException: No process is associated with this object. } ```	process	properties of system diagnostics process should be null rather than throwing exceptions the following code throws system invalidoperationexception no process is associated with this object when trying to access the processname property although it has a null check i mean for some reason accessing all properties of the process object throws an invalidoperationexception wouldn t it make more sense that they are just null if the underlying process is disposed otherwise something like this public string processname proc processname is not possible and always requires a try catch method body like this public string processname get try return proc processname catch invalidoperationexception return repro public mainwindow initializecomponent proc process getcurrentprocess private process proc public string processname proc processname private void button click object sender routedeventargs e messagebox show processname proc close proc dispose messagebox show processname throws system invalidoperationexception no process is associated with this object	1
64,478	15,890,505,520	IssuesEvent	2021-04-10 15:41:25	ARMmaster17/Captain	https://api.github.com/repos/ARMmaster17/Captain	closed	Builder does not catch IPAM errors	bug component:Builder	Need to wrap IPAM requests the same way Proxmox requests were wrapped in #15 with some basic error catching. No idea what the format is for IPAM errors other than the server returns a 5XX error code.	1.0	Builder does not catch IPAM errors - Need to wrap IPAM requests the same way Proxmox requests were wrapped in #15 with some basic error catching. No idea what the format is for IPAM errors other than the server returns a 5XX error code.	non_process	builder does not catch ipam errors need to wrap ipam requests the same way proxmox requests were wrapped in with some basic error catching no idea what the format is for ipam errors other than the server returns a error code	0
179,801	14,713,023,716	IssuesEvent	2021-01-05 09:45:41	SketchUp/api-issue-tracker	https://api.github.com/repos/SketchUp/api-issue-tracker	opened	Document UI::Notification icon size and supported fileformat	Ruby API SketchUp documentation	Ref: https://github.com/SketchUp/api-issue-tracker/issues/484 > There is no info of what icon file types are supported (what file types are supported?) > Thanks! I maybe spent an hour in total with SVG with locals and global paths, giving up up and switching to PNGs only to have them weirdly cropped. I made sure to have some excess size for the icon to look crisp on a high DPI machine, but nope.	1.0	Document UI::Notification icon size and supported fileformat - Ref: https://github.com/SketchUp/api-issue-tracker/issues/484 > There is no info of what icon file types are supported (what file types are supported?) > Thanks! I maybe spent an hour in total with SVG with locals and global paths, giving up up and switching to PNGs only to have them weirdly cropped. I made sure to have some excess size for the icon to look crisp on a high DPI machine, but nope.	non_process	document ui notification icon size and supported fileformat ref there is no info of what icon file types are supported what file types are supported thanks i maybe spent an hour in total with svg with locals and global paths giving up up and switching to pngs only to have them weirdly cropped i made sure to have some excess size for the icon to look crisp on a high dpi machine but nope	0
17,617	23,433,907,266	IssuesEvent	2022-08-15 07:33:26	NationalSecurityAgency/ghidra	https://api.github.com/repos/NationalSecurityAgency/ghidra	closed	dsPIC30 mov issue	Feature: Processor/PIC Reason: OBE	Not sure if this is working as intended, or a bug. Here is a small program for the dsPIC30. ``` #include <stdio.h> int main(void) { printf("Hello World\n"); } ``` <img width="524" alt="Capture" src="https://user-images.githubusercontent.com/5378554/77928375-52dacd80-7276-11ea-9b32-af41fcf8dbde.PNG"> [Test.X.production.zip](https://github.com/NationalSecurityAgency/ghidra/files/4403699/Test.X.production.zip) At `rom:0007a2`, it looks like it's moving a constant into `W0` rather than a memory address. When you double click on `#0x870e`, it tries to navigate to `rom:00870e` rather than `ram`. Is there anything than can be done to fix this behavior? The decompiler seems to get it right.	1.0	dsPIC30 mov issue - Not sure if this is working as intended, or a bug. Here is a small program for the dsPIC30. ``` #include <stdio.h> int main(void) { printf("Hello World\n"); } ``` <img width="524" alt="Capture" src="https://user-images.githubusercontent.com/5378554/77928375-52dacd80-7276-11ea-9b32-af41fcf8dbde.PNG"> [Test.X.production.zip](https://github.com/NationalSecurityAgency/ghidra/files/4403699/Test.X.production.zip) At `rom:0007a2`, it looks like it's moving a constant into `W0` rather than a memory address. When you double click on `#0x870e`, it tries to navigate to `rom:00870e` rather than `ram`. Is there anything than can be done to fix this behavior? The decompiler seems to get it right.	process	mov issue not sure if this is working as intended or a bug here is a small program for the include int main void printf hello world n img width alt capture src at rom it looks like it s moving a constant into rather than a memory address when you double click on it tries to navigate to rom rather than ram is there anything than can be done to fix this behavior the decompiler seems to get it right	1
20,142	26,688,504,042	IssuesEvent	2023-01-27 01:01:50	opensearch-project/data-prepper	https://api.github.com/repos/opensearch-project/data-prepper	closed	Add metrics for otel_trace_raw: traceGroupCacheCount and spanSetCount	enhancement plugin - processor	Is your feature request related to a problem? Please describe. The otel_trace_raw processor has some large collections of data, but does not report metrics on these. Describe the solution you'd like Provide two new metrics: * `traceGroupCacheCount` - The size of the cache holding trace groups * `spanSetCount` - The number of span-sets	1.0	Add metrics for otel_trace_raw: traceGroupCacheCount and spanSetCount - Is your feature request related to a problem? Please describe. The otel_trace_raw processor has some large collections of data, but does not report metrics on these. Describe the solution you'd like Provide two new metrics: * `traceGroupCacheCount` - The size of the cache holding trace groups * `spanSetCount` - The number of span-sets	process	add metrics for otel trace raw tracegroupcachecount and spansetcount is your feature request related to a problem please describe the otel trace raw processor has some large collections of data but does not report metrics on these describe the solution you d like provide two new metrics tracegroupcachecount the size of the cache holding trace groups spansetcount the number of span sets	1
235,802	25,962,067,195	IssuesEvent	2022-12-19 01:02:55	rvvergara/flight-booker	https://api.github.com/repos/rvvergara/flight-booker	opened	CVE-2022-23516 (High) detected in loofah-2.2.3.gem	security vulnerability	## CVE-2022-23516 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>loofah-2.2.3.gem</b></p></summary> <p>Loofah is a general library for manipulating and transforming HTML/XML documents and fragments. It's built on top of Nokogiri and libxml2, so it's fast and has a nice API. Loofah excels at HTML sanitization (XSS prevention). It includes some nice HTML sanitizers, which are based on HTML5lib's whitelist, so it most likely won't make your codes less secure. (These statements have not been evaluated by Netexperts.) ActiveRecord extensions for sanitization are available in the [`loofah-activerecord` gem](https://github.com/flavorjones/loofah-activerecord).</p> <p>Library home page: <a href="https://rubygems.org/gems/loofah-2.2.3.gem">https://rubygems.org/gems/loofah-2.2.3.gem</a></p> <p> Dependency Hierarchy: - rails-5.2.3.gem (Root Library) - actioncable-5.2.3.gem - actionpack-5.2.3.gem - actionview-5.2.3.gem - rails-html-sanitizer-1.0.4.gem - :x: loofah-2.2.3.gem (Vulnerable Library) <p>Found in HEAD commit: <a href="https://github.com/rvvergara/flight-booker/commit/5415a961bf0fcfe4baefcc9d86c9cd837862c738">5415a961bf0fcfe4baefcc9d86c9cd837862c738</a></p> <p>Found in base branch: <b>master</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> <p> Loofah is a general library for manipulating and transforming HTML/XML documents and fragments, built on top of Nokogiri. Loofah >= 2.2.0, < 2.19.1 uses recursion for sanitizing CDATA sections, making it susceptible to stack exhaustion and raising a SystemStackError exception. This may lead to a denial of service through CPU resource consumption. This issue is patched in version 2.19.1. Users who are unable to upgrade may be able to mitigate this vulnerability by limiting the length of the strings that are sanitized. <p>Publish Date: 2022-12-14 <p>URL: <a href=https://www.mend.io/vulnerability-database/CVE-2022-23516>CVE-2022-23516</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>7.5</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: None - Integrity Impact: None - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Origin: <a href="https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm">https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm</a></p> <p>Release Date: 2022-12-14</p> <p>Fix Resolution: loofah - 2.19.1</p> </p> </details> <p></p> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)	True	CVE-2022-23516 (High) detected in loofah-2.2.3.gem - ## CVE-2022-23516 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>loofah-2.2.3.gem</b></p></summary> <p>Loofah is a general library for manipulating and transforming HTML/XML documents and fragments. It's built on top of Nokogiri and libxml2, so it's fast and has a nice API. Loofah excels at HTML sanitization (XSS prevention). It includes some nice HTML sanitizers, which are based on HTML5lib's whitelist, so it most likely won't make your codes less secure. (These statements have not been evaluated by Netexperts.) ActiveRecord extensions for sanitization are available in the [`loofah-activerecord` gem](https://github.com/flavorjones/loofah-activerecord).</p> <p>Library home page: <a href="https://rubygems.org/gems/loofah-2.2.3.gem">https://rubygems.org/gems/loofah-2.2.3.gem</a></p> <p> Dependency Hierarchy: - rails-5.2.3.gem (Root Library) - actioncable-5.2.3.gem - actionpack-5.2.3.gem - actionview-5.2.3.gem - rails-html-sanitizer-1.0.4.gem - :x: loofah-2.2.3.gem (Vulnerable Library) <p>Found in HEAD commit: <a href="https://github.com/rvvergara/flight-booker/commit/5415a961bf0fcfe4baefcc9d86c9cd837862c738">5415a961bf0fcfe4baefcc9d86c9cd837862c738</a></p> <p>Found in base branch: <b>master</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> <p> Loofah is a general library for manipulating and transforming HTML/XML documents and fragments, built on top of Nokogiri. Loofah >= 2.2.0, < 2.19.1 uses recursion for sanitizing CDATA sections, making it susceptible to stack exhaustion and raising a SystemStackError exception. This may lead to a denial of service through CPU resource consumption. This issue is patched in version 2.19.1. Users who are unable to upgrade may be able to mitigate this vulnerability by limiting the length of the strings that are sanitized. <p>Publish Date: 2022-12-14 <p>URL: <a href=https://www.mend.io/vulnerability-database/CVE-2022-23516>CVE-2022-23516</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>7.5</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: None - Integrity Impact: None - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Origin: <a href="https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm">https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm</a></p> <p>Release Date: 2022-12-14</p> <p>Fix Resolution: loofah - 2.19.1</p> </p> </details> <p></p> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)	non_process	cve high detected in loofah gem cve high severity vulnerability vulnerable library loofah gem loofah is a general library for manipulating and transforming html xml documents and fragments it s built on top of nokogiri and so it s fast and has a nice api loofah excels at html sanitization xss prevention it includes some nice html sanitizers which are based on s whitelist so it most likely won t make your codes less secure these statements have not been evaluated by netexperts activerecord extensions for sanitization are available in the library home page a href dependency hierarchy rails gem root library actioncable gem actionpack gem actionview gem rails html sanitizer gem x loofah gem vulnerable library found in head commit a href found in base branch master vulnerability details loofah is a general library for manipulating and transforming html xml documents and fragments built on top of nokogiri loofah uses recursion for sanitizing cdata sections making it susceptible to stack exhaustion and raising a systemstackerror exception this may lead to a denial of service through cpu resource consumption this issue is patched in version users who are unable to upgrade may be able to mitigate this vulnerability by limiting the length of the strings that are sanitized publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction none scope unchanged impact metrics confidentiality impact none integrity impact none availability impact high for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution loofah step up your open source security game with mend	0
5,467	8,335,489,130	IssuesEvent	2018-09-28 02:36:23	mozilla-tw/ScreenshotGo	https://api.github.com/repos/mozilla-tw/ScreenshotGo	closed	Integrate Firebase	P0 others process	learned we haven't done that so submit an issue for tracking. should be something to include before official launch so P1.	1.0	Integrate Firebase - learned we haven't done that so submit an issue for tracking. should be something to include before official launch so P1.	process	integrate firebase learned we haven t done that so submit an issue for tracking should be something to include before official launch so	1
140,599	12,943,076,681	IssuesEvent	2020-07-18 05:06:17	moxfield/moxfield-public	https://api.github.com/repos/moxfield/moxfield-public	closed	Patreon Reward: Subdomains	documentation	For your future Patreon, it would be great to have a reward tier where a user would be given a subdomain redirect for their decks. To ease your life, this should be able to be provision/deprovision automatically as patreon rewards lapse/unlapse. On the flipside, it could become permanent if a user were to donate for 3 (or 6 or 12) months straight a specific tier, or hit a certain financial threshold over time (even when subscribing/unsubscribing).	1.0	Patreon Reward: Subdomains - For your future Patreon, it would be great to have a reward tier where a user would be given a subdomain redirect for their decks. To ease your life, this should be able to be provision/deprovision automatically as patreon rewards lapse/unlapse. On the flipside, it could become permanent if a user were to donate for 3 (or 6 or 12) months straight a specific tier, or hit a certain financial threshold over time (even when subscribing/unsubscribing).	non_process	patreon reward subdomains for your future patreon it would be great to have a reward tier where a user would be given a subdomain redirect for their decks to ease your life this should be able to be provision deprovision automatically as patreon rewards lapse unlapse on the flipside it could become permanent if a user were to donate for or or months straight a specific tier or hit a certain financial threshold over time even when subscribing unsubscribing	0
68,823	8,355,169,835	IssuesEvent	2018-10-02 15:03:48	alerj/aloalerjsite	https://api.github.com/repos/alerj/aloalerjsite	closed	Alinhar label no centro do controle	bug web design	- [ ] Alinhamento vertical ao label no centro - [ ] Alinhamento à esquerda em relação ao outros controles acima e abaixo dele ![image](https://user-images.githubusercontent.com/3182864/45510342-4fedba00-b770-11e8-8d4d-40c11d2a65b3.png)	1.0	Alinhar label no centro do controle - - [ ] Alinhamento vertical ao label no centro - [ ] Alinhamento à esquerda em relação ao outros controles acima e abaixo dele ![image](https://user-images.githubusercontent.com/3182864/45510342-4fedba00-b770-11e8-8d4d-40c11d2a65b3.png)	non_process	alinhar label no centro do controle alinhamento vertical ao label no centro alinhamento à esquerda em relação ao outros controles acima e abaixo dele	0
16,281	20,905,499,503	IssuesEvent	2022-03-24 01:24:29	crim-ca/weaver	https://api.github.com/repos/crim-ca/weaver	closed	[BUG] wrong workflow_input_value usage	triage/bug feature/CWL process/wps3 project/OGC feature/job process/workflow	Describe the bug `workflow_input_value` can have multiple types (eg: href, float, int) but `wps3_process.execute()` expects it to be a href. When it isn't and href, `wps3_process.execute()` fails. Either when trying `workflow_input_value["location"]` when `workflow_input_value` is a float (which isn't subscriptable), or later on, during the `.startswith()` validation, which fails since type mismatch. To Reproduce 1. Run a workflow, for eg: `pytest tests/functional/test_ems_end2end.py -k "test_workflow_subset_picker"` 2. Notice the TypeError message in the logs (see Screenshots section) Expected behavior Having data other than `href` parsed as `data`, to avoid type mismatch with `LiteralData`. Screenshots ``` weaver_1 \| File "/opt/local/src/weaver/weaver/processes/wps_workflow.py", line 455, in execute weaver_1 \| self.results = self.wps_process.execute(self.builder.job, self.outdir, self.expected_outputs) weaver_1 \| File "/opt/local/src/weaver/weaver/processes/wps3_process.py", line 256, in execute weaver_1 \| execute_req_input_val: workflow_input_value["location"]}) weaver_1 \| TypeError: 'float' object is not subscriptable ``` Context (please complete the following information): - OS: linux - Instance: local EMS - Version 1.13.1 Note that although `test_workflow_subset_picker` is not currently working, in the past this test passed successfully on a local EMS.	2.0	[BUG] wrong workflow_input_value usage - Describe the bug `workflow_input_value` can have multiple types (eg: href, float, int) but `wps3_process.execute()` expects it to be a href. When it isn't and href, `wps3_process.execute()` fails. Either when trying `workflow_input_value["location"]` when `workflow_input_value` is a float (which isn't subscriptable), or later on, during the `.startswith()` validation, which fails since type mismatch. To Reproduce 1. Run a workflow, for eg: `pytest tests/functional/test_ems_end2end.py -k "test_workflow_subset_picker"` 2. Notice the TypeError message in the logs (see Screenshots section) Expected behavior Having data other than `href` parsed as `data`, to avoid type mismatch with `LiteralData`. Screenshots ``` weaver_1 \| File "/opt/local/src/weaver/weaver/processes/wps_workflow.py", line 455, in execute weaver_1 \| self.results = self.wps_process.execute(self.builder.job, self.outdir, self.expected_outputs) weaver_1 \| File "/opt/local/src/weaver/weaver/processes/wps3_process.py", line 256, in execute weaver_1 \| execute_req_input_val: workflow_input_value["location"]}) weaver_1 \| TypeError: 'float' object is not subscriptable ``` Context (please complete the following information): - OS: linux - Instance: local EMS - Version 1.13.1 Note that although `test_workflow_subset_picker` is not currently working, in the past this test passed successfully on a local EMS.	process	wrong workflow input value usage describe the bug workflow input value can have multiple types eg href float int but process execute expects it to be a href when it isn t and href process execute fails either when trying workflow input value when workflow input value is a float which isn t subscriptable or later on during the startswith validation which fails since type mismatch to reproduce run a workflow for eg pytest tests functional test ems py k test workflow subset picker notice the typeerror message in the logs see screenshots section expected behavior having data other than href parsed as data to avoid type mismatch with literaldata screenshots weaver file opt local src weaver weaver processes wps workflow py line in execute weaver self results self wps process execute self builder job self outdir self expected outputs weaver file opt local src weaver weaver processes process py line in execute weaver execute req input val workflow input value weaver typeerror float object is not subscriptable context please complete the following information os linux instance local ems version note that although test workflow subset picker is not currently working in the past this test passed successfully on a local ems	1
498	2,941,516,116	IssuesEvent	2015-07-02 08:31:02	tim-m89/Download-Scheduler	https://api.github.com/repos/tim-m89/Download-Scheduler	opened	Multiprocess / Electrolysis support	multiprocess (Electrolysis)	[Multiprocess Firefox](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox) is coming. This is a good thing really. See the [motivation behind this](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox/Motivation) Unfortunately this means without doing anything, this add-on may just not work properly in a future Firefox release. I think the current eta is Firefox 40 (August). The major code restructuring I've done on this add-on recently has been driven by this so that I can implement the changes as simply as possible. Switching from overlay based to bootstrap more closely matches the dynamic paradigm of implementing multiprocess.	1.0	Multiprocess / Electrolysis support - [Multiprocess Firefox](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox) is coming. This is a good thing really. See the [motivation behind this](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox/Motivation) Unfortunately this means without doing anything, this add-on may just not work properly in a future Firefox release. I think the current eta is Firefox 40 (August). The major code restructuring I've done on this add-on recently has been driven by this so that I can implement the changes as simply as possible. Switching from overlay based to bootstrap more closely matches the dynamic paradigm of implementing multiprocess.	process	multiprocess electrolysis support is coming this is a good thing really see the unfortunately this means without doing anything this add on may just not work properly in a future firefox release i think the current eta is firefox august the major code restructuring i ve done on this add on recently has been driven by this so that i can implement the changes as simply as possible switching from overlay based to bootstrap more closely matches the dynamic paradigm of implementing multiprocess	1
6,404	9,487,212,305	IssuesEvent	2019-04-22 16:12:14	brandon1roadgears/Interpreter-of-programming-language-of-Turing-Machine	https://api.github.com/repos/brandon1roadgears/Interpreter-of-programming-language-of-Turing-Machine	opened	написать функцию выводящую с правилами ввода для пользователя	C++ Work in process	*## Нужно написать функцию в которой будут написаны правила вода КОМАНД и ГЛАВНОЙ строки.	1.0	написать функцию выводящую с правилами ввода для пользователя - *## Нужно написать функцию в которой будут написаны правила вода КОМАНД и ГЛАВНОЙ строки.	process	написать функцию выводящую с правилами ввода для пользователя нужно написать функцию в которой будут написаны правила вода команд и главной строки	1
21,625	3,908,852,955	IssuesEvent	2016-04-19 17:13:46	kubernetes/kubernetes	https://api.github.com/repos/kubernetes/kubernetes	closed	e2e flake: Preparation for creating a cluster are ridiculously long (45+ minutes)	kind/flake priority/P0 team/test-infra	In this example: http://kubekins.dls.corp.google.com/view/Scalability/job/kubernetes-kubemark-5-gce/1469/consoleFull It took 45 minutes before we even started creating a cluster. Few log lines: 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by upstream project "kubernetes-build" build number 9424 ... 00:17:12 + gsutil -mq cp gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes.tar.gz gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes-test.tar.gz . 00:21:16 + unpack_binaries 00:21:16 + md5sum kubernetes.tar.gz kubernetes-test.tar.gz 00:21:21 b3f9851f4bf241f0d87fb4b094c89ef1 kubernetes.tar.gz 00:21:24 f49f8a8da9579df2a9a29177651d394d kubernetes-test.tar.gz 00:21:24 + tar -xzf kubernetes.tar.gz 00:39:19 + tar -xzf kubernetes-test.tar.gz 01:03:57 ++ cut -c 2- 01:03:57 ++ echo v1.3.0-alpha.2.181+8f3c623287e2f4 ... After 45 minutes we didn't even call kube-up yet. This is some disaster. We need to fix it, or we will be blocking merge-bot frequently because of timeouts. @kubernetes/goog-testing @fejta @spxtr @ixdy	1.0	e2e flake: Preparation for creating a cluster are ridiculously long (45+ minutes) - In this example: http://kubekins.dls.corp.google.com/view/Scalability/job/kubernetes-kubemark-5-gce/1469/consoleFull It took 45 minutes before we even started creating a cluster. Few log lines: 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by upstream project "kubernetes-build" build number 9424 ... 00:17:12 + gsutil -mq cp gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes.tar.gz gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes-test.tar.gz . 00:21:16 + unpack_binaries 00:21:16 + md5sum kubernetes.tar.gz kubernetes-test.tar.gz 00:21:21 b3f9851f4bf241f0d87fb4b094c89ef1 kubernetes.tar.gz 00:21:24 f49f8a8da9579df2a9a29177651d394d kubernetes-test.tar.gz 00:21:24 + tar -xzf kubernetes.tar.gz 00:39:19 + tar -xzf kubernetes-test.tar.gz 01:03:57 ++ cut -c 2- 01:03:57 ++ echo v1.3.0-alpha.2.181+8f3c623287e2f4 ... After 45 minutes we didn't even call kube-up yet. This is some disaster. We need to fix it, or we will be blocking merge-bot frequently because of timeouts. @kubernetes/goog-testing @fejta @spxtr @ixdy	non_process	flake preparation for creating a cluster are ridiculously long minutes in this example it took minutes before we even started creating a cluster few log lines started by timer started by timer started by timer started by timer started by upstream project kubernetes build build number gsutil mq cp gs kubernetes release ci alpha kubernetes tar gz gs kubernetes release ci alpha kubernetes test tar gz unpack binaries kubernetes tar gz kubernetes test tar gz kubernetes tar gz kubernetes test tar gz tar xzf kubernetes tar gz tar xzf kubernetes test tar gz cut c echo alpha after minutes we didn t even call kube up yet this is some disaster we need to fix it or we will be blocking merge bot frequently because of timeouts kubernetes goog testing fejta spxtr ixdy	0
14,208	17,106,136,124	IssuesEvent	2021-07-09 18:03:40	2i2c-org/team-compass	https://api.github.com/repos/2i2c-org/team-compass	opened	Create a bi-weekly deliverables review meeting	:label: team-process type: enhancement	# Summary In #144 we discussed creating a deliverables review meeting. The goal of this meeting would be to do the following things: - Go over our major deliverables on the development board - Have discussion / refinement around deliverables that need conversation and planning - Encourage team members to "pick up" deliverables for development - Provide assistance to others that are stuck on some deliverables - Discuss which deliverables we should prioritize next - Go over our major support items - Discuss how support has gone for the last 2 weeks - Hand off the Support Steward role to the next person # User Stories - As a team member, this meeting should tell me what are the most important issues to work on next, and which I should focus on myself. - As the next Support Steward, this meeting should tell me any context information needed for pre-existing support tickets, and where to focus my efforts next. # Acceptance criteria We've got this process written up in the Team Compass describing when/how this meeting works. # Tasks to complete - [ ] Agree on the general structure and cadence of this meeting - [ ] Create a calendar event for it - [ ] Create a meeting notes template to follow - [ ] Have the first meeting - [ ] Make edits as necessary - [ ] Write it all up in the Team Compass	1.0	Create a bi-weekly deliverables review meeting - # Summary In #144 we discussed creating a deliverables review meeting. The goal of this meeting would be to do the following things: - Go over our major deliverables on the development board - Have discussion / refinement around deliverables that need conversation and planning - Encourage team members to "pick up" deliverables for development - Provide assistance to others that are stuck on some deliverables - Discuss which deliverables we should prioritize next - Go over our major support items - Discuss how support has gone for the last 2 weeks - Hand off the Support Steward role to the next person # User Stories - As a team member, this meeting should tell me what are the most important issues to work on next, and which I should focus on myself. - As the next Support Steward, this meeting should tell me any context information needed for pre-existing support tickets, and where to focus my efforts next. # Acceptance criteria We've got this process written up in the Team Compass describing when/how this meeting works. # Tasks to complete - [ ] Agree on the general structure and cadence of this meeting - [ ] Create a calendar event for it - [ ] Create a meeting notes template to follow - [ ] Have the first meeting - [ ] Make edits as necessary - [ ] Write it all up in the Team Compass	process	create a bi weekly deliverables review meeting summary in we discussed creating a deliverables review meeting the goal of this meeting would be to do the following things go over our major deliverables on the development board have discussion refinement around deliverables that need conversation and planning encourage team members to pick up deliverables for development provide assistance to others that are stuck on some deliverables discuss which deliverables we should prioritize next go over our major support items discuss how support has gone for the last weeks hand off the support steward role to the next person user stories as a team member this meeting should tell me what are the most important issues to work on next and which i should focus on myself as the next support steward this meeting should tell me any context information needed for pre existing support tickets and where to focus my efforts next acceptance criteria we ve got this process written up in the team compass describing when how this meeting works tasks to complete agree on the general structure and cadence of this meeting create a calendar event for it create a meeting notes template to follow have the first meeting make edits as necessary write it all up in the team compass	1
11,615	14,480,634,955	IssuesEvent	2020-12-10 11:27:35	Edher-Nava/5a	https://api.github.com/repos/Edher-Nava/5a	closed	complete_size_estimating_template	process-dashboard	with the obtained real values complete the LOC estimation form	1.0	complete_size_estimating_template - with the obtained real values complete the LOC estimation form	process	complete size estimating template with the obtained real values complete the loc estimation form	1
71,925	18,927,534,403	IssuesEvent	2021-11-17 11:07:39	appsmithorg/appsmith	https://api.github.com/repos/appsmithorg/appsmith	closed	[Bug]: Input widget of phone number type must read spaces in bound data and accept spaces while entering number	Bug Input Widget App Viewers Pod High Production UI Building Pod	### Is there an existing issue for this? - [X] I have searched the existing issues ### Current Behavior Currently, phone number type input widget does not read spaces when present in bound data causing an error. Also, one is unable to use spaces in between numbers. This needs to be handled ![image](https://user-images.githubusercontent.com/79509062/137742333-8de7215c-a1e6-4bf7-822f-c56aa50cbb62.png) ### Steps To Reproduce ```markdown 1. Use a datasource that contains phone numbers that are formatted according to local "readbility" style and bind it to an input widget of Phone number type and observe the error ``` ### Environment Production ### Version Cloud	1.0	[Bug]: Input widget of phone number type must read spaces in bound data and accept spaces while entering number - ### Is there an existing issue for this? - [X] I have searched the existing issues ### Current Behavior Currently, phone number type input widget does not read spaces when present in bound data causing an error. Also, one is unable to use spaces in between numbers. This needs to be handled ![image](https://user-images.githubusercontent.com/79509062/137742333-8de7215c-a1e6-4bf7-822f-c56aa50cbb62.png) ### Steps To Reproduce ```markdown 1. Use a datasource that contains phone numbers that are formatted according to local "readbility" style and bind it to an input widget of Phone number type and observe the error ``` ### Environment Production ### Version Cloud	non_process	input widget of phone number type must read spaces in bound data and accept spaces while entering number is there an existing issue for this i have searched the existing issues current behavior currently phone number type input widget does not read spaces when present in bound data causing an error also one is unable to use spaces in between numbers this needs to be handled steps to reproduce markdown use a datasource that contains phone numbers that are formatted according to local readbility style and bind it to an input widget of phone number type and observe the error environment production version cloud	0
8,161	11,384,109,703	IssuesEvent	2020-01-29 08:11:12	google/gvisor	https://api.github.com/repos/google/gvisor	closed	Getting an Arm64 CI for gVisor	arch: arm area: tooling type: process	Hi all, Since more and more arm64 codes are being merged. It would be good to have a CI setup for arm64, so that future stuff doesn't break on it. Tasks: * [ ] cross-building * [ ] add a Arm64 server into Kokoro ? * [ ] setup syscall test with ptrace on Arm64 * [ ] setup syscall test with kvm on Arm64 * [ ] e2e test on Arm64 * [ ] image test on Arm64 .... Known issues: 1, Regarding ptrace platform, it seems that the kernel provided by Travis is too old. Because we just added the sysemu feature to the Linux kernel in last year. The kernel version should bigger than 5.3. 2, Regarding kvm platform, the nested virtualization is a problem. At present, officially announced that arm8.3 hardware will support this feature. Currently, as far as I know, there is no such arm64 server using Arm8.3. cc @xiaobo55x @avagin @prattmic	1.0	Getting an Arm64 CI for gVisor - Hi all, Since more and more arm64 codes are being merged. It would be good to have a CI setup for arm64, so that future stuff doesn't break on it. Tasks: * [ ] cross-building * [ ] add a Arm64 server into Kokoro ? * [ ] setup syscall test with ptrace on Arm64 * [ ] setup syscall test with kvm on Arm64 * [ ] e2e test on Arm64 * [ ] image test on Arm64 .... Known issues: 1, Regarding ptrace platform, it seems that the kernel provided by Travis is too old. Because we just added the sysemu feature to the Linux kernel in last year. The kernel version should bigger than 5.3. 2, Regarding kvm platform, the nested virtualization is a problem. At present, officially announced that arm8.3 hardware will support this feature. Currently, as far as I know, there is no such arm64 server using Arm8.3. cc @xiaobo55x @avagin @prattmic	process	getting an ci for gvisor hi all since more and more codes are being merged it would be good to have a ci setup for so that future stuff doesn t break on it tasks cross building add a server into kokoro setup syscall test with ptrace on setup syscall test with kvm on test on image test on known issues regarding ptrace platform it seems that the kernel provided by travis is too old because we just added the sysemu feature to the linux kernel in last year the kernel version should bigger than regarding kvm platform the nested virtualization is a problem at present officially announced that hardware will support this feature currently as far as i know there is no such server using cc avagin prattmic	1
22,606	31,831,309,928	IssuesEvent	2023-09-14 10:45:30	awslabs/clickstream-analytics-on-aws	https://api.github.com/repos/awslabs/clickstream-analytics-on-aws	closed	fail to deploy data processing stack due to the error when creating emr serverless application	bug data-processing	## Summary <!-- description of the bug: --> fail to create data pipeline stack in a new aws account with below error > Received response status [FAILED] from custom resource. Message returned: SubnetIds and SecurityGroupIds are required ## Steps to reproduce <!-- minimal amount of steps that causes the bug (if possible) or a reference: --> 1. create a project 2. create a pipeline with data pipeline ## What is the current bug behavior? <!-- What actually happens --> the pipeline creation failed due to above error ## What is the expected correct behavior? <!-- What you should see instead --> ## Relevant logs and/or screenshots <!-- what is the error message you are seeing? --> It failed on the custom resource to create EMR serverless application. Below is the log of Lambda function, > { "level": "INFO", "message": "{\"RequestType\":\"Create\",\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"ResponseURL\":\"https://cloudformation-custom-resource-response-apnortheast1.s3-ap-northeast-1.amazonaws.com/arn%3Aaws%3Acloudformation%3Aap-northeast-1%3A057016563434%3Astack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b%7CNestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E%7C91b8004e-344b-4f1e-b4c1-2c9c13fafb9f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230914T082245Z&X-Amz-SignedHeaders=host&X-Amz-Expires=7200&X-Amz-Credential=AKIASU3QWAIZA3EFKAWZ%2F20230914%2Fap-northeast-1%2Fs3%2Faws4_request&X-Amz-Signature=c69a882cdbc0079e42bf57a5b88d69f66d85112b4e7781139f3981dde1d8578c\",\"StackId\":\"arn:aws:cloudformation:ap-northeast-1:057016563434:stack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b\",\"RequestId\":\"91b8004e-344b-4f1e-b4c1-2c9c13fafb9f\",\"LogicalResourceId\":\"NestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E\",\"ResourceType\":\"AWS::CloudFormation::CustomResource\",\"ResourceProperties\":{\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"idleTimeoutMinutes\":\"5\",\"name\":\"Clickstream-Spark-APP-gcr_solutions\",\"pipelineS3BucketName\":\"aws-gcr-solutions-clickstream-data\",\"pipelineS3Prefix\":\"clickstream/gcr_solutions/data/pipeline-temp/\",\"projectId\":\"gcr_solutions\",\"version\":\"emr-6.11.0\",\"secourityGroupId\":\"sg-04d7b916ad7eb7649\",\"subnetIds\":\"subnet-0742bf553bc6d7a30,subnet-08705f4ee04cd6ee6,subnet-0b6af7bf03fc44e53\"}}", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.193Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861" } But the security group id were missing when calling EMR serverless API. > { "level": "INFO", "message": "CreateApplicationCommand input", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.201Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861", "input": { "name": "Clickstream-Spark-APP-gcr_solutions", "releaseLabel": "emr-6.11.0", "type": "SPARK", "architecture": "X86_64", "networkConfiguration": { "subnetIds": [ "subnet-0742bf553bc6d7a30", "subnet-08705f4ee04cd6ee6", "subnet-0b6af7bf03fc44e53" ], "securityGroupIds": [ null ] }, "autoStartConfiguration": { "enabled": true }, "autoStopConfiguration": { "enabled": true, "idleTimeoutMinutes": 5 } } } ## Possible fixes <!-- If you can, link to the line of code that might be responsible for the problem --> --- This is :bug: Bug Report	1.0	fail to deploy data processing stack due to the error when creating emr serverless application - ## Summary <!-- description of the bug: --> fail to create data pipeline stack in a new aws account with below error > Received response status [FAILED] from custom resource. Message returned: SubnetIds and SecurityGroupIds are required ## Steps to reproduce <!-- minimal amount of steps that causes the bug (if possible) or a reference: --> 1. create a project 2. create a pipeline with data pipeline ## What is the current bug behavior? <!-- What actually happens --> the pipeline creation failed due to above error ## What is the expected correct behavior? <!-- What you should see instead --> ## Relevant logs and/or screenshots <!-- what is the error message you are seeing? --> It failed on the custom resource to create EMR serverless application. Below is the log of Lambda function, > { "level": "INFO", "message": "{\"RequestType\":\"Create\",\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"ResponseURL\":\"https://cloudformation-custom-resource-response-apnortheast1.s3-ap-northeast-1.amazonaws.com/arn%3Aaws%3Acloudformation%3Aap-northeast-1%3A057016563434%3Astack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b%7CNestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E%7C91b8004e-344b-4f1e-b4c1-2c9c13fafb9f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230914T082245Z&X-Amz-SignedHeaders=host&X-Amz-Expires=7200&X-Amz-Credential=AKIASU3QWAIZA3EFKAWZ%2F20230914%2Fap-northeast-1%2Fs3%2Faws4_request&X-Amz-Signature=c69a882cdbc0079e42bf57a5b88d69f66d85112b4e7781139f3981dde1d8578c\",\"StackId\":\"arn:aws:cloudformation:ap-northeast-1:057016563434:stack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b\",\"RequestId\":\"91b8004e-344b-4f1e-b4c1-2c9c13fafb9f\",\"LogicalResourceId\":\"NestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E\",\"ResourceType\":\"AWS::CloudFormation::CustomResource\",\"ResourceProperties\":{\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"idleTimeoutMinutes\":\"5\",\"name\":\"Clickstream-Spark-APP-gcr_solutions\",\"pipelineS3BucketName\":\"aws-gcr-solutions-clickstream-data\",\"pipelineS3Prefix\":\"clickstream/gcr_solutions/data/pipeline-temp/\",\"projectId\":\"gcr_solutions\",\"version\":\"emr-6.11.0\",\"secourityGroupId\":\"sg-04d7b916ad7eb7649\",\"subnetIds\":\"subnet-0742bf553bc6d7a30,subnet-08705f4ee04cd6ee6,subnet-0b6af7bf03fc44e53\"}}", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.193Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861" } But the security group id were missing when calling EMR serverless API. > { "level": "INFO", "message": "CreateApplicationCommand input", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.201Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861", "input": { "name": "Clickstream-Spark-APP-gcr_solutions", "releaseLabel": "emr-6.11.0", "type": "SPARK", "architecture": "X86_64", "networkConfiguration": { "subnetIds": [ "subnet-0742bf553bc6d7a30", "subnet-08705f4ee04cd6ee6", "subnet-0b6af7bf03fc44e53" ], "securityGroupIds": [ null ] }, "autoStartConfiguration": { "enabled": true }, "autoStopConfiguration": { "enabled": true, "idleTimeoutMinutes": 5 } } } ## Possible fixes <!-- If you can, link to the line of code that might be responsible for the problem --> --- This is :bug: Bug Report	process	fail to deploy data processing stack due to the error when creating emr serverless application summary fail to create data pipeline stack in a new aws account with below error received response status from custom resource message returned subnetids and securitygroupids are required steps to reproduce create a project create a pipeline with data pipeline what is the current bug behavior the pipeline creation failed due to above error what is the expected correct behavior relevant logs and or screenshots it failed on the custom resource to create emr serverless application below is the log of lambda function level info message requesttype create servicetoken arn aws lambda ap northeast function clickstream dataprocessin nestedstackcreateemrserv responseurl sampling rate service clickstreamanalyticsonaws timestamp xray trace id but the security group id were missing when calling emr serverless api level info message createapplicationcommand input sampling rate service clickstreamanalyticsonaws timestamp xray trace id input name clickstream spark app gcr solutions releaselabel emr type spark architecture networkconfiguration subnetids subnet subnet subnet securitygroupids null autostartconfiguration enabled true autostopconfiguration enabled true idletimeoutminutes possible fixes this is bug bug report	1
144,867	13,128,810,056	IssuesEvent	2020-08-06 12:56:33	cloudevents/sdk-rust	https://api.github.com/repos/cloudevents/sdk-rust	closed	Docs in reqwest, actix-web and rdkafka integration	documentation enhancement good first issue	At the moment our "subcrates" doesn't have any docs in the main crate file `lib.rs`, we should add a doc with a little code example to explain the features of the crate and some pointers to the main doc of `cloudevents-sdk`	1.0	Docs in reqwest, actix-web and rdkafka integration - At the moment our "subcrates" doesn't have any docs in the main crate file `lib.rs`, we should add a doc with a little code example to explain the features of the crate and some pointers to the main doc of `cloudevents-sdk`	non_process	docs in reqwest actix web and rdkafka integration at the moment our subcrates doesn t have any docs in the main crate file lib rs we should add a doc with a little code example to explain the features of the crate and some pointers to the main doc of cloudevents sdk	0
148,710	5,694,618,117	IssuesEvent	2017-04-15 14:58:19	FreezingMoon/AncientBeast	https://api.github.com/repos/FreezingMoon/AncientBeast	opened	not turning around to hit when replaying	Coding Easy Priority Visuals	When replaying a combat log, Cyber Hound is not turning around when hitting a unit behind. Need to check if this is a more generic issue.	1.0	not turning around to hit when replaying - When replaying a combat log, Cyber Hound is not turning around when hitting a unit behind. Need to check if this is a more generic issue.	non_process	not turning around to hit when replaying when replaying a combat log cyber hound is not turning around when hitting a unit behind need to check if this is a more generic issue	0
18,084	24,105,779,954	IssuesEvent	2022-09-20 07:19:42	arcangelo7/issues	https://api.github.com/repos/arcangelo7/issues	closed	deposit localhost:330 doi:10.1007/978-3-030-00668-6_8	deposit to be processed test	"id","title","author","pub_date","venue","volume","issue","page","type","publisher","editor" "doi:10.1007/978-3-662-07918-8_3","Influence of Dielectric Properties, State, and Electrodes on Electric Strength","Ushakov, Vasily Y.","2004","Insulation of High-Voltage Equipment [isbn:9783642058530 isbn:9783662079188]","","","27-82","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1016/0021-9991(73)90147-2","Flux-corrected transport. I. SHASTA, a fluid transport algorithm that works","Boris, Jay P; Book, David L","1973-1","Journal of Computational Physics [issn:0021-9991]","11","1","38-69","journal article","Elsevier BV [crossref:78]","" "doi:10.1109/20.877674","An investigation of FEM-FCT method for streamer corona simulation","Woong-Gee Min, ; Hyeong-Seok Kim, ; Seok-Hyun Lee, ; Song-Yop Hahn, ","2000-7","IEEE Transactions on Magnetics [issn:0018-9464]","36","4","1280-1284","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1109/tps.2003.815469","Numerical study on influences of barrier arrangements on dielectric barrier discharge characteristics","Woo Seok Kang, ; Jin Myung Park, ; Yongho Kim, ; Sang Hee Hong, ","2003-8","IEEE Transactions on Plasma Science [issn:0093-3813]","31","4","504-510","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "","Spatial Distribution of Ion Current Around HVDC Bundle Conductors","Zhou, Xiangxian; Cui, Xiang; Lu, Tiebing; Fang, Chao; Zhen, Yongzan","2012-1","IEEE Transactions on Power Delivery [issn:0885-8977 issn:1937-4208]","27","1","380-390","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1007/978-1-4615-3786-1_11","The Solution of the Continuity Equations in Ionization and Plasma Growth","Davies, A. J.; Niessen, W.","1990","Physics and Applications of Pseudosparks [isbn:9781461366874 isbn:9781461537861]","","","197-217","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1088/0022-3727/13/1/002","Discharge current induced by the motion of charged particles","Sato, N","1980-1-14","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","13","1","3-6","journal article","IOP Publishing [crossref:266]","" "doi:10.1109/27.106800","Particle-in-cell charged-particle simulations, plus Monte Carlo collisions with neutral atoms, PIC-MCC","Birdsall, C.K.","1991-4","IEEE Transactions on Plasma Science [issn:0093-3813]","19","2","65-85","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1016/0021-9991(79)90051-2","Fully multidimensional flux-corrected transport algorithms for fluids","Zalesak, Steven T","1979-6","Journal of Computational Physics [issn:0021-9991]","31","3","335-362","journal article","Elsevier BV [crossref:78]","" "doi:10.1088/0022-3727/39/14/017","Diffusion correction to the Raether–Meek criterion for the avalanche-to-streamer transition","Montijn, Carolynne; Ebert, Ute [orcid:0000-0003-3891-6869]","2006-6-30","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","39","14","2979-2992","journal article","IOP Publishing [crossref:266]","" "doi:10.1007/978-3-663-14090-0 isbn:9783528085995 isbn:9783663140900","High-Voltage Insulation Technology","Kind, Dieter; Kärner, Hermann","1985","","","","","book","Springer Science and Business Media LLC [crossref:297]","" "","Space-charge effects in high-density plasmas","Morrow, R","1982-6","Journal of Computational Physics [issn:0021-9991]","46","3","454-461","journal article","Elsevier BV [crossref:78]","" "doi:10.1007/s42835-022-01029-y","Numerical Simulation of Gas Discharge Using SUPG-FEM-FCT Method with Adaptive Mesh Refinement","Choi, Chan Young; Park, Il Han [orcid:0000-0002-9383-6856]","2022-2-28","Journal of Electrical Engineering & Technology [issn:1975-0102 issn:2093-7423]","17","3","1873-1881","journal article","Springer Science and Business Media LLC [crossref:297]","" ===###===@@@=== "citing_id","citing_publication_date","cited_id","cited_publication_date" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-662-07918-8_3","2004" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(73)90147-2","1973-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/20.877674","2000-7" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tps.2003.815469","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tpwrd.2011.2172694","2012-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-1-4615-3786-1_11","1990" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/13/1/002","1980-1-14" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/27.106800","1991-4" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(79)90051-2","1979-6" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/39/14/017","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-663-14090-0","1985" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(82)90026-2",""	1.0	deposit localhost:330 doi:10.1007/978-3-030-00668-6_8 - "id","title","author","pub_date","venue","volume","issue","page","type","publisher","editor" "doi:10.1007/978-3-662-07918-8_3","Influence of Dielectric Properties, State, and Electrodes on Electric Strength","Ushakov, Vasily Y.","2004","Insulation of High-Voltage Equipment [isbn:9783642058530 isbn:9783662079188]","","","27-82","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1016/0021-9991(73)90147-2","Flux-corrected transport. I. SHASTA, a fluid transport algorithm that works","Boris, Jay P; Book, David L","1973-1","Journal of Computational Physics [issn:0021-9991]","11","1","38-69","journal article","Elsevier BV [crossref:78]","" "doi:10.1109/20.877674","An investigation of FEM-FCT method for streamer corona simulation","Woong-Gee Min, ; Hyeong-Seok Kim, ; Seok-Hyun Lee, ; Song-Yop Hahn, ","2000-7","IEEE Transactions on Magnetics [issn:0018-9464]","36","4","1280-1284","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1109/tps.2003.815469","Numerical study on influences of barrier arrangements on dielectric barrier discharge characteristics","Woo Seok Kang, ; Jin Myung Park, ; Yongho Kim, ; Sang Hee Hong, ","2003-8","IEEE Transactions on Plasma Science [issn:0093-3813]","31","4","504-510","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "","Spatial Distribution of Ion Current Around HVDC Bundle Conductors","Zhou, Xiangxian; Cui, Xiang; Lu, Tiebing; Fang, Chao; Zhen, Yongzan","2012-1","IEEE Transactions on Power Delivery [issn:0885-8977 issn:1937-4208]","27","1","380-390","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1007/978-1-4615-3786-1_11","The Solution of the Continuity Equations in Ionization and Plasma Growth","Davies, A. J.; Niessen, W.","1990","Physics and Applications of Pseudosparks [isbn:9781461366874 isbn:9781461537861]","","","197-217","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1088/0022-3727/13/1/002","Discharge current induced by the motion of charged particles","Sato, N","1980-1-14","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","13","1","3-6","journal article","IOP Publishing [crossref:266]","" "doi:10.1109/27.106800","Particle-in-cell charged-particle simulations, plus Monte Carlo collisions with neutral atoms, PIC-MCC","Birdsall, C.K.","1991-4","IEEE Transactions on Plasma Science [issn:0093-3813]","19","2","65-85","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1016/0021-9991(79)90051-2","Fully multidimensional flux-corrected transport algorithms for fluids","Zalesak, Steven T","1979-6","Journal of Computational Physics [issn:0021-9991]","31","3","335-362","journal article","Elsevier BV [crossref:78]","" "doi:10.1088/0022-3727/39/14/017","Diffusion correction to the Raether–Meek criterion for the avalanche-to-streamer transition","Montijn, Carolynne; Ebert, Ute [orcid:0000-0003-3891-6869]","2006-6-30","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","39","14","2979-2992","journal article","IOP Publishing [crossref:266]","" "doi:10.1007/978-3-663-14090-0 isbn:9783528085995 isbn:9783663140900","High-Voltage Insulation Technology","Kind, Dieter; Kärner, Hermann","1985","","","","","book","Springer Science and Business Media LLC [crossref:297]","" "","Space-charge effects in high-density plasmas","Morrow, R","1982-6","Journal of Computational Physics [issn:0021-9991]","46","3","454-461","journal article","Elsevier BV [crossref:78]","" "doi:10.1007/s42835-022-01029-y","Numerical Simulation of Gas Discharge Using SUPG-FEM-FCT Method with Adaptive Mesh Refinement","Choi, Chan Young; Park, Il Han [orcid:0000-0002-9383-6856]","2022-2-28","Journal of Electrical Engineering & Technology [issn:1975-0102 issn:2093-7423]","17","3","1873-1881","journal article","Springer Science and Business Media LLC [crossref:297]","" ===###===@@@=== "citing_id","citing_publication_date","cited_id","cited_publication_date" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-662-07918-8_3","2004" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(73)90147-2","1973-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/20.877674","2000-7" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tps.2003.815469","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tpwrd.2011.2172694","2012-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-1-4615-3786-1_11","1990" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/13/1/002","1980-1-14" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/27.106800","1991-4" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(79)90051-2","1979-6" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/39/14/017","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-663-14090-0","1985" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(82)90026-2",""	process	deposit localhost doi id title author pub date venue volume issue page type publisher editor doi influence of dielectric properties state and electrodes on electric strength ushakov vasily y insulation of high voltage equipment book chapter springer science and business media llc doi flux corrected transport i shasta a fluid transport algorithm that works boris jay p book david l journal of computational physics journal article elsevier bv doi an investigation of fem fct method for streamer corona simulation woong gee min hyeong seok kim seok hyun lee song yop hahn ieee transactions on magnetics journal article institute of electrical and electronics engineers ieee doi tps numerical study on influences of barrier arrangements on dielectric barrier discharge characteristics woo seok kang jin myung park yongho kim sang hee hong ieee transactions on plasma science journal article institute of electrical and electronics engineers ieee spatial distribution of ion current around hvdc bundle conductors zhou xiangxian cui xiang lu tiebing fang chao zhen yongzan ieee transactions on power delivery journal article institute of electrical and electronics engineers ieee doi the solution of the continuity equations in ionization and plasma growth davies a j niessen w physics and applications of pseudosparks book chapter springer science and business media llc doi discharge current induced by the motion of charged particles sato n journal of physics d applied physics journal article iop publishing doi particle in cell charged particle simulations plus monte carlo collisions with neutral atoms pic mcc birdsall c k ieee transactions on plasma science journal article institute of electrical and electronics engineers ieee doi fully multidimensional flux corrected transport algorithms for fluids zalesak steven t journal of computational physics journal article elsevier bv doi diffusion correction to the raether–meek criterion for the avalanche to streamer transition montijn carolynne ebert ute journal of physics d applied physics journal article iop publishing doi isbn isbn high voltage insulation technology kind dieter kärner hermann book springer science and business media llc space charge effects in high density plasmas morrow r journal of computational physics journal article elsevier bv doi y numerical simulation of gas discharge using supg fem fct method with adaptive mesh refinement choi chan young park il han journal of electrical engineering technology journal article springer science and business media llc citing id citing publication date cited id cited publication date doi y doi doi y doi doi y doi doi y doi tps doi y doi tpwrd doi y doi doi y doi doi y doi doi y doi doi y doi doi y doi doi y doi	1
220,031	7,349,155,600	IssuesEvent	2018-03-08 09:41:34	xwikisas/application-licensing	https://api.github.com/repos/xwikisas/application-licensing	closed	The French translation page is not hidden	Priority: Major Type: Bug	This makes the Licenses page appear in the page tree in the navigation panel.	1.0	The French translation page is not hidden - This makes the Licenses page appear in the page tree in the navigation panel.	non_process	the french translation page is not hidden this makes the licenses page appear in the page tree in the navigation panel	0
4,642	7,482,419,393	IssuesEvent	2018-04-05 01:15:56	UnbFeelings/unb-feelings-GQA	https://api.github.com/repos/UnbFeelings/unb-feelings-GQA	closed	Reorganizar o subprocesso que lida com as não conformidades	process wiki	- Explicitar no processo que a não conformidade deve ser acompanhada por um prazo diferente das auditorias. Por exemplo, o prazo para resolver o problema X é dois dias, e não até a próxima auditoria - Colocar na descrição da atividade do processo: ao registrar a não conformidade, é necessário informar a complexidade dela	1.0	Reorganizar o subprocesso que lida com as não conformidades - - Explicitar no processo que a não conformidade deve ser acompanhada por um prazo diferente das auditorias. Por exemplo, o prazo para resolver o problema X é dois dias, e não até a próxima auditoria - Colocar na descrição da atividade do processo: ao registrar a não conformidade, é necessário informar a complexidade dela	process	reorganizar o subprocesso que lida com as não conformidades explicitar no processo que a não conformidade deve ser acompanhada por um prazo diferente das auditorias por exemplo o prazo para resolver o problema x é dois dias e não até a próxima auditoria colocar na descrição da atividade do processo ao registrar a não conformidade é necessário informar a complexidade dela	1
8,081	11,253,936,260	IssuesEvent	2020-01-11 19:45:49	GoogleCloudPlatform/wombat-dressing-room	https://api.github.com/repos/GoogleCloudPlatform/wombat-dressing-room	closed	add CI/CD	type: process	we should add our standard kokoro CI/CD setup to this project. This is blocked by #16 , which is preventing the build from passing in various environments.	1.0	add CI/CD - we should add our standard kokoro CI/CD setup to this project. This is blocked by #16 , which is preventing the build from passing in various environments.	process	add ci cd we should add our standard kokoro ci cd setup to this project this is blocked by which is preventing the build from passing in various environments	1
11,433	14,248,237,714	IssuesEvent	2020-11-19 12:39:15	tikv/tikv	https://api.github.com/repos/tikv/tikv	closed	Optimize Coprocessor Analyze performance	difficulty/medium sig/coprocessor status/help-wanted	## Feature Request The performance of Coprocessor Analyze might be improved. We can use FlameGraph to check the current hotspot and do optimization. We can at least change to use batch table scanners.	1.0	Optimize Coprocessor Analyze performance - ## Feature Request The performance of Coprocessor Analyze might be improved. We can use FlameGraph to check the current hotspot and do optimization. We can at least change to use batch table scanners.	process	optimize coprocessor analyze performance feature request the performance of coprocessor analyze might be improved we can use flamegraph to check the current hotspot and do optimization we can at least change to use batch table scanners	1
93,079	11,738,729,659	IssuesEvent	2020-03-11 16:31:36	department-of-veterans-affairs/caseflow	https://api.github.com/repos/department-of-veterans-affairs/caseflow	opened	User Research Study: Contested Claims \| Attorney Fees or Full Contested Claims	Team: Foxtrot 🦊 Type: design 💅	## User story BVA Intake users need a way to intake contested claims in Caseflow, starting with attorney fees claims (which may be one of the more frequent claims we see coming in). ## Problem statement The team needs to establish an understanding of what contested claims are, the various types, and what the general process is for someone to submit such a claim. Via user research, we need to clarify how users are currently processing contested claims, what their workarounds in Caseflow are, and what their pain points and needs are. ## What is out of scope? - What happens after a user intakes a contested claim (this belongs in Team Echo's territory) ## Background/context - Contested claims occur when two or more people are fighting over the same benefit (they don't have to be related to the veteran e.g. an attorney, funeral home etc. - Some work might have been done on this by a previous Caseflow team, however the functionality does not fully exist in Caseflow and we are unclear what user needs look like ## What are the unknowns? <!-- If there are key unknowns or assumptions, add them here. If we're accepting the risks associated with the unknowns or assumptions, let us know that too. --> - Apportionments: issues that are covered, history of apportionments in the dept, more context information - Do we need to design for apportionments? - What does the process of filing such a claim look like? - How are issues impacted? - What use cases do we need to consider? - What is happening behind the scenes for each contested claim type? ## For research tickets only: What questions do we hope to answer? What are our learning goals? <!-- It can be useful to also include questions we're not trying to answer. --> - What are the various types of contested claims? What is the general process? - How are users currently processing contested claims? What is working well and what are they struggling with? What are their workarounds? - What does an ideal future look like? What does success look like to our users? ## Existing design and content <!--If we're replacing something, please include a screenshot(s) or link to the existing solution, even if it's not in Caseflow. --> - Currently this functionality exists in VACOLS but the purpose of this research study is to clarify what this looks like - Users might have established their own workaround in Caseflow, that we need to learn more about ## Success criteria <!-- Include as needed, especially for issues that aren't part of epics. if no measurable success criteria, what does success look like? --> - Users can successfully intake attorney fees claims - Users can successfully enter a claimant who isn't currently listed in Caseflow - Users can successfully intake all other contested claims ## Technical/logistical constraints (if known) <!-- Are there technical constraints that will impact any design or writing solution? Logistical constraints that will impact user research? --> - Existing Intake bugs - Scheduling with intake users	1.0	User Research Study: Contested Claims \| Attorney Fees or Full Contested Claims - ## User story BVA Intake users need a way to intake contested claims in Caseflow, starting with attorney fees claims (which may be one of the more frequent claims we see coming in). ## Problem statement The team needs to establish an understanding of what contested claims are, the various types, and what the general process is for someone to submit such a claim. Via user research, we need to clarify how users are currently processing contested claims, what their workarounds in Caseflow are, and what their pain points and needs are. ## What is out of scope? - What happens after a user intakes a contested claim (this belongs in Team Echo's territory) ## Background/context - Contested claims occur when two or more people are fighting over the same benefit (they don't have to be related to the veteran e.g. an attorney, funeral home etc. - Some work might have been done on this by a previous Caseflow team, however the functionality does not fully exist in Caseflow and we are unclear what user needs look like ## What are the unknowns? <!-- If there are key unknowns or assumptions, add them here. If we're accepting the risks associated with the unknowns or assumptions, let us know that too. --> - Apportionments: issues that are covered, history of apportionments in the dept, more context information - Do we need to design for apportionments? - What does the process of filing such a claim look like? - How are issues impacted? - What use cases do we need to consider? - What is happening behind the scenes for each contested claim type? ## For research tickets only: What questions do we hope to answer? What are our learning goals? <!-- It can be useful to also include questions we're not trying to answer. --> - What are the various types of contested claims? What is the general process? - How are users currently processing contested claims? What is working well and what are they struggling with? What are their workarounds? - What does an ideal future look like? What does success look like to our users? ## Existing design and content <!--If we're replacing something, please include a screenshot(s) or link to the existing solution, even if it's not in Caseflow. --> - Currently this functionality exists in VACOLS but the purpose of this research study is to clarify what this looks like - Users might have established their own workaround in Caseflow, that we need to learn more about ## Success criteria <!-- Include as needed, especially for issues that aren't part of epics. if no measurable success criteria, what does success look like? --> - Users can successfully intake attorney fees claims - Users can successfully enter a claimant who isn't currently listed in Caseflow - Users can successfully intake all other contested claims ## Technical/logistical constraints (if known) <!-- Are there technical constraints that will impact any design or writing solution? Logistical constraints that will impact user research? --> - Existing Intake bugs - Scheduling with intake users	non_process	user research study contested claims attorney fees or full contested claims user story bva intake users need a way to intake contested claims in caseflow starting with attorney fees claims which may be one of the more frequent claims we see coming in problem statement the team needs to establish an understanding of what contested claims are the various types and what the general process is for someone to submit such a claim via user research we need to clarify how users are currently processing contested claims what their workarounds in caseflow are and what their pain points and needs are what is out of scope what happens after a user intakes a contested claim this belongs in team echo s territory background context contested claims occur when two or more people are fighting over the same benefit they don t have to be related to the veteran e g an attorney funeral home etc some work might have been done on this by a previous caseflow team however the functionality does not fully exist in caseflow and we are unclear what user needs look like what are the unknowns apportionments issues that are covered history of apportionments in the dept more context information do we need to design for apportionments what does the process of filing such a claim look like how are issues impacted what use cases do we need to consider what is happening behind the scenes for each contested claim type for research tickets only what questions do we hope to answer what are our learning goals what are the various types of contested claims what is the general process how are users currently processing contested claims what is working well and what are they struggling with what are their workarounds what does an ideal future look like what does success look like to our users existing design and content currently this functionality exists in vacols but the purpose of this research study is to clarify what this looks like users might have established their own workaround in caseflow that we need to learn more about success criteria users can successfully intake attorney fees claims users can successfully enter a claimant who isn t currently listed in caseflow users can successfully intake all other contested claims technical logistical constraints if known existing intake bugs scheduling with intake users	0
358,853	10,650,901,224	IssuesEvent	2019-10-17 09:16:38	wso2/product-apim	https://api.github.com/repos/wso2/product-apim	closed	[Publisher][UI] Exception thrown when selecting the user name twice	3.0.0-alpha Priority/High Severity/Major Type/React-UI	Select the user name in the publisher ui <img width="113" alt="Screen Shot 2019-09-23 at 10 35 36 AM" src="https://user-images.githubusercontent.com/4861150/65402759-fa6dd380-dded-11e9-845a-2f261383a6ad.png"> First time logout popup is shown. Instead of selecting any button, select the user name again and following <img width="1083" alt="Screen Shot 2019-09-23 at 10 32 53 AM" src="https://user-images.githubusercontent.com/4861150/65402760-fe015a80-dded-11e9-8beb-211fc40bae9f.png">	1.0	[Publisher][UI] Exception thrown when selecting the user name twice - Select the user name in the publisher ui <img width="113" alt="Screen Shot 2019-09-23 at 10 35 36 AM" src="https://user-images.githubusercontent.com/4861150/65402759-fa6dd380-dded-11e9-845a-2f261383a6ad.png"> First time logout popup is shown. Instead of selecting any button, select the user name again and following <img width="1083" alt="Screen Shot 2019-09-23 at 10 32 53 AM" src="https://user-images.githubusercontent.com/4861150/65402760-fe015a80-dded-11e9-8beb-211fc40bae9f.png">	non_process	exception thrown when selecting the user name twice select the user name in the publisher ui img width alt screen shot at am src first time logout popup is shown instead of selecting any button select the user name again and following img width alt screen shot at am src	0
7,158	10,308,215,462	IssuesEvent	2019-08-29 10:24:07	natario1/CameraView	https://api.github.com/repos/natario1/CameraView	closed	Frame.getSize returns null	about:frame processing is:bug status:has pr	See screenshot what code I had written. I got only one error like this java.lang.IllegalStateException: frame.size must not be null ``` FATAL EXCEPTION: FrameProcessorsWorker Process: com.royalbimrah.ai, PID: 12887 java.lang.IllegalStateException: frame.size must not be null at com.royalbimrah.ai.facedetection.FaceDetectionActivity.process(FaceDetectionActivity.kt:67) at com.otaliastudios.cameraview.CameraView$Callbacks$13.run(CameraView.java:1742) at android.os.Handler.handleCallback(Handler.java:751) at android.os.Handler.dispatchMessage(Handler.java:95) at android.os.Looper.loop(Looper.java:154) at android.os.HandlerThread.run(HandlerThread.java:61) ``` Also, you can see code and let me know how to solve this error soon. ![image](https://user-images.githubusercontent.com/39325207/62851469-c50c9b00-bd03-11e9-8fed-b14053b82165.png)	1.0	Frame.getSize returns null - See screenshot what code I had written. I got only one error like this java.lang.IllegalStateException: frame.size must not be null ``` FATAL EXCEPTION: FrameProcessorsWorker Process: com.royalbimrah.ai, PID: 12887 java.lang.IllegalStateException: frame.size must not be null at com.royalbimrah.ai.facedetection.FaceDetectionActivity.process(FaceDetectionActivity.kt:67) at com.otaliastudios.cameraview.CameraView$Callbacks$13.run(CameraView.java:1742) at android.os.Handler.handleCallback(Handler.java:751) at android.os.Handler.dispatchMessage(Handler.java:95) at android.os.Looper.loop(Looper.java:154) at android.os.HandlerThread.run(HandlerThread.java:61) ``` Also, you can see code and let me know how to solve this error soon. ![image](https://user-images.githubusercontent.com/39325207/62851469-c50c9b00-bd03-11e9-8fed-b14053b82165.png)	process	frame getsize returns null see screenshot what code i had written i got only one error like this java lang illegalstateexception frame size must not be null fatal exception frameprocessorsworker process com royalbimrah ai pid java lang illegalstateexception frame size must not be null at com royalbimrah ai facedetection facedetectionactivity process facedetectionactivity kt at com otaliastudios cameraview cameraview callbacks run cameraview java at android os handler handlecallback handler java at android os handler dispatchmessage handler java at android os looper loop looper java at android os handlerthread run handlerthread java also you can see code and let me know how to solve this error soon	1
149,807	11,924,462,219	IssuesEvent	2020-04-01 09:34:09	input-output-hk/ouroboros-network	https://api.github.com/repos/input-output-hk/ouroboros-network	closed	Chain DB: simulate VolatileDB corruption	byron chain db consensus priority medium testing	#1625 fixes #1624, but doesn't test it, because it's quite an edge case to test it: the VolatileDB has to be empty and the block we're adding must be an EBB with the same block as the block at the current tip. Suggested approach: add a `WipeVolDB` command to the ChainDB q-s-m tests that closes the ChainDB, wipes the entire VolatileDB, and reopens the ChainDB. This could trigger #1624. Doing partial truncation of the VolatileDB is much more complicated, as it will depend on implementation details of the VolatileDB.	1.0	Chain DB: simulate VolatileDB corruption - #1625 fixes #1624, but doesn't test it, because it's quite an edge case to test it: the VolatileDB has to be empty and the block we're adding must be an EBB with the same block as the block at the current tip. Suggested approach: add a `WipeVolDB` command to the ChainDB q-s-m tests that closes the ChainDB, wipes the entire VolatileDB, and reopens the ChainDB. This could trigger #1624. Doing partial truncation of the VolatileDB is much more complicated, as it will depend on implementation details of the VolatileDB.	non_process	chain db simulate volatiledb corruption fixes but doesn t test it because it s quite an edge case to test it the volatiledb has to be empty and the block we re adding must be an ebb with the same block as the block at the current tip suggested approach add a wipevoldb command to the chaindb q s m tests that closes the chaindb wipes the entire volatiledb and reopens the chaindb this could trigger doing partial truncation of the volatiledb is much more complicated as it will depend on implementation details of the volatiledb	0
175,015	27,774,137,917	IssuesEvent	2023-03-16 16:09:06	phetsims/calculus-grapher	https://api.github.com/repos/phetsims/calculus-grapher	closed	phetioFeatured overrides	status:ready-for-review design:phet-io	Use Studio to feature/unfeature elements, and generate `calculus-grapher-phet-io-overrides.js`. If you're clear on the process for this, contact @arouinfar. Because this sim does not yet have an API, you can safely commit changes to `calculus-grapher-phet-io-overrides.js` without having to regenerate the API. This should be completed after https://github.com/phetsims/calculus-grapher/issues/224 (PhET-iO design review).	1.0	phetioFeatured overrides - Use Studio to feature/unfeature elements, and generate `calculus-grapher-phet-io-overrides.js`. If you're clear on the process for this, contact @arouinfar. Because this sim does not yet have an API, you can safely commit changes to `calculus-grapher-phet-io-overrides.js` without having to regenerate the API. This should be completed after https://github.com/phetsims/calculus-grapher/issues/224 (PhET-iO design review).	non_process	phetiofeatured overrides use studio to feature unfeature elements and generate calculus grapher phet io overrides js if you re clear on the process for this contact arouinfar because this sim does not yet have an api you can safely commit changes to calculus grapher phet io overrides js without having to regenerate the api this should be completed after phet io design review	0
3,375	6,501,055,061	IssuesEvent	2017-08-23 08:06:11	gaocegege/Processing.R	https://api.github.com/repos/gaocegege/Processing.R	closed	Add tutorials	community/processing difficulty/low priority/p1 size/no-idea status/to-be-claimed type/enhancement	* Documentation is important, although it is so big that it could be done forever -- so maybe a few crucial improvements, then leave the rest for contributors.	1.0	Add tutorials - * Documentation is important, although it is so big that it could be done forever -- so maybe a few crucial improvements, then leave the rest for contributors.	process	add tutorials documentation is important although it is so big that it could be done forever so maybe a few crucial improvements then leave the rest for contributors	1
22,226	11,701,749,781	IssuesEvent	2020-03-06 20:23:33	kennyrkun/rmm	https://api.github.com/repos/kennyrkun/rmm	opened	windows service will not start	bug: unconfirmed m:windows-service p:windows	don't really know why. click the start button in the task manager and it says starting but goes back to stopped. don't really know why, we oughtta figure it out though.	1.0	windows service will not start - don't really know why. click the start button in the task manager and it says starting but goes back to stopped. don't really know why, we oughtta figure it out though.	non_process	windows service will not start don t really know why click the start button in the task manager and it says starting but goes back to stopped don t really know why we oughtta figure it out though	0
223,560	24,711,915,683	IssuesEvent	2022-10-20 01:59:22	alpersonalwebsite/react-todo-board	https://api.github.com/repos/alpersonalwebsite/react-todo-board	opened	CVE-2022-37601 (High) detected in loader-utils-1.2.3.tgz, loader-utils-1.4.0.tgz	security vulnerability	## CVE-2022-37601 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - <b>loader-utils-1.2.3.tgz</b>, <b>loader-utils-1.4.0.tgz</b></p></summary> <p> <details><summary><b>loader-utils-1.2.3.tgz</b></p></summary> <p>utils for webpack loaders</p> <p>Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz</a></p> <p>Path to dependency file: /package.json</p> <p>Path to vulnerable library: /node_modules/react-dev-utils/node_modules/loader-utils/package.json</p> <p> Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - react-dev-utils-9.1.0.tgz - :x: loader-utils-1.2.3.tgz (Vulnerable Library) </details> <details><summary><b>loader-utils-1.4.0.tgz</b></p></summary> <p>utils for webpack loaders</p> <p>Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz</a></p> <p>Path to dependency file: /package.json</p> <p>Path to vulnerable library: /node_modules/loader-utils/package.json</p> <p> Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - webpack-4.1.0.tgz - :x: loader-utils-1.4.0.tgz (Vulnerable Library) </details> <p>Found in base branch: <b>master</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> <p> Prototype pollution vulnerability in function parseQuery in parseQuery.js in webpack loader-utils 2.0.0 via the name variable in parseQuery.js. <p>Publish Date: 2022-10-12 <p>URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2022-37601>CVE-2022-37601</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>9.8</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Release Date: 2022-10-12</p> <p>Fix Resolution (loader-utils): 2.0.0</p> <p>Direct dependency fix Resolution (react-scripts): 5.0.1</p><p>Fix Resolution (loader-utils): 2.0.0</p> <p>Direct dependency fix Resolution (react-scripts): 5.0.1</p> </p> </details> <p></p> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)	True	CVE-2022-37601 (High) detected in loader-utils-1.2.3.tgz, loader-utils-1.4.0.tgz - ## CVE-2022-37601 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - <b>loader-utils-1.2.3.tgz</b>, <b>loader-utils-1.4.0.tgz</b></p></summary> <p> <details><summary><b>loader-utils-1.2.3.tgz</b></p></summary> <p>utils for webpack loaders</p> <p>Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz</a></p> <p>Path to dependency file: /package.json</p> <p>Path to vulnerable library: /node_modules/react-dev-utils/node_modules/loader-utils/package.json</p> <p> Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - react-dev-utils-9.1.0.tgz - :x: loader-utils-1.2.3.tgz (Vulnerable Library) </details> <details><summary><b>loader-utils-1.4.0.tgz</b></p></summary> <p>utils for webpack loaders</p> <p>Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz</a></p> <p>Path to dependency file: /package.json</p> <p>Path to vulnerable library: /node_modules/loader-utils/package.json</p> <p> Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - webpack-4.1.0.tgz - :x: loader-utils-1.4.0.tgz (Vulnerable Library) </details> <p>Found in base branch: <b>master</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> <p> Prototype pollution vulnerability in function parseQuery in parseQuery.js in webpack loader-utils 2.0.0 via the name variable in parseQuery.js. <p>Publish Date: 2022-10-12 <p>URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2022-37601>CVE-2022-37601</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>9.8</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Release Date: 2022-10-12</p> <p>Fix Resolution (loader-utils): 2.0.0</p> <p>Direct dependency fix Resolution (react-scripts): 5.0.1</p><p>Fix Resolution (loader-utils): 2.0.0</p> <p>Direct dependency fix Resolution (react-scripts): 5.0.1</p> </p> </details> <p></p> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)	non_process	cve high detected in loader utils tgz loader utils tgz cve high severity vulnerability vulnerable libraries loader utils tgz loader utils tgz loader utils tgz utils for webpack loaders library home page a href path to dependency file package json path to vulnerable library node modules react dev utils node modules loader utils package json dependency hierarchy react scripts tgz root library react dev utils tgz x loader utils tgz vulnerable library loader utils tgz utils for webpack loaders library home page a href path to dependency file package json path to vulnerable library node modules loader utils package json dependency hierarchy react scripts tgz root library webpack tgz x loader utils tgz vulnerable library found in base branch master vulnerability details prototype pollution vulnerability in function parsequery in parsequery js in webpack loader utils via the name variable in parsequery js publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction none scope unchanged impact metrics confidentiality impact high integrity impact high availability impact high for more information on scores click a href suggested fix type upgrade version release date fix resolution loader utils direct dependency fix resolution react scripts fix resolution loader utils direct dependency fix resolution react scripts step up your open source security game with mend	0
65,824	14,761,947,162	IssuesEvent	2021-01-09 01:06:29	rsoreq/zaproxy	https://api.github.com/repos/rsoreq/zaproxy	opened	CVE-2020-36184 (Medium) detected in jackson-databind-2.9.10.jar, jackson-databind-2.9.2.jar	security vulnerability	## CVE-2020-36184 - Medium Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - <b>jackson-databind-2.9.10.jar</b>, <b>jackson-databind-2.9.2.jar</b></p></summary> <p> <details><summary><b>jackson-databind-2.9.10.jar</b></p></summary> <p>General data-binding functionality for Jackson: works on core streaming API</p> <p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p> <p>Path to dependency file: zaproxy</p> <p>Path to vulnerable library: /tmp/ws-ua_20200729112444_WCAEYA/downloadResource_JMENZF/20200729112922/jackson-databind-2.9.10.jar</p> <p> Dependency Hierarchy: - wiremock-jre8-2.25.1.jar (Root Library) - zjsonpatch-0.4.4.jar - :x: jackson-databind-2.9.10.jar (Vulnerable Library) </details> <details><summary><b>jackson-databind-2.9.2.jar</b></p></summary> <p>General data-binding functionality for Jackson: works on core streaming API</p> <p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p> <p>Path to dependency file: zaproxy/buildSrc/build.gradle.kts</p> <p>Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.9.2/1d8d8cb7cf26920ba57fb61fa56da88cc123b21f/jackson-databind-2.9.2.jar</p> <p> Dependency Hierarchy: - kotlin-reflect-1.3.72.jar (Root Library) - :x: jackson-databind-2.9.2.jar (Vulnerable Library) </details> <p>Found in base branch: <b>develop</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/medium_vul.png' width=19 height=20> Vulnerability Details</summary> <p> FasterXML jackson-databind 2.x before 2.9.10.8 mishandles the interaction between serialization gadgets and typing, related to org.apache.tomcat.dbcp.dbcp2.datasources.PerUserPoolDataSource. <p>Publish Date: 2021-01-06 <p>URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36184>CVE-2020-36184</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>4.2</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Local - Attack Complexity: High - Privileges Required: Low - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: Low - Integrity Impact: Low - Availability Impact: Low </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Origin: <a href="https://github.com/FasterXML/jackson-databind/issues/2998">https://github.com/FasterXML/jackson-databind/issues/2998</a></p> <p>Release Date: 2021-01-06</p> <p>Fix Resolution: com.fasterxml.jackson.core:jackson-databind:2.9.10.8</p> </p> </details> <p></p> <!-- <REMEDIATE>{"isOpenPROnVulnerability":true,"isPackageBased":true,"isDefaultBranch":true,"packages":[{"packageType":"Java","groupId":"com.fasterxml.jackson.core","packageName":"jackson-databind","packageVersion":"2.9.10","isTransitiveDependency":true,"dependencyTree":"com.github.tomakehurst:wiremock-jre8:2.25.1;com.flipkart.zjsonpatch:zjsonpatch:0.4.4;com.fasterxml.jackson.core:jackson-databind:2.9.10","isMinimumFixVersionAvailable":true,"minimumFixVersion":"com.fasterxml.jackson.core:jackson-databind:2.9.10.8"},{"packageType":"Java","groupId":"com.fasterxml.jackson.core","packageName":"jackson-databind","packageVersion":"2.9.2","isTransitiveDependency":true,"dependencyTree":"org.jetbrains.kotlin:kotlin-reflect:1.3.72;com.fasterxml.jackson.core:jackson-databind:2.9.2","isMinimumFixVersionAvailable":true,"minimumFixVersion":"com.fasterxml.jackson.core:jackson-databind:2.9.10.8"}],"vulnerabilityIdentifier":"CVE-2020-36184","vulnerabilityDetails":"FasterXML jackson-databind 2.x before 2.9.10.8 mishandles the interaction between serialization gadgets and typing, related to org.apache.tomcat.dbcp.dbcp2.datasources.PerUserPoolDataSource.","vulnerabilityUrl":"https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36184","cvss3Severity":"medium","cvss3Score":"4.2","cvss3Metrics":{"A":"Low","AC":"High","PR":"Low","S":"Unchanged","C":"Low","UI":"Required","AV":"Local","I":"Low"},"extraData":{}}</REMEDIATE> -->	True	CVE-2020-36184 (Medium) detected in jackson-databind-2.9.10.jar, jackson-databind-2.9.2.jar - ## CVE-2020-36184 - Medium Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - <b>jackson-databind-2.9.10.jar</b>, <b>jackson-databind-2.9.2.jar</b></p></summary> <p> <details><summary><b>jackson-databind-2.9.10.jar</b></p></summary> <p>General data-binding functionality for Jackson: works on core streaming API</p> <p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p> <p>Path to dependency file: zaproxy</p> <p>Path to vulnerable library: /tmp/ws-ua_20200729112444_WCAEYA/downloadResource_JMENZF/20200729112922/jackson-databind-2.9.10.jar</p> <p> Dependency Hierarchy: - wiremock-jre8-2.25.1.jar (Root Library) - zjsonpatch-0.4.4.jar - :x: jackson-databind-2.9.10.jar (Vulnerable Library) </details> <details><summary><b>jackson-databind-2.9.2.jar</b></p></summary> <p>General data-binding functionality for Jackson: works on core streaming API</p> <p>Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a></p> <p>Path to dependency file: zaproxy/buildSrc/build.gradle.kts</p> <p>Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.9.2/1d8d8cb7cf26920ba57fb61fa56da88cc123b21f/jackson-databind-2.9.2.jar</p> <p> Dependency Hierarchy: - kotlin-reflect-1.3.72.jar (Root Library) - :x: jackson-databind-2.9.2.jar (Vulnerable Library) </details> <p>Found in base branch: <b>develop</b></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/medium_vul.png' width=19 height=20> Vulnerability Details</summary> <p> FasterXML jackson-databind 2.x before 2.9.10.8 mishandles the interaction between serialization gadgets and typing, related to org.apache.tomcat.dbcp.dbcp2.datasources.PerUserPoolDataSource. <p>Publish Date: 2021-01-06 <p>URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36184>CVE-2020-36184</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>4.2</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Local - Attack Complexity: High - Privileges Required: Low - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: Low - Integrity Impact: Low - Availability Impact: Low </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Upgrade version</p> <p>Origin: <a href="https://github.com/FasterXML/jackson-databind/issues/2998">https://github.com/FasterXML/jackson-databind/issues/2998</a></p> <p>Release Date: 2021-01-06</p> <p>Fix Resolution: com.fasterxml.jackson.core:jackson-databind:2.9.10.8</p> </p> </details> <p></p> <!-- <REMEDIATE>{"isOpenPROnVulnerability":true,"isPackageBased":true,"isDefaultBranch":true,"packages":[{"packageType":"Java","groupId":"com.fasterxml.jackson.core","packageName":"jackson-databind","packageVersion":"2.9.10","isTransitiveDependency":true,"dependencyTree":"com.github.tomakehurst:wiremock-jre8:2.25.1;com.flipkart.zjsonpatch:zjsonpatch:0.4.4;com.fasterxml.jackson.core:jackson-databind:2.9.10","isMinimumFixVersionAvailable":true,"minimumFixVersion":"com.fasterxml.jackson.core:jackson-databind:2.9.10.8"},{"packageType":"Java","groupId":"com.fasterxml.jackson.core","packageName":"jackson-databind","packageVersion":"2.9.2","isTransitiveDependency":true,"dependencyTree":"org.jetbrains.kotlin:kotlin-reflect:1.3.72;com.fasterxml.jackson.core:jackson-databind:2.9.2","isMinimumFixVersionAvailable":true,"minimumFixVersion":"com.fasterxml.jackson.core:jackson-databind:2.9.10.8"}],"vulnerabilityIdentifier":"CVE-2020-36184","vulnerabilityDetails":"FasterXML jackson-databind 2.x before 2.9.10.8 mishandles the interaction between serialization gadgets and typing, related to org.apache.tomcat.dbcp.dbcp2.datasources.PerUserPoolDataSource.","vulnerabilityUrl":"https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36184","cvss3Severity":"medium","cvss3Score":"4.2","cvss3Metrics":{"A":"Low","AC":"High","PR":"Low","S":"Unchanged","C":"Low","UI":"Required","AV":"Local","I":"Low"},"extraData":{}}</REMEDIATE> -->	non_process	cve medium detected in jackson databind jar jackson databind jar cve medium severity vulnerability vulnerable libraries jackson databind jar jackson databind jar jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to dependency file zaproxy path to vulnerable library tmp ws ua wcaeya downloadresource jmenzf jackson databind jar dependency hierarchy wiremock jar root library zjsonpatch jar x jackson databind jar vulnerable library jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to dependency file zaproxy buildsrc build gradle kts path to vulnerable library home wss scanner gradle caches modules files com fasterxml jackson core jackson databind jackson databind jar dependency hierarchy kotlin reflect jar root library x jackson databind jar vulnerable library found in base branch develop vulnerability details fasterxml jackson databind x before mishandles the interaction between serialization gadgets and typing related to org apache tomcat dbcp datasources peruserpooldatasource publish date url a href cvss score details base score metrics exploitability metrics attack vector local attack complexity high privileges required low user interaction required scope unchanged impact metrics confidentiality impact low integrity impact low availability impact low for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution com fasterxml jackson core jackson databind isopenpronvulnerability true ispackagebased true isdefaultbranch true packages vulnerabilityidentifier cve vulnerabilitydetails fasterxml jackson databind x before mishandles the interaction between serialization gadgets and typing related to org apache tomcat dbcp datasources peruserpooldatasource vulnerabilityurl	0
16,126	9,693,785,412	IssuesEvent	2019-05-24 17:02:48	pcrane70/blackrock	https://api.github.com/repos/pcrane70/blackrock	opened	CVE-2018-8014 (High) detected in tomcat-embed-core-8.5.20.jar	security vulnerability	## CVE-2018-8014 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>tomcat-embed-core-8.5.20.jar</b></p></summary> <p>Core Tomcat implementation</p> <p>Library home page: <a href="http://tomcat.apache.org/">http://tomcat.apache.org/</a></p> <p>Path to dependency file: /blackrock/dataguard-gateway/pom.xml</p> <p>Path to vulnerable library: /root/.m2/repository/org/apache/tomcat/embed/tomcat-embed-core/8.5.20/tomcat-embed-core-8.5.20.jar</p> <p> Dependency Hierarchy: - spring-boot-starter-jersey-1.5.7.RELEASE.jar (Root Library) - spring-boot-starter-tomcat-1.5.7.RELEASE.jar - :x: tomcat-embed-core-8.5.20.jar (Vulnerable Library) </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> <p> The defaults settings for the CORS filter provided in Apache Tomcat 9.0.0.M1 to 9.0.8, 8.5.0 to 8.5.31, 8.0.0.RC1 to 8.0.52, 7.0.41 to 7.0.88 are insecure and enable 'supportsCredentials' for all origins. It is expected that users of the CORS filter will have configured it appropriately for their environment rather than using it in the default configuration. Therefore, it is expected that most users will not be impacted by this issue. <p>Publish Date: 2018-05-16 <p>URL: <a href=https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-8014>CVE-2018-8014</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>9.8</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Change files</p> <p>Origin: <a href="https://nvd.nist.gov/vuln/detail/CVE-2018-8014">https://nvd.nist.gov/vuln/detail/CVE-2018-8014</a></p> <p>Release Date: 2019-04-08</p> <p>Fix Resolution: Replace or update the following files: 7.0.89, 8.0.53, 8.5.32, 9.0.9</p> </p> </details> <p></p> *** Step up your Open Source Security Game with WhiteSource [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)	True	CVE-2018-8014 (High) detected in tomcat-embed-core-8.5.20.jar - ## CVE-2018-8014 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - <b>tomcat-embed-core-8.5.20.jar</b></p></summary> <p>Core Tomcat implementation</p> <p>Library home page: <a href="http://tomcat.apache.org/">http://tomcat.apache.org/</a></p> <p>Path to dependency file: /blackrock/dataguard-gateway/pom.xml</p> <p>Path to vulnerable library: /root/.m2/repository/org/apache/tomcat/embed/tomcat-embed-core/8.5.20/tomcat-embed-core-8.5.20.jar</p> <p> Dependency Hierarchy: - spring-boot-starter-jersey-1.5.7.RELEASE.jar (Root Library) - spring-boot-starter-tomcat-1.5.7.RELEASE.jar - :x: tomcat-embed-core-8.5.20.jar (Vulnerable Library) </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> <p> The defaults settings for the CORS filter provided in Apache Tomcat 9.0.0.M1 to 9.0.8, 8.5.0 to 8.5.31, 8.0.0.RC1 to 8.0.52, 7.0.41 to 7.0.88 are insecure and enable 'supportsCredentials' for all origins. It is expected that users of the CORS filter will have configured it appropriately for their environment rather than using it in the default configuration. Therefore, it is expected that most users will not be impacted by this issue. <p>Publish Date: 2018-05-16 <p>URL: <a href=https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-8014>CVE-2018-8014</a></p> </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (<b>9.8</b>)</summary> <p> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High </p> For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </p> </details> <p></p> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> <p> <p>Type: Change files</p> <p>Origin: <a href="https://nvd.nist.gov/vuln/detail/CVE-2018-8014">https://nvd.nist.gov/vuln/detail/CVE-2018-8014</a></p> <p>Release Date: 2019-04-08</p> <p>Fix Resolution: Replace or update the following files: 7.0.89, 8.0.53, 8.5.32, 9.0.9</p> </p> </details> <p></p> *** Step up your Open Source Security Game with WhiteSource [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)	non_process	cve high detected in tomcat embed core jar cve high severity vulnerability vulnerable library tomcat embed core jar core tomcat implementation library home page a href path to dependency file blackrock dataguard gateway pom xml path to vulnerable library root repository org apache tomcat embed tomcat embed core tomcat embed core jar dependency hierarchy spring boot starter jersey release jar root library spring boot starter tomcat release jar x tomcat embed core jar vulnerable library vulnerability details the defaults settings for the cors filter provided in apache tomcat to to to to are insecure and enable supportscredentials for all origins it is expected that users of the cors filter will have configured it appropriately for their environment rather than using it in the default configuration therefore it is expected that most users will not be impacted by this issue publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction none scope unchanged impact metrics confidentiality impact high integrity impact high availability impact high for more information on scores click a href suggested fix type change files origin a href release date fix resolution replace or update the following files step up your open source security game with whitesource	0
627,387	19,903,822,744	IssuesEvent	2022-01-25 10:40:40	iteles/blw-baby	https://api.github.com/repos/iteles/blw-baby	opened	Homemade baby puffs	priority-2	`Baby puffs` are sold everywhere in every country and are super popular because they are an easy grab-n-go snack that makes practically zero mess and the baby can serve themselves _and_ hold their snacks all by their lonesome. This kind of thing: ![image](https://user-images.githubusercontent.com/4185328/150883076-0b5bf2be-acae-420e-af54-edd4ca056cae.png) I'm not a fan of hidden ingredients but _especially_ not a fan of the mega packaging, so I though we would do well to try our own. A quick google has yielded two recipes we can try to start: 1. https://whatgreatgrandmaate.com/healthy-homemade-baby-puffs/ I'm thinking apple sauce (which we already have frozen), pear & a little kale and a starter. 2. https://feedingtinybellies.com/strawberry-kiwi-puffs/ Note: the _size_ of the puffs is _extremely_ important in order to guarantee proper cooking.	1.0	Homemade baby puffs - `Baby puffs` are sold everywhere in every country and are super popular because they are an easy grab-n-go snack that makes practically zero mess and the baby can serve themselves _and_ hold their snacks all by their lonesome. This kind of thing: ![image](https://user-images.githubusercontent.com/4185328/150883076-0b5bf2be-acae-420e-af54-edd4ca056cae.png) I'm not a fan of hidden ingredients but _especially_ not a fan of the mega packaging, so I though we would do well to try our own. A quick google has yielded two recipes we can try to start: 1. https://whatgreatgrandmaate.com/healthy-homemade-baby-puffs/ I'm thinking apple sauce (which we already have frozen), pear & a little kale and a starter. 2. https://feedingtinybellies.com/strawberry-kiwi-puffs/ Note: the _size_ of the puffs is _extremely_ important in order to guarantee proper cooking.	non_process	homemade baby puffs baby puffs are sold everywhere in every country and are super popular because they are an easy grab n go snack that makes practically zero mess and the baby can serve themselves and hold their snacks all by their lonesome this kind of thing i m not a fan of hidden ingredients but especially not a fan of the mega packaging so i though we would do well to try our own a quick google has yielded two recipes we can try to start i m thinking apple sauce which we already have frozen pear a little kale and a starter note the size of the puffs is extremely important in order to guarantee proper cooking	0
3,171	6,224,315,048	IssuesEvent	2017-07-10 14:01:12	nodejs/node	https://api.github.com/repos/nodejs/node	reopened	stdout/stderr buffering with TTY	process repro-exists tty	Recently I discovered that at least with the latest io.js (currently testing with the `next` branch in particular) and possibly older versions, stdout and stderr seem to be buffering when those streams refer to a TTY. Here's a simple example that replicates the issue for me: ``` javascript while (true) { console.error('stderr is tty? ' + process.stderr.isTTY); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); require('fs').writeFileSync('/tmp/stderr_buffer_len', 'stderr buffer length=' + process.stderr._writableState.length + '\n'); } ``` In one terminal do `tail -F /tmp/stderr_buffer_len` and then in another do `iojs test.js`. I also tested the same script with node v0.10.30 and the stderr buffer length is always 0.	1.0	stdout/stderr buffering with TTY - Recently I discovered that at least with the latest io.js (currently testing with the `next` branch in particular) and possibly older versions, stdout and stderr seem to be buffering when those streams refer to a TTY. Here's a simple example that replicates the issue for me: ``` javascript while (true) { console.error('stderr is tty? ' + process.stderr.isTTY); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); require('fs').writeFileSync('/tmp/stderr_buffer_len', 'stderr buffer length=' + process.stderr._writableState.length + '\n'); } ``` In one terminal do `tail -F /tmp/stderr_buffer_len` and then in another do `iojs test.js`. I also tested the same script with node v0.10.30 and the stderr buffer length is always 0.	process	stdout stderr buffering with tty recently i discovered that at least with the latest io js currently testing with the next branch in particular and possibly older versions stdout and stderr seem to be buffering when those streams refer to a tty here s a simple example that replicates the issue for me javascript while true console error stderr is tty process stderr istty console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc require fs writefilesync tmp stderr buffer len stderr buffer length process stderr writablestate length n in one terminal do tail f tmp stderr buffer len and then in another do iojs test js i also tested the same script with node and the stderr buffer length is always	1
8,503	11,685,550,937	IssuesEvent	2020-03-05 09:18:24	storm-fsv-cvut/smoderp2d	https://api.github.com/repos/storm-fsv-cvut/smoderp2d	opened	internal poit and stream to output	Output post-processing	Internal point (shp layer) save to core output stream - part in computed area (with added outputs for each segment - other issue from Dec 11) save also to core output output point data (.dat) save also to core output or subfolder of core	1.0	internal poit and stream to output - Internal point (shp layer) save to core output stream - part in computed area (with added outputs for each segment - other issue from Dec 11) save also to core output output point data (.dat) save also to core output or subfolder of core	process	internal poit and stream to output internal point shp layer save to core output stream part in computed area with added outputs for each segment other issue from dec save also to core output output point data dat save also to core output or subfolder of core	1
17,538	23,346,359,160	IssuesEvent	2022-08-09 18:22:34	GSA/EDX	https://api.github.com/repos/GSA/EDX	closed	Review Web Manager Position Descriptions for requirements (Skills, Competencies, Training)	process professional-development strategy	- [x] Develop a roster of website managers from the Touchpoints survey - [x] Identify PDs of each website manager - [x] [Fix Roster with lessons learned from survey and review new PDs](https://github.com/GSA/EDX/issues/113#issuecomment-1059581758) - [x] Determine the number of unique PDs (and standardized PDs vs. specific PDs) - [x] Pull a sample of PDs - [x] Review PDs for requirements (skills, competencies, training) - [x] Identify opportunities to include requirements How can we elevate the role of the website manager so 1) that role is clearly identified & delinated 2) site managers have the required skills & competencies 3) site managers are held accountable to be in compliance with all regulations? In Issue 55, we reviewed a sample of site manager PDs and found that none of them addressed skills, competencies, regulations, or any requirements pertaining to website management compliance. After consultation with Classification, we decided to review the PDs of every site manager to determine the 1) series 2) bargaining unit status 3) skills, competencies required 4) regulations. We'll analyze that information and make a recommendation if site manager PDs should include certain requirements. We'll continue to collaborate with Classification and consult with Labor Relations.	1.0	Review Web Manager Position Descriptions for requirements (Skills, Competencies, Training) - - [x] Develop a roster of website managers from the Touchpoints survey - [x] Identify PDs of each website manager - [x] [Fix Roster with lessons learned from survey and review new PDs](https://github.com/GSA/EDX/issues/113#issuecomment-1059581758) - [x] Determine the number of unique PDs (and standardized PDs vs. specific PDs) - [x] Pull a sample of PDs - [x] Review PDs for requirements (skills, competencies, training) - [x] Identify opportunities to include requirements How can we elevate the role of the website manager so 1) that role is clearly identified & delinated 2) site managers have the required skills & competencies 3) site managers are held accountable to be in compliance with all regulations? In Issue 55, we reviewed a sample of site manager PDs and found that none of them addressed skills, competencies, regulations, or any requirements pertaining to website management compliance. After consultation with Classification, we decided to review the PDs of every site manager to determine the 1) series 2) bargaining unit status 3) skills, competencies required 4) regulations. We'll analyze that information and make a recommendation if site manager PDs should include certain requirements. We'll continue to collaborate with Classification and consult with Labor Relations.	process	review web manager position descriptions for requirements skills competencies training develop a roster of website managers from the touchpoints survey identify pds of each website manager determine the number of unique pds and standardized pds vs specific pds pull a sample of pds review pds for requirements skills competencies training identify opportunities to include requirements how can we elevate the role of the website manager so that role is clearly identified delinated site managers have the required skills competencies site managers are held accountable to be in compliance with all regulations in issue we reviewed a sample of site manager pds and found that none of them addressed skills competencies regulations or any requirements pertaining to website management compliance after consultation with classification we decided to review the pds of every site manager to determine the series bargaining unit status skills competencies required regulations we ll analyze that information and make a recommendation if site manager pds should include certain requirements we ll continue to collaborate with classification and consult with labor relations	1
291,952	25,187,597,348	IssuesEvent	2022-11-11 19:46:06	ValveSoftware/Proton	https://api.github.com/repos/ValveSoftware/Proton	closed	The Ultimate Doom (2280) rerelease	Game compatibility Need Retest	# Compatibility Report - Name of the game with compatibility issues: The Ultimate Doom - Steam AppID of the game: 2280 ## System Information - GPU: AMD Radeon RX5700 - Driver/LLVM version: Mesa 20.1.7 llvm 10.0.1 - Kernel version: 5.8.5 - Link to full system information report as [Gist](https://gist.github.com/): https://gist.github.com/xDShot/8e9ae635b715265059e09bb21f592afa - Proton version: 5.0-9 ## I confirm: - [x] that I haven't found an existing compatibility report for this game. - [x] that I have checked whether there are updates for my system available. <!-- Please add `PROTON_LOG=1 %command%` to the game's launch options and drag and drop the generated `$HOME/steam-$APPID.log` into this issue report --> ## Symptoms <!-- What's the problem? --> Black screen when launched from Steam. Works if launched directly outside of Steam with system Wine.	1.0	The Ultimate Doom (2280) rerelease - # Compatibility Report - Name of the game with compatibility issues: The Ultimate Doom - Steam AppID of the game: 2280 ## System Information - GPU: AMD Radeon RX5700 - Driver/LLVM version: Mesa 20.1.7 llvm 10.0.1 - Kernel version: 5.8.5 - Link to full system information report as [Gist](https://gist.github.com/): https://gist.github.com/xDShot/8e9ae635b715265059e09bb21f592afa - Proton version: 5.0-9 ## I confirm: - [x] that I haven't found an existing compatibility report for this game. - [x] that I have checked whether there are updates for my system available. <!-- Please add `PROTON_LOG=1 %command%` to the game's launch options and drag and drop the generated `$HOME/steam-$APPID.log` into this issue report --> ## Symptoms <!-- What's the problem? --> Black screen when launched from Steam. Works if launched directly outside of Steam with system Wine.	non_process	the ultimate doom rerelease compatibility report name of the game with compatibility issues the ultimate doom steam appid of the game system information gpu amd radeon driver llvm version mesa llvm kernel version link to full system information report as proton version i confirm that i haven t found an existing compatibility report for this game that i have checked whether there are updates for my system available please add proton log command to the game s launch options and drag and drop the generated home steam appid log into this issue report symptoms black screen when launched from steam works if launched directly outside of steam with system wine	0
19,450	25,732,054,973	IssuesEvent	2022-12-07 21:07:38	LLNL/maestrowf	https://api.github.com/repos/LLNL/maestrowf	opened	Version info syncing issues	Release Process Discussion	Currently we seem to have 3 sources of package version info which is not in sync: * git tags: develop's latest tag appears to be 1.1.7dev0, master is 1.1.4, and more concerning develop has inconsistent use of dev, dev0, and no dev tag at all * pyproject.toml: most up to date source since the switch to poetry * top level `__init__.py` is still on 1.1.9dev1 I propose switching this to a single source and automating it if possible and potentially a different versioning scheme (dev tags). Importlib appears able to read from the pyproject.toml now, so I think that single source could be the toml file now. Additionally, there's a few interesting options for at least semi-automated version bumping: * poetry's built in 'version' command: it has some potentially useful bump rules like prepatch/preminor/prerelease. There are some quirks though: repeated use of prerelease does tick the release (-alpha) tag numbers, but repeated use of prepatch doesn't? Also not clear if this can use the abbreviated form 'a0' instead of the default '-alpha.0' * bumpver package supports more customization on what the actual tag is, but still limited to alpha/beta/release/final/post, and no dev. dev could likely be added if we make a pr to that package and it gets accepted. Either one would likely be able to use hook into an automated tag creation since both have a version query on the cli. Potentially bigger changes to the versioning scheme beyond just possible switching away from dev if jumping on an existing solution right away: * have explicit version commits/tagging post pushing to master, which isn't really changing anything * drop pre-release/dev tagging/version bumps altogether on develop beyond an initial alpha/dev tag upon cutting a release? we don't currently release those anyway, so much as it's nice in principle, is it worth the effort of maintaining tags/version bumps on every PR/commit onto develop? Can always do some one off pre-release tags/bumps for dev version releases if needed	1.0	Version info syncing issues - Currently we seem to have 3 sources of package version info which is not in sync: * git tags: develop's latest tag appears to be 1.1.7dev0, master is 1.1.4, and more concerning develop has inconsistent use of dev, dev0, and no dev tag at all * pyproject.toml: most up to date source since the switch to poetry * top level `__init__.py` is still on 1.1.9dev1 I propose switching this to a single source and automating it if possible and potentially a different versioning scheme (dev tags). Importlib appears able to read from the pyproject.toml now, so I think that single source could be the toml file now. Additionally, there's a few interesting options for at least semi-automated version bumping: * poetry's built in 'version' command: it has some potentially useful bump rules like prepatch/preminor/prerelease. There are some quirks though: repeated use of prerelease does tick the release (-alpha) tag numbers, but repeated use of prepatch doesn't? Also not clear if this can use the abbreviated form 'a0' instead of the default '-alpha.0' * bumpver package supports more customization on what the actual tag is, but still limited to alpha/beta/release/final/post, and no dev. dev could likely be added if we make a pr to that package and it gets accepted. Either one would likely be able to use hook into an automated tag creation since both have a version query on the cli. Potentially bigger changes to the versioning scheme beyond just possible switching away from dev if jumping on an existing solution right away: * have explicit version commits/tagging post pushing to master, which isn't really changing anything * drop pre-release/dev tagging/version bumps altogether on develop beyond an initial alpha/dev tag upon cutting a release? we don't currently release those anyway, so much as it's nice in principle, is it worth the effort of maintaining tags/version bumps on every PR/commit onto develop? Can always do some one off pre-release tags/bumps for dev version releases if needed	process	version info syncing issues currently we seem to have sources of package version info which is not in sync git tags develop s latest tag appears to be master is and more concerning develop has inconsistent use of dev and no dev tag at all pyproject toml most up to date source since the switch to poetry top level init py is still on i propose switching this to a single source and automating it if possible and potentially a different versioning scheme dev tags importlib appears able to read from the pyproject toml now so i think that single source could be the toml file now additionally there s a few interesting options for at least semi automated version bumping poetry s built in version command it has some potentially useful bump rules like prepatch preminor prerelease there are some quirks though repeated use of prerelease does tick the release alpha tag numbers but repeated use of prepatch doesn t also not clear if this can use the abbreviated form instead of the default alpha bumpver package supports more customization on what the actual tag is but still limited to alpha beta release final post and no dev dev could likely be added if we make a pr to that package and it gets accepted either one would likely be able to use hook into an automated tag creation since both have a version query on the cli potentially bigger changes to the versioning scheme beyond just possible switching away from dev if jumping on an existing solution right away have explicit version commits tagging post pushing to master which isn t really changing anything drop pre release dev tagging version bumps altogether on develop beyond an initial alpha dev tag upon cutting a release we don t currently release those anyway so much as it s nice in principle is it worth the effort of maintaining tags version bumps on every pr commit onto develop can always do some one off pre release tags bumps for dev version releases if needed	1
21,629	30,033,459,318	IssuesEvent	2023-06-27 11:10:05	metabase/metabase	https://api.github.com/repos/metabase/metabase	closed	Join is removed with "Removing invalid MBQL clause" joining two columns together with the same field ID	Type:Bug Priority:P2 .Regression .metabase-lib .Team/QueryProcessor :hammer_and_wrench:	### Describe the bug If you join two tables together on a column with the same field ID in the notebook editor, the join gets removed on save. ### To Reproduce Following the first three steps from https://github.com/metabase/metabase/issues/12930: 1. Create question Q1 - Orders join with Products and People, summarized by Count, grouped by Category and Source ![image](https://user-images.githubusercontent.com/1447303/87708713-6e290480-c7a3-11ea-861a-72d09dac8c30.png) 2. Create question Q2 - Products, summarized by Average Rating, grouped by Category ![image](https://user-images.githubusercontent.com/1447303/87715536-c9f88b00-c7ad-11ea-8c81-4c57662acf5e.png) 3. Custom question > Saved Questions > Q1 and join Q2 on Category. <img width="593" alt="image" src="https://github.com/metabase/metabase/assets/39073188/31a3d51a-f550-45f9-8f18-d0c9e6f5235e"> 4. Click "Save". The Join should be removed with only Q1 remaining: <img width="666" alt="image" src="https://github.com/metabase/metabase/assets/39073188/38925aed-73ea-499a-aff8-c3bc84c7490c"> ### Expected behavior Join should work ### Logs The browser console shows this warning on save: ![image](https://github.com/metabase/metabase/assets/39073188/4faef6d5-4110-46a6-9cfd-1ee24245c1d9) ### Information about your Metabase installation ```JSON master, commit 7587d3cf1a6e35e593f900f98d266591a6160a31 ``` ### Severity High ### Additional context _No response_	1.0	Join is removed with "Removing invalid MBQL clause" joining two columns together with the same field ID - ### Describe the bug If you join two tables together on a column with the same field ID in the notebook editor, the join gets removed on save. ### To Reproduce Following the first three steps from https://github.com/metabase/metabase/issues/12930: 1. Create question Q1 - Orders join with Products and People, summarized by Count, grouped by Category and Source ![image](https://user-images.githubusercontent.com/1447303/87708713-6e290480-c7a3-11ea-861a-72d09dac8c30.png) 2. Create question Q2 - Products, summarized by Average Rating, grouped by Category ![image](https://user-images.githubusercontent.com/1447303/87715536-c9f88b00-c7ad-11ea-8c81-4c57662acf5e.png) 3. Custom question > Saved Questions > Q1 and join Q2 on Category. <img width="593" alt="image" src="https://github.com/metabase/metabase/assets/39073188/31a3d51a-f550-45f9-8f18-d0c9e6f5235e"> 4. Click "Save". The Join should be removed with only Q1 remaining: <img width="666" alt="image" src="https://github.com/metabase/metabase/assets/39073188/38925aed-73ea-499a-aff8-c3bc84c7490c"> ### Expected behavior Join should work ### Logs The browser console shows this warning on save: ![image](https://github.com/metabase/metabase/assets/39073188/4faef6d5-4110-46a6-9cfd-1ee24245c1d9) ### Information about your Metabase installation ```JSON master, commit 7587d3cf1a6e35e593f900f98d266591a6160a31 ``` ### Severity High ### Additional context _No response_	process	join is removed with removing invalid mbql clause joining two columns together with the same field id describe the bug if you join two tables together on a column with the same field id in the notebook editor the join gets removed on save to reproduce following the first three steps from create question orders join with products and people summarized by count grouped by category and source create question products summarized by average rating grouped by category custom question saved questions and join on category img width alt image src click save the join should be removed with only remaining img width alt image src expected behavior join should work logs the browser console shows this warning on save information about your metabase installation json master commit severity high additional context no response	1
22,010	6,228,039,713	IssuesEvent	2017-07-10 22:10:00	XceedBoucherS/TestImport5	https://api.github.com/repos/XceedBoucherS/TestImport5	closed	RichTextBox TextFormatter Not Supporting Flowdocument Property?	CodePlex	<b>LastDefense[CodePlex]</b> <br />I tried to Bind the RichTextBox to a FlowDocument Property Type using custom formatter. I actually used the one in your code example with no other changes. When the RTB tries to bind I get the following error: Cannot create default converter to perform 'two-way' conversions between types 'System.Windows.Documents.FlowDocument' and 'System.String'. nbsp I changed the property type to string and the format being returned is plain text event though I have the custom formatter applied. nbsp I used the code in your example for the RTB and the custom formatter so I am not sure what I am doing wrong. Can you explain in more detail how the custom formatter is supposed to work? I have a Flowdocument to bind so it does not make sense to convert it to a string or xaml just to be converted back to a Flowdocument. nbsp Thanks	1.0	RichTextBox TextFormatter Not Supporting Flowdocument Property? - <b>LastDefense[CodePlex]</b> <br />I tried to Bind the RichTextBox to a FlowDocument Property Type using custom formatter. I actually used the one in your code example with no other changes. When the RTB tries to bind I get the following error: Cannot create default converter to perform 'two-way' conversions between types 'System.Windows.Documents.FlowDocument' and 'System.String'. nbsp I changed the property type to string and the format being returned is plain text event though I have the custom formatter applied. nbsp I used the code in your example for the RTB and the custom formatter so I am not sure what I am doing wrong. Can you explain in more detail how the custom formatter is supposed to work? I have a Flowdocument to bind so it does not make sense to convert it to a string or xaml just to be converted back to a Flowdocument. nbsp Thanks	non_process	richtextbox textformatter not supporting flowdocument property lastdefense i tried to bind the richtextbox to a flowdocument property type using custom formatter i actually used the one in your code example with no other changes when the rtb tries to bind i get the following error cannot create default converter to perform two way conversions between types system windows documents flowdocument and system string nbsp i changed the property type to string and the format being returned is plain text event though i have the custom formatter applied nbsp i used the code in your example for the rtb and the custom formatter so i am not sure what i am doing wrong can you explain in more detail how the custom formatter is supposed to work i have a flowdocument to bind so it does not make sense to convert it to a string or xaml just to be converted back to a flowdocument nbsp thanks	0
108,899	13,685,793,384	IssuesEvent	2020-09-30 07:43:57	mdn/mdn-minimalist	https://api.github.com/repos/mdn/mdn-minimalist	closed	Forms	design-system enhancement	A couple of tweaks to form styles: - [x] When a piece of text is wrapped in a `code` element inside a highlighted `legend` element, set the `background-color` to transparent - [x] Remove left padding from highlighted `legend` elements	1.0	Forms - A couple of tweaks to form styles: - [x] When a piece of text is wrapped in a `code` element inside a highlighted `legend` element, set the `background-color` to transparent - [x] Remove left padding from highlighted `legend` elements	non_process	forms a couple of tweaks to form styles when a piece of text is wrapped in a code element inside a highlighted legend element set the background color to transparent remove left padding from highlighted legend elements	0
223,875	7,461,529,594	IssuesEvent	2018-03-31 04:13:57	rivine/rivine	https://api.github.com/repos/rivine/rivine	closed	make it possible to send block stakes to multiple people at once using rivinec	priority_major state_inprogress type_feature	currently `rivinec wallet send blockstakes` only allows to send to one person at a time, would be great if we can send to multiple people at once	1.0	make it possible to send block stakes to multiple people at once using rivinec - currently `rivinec wallet send blockstakes` only allows to send to one person at a time, would be great if we can send to multiple people at once	non_process	make it possible to send block stakes to multiple people at once using rivinec currently rivinec wallet send blockstakes only allows to send to one person at a time would be great if we can send to multiple people at once	0
22,072	30,594,928,893	IssuesEvent	2023-07-21 20:50:19	USGS-WiM/StreamStats	https://api.github.com/repos/USGS-WiM/StreamStats	opened	Disable Reorder Queue button when there are no batches in the queue	Batch Processor	![image](https://github.com/USGS-WiM/StreamStats/assets/40237491/7f0b211b-ac79-456a-bf65-6056e0ad6b44) The Reorder Queue button should be disabled because there are no batches that are able to be reordered Low priority issue	1.0	Disable Reorder Queue button when there are no batches in the queue - ![image](https://github.com/USGS-WiM/StreamStats/assets/40237491/7f0b211b-ac79-456a-bf65-6056e0ad6b44) The Reorder Queue button should be disabled because there are no batches that are able to be reordered Low priority issue	process	disable reorder queue button when there are no batches in the queue the reorder queue button should be disabled because there are no batches that are able to be reordered low priority issue	1
1,999	4,818,389,008	IssuesEvent	2016-11-04 16:14:10	bbcarchdev/anansi	https://api.github.com/repos/bbcarchdev/anansi	opened	UTF8 resources can not be added to the queue	bug processor:rdf	They all result in a parsing error. These are the errors generated when adding dbpedia:Cardiff to the queue: `anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Cardiff_Blues_vs_Leicester_Tigers_(2008–09_Heineken_Cup)> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ja.dbpedia.org/resource/カーディフ> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ko.dbpedia.org/resource/카디프> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://el.dbpedia.org/resource/Κάρντιφ> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Tŷ_Pont_Haearn> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/6/6/00_–_Cardiff,_Wales> `	1.0	UTF8 resources can not be added to the queue - They all result in a parsing error. These are the errors generated when adding dbpedia:Cardiff to the queue: `anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Cardiff_Blues_vs_Leicester_Tigers_(2008–09_Heineken_Cup)> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ja.dbpedia.org/resource/カーディフ> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ko.dbpedia.org/resource/카디프> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://el.dbpedia.org/resource/Κάρντιφ> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Tŷ_Pont_Haearn> anansi_1 \| crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/6/6/00_–_Cardiff,_Wales> `	process	resources can not be added to the queue they all result in a parsing error these are the errors generated when adding dbpedia cardiff to the queue anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri	1
285,139	24,645,098,398	IssuesEvent	2022-10-17 14:21:39	streamnative/pulsar	https://api.github.com/repos/streamnative/pulsar	opened	ISSUE-18069: Flaky-test: NamespacesTest.testSplitBundleForMultiTimes	component/test flaky-tests	Original Issue: apache/pulsar#18069 --- ### Search before asking - [X] I searched in the [issues](https://github.com/apache/pulsar/issues) and found nothing similar. ### Example failure https://github.com/apache/pulsar/actions/runs/3224912715/jobs/5276751731#step:10:787 ### Exception stacktrace ``` org.apache.pulsar.client.admin.PulsarAdminException$ServerSideErrorException: --- An unexpected error occurred in the server --- Message: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded Stacktrace: java.lang.IllegalStateException: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$9(NamespaceService.java:419) at java.base/java.util.concurrent.CompletableFuture.uniAcceptNow(CompletableFuture.java:757) at java.base/java.util.concurrent.CompletableFuture.uniAcceptStage(CompletableFuture.java:735) at java.base/java.util.concurrent.CompletableFuture.thenAccept(CompletableFuture.java:2182) at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$13(NamespaceService.java:404) at org.apache.pulsar.common.util.collections.ConcurrentOpenHashMap$Section.put(ConcurrentOpenHashMap.java:409) ``` ### Are you willing to submit a PR? - [ ] I'm willing to submit a PR!	2.0	ISSUE-18069: Flaky-test: NamespacesTest.testSplitBundleForMultiTimes - Original Issue: apache/pulsar#18069 --- ### Search before asking - [X] I searched in the [issues](https://github.com/apache/pulsar/issues) and found nothing similar. ### Example failure https://github.com/apache/pulsar/actions/runs/3224912715/jobs/5276751731#step:10:787 ### Exception stacktrace ``` org.apache.pulsar.client.admin.PulsarAdminException$ServerSideErrorException: --- An unexpected error occurred in the server --- Message: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded Stacktrace: java.lang.IllegalStateException: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$9(NamespaceService.java:419) at java.base/java.util.concurrent.CompletableFuture.uniAcceptNow(CompletableFuture.java:757) at java.base/java.util.concurrent.CompletableFuture.uniAcceptStage(CompletableFuture.java:735) at java.base/java.util.concurrent.CompletableFuture.thenAccept(CompletableFuture.java:2182) at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$13(NamespaceService.java:404) at org.apache.pulsar.common.util.collections.ConcurrentOpenHashMap$Section.put(ConcurrentOpenHashMap.java:409) ``` ### Are you willing to submit a PR? - [ ] I'm willing to submit a PR!	non_process	issue flaky test namespacestest testsplitbundleformultitimes original issue apache pulsar search before asking i searched in the and found nothing similar example failure exception stacktrace org apache pulsar client admin pulsaradminexception serversideerrorexception an unexpected error occurred in the server message namespace bundle my tenant namespace is being unloaded stacktrace java lang illegalstateexception namespace bundle my tenant namespace is being unloaded at org apache pulsar broker namespace namespaceservice lambda findbrokerserviceurl namespaceservice java at java base java util concurrent completablefuture uniacceptnow completablefuture java at java base java util concurrent completablefuture uniacceptstage completablefuture java at java base java util concurrent completablefuture thenaccept completablefuture java at org apache pulsar broker namespace namespaceservice lambda findbrokerserviceurl namespaceservice java at org apache pulsar common util collections concurrentopenhashmap section put concurrentopenhashmap java are you willing to submit a pr i m willing to submit a pr	0
414,160	27,979,700,282	IssuesEvent	2023-03-26 01:42:23	opentiny/tiny-vue	https://api.github.com/repos/opentiny/tiny-vue	opened	[官网] Steps组件中混合了多个Timeline组件的demo	documentation	以下两个 demo 应该是 Timeline 组件的 demo，却用在了 Steps 组件中，建议移到 Steps 组件的 demo 中。 - [普通步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#normal-steps) - [时间线步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#timeline-steps) <img width="1357" alt="image" src="https://user-images.githubusercontent.com/9566362/227750633-000ba3c1-a278-43c9-94d9-3a01902dc13f.png">	1.0	[官网] Steps组件中混合了多个Timeline组件的demo - 以下两个 demo 应该是 Timeline 组件的 demo，却用在了 Steps 组件中，建议移到 Steps 组件的 demo 中。 - [普通步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#normal-steps) - [时间线步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#timeline-steps) <img width="1357" alt="image" src="https://user-images.githubusercontent.com/9566362/227750633-000ba3c1-a278-43c9-94d9-3a01902dc13f.png">	non_process	steps组件中混合了多个timeline组件的demo 以下两个 demo 应该是 timeline 组件的 demo，却用在了 steps 组件中，建议移到 steps 组件的 demo 中。 img width alt image src	0
312,059	23,414,342,951	IssuesEvent	2022-08-12 21:38:48	UnBArqDsw2022-1/2022_1_G5_SerFit	https://api.github.com/repos/UnBArqDsw2022-1/2022_1_G5_SerFit	closed	GOF Estrutural - Bridge	documentation	### Contact Details (optional) _No response_ ### Summary Documentar GOF Estrutural Bridge ### Motivation Entrega 3 ### Alternatives _No response_ ### Additional Context _No response_ ### Code of Conduct - [X] I agree to follow this project's Code of Conduct	1.0	GOF Estrutural - Bridge - ### Contact Details (optional) _No response_ ### Summary Documentar GOF Estrutural Bridge ### Motivation Entrega 3 ### Alternatives _No response_ ### Additional Context _No response_ ### Code of Conduct - [X] I agree to follow this project's Code of Conduct	non_process	gof estrutural bridge contact details optional no response summary documentar gof estrutural bridge motivation entrega alternatives no response additional context no response code of conduct i agree to follow this project s code of conduct	0
73,310	9,659,491,929	IssuesEvent	2019-05-20 13:34:40	wc-duck/datalibrary	https://api.github.com/repos/wc-duck/datalibrary	opened	doc: document dl-json	documentation	DL uses json... isch ;) Document the difference. * Valid json IS valid dl-json * DL-json support comments // and /*/ DL json can use '' or "" for strings/keys * in dl-json '' or "" is optional on keys * Keys need to be valid c-identifiers * , is accepted for last item in array and map * new-line in strings are valid, or 'my_str' : "whoo Whaaa Whiiii"	1.0	doc: document dl-json - DL uses json... isch ;) Document the difference. * Valid json IS valid dl-json * DL-json support comments // and /*/ DL json can use '' or "" for strings/keys * in dl-json '' or "" is optional on keys * Keys need to be valid c-identifiers * , is accepted for last item in array and map * new-line in strings are valid, or 'my_str' : "whoo Whaaa Whiiii"	non_process	doc document dl json dl uses json isch document the difference valid json is valid dl json dl json support comments and dl json can use or for strings keys in dl json or is optional on keys keys need to be valid c identifiers is accepted for last item in array and map new line in strings are valid or my str whoo whaaa whiiii	0
1,301	3,840,864,220	IssuesEvent	2016-04-04 00:33:13	moxie-leean/ng-web-app	https://api.github.com/repos/moxie-leean/ng-web-app	closed	Config watch	Pending Deploy process	Add a config watch script to package.json and include it in the general watch job.	1.0	Config watch - Add a config watch script to package.json and include it in the general watch job.	process	config watch add a config watch script to package json and include it in the general watch job	1
55,347	11,425,209,385	IssuesEvent	2020-02-03 19:22:05	joomla/joomla-cms	https://api.github.com/repos/joomla/joomla-cms	closed	Wrong ACL behaviour with ‘modify state’ action in interaction of articles and categories	J3 Issue No Code Attached Yet	ACL !!! FRONTEND !!! For a given usergroup of article writers: Don’t grant any action rights on articles for this usergroup in System>Config>Articles. Then have a category and grant all action rights to the usergroup there. The writers will then be able, to create and edit articles, that are stored in that category (or a subcategory). This worked fine in the past. And it works fine currently: BUT: Does no longer work correct for ‘modify state’ action (although the computed inherited rights are shown correct in the category – but system in reality does not behave that way any longer). ### Steps to reproduce the issue Create a new top-level category ‘TestCatego’ Create a new usergroup ‘TestGroup’ as direct subgroup of ‘registerred’ Create a new accesslevel ‘TextAccLevel’ containing just ‘TestGroup’ Register a user ‘TestUser’ an attach it with ‘TestGroup’ Create a menuitem with TestAccLevel showing the articles in TestCatego as a blog System > Configuration > Articles > Rights: Ensure that you do not explicitly enable oder disable any rights to TestGroup. This shall lead to ‘disabled (inherited)’ for all article actions for TestGroup. Content > Categories > Select category ‘TestCatego’ > Rights: For TestGroup explicitly grant the following rights: create / edit / edit your own / modify status ### Expected result The calculated rights (after saving) will show, that a user of this group will be able to do the granted things in category TestCatego: Especially: Will be able to modify the status of any article in this group. That is the behavior to be expected. ### Actual result The system does NOT behave in this way: The user will not be able to modify the state of an article in this category IN THE FRONTEND. The expected behavior might be achieved by a workaround: Explicitly allow ‘modify status’ in ‘System > Configuration > Articles > Rights’ for TestGroup. BUT: From now on any user in TestGroup will be shown any hidden or no longer published article on the website – which is not the expected behavior again. In my eyes there must be a piece of code, dealing with ‘modify state’ in a wrong way? ### System information (as much as possible) Joomla 3.9.6	1.0	Wrong ACL behaviour with ‘modify state’ action in interaction of articles and categories - ACL !!! FRONTEND !!! For a given usergroup of article writers: Don’t grant any action rights on articles for this usergroup in System>Config>Articles. Then have a category and grant all action rights to the usergroup there. The writers will then be able, to create and edit articles, that are stored in that category (or a subcategory). This worked fine in the past. And it works fine currently: BUT: Does no longer work correct for ‘modify state’ action (although the computed inherited rights are shown correct in the category – but system in reality does not behave that way any longer). ### Steps to reproduce the issue Create a new top-level category ‘TestCatego’ Create a new usergroup ‘TestGroup’ as direct subgroup of ‘registerred’ Create a new accesslevel ‘TextAccLevel’ containing just ‘TestGroup’ Register a user ‘TestUser’ an attach it with ‘TestGroup’ Create a menuitem with TestAccLevel showing the articles in TestCatego as a blog System > Configuration > Articles > Rights: Ensure that you do not explicitly enable oder disable any rights to TestGroup. This shall lead to ‘disabled (inherited)’ for all article actions for TestGroup. Content > Categories > Select category ‘TestCatego’ > Rights: For TestGroup explicitly grant the following rights: create / edit / edit your own / modify status ### Expected result The calculated rights (after saving) will show, that a user of this group will be able to do the granted things in category TestCatego: Especially: Will be able to modify the status of any article in this group. That is the behavior to be expected. ### Actual result The system does NOT behave in this way: The user will not be able to modify the state of an article in this category IN THE FRONTEND. The expected behavior might be achieved by a workaround: Explicitly allow ‘modify status’ in ‘System > Configuration > Articles > Rights’ for TestGroup. BUT: From now on any user in TestGroup will be shown any hidden or no longer published article on the website – which is not the expected behavior again. In my eyes there must be a piece of code, dealing with ‘modify state’ in a wrong way? ### System information (as much as possible) Joomla 3.9.6	non_process	wrong acl behaviour with ‘modify state’ action in interaction of articles and categories acl frontend for a given usergroup of article writers don’t grant any action rights on articles for this usergroup in system config articles then have a category and grant all action rights to the usergroup there the writers will then be able to create and edit articles that are stored in that category or a subcategory this worked fine in the past and it works fine currently but does no longer work correct for ‘modify state’ action although the computed inherited rights are shown correct in the category – but system in reality does not behave that way any longer steps to reproduce the issue create a new top level category ‘testcatego’ create a new usergroup ‘testgroup’ as direct subgroup of ‘registerred’ create a new accesslevel ‘textacclevel’ containing just ‘testgroup’ register a user ‘testuser’ an attach it with ‘testgroup’ create a menuitem with testacclevel showing the articles in testcatego as a blog system configuration articles rights ensure that you do not explicitly enable oder disable any rights to testgroup this shall lead to ‘disabled inherited ’ for all article actions for testgroup content categories select category ‘testcatego’ rights for testgroup explicitly grant the following rights create edit edit your own modify status expected result the calculated rights after saving will show that a user of this group will be able to do the granted things in category testcatego especially will be able to modify the status of any article in this group that is the behavior to be expected actual result the system does not behave in this way the user will not be able to modify the state of an article in this category in the frontend the expected behavior might be achieved by a workaround explicitly allow ‘modify status’ in ‘system configuration articles rights’ for testgroup but from now on any user in testgroup will be shown any hidden or no longer published article on the website – which is not the expected behavior again in my eyes there must be a piece of code dealing with ‘modify state’ in a wrong way system information as much as possible joomla	0
841	3,309,449,918	IssuesEvent	2015-11-05 01:08:50	nodejs/node	https://api.github.com/repos/nodejs/node	closed	process.stdout/.stderr might loose data when calling process.exit()	duplicate process	this won't work reliable (but it should according to the docs): ```javascript var empty = new Buffer(0) process.stdout.write(empty, function() { process.stderr.write(empty, function() { process.exit(code); }); }); ``` Not all data is flushed when node.js ends and thus another process using its output will not get all written data. It also doesn't help to use something like ```javascript stream.pipe(process.stdout); stream.push(null); ``` That .end() throws and an finish is never emitted makes using process.stdout/.stderr something like lottery. Also trying to workaround via ```javascript fs.createWritableStream(null, {fd:1}); ``` leads to another bunch of problems like EAGAIN errors. I would suggest to implement .end() and finish events so that both process.stdout and .stderr behave like any other stream.	1.0	process.stdout/.stderr might loose data when calling process.exit() - this won't work reliable (but it should according to the docs): ```javascript var empty = new Buffer(0) process.stdout.write(empty, function() { process.stderr.write(empty, function() { process.exit(code); }); }); ``` Not all data is flushed when node.js ends and thus another process using its output will not get all written data. It also doesn't help to use something like ```javascript stream.pipe(process.stdout); stream.push(null); ``` That .end() throws and an finish is never emitted makes using process.stdout/.stderr something like lottery. Also trying to workaround via ```javascript fs.createWritableStream(null, {fd:1}); ``` leads to another bunch of problems like EAGAIN errors. I would suggest to implement .end() and finish events so that both process.stdout and .stderr behave like any other stream.	process	process stdout stderr might loose data when calling process exit this won t work reliable but it should according to the docs javascript var empty new buffer process stdout write empty function process stderr write empty function process exit code not all data is flushed when node js ends and thus another process using its output will not get all written data it also doesn t help to use something like javascript stream pipe process stdout stream push null that end throws and an finish is never emitted makes using process stdout stderr something like lottery also trying to workaround via javascript fs createwritablestream null fd leads to another bunch of problems like eagain errors i would suggest to implement end and finish events so that both process stdout and stderr behave like any other stream	1
20,034	26,518,407,169	IssuesEvent	2023-01-18 23:09:07	dotnet/runtime	https://api.github.com/repos/dotnet/runtime	closed	System.Diagnostics.Process.OutputDataReceived - determine final newline	area-System.Diagnostics.Process untriaged	### Description When using System.Diagnostics.Process.OutputDataReceived there is no way to determine if output has or doesn't have final newline ### Reproduction Steps Test that shows the issue: ```cs using System; using System.Collections.Generic; using System.Diagnostics; using System.Linq; using System.Text; using Xunit; namespace ProcessRedirectOutputApiIssue; public static class ProcessRedirectOutputTests { private static Process EchoProcess(string output) => new() { StartInfo = { FileName = "echo", ArgumentList = { "-n", output }, RedirectStandardOutput = true } }; // Capture output from Process.StandardOutput for the reference private static string Output_From_Stream(Process process) { process.WaitForExit(); return process.StandardOutput.ReadToEnd(); } // Capture output via Process.OutputDataReceived the way it is stated in the official documentation private static string Output_From_Event(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { output.Append(e.Data); output.Append('\n'); } }; process.WaitForExit(); return output.ToString(); } // Alternative way to capture output via Process.OutputDataReceived - skip final newline private static string Output_From_Event_Skip_Final_Newline(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { if (output.Length > 0) output.Append('\n'); output.Append(e.Data); } }; process.WaitForExit(); return output.ToString(); } private static readonly string[] Outputs = { "", "\n", "ab", "ab\n", "ab\ncd", "ab\ncd\n", }; private static readonly Func<Process, string>[] OutputCapturers = { Output_From_Stream, Output_From_Event, Output_From_Event_Skip_Final_Newline }; public static IEnumerable<object[]> TestData => from o in Outputs from oc in OutputCapturers select new object[] {o, oc}; [Theory] [MemberData(nameof(TestData))] public static void Process_Redirect_Output_Test(string output, Func<Process, string> outputCapturer) { using var process = EchoProcess(output); process.Start(); var capturedOutput = outputCapturer(process); Assert.Equal(output, capturedOutput); } } ``` csproj: ```csproj <Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <TargetFramework>net6.0</TargetFramework> </PropertyGroup> <ItemGroup> <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.4.0" /> <PackageReference Include="xunit.runner.visualstudio" Version="2.4.5" /> <PackageReference Include="xunit" Version="2.4.2" /> </ItemGroup> </Project> ``` ### Expected behavior There should be some way to determine if there is a final new line or not. Maybe the case above where final newline is skipped should succeed ### Actual behavior There is no way to determine from OutputDataReceived event if final newline exists or doesn't exist in the standard output: ![image](https://user-images.githubusercontent.com/4021496/202765092-2cd2b0b4-284c-4bca-8414-5bae9e5207e8.png) ### Regression? _No response_ ### Known Workarounds _No response_ ### Configuration Latest .NET 6 (or 7) SDK on Ubuntu 18.04 ### Other information _No response_	1.0	System.Diagnostics.Process.OutputDataReceived - determine final newline - ### Description When using System.Diagnostics.Process.OutputDataReceived there is no way to determine if output has or doesn't have final newline ### Reproduction Steps Test that shows the issue: ```cs using System; using System.Collections.Generic; using System.Diagnostics; using System.Linq; using System.Text; using Xunit; namespace ProcessRedirectOutputApiIssue; public static class ProcessRedirectOutputTests { private static Process EchoProcess(string output) => new() { StartInfo = { FileName = "echo", ArgumentList = { "-n", output }, RedirectStandardOutput = true } }; // Capture output from Process.StandardOutput for the reference private static string Output_From_Stream(Process process) { process.WaitForExit(); return process.StandardOutput.ReadToEnd(); } // Capture output via Process.OutputDataReceived the way it is stated in the official documentation private static string Output_From_Event(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { output.Append(e.Data); output.Append('\n'); } }; process.WaitForExit(); return output.ToString(); } // Alternative way to capture output via Process.OutputDataReceived - skip final newline private static string Output_From_Event_Skip_Final_Newline(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { if (output.Length > 0) output.Append('\n'); output.Append(e.Data); } }; process.WaitForExit(); return output.ToString(); } private static readonly string[] Outputs = { "", "\n", "ab", "ab\n", "ab\ncd", "ab\ncd\n", }; private static readonly Func<Process, string>[] OutputCapturers = { Output_From_Stream, Output_From_Event, Output_From_Event_Skip_Final_Newline }; public static IEnumerable<object[]> TestData => from o in Outputs from oc in OutputCapturers select new object[] {o, oc}; [Theory] [MemberData(nameof(TestData))] public static void Process_Redirect_Output_Test(string output, Func<Process, string> outputCapturer) { using var process = EchoProcess(output); process.Start(); var capturedOutput = outputCapturer(process); Assert.Equal(output, capturedOutput); } } ``` csproj: ```csproj <Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <TargetFramework>net6.0</TargetFramework> </PropertyGroup> <ItemGroup> <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.4.0" /> <PackageReference Include="xunit.runner.visualstudio" Version="2.4.5" /> <PackageReference Include="xunit" Version="2.4.2" /> </ItemGroup> </Project> ``` ### Expected behavior There should be some way to determine if there is a final new line or not. Maybe the case above where final newline is skipped should succeed ### Actual behavior There is no way to determine from OutputDataReceived event if final newline exists or doesn't exist in the standard output: ![image](https://user-images.githubusercontent.com/4021496/202765092-2cd2b0b4-284c-4bca-8414-5bae9e5207e8.png) ### Regression? _No response_ ### Known Workarounds _No response_ ### Configuration Latest .NET 6 (or 7) SDK on Ubuntu 18.04 ### Other information _No response_	process	system diagnostics process outputdatareceived determine final newline description when using system diagnostics process outputdatareceived there is no way to determine if output has or doesn t have final newline reproduction steps test that shows the issue cs using system using system collections generic using system diagnostics using system linq using system text using xunit namespace processredirectoutputapiissue public static class processredirectoutputtests private static process echoprocess string output new startinfo filename echo argumentlist n output redirectstandardoutput true capture output from process standardoutput for the reference private static string output from stream process process process waitforexit return process standardoutput readtoend capture output via process outputdatareceived the way it is stated in the official documentation private static string output from event process process process beginoutputreadline var output new stringbuilder process outputdatareceived e if e data null output append e data output append n process waitforexit return output tostring alternative way to capture output via process outputdatareceived skip final newline private static string output from event skip final newline process process process beginoutputreadline var output new stringbuilder process outputdatareceived e if e data null if output length output append n output append e data process waitforexit return output tostring private static readonly string outputs n ab ab n ab ncd ab ncd n private static readonly func outputcapturers output from stream output from event output from event skip final newline public static ienumerable testdata from o in outputs from oc in outputcapturers select new object o oc public static void process redirect output test string output func outputcapturer using var process echoprocess output process start var capturedoutput outputcapturer process assert equal output capturedoutput csproj csproj expected behavior there should be some way to determine if there is a final new line or not maybe the case above where final newline is skipped should succeed actual behavior there is no way to determine from outputdatareceived event if final newline exists or doesn t exist in the standard output regression no response known workarounds no response configuration latest net or sdk on ubuntu other information no response	1
466,253	13,399,058,488	IssuesEvent	2020-09-03 13:59:54	jules008/CDCTracker	https://api.github.com/repos/jules008/CDCTracker	opened	Change link button to copy data button	1 - High Priority enhancement	Copies data into clipboard AU23 - Mohr's username CDSAR - Members full SSN	1.0	Change link button to copy data button - Copies data into clipboard AU23 - Mohr's username CDSAR - Members full SSN	non_process	change link button to copy data button copies data into clipboard mohr s username cdsar members full ssn	0
7,549	10,674,679,330	IssuesEvent	2019-10-21 09:56:05	prisma/prisma2	https://api.github.com/repos/prisma/prisma2	opened	Remove panics	area/binaries kind/improvement process/candidate	Everytime I see one of these my eyes glaze over & I feel helpless: ``` ➜ prisma prisma2 lift save Error: Error in migration engine: thread 'tokio-runtime-worker-1' panicked at 'get data_type', src/libcore/option.rs:1034:5 stack backtrace: 0: std::panicking::default_hook::{{closure}} 1: std::panicking::default_hook 2: std::panicking::rust_panic_with_hook 3: std::panicking::continue_panic_fmt 4: rust_begin_unwind 5: core::panicking::panic_fmt 6: core::option::expect_failed 7: core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &mut F>::call_once 8: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 9: sql_schema_describer::mysql::SqlSchemaDescriber::get_table 10: <core::iter::adapters::Map<I,F> as core::iter::traits::iterator::Iterator>::fold 11: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 12: <sql_schema_describer::mysql::SqlSchemaDescriber as sql_schema_describer::SqlSchemaDescriberBackend>::describe 13: <sql_migration_connector::sql_database_migration_inferrer::SqlDatabaseMigrationInferrer as migration_connector::database_migration_inferrer::DatabaseMigrationInferrer<sql_migration_connector::sql_migration::SqlMigration>>::infer 14: <migration_core::api::MigrationApi<C,D> as migration_core::api::GenericApi>::infer_migration_steps 15: migration_core::api::rpc::RpcApi::create_sync_handler 16: tokio_executor::enter::exit 17: tokio_threadpool::blocking::blocking 18: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 19: futures::future::chain::Chain<A,B,C>::poll 20: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 21: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 22: futures::future::chain::Chain<A,B,C>::poll 23: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 24: <futures::future::map::Map<A,F> as futures::future::Future>::poll 25: <futures::future::either::Either<A,B> as futures::future::Future>::poll 26: <futures::future::map::Map<A,F> as futures::future::Future>::poll 27: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 28: <futures::stream::and_then::AndThen<S,F,U> as futures::stream::Stream>::poll 29: <futures::stream::forward::Forward<T,U> as futures::future::Future>::poll 30: <futures::future::map::Map<A,F> as futures::future::Future>::poll 31: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 32: futures::task_impl::std::set 33: std::panicking::try::do_call 34: __rust_maybe_catch_panic 35: tokio_threadpool::task::Task::run 36: tokio_threadpool::worker::Worker::run_task 37: tokio_threadpool::worker::Worker::run 38: std::thread::local::LocalKey<T>::with 39: std::thread::local::LocalKey<T>::with 40: tokio_reactor::with_default 41: tokio::runtime::threadpool::builder::Builder::build::{{closure}} 42: std::thread::local::LocalKey<T>::with 43: std::thread::local::LocalKey<T>::with note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace. ``` I'd us to return errors for these cases, rather than panic. Concretely this means turning code fragments like `.expect(...)` to match statements. Here's an example: https://github.com/prisma/prisma-engine/blob/a348799e88f99b9d287bedf9149abb764b0382f4/libs/sql-schema-describer/src/mysql.rs#L93 Does rust have a linter that will catch possible panic-able code?	1.0	Remove panics - Everytime I see one of these my eyes glaze over & I feel helpless: ``` ➜ prisma prisma2 lift save Error: Error in migration engine: thread 'tokio-runtime-worker-1' panicked at 'get data_type', src/libcore/option.rs:1034:5 stack backtrace: 0: std::panicking::default_hook::{{closure}} 1: std::panicking::default_hook 2: std::panicking::rust_panic_with_hook 3: std::panicking::continue_panic_fmt 4: rust_begin_unwind 5: core::panicking::panic_fmt 6: core::option::expect_failed 7: core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &mut F>::call_once 8: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 9: sql_schema_describer::mysql::SqlSchemaDescriber::get_table 10: <core::iter::adapters::Map<I,F> as core::iter::traits::iterator::Iterator>::fold 11: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 12: <sql_schema_describer::mysql::SqlSchemaDescriber as sql_schema_describer::SqlSchemaDescriberBackend>::describe 13: <sql_migration_connector::sql_database_migration_inferrer::SqlDatabaseMigrationInferrer as migration_connector::database_migration_inferrer::DatabaseMigrationInferrer<sql_migration_connector::sql_migration::SqlMigration>>::infer 14: <migration_core::api::MigrationApi<C,D> as migration_core::api::GenericApi>::infer_migration_steps 15: migration_core::api::rpc::RpcApi::create_sync_handler 16: tokio_executor::enter::exit 17: tokio_threadpool::blocking::blocking 18: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 19: futures::future::chain::Chain<A,B,C>::poll 20: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 21: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 22: futures::future::chain::Chain<A,B,C>::poll 23: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 24: <futures::future::map::Map<A,F> as futures::future::Future>::poll 25: <futures::future::either::Either<A,B> as futures::future::Future>::poll 26: <futures::future::map::Map<A,F> as futures::future::Future>::poll 27: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 28: <futures::stream::and_then::AndThen<S,F,U> as futures::stream::Stream>::poll 29: <futures::stream::forward::Forward<T,U> as futures::future::Future>::poll 30: <futures::future::map::Map<A,F> as futures::future::Future>::poll 31: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 32: futures::task_impl::std::set 33: std::panicking::try::do_call 34: __rust_maybe_catch_panic 35: tokio_threadpool::task::Task::run 36: tokio_threadpool::worker::Worker::run_task 37: tokio_threadpool::worker::Worker::run 38: std::thread::local::LocalKey<T>::with 39: std::thread::local::LocalKey<T>::with 40: tokio_reactor::with_default 41: tokio::runtime::threadpool::builder::Builder::build::{{closure}} 42: std::thread::local::LocalKey<T>::with 43: std::thread::local::LocalKey<T>::with note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace. ``` I'd us to return errors for these cases, rather than panic. Concretely this means turning code fragments like `.expect(...)` to match statements. Here's an example: https://github.com/prisma/prisma-engine/blob/a348799e88f99b9d287bedf9149abb764b0382f4/libs/sql-schema-describer/src/mysql.rs#L93 Does rust have a linter that will catch possible panic-able code?	process	remove panics everytime i see one of these my eyes glaze over i feel helpless ➜ prisma lift save error error in migration engine thread tokio runtime worker panicked at get data type src libcore option rs stack backtrace std panicking default hook closure std panicking default hook std panicking rust panic with hook std panicking continue panic fmt rust begin unwind core panicking panic fmt core option expect failed core ops function impls for mut f call once as alloc vec specextend from iter sql schema describer mysql sqlschemadescriber get table as core iter traits iterator iterator fold as alloc vec specextend from iter describe infer as migration core api genericapi infer migration steps migration core api rpc rpcapi create sync handler tokio executor enter exit tokio threadpool blocking blocking as futures future future poll futures future chain chain poll as futures future future poll as futures future future poll futures future chain chain poll as futures future future poll as futures future future poll as futures future future poll as futures future future poll as futures future future poll as futures stream stream poll as futures future future poll as futures future future poll as futures future future poll futures task impl std set std panicking try do call rust maybe catch panic tokio threadpool task task run tokio threadpool worker worker run task tokio threadpool worker worker run std thread local localkey with std thread local localkey with tokio reactor with default tokio runtime threadpool builder builder build closure std thread local localkey with std thread local localkey with note some details are omitted run with rust backtrace full for a verbose backtrace i d us to return errors for these cases rather than panic concretely this means turning code fragments like expect to match statements here s an example does rust have a linter that will catch possible panic able code	1
9,138	12,203,186,487	IssuesEvent	2020-04-30 10:10:13	MHRA/products	https://api.github.com/repos/MHRA/products	closed	Observability \| Prometheus config	EPIC - Auto Batch Process :oncoming_automobile:	## User want As a technical user I want set Prometheus to collect and digest doc index updater logs So that I can enable monitoring and alerting tools ## Acceptance criteria - [ ] Prometheus should collect and digest logs from doc index updater - [ ] [Docs should explain how to see these logs ](https://github.com/MHRA/products/blob/master/infrastructure/docs/logs.md) ## Data - Potential impact Size Value Effort ### Exit Criteria met - [x] Backlog - [x] Discovery - [x] DUXD - [x] Development - [ ] Quality Assurance - [ ] Release and Validate	1.0	Observability \| Prometheus config - ## User want As a technical user I want set Prometheus to collect and digest doc index updater logs So that I can enable monitoring and alerting tools ## Acceptance criteria - [ ] Prometheus should collect and digest logs from doc index updater - [ ] [Docs should explain how to see these logs ](https://github.com/MHRA/products/blob/master/infrastructure/docs/logs.md) ## Data - Potential impact Size Value Effort ### Exit Criteria met - [x] Backlog - [x] Discovery - [x] DUXD - [x] Development - [ ] Quality Assurance - [ ] Release and Validate	process	observability prometheus config user want as a technical user i want set prometheus to collect and digest doc index updater logs so that i can enable monitoring and alerting tools acceptance criteria prometheus should collect and digest logs from doc index updater docs should explain how to see these logs data potential impact size value effort exit criteria met backlog discovery duxd development quality assurance release and validate	1

684,040

23,404,251,696

IssuesEvent

2022-08-12 11:09:13

webcompat/web-bugs

https://api.github.com/repos/webcompat/web-bugs

closed

www.ancestry.com - design is broken

browser-firefox priority-normal engine-gecko

**URL**: https://www.ancestry.com/dna/insights **Browser / Version**: Firefox 103.0 **Operating System**: Windows 10 **Tested Another Browser**: Yes Edge **Problem type**: Design is broken **Description**: Images not loaded **Steps to Reproduce**: Pics dont load after Ffox upgrade <details> <summary>View the screenshot</summary> <img alt="Screenshot" src="https://webcompat.com/uploads/2022/8/2a62d5ff-4384-479f-a15b-53a3e147ebaf.jpeg"> </details> <details> <summary>Browser Configuration</summary> <ul> <li>None</li> </ul> </details> _From [webcompat.com](https://webcompat.com/) with ❤️_

1.0

www.ancestry.com - design is broken -     **URL**: https://www.ancestry.com/dna/insights **Browser / Version**: Firefox 103.0 **Operating System**: Windows 10 **Tested Another Browser**: Yes Edge **Problem type**: Design is broken **Description**: Images not loaded **Steps to Reproduce**: Pics dont load after Ffox upgrade <details> <summary>View the screenshot</summary> <img alt="Screenshot" src="https://webcompat.com/uploads/2022/8/2a62d5ff-4384-479f-a15b-53a3e147ebaf.jpeg"> </details> <details> <summary>Browser Configuration</summary> <ul> <li>None</li> </ul> </details> _From [webcompat.com](https://webcompat.com/) with ❤️_

non_process

design is broken url browser version firefox operating system windows tested another browser yes edge problem type design is broken description images not loaded steps to reproduce pics dont load after ffox upgrade view the screenshot img alt screenshot src browser configuration none from with ❤️

0

10,244

13,099,477,472

IssuesEvent

2020-08-03 21:41:52

GoogleCloudPlatform/python-docs-samples

https://api.github.com/repos/GoogleCloudPlatform/python-docs-samples

closed

Restrict noxfile

type: process

Currently we allow importing of a pytest.ini: https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/noxfile-template.py#L148-L159 we should restrict that to only let users ignore certain folders instead

1.0

Restrict noxfile - Currently we allow importing of a pytest.ini: https://github.com/GoogleCloudPlatform/python-docs-samples/blob/master/noxfile-template.py#L148-L159 we should restrict that to only let users ignore certain folders instead

process

restrict noxfile currently we allow importing of a pytest ini we should restrict that to only let users ignore certain folders instead

1

14,439

17,496,527,830

IssuesEvent

2021-08-10 01:36:33

emily-writes-poems/emily-writes-poems-scripts

https://api.github.com/repos/emily-writes-poems/emily-writes-poems-scripts

closed

don't process details unless poem exists in mongo

processing

check if poem exists in mongo before trying to update details

1.0

don't process details unless poem exists in mongo - check if poem exists in mongo before trying to update details

process

don t process details unless poem exists in mongo check if poem exists in mongo before trying to update details

1

9,900

12,906,360,476

IssuesEvent

2020-07-15 01:23:44

allinurl/goaccess

https://api.github.com/repos/allinurl/goaccess

closed

Filtering referrers out

command-line options log-processing question

How do hide-referer and ignore-referer work exactly? Are they matching against referrer domain or the whole URL? Does * match dots? Does ignore-referer exclude log line from all stats or just from referrer ones? I have this defined in config: ``` hide-referer *.domain.com hide-referer domain.com ``` but I can still see *domain.com*, *www.domain.com* and *DOMAIN.COM* (without protocol) in Referrers URLs pane. Domain is partially removed from Referring Sites pane, but domains like these are still there: ``` www.sub.domain.com sub2.domain2.tld.domain.com domain.com. ```

1.0

Filtering referrers out - How do hide-referer and ignore-referer work exactly? Are they matching against referrer domain or the whole URL? Does * match dots? Does ignore-referer exclude log line from all stats or just from referrer ones? I have this defined in config: ``` hide-referer *.domain.com hide-referer domain.com ``` but I can still see *domain.com*, *www.domain.com* and *DOMAIN.COM* (without protocol) in Referrers URLs pane. Domain is partially removed from Referring Sites pane, but domains like these are still there: ``` www.sub.domain.com sub2.domain2.tld.domain.com domain.com. ```

process

filtering referrers out how do hide referer and ignore referer work exactly are they matching against referrer domain or the whole url does match dots does ignore referer exclude log line from all stats or just from referrer ones i have this defined in config hide referer domain com hide referer domain com but i can still see domain com and domain com without protocol in referrers urls pane domain is partially removed from referring sites pane but domains like these are still there tld domain com domain com

1

333,640

24,383,900,757

IssuesEvent

2022-10-04 10:05:11

oleksandrblazhko/ai201-tsigankova

https://api.github.com/repos/oleksandrblazhko/ai201-tsigankova

opened

CW2

documentation

Завдання 1 1) Користувач проводить експерименти та формує прогноз погоди. 2) Офісний працівник працює. 3) Користувач пише технічні запитання. Завдання 2 1) Показник «Розподілена система» - значення 3. Мій ПП має середню завантаженість різними функціями та процесами. Лише дії, які можуть виконувати користувачі з певними даними, або офісні робітники з інформацією бази даних. 2) Показник «Спеціальні вимоги до навчання користувачів» - значення 3. Мій ПП вимагає від кожного типу користувачів певний рівень освіти, а саме середній та вище для науковців (з додатковим досвідом роботи), або середній для офісних робітників. Тобто більшість робітників повинні мати хоча б середній рівень освіти.

1.0

CW2 - Завдання 1 1) Користувач проводить експерименти та формує прогноз погоди. 2) Офісний працівник працює. 3) Користувач пише технічні запитання. Завдання 2 1) Показник «Розподілена система» - значення 3. Мій ПП має середню завантаженість різними функціями та процесами. Лише дії, які можуть виконувати користувачі з певними даними, або офісні робітники з інформацією бази даних. 2) Показник «Спеціальні вимоги до навчання користувачів» - значення 3. Мій ПП вимагає від кожного типу користувачів певний рівень освіти, а саме середній та вище для науковців (з додатковим досвідом роботи), або середній для офісних робітників. Тобто більшість робітників повинні мати хоча б середній рівень освіти.

non_process

завдання користувач проводить експерименти та формує прогноз погоди офісний працівник працює користувач пише технічні запитання завдання показник «розподілена система» значення мій пп має середню завантаженість різними функціями та процесами лише дії які можуть виконувати користувачі з певними даними або офісні робітники з інформацією бази даних показник «спеціальні вимоги до навчання користувачів» значення мій пп вимагає від кожного типу користувачів певний рівень освіти а саме середній та вище для науковців з додатковим досвідом роботи або середній для офісних робітників тобто більшість робітників повинні мати хоча б середній рівень освіти

0

21,270

28,441,964,718

IssuesEvent

2023-04-16 02:00:07

lizhihao6/get-daily-arxiv-noti

https://api.github.com/repos/lizhihao6/get-daily-arxiv-noti

opened

New submissions for Fri, 14 Apr 23

event camera white balance isp compression image signal processing image signal process raw raw image events camera color contrast events AWB

## Keyword: events ### Event-based tracking of human hands - **Authors:** Laura Duarte, Mohammad Safeea, Pedro Neto - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06534 - **Pdf link:** https://arxiv.org/pdf/2304.06534 - **Abstract** This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: event camera ### Neuromorphic Event-based Facial Expression Recognition - **Authors:** Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti, Andrea Leonardo, Sara Picchioni, Federico Becattini, Alberto Del Bimbo - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06351 - **Pdf link:** https://arxiv.org/pdf/2304.06351 - **Abstract** Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal. ### Event-based tracking of human hands - **Authors:** Laura Duarte, Mohammad Safeea, Pedro Neto - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06534 - **Pdf link:** https://arxiv.org/pdf/2304.06534 - **Abstract** This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - **Authors:** Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - **Arxiv link:** https://arxiv.org/abs/2304.06531 - **Pdf link:** https://arxiv.org/pdf/2304.06531 - **Abstract** 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ## Keyword: ISP ### CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input - **Authors:** Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Yurong Chen, Shunli Zhang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06454 - **Pdf link:** https://arxiv.org/pdf/2304.06454 - **Abstract** With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Learning-based Spatial and Angular Information Separation for Light Field Compression - **Authors:** Jinglei Shi, Yihong Xu, Christine Guillemot - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV) - **Arxiv link:** https://arxiv.org/abs/2304.06322 - **Pdf link:** https://arxiv.org/pdf/2304.06322 - **Abstract** Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations. In this context, spatial information is defined as features that remain static regardless of perspectives, while angular information refers to features that vary between viewpoints. We propose a novel neural network that, by design, can separate angular and spatial information of a light field. The network represents spatial information using spatial kernels shared among all Sub-Aperture Images (SAIs), and angular information using sets of angular kernels for each SAI. To further improve the representation capability of the network without increasing parameter number, we also introduce angular kernel allocation and kernel tensor decomposition mechanisms. Extensive experiments demonstrate the benefits of information separation: when applied to the compression task, our network outperforms other state-of-the-art methods by a large margin. And angular information can be easily transferred to other scenes for rendering dense views, showing the successful separation and the potential use case for the view synthesis task. We plan to release the code upon acceptance of the paper to encourage further research on this topic. ### Learning Accurate Performance Predictors for Ultrafast Automated Model Compression - **Authors:** Ziwei Wang, Jiwen Lu, Han Xiao, Shengyu Liu, Jie Zhou - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - **Arxiv link:** https://arxiv.org/abs/2304.06393 - **Pdf link:** https://arxiv.org/pdf/2304.06393 - **Abstract** In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost. ### DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos - **Authors:** Qi Zhao, M. Salman Asif, Zhan Ma - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06544 - **Pdf link:** https://arxiv.org/pdf/2304.06544 - **Abstract** Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for $960 \times 1920$ videos. ## Keyword: RAW ### TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval - **Authors:** Trung-Nghia Le, Tam V. Nguyen c, Minh-Quan Le, Trong-Thuan Nguyen, Viet-Tham Huynh, Trong-Le Do, Khanh-Duy Le, Mai-Khiem Tran, Nhat Hoang-Xuan, Thang-Long Nguyen-Ho, Vinh-Tiep Nguyen, Tuong-Nghiem Diep, Khanh-Duy Ho, Xuan-Hieu Nguyen, Thien-Phuc Tran, Tuan-Anh Yang, Kim-Phat Tran, Nhu-Vinh Hoang, Minh-Quang Nguyen, E-Ro Nguyen, Minh-Khoi Nguyen-Nhat, Tuan-An To, Trung-Truc Huynh-Le, Nham-Tan Nguyen, Hoang-Chau Luong, Truong Hoai Phong, Nhat-Quynh Le-Pham, Huu-Phuc Pham, Trong-Vu Hoang, Quang-Binh Nguyen, Hai-Dang Nguyen, Akihiro Sugimoto, Minh-Triet Tran - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06053 - **Pdf link:** https://arxiv.org/pdf/2304.06053 - **Abstract** 3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies. ### UniverSeg: Universal Medical Image Segmentation - **Authors:** Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John Guttag, Adrian V. Dalca - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - **Arxiv link:** https://arxiv.org/abs/2304.06131 - **Pdf link:** https://arxiv.org/pdf/2304.06131 - **Abstract** While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu ### TransHP: Image Classification with Hierarchical Prompting - **Authors:** Wenhao Wang, Yifan Sun, Wei Li, Yi Yang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06385 - **Pdf link:** https://arxiv.org/pdf/2304.06385 - **Abstract** This paper explores a hierarchical prompting mechanism for the hierarchical image classification (HIC) task. Different from prior HIC methods, our hierarchical prompting is the first to explicitly inject ancestor-class information as a tokenized hint that benefits the descendant-class discrimination. We think it well imitates human visual recognition, i.e., humans may use the ancestor class as a prompt to draw focus on the subtle differences among descendant classes. We model this prompting mechanism into a Transformer with Hierarchical Prompting (TransHP). TransHP consists of three steps: 1) learning a set of prompt tokens to represent the coarse (ancestor) classes, 2) on-the-fly predicting the coarse class of the input image at an intermediate block, and 3) injecting the prompt token of the predicted coarse class into the intermediate feature. Though the parameters of TransHP maintain the same for all input images, the injected coarse-class prompt conditions (modifies) the subsequent feature extraction and encourages a dynamic focus on relatively subtle differences among the descendant classes. Extensive experiments show that TransHP improves image classification on accuracy (e.g., improving ViT-B/16 by +2.83% ImageNet classification accuracy), training data efficiency (e.g., +12.69% improvement under 10% ImageNet training data), and model explainability. Moreover, TransHP also performs favorably against prior HIC methods, showing that TransHP well exploits the hierarchical information. ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - **Authors:** Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - **Arxiv link:** https://arxiv.org/abs/2304.06531 - **Pdf link:** https://arxiv.org/pdf/2304.06531 - **Abstract** 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - **Authors:** Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06703 - **Pdf link:** https://arxiv.org/pdf/2304.06703 - **Abstract** Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. ### What does CLIP know about a red circle? Visual prompt engineering for VLMs - **Authors:** Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06712 - **Pdf link:** https://arxiv.org/pdf/2304.06712 - **Abstract** Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation. Despite that, their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models, such as GPT-3. Here we explore the idea of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text. In particular, we discover an emergent ability of CLIP, where, by simply drawing a red circle around an object, we can direct the model's attention to that region, while also maintaining global information. We show the power of this simple approach by achieving state-of-the-art in zero-shot referring expressions comprehension and strong performance in keypoint localization tasks. Finally, we draw attention to some potential ethical concerns of large language-vision models. ## Keyword: raw image ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - **Authors:** Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06703 - **Pdf link:** https://arxiv.org/pdf/2304.06703 - **Abstract** Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.

2.0

New submissions for Fri, 14 Apr 23 - ## Keyword: events ### Event-based tracking of human hands - **Authors:** Laura Duarte, Mohammad Safeea, Pedro Neto - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06534 - **Pdf link:** https://arxiv.org/pdf/2304.06534 - **Abstract** This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: event camera ### Neuromorphic Event-based Facial Expression Recognition - **Authors:** Lorenzo Berlincioni, Luca Cultrera, Chiara Albisani, Lisa Cresti, Andrea Leonardo, Sara Picchioni, Federico Becattini, Alberto Del Bimbo - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06351 - **Pdf link:** https://arxiv.org/pdf/2304.06351 - **Abstract** Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal. ### Event-based tracking of human hands - **Authors:** Laura Duarte, Mohammad Safeea, Pedro Neto - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06534 - **Pdf link:** https://arxiv.org/pdf/2304.06534 - **Abstract** This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space). ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - **Authors:** Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - **Arxiv link:** https://arxiv.org/abs/2304.06531 - **Pdf link:** https://arxiv.org/pdf/2304.06531 - **Abstract** 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ## Keyword: ISP ### CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input - **Authors:** Senmao Tian, Ming Lu, Jiaming Liu, Yandong Guo, Yurong Chen, Shunli Zhang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06454 - **Pdf link:** https://arxiv.org/pdf/2304.06454 - **Abstract** With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Learning-based Spatial and Angular Information Separation for Light Field Compression - **Authors:** Jinglei Shi, Yihong Xu, Christine Guillemot - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV) - **Arxiv link:** https://arxiv.org/abs/2304.06322 - **Pdf link:** https://arxiv.org/pdf/2304.06322 - **Abstract** Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations. In this context, spatial information is defined as features that remain static regardless of perspectives, while angular information refers to features that vary between viewpoints. We propose a novel neural network that, by design, can separate angular and spatial information of a light field. The network represents spatial information using spatial kernels shared among all Sub-Aperture Images (SAIs), and angular information using sets of angular kernels for each SAI. To further improve the representation capability of the network without increasing parameter number, we also introduce angular kernel allocation and kernel tensor decomposition mechanisms. Extensive experiments demonstrate the benefits of information separation: when applied to the compression task, our network outperforms other state-of-the-art methods by a large margin. And angular information can be easily transferred to other scenes for rendering dense views, showing the successful separation and the potential use case for the view synthesis task. We plan to release the code upon acceptance of the paper to encourage further research on this topic. ### Learning Accurate Performance Predictors for Ultrafast Automated Model Compression - **Authors:** Ziwei Wang, Jiwen Lu, Han Xiao, Shengyu Liu, Jie Zhou - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - **Arxiv link:** https://arxiv.org/abs/2304.06393 - **Pdf link:** https://arxiv.org/pdf/2304.06393 - **Abstract** In this paper, we propose an ultrafast automated model compression framework called SeerNet for flexible network deployment. Conventional non-differen-tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models, and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment. They both cause heavy computational cost due to the complex compression policy search and evaluation process. On the contrary, we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor, where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation. Specifically, we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search, so that informative supervision is provided to learn the accurate performance predictor with acceptable cost. Then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy, where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy. Compared with the state-of-the-art automated model compression methods, experimental results on image classification and object detection show that our method achieves competitive accuracy-complexity trade-offs with significant reduction of the search cost. ### DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos - **Authors:** Qi Zhao, M. Salman Asif, Zhan Ma - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06544 - **Pdf link:** https://arxiv.org/pdf/2304.06544 - **Abstract** Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for $960 \times 1920$ videos. ## Keyword: RAW ### TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval - **Authors:** Trung-Nghia Le, Tam V. Nguyen c, Minh-Quan Le, Trong-Thuan Nguyen, Viet-Tham Huynh, Trong-Le Do, Khanh-Duy Le, Mai-Khiem Tran, Nhat Hoang-Xuan, Thang-Long Nguyen-Ho, Vinh-Tiep Nguyen, Tuong-Nghiem Diep, Khanh-Duy Ho, Xuan-Hieu Nguyen, Thien-Phuc Tran, Tuan-Anh Yang, Kim-Phat Tran, Nhu-Vinh Hoang, Minh-Quang Nguyen, E-Ro Nguyen, Minh-Khoi Nguyen-Nhat, Tuan-An To, Trung-Truc Huynh-Le, Nham-Tan Nguyen, Hoang-Chau Luong, Truong Hoai Phong, Nhat-Quynh Le-Pham, Huu-Phuc Pham, Trong-Vu Hoang, Quang-Binh Nguyen, Hai-Dang Nguyen, Akihiro Sugimoto, Minh-Triet Tran - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06053 - **Pdf link:** https://arxiv.org/pdf/2304.06053 - **Abstract** 3D object retrieval is an important yet challenging task, which has drawn more and more attention in recent years. While existing approaches have made strides in addressing this issue, they are often limited to restricted settings such as image and sketch queries, which are often unfriendly interactions for common users. In order to overcome these limitations, this paper presents a novel SHREC challenge track focusing on text-based fine-grained retrieval of 3D animal models. Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval. Despite the increased difficulty, we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with 3D objects. Five groups participated in our competition, submitting a total of 114 runs. While the results obtained in our competition are satisfactory, we note that the challenges presented by this task are far from being fully solved. As such, we provide insights into potential areas for future research and improvements. We believe that we can help push the boundaries of 3D object retrieval and facilitate more user-friendly interactions via vision-language technologies. ### UniverSeg: Universal Medical Image Segmentation - **Authors:** Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John Guttag, Adrian V. Dalca - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - **Arxiv link:** https://arxiv.org/abs/2304.06131 - **Pdf link:** https://arxiv.org/pdf/2304.06131 - **Abstract** While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu ### TransHP: Image Classification with Hierarchical Prompting - **Authors:** Wenhao Wang, Yifan Sun, Wei Li, Yi Yang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06385 - **Pdf link:** https://arxiv.org/pdf/2304.06385 - **Abstract** This paper explores a hierarchical prompting mechanism for the hierarchical image classification (HIC) task. Different from prior HIC methods, our hierarchical prompting is the first to explicitly inject ancestor-class information as a tokenized hint that benefits the descendant-class discrimination. We think it well imitates human visual recognition, i.e., humans may use the ancestor class as a prompt to draw focus on the subtle differences among descendant classes. We model this prompting mechanism into a Transformer with Hierarchical Prompting (TransHP). TransHP consists of three steps: 1) learning a set of prompt tokens to represent the coarse (ancestor) classes, 2) on-the-fly predicting the coarse class of the input image at an intermediate block, and 3) injecting the prompt token of the predicted coarse class into the intermediate feature. Though the parameters of TransHP maintain the same for all input images, the injected coarse-class prompt conditions (modifies) the subsequent feature extraction and encourages a dynamic focus on relatively subtle differences among the descendant classes. Extensive experiments show that TransHP improves image classification on accuracy (e.g., improving ViT-B/16 by +2.83% ImageNet classification accuracy), training data efficiency (e.g., +12.69% improvement under 10% ImageNet training data), and model explainability. Moreover, TransHP also performs favorably against prior HIC methods, showing that TransHP well exploits the hierarchical information. ### SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes - **Authors:** Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR) - **Arxiv link:** https://arxiv.org/abs/2304.06531 - **Pdf link:** https://arxiv.org/pdf/2304.06531 - **Abstract** 3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of fine details, or the edges of an object. We introduce SepicNet, a novel deep network for the detection and parametrization of sharp edges in 3D shapes as primitive curves. To make the network end-to-end trainable, we formulate the curve fitting in a differentiable manner. We develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling. The experiments were conducted on a newly introduced large-scale dataset of 50k 3D scans, where the sharp edge annotations were extracted from their parametric CAD models, and demonstrate significant improvement over state-of-the-art methods. ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - **Authors:** Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06703 - **Pdf link:** https://arxiv.org/pdf/2304.06703 - **Abstract** Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement. ### What does CLIP know about a red circle? Visual prompt engineering for VLMs - **Authors:** Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06712 - **Pdf link:** https://arxiv.org/pdf/2304.06712 - **Abstract** Large-scale Vision-Language Models, such as CLIP, learn powerful image-text representations that have found numerous applications, from zero-shot classification to text-to-image generation. Despite that, their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models, such as GPT-3. Here we explore the idea of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text. In particular, we discover an emergent ability of CLIP, where, by simply drawing a red circle around an object, we can direct the model's attention to that region, while also maintaining global information. We show the power of this simple approach by achieving state-of-the-art in zero-shot referring expressions comprehension and strong performance in keypoint localization tasks. Finally, we draw attention to some potential ethical concerns of large language-vision models. ## Keyword: raw image ### Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement - **Authors:** Nancy Mehta, Akshay Dudhane, Subrahmanyam Murala, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2304.06703 - **Pdf link:** https://arxiv.org/pdf/2304.06703 - **Abstract** Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts. Existing burst restoration methods usually do not consider the mutual correlation and non-local contextual information among burst frames, which tends to limit these approaches in challenging cases. Another key challenge lies in the robust up-sampling of burst frames. The existing up-sampling methods cannot effectively utilize the advantages of single-stage and progressive up-sampling strategies with conventional and/or recent up-samplers at the same time. To address these challenges, we propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. GMTNet consists of three modules optimized for burst processing tasks: Multi-scale Burst Feature Alignment (MBFA) for feature denoising and alignment, Transposed-Attention Feature Merging (TAFM) for multi-frame feature aggregation, and Resolution Transfer Feature Up-sampler (RTFU) to up-scale merged features and construct a high-quality output image. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.

process

new submissions for fri apr keyword events event based tracking of human hands authors laura duarte mohammad safeea pedro neto subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this paper proposes a novel method for human hands tracking using data from an event camera the event camera detects changes in brightness measuring motion with low latency no motion blur low power consumption and high dynamic range captured frames are analysed using lightweight algorithms reporting hand position data the chosen pick and place scenario serves as an example input for collaborative human robot interactions and in obstacle avoidance for human robot safety applications events data are pre processed into intensity frames the regions of interest roi are defined through object edge event activity reducing noise roi features are extracted for use in depth perception event based tracking of human hand demonstrated feasible in real time and at a low computational cost the proposed roi finding method reduces noise from intensity images achieving up to of data reduction in relation to the original while preserving the features the depth estimation error in relation to ground truth measured with wearables measured using dynamic time warping and using a single event camera is from to millimetres depending on the plane it is measured tracking of human hands in space using a single event camera data and lightweight algorithms to define roi features hands tracking in space keyword event camera neuromorphic event based facial expression recognition authors lorenzo berlincioni luca cultrera chiara albisani lisa cresti andrea leonardo sara picchioni federico becattini alberto del bimbo subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract recently event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution in this work we investigate the usage of such kind of data for emotion recognition by presenting nefer a dataset for neuromorphic event based facial expression recognition nefer is composed of paired rgb and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks we detail the data acquisition process as well as providing a baseline method for rgb and event data the collected data captures subtle micro expressions which are hard to spot with rgb data yet emerge in the event domain we report a double recognition accuracy for the event based approach proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal event based tracking of human hands authors laura duarte mohammad safeea pedro neto subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this paper proposes a novel method for human hands tracking using data from an event camera the event camera detects changes in brightness measuring motion with low latency no motion blur low power consumption and high dynamic range captured frames are analysed using lightweight algorithms reporting hand position data the chosen pick and place scenario serves as an example input for collaborative human robot interactions and in obstacle avoidance for human robot safety applications events data are pre processed into intensity frames the regions of interest roi are defined through object edge event activity reducing noise roi features are extracted for use in depth perception event based tracking of human hand demonstrated feasible in real time and at a low computational cost the proposed roi finding method reduces noise from intensity images achieving up to of data reduction in relation to the original while preserving the features the depth estimation error in relation to ground truth measured with wearables measured using dynamic time warping and using a single event camera is from to millimetres depending on the plane it is measured tracking of human hands in space using a single event camera data and lightweight algorithms to define roi features hands tracking in space keyword events camera there is no result keyword white balance there is no result keyword color contrast there is no result keyword awb sepicnet sharp edges recovery by parametric inference of curves in shapes authors kseniya cherenkova elona dupont anis kacem ilya arzhannikov gleb gusev djamila aouada subjects computer vision and pattern recognition cs cv graphics cs gr arxiv link pdf link abstract scanning as a technique to digitize objects in reality and create their models is used in many fields and areas though the quality of scans depends on the technical characteristics of the scanner the common drawback is the smoothing of fine details or the edges of an object we introduce sepicnet a novel deep network for the detection and parametrization of sharp edges in shapes as primitive curves to make the network end to end trainable we formulate the curve fitting in a differentiable manner we develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling the experiments were conducted on a newly introduced large scale dataset of scans where the sharp edge annotations were extracted from their parametric cad models and demonstrate significant improvement over state of the art methods keyword isp cabm content aware bit mapping for single image super resolution network with large input authors senmao tian ming lu jiaming liu yandong guo yurong chen shunli zhang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract with the development of high definition display devices the practical scenario of super resolution sr usually needs to super resolve large input like to higher resolution to reduce the computational and memory cost current methods first split the large input into local patches and then merge the sr patches into the output these methods adaptively allocate a subnet for each patch quantization is a very important technique for network acceleration and has been used to design the subnets current methods train an mlp bit selector to determine the propoer bit for each layer however they uniformly sample subnets for training making simple subnets overfitted and complicated subnets underfitted therefore the trained bit selector fails to determine the optimal bit apart from this the introduced bit selector brings additional cost to each layer of the sr network in this paper we propose a novel method named content aware bit mapping cabm which can remove the bit selector without any performance loss cabm also learns a bit selector for each layer during training after training we analyze the relation between the edge information of an input patch and the bit of each layer we observe that the edge information can be an effective metric for the selected bit therefore we design a strategy to build an edge to bit lookup table that maps the edge score of a patch to the bit of each layer during inference the bit configuration of sr network can be determined by the lookup tables of all layers our strategy can find better bit configuration resulting in more efficient mixed precision networks we conduct detailed experiments to demonstrate the generalization ability of our method the code will be released keyword image signal processing there is no result keyword image signal process there is no result keyword compression learning based spatial and angular information separation for light field compression authors jinglei shi yihong xu christine guillemot subjects computer vision and pattern recognition cs cv image and video processing eess iv arxiv link pdf link abstract light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations in this context spatial information is defined as features that remain static regardless of perspectives while angular information refers to features that vary between viewpoints we propose a novel neural network that by design can separate angular and spatial information of a light field the network represents spatial information using spatial kernels shared among all sub aperture images sais and angular information using sets of angular kernels for each sai to further improve the representation capability of the network without increasing parameter number we also introduce angular kernel allocation and kernel tensor decomposition mechanisms extensive experiments demonstrate the benefits of information separation when applied to the compression task our network outperforms other state of the art methods by a large margin and angular information can be easily transferred to other scenes for rendering dense views showing the successful separation and the potential use case for the view synthesis task we plan to release the code upon acceptance of the paper to encourage further research on this topic learning accurate performance predictors for ultrafast automated model compression authors ziwei wang jiwen lu han xiao shengyu liu jie zhou subjects computer vision and pattern recognition cs cv machine learning cs lg arxiv link pdf link abstract in this paper we propose an ultrafast automated model compression framework called seernet for flexible network deployment conventional non differen tiable methods discretely search the desirable compression policy based on the accuracy from exhaustively trained lightweight models and existing differentiable methods optimize an extremely large supernet to obtain the required compressed model for deployment they both cause heavy computational cost due to the complex compression policy search and evaluation process on the contrary we obtain the optimal efficient networks by directly optimizing the compression policy with an accurate performance predictor where the ultrafast automated model compression for various computational cost constraint is achieved without complex compression policy search and evaluation specifically we first train the performance predictor based on the accuracy from uncertain compression policies actively selected by efficient evolutionary search so that informative supervision is provided to learn the accurate performance predictor with acceptable cost then we leverage the gradient that maximizes the predicted performance under the barrier complexity constraint for ultrafast acquisition of the desirable compression policy where adaptive update stepsizes with momentum are employed to enhance optimality of the acquired pruning and quantization strategy compared with the state of the art automated model compression methods experimental results on image classification and object detection show that our method achieves competitive accuracy complexity trade offs with significant reduction of the search cost dnerv modeling inherent dynamics via difference neural representation for videos authors qi zhao m salman asif zhan ma subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract existing implicit neural representation inr methods do not fully exploit spatiotemporal redundancies in videos index based inrs ignore the content specific spatial features and hybrid inrs ignore the contextual dependency on adjacent frames leading to poor modeling capability for scenes with large motion or dynamics we analyze this limitation from the perspective of function fitting and reveal the importance of frame difference to use explicit motion information we propose difference neural representation for videos dnerv which consists of two streams for content and frame difference we also introduce a collaborative content unit for effective feature fusion we test dnerv for video compression inpainting and interpolation dnerv achieves competitive results against the state of the art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for times videos keyword raw textanimar text based animal fine grained retrieval authors trung nghia le tam v nguyen c minh quan le trong thuan nguyen viet tham huynh trong le do khanh duy le mai khiem tran nhat hoang xuan thang long nguyen ho vinh tiep nguyen tuong nghiem diep khanh duy ho xuan hieu nguyen thien phuc tran tuan anh yang kim phat tran nhu vinh hoang minh quang nguyen e ro nguyen minh khoi nguyen nhat tuan an to trung truc huynh le nham tan nguyen hoang chau luong truong hoai phong nhat quynh le pham huu phuc pham trong vu hoang quang binh nguyen hai dang nguyen akihiro sugimoto minh triet tran subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract object retrieval is an important yet challenging task which has drawn more and more attention in recent years while existing approaches have made strides in addressing this issue they are often limited to restricted settings such as image and sketch queries which are often unfriendly interactions for common users in order to overcome these limitations this paper presents a novel shrec challenge track focusing on text based fine grained retrieval of animal models unlike previous shrec challenge tracks the proposed task is considerably more challenging requiring participants to develop innovative approaches to tackle the problem of text based retrieval despite the increased difficulty we believe that this task has the potential to drive useful applications in practice and facilitate more intuitive interactions with objects five groups participated in our competition submitting a total of runs while the results obtained in our competition are satisfactory we note that the challenges presented by this task are far from being fully solved as such we provide insights into potential areas for future research and improvements we believe that we can help push the boundaries of object retrieval and facilitate more user friendly interactions via vision language technologies universeg universal medical image segmentation authors victor ion butoi jose javier gonzalez ortiz tianyu ma mert r sabuncu john guttag adrian v dalca subjects computer vision and pattern recognition cs cv machine learning cs lg arxiv link pdf link abstract while deep learning models have become the predominant method for medical image segmentation they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies image modalities or labels given a new segmentation task researchers generally have to train or fine tune models which is time consuming and poses a substantial barrier for clinical researchers who often lack the resources and expertise to train neural networks we present universeg a method for solving unseen medical segmentation tasks without additional training given a query image and example set of image label pairs that define a new segmentation task universeg employs a new cross block mechanism to produce accurate segmentation maps without the need for additional training to achieve generalization to new tasks we have gathered and standardized a collection of open access medical segmentation datasets with over scans which we refer to as megamedical we used this collection to train universeg on a diverse set of anatomies and imaging modalities we demonstrate that universeg substantially outperforms several related methods on unseen tasks and thoroughly analyze and draw insights about important aspects of the proposed system the universeg source code and model weights are freely available at transhp image classification with hierarchical prompting authors wenhao wang yifan sun wei li yi yang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract this paper explores a hierarchical prompting mechanism for the hierarchical image classification hic task different from prior hic methods our hierarchical prompting is the first to explicitly inject ancestor class information as a tokenized hint that benefits the descendant class discrimination we think it well imitates human visual recognition i e humans may use the ancestor class as a prompt to draw focus on the subtle differences among descendant classes we model this prompting mechanism into a transformer with hierarchical prompting transhp transhp consists of three steps learning a set of prompt tokens to represent the coarse ancestor classes on the fly predicting the coarse class of the input image at an intermediate block and injecting the prompt token of the predicted coarse class into the intermediate feature though the parameters of transhp maintain the same for all input images the injected coarse class prompt conditions modifies the subsequent feature extraction and encourages a dynamic focus on relatively subtle differences among the descendant classes extensive experiments show that transhp improves image classification on accuracy e g improving vit b by imagenet classification accuracy training data efficiency e g improvement under imagenet training data and model explainability moreover transhp also performs favorably against prior hic methods showing that transhp well exploits the hierarchical information sepicnet sharp edges recovery by parametric inference of curves in shapes authors kseniya cherenkova elona dupont anis kacem ilya arzhannikov gleb gusev djamila aouada subjects computer vision and pattern recognition cs cv graphics cs gr arxiv link pdf link abstract scanning as a technique to digitize objects in reality and create their models is used in many fields and areas though the quality of scans depends on the technical characteristics of the scanner the common drawback is the smoothing of fine details or the edges of an object we introduce sepicnet a novel deep network for the detection and parametrization of sharp edges in shapes as primitive curves to make the network end to end trainable we formulate the curve fitting in a differentiable manner we develop an adaptive point cloud sampling technique that captures the sharp features better than uniform sampling the experiments were conducted on a newly introduced large scale dataset of scans where the sharp edge annotations were extracted from their parametric cad models and demonstrate significant improvement over state of the art methods gated multi resolution transfer network for burst restoration and enhancement authors nancy mehta akshay dudhane subrahmanyam murala syed waqas zamir salman khan fahad shahbaz khan subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract burst image processing is becoming increasingly popular in recent years however it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts existing burst restoration methods usually do not consider the mutual correlation and non local contextual information among burst frames which tends to limit these approaches in challenging cases another key challenge lies in the robust up sampling of burst frames the existing up sampling methods cannot effectively utilize the advantages of single stage and progressive up sampling strategies with conventional and or recent up samplers at the same time to address these challenges we propose a novel gated multi resolution transfer network gmtnet to reconstruct a spatially precise high quality image from a burst of low quality raw images gmtnet consists of three modules optimized for burst processing tasks multi scale burst feature alignment mbfa for feature denoising and alignment transposed attention feature merging tafm for multi frame feature aggregation and resolution transfer feature up sampler rtfu to up scale merged features and construct a high quality output image detailed experimental analysis on five datasets validates our approach and sets a state of the art for burst super resolution burst denoising and low light burst enhancement what does clip know about a red circle visual prompt engineering for vlms authors aleksandar shtedritski christian rupprecht andrea vedaldi subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract large scale vision language models such as clip learn powerful image text representations that have found numerous applications from zero shot classification to text to image generation despite that their capabilities for solving novel discriminative tasks via prompting fall behind those of large language models such as gpt here we explore the idea of visual prompt engineering for solving computer vision tasks beyond classification by editing in image space instead of text in particular we discover an emergent ability of clip where by simply drawing a red circle around an object we can direct the model s attention to that region while also maintaining global information we show the power of this simple approach by achieving state of the art in zero shot referring expressions comprehension and strong performance in keypoint localization tasks finally we draw attention to some potential ethical concerns of large language vision models keyword raw image gated multi resolution transfer network for burst restoration and enhancement authors nancy mehta akshay dudhane subrahmanyam murala syed waqas zamir salman khan fahad shahbaz khan subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract burst image processing is becoming increasingly popular in recent years however it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghosting and zipper artifacts existing burst restoration methods usually do not consider the mutual correlation and non local contextual information among burst frames which tends to limit these approaches in challenging cases another key challenge lies in the robust up sampling of burst frames the existing up sampling methods cannot effectively utilize the advantages of single stage and progressive up sampling strategies with conventional and or recent up samplers at the same time to address these challenges we propose a novel gated multi resolution transfer network gmtnet to reconstruct a spatially precise high quality image from a burst of low quality raw images gmtnet consists of three modules optimized for burst processing tasks multi scale burst feature alignment mbfa for feature denoising and alignment transposed attention feature merging tafm for multi frame feature aggregation and resolution transfer feature up sampler rtfu to up scale merged features and construct a high quality output image detailed experimental analysis on five datasets validates our approach and sets a state of the art for burst super resolution burst denoising and low light burst enhancement

1

14,944

18,420,144,265

IssuesEvent

2021-10-13 15:17:49

python-trio/trio

https://api.github.com/repos/python-trio/trio

closed

Should we have a conventional way to "spawn a process into a nursery"?

subprocesses

We have `run_process`, which is a convenient way to run a process like it's a regular subroutine, with error checking (`check=`), cancellation support, automatic waiting, etc. And we have `trio.Process`, which is a fully general way to spawn a process and then do *whatever* with it. In the discussion in #872, @Badg [brought up](https://github.com/python-trio/trio/pull/872#discussion_r261434230) some dissatisfaction with both of these options. I *think* I mostly get the issue now, so I'll try to rephrase in my own words: In some ways, when a task spawns a subprocess it's very similar to spawning a new task: the subprocess is a chunk of code that runs simultaneously with the Trio task, and they can potentially interact. Trio has some strong and convenient conventions for spawning tasks: - trio always makes sure that both tasks are finished before exiting the nursery block - if the new task crashes, the exception propagates until caught, and automatically cancels sibling tasks - if the original task crashes, the new task is automatically cancelled too - if the surrounding context is cancelled, then this automatically cancels both tasks It would be nice to be able to get this combination of features for subprocesses too. But we don't currently have any simple way to do that. The closest thing we have is to use `async with` on a `Process` object. But that's stuck between two conflicting sets of conventions. The `Process` API mostly treats processes as independent *objects*, and our `async with process` mostly follows the general conventions for `async with <resource>`: it never cancels the body of the `with` (even if the process crashes). It doesn't care whether the body of the `with` block completed with an exception or not. And it doesn't do any particular checks on the subprocess's return code. This is also reasonable and what you'd expect for like, `with file_obj`. OTOH, the conventions above are specific to how Trio handles *code*, and really only show up with nurseries and nursery-like objects. Which a `Process`... kind of is, and kind of isn't. I don't think it makes sense to try to wedge all those nursery semantics into `async with process_obj`. In particular, it would be very weird for `async with process_obj` to suddenly enable the equivalent of `run_process`'s `check=True`. And then we'd need some way to allow it to be overridden with `check=False` on a case-by-case basis, and there's no obvious way to spell that. And if we use `check=True` and the subprocess fails and the body fails, do we raise a `MultiError`? In multierror v2, does that mean we need to wrap all outgoing exceptions in `MultiError`? It would involve a lot of weirdness. Another idea that's been proposed is to extend `run_process` so that it can be used like: ```python process_obj = await nursery.start(run_process, ...) ``` This has several attractive aspects: `run_process`'s job is to "domesticate" a subprocess so it acts like a subroutine – for example, the `check=` argument translates error-reporting from subprocess-style to subroutine-style. So here we're reusing that, plus a nursery's general ability to take any subroutine and push it off into a parallel task. You get nursery semantics because you used a nursery. Elegant! It's also kinda weird. It's effectively a totally different 'mode' of using `run_process`: normally `run_process` treats the `process_obj` as hidden, and the return value as important; this flips that around completely, so you can mess with the `process_obj`, and never see the return value. Normally, the `capture_*` arguments are a major part of why people use `run_process`; here, they're completely useless, and would probably need to be forbidden. The use of `start` is sorta gratuitous: ~~the actual process startup is synchronous, so you could just as well have a synchronous version. The `start` is just a trick to start a task and get a return value at the same time.~~ (never mind, see #1109) Are there other options to consider? Brainstorming: ```python async with trio.open_process(...) as process_obj: # creates a nursery ... process_obj = nursery.process_start_soon(...) # or start_process_soon or start_soon_process? async with trio.open_nursery() as nursery: await nursery.start(trio.process_as_task, ...) async with trio.open_nursery() as nursery: process_obj = trio.start_process(nursery, ...) ```

1.0

Should we have a conventional way to "spawn a process into a nursery"? - We have `run_process`, which is a convenient way to run a process like it's a regular subroutine, with error checking (`check=`), cancellation support, automatic waiting, etc. And we have `trio.Process`, which is a fully general way to spawn a process and then do *whatever* with it. In the discussion in #872, @Badg [brought up](https://github.com/python-trio/trio/pull/872#discussion_r261434230) some dissatisfaction with both of these options. I *think* I mostly get the issue now, so I'll try to rephrase in my own words: In some ways, when a task spawns a subprocess it's very similar to spawning a new task: the subprocess is a chunk of code that runs simultaneously with the Trio task, and they can potentially interact. Trio has some strong and convenient conventions for spawning tasks: - trio always makes sure that both tasks are finished before exiting the nursery block - if the new task crashes, the exception propagates until caught, and automatically cancels sibling tasks - if the original task crashes, the new task is automatically cancelled too - if the surrounding context is cancelled, then this automatically cancels both tasks It would be nice to be able to get this combination of features for subprocesses too. But we don't currently have any simple way to do that. The closest thing we have is to use `async with` on a `Process` object. But that's stuck between two conflicting sets of conventions. The `Process` API mostly treats processes as independent *objects*, and our `async with process` mostly follows the general conventions for `async with <resource>`: it never cancels the body of the `with` (even if the process crashes). It doesn't care whether the body of the `with` block completed with an exception or not. And it doesn't do any particular checks on the subprocess's return code. This is also reasonable and what you'd expect for like, `with file_obj`. OTOH, the conventions above are specific to how Trio handles *code*, and really only show up with nurseries and nursery-like objects. Which a `Process`... kind of is, and kind of isn't. I don't think it makes sense to try to wedge all those nursery semantics into `async with process_obj`. In particular, it would be very weird for `async with process_obj` to suddenly enable the equivalent of `run_process`'s `check=True`. And then we'd need some way to allow it to be overridden with `check=False` on a case-by-case basis, and there's no obvious way to spell that. And if we use `check=True` and the subprocess fails and the body fails, do we raise a `MultiError`? In multierror v2, does that mean we need to wrap all outgoing exceptions in `MultiError`? It would involve a lot of weirdness. Another idea that's been proposed is to extend `run_process` so that it can be used like: ```python process_obj = await nursery.start(run_process, ...) ``` This has several attractive aspects: `run_process`'s job is to "domesticate" a subprocess so it acts like a subroutine – for example, the `check=` argument translates error-reporting from subprocess-style to subroutine-style. So here we're reusing that, plus a nursery's general ability to take any subroutine and push it off into a parallel task. You get nursery semantics because you used a nursery. Elegant! It's also kinda weird. It's effectively a totally different 'mode' of using `run_process`: normally `run_process` treats the `process_obj` as hidden, and the return value as important; this flips that around completely, so you can mess with the `process_obj`, and never see the return value. Normally, the `capture_*` arguments are a major part of why people use `run_process`; here, they're completely useless, and would probably need to be forbidden. The use of `start` is sorta gratuitous: ~~the actual process startup is synchronous, so you could just as well have a synchronous version. The `start` is just a trick to start a task and get a return value at the same time.~~ (never mind, see #1109) Are there other options to consider? Brainstorming: ```python async with trio.open_process(...) as process_obj: # creates a nursery ... process_obj = nursery.process_start_soon(...) # or start_process_soon or start_soon_process? async with trio.open_nursery() as nursery: await nursery.start(trio.process_as_task, ...) async with trio.open_nursery() as nursery: process_obj = trio.start_process(nursery, ...) ```

process

should we have a conventional way to spawn a process into a nursery we have run process which is a convenient way to run a process like it s a regular subroutine with error checking check cancellation support automatic waiting etc and we have trio process which is a fully general way to spawn a process and then do whatever with it in the discussion in badg some dissatisfaction with both of these options i think i mostly get the issue now so i ll try to rephrase in my own words in some ways when a task spawns a subprocess it s very similar to spawning a new task the subprocess is a chunk of code that runs simultaneously with the trio task and they can potentially interact trio has some strong and convenient conventions for spawning tasks trio always makes sure that both tasks are finished before exiting the nursery block if the new task crashes the exception propagates until caught and automatically cancels sibling tasks if the original task crashes the new task is automatically cancelled too if the surrounding context is cancelled then this automatically cancels both tasks it would be nice to be able to get this combination of features for subprocesses too but we don t currently have any simple way to do that the closest thing we have is to use async with on a process object but that s stuck between two conflicting sets of conventions the process api mostly treats processes as independent objects and our async with process mostly follows the general conventions for async with it never cancels the body of the with even if the process crashes it doesn t care whether the body of the with block completed with an exception or not and it doesn t do any particular checks on the subprocess s return code this is also reasonable and what you d expect for like with file obj otoh the conventions above are specific to how trio handles code and really only show up with nurseries and nursery like objects which a process kind of is and kind of isn t i don t think it makes sense to try to wedge all those nursery semantics into async with process obj in particular it would be very weird for async with process obj to suddenly enable the equivalent of run process s check true and then we d need some way to allow it to be overridden with check false on a case by case basis and there s no obvious way to spell that and if we use check true and the subprocess fails and the body fails do we raise a multierror in multierror does that mean we need to wrap all outgoing exceptions in multierror it would involve a lot of weirdness another idea that s been proposed is to extend run process so that it can be used like python process obj await nursery start run process this has several attractive aspects run process s job is to domesticate a subprocess so it acts like a subroutine – for example the check argument translates error reporting from subprocess style to subroutine style so here we re reusing that plus a nursery s general ability to take any subroutine and push it off into a parallel task you get nursery semantics because you used a nursery elegant it s also kinda weird it s effectively a totally different mode of using run process normally run process treats the process obj as hidden and the return value as important this flips that around completely so you can mess with the process obj and never see the return value normally the capture arguments are a major part of why people use run process here they re completely useless and would probably need to be forbidden the use of start is sorta gratuitous the actual process startup is synchronous so you could just as well have a synchronous version the start is just a trick to start a task and get a return value at the same time never mind see are there other options to consider brainstorming python async with trio open process as process obj creates a nursery process obj nursery process start soon or start process soon or start soon process async with trio open nursery as nursery await nursery start trio process as task async with trio open nursery as nursery process obj trio start process nursery

1

3,394

6,516,741,198

IssuesEvent

2017-08-27 13:47:06

gaocegege/Processing.R

https://api.github.com/repos/gaocegege/Processing.R

closed

Support CI in macOS via Travis CI

community/processing for-new-contributors priority/p3 size/small status/to-be-claimed type/enhancement

Now we have Windows CI(AppVeyor) and Linux CI(Travis CI), macOS CI should be supported.

1.0

Support CI in macOS via Travis CI - Now we have Windows CI(AppVeyor) and Linux CI(Travis CI), macOS CI should be supported.

process

support ci in macos via travis ci now we have windows ci appveyor and linux ci travis ci macos ci should be supported

1

10,753

13,543,129,986

IssuesEvent

2020-09-16 18:27:50

googleapis/nodejs-assured-workloads

https://api.github.com/repos/googleapis/nodejs-assured-workloads

opened

GA Release of @google-cloud/assured-workloads

type: process

Package name: **FIXME** Current release: **beta** Proposed release: **GA** ## Instructions Check the lists below, adding tests / documentation as required. Once all the "required" boxes are ticked, please create a release and close this issue. ## Required - [ ] 28 days elapsed since last beta release with new API surface - [ ] Server API is GA - [ ] Package API is stable, and we can commit to backward compatibility - [x] All dependencies are GA ## Optional - [ ] Most common / important scenarios have descriptive samples - [ ] Public manual methods have at least one usage sample each (excluding overloads) - [ ] Per-API README includes a full description of the API - [ ] Per-API README contains at least one “getting started” sample using the most common API scenario - [ ] Manual code has been reviewed by API producer - [ ] Manual code has been reviewed by a DPE responsible for samples - [ ] 'Client Libraries' page is added to the product documentation in 'APIs & Reference' section of the product's documentation on Cloud Site

1.0

GA Release of @google-cloud/assured-workloads - Package name: **FIXME** Current release: **beta** Proposed release: **GA** ## Instructions Check the lists below, adding tests / documentation as required. Once all the "required" boxes are ticked, please create a release and close this issue. ## Required - [ ] 28 days elapsed since last beta release with new API surface - [ ] Server API is GA - [ ] Package API is stable, and we can commit to backward compatibility - [x] All dependencies are GA ## Optional - [ ] Most common / important scenarios have descriptive samples - [ ] Public manual methods have at least one usage sample each (excluding overloads) - [ ] Per-API README includes a full description of the API - [ ] Per-API README contains at least one “getting started” sample using the most common API scenario - [ ] Manual code has been reviewed by API producer - [ ] Manual code has been reviewed by a DPE responsible for samples - [ ] 'Client Libraries' page is added to the product documentation in 'APIs & Reference' section of the product's documentation on Cloud Site

process

ga release of google cloud assured workloads package name fixme current release beta proposed release ga instructions check the lists below adding tests documentation as required once all the required boxes are ticked please create a release and close this issue required days elapsed since last beta release with new api surface server api is ga package api is stable and we can commit to backward compatibility all dependencies are ga optional most common important scenarios have descriptive samples public manual methods have at least one usage sample each excluding overloads per api readme includes a full description of the api per api readme contains at least one “getting started” sample using the most common api scenario manual code has been reviewed by api producer manual code has been reviewed by a dpe responsible for samples client libraries page is added to the product documentation in apis reference section of the product s documentation on cloud site

1

10,119

13,044,162,246

IssuesEvent

2020-07-29 03:47:31

tikv/tikv

https://api.github.com/repos/tikv/tikv

closed

UCP: Migrate scalar function `TimeTimeTimeDiff` from TiDB

challenge-program-2 component/coprocessor difficulty/easy sig/coprocessor

## Description Port the scalar function `TimeTimeTimeDiff` from TiDB to coprocessor. ## Score * 50 ## Mentor(s) * @breeswish ## Recommended Skills * Rust programming ## Learning Materials Already implemented expressions ported from TiDB - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/rpn_expr) - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/expr)

2.0

UCP: Migrate scalar function `TimeTimeTimeDiff` from TiDB - ## Description Port the scalar function `TimeTimeTimeDiff` from TiDB to coprocessor. ## Score * 50 ## Mentor(s) * @breeswish ## Recommended Skills * Rust programming ## Learning Materials Already implemented expressions ported from TiDB - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/rpn_expr) - https://github.com/tikv/tikv/tree/master/components/tidb_query/src/expr)

process

ucp migrate scalar function timetimetimediff from tidb description port the scalar function timetimetimediff from tidb to coprocessor score mentor s breeswish recommended skills rust programming learning materials already implemented expressions ported from tidb

1

4,295

7,192,445,960

IssuesEvent

2018-02-03 03:39:15

Great-Hill-Corporation/quickBlocks

https://api.github.com/repos/Great-Hill-Corporation/quickBlocks

closed

Add --count to isContract (or getAccounts) to show account nonce.

status-inprocess tools-getAccounts tools-isContract type-enhancement

eth_getTransactionCount is simple to implement. It returns the account's nonce, so maybe not so interesting for my work, but it's easy to add this option.

1.0

Add --count to isContract (or getAccounts) to show account nonce. - eth_getTransactionCount is simple to implement. It returns the account's nonce, so maybe not so interesting for my work, but it's easy to add this option.

process

add count to iscontract or getaccounts to show account nonce eth gettransactioncount is simple to implement it returns the account s nonce so maybe not so interesting for my work but it s easy to add this option

1

15,585

19,706,806,846

IssuesEvent

2022-01-12 23:10:08

googleapis/java-containeranalysis

https://api.github.com/repos/googleapis/java-containeranalysis

closed

Your .repo-metadata.json file has a problem 🤒

type: process api: containeranalysis repo-metadata: lint

You have a problem with your .repo-metadata.json file: Result of scan 📈: * release_level must be equal to one of the allowed values in .repo-metadata.json ☝️ Once you correct these problems, you can close this issue. Reach out to **go/github-automation** if you have any questions.

1.0

Your .repo-metadata.json file has a problem 🤒 - You have a problem with your .repo-metadata.json file: Result of scan 📈: * release_level must be equal to one of the allowed values in .repo-metadata.json ☝️ Once you correct these problems, you can close this issue. Reach out to **go/github-automation** if you have any questions.

process

your repo metadata json file has a problem 🤒 you have a problem with your repo metadata json file result of scan 📈 release level must be equal to one of the allowed values in repo metadata json ☝️ once you correct these problems you can close this issue reach out to go github automation if you have any questions

1

203,610

7,067,688,676

IssuesEvent

2018-01-08 02:05:40

xcat2/xcat-core

https://api.github.com/repos/xcat2/xcat-core

closed

Should goconserver logs be in /var/log/consoles ?

component:console priority:low

@chenglch Should goconserver logs be in `/var/log/consoles`? It seems like they do not conflict since the logs are under a `nodes` directory AND have `.log` in their filename.... ``` [root@briggs01 log]# ls -ltr goconserver/ total 92948 drwx------ 2 root root 4096 Dec 21 15:47 nodes -rw------- 1 root root 95169175 Dec 21 15:47 server.log [root@briggs01 log]# ls goconserver/nodes/ dummytest.log mid05tor12cn02.log mid05tor12cn05.log mid05tor12cn13.log mid05tor12cn15.log mid05tor12cn16.log mid05tor12cn18.log node-8335-gtc-1318c4a.log sn01.log sn02.log [root@briggs01 log]# ``` And `/var/log/consoles`.... ``` [root@briggs01 log]# ls -ltr consoles/ total 2024728 -rw-r--r-- 1 root root 239270 Nov 15 10:23 mid05tor12cn01 -rw-r--r-- 1 root root 185269 Nov 15 10:23 mid05tor12cn06 -rw-r--r-- 1 root root 189775 Nov 15 10:23 mid05tor12cn07 -rw-r--r-- 1 root root 275528624 Nov 15 10:23 mid05tor12cn08 ``` I thought that console was not working when looking at the logs but then realized it was in a different location. So having it like this would trip up some people if we eventually want to move to using gocons...

1.0

Should goconserver logs be in /var/log/consoles ? - @chenglch Should goconserver logs be in `/var/log/consoles`? It seems like they do not conflict since the logs are under a `nodes` directory AND have `.log` in their filename.... ``` [root@briggs01 log]# ls -ltr goconserver/ total 92948 drwx------ 2 root root 4096 Dec 21 15:47 nodes -rw------- 1 root root 95169175 Dec 21 15:47 server.log [root@briggs01 log]# ls goconserver/nodes/ dummytest.log mid05tor12cn02.log mid05tor12cn05.log mid05tor12cn13.log mid05tor12cn15.log mid05tor12cn16.log mid05tor12cn18.log node-8335-gtc-1318c4a.log sn01.log sn02.log [root@briggs01 log]# ``` And `/var/log/consoles`.... ``` [root@briggs01 log]# ls -ltr consoles/ total 2024728 -rw-r--r-- 1 root root 239270 Nov 15 10:23 mid05tor12cn01 -rw-r--r-- 1 root root 185269 Nov 15 10:23 mid05tor12cn06 -rw-r--r-- 1 root root 189775 Nov 15 10:23 mid05tor12cn07 -rw-r--r-- 1 root root 275528624 Nov 15 10:23 mid05tor12cn08 ``` I thought that console was not working when looking at the logs but then realized it was in a different location. So having it like this would trip up some people if we eventually want to move to using gocons...

non_process

should goconserver logs be in var log consoles chenglch should goconserver logs be in var log consoles it seems like they do not conflict since the logs are under a nodes directory and have log in their filename ls ltr goconserver total drwx root root dec nodes rw root root dec server log ls goconserver nodes dummytest log log log log log log log node gtc log log log and var log consoles ls ltr consoles total rw r r root root nov rw r r root root nov rw r r root root nov rw r r root root nov i thought that console was not working when looking at the logs but then realized it was in a different location so having it like this would trip up some people if we eventually want to move to using gocons

0

85,392

24,590,835,023

IssuesEvent

2022-10-14 01:56:52

sktime/BaseObject

https://api.github.com/repos/sktime/BaseObject

opened

Update Github Action Versions

ci / build good first issue

**Describe Maintenance Item.** Several of the Github actions used in our CI workflow are not the current versions. We should update them. **Describe the solution you'd like** We should make the following updates to our Github Actions: - `actions/checkout@v2` to `actions/checkout@v3` - `actions/setup-python@v2` to `actions/setup-python@v4` - `pre-commit/action@v2.0.0` to `pre-commit/action@v3.0.0` - `codecov/codecov-action@v2` to `codecov/codecov-action@v3` **Additional context** These changes did not appear to cause any problems on main `sktime` repository, so this will hopefully be an easy update.

1.0

Update Github Action Versions - **Describe Maintenance Item.** Several of the Github actions used in our CI workflow are not the current versions. We should update them. **Describe the solution you'd like** We should make the following updates to our Github Actions: - `actions/checkout@v2` to `actions/checkout@v3` - `actions/setup-python@v2` to `actions/setup-python@v4` - `pre-commit/action@v2.0.0` to `pre-commit/action@v3.0.0` - `codecov/codecov-action@v2` to `codecov/codecov-action@v3` **Additional context** These changes did not appear to cause any problems on main `sktime` repository, so this will hopefully be an easy update.

non_process

update github action versions describe maintenance item several of the github actions used in our ci workflow are not the current versions we should update them describe the solution you d like we should make the following updates to our github actions actions checkout to actions checkout actions setup python to actions setup python pre commit action to pre commit action codecov codecov action to codecov codecov action additional context these changes did not appear to cause any problems on main sktime repository so this will hopefully be an easy update

0

19,439

25,707,103,012

IssuesEvent

2022-12-07 02:00:09

lizhihao6/get-daily-arxiv-noti

https://api.github.com/repos/lizhihao6/get-daily-arxiv-noti

opened

New submissions for Wed, 7 Dec 22

event camera white balance isp compression image signal processing image signal process raw raw image events camera color contrast events AWB

## Keyword: events ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - **Authors:** Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.02791 - **Pdf link:** https://arxiv.org/pdf/2212.02791 - **Abstract** Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ## Keyword: event camera ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - **Authors:** Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.02791 - **Pdf link:** https://arxiv.org/pdf/2212.02791 - **Abstract** Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ### Privacy-Preserving Visual Localization with Event Cameras - **Authors:** Junho Kim, Young Min Kim, Yicheng Wu, Ramzi Zahreddine, Weston A. Welge, Gurunandan Krishnan, Sizhuo Ma, Jian Wang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.03177 - **Pdf link:** https://arxiv.org/pdf/2212.03177 - **Abstract** We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difficult to directly apply conventional image-based localization algorithms. To mitigate the gap, we propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras, and thus can naturally hide sensitive visual details. To further enhance the privacy protection in our event-based pipeline, we introduce privacy protection at two levels, namely sensor and network level. Sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user's view in private scene applications using a novel neural network inference pipeline. Both levels of protection involve light-weight computation and incur only a small performance loss. We thus project our method to serve as a building block for practical location-based services using event cameras. The code and dataset will be made public through the following link: https://github.com/82magnolia/event_localization. ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB There is no result ## Keyword: ISP ### SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud - **Authors:** Yan Wang, Junbo Yin, Wei Li, Pascal Frossard, Ruigang Yang, Jianbing Shen - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) - **Arxiv link:** https://arxiv.org/abs/2212.02845 - **Pdf link:** https://arxiv.org/pdf/2212.02845 - **Abstract** LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D. ### Towards Energy Efficient Mobile Eye Tracking for AR Glasses through Optical Sensor Technology - **Authors:** Johannes Meyer - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Image and Video Processing (eess.IV) - **Arxiv link:** https://arxiv.org/abs/2212.03189 - **Pdf link:** https://arxiv.org/pdf/2212.03189 - **Abstract** After the introduction of smartphones and smartwatches, AR glasses are considered the next breakthrough in the field of wearables. While the transition from smartphones to smartwatches was based mainly on established display technologies, the display technology of AR glasses presents a technological challenge. Many display technologies, such as retina projectors, are based on continuous adaptive control of the display based on the user's pupil position. Furthermore, head-mounted systems require an adaptation and extension of established interaction concepts to provide the user with an immersive experience. Eye-tracking is a crucial technology to help AR glasses achieve a breakthrough through optimized display technology and gaze-based interaction concepts. Available eye-tracking technologies, such as VOG, do not meet the requirements of AR glasses, especially regarding power consumption, robustness, and integrability. To further overcome these limitations and push mobile eye-tracking for AR glasses forward, novel laser-based eye-tracking sensor technologies are researched in this thesis. The thesis contributes to a significant scientific advancement towards energy-efficient mobile eye-tracking for AR glasses. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Leveraging Different Learning Styles for Improved Knowledge Distillation - **Authors:** Usma Niyaz, Deepti R. Bathula - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - **Arxiv link:** https://arxiv.org/abs/2212.02931 - **Pdf link:** https://arxiv.org/pdf/2212.02931 - **Abstract** Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences like visual, auditory, etc., for acquiring and effectively processing information. Inspired by this concept, our work explores the idea of mixed information sharing with model compression in the context of Knowledge Distillation (KD) and Mutual Learning (ML). Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process. We formulate a combined KD and ML framework with one teacher and two student networks that share or exchange information in the form of predictions and feature maps. Our comprehensive experiments with benchmark classification and segmentation datasets demonstrate that with 15% compression, the ensemble performance of networks trained with diverse forms of knowledge outperforms the conventional techniques both quantitatively and qualitatively. ### Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis - **Authors:** Yuchao Gu, Xintao Wang, Yixiao Ge, Ying Shan, Xiaohu Qie, Mike Zheng Shou - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.03185 - **Pdf link:** https://arxiv.org/pdf/2212.03185 - **Abstract** Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper, we surprisingly find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead, learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability to capture textures and structures. We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation. Different from previous work that only pursues better details preservation, we propose Semantic-Quantized GAN (SeQ-GAN) with two learning phases to balance the two objectives. In the first phase, we propose a semantic-enhanced perceptual loss for better semantic compression. In the second phase, we fix the encoder and codebook, but enhance and finetune the decoder to achieve better details preservation. The proposed SeQ-GAN greatly improves VQ-based generative models and surpasses the GAN and Diffusion Models on both unconditional and conditional image generation. Our SeQ-GAN (364M) achieves Frechet Inception Distance (FID) of 6.25 and Inception Score (IS) of 140.9 on 256x256 ImageNet generation, a remarkable improvement over VIT-VQGAN (714M), which obtains 11.2 FID and 97.2 IS. ## Keyword: RAW ### Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances - **Authors:** Ajay Jaiswal, Tianlong Chen, Justin F. Rousseau, Yifan Peng, Ying Ding, Zhangyang Wang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.02675 - **Pdf link:** https://arxiv.org/pdf/2212.02675 - **Abstract** Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify \textit{hard-to-learn} (HTL) training samples, and improve pathology localization by attending them explicitly, during training in \textit{supervised, semi-supervised, and weakly-supervised} settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning \cite{hooker2019compressed} - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by $\sim$2-3\%. ## Keyword: raw image There is no result

2.0

New submissions for Wed, 7 Dec 22 - ## Keyword: events ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - **Authors:** Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.02791 - **Pdf link:** https://arxiv.org/pdf/2212.02791 - **Abstract** Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ## Keyword: event camera ### Event-based Monocular Dense Depth Estimation with Recurrent Transformers - **Authors:** Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.02791 - **Pdf link:** https://arxiv.org/pdf/2212.02791 - **Abstract** Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material. ### Privacy-Preserving Visual Localization with Event Cameras - **Authors:** Junho Kim, Young Min Kim, Yicheng Wu, Ramzi Zahreddine, Weston A. Welge, Gurunandan Krishnan, Sizhuo Ma, Jian Wang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.03177 - **Pdf link:** https://arxiv.org/pdf/2212.03177 - **Abstract** We present a robust, privacy-preserving visual localization algorithm using event cameras. While event cameras can potentially make robust localization due to high dynamic range and small motion blur, the sensors exhibit large domain gaps making it difficult to directly apply conventional image-based localization algorithms. To mitigate the gap, we propose applying event-to-image conversion prior to localization which leads to stable localization. In the privacy perspective, event cameras capture only a fraction of visual information compared to normal cameras, and thus can naturally hide sensitive visual details. To further enhance the privacy protection in our event-based pipeline, we introduce privacy protection at two levels, namely sensor and network level. Sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user's view in private scene applications using a novel neural network inference pipeline. Both levels of protection involve light-weight computation and incur only a small performance loss. We thus project our method to serve as a building block for practical location-based services using event cameras. The code and dataset will be made public through the following link: https://github.com/82magnolia/event_localization. ## Keyword: events camera There is no result ## Keyword: white balance There is no result ## Keyword: color contrast There is no result ## Keyword: AWB There is no result ## Keyword: ISP ### SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud - **Authors:** Yan Wang, Junbo Yin, Wei Li, Pascal Frossard, Ruigang Yang, Jianbing Shen - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) - **Arxiv link:** https://arxiv.org/abs/2212.02845 - **Pdf link:** https://arxiv.org/pdf/2212.02845 - **Abstract** LiDAR-based 3D object detection is an indispensable task in advanced autonomous driving systems. Though impressive detection results have been achieved by superior 3D detectors, they suffer from significant performance degeneration when facing unseen domains, such as different LiDAR configurations, different cities, and weather conditions. The mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation (UDA) techniques. However, these UDA solutions just yield unsatisfactory 3D detection results when there is a severe domain shift, e.g., from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D), where only a few labeled target data is available, yet can significantly improve the adaptation performance. In particular, our SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the first stage, an Inter-domain Point-CutMix module is presented to efficiently align the point cloud distribution across domains. The Point-CutMix generates mixed samples of an intermediate domain, thus encouraging to learn domain-invariant knowledge. Then, in the second stage, we further enhance the model for better generalization on the unlabeled target set. This is achieved by exploring Intra-domain Point-MixUp in semi-supervised learning, which essentially regularizes the pseudo label distribution. Experiments from Waymo to nuScenes show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label. Our code is available at https://github.com/yinjunbo/SSDA3D. ### Towards Energy Efficient Mobile Eye Tracking for AR Glasses through Optical Sensor Technology - **Authors:** Johannes Meyer - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Image and Video Processing (eess.IV) - **Arxiv link:** https://arxiv.org/abs/2212.03189 - **Pdf link:** https://arxiv.org/pdf/2212.03189 - **Abstract** After the introduction of smartphones and smartwatches, AR glasses are considered the next breakthrough in the field of wearables. While the transition from smartphones to smartwatches was based mainly on established display technologies, the display technology of AR glasses presents a technological challenge. Many display technologies, such as retina projectors, are based on continuous adaptive control of the display based on the user's pupil position. Furthermore, head-mounted systems require an adaptation and extension of established interaction concepts to provide the user with an immersive experience. Eye-tracking is a crucial technology to help AR glasses achieve a breakthrough through optimized display technology and gaze-based interaction concepts. Available eye-tracking technologies, such as VOG, do not meet the requirements of AR glasses, especially regarding power consumption, robustness, and integrability. To further overcome these limitations and push mobile eye-tracking for AR glasses forward, novel laser-based eye-tracking sensor technologies are researched in this thesis. The thesis contributes to a significant scientific advancement towards energy-efficient mobile eye-tracking for AR glasses. ## Keyword: image signal processing There is no result ## Keyword: image signal process There is no result ## Keyword: compression ### Leveraging Different Learning Styles for Improved Knowledge Distillation - **Authors:** Usma Niyaz, Deepti R. Bathula - **Subjects:** Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) - **Arxiv link:** https://arxiv.org/abs/2212.02931 - **Pdf link:** https://arxiv.org/pdf/2212.02931 - **Abstract** Learning style refers to a type of training mechanism adopted by an individual to gain new knowledge. As suggested by the VARK model, humans have different learning preferences like visual, auditory, etc., for acquiring and effectively processing information. Inspired by this concept, our work explores the idea of mixed information sharing with model compression in the context of Knowledge Distillation (KD) and Mutual Learning (ML). Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process. We formulate a combined KD and ML framework with one teacher and two student networks that share or exchange information in the form of predictions and feature maps. Our comprehensive experiments with benchmark classification and segmentation datasets demonstrate that with 15% compression, the ensemble performance of networks trained with diverse forms of knowledge outperforms the conventional techniques both quantitatively and qualitatively. ### Rethinking the Objectives of Vector-Quantized Tokenizers for Image Synthesis - **Authors:** Yuchao Gu, Xintao Wang, Yixiao Ge, Ying Shan, Xiaohu Qie, Mike Zheng Shou - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.03185 - **Pdf link:** https://arxiv.org/pdf/2212.03185 - **Abstract** Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper, we surprisingly find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead, learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability to capture textures and structures. We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation. Different from previous work that only pursues better details preservation, we propose Semantic-Quantized GAN (SeQ-GAN) with two learning phases to balance the two objectives. In the first phase, we propose a semantic-enhanced perceptual loss for better semantic compression. In the second phase, we fix the encoder and codebook, but enhance and finetune the decoder to achieve better details preservation. The proposed SeQ-GAN greatly improves VQ-based generative models and surpasses the GAN and Diffusion Models on both unconditional and conditional image generation. Our SeQ-GAN (364M) achieves Frechet Inception Distance (FID) of 6.25 and Inception Score (IS) of 140.9 on 256x256 ImageNet generation, a remarkable improvement over VIT-VQGAN (714M), which obtains 11.2 FID and 97.2 IS. ## Keyword: RAW ### Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances - **Authors:** Ajay Jaiswal, Tianlong Chen, Justin F. Rousseau, Yifan Peng, Ying Ding, Zhangyang Wang - **Subjects:** Computer Vision and Pattern Recognition (cs.CV) - **Arxiv link:** https://arxiv.org/abs/2212.02675 - **Pdf link:** https://arxiv.org/pdf/2212.02675 - **Abstract** Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify \textit{hard-to-learn} (HTL) training samples, and improve pathology localization by attending them explicitly, during training in \textit{supervised, semi-supervised, and weakly-supervised} settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning \cite{hooker2019compressed} - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by $\sim$2-3\%. ## Keyword: raw image There is no result

process

new submissions for wed dec keyword events event based monocular dense depth estimation with recurrent transformers authors xu liu jianing li xiaopeng fan yonghong tian subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract event cameras offering high temporal resolutions and high dynamic ranges have brought a new perspective to address common challenges e g motion blur and low light in monocular depth estimation however how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor to this end we propose a novel event based monocular depth estimator with recurrent transformers namely ereformer which is the first pure transformer with a recursive mechanism to process continuous event streams technically for spatial modeling a novel transformer based encoder decoder with a spatial transformer fusion module is presented having better global context information modeling capabilities than cnn based methods for temporal modeling we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers improving temporal modeling capabilities while alleviating the expensive gpu memory cost the experimental results show that our ereformer outperforms state of the art methods by a margin on both synthetic and real world datasets we hope that our work will attract further research to develop stunning transformers in the event based vision community our open source code can be found in the supplemental material keyword event camera event based monocular dense depth estimation with recurrent transformers authors xu liu jianing li xiaopeng fan yonghong tian subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract event cameras offering high temporal resolutions and high dynamic ranges have brought a new perspective to address common challenges e g motion blur and low light in monocular depth estimation however how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor to this end we propose a novel event based monocular depth estimator with recurrent transformers namely ereformer which is the first pure transformer with a recursive mechanism to process continuous event streams technically for spatial modeling a novel transformer based encoder decoder with a spatial transformer fusion module is presented having better global context information modeling capabilities than cnn based methods for temporal modeling we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers improving temporal modeling capabilities while alleviating the expensive gpu memory cost the experimental results show that our ereformer outperforms state of the art methods by a margin on both synthetic and real world datasets we hope that our work will attract further research to develop stunning transformers in the event based vision community our open source code can be found in the supplemental material privacy preserving visual localization with event cameras authors junho kim young min kim yicheng wu ramzi zahreddine weston a welge gurunandan krishnan sizhuo ma jian wang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract we present a robust privacy preserving visual localization algorithm using event cameras while event cameras can potentially make robust localization due to high dynamic range and small motion blur the sensors exhibit large domain gaps making it difficult to directly apply conventional image based localization algorithms to mitigate the gap we propose applying event to image conversion prior to localization which leads to stable localization in the privacy perspective event cameras capture only a fraction of visual information compared to normal cameras and thus can naturally hide sensitive visual details to further enhance the privacy protection in our event based pipeline we introduce privacy protection at two levels namely sensor and network level sensor level protection aims at hiding facial details with lightweight filtering while network level protection targets hiding the entire user s view in private scene applications using a novel neural network inference pipeline both levels of protection involve light weight computation and incur only a small performance loss we thus project our method to serve as a building block for practical location based services using event cameras the code and dataset will be made public through the following link keyword events camera there is no result keyword white balance there is no result keyword color contrast there is no result keyword awb there is no result keyword isp semi supervised domain adaptation for object detection from point cloud authors yan wang junbo yin wei li pascal frossard ruigang yang jianbing shen subjects computer vision and pattern recognition cs cv artificial intelligence cs ai arxiv link pdf link abstract lidar based object detection is an indispensable task in advanced autonomous driving systems though impressive detection results have been achieved by superior detectors they suffer from significant performance degeneration when facing unseen domains such as different lidar configurations different cities and weather conditions the mainstream approaches tend to solve these challenges by leveraging unsupervised domain adaptation uda techniques however these uda solutions just yield unsatisfactory detection results when there is a severe domain shift e g from waymo beam to nuscenes beam to address this we present a novel semi supervised domain adaptation method for object detection where only a few labeled target data is available yet can significantly improve the adaptation performance in particular our includes an inter domain adaptation stage and an intra domain generalization stage in the first stage an inter domain point cutmix module is presented to efficiently align the point cloud distribution across domains the point cutmix generates mixed samples of an intermediate domain thus encouraging to learn domain invariant knowledge then in the second stage we further enhance the model for better generalization on the unlabeled target set this is achieved by exploring intra domain point mixup in semi supervised learning which essentially regularizes the pseudo label distribution experiments from waymo to nuscenes show that with only labeled target data our can surpass the fully supervised oracle model with target label our code is available at towards energy efficient mobile eye tracking for ar glasses through optical sensor technology authors johannes meyer subjects computer vision and pattern recognition cs cv human computer interaction cs hc image and video processing eess iv arxiv link pdf link abstract after the introduction of smartphones and smartwatches ar glasses are considered the next breakthrough in the field of wearables while the transition from smartphones to smartwatches was based mainly on established display technologies the display technology of ar glasses presents a technological challenge many display technologies such as retina projectors are based on continuous adaptive control of the display based on the user s pupil position furthermore head mounted systems require an adaptation and extension of established interaction concepts to provide the user with an immersive experience eye tracking is a crucial technology to help ar glasses achieve a breakthrough through optimized display technology and gaze based interaction concepts available eye tracking technologies such as vog do not meet the requirements of ar glasses especially regarding power consumption robustness and integrability to further overcome these limitations and push mobile eye tracking for ar glasses forward novel laser based eye tracking sensor technologies are researched in this thesis the thesis contributes to a significant scientific advancement towards energy efficient mobile eye tracking for ar glasses keyword image signal processing there is no result keyword image signal process there is no result keyword compression leveraging different learning styles for improved knowledge distillation authors usma niyaz deepti r bathula subjects computer vision and pattern recognition cs cv machine learning cs lg arxiv link pdf link abstract learning style refers to a type of training mechanism adopted by an individual to gain new knowledge as suggested by the vark model humans have different learning preferences like visual auditory etc for acquiring and effectively processing information inspired by this concept our work explores the idea of mixed information sharing with model compression in the context of knowledge distillation kd and mutual learning ml unlike conventional techniques that share the same type of knowledge with all networks we propose to train individual networks with different forms of information to enhance the learning process we formulate a combined kd and ml framework with one teacher and two student networks that share or exchange information in the form of predictions and feature maps our comprehensive experiments with benchmark classification and segmentation datasets demonstrate that with compression the ensemble performance of networks trained with diverse forms of knowledge outperforms the conventional techniques both quantitatively and qualitatively rethinking the objectives of vector quantized tokenizers for image synthesis authors yuchao gu xintao wang yixiao ge ying shan xiaohu qie mike zheng shou subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract vector quantized vq based generative models usually consist of two basic components i e vq tokenizers and generative transformers prior research focuses on improving the reconstruction fidelity of vq tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers in this paper we surprisingly find that improving the reconstruction fidelity of vq tokenizers does not necessarily improve the generation instead learning to compress semantic features within vq tokenizers significantly improves generative transformers ability to capture textures and structures we thus highlight two competing objectives of vq tokenizers for image synthesis semantic compression and details preservation different from previous work that only pursues better details preservation we propose semantic quantized gan seq gan with two learning phases to balance the two objectives in the first phase we propose a semantic enhanced perceptual loss for better semantic compression in the second phase we fix the encoder and codebook but enhance and finetune the decoder to achieve better details preservation the proposed seq gan greatly improves vq based generative models and surpasses the gan and diffusion models on both unconditional and conditional image generation our seq gan achieves frechet inception distance fid of and inception score is of on imagenet generation a remarkable improvement over vit vqgan which obtains fid and is keyword raw attend who is weak pruning assisted medical image localization under sophisticated and implicit imbalances authors ajay jaiswal tianlong chen justin f rousseau yifan peng ying ding zhangyang wang subjects computer vision and pattern recognition cs cv arxiv link pdf link abstract deep neural networks dnns have rapidly become a textit de facto choice for medical image understanding tasks however dnns are notoriously fragile to the class imbalance in image classification we further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization as imbalances in such problems can have highly complex and often implicit forms of presence for example different pathology can have different sizes or colors w r t the background different underlying demographic distributions and in general different difficulty levels to recognize even in a meticulously curated balanced distribution of training data in this paper we propose to use pruning to automatically and adaptively identify textit hard to learn htl training samples and improve pathology localization by attending them explicitly during training in textit supervised semi supervised and weakly supervised settings our main inspiration is drawn from the recent finding that deep classification models have difficult to memorize samples and those may be effectively exposed through network pruning cite and we extend such observation beyond classification for the first time we also present an interesting demographic analysis which illustrates htls ability to capture complex demographic imbalances our extensive experiments on the skin lesion localization task in multiple training settings by paying additional attention to htls show significant improvement of localization performance by sim keyword raw image there is no result

1

882

3,348,216,394

IssuesEvent

2015-11-17 00:23:59

beesmart-it/trend-hrm

https://api.github.com/repos/beesmart-it/trend-hrm

opened

Can't remove client if have active processes associated

client company requirement selection process

Can't remove client if have active processes associated. One option is to inform user and confirm if agreed to cancell all processes.

1.0

Can't remove client if have active processes associated - Can't remove client if have active processes associated. One option is to inform user and confirm if agreed to cancell all processes.

process

can t remove client if have active processes associated can t remove client if have active processes associated one option is to inform user and confirm if agreed to cancell all processes

1

139

2,575,496,616

IssuesEvent

2015-02-11 23:32:13

tinkerpop/tinkerpop3

https://api.github.com/repos/tinkerpop/tinkerpop3

closed

Make Traversal serializable and work towards the .submit() model for OLAP and Server.

enhancement io process server

Many many moons ago `Traversal` implemented `Serializable`. Unfortunately, in Java8 (and Groovy), you can not serialize lambdas across JVM boundaries. With recent work around anonymous traversals, the need for lambdas is quickly fading to 0. With that, traversals (presumably) can now be serialized and sent over the wire. There are two situations in which traversal serialization is good: * `g.V.out.name.submit(g.compute())` // GraphComputer (OLAP) * `g.V.out.name.submit(server)` // Gremlin Server (OLTP or OLAP) With this, the `:>` model goes away in favor of `.submit()`. Why is this good? Over the wire communication is not "fat string", but native language embedding (for JVM-based languages). We should still keep `:>` around for non-JVM languages (e.g. Gremlin-Go -> GremlinServer) and for computations that will use lambdas (e.g. `g.V.filter{complex}` -> OLAP).

1.0

Make Traversal serializable and work towards the .submit() model for OLAP and Server. - Many many moons ago `Traversal` implemented `Serializable`. Unfortunately, in Java8 (and Groovy), you can not serialize lambdas across JVM boundaries. With recent work around anonymous traversals, the need for lambdas is quickly fading to 0. With that, traversals (presumably) can now be serialized and sent over the wire. There are two situations in which traversal serialization is good: * `g.V.out.name.submit(g.compute())` // GraphComputer (OLAP) * `g.V.out.name.submit(server)` // Gremlin Server (OLTP or OLAP) With this, the `:>` model goes away in favor of `.submit()`. Why is this good? Over the wire communication is not "fat string", but native language embedding (for JVM-based languages). We should still keep `:>` around for non-JVM languages (e.g. Gremlin-Go -> GremlinServer) and for computations that will use lambdas (e.g. `g.V.filter{complex}` -> OLAP).

process

make traversal serializable and work towards the submit model for olap and server many many moons ago traversal implemented serializable unfortunately in and groovy you can not serialize lambdas across jvm boundaries with recent work around anonymous traversals the need for lambdas is quickly fading to with that traversals presumably can now be serialized and sent over the wire there are two situations in which traversal serialization is good g v out name submit g compute graphcomputer olap g v out name submit server gremlin server oltp or olap with this the model goes away in favor of submit why is this good over the wire communication is not fat string but native language embedding for jvm based languages we should still keep around for non jvm languages e g gremlin go gremlinserver and for computations that will use lambdas e g g v filter complex olap

1

12,659

15,031,232,256

IssuesEvent

2021-02-02 08:39:43

GoogleCloudPlatform/fda-mystudies

https://api.github.com/repos/GoogleCloudPlatform/fda-mystudies

closed

[Mobile Apps] Responses are not saving from mobile apps

Blocker Bug P0 Process: Fixed Process: Tested dev Response datastore

Responses are not saving from mobile apps for any of the study activities Issue observed in aut-deployment environment and not observed in Dev environment

2.0

[Mobile Apps] Responses are not saving from mobile apps - Responses are not saving from mobile apps for any of the study activities Issue observed in aut-deployment environment and not observed in Dev environment

process

responses are not saving from mobile apps responses are not saving from mobile apps for any of the study activities issue observed in aut deployment environment and not observed in dev environment

1

6,005

8,809,528,341

IssuesEvent

2018-12-27 20:12:01

shirou/gopsutil

https://api.github.com/repos/shirou/gopsutil

closed

Many undefined functions in process on OpenBSD/amd64

os:openbsd package:process

``` src/github.com/shirou/gopsutil/process/process.go:135:15: undefined: Pids src/github.com/shirou/gopsutil/process/process.go:156:20: p.Times undefined (type *Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:166:20: p.Times undefined (type *Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:209:25: p.MemoryInfo undefined (type *Process has no field or method MemoryInfo) src/github.com/shirou/gopsutil/process/process.go:224:20: p.CreateTime undefined (type *Process has no field or method CreateTime) src/github.com/shirou/gopsutil/process/process.go:229:16: p.Times undefined (type *Process has no field or method Times) src/github.com/shirou/gopsutil/process/process_posix.go:128:16: p.Uids undefined (type *Process has no field or method Uids, but does have uids) *** Error code 2 Stop. make: stopped in /z/go/bhyve-vm-goagent ``` _Originally posted by @araujobsd in https://github.com/shirou/gopsutil/issues/349#issuecomment-359325468_

1.0

Many undefined functions in process on OpenBSD/amd64 - ``` src/github.com/shirou/gopsutil/process/process.go:135:15: undefined: Pids src/github.com/shirou/gopsutil/process/process.go:156:20: p.Times undefined (type *Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:166:20: p.Times undefined (type *Process has no field or method Times) src/github.com/shirou/gopsutil/process/process.go:209:25: p.MemoryInfo undefined (type *Process has no field or method MemoryInfo) src/github.com/shirou/gopsutil/process/process.go:224:20: p.CreateTime undefined (type *Process has no field or method CreateTime) src/github.com/shirou/gopsutil/process/process.go:229:16: p.Times undefined (type *Process has no field or method Times) src/github.com/shirou/gopsutil/process/process_posix.go:128:16: p.Uids undefined (type *Process has no field or method Uids, but does have uids) *** Error code 2 Stop. make: stopped in /z/go/bhyve-vm-goagent ``` _Originally posted by @araujobsd in https://github.com/shirou/gopsutil/issues/349#issuecomment-359325468_

process

many undefined functions in process on openbsd src github com shirou gopsutil process process go undefined pids src github com shirou gopsutil process process go p times undefined type process has no field or method times src github com shirou gopsutil process process go p times undefined type process has no field or method times src github com shirou gopsutil process process go p memoryinfo undefined type process has no field or method memoryinfo src github com shirou gopsutil process process go p createtime undefined type process has no field or method createtime src github com shirou gopsutil process process go p times undefined type process has no field or method times src github com shirou gopsutil process process posix go p uids undefined type process has no field or method uids but does have uids error code stop make stopped in z go bhyve vm goagent originally posted by araujobsd in

1

2,649

5,427,981,038

IssuesEvent

2017-03-03 14:50:36

openvstorage/volumedriver

https://api.github.com/repos/openvstorage/volumedriver

closed

cannot create vdisk use qemu

process_wontfix state_question

_From @hoanhdo on December 12, 2016 11:16_ I running openvstorage in ubuntu 16.04. I create disk use `qemu-img convert CentOS-6-x86_64-GenericCloud.qcow2 openvstorage+tcp:172.16.86.132:26203/vdiskdemo` Error log `qemu-img: openvstorage+tcp:172.16.86.132:26203/vdiskdemo: error while converting raw: cannot create volume: Input/output error` _Copied from original issue: openvstorage/framework#1262_

1.0

cannot create vdisk use qemu - _From @hoanhdo on December 12, 2016 11:16_ I running openvstorage in ubuntu 16.04. I create disk use `qemu-img convert CentOS-6-x86_64-GenericCloud.qcow2 openvstorage+tcp:172.16.86.132:26203/vdiskdemo` Error log `qemu-img: openvstorage+tcp:172.16.86.132:26203/vdiskdemo: error while converting raw: cannot create volume: Input/output error` _Copied from original issue: openvstorage/framework#1262_

process

cannot create vdisk use qemu from hoanhdo on december i running openvstorage in ubuntu i create disk use qemu img convert centos genericcloud openvstorage tcp vdiskdemo error log qemu img openvstorage tcp vdiskdemo error while converting raw cannot create volume input output error copied from original issue openvstorage framework

1

9,392

12,394,947,880

IssuesEvent

2020-05-20 17:46:46

hashicorp/packer

https://api.github.com/repos/hashicorp/packer

closed

Vagrant post processor using artifice ova file

post-processor/artifice post-processor/vagrant question

It is possible to use an ova file created by virtualbo-ovf to vagrant post-processor using artifice post-processor ? ``` sample code: "builders":[ { "name": "vagrant-box", "type": "null", "communicator": "none" } ], "post-processors": [ [ { "type": "artifice", "files": [ "sample.ova" ] }, { "name": "vbox_ova_vagrant_post", "type": "vagrant", "output": "sample.box" } ] ] ``` I get this error: > `"Post-processor` failed: Unknown artifact type, can't build box: packer.post-processor.artifice" what's the difference from vagrant point of view if artifact come from virtualbox-ovf builder o from "null builder + artifice post-processor" ?

2.0

Vagrant post processor using artifice ova file - It is possible to use an ova file created by virtualbo-ovf to vagrant post-processor using artifice post-processor ? ``` sample code: "builders":[ { "name": "vagrant-box", "type": "null", "communicator": "none" } ], "post-processors": [ [ { "type": "artifice", "files": [ "sample.ova" ] }, { "name": "vbox_ova_vagrant_post", "type": "vagrant", "output": "sample.box" } ] ] ``` I get this error: > `"Post-processor` failed: Unknown artifact type, can't build box: packer.post-processor.artifice" what's the difference from vagrant point of view if artifact come from virtualbox-ovf builder o from "null builder + artifice post-processor" ?

process

vagrant post processor using artifice ova file it is possible to use an ova file created by virtualbo ovf to vagrant post processor using artifice post processor sample code builders name vagrant box type null communicator none post processors type artifice files name vbox ova vagrant post type vagrant output sample box i get this error post processor failed unknown artifact type can t build box packer post processor artifice what s the difference from vagrant point of view if artifact come from virtualbox ovf builder o from null builder artifice post processor

1

7,871

11,044,763,900

IssuesEvent

2019-12-09 13:56:56

prisma/lift

https://api.github.com/repos/prisma/lift

closed

Lift is trying to recreate type aliases

bug/2-confirmed kind/bug process/candidate

Right now lift is trying to recreate type aliases that already exists. ![image](https://user-images.githubusercontent.com/22195362/69784432-c511a780-11db-11ea-9f1f-639703e16092.png) In the above screenshot, type CUID already exists but lift is trying to recreate it and it is failing to do so. To Reproduce: 1. Create a new Prisma 2 project using the CLI 2. Add a type alias, for example `type CUID = String @id @default(cuid())` and use it in one place 3. Migrate the database 4. Add another field of any type to any model. 5. Try to migrate the database again, you will see the error mentioned in the screenshot above. Prisma2 version : `prisma2@2.0.0-preview017.1, binary version: 6159bf3a263921c3c28ee68e2c9e130b5a69c293`

1.0

Lift is trying to recreate type aliases - Right now lift is trying to recreate type aliases that already exists. ![image](https://user-images.githubusercontent.com/22195362/69784432-c511a780-11db-11ea-9f1f-639703e16092.png) In the above screenshot, type CUID already exists but lift is trying to recreate it and it is failing to do so. To Reproduce: 1. Create a new Prisma 2 project using the CLI 2. Add a type alias, for example `type CUID = String @id @default(cuid())` and use it in one place 3. Migrate the database 4. Add another field of any type to any model. 5. Try to migrate the database again, you will see the error mentioned in the screenshot above. Prisma2 version : `prisma2@2.0.0-preview017.1, binary version: 6159bf3a263921c3c28ee68e2c9e130b5a69c293`

process

lift is trying to recreate type aliases right now lift is trying to recreate type aliases that already exists in the above screenshot type cuid already exists but lift is trying to recreate it and it is failing to do so to reproduce create a new prisma project using the cli add a type alias for example type cuid string id default cuid and use it in one place migrate the database add another field of any type to any model try to migrate the database again you will see the error mentioned in the screenshot above version binary version

1

21,716

30,217,419,182

IssuesEvent

2023-07-05 16:33:56

q191201771/lal

https://api.github.com/repos/q191201771/lal

closed

Cannot play HLS stream on browser

#Question *In process

I think the problem is not directly related to LAL, but anyway I'm asking for some help... I'm using LAL to capture live streaming from camera and mic and pushing and serving the stream to/from my internet server. The streaming is captured and shown without any issues using VLC or browser HLS plugins. But when I try hls.js on my HTML page (without plugins) to show the streaming I got a message from hls.js saying "ManifestParsingError". Am I missing some detail to make this work? Are there some HTML sample page that I can examine and could play my stream? Can you recommend some another HTML HLS player? By the way, LAL is an extraordinary piece of software! Congratulations!

1.0

Cannot play HLS stream on browser - I think the problem is not directly related to LAL, but anyway I'm asking for some help... I'm using LAL to capture live streaming from camera and mic and pushing and serving the stream to/from my internet server. The streaming is captured and shown without any issues using VLC or browser HLS plugins. But when I try hls.js on my HTML page (without plugins) to show the streaming I got a message from hls.js saying "ManifestParsingError". Am I missing some detail to make this work? Are there some HTML sample page that I can examine and could play my stream? Can you recommend some another HTML HLS player? By the way, LAL is an extraordinary piece of software! Congratulations!

process

cannot play hls stream on browser i think the problem is not directly related to lal but anyway i m asking for some help i m using lal to capture live streaming from camera and mic and pushing and serving the stream to from my internet server the streaming is captured and shown without any issues using vlc or browser hls plugins but when i try hls js on my html page without plugins to show the streaming i got a message from hls js saying manifestparsingerror am i missing some detail to make this work are there some html sample page that i can examine and could play my stream can you recommend some another html hls player by the way lal is an extraordinary piece of software congratulations

1

396

2,845,497,180

IssuesEvent

2015-05-29 03:42:08

cyanisaac/openterminalos

https://api.github.com/repos/cyanisaac/openterminalos

closed

Filesystem Shield

Background Process

# The Filesystem Shield This feature will allow OTOS to protect itself from malicious scripts and similar. It will work to backup directories and verify that they are intact. They will also remove certain files that should not be there (at my choosing. A good example is _safeBootLock, which is my friend's special implemented secure bootlocker in his server's rom.) It will actively look for faults in the OTOS directory and replace any faulty files with a hidden backup copy. It will **not** prevent modification, it will only replace files that are deleted, so it is not a perfect system. ## Howto submit malicious script deletions If you would like to delete a specific malicious script, please comment it as a request here in the issues and I will take a look at the script in question. I need both a link to the program/pastebin id/text, and I need the name of the file.

1.0

Filesystem Shield - # The Filesystem Shield This feature will allow OTOS to protect itself from malicious scripts and similar. It will work to backup directories and verify that they are intact. They will also remove certain files that should not be there (at my choosing. A good example is _safeBootLock, which is my friend's special implemented secure bootlocker in his server's rom.) It will actively look for faults in the OTOS directory and replace any faulty files with a hidden backup copy. It will **not** prevent modification, it will only replace files that are deleted, so it is not a perfect system. ## Howto submit malicious script deletions If you would like to delete a specific malicious script, please comment it as a request here in the issues and I will take a look at the script in question. I need both a link to the program/pastebin id/text, and I need the name of the file.

process

filesystem shield the filesystem shield this feature will allow otos to protect itself from malicious scripts and similar it will work to backup directories and verify that they are intact they will also remove certain files that should not be there at my choosing a good example is safebootlock which is my friend s special implemented secure bootlocker in his server s rom it will actively look for faults in the otos directory and replace any faulty files with a hidden backup copy it will not prevent modification it will only replace files that are deleted so it is not a perfect system howto submit malicious script deletions if you would like to delete a specific malicious script please comment it as a request here in the issues and i will take a look at the script in question i need both a link to the program pastebin id text and i need the name of the file

1

13,103

15,496,494,983

IssuesEvent

2021-03-11 02:47:09

dluiscosta/weather_api

https://api.github.com/repos/dluiscosta/weather_api

opened

test_get_weather_cache_expiration takes to long to run

development process enhancement question

The ```test_get_weather_cache_expiration``` test currently is taking too long to run. Even though this test reduces the configured cache expiration time, it still needs to wait at least 60 seconds in order to reliably assert if a given saved cache expired, since that is the duration of the cycle in which MongoDB checks for documents with expired TTL. Is there a workaround this?

1.0

test_get_weather_cache_expiration takes to long to run - The ```test_get_weather_cache_expiration``` test currently is taking too long to run. Even though this test reduces the configured cache expiration time, it still needs to wait at least 60 seconds in order to reliably assert if a given saved cache expired, since that is the duration of the cycle in which MongoDB checks for documents with expired TTL. Is there a workaround this?

process

test get weather cache expiration takes to long to run the test get weather cache expiration test currently is taking too long to run even though this test reduces the configured cache expiration time it still needs to wait at least seconds in order to reliably assert if a given saved cache expired since that is the duration of the cycle in which mongodb checks for documents with expired ttl is there a workaround this

1

6,922

10,082,804,969

IssuesEvent

2019-07-25 12:13:54

linnovate/root

https://api.github.com/repos/linnovate/root

closed

document / item it is not deleted

2.0.7 Fixed Process bug

Documents - When you delete a specific document / item it is not deleted in the Details pane

1.0

document / item it is not deleted - Documents - When you delete a specific document / item it is not deleted in the Details pane

process

document item it is not deleted documents when you delete a specific document item it is not deleted in the details pane

1

2,671

5,475,622,933

IssuesEvent

2017-03-11 13:10:46

rubberduck-vba/Rubberduck

https://api.github.com/repos/rubberduck-vba/Rubberduck

opened

Usages of _HiddenModule's Width procedure don't resolve

bug parse-tree-processing

The RD context bar doesn't recognize the `Width` procedure in a usage like ```vb Width #i, j ``` I suspect there's a parser bug because `Width`'s first argument is a file number that must be prefixed with a `#`. Linking #1487 and #2834

1.0

Usages of _HiddenModule's Width procedure don't resolve - The RD context bar doesn't recognize the `Width` procedure in a usage like ```vb Width #i, j ``` I suspect there's a parser bug because `Width`'s first argument is a file number that must be prefixed with a `#`. Linking #1487 and #2834

process

usages of hiddenmodule s width procedure don t resolve the rd context bar doesn t recognize the width procedure in a usage like vb width i j i suspect there s a parser bug because width s first argument is a file number that must be prefixed with a linking and

1

17,751

23,665,767,415

IssuesEvent

2022-08-26 20:43:14

googleapis/python-api-common-protos

https://api.github.com/repos/googleapis/python-api-common-protos

closed

Dependency Dashboard

type: process

This issue lists Renovate updates and detected dependencies. Read the [Dependency Dashboard](https://docs.renovatebot.com/key-concepts/dashboard/) docs to learn more. ## Edited/Blocked These updates have been manually edited so Renovate will no longer make changes. To discard all commits and start over, click on a checkbox. - [ ] [chore(deps): update dependency distlib to v0.3.6](../pull/126) ## Ignored or Blocked These are blocked by an existing closed PR and will not be recreated unless you click a checkbox below. - [ ] [chore(deps): update dependency click to v8.1.3](../pull/124) - [ ] [chore(deps): update dependency setuptools to v65.3.0](../pull/125) - [ ] [chore(deps): update dependency protobuf to v4](../pull/107) ## Detected dependencies <details><summary>dockerfile</summary> <blockquote> <details><summary>.kokoro/docker/docs/Dockerfile</summary> - `ubuntu 22.04` </details> </blockquote> </details> <details><summary>pip_requirements</summary> <blockquote> <details><summary>.kokoro/requirements.txt</summary> - `argcomplete ==2.0.0` - `attrs ==22.1.0` - `bleach ==5.0.1` - `cachetools ==5.2.0` - `certifi ==2022.6.15` - `cffi ==1.15.1` - `charset-normalizer ==2.1.1` - `click ==8.0.4` - `colorlog ==6.6.0` - `commonmark ==0.9.1` - `cryptography ==37.0.4` - `distlib ==0.3.5` - `docutils ==0.19` - `filelock ==3.8.0` - `gcp-docuploader ==0.6.3` - `gcp-releasetool ==1.8.6` - `google-api-core ==2.8.2` - `google-auth ==2.11.0` - `google-cloud-core ==2.3.2` - `google-cloud-storage ==2.5.0` - `google-crc32c ==1.3.0` - `google-resumable-media ==2.3.3` - `googleapis-common-protos ==1.56.4` - `idna ==3.3` - `importlib-metadata ==4.12.0` - `jeepney ==0.8.0` - `jinja2 ==3.1.2` - `keyring ==23.8.2` - `markupsafe ==2.1.1` - `nox ==2022.8.7` - `packaging ==21.3` - `pkginfo ==1.8.3` - `platformdirs ==2.5.2` - `protobuf ==3.20.1` - `py ==1.11.0` - `pyasn1 ==0.4.8` - `pyasn1-modules ==0.2.8` - `pycparser ==2.21` - `pygments ==2.13.0` - `pyjwt ==2.4.0` - `pyparsing ==3.0.9` - `pyperclip ==1.8.2` - `python-dateutil ==2.8.2` - `readme-renderer ==37.0` - `requests ==2.28.1` - `requests-toolbelt ==0.9.1` - `rfc3986 ==2.0.0` - `rich ==12.5.1` - `rsa ==4.9` - `secretstorage ==3.3.3` - `six ==1.16.0` - `twine ==4.0.1` - `typing-extensions ==4.3.0` - `urllib3 ==1.26.12` - `virtualenv ==20.16.3` - `webencodings ==0.5.1` - `wheel ==0.37.1` - `zipp ==3.8.1` - `setuptools ==65.2.0` </details> </blockquote> </details> <details><summary>pip_setup</summary> <blockquote> <details><summary>setup.py</summary> - `protobuf >= 3.15.0, <5.0.0dev` - `grpcio >= 1.0.0, <2.0.0dev` </details> </blockquote> </details> --- - [ ] Check this box to trigger a request for Renovate to run again on this repository

1.0

Dependency Dashboard - This issue lists Renovate updates and detected dependencies. Read the [Dependency Dashboard](https://docs.renovatebot.com/key-concepts/dashboard/) docs to learn more. ## Edited/Blocked These updates have been manually edited so Renovate will no longer make changes. To discard all commits and start over, click on a checkbox. - [ ] [chore(deps): update dependency distlib to v0.3.6](../pull/126) ## Ignored or Blocked These are blocked by an existing closed PR and will not be recreated unless you click a checkbox below. - [ ] [chore(deps): update dependency click to v8.1.3](../pull/124) - [ ] [chore(deps): update dependency setuptools to v65.3.0](../pull/125) - [ ] [chore(deps): update dependency protobuf to v4](../pull/107) ## Detected dependencies <details><summary>dockerfile</summary> <blockquote> <details><summary>.kokoro/docker/docs/Dockerfile</summary> - `ubuntu 22.04` </details> </blockquote> </details> <details><summary>pip_requirements</summary> <blockquote> <details><summary>.kokoro/requirements.txt</summary> - `argcomplete ==2.0.0` - `attrs ==22.1.0` - `bleach ==5.0.1` - `cachetools ==5.2.0` - `certifi ==2022.6.15` - `cffi ==1.15.1` - `charset-normalizer ==2.1.1` - `click ==8.0.4` - `colorlog ==6.6.0` - `commonmark ==0.9.1` - `cryptography ==37.0.4` - `distlib ==0.3.5` - `docutils ==0.19` - `filelock ==3.8.0` - `gcp-docuploader ==0.6.3` - `gcp-releasetool ==1.8.6` - `google-api-core ==2.8.2` - `google-auth ==2.11.0` - `google-cloud-core ==2.3.2` - `google-cloud-storage ==2.5.0` - `google-crc32c ==1.3.0` - `google-resumable-media ==2.3.3` - `googleapis-common-protos ==1.56.4` - `idna ==3.3` - `importlib-metadata ==4.12.0` - `jeepney ==0.8.0` - `jinja2 ==3.1.2` - `keyring ==23.8.2` - `markupsafe ==2.1.1` - `nox ==2022.8.7` - `packaging ==21.3` - `pkginfo ==1.8.3` - `platformdirs ==2.5.2` - `protobuf ==3.20.1` - `py ==1.11.0` - `pyasn1 ==0.4.8` - `pyasn1-modules ==0.2.8` - `pycparser ==2.21` - `pygments ==2.13.0` - `pyjwt ==2.4.0` - `pyparsing ==3.0.9` - `pyperclip ==1.8.2` - `python-dateutil ==2.8.2` - `readme-renderer ==37.0` - `requests ==2.28.1` - `requests-toolbelt ==0.9.1` - `rfc3986 ==2.0.0` - `rich ==12.5.1` - `rsa ==4.9` - `secretstorage ==3.3.3` - `six ==1.16.0` - `twine ==4.0.1` - `typing-extensions ==4.3.0` - `urllib3 ==1.26.12` - `virtualenv ==20.16.3` - `webencodings ==0.5.1` - `wheel ==0.37.1` - `zipp ==3.8.1` - `setuptools ==65.2.0` </details> </blockquote> </details> <details><summary>pip_setup</summary> <blockquote> <details><summary>setup.py</summary> - `protobuf >= 3.15.0, <5.0.0dev` - `grpcio >= 1.0.0, <2.0.0dev` </details> </blockquote> </details> --- - [ ] Check this box to trigger a request for Renovate to run again on this repository

process

dependency dashboard this issue lists renovate updates and detected dependencies read the docs to learn more edited blocked these updates have been manually edited so renovate will no longer make changes to discard all commits and start over click on a checkbox pull ignored or blocked these are blocked by an existing closed pr and will not be recreated unless you click a checkbox below pull pull pull detected dependencies dockerfile kokoro docker docs dockerfile ubuntu pip requirements kokoro requirements txt argcomplete attrs bleach cachetools certifi cffi charset normalizer click colorlog commonmark cryptography distlib docutils filelock gcp docuploader gcp releasetool google api core google auth google cloud core google cloud storage google google resumable media googleapis common protos idna importlib metadata jeepney keyring markupsafe nox packaging pkginfo platformdirs protobuf py modules pycparser pygments pyjwt pyparsing pyperclip python dateutil readme renderer requests requests toolbelt rich rsa secretstorage six twine typing extensions virtualenv webencodings wheel zipp setuptools pip setup setup py protobuf grpcio check this box to trigger a request for renovate to run again on this repository

1

16,573

21,581,211,694

IssuesEvent

2022-05-02 18:56:43

bazelbuild/bazel

https://api.github.com/repos/bazelbuild/bazel

closed

Does bazel support visual studio vs2019 debug mode?

type: support / not a bug (process) untriaged team-OSS

When tensorflow .lilb and tensorflow.dll are generated by ` bazel build --config=v1 --config=opt //tensorflow ` they can be loaded and works well in visual studio 2019 Release mode, BUT failed in debug mode So if I want to build tensorlfow.lib which can be used by vs2019's debug mode, how to choose bazel options ? in other words, selecting bazel build options equals to cmake's Debug option

1.0

Does bazel support visual studio vs2019 debug mode? - When tensorflow .lilb and tensorflow.dll are generated by ` bazel build --config=v1 --config=opt //tensorflow ` they can be loaded and works well in visual studio 2019 Release mode, BUT failed in debug mode So if I want to build tensorlfow.lib which can be used by vs2019's debug mode, how to choose bazel options ? in other words, selecting bazel build options equals to cmake's Debug option

process

does bazel support visual studio debug mode when tensorflow lilb and tensorflow dll are generated by bazel build config config opt tensorflow they can be loaded and works well in visual studio release mode but failed in debug mode so if i want to build tensorlfow lib which can be used by s debug mode how to choose bazel options in other words selecting bazel build options equals to cmake s debug option

1

34,845

7,872,344,478

IssuesEvent

2018-06-25 10:53:46

mozilla/addons-server

https://api.github.com/repos/mozilla/addons-server

closed

Remove ipython from packages on travis

component: code quality priority: p4 triaged

We install IPython and ipdb by default in `dev.txt` which is fine, and should potentially be run by CircleCI UI Tests (since that's the only place where we install **everything** from scratch already). But let's maybe try to simplify our travis installation a bit and don't install ipython / ipdb there.

1.0

Remove ipython from packages on travis - We install IPython and ipdb by default in `dev.txt` which is fine, and should potentially be run by CircleCI UI Tests (since that's the only place where we install **everything** from scratch already). But let's maybe try to simplify our travis installation a bit and don't install ipython / ipdb there.

non_process

remove ipython from packages on travis we install ipython and ipdb by default in dev txt which is fine and should potentially be run by circleci ui tests since that s the only place where we install everything from scratch already but let s maybe try to simplify our travis installation a bit and don t install ipython ipdb there

0

3,840

2,540,840,681

IssuesEvent

2015-01-28 00:58:23

CienProject2014/OneLevelHero_ProposalTeam

https://api.github.com/repos/CienProject2014/OneLevelHero_ProposalTeam

opened

엔딩 보상 시스템

priority:Middle

1. 엔딩에 따른 보상 종류 정하기 a. 아이템 b. 스테이터스 c. 스킬 d. 기타 2. 엔딩 보상 구체적으로 정하기 a. 어떤 방식으로 ? b. 얼마나? c. 기타 등등... 3. 엔딩 보상에 고려해야할 것들 a. 성취감을 충분히 느끼게 하느냐 b. 두번 이상 플레이시, 난이도, 흥미, 동기 를 해치지 않을 것 c. 플레이 중 획득되는 것과 차별을 둘 것 d. 설정 상 엔딩 보상은 "용사를 돕기위해 신이 주신 선물"

1.0

엔딩 보상 시스템 - 1. 엔딩에 따른 보상 종류 정하기 a. 아이템 b. 스테이터스 c. 스킬 d. 기타 2. 엔딩 보상 구체적으로 정하기 a. 어떤 방식으로 ? b. 얼마나? c. 기타 등등... 3. 엔딩 보상에 고려해야할 것들 a. 성취감을 충분히 느끼게 하느냐 b. 두번 이상 플레이시, 난이도, 흥미, 동기 를 해치지 않을 것 c. 플레이 중 획득되는 것과 차별을 둘 것 d. 설정 상 엔딩 보상은 "용사를 돕기위해 신이 주신 선물"

non_process

엔딩 보상 시스템 엔딩에 따른 보상 종류 정하기 a 아이템 b 스테이터스 c 스킬 d 기타 엔딩 보상 구체적으로 정하기 a 어떤 방식으로 b 얼마나 c 기타 등등 엔딩 보상에 고려해야할 것들 a 성취감을 충분히 느끼게 하느냐 b 두번 이상 플레이시 난이도 흥미 동기 를 해치지 않을 것 c 플레이 중 획득되는 것과 차별을 둘 것 d 설정 상 엔딩 보상은 용사를 돕기위해 신이 주신 선물

0

15,890

20,075,037,203

IssuesEvent

2022-02-04 11:43:37

climatepolicyradar/navigator

https://api.github.com/repos/climatepolicyradar/navigator

opened

Detect and store language for passage

Document processing

When processing a Document, the language of each passage which is extracted from that document should be detected. The language detected should be stored in the database.

1.0

Detect and store language for passage - When processing a Document, the language of each passage which is extracted from that document should be detected. The language detected should be stored in the database.

process

detect and store language for passage when processing a document the language of each passage which is extracted from that document should be detected the language detected should be stored in the database

1

18,372

24,498,948,210

IssuesEvent

2022-10-10 11:08:47

deepset-ai/haystack

https://api.github.com/repos/deepset-ai/haystack

closed

Pipeline validation fails for PreProcessor initialised with strings containing spaces for the remove_substrings param

type:bug topic:preprocessing journey:intermediate

**Describe the bug** When using the `PreProcessor` with the `remove_substrings` parameter with a configuration where the PreProcessor should remove substrings with a space in the string, pipeline validation fails if using `load_from_config` or `load_from_yaml`. **Error message** ``` raise PipelineConfigError( haystack.errors.PipelineConfigError: 'with space' is not a valid variable name or value. Use alphanumeric characters or dash, underscore and colon only.) ``` This is also true for any parameter value in custom nodes which is very limiting regarding what you can implement. **Expected behavior** Pipeline validation does not care about the formatting of the values that are passed in. Checking variable names is fine but values shouldn't be checked. **Additional context** **To Reproduce** ```python from haystack.pipelines import Pipeline if __name__ == '__main__': pipeline_cfg = { 'version': '1.8', 'components': [ { 'name': 'preprocessor', 'type': 'PreProcessor', 'params': { 'remove_substrings': ['with space'] } } ], 'pipelines': [ { 'name': 'indexing', 'nodes': [ {'name': 'preprocessor', 'inputs': ['File']} ] } ] } pp = Pipeline.load_from_config(pipeline_cfg, pipeline_name='indexing') ``` **FAQ Check** - [x ] Have you had a look at [our new FAQ page](https://haystack.deepset.ai/overview/faq)? **System:** - OS: - GPU/CPU: - Haystack version (commit or version number): 1.8 - DocumentStore: - Reader: - Retriever:

1.0

Pipeline validation fails for PreProcessor initialised with strings containing spaces for the remove_substrings param - **Describe the bug** When using the `PreProcessor` with the `remove_substrings` parameter with a configuration where the PreProcessor should remove substrings with a space in the string, pipeline validation fails if using `load_from_config` or `load_from_yaml`. **Error message** ``` raise PipelineConfigError( haystack.errors.PipelineConfigError: 'with space' is not a valid variable name or value. Use alphanumeric characters or dash, underscore and colon only.) ``` This is also true for any parameter value in custom nodes which is very limiting regarding what you can implement. **Expected behavior** Pipeline validation does not care about the formatting of the values that are passed in. Checking variable names is fine but values shouldn't be checked. **Additional context** **To Reproduce** ```python from haystack.pipelines import Pipeline if __name__ == '__main__': pipeline_cfg = { 'version': '1.8', 'components': [ { 'name': 'preprocessor', 'type': 'PreProcessor', 'params': { 'remove_substrings': ['with space'] } } ], 'pipelines': [ { 'name': 'indexing', 'nodes': [ {'name': 'preprocessor', 'inputs': ['File']} ] } ] } pp = Pipeline.load_from_config(pipeline_cfg, pipeline_name='indexing') ``` **FAQ Check** - [x ] Have you had a look at [our new FAQ page](https://haystack.deepset.ai/overview/faq)? **System:** - OS: - GPU/CPU: - Haystack version (commit or version number): 1.8 - DocumentStore: - Reader: - Retriever:

process

pipeline validation fails for preprocessor initialised with strings containing spaces for the remove substrings param describe the bug when using the preprocessor with the remove substrings parameter with a configuration where the preprocessor should remove substrings with a space in the string pipeline validation fails if using load from config or load from yaml error message raise pipelineconfigerror haystack errors pipelineconfigerror with space is not a valid variable name or value use alphanumeric characters or dash underscore and colon only this is also true for any parameter value in custom nodes which is very limiting regarding what you can implement expected behavior pipeline validation does not care about the formatting of the values that are passed in checking variable names is fine but values shouldn t be checked additional context to reproduce python from haystack pipelines import pipeline if name main pipeline cfg version components name preprocessor type preprocessor params remove substrings pipelines name indexing nodes name preprocessor inputs pp pipeline load from config pipeline cfg pipeline name indexing faq check have you had a look at system os gpu cpu haystack version commit or version number documentstore reader retriever

1

260,919

19,688,987,366

IssuesEvent

2022-01-12 03:20:02

chaos-mesh/chaos-mesh

https://api.github.com/repos/chaos-mesh/chaos-mesh

closed

does test environment dashboard could add user token permission?

documentation

when use k3s environment, installing chaos-mesh, i can't find ```add user token``` option in settings on dashboard；how can i set ```user token``` in k3s environment？ version: ``` v2.1.0 ```

1.0

does test environment dashboard could add user token permission? - when use k3s environment, installing chaos-mesh, i can't find ```add user token``` option in settings on dashboard；how can i set ```user token``` in k3s environment？ version: ``` v2.1.0 ```

non_process

does test environment dashboard could add user token permission when use environment installing chaos mesh i can t find add user token option in settings on dashboard；how can i set user token in environment？ version

0

42,164

5,428,801,102

IssuesEvent

2017-03-03 16:45:55

phetsims/circuit-construction-kit-common

https://api.github.com/repos/phetsims/circuit-construction-kit-common

opened

Create artwork for the objects

design:artwork

@arouinfar can you please work with our artist to create artwork for the objects to be used in the 2nd screen?

1.0

Create artwork for the objects - @arouinfar can you please work with our artist to create artwork for the objects to be used in the 2nd screen?

non_process

create artwork for the objects arouinfar can you please work with our artist to create artwork for the objects to be used in the screen

0

8,343

11,497,832,785

IssuesEvent

2020-02-12 10:45:53

18F/tts-tech-portfolio

https://api.github.com/repos/18F/tts-tech-portfolio

closed

create `prep step` issues from icebox/backlog issues

Jan2020-inperson epic: customer service epic: internal workflow/procedures prep step workflow: process

As a team performing sprint planning and grooming; we should ensure we dedicate a portion of the time to break out preparation tasks to ensure issues are ready to be worked when appropriate. E.g. a proactive issue to perform a small/short engagement and communication (email/meeting/outline/etc) to obtain guidance and understanding around an issue / to get a head of a long lead issue and direction. #### Acceptance Criteria: - [x] create an established label - https://github.com/18F/tts-tech-portfolio/labels/prep%20step - [ ] Update sprint/grooming documentation - [ ] Complete https://github.com/18F/tts-tech-portfolio/issues/222

1.0

create `prep step` issues from icebox/backlog issues - As a team performing sprint planning and grooming; we should ensure we dedicate a portion of the time to break out preparation tasks to ensure issues are ready to be worked when appropriate. E.g. a proactive issue to perform a small/short engagement and communication (email/meeting/outline/etc) to obtain guidance and understanding around an issue / to get a head of a long lead issue and direction. #### Acceptance Criteria: - [x] create an established label - https://github.com/18F/tts-tech-portfolio/labels/prep%20step - [ ] Update sprint/grooming documentation - [ ] Complete https://github.com/18F/tts-tech-portfolio/issues/222

process

create prep step issues from icebox backlog issues as a team performing sprint planning and grooming we should ensure we dedicate a portion of the time to break out preparation tasks to ensure issues are ready to be worked when appropriate e g a proactive issue to perform a small short engagement and communication email meeting outline etc to obtain guidance and understanding around an issue to get a head of a long lead issue and direction acceptance criteria create an established label update sprint grooming documentation complete

1

29,733

5,846,049,814

IssuesEvent

2017-05-10 15:24:41

jOOQ/jOOQ

https://api.github.com/repos/jOOQ/jOOQ

closed

INSERT INTO .. SET Record statement should not take defaulted null values into consideration

C: Functionality P: Medium R: Invalid T: Defect

Similar to other API elements, the `INSERT INTO .. SET record` API should not explicitly set values that are: - `null` (in Java) - `NOT NULL` (in the database) --- See also - #2700 - #4161 - https://groups.google.com/forum/#!topic/jooq-user/8hwhDanETYs

1.0

INSERT INTO .. SET Record statement should not take defaulted null values into consideration - Similar to other API elements, the `INSERT INTO .. SET record` API should not explicitly set values that are: - `null` (in Java) - `NOT NULL` (in the database) --- See also - #2700 - #4161 - https://groups.google.com/forum/#!topic/jooq-user/8hwhDanETYs

non_process

insert into set record statement should not take defaulted null values into consideration similar to other api elements the insert into set record api should not explicitly set values that are null in java not null in the database see also

0

35,855

8,016,937,622

IssuesEvent

2018-07-25 14:40:15

MicrosoftDocs/live-share

https://api.github.com/repos/MicrosoftDocs/live-share

closed

Support VS Code's task running features for guests

feature-request vscode

> Note that developers starting the collaboration session (hosts) do not have this limitation. It is specific to participants joining a session. Visual Studio Code supports the concept of [running tasks via the command palette](https://code.visualstudio.com/Docs/editor/tasks). While developers starting the collaboration session (hosts) can use this feature freely, participants that join the session are not able to take advantage of the capability as VS Code attempts to run these tasks locally. We should enable remote execution of these tasks for the guest so they are also able to take advantage of the feature.

1.0

Support VS Code's task running features for guests - > Note that developers starting the collaboration session (hosts) do not have this limitation. It is specific to participants joining a session. Visual Studio Code supports the concept of [running tasks via the command palette](https://code.visualstudio.com/Docs/editor/tasks). While developers starting the collaboration session (hosts) can use this feature freely, participants that join the session are not able to take advantage of the capability as VS Code attempts to run these tasks locally. We should enable remote execution of these tasks for the guest so they are also able to take advantage of the feature.

non_process

support vs code s task running features for guests note that developers starting the collaboration session hosts do not have this limitation it is specific to participants joining a session visual studio code supports the concept of while developers starting the collaboration session hosts can use this feature freely participants that join the session are not able to take advantage of the capability as vs code attempts to run these tasks locally we should enable remote execution of these tasks for the guest so they are also able to take advantage of the feature

0

272,140

29,794,985,385

IssuesEvent

2023-06-16 01:02:03

billmcchesney1/hadoop

https://api.github.com/repos/billmcchesney1/hadoop

closed

CVE-2020-11111 (High) detected in jackson-databind-2.9.10.1.jar - autoclosed

Mend: dependency security vulnerability

## CVE-2020-11111 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - jackson-databind-2.9.10.1.jar</summary> General data-binding functionality for Jackson: works on core streaming API Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a> Path to vulnerable library: /hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-documentstore/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-common/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-client/target/lib/jackson-databind-2.9.10.1.jar Dependency Hierarchy: - :x: **jackson-databind-2.9.10.1.jar** (Vulnerable Library) Found in HEAD commit: <a href="https://github.com/billmcchesney1/hadoop/commit/6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a">6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a</a> Found in base branch: trunk </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png?' width=19 height=20> Vulnerability Details</summary> FasterXML jackson-databind 2.x before 2.9.10.4 mishandles the interaction between serialization gadgets and typing, related to org.apache.activemq.* (aka activemq-jms, activemq-core, activemq-pool, and activemq-pool-jms). Publish Date: 2020-03-31 URL: <a href=https://www.mend.io/vulnerability-database/CVE-2020-11111>CVE-2020-11111</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (8.8)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Origin: <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113">https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113</a> Release Date: 2020-03-31 Fix Resolution: 2.9.10.4 </details>

True

CVE-2020-11111 (High) detected in jackson-databind-2.9.10.1.jar - autoclosed - ## CVE-2020-11111 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - jackson-databind-2.9.10.1.jar</summary> General data-binding functionality for Jackson: works on core streaming API Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a> Path to vulnerable library: /hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-documentstore/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-common/target/lib/jackson-databind-2.9.10.1.jar,/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-server/hadoop-yarn-server-timelineservice-hbase/hadoop-yarn-server-timelineservice-hbase-client/target/lib/jackson-databind-2.9.10.1.jar Dependency Hierarchy: - :x: **jackson-databind-2.9.10.1.jar** (Vulnerable Library) Found in HEAD commit: <a href="https://github.com/billmcchesney1/hadoop/commit/6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a">6dcd8400219941dcbd7fb0f6b980cc2c6a2a6b0a</a> Found in base branch: trunk </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png?' width=19 height=20> Vulnerability Details</summary> FasterXML jackson-databind 2.x before 2.9.10.4 mishandles the interaction between serialization gadgets and typing, related to org.apache.activemq.* (aka activemq-jms, activemq-core, activemq-pool, and activemq-pool-jms). Publish Date: 2020-03-31 URL: <a href=https://www.mend.io/vulnerability-database/CVE-2020-11111>CVE-2020-11111</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (8.8)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Origin: <a href="https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113">https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2020-11113</a> Release Date: 2020-03-31 Fix Resolution: 2.9.10.4 </details>

non_process

cve high detected in jackson databind jar autoclosed cve high severity vulnerability vulnerable library jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to vulnerable library hadoop yarn project hadoop yarn hadoop yarn server hadoop yarn server timelineservice documentstore target lib jackson databind jar hadoop yarn project hadoop yarn hadoop yarn server hadoop yarn server timelineservice hbase hadoop yarn server timelineservice hbase common target lib jackson databind jar hadoop yarn project hadoop yarn hadoop yarn server hadoop yarn server timelineservice hbase hadoop yarn server timelineservice hbase client target lib jackson databind jar dependency hierarchy x jackson databind jar vulnerable library found in head commit a href found in base branch trunk vulnerability details fasterxml jackson databind x before mishandles the interaction between serialization gadgets and typing related to org apache activemq aka activemq jms activemq core activemq pool and activemq pool jms publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction required scope unchanged impact metrics confidentiality impact high integrity impact high availability impact high for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution

0

7,415

10,540,552,982

IssuesEvent

2019-10-02 08:41:32

threefoldtech/jumpscaleX_threebot

https://api.github.com/repos/threefoldtech/jumpscaleX_threebot

closed

Threebot: can not start directory package

process_wontfix type_bug

The package started the first time, but when I stopped it and tried to start it again. I keep getting this same error: `can not release un-acquired lock` ![image](https://user-images.githubusercontent.com/10920323/65828102-7f963400-e297-11e9-8d02-7ef1e66d7b29.png)

1.0

Threebot: can not start directory package - The package started the first time, but when I stopped it and tried to start it again. I keep getting this same error: `can not release un-acquired lock` ![image](https://user-images.githubusercontent.com/10920323/65828102-7f963400-e297-11e9-8d02-7ef1e66d7b29.png)

process

threebot can not start directory package the package started the first time but when i stopped it and tried to start it again i keep getting this same error can not release un acquired lock

1

171,133

27,065,571,052

IssuesEvent

2023-02-14 00:01:03

department-of-veterans-affairs/vets-design-system-documentation

https://api.github.com/repos/department-of-veterans-affairs/vets-design-system-documentation

closed

Update Sketch with Radio button tile variation

vsp-design-system-team va-radio

## Description There are teams interested in using the Tile version of Radio button. It's only in Storybook right now. Update Sketch library with the Radio button with tile and description text. ## Details Storybook link https://design.va.gov/storybook/?path=/docs/components-va-radio--default#with-description-text ## Tasks - [ ] Update Sketch library to include Radio button with tile and description text - [ ] Get review and sign off ## Acceptance Criteria - [ ] Sketch library has been updated - [ ] Update to Radio button in Sketch has been announced to designers

1.0

Update Sketch with Radio button tile variation - ## Description There are teams interested in using the Tile version of Radio button. It's only in Storybook right now. Update Sketch library with the Radio button with tile and description text. ## Details Storybook link https://design.va.gov/storybook/?path=/docs/components-va-radio--default#with-description-text ## Tasks - [ ] Update Sketch library to include Radio button with tile and description text - [ ] Get review and sign off ## Acceptance Criteria - [ ] Sketch library has been updated - [ ] Update to Radio button in Sketch has been announced to designers

non_process

update sketch with radio button tile variation description there are teams interested in using the tile version of radio button it s only in storybook right now update sketch library with the radio button with tile and description text details storybook link tasks update sketch library to include radio button with tile and description text get review and sign off acceptance criteria sketch library has been updated update to radio button in sketch has been announced to designers

0

18,328

24,445,771,477

IssuesEvent

2022-10-06 17:49:42

microsoft/vscode

https://api.github.com/repos/microsoft/vscode

closed

Detaching a terminal editor doesn't fire vscode.window.onDidCloseTerminal event

bug api terminal-process

- VS Code Version: stable and from sources Steps to Reproduce: 1. Create a terminal editor 2. Run `workbench.action.terminal.detachSession` command 3. :bug: `vscode.window.onDidCloseTerminal` isn't fired

1.0

Detaching a terminal editor doesn't fire vscode.window.onDidCloseTerminal event - - VS Code Version: stable and from sources Steps to Reproduce: 1. Create a terminal editor 2. Run `workbench.action.terminal.detachSession` command 3. :bug: `vscode.window.onDidCloseTerminal` isn't fired

process

detaching a terminal editor doesn t fire vscode window ondidcloseterminal event vs code version stable and from sources steps to reproduce create a terminal editor run workbench action terminal detachsession command bug vscode window ondidcloseterminal isn t fired

1

7,099

10,252,919,377

IssuesEvent

2019-08-21 10:01:23

dotnet/corefx

https://api.github.com/repos/dotnet/corefx

closed

Properties of System.Diagnostics.Process should be null rather than throwing exceptions

area-System.Diagnostics.Process

The following code throws `System.InvalidOperationException: No process is associated with this object.` when trying to access the ProcessName property although it has a null check. I mean for some reason accessing all properties of the Process object throws an InvalidOperationException. Wouldn't it make more sense that they are just null if the underlying process is disposed? Otherwise something like this: `public string ProcessName => proc?.ProcessName ?? "<empty>";` is not possible and always requires a try catch method body like this: ``` public string ProcessName { get { try { return proc.ProcessName; } catch (InvalidOperationException) { return "<empty>"; } } } ``` Repro: ``` public MainWindow() { InitializeComponent(); proc = Process.GetCurrentProcess(); } private Process proc; public string ProcessName => proc?.ProcessName ?? "<empty>"; private void Button_Click(object sender, RoutedEventArgs e) { MessageBox.Show(ProcessName); proc?.Close(); proc?.Dispose(); MessageBox.Show(ProcessName); //Throws System.InvalidOperationException: No process is associated with this object. } ```

1.0

Properties of System.Diagnostics.Process should be null rather than throwing exceptions - The following code throws `System.InvalidOperationException: No process is associated with this object.` when trying to access the ProcessName property although it has a null check. I mean for some reason accessing all properties of the Process object throws an InvalidOperationException. Wouldn't it make more sense that they are just null if the underlying process is disposed? Otherwise something like this: `public string ProcessName => proc?.ProcessName ?? "<empty>";` is not possible and always requires a try catch method body like this: ``` public string ProcessName { get { try { return proc.ProcessName; } catch (InvalidOperationException) { return "<empty>"; } } } ``` Repro: ``` public MainWindow() { InitializeComponent(); proc = Process.GetCurrentProcess(); } private Process proc; public string ProcessName => proc?.ProcessName ?? "<empty>"; private void Button_Click(object sender, RoutedEventArgs e) { MessageBox.Show(ProcessName); proc?.Close(); proc?.Dispose(); MessageBox.Show(ProcessName); //Throws System.InvalidOperationException: No process is associated with this object. } ```

process

properties of system diagnostics process should be null rather than throwing exceptions the following code throws system invalidoperationexception no process is associated with this object when trying to access the processname property although it has a null check i mean for some reason accessing all properties of the process object throws an invalidoperationexception wouldn t it make more sense that they are just null if the underlying process is disposed otherwise something like this public string processname proc processname is not possible and always requires a try catch method body like this public string processname get try return proc processname catch invalidoperationexception return repro public mainwindow initializecomponent proc process getcurrentprocess private process proc public string processname proc processname private void button click object sender routedeventargs e messagebox show processname proc close proc dispose messagebox show processname throws system invalidoperationexception no process is associated with this object

1

64,478

15,890,505,520

IssuesEvent

2021-04-10 15:41:25

ARMmaster17/Captain

https://api.github.com/repos/ARMmaster17/Captain

closed

Builder does not catch IPAM errors

bug component:Builder

Need to wrap IPAM requests the same way Proxmox requests were wrapped in #15 with some basic error catching. No idea what the format is for IPAM errors other than the server returns a 5XX error code.

1.0

Builder does not catch IPAM errors - Need to wrap IPAM requests the same way Proxmox requests were wrapped in #15 with some basic error catching. No idea what the format is for IPAM errors other than the server returns a 5XX error code.

non_process

builder does not catch ipam errors need to wrap ipam requests the same way proxmox requests were wrapped in with some basic error catching no idea what the format is for ipam errors other than the server returns a error code

0

179,801

14,713,023,716

IssuesEvent

2021-01-05 09:45:41

SketchUp/api-issue-tracker

https://api.github.com/repos/SketchUp/api-issue-tracker

opened

Document UI::Notification icon size and supported fileformat

Ruby API SketchUp documentation

Ref: https://github.com/SketchUp/api-issue-tracker/issues/484 > There is no info of what icon file types are supported (what file types are supported?) > Thanks! I maybe spent an hour in total with SVG with locals and global paths, giving up up and switching to PNGs only to have them weirdly cropped. I made sure to have some excess size for the icon to look crisp on a high DPI machine, but nope.

1.0

Document UI::Notification icon size and supported fileformat - Ref: https://github.com/SketchUp/api-issue-tracker/issues/484 > There is no info of what icon file types are supported (what file types are supported?) > Thanks! I maybe spent an hour in total with SVG with locals and global paths, giving up up and switching to PNGs only to have them weirdly cropped. I made sure to have some excess size for the icon to look crisp on a high DPI machine, but nope.

non_process

document ui notification icon size and supported fileformat ref there is no info of what icon file types are supported what file types are supported thanks i maybe spent an hour in total with svg with locals and global paths giving up up and switching to pngs only to have them weirdly cropped i made sure to have some excess size for the icon to look crisp on a high dpi machine but nope

0

17,617

23,433,907,266

IssuesEvent

2022-08-15 07:33:26

NationalSecurityAgency/ghidra

https://api.github.com/repos/NationalSecurityAgency/ghidra

closed

dsPIC30 mov issue

Feature: Processor/PIC Reason: OBE

Not sure if this is working as intended, or a bug. Here is a small program for the dsPIC30. ``` #include <stdio.h> int main(void) { printf("Hello World\n"); } ``` <img width="524" alt="Capture" src="https://user-images.githubusercontent.com/5378554/77928375-52dacd80-7276-11ea-9b32-af41fcf8dbde.PNG"> [Test.X.production.zip](https://github.com/NationalSecurityAgency/ghidra/files/4403699/Test.X.production.zip) At `rom:0007a2`, it looks like it's moving a constant into `W0` rather than a memory address. When you double click on `#0x870e`, it tries to navigate to `rom:00870e` rather than `ram`. Is there anything than can be done to fix this behavior? The decompiler seems to get it right.

1.0

dsPIC30 mov issue - Not sure if this is working as intended, or a bug. Here is a small program for the dsPIC30. ``` #include <stdio.h> int main(void) { printf("Hello World\n"); } ``` <img width="524" alt="Capture" src="https://user-images.githubusercontent.com/5378554/77928375-52dacd80-7276-11ea-9b32-af41fcf8dbde.PNG"> [Test.X.production.zip](https://github.com/NationalSecurityAgency/ghidra/files/4403699/Test.X.production.zip) At `rom:0007a2`, it looks like it's moving a constant into `W0` rather than a memory address. When you double click on `#0x870e`, it tries to navigate to `rom:00870e` rather than `ram`. Is there anything than can be done to fix this behavior? The decompiler seems to get it right.

process

mov issue not sure if this is working as intended or a bug here is a small program for the include int main void printf hello world n img width alt capture src at rom it looks like it s moving a constant into rather than a memory address when you double click on it tries to navigate to rom rather than ram is there anything than can be done to fix this behavior the decompiler seems to get it right

1

20,142

26,688,504,042

IssuesEvent

2023-01-27 01:01:50

opensearch-project/data-prepper

https://api.github.com/repos/opensearch-project/data-prepper

closed

Add metrics for otel_trace_raw: traceGroupCacheCount and spanSetCount

enhancement plugin - processor

**Is your feature request related to a problem? Please describe.** The otel_trace_raw processor has some large collections of data, but does not report metrics on these. **Describe the solution you'd like** Provide two new metrics: * `traceGroupCacheCount` - The size of the cache holding trace groups * `spanSetCount` - The number of span-sets

1.0

Add metrics for otel_trace_raw: traceGroupCacheCount and spanSetCount - **Is your feature request related to a problem? Please describe.** The otel_trace_raw processor has some large collections of data, but does not report metrics on these. **Describe the solution you'd like** Provide two new metrics: * `traceGroupCacheCount` - The size of the cache holding trace groups * `spanSetCount` - The number of span-sets

process

add metrics for otel trace raw tracegroupcachecount and spansetcount is your feature request related to a problem please describe the otel trace raw processor has some large collections of data but does not report metrics on these describe the solution you d like provide two new metrics tracegroupcachecount the size of the cache holding trace groups spansetcount the number of span sets

1

235,802

25,962,067,195

IssuesEvent

2022-12-19 01:02:55

rvvergara/flight-booker

https://api.github.com/repos/rvvergara/flight-booker

opened

CVE-2022-23516 (High) detected in loofah-2.2.3.gem

security vulnerability

## CVE-2022-23516 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - loofah-2.2.3.gem</summary> Loofah is a general library for manipulating and transforming HTML/XML documents and fragments. It's built on top of Nokogiri and libxml2, so it's fast and has a nice API. Loofah excels at HTML sanitization (XSS prevention). It includes some nice HTML sanitizers, which are based on HTML5lib's whitelist, so it most likely won't make your codes less secure. (These statements have not been evaluated by Netexperts.) ActiveRecord extensions for sanitization are available in the [`loofah-activerecord` gem](https://github.com/flavorjones/loofah-activerecord). Library home page: <a href="https://rubygems.org/gems/loofah-2.2.3.gem">https://rubygems.org/gems/loofah-2.2.3.gem</a> Dependency Hierarchy: - rails-5.2.3.gem (Root Library) - actioncable-5.2.3.gem - actionpack-5.2.3.gem - actionview-5.2.3.gem - rails-html-sanitizer-1.0.4.gem - :x: **loofah-2.2.3.gem** (Vulnerable Library) Found in HEAD commit: <a href="https://github.com/rvvergara/flight-booker/commit/5415a961bf0fcfe4baefcc9d86c9cd837862c738">5415a961bf0fcfe4baefcc9d86c9cd837862c738</a> Found in base branch: master </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> Loofah is a general library for manipulating and transforming HTML/XML documents and fragments, built on top of Nokogiri. Loofah >= 2.2.0, < 2.19.1 uses recursion for sanitizing CDATA sections, making it susceptible to stack exhaustion and raising a SystemStackError exception. This may lead to a denial of service through CPU resource consumption. This issue is patched in version 2.19.1. Users who are unable to upgrade may be able to mitigate this vulnerability by limiting the length of the strings that are sanitized. Publish Date: 2022-12-14 URL: <a href=https://www.mend.io/vulnerability-database/CVE-2022-23516>CVE-2022-23516</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (7.5)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: None - Integrity Impact: None - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Origin: <a href="https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm">https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm</a> Release Date: 2022-12-14 Fix Resolution: loofah - 2.19.1 </details> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)

True

CVE-2022-23516 (High) detected in loofah-2.2.3.gem - ## CVE-2022-23516 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - loofah-2.2.3.gem</summary> Loofah is a general library for manipulating and transforming HTML/XML documents and fragments. It's built on top of Nokogiri and libxml2, so it's fast and has a nice API. Loofah excels at HTML sanitization (XSS prevention). It includes some nice HTML sanitizers, which are based on HTML5lib's whitelist, so it most likely won't make your codes less secure. (These statements have not been evaluated by Netexperts.) ActiveRecord extensions for sanitization are available in the [`loofah-activerecord` gem](https://github.com/flavorjones/loofah-activerecord). Library home page: <a href="https://rubygems.org/gems/loofah-2.2.3.gem">https://rubygems.org/gems/loofah-2.2.3.gem</a> Dependency Hierarchy: - rails-5.2.3.gem (Root Library) - actioncable-5.2.3.gem - actionpack-5.2.3.gem - actionview-5.2.3.gem - rails-html-sanitizer-1.0.4.gem - :x: **loofah-2.2.3.gem** (Vulnerable Library) Found in HEAD commit: <a href="https://github.com/rvvergara/flight-booker/commit/5415a961bf0fcfe4baefcc9d86c9cd837862c738">5415a961bf0fcfe4baefcc9d86c9cd837862c738</a> Found in base branch: master </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> Loofah is a general library for manipulating and transforming HTML/XML documents and fragments, built on top of Nokogiri. Loofah >= 2.2.0, < 2.19.1 uses recursion for sanitizing CDATA sections, making it susceptible to stack exhaustion and raising a SystemStackError exception. This may lead to a denial of service through CPU resource consumption. This issue is patched in version 2.19.1. Users who are unable to upgrade may be able to mitigate this vulnerability by limiting the length of the strings that are sanitized. Publish Date: 2022-12-14 URL: <a href=https://www.mend.io/vulnerability-database/CVE-2022-23516>CVE-2022-23516</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (7.5)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: None - Integrity Impact: None - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Origin: <a href="https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm">https://github.com/flavorjones/loofah/security/advisories/GHSA-3x8r-x6xp-q4vm</a> Release Date: 2022-12-14 Fix Resolution: loofah - 2.19.1 </details> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)

non_process

cve high detected in loofah gem cve high severity vulnerability vulnerable library loofah gem loofah is a general library for manipulating and transforming html xml documents and fragments it s built on top of nokogiri and so it s fast and has a nice api loofah excels at html sanitization xss prevention it includes some nice html sanitizers which are based on s whitelist so it most likely won t make your codes less secure these statements have not been evaluated by netexperts activerecord extensions for sanitization are available in the library home page a href dependency hierarchy rails gem root library actioncable gem actionpack gem actionview gem rails html sanitizer gem x loofah gem vulnerable library found in head commit a href found in base branch master vulnerability details loofah is a general library for manipulating and transforming html xml documents and fragments built on top of nokogiri loofah uses recursion for sanitizing cdata sections making it susceptible to stack exhaustion and raising a systemstackerror exception this may lead to a denial of service through cpu resource consumption this issue is patched in version users who are unable to upgrade may be able to mitigate this vulnerability by limiting the length of the strings that are sanitized publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction none scope unchanged impact metrics confidentiality impact none integrity impact none availability impact high for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution loofah step up your open source security game with mend

0

5,467

8,335,489,130

IssuesEvent

2018-09-28 02:36:23

mozilla-tw/ScreenshotGo

https://api.github.com/repos/mozilla-tw/ScreenshotGo

closed

Integrate Firebase

P0 others process

learned we haven't done that so submit an issue for tracking. should be something to include before official launch so P1.

1.0

Integrate Firebase - learned we haven't done that so submit an issue for tracking. should be something to include before official launch so P1.

process

integrate firebase learned we haven t done that so submit an issue for tracking should be something to include before official launch so

1

140,599

12,943,076,681

IssuesEvent

2020-07-18 05:06:17

moxfield/moxfield-public

https://api.github.com/repos/moxfield/moxfield-public

closed

Patreon Reward: Subdomains

documentation

For your future Patreon, it would be great to have a reward tier where a user would be given a subdomain redirect for their decks. To ease your life, this should be able to be provision/deprovision automatically as patreon rewards lapse/unlapse. On the flipside, it could become permanent if a user were to donate for 3 (or 6 or 12) months straight a specific tier, or hit a certain financial threshold over time (even when subscribing/unsubscribing).

1.0

Patreon Reward: Subdomains - For your future Patreon, it would be great to have a reward tier where a user would be given a subdomain redirect for their decks. To ease your life, this should be able to be provision/deprovision automatically as patreon rewards lapse/unlapse. On the flipside, it could become permanent if a user were to donate for 3 (or 6 or 12) months straight a specific tier, or hit a certain financial threshold over time (even when subscribing/unsubscribing).

non_process

patreon reward subdomains for your future patreon it would be great to have a reward tier where a user would be given a subdomain redirect for their decks to ease your life this should be able to be provision deprovision automatically as patreon rewards lapse unlapse on the flipside it could become permanent if a user were to donate for or or months straight a specific tier or hit a certain financial threshold over time even when subscribing unsubscribing

0

68,823

8,355,169,835

IssuesEvent

2018-10-02 15:03:48

alerj/aloalerjsite

https://api.github.com/repos/alerj/aloalerjsite

closed

Alinhar label no centro do controle

bug web design

- [ ] Alinhamento vertical ao label no centro - [ ] Alinhamento à esquerda em relação ao outros controles acima e abaixo dele ![image](https://user-images.githubusercontent.com/3182864/45510342-4fedba00-b770-11e8-8d4d-40c11d2a65b3.png)

1.0

Alinhar label no centro do controle - - [ ] Alinhamento vertical ao label no centro - [ ] Alinhamento à esquerda em relação ao outros controles acima e abaixo dele ![image](https://user-images.githubusercontent.com/3182864/45510342-4fedba00-b770-11e8-8d4d-40c11d2a65b3.png)

non_process

alinhar label no centro do controle alinhamento vertical ao label no centro alinhamento à esquerda em relação ao outros controles acima e abaixo dele

0

16,281

20,905,499,503

IssuesEvent

2022-03-24 01:24:29

crim-ca/weaver

https://api.github.com/repos/crim-ca/weaver

closed

[BUG] wrong workflow_input_value usage

triage/bug feature/CWL process/wps3 project/OGC feature/job process/workflow

**Describe the bug** `workflow_input_value` can have multiple types (eg: href, float, int) but `wps3_process.execute()` expects it to be a href. When it isn't and href, `wps3_process.execute()` fails. Either when trying `workflow_input_value["location"]` when `workflow_input_value` is a float (which isn't subscriptable), or later on, during the `.startswith()` validation, which fails since type mismatch. **To Reproduce** 1. Run a workflow, for eg: `pytest tests/functional/test_ems_end2end.py -k "test_workflow_subset_picker"` 2. Notice the TypeError message in the logs (see Screenshots section) **Expected behavior** Having data other than `href` parsed as `data`, to avoid type mismatch with `LiteralData`. **Screenshots** ``` weaver_1 | File "/opt/local/src/weaver/weaver/processes/wps_workflow.py", line 455, in execute weaver_1 | self.results = self.wps_process.execute(self.builder.job, self.outdir, self.expected_outputs) weaver_1 | File "/opt/local/src/weaver/weaver/processes/wps3_process.py", line 256, in execute weaver_1 | execute_req_input_val: workflow_input_value["location"]}) weaver_1 | TypeError: 'float' object is not subscriptable ``` **Context (please complete the following information):** - OS: linux - Instance: local EMS - Version 1.13.1 Note that although `test_workflow_subset_picker` is not currently working, in the past this test passed successfully on a local EMS.

2.0

[BUG] wrong workflow_input_value usage - **Describe the bug** `workflow_input_value` can have multiple types (eg: href, float, int) but `wps3_process.execute()` expects it to be a href. When it isn't and href, `wps3_process.execute()` fails. Either when trying `workflow_input_value["location"]` when `workflow_input_value` is a float (which isn't subscriptable), or later on, during the `.startswith()` validation, which fails since type mismatch. **To Reproduce** 1. Run a workflow, for eg: `pytest tests/functional/test_ems_end2end.py -k "test_workflow_subset_picker"` 2. Notice the TypeError message in the logs (see Screenshots section) **Expected behavior** Having data other than `href` parsed as `data`, to avoid type mismatch with `LiteralData`. **Screenshots** ``` weaver_1 | File "/opt/local/src/weaver/weaver/processes/wps_workflow.py", line 455, in execute weaver_1 | self.results = self.wps_process.execute(self.builder.job, self.outdir, self.expected_outputs) weaver_1 | File "/opt/local/src/weaver/weaver/processes/wps3_process.py", line 256, in execute weaver_1 | execute_req_input_val: workflow_input_value["location"]}) weaver_1 | TypeError: 'float' object is not subscriptable ``` **Context (please complete the following information):** - OS: linux - Instance: local EMS - Version 1.13.1 Note that although `test_workflow_subset_picker` is not currently working, in the past this test passed successfully on a local EMS.

process

wrong workflow input value usage describe the bug workflow input value can have multiple types eg href float int but process execute expects it to be a href when it isn t and href process execute fails either when trying workflow input value when workflow input value is a float which isn t subscriptable or later on during the startswith validation which fails since type mismatch to reproduce run a workflow for eg pytest tests functional test ems py k test workflow subset picker notice the typeerror message in the logs see screenshots section expected behavior having data other than href parsed as data to avoid type mismatch with literaldata screenshots weaver file opt local src weaver weaver processes wps workflow py line in execute weaver self results self wps process execute self builder job self outdir self expected outputs weaver file opt local src weaver weaver processes process py line in execute weaver execute req input val workflow input value weaver typeerror float object is not subscriptable context please complete the following information os linux instance local ems version note that although test workflow subset picker is not currently working in the past this test passed successfully on a local ems

1

498

2,941,516,116

IssuesEvent

2015-07-02 08:31:02

tim-m89/Download-Scheduler

https://api.github.com/repos/tim-m89/Download-Scheduler

opened

Multiprocess / Electrolysis support

multiprocess (Electrolysis)

[Multiprocess Firefox](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox) is coming. This is a good thing really. See the [motivation behind this](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox/Motivation) Unfortunately this means without doing anything, this add-on may just not work properly in a future Firefox release. I think the current eta is Firefox 40 (August). The major code restructuring I've done on this add-on recently has been driven by this so that I can implement the changes as simply as possible. Switching from overlay based to bootstrap more closely matches the dynamic paradigm of implementing multiprocess.

1.0

Multiprocess / Electrolysis support - [Multiprocess Firefox](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox) is coming. This is a good thing really. See the [motivation behind this](https://developer.mozilla.org/en-US/Firefox/Multiprocess_Firefox/Motivation) Unfortunately this means without doing anything, this add-on may just not work properly in a future Firefox release. I think the current eta is Firefox 40 (August). The major code restructuring I've done on this add-on recently has been driven by this so that I can implement the changes as simply as possible. Switching from overlay based to bootstrap more closely matches the dynamic paradigm of implementing multiprocess.

process

multiprocess electrolysis support is coming this is a good thing really see the unfortunately this means without doing anything this add on may just not work properly in a future firefox release i think the current eta is firefox august the major code restructuring i ve done on this add on recently has been driven by this so that i can implement the changes as simply as possible switching from overlay based to bootstrap more closely matches the dynamic paradigm of implementing multiprocess

1

6,404

9,487,212,305

IssuesEvent

2019-04-22 16:12:14

brandon1roadgears/Interpreter-of-programming-language-of-Turing-Machine

https://api.github.com/repos/brandon1roadgears/Interpreter-of-programming-language-of-Turing-Machine

opened

написать функцию выводящую с правилами ввода для пользователя

C++ Work in process

*## Нужно написать функцию в которой будут написаны правила вода КОМАНД и ГЛАВНОЙ строки.

1.0

написать функцию выводящую с правилами ввода для пользователя - *## Нужно написать функцию в которой будут написаны правила вода КОМАНД и ГЛАВНОЙ строки.

process

написать функцию выводящую с правилами ввода для пользователя нужно написать функцию в которой будут написаны правила вода команд и главной строки

1

21,625

3,908,852,955

IssuesEvent

2016-04-19 17:13:46

kubernetes/kubernetes

https://api.github.com/repos/kubernetes/kubernetes

closed

e2e flake: Preparation for creating a cluster are ridiculously long (45+ minutes)

kind/flake priority/P0 team/test-infra

In this example: http://kubekins.dls.corp.google.com/view/Scalability/job/kubernetes-kubemark-5-gce/1469/consoleFull It took 45 minutes before we even started creating a cluster. Few log lines: 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by upstream project "kubernetes-build" build number 9424 ... 00:17:12 + gsutil -mq cp gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes.tar.gz gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes-test.tar.gz . 00:21:16 + unpack_binaries 00:21:16 + md5sum kubernetes.tar.gz kubernetes-test.tar.gz 00:21:21 b3f9851f4bf241f0d87fb4b094c89ef1 kubernetes.tar.gz 00:21:24 f49f8a8da9579df2a9a29177651d394d kubernetes-test.tar.gz 00:21:24 + tar -xzf kubernetes.tar.gz 00:39:19 + tar -xzf kubernetes-test.tar.gz 01:03:57 ++ cut -c 2- 01:03:57 ++ echo v1.3.0-alpha.2.181+8f3c623287e2f4 ... After 45 minutes we didn't even call kube-up yet. This is some disaster. We need to fix it, or we will be blocking merge-bot frequently because of timeouts. @kubernetes/goog-testing @fejta @spxtr @ixdy

1.0

e2e flake: Preparation for creating a cluster are ridiculously long (45+ minutes) - In this example: http://kubekins.dls.corp.google.com/view/Scalability/job/kubernetes-kubemark-5-gce/1469/consoleFull It took 45 minutes before we even started creating a cluster. Few log lines: 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by timer 00:17:08 Started by upstream project "kubernetes-build" build number 9424 ... 00:17:12 + gsutil -mq cp gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes.tar.gz gs://kubernetes-release/ci/v1.3.0-alpha.2.181+8f3c623287e2f4/kubernetes-test.tar.gz . 00:21:16 + unpack_binaries 00:21:16 + md5sum kubernetes.tar.gz kubernetes-test.tar.gz 00:21:21 b3f9851f4bf241f0d87fb4b094c89ef1 kubernetes.tar.gz 00:21:24 f49f8a8da9579df2a9a29177651d394d kubernetes-test.tar.gz 00:21:24 + tar -xzf kubernetes.tar.gz 00:39:19 + tar -xzf kubernetes-test.tar.gz 01:03:57 ++ cut -c 2- 01:03:57 ++ echo v1.3.0-alpha.2.181+8f3c623287e2f4 ... After 45 minutes we didn't even call kube-up yet. This is some disaster. We need to fix it, or we will be blocking merge-bot frequently because of timeouts. @kubernetes/goog-testing @fejta @spxtr @ixdy

non_process

flake preparation for creating a cluster are ridiculously long minutes in this example it took minutes before we even started creating a cluster few log lines started by timer started by timer started by timer started by timer started by upstream project kubernetes build build number gsutil mq cp gs kubernetes release ci alpha kubernetes tar gz gs kubernetes release ci alpha kubernetes test tar gz unpack binaries kubernetes tar gz kubernetes test tar gz kubernetes tar gz kubernetes test tar gz tar xzf kubernetes tar gz tar xzf kubernetes test tar gz cut c echo alpha after minutes we didn t even call kube up yet this is some disaster we need to fix it or we will be blocking merge bot frequently because of timeouts kubernetes goog testing fejta spxtr ixdy

0

14,208

17,106,136,124

IssuesEvent

2021-07-09 18:03:40

2i2c-org/team-compass

https://api.github.com/repos/2i2c-org/team-compass

opened

Create a bi-weekly deliverables review meeting

:label: team-process type: enhancement

# Summary In #144 we discussed creating a **deliverables review meeting**. The goal of this meeting would be to do the following things: - Go over our major deliverables on the development board - Have discussion / refinement around deliverables that need conversation and planning - Encourage team members to "pick up" deliverables for development - Provide assistance to others that are stuck on some deliverables - Discuss which deliverables we should prioritize next - Go over our major support items - Discuss how support has gone for the last 2 weeks - Hand off the Support Steward role to the next person # User Stories - As a team member, this meeting should tell me what are the most important issues to work on next, and which I should focus on myself. - As the next Support Steward, this meeting should tell me any context information needed for pre-existing support tickets, and where to focus my efforts next. # Acceptance criteria We've got this process written up in the Team Compass describing when/how this meeting works. # Tasks to complete - [ ] Agree on the general structure and cadence of this meeting - [ ] Create a calendar event for it - [ ] Create a meeting notes template to follow - [ ] Have the first meeting - [ ] Make edits as necessary - [ ] Write it all up in the Team Compass

1.0

Create a bi-weekly deliverables review meeting - # Summary In #144 we discussed creating a **deliverables review meeting**. The goal of this meeting would be to do the following things: - Go over our major deliverables on the development board - Have discussion / refinement around deliverables that need conversation and planning - Encourage team members to "pick up" deliverables for development - Provide assistance to others that are stuck on some deliverables - Discuss which deliverables we should prioritize next - Go over our major support items - Discuss how support has gone for the last 2 weeks - Hand off the Support Steward role to the next person # User Stories - As a team member, this meeting should tell me what are the most important issues to work on next, and which I should focus on myself. - As the next Support Steward, this meeting should tell me any context information needed for pre-existing support tickets, and where to focus my efforts next. # Acceptance criteria We've got this process written up in the Team Compass describing when/how this meeting works. # Tasks to complete - [ ] Agree on the general structure and cadence of this meeting - [ ] Create a calendar event for it - [ ] Create a meeting notes template to follow - [ ] Have the first meeting - [ ] Make edits as necessary - [ ] Write it all up in the Team Compass

process

create a bi weekly deliverables review meeting summary in we discussed creating a deliverables review meeting the goal of this meeting would be to do the following things go over our major deliverables on the development board have discussion refinement around deliverables that need conversation and planning encourage team members to pick up deliverables for development provide assistance to others that are stuck on some deliverables discuss which deliverables we should prioritize next go over our major support items discuss how support has gone for the last weeks hand off the support steward role to the next person user stories as a team member this meeting should tell me what are the most important issues to work on next and which i should focus on myself as the next support steward this meeting should tell me any context information needed for pre existing support tickets and where to focus my efforts next acceptance criteria we ve got this process written up in the team compass describing when how this meeting works tasks to complete agree on the general structure and cadence of this meeting create a calendar event for it create a meeting notes template to follow have the first meeting make edits as necessary write it all up in the team compass

1

11,615

14,480,634,955

IssuesEvent

2020-12-10 11:27:35

Edher-Nava/5a

https://api.github.com/repos/Edher-Nava/5a

closed

complete_size_estimating_template

process-dashboard

with the obtained real values complete the LOC estimation form

1.0

complete_size_estimating_template - with the obtained real values complete the LOC estimation form

process

complete size estimating template with the obtained real values complete the loc estimation form

1

71,925

18,927,534,403

IssuesEvent

2021-11-17 11:07:39

appsmithorg/appsmith

https://api.github.com/repos/appsmithorg/appsmith

closed

[Bug]: Input widget of phone number type must read spaces in bound data and accept spaces while entering number

Bug Input Widget App Viewers Pod High Production UI Building Pod

### Is there an existing issue for this? - [X] I have searched the existing issues ### Current Behavior Currently, phone number type input widget does not read spaces when present in bound data causing an error. Also, one is unable to use spaces in between numbers. This needs to be handled ![image](https://user-images.githubusercontent.com/79509062/137742333-8de7215c-a1e6-4bf7-822f-c56aa50cbb62.png) ### Steps To Reproduce ```markdown 1. Use a datasource that contains phone numbers that are formatted according to local "readbility" style and bind it to an input widget of Phone number type and observe the error ``` ### Environment Production ### Version Cloud

1.0

[Bug]: Input widget of phone number type must read spaces in bound data and accept spaces while entering number - ### Is there an existing issue for this? - [X] I have searched the existing issues ### Current Behavior Currently, phone number type input widget does not read spaces when present in bound data causing an error. Also, one is unable to use spaces in between numbers. This needs to be handled ![image](https://user-images.githubusercontent.com/79509062/137742333-8de7215c-a1e6-4bf7-822f-c56aa50cbb62.png) ### Steps To Reproduce ```markdown 1. Use a datasource that contains phone numbers that are formatted according to local "readbility" style and bind it to an input widget of Phone number type and observe the error ``` ### Environment Production ### Version Cloud

non_process

input widget of phone number type must read spaces in bound data and accept spaces while entering number is there an existing issue for this i have searched the existing issues current behavior currently phone number type input widget does not read spaces when present in bound data causing an error also one is unable to use spaces in between numbers this needs to be handled steps to reproduce markdown use a datasource that contains phone numbers that are formatted according to local readbility style and bind it to an input widget of phone number type and observe the error environment production version cloud

0

8,161

11,384,109,703

IssuesEvent

2020-01-29 08:11:12

google/gvisor

https://api.github.com/repos/google/gvisor

closed

Getting an Arm64 CI for gVisor

arch: arm area: tooling type: process

Hi all, Since more and more arm64 codes are being merged. It would be good to have a CI setup for arm64, so that future stuff doesn't break on it. Tasks: * [ ] cross-building * [ ] add a Arm64 server into Kokoro ? * [ ] setup syscall test with ptrace on Arm64 * [ ] setup syscall test with kvm on Arm64 * [ ] e2e test on Arm64 * [ ] image test on Arm64 .... Known issues: 1, Regarding ptrace platform, it seems that the kernel provided by Travis is too old. Because we just added the sysemu feature to the Linux kernel in last year. The kernel version should bigger than 5.3. 2, Regarding kvm platform, the nested virtualization is a problem. At present, officially announced that arm8.3 hardware will support this feature. Currently, as far as I know, there is no such arm64 server using Arm8.3. cc @xiaobo55x @avagin @prattmic

1.0

Getting an Arm64 CI for gVisor - Hi all, Since more and more arm64 codes are being merged. It would be good to have a CI setup for arm64, so that future stuff doesn't break on it. Tasks: * [ ] cross-building * [ ] add a Arm64 server into Kokoro ? * [ ] setup syscall test with ptrace on Arm64 * [ ] setup syscall test with kvm on Arm64 * [ ] e2e test on Arm64 * [ ] image test on Arm64 .... Known issues: 1, Regarding ptrace platform, it seems that the kernel provided by Travis is too old. Because we just added the sysemu feature to the Linux kernel in last year. The kernel version should bigger than 5.3. 2, Regarding kvm platform, the nested virtualization is a problem. At present, officially announced that arm8.3 hardware will support this feature. Currently, as far as I know, there is no such arm64 server using Arm8.3. cc @xiaobo55x @avagin @prattmic

process

getting an ci for gvisor hi all since more and more codes are being merged it would be good to have a ci setup for so that future stuff doesn t break on it tasks cross building add a server into kokoro setup syscall test with ptrace on setup syscall test with kvm on test on image test on known issues regarding ptrace platform it seems that the kernel provided by travis is too old because we just added the sysemu feature to the linux kernel in last year the kernel version should bigger than regarding kvm platform the nested virtualization is a problem at present officially announced that hardware will support this feature currently as far as i know there is no such server using cc avagin prattmic

1

22,606

31,831,309,928

IssuesEvent

2023-09-14 10:45:30

awslabs/clickstream-analytics-on-aws

https://api.github.com/repos/awslabs/clickstream-analytics-on-aws

closed

fail to deploy data processing stack due to the error when creating emr serverless application

bug data-processing

## Summary  fail to create data pipeline stack in a new aws account with below error > Received response status [FAILED] from custom resource. Message returned: SubnetIds and SecurityGroupIds are required ## Steps to reproduce  1. create a project 2. create a pipeline with data pipeline ## What is the current bug behavior?  the pipeline creation failed due to above error ## What is the expected correct behavior?  ## Relevant logs and/or screenshots  It failed on the custom resource to create EMR serverless application. Below is the log of Lambda function, > { "level": "INFO", "message": "{\"RequestType\":\"Create\",\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"ResponseURL\":\"https://cloudformation-custom-resource-response-apnortheast1.s3-ap-northeast-1.amazonaws.com/arn%3Aaws%3Acloudformation%3Aap-northeast-1%3A057016563434%3Astack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b%7CNestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E%7C91b8004e-344b-4f1e-b4c1-2c9c13fafb9f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230914T082245Z&X-Amz-SignedHeaders=host&X-Amz-Expires=7200&X-Amz-Credential=AKIASU3QWAIZA3EFKAWZ%2F20230914%2Fap-northeast-1%2Fs3%2Faws4_request&X-Amz-Signature=c69a882cdbc0079e42bf57a5b88d69f66d85112b4e7781139f3981dde1d8578c\",\"StackId\":\"arn:aws:cloudformation:ap-northeast-1:057016563434:stack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b\",\"RequestId\":\"91b8004e-344b-4f1e-b4c1-2c9c13fafb9f\",\"LogicalResourceId\":\"NestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E\",\"ResourceType\":\"AWS::CloudFormation::CustomResource\",\"ResourceProperties\":{\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"idleTimeoutMinutes\":\"5\",\"name\":\"Clickstream-Spark-APP-gcr_solutions\",\"pipelineS3BucketName\":\"aws-gcr-solutions-clickstream-data\",\"pipelineS3Prefix\":\"clickstream/gcr_solutions/data/pipeline-temp/\",\"projectId\":\"gcr_solutions\",\"version\":\"emr-6.11.0\",\"secourityGroupId\":\"sg-04d7b916ad7eb7649\",\"subnetIds\":\"subnet-0742bf553bc6d7a30,subnet-08705f4ee04cd6ee6,subnet-0b6af7bf03fc44e53\"}}", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.193Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861" } But the security group id were missing when calling EMR serverless API. > { "level": "INFO", "message": "CreateApplicationCommand input", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.201Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861", "input": { "name": "Clickstream-Spark-APP-gcr_solutions", "releaseLabel": "emr-6.11.0", "type": "SPARK", "architecture": "X86_64", "networkConfiguration": { "subnetIds": [ "subnet-0742bf553bc6d7a30", "subnet-08705f4ee04cd6ee6", "subnet-0b6af7bf03fc44e53" ], "securityGroupIds": [ null ] }, "autoStartConfiguration": { "enabled": true }, "autoStopConfiguration": { "enabled": true, "idleTimeoutMinutes": 5 } } } ## Possible fixes  --- This is :bug: Bug Report

1.0

fail to deploy data processing stack due to the error when creating emr serverless application - ## Summary  fail to create data pipeline stack in a new aws account with below error > Received response status [FAILED] from custom resource. Message returned: SubnetIds and SecurityGroupIds are required ## Steps to reproduce  1. create a project 2. create a pipeline with data pipeline ## What is the current bug behavior?  the pipeline creation failed due to above error ## What is the expected correct behavior?  ## Relevant logs and/or screenshots  It failed on the custom resource to create EMR serverless application. Below is the log of Lambda function, > { "level": "INFO", "message": "{\"RequestType\":\"Create\",\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"ResponseURL\":\"https://cloudformation-custom-resource-response-apnortheast1.s3-ap-northeast-1.amazonaws.com/arn%3Aaws%3Acloudformation%3Aap-northeast-1%3A057016563434%3Astack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b%7CNestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E%7C91b8004e-344b-4f1e-b4c1-2c9c13fafb9f?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20230914T082245Z&X-Amz-SignedHeaders=host&X-Amz-Expires=7200&X-Amz-Credential=AKIASU3QWAIZA3EFKAWZ%2F20230914%2Fap-northeast-1%2Fs3%2Faws4_request&X-Amz-Signature=c69a882cdbc0079e42bf57a5b88d69f66d85112b4e7781139f3981dde1d8578c\",\"StackId\":\"arn:aws:cloudformation:ap-northeast-1:057016563434:stack/Clickstream-DataProcessing-f17cee774bc64798-DataPipelineWithoutCustomPluginsNestedStac-1N60B7BJ3NKY2/d3d7c8f0-52d5-11ee-bb30-0ae71090731b\",\"RequestId\":\"91b8004e-344b-4f1e-b4c1-2c9c13fafb9f\",\"LogicalResourceId\":\"NestedStackCreateEMRServelsssApplicationCustomResourceB1D0767E\",\"ResourceType\":\"AWS::CloudFormation::CustomResource\",\"ResourceProperties\":{\"ServiceToken\":\"arn:aws:lambda:ap-northeast-1:057016563434:function:Clickstream-DataProcessin-NestedStackCreateEMRServ-hiaDwWF4jWfO\",\"idleTimeoutMinutes\":\"5\",\"name\":\"Clickstream-Spark-APP-gcr_solutions\",\"pipelineS3BucketName\":\"aws-gcr-solutions-clickstream-data\",\"pipelineS3Prefix\":\"clickstream/gcr_solutions/data/pipeline-temp/\",\"projectId\":\"gcr_solutions\",\"version\":\"emr-6.11.0\",\"secourityGroupId\":\"sg-04d7b916ad7eb7649\",\"subnetIds\":\"subnet-0742bf553bc6d7a30,subnet-08705f4ee04cd6ee6,subnet-0b6af7bf03fc44e53\"}}", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.193Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861" } But the security group id were missing when calling EMR serverless API. > { "level": "INFO", "message": "CreateApplicationCommand input", "sampling_rate": 1, "service": "ClickStreamAnalyticsOnAWS", "timestamp": "2023-09-14T08:22:47.201Z", "xray_trace_id": "1-6502c2d5-601d3208242beb6850e2d861", "input": { "name": "Clickstream-Spark-APP-gcr_solutions", "releaseLabel": "emr-6.11.0", "type": "SPARK", "architecture": "X86_64", "networkConfiguration": { "subnetIds": [ "subnet-0742bf553bc6d7a30", "subnet-08705f4ee04cd6ee6", "subnet-0b6af7bf03fc44e53" ], "securityGroupIds": [ null ] }, "autoStartConfiguration": { "enabled": true }, "autoStopConfiguration": { "enabled": true, "idleTimeoutMinutes": 5 } } } ## Possible fixes  --- This is :bug: Bug Report

process

fail to deploy data processing stack due to the error when creating emr serverless application summary fail to create data pipeline stack in a new aws account with below error received response status from custom resource message returned subnetids and securitygroupids are required steps to reproduce create a project create a pipeline with data pipeline what is the current bug behavior the pipeline creation failed due to above error what is the expected correct behavior relevant logs and or screenshots it failed on the custom resource to create emr serverless application below is the log of lambda function level info message requesttype create servicetoken arn aws lambda ap northeast function clickstream dataprocessin nestedstackcreateemrserv responseurl sampling rate service clickstreamanalyticsonaws timestamp xray trace id but the security group id were missing when calling emr serverless api level info message createapplicationcommand input sampling rate service clickstreamanalyticsonaws timestamp xray trace id input name clickstream spark app gcr solutions releaselabel emr type spark architecture networkconfiguration subnetids subnet subnet subnet securitygroupids null autostartconfiguration enabled true autostopconfiguration enabled true idletimeoutminutes possible fixes this is bug bug report

1

144,867

13,128,810,056

IssuesEvent

2020-08-06 12:56:33

cloudevents/sdk-rust

https://api.github.com/repos/cloudevents/sdk-rust

closed

Docs in reqwest, actix-web and rdkafka integration

documentation enhancement good first issue

At the moment our "subcrates" doesn't have any docs in the main crate file `lib.rs`, we should add a doc with a little code example to explain the features of the crate and some pointers to the main doc of `cloudevents-sdk`

1.0

Docs in reqwest, actix-web and rdkafka integration - At the moment our "subcrates" doesn't have any docs in the main crate file `lib.rs`, we should add a doc with a little code example to explain the features of the crate and some pointers to the main doc of `cloudevents-sdk`

non_process

docs in reqwest actix web and rdkafka integration at the moment our subcrates doesn t have any docs in the main crate file lib rs we should add a doc with a little code example to explain the features of the crate and some pointers to the main doc of cloudevents sdk

0

148,710

5,694,618,117

IssuesEvent

2017-04-15 14:58:19

FreezingMoon/AncientBeast

https://api.github.com/repos/FreezingMoon/AncientBeast

opened

not turning around to hit when replaying

Coding Easy Priority Visuals

When replaying a combat log, Cyber Hound is not turning around when hitting a unit behind. Need to check if this is a more generic issue.

1.0

not turning around to hit when replaying - When replaying a combat log, Cyber Hound is not turning around when hitting a unit behind. Need to check if this is a more generic issue.

non_process

not turning around to hit when replaying when replaying a combat log cyber hound is not turning around when hitting a unit behind need to check if this is a more generic issue

0

18,084

24,105,779,954

IssuesEvent

2022-09-20 07:19:42

arcangelo7/issues

https://api.github.com/repos/arcangelo7/issues

closed

deposit localhost:330 doi:10.1007/978-3-030-00668-6_8

deposit to be processed test

"id","title","author","pub_date","venue","volume","issue","page","type","publisher","editor" "doi:10.1007/978-3-662-07918-8_3","Influence of Dielectric Properties, State, and Electrodes on Electric Strength","Ushakov, Vasily Y.","2004","Insulation of High-Voltage Equipment [isbn:9783642058530 isbn:9783662079188]","","","27-82","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1016/0021-9991(73)90147-2","Flux-corrected transport. I. SHASTA, a fluid transport algorithm that works","Boris, Jay P; Book, David L","1973-1","Journal of Computational Physics [issn:0021-9991]","11","1","38-69","journal article","Elsevier BV [crossref:78]","" "doi:10.1109/20.877674","An investigation of FEM-FCT method for streamer corona simulation","Woong-Gee Min, ; Hyeong-Seok Kim, ; Seok-Hyun Lee, ; Song-Yop Hahn, ","2000-7","IEEE Transactions on Magnetics [issn:0018-9464]","36","4","1280-1284","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1109/tps.2003.815469","Numerical study on influences of barrier arrangements on dielectric barrier discharge characteristics","Woo Seok Kang, ; Jin Myung Park, ; Yongho Kim, ; Sang Hee Hong, ","2003-8","IEEE Transactions on Plasma Science [issn:0093-3813]","31","4","504-510","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "","Spatial Distribution of Ion Current Around HVDC Bundle Conductors","Zhou, Xiangxian; Cui, Xiang; Lu, Tiebing; Fang, Chao; Zhen, Yongzan","2012-1","IEEE Transactions on Power Delivery [issn:0885-8977 issn:1937-4208]","27","1","380-390","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1007/978-1-4615-3786-1_11","The Solution of the Continuity Equations in Ionization and Plasma Growth","Davies, A. J.; Niessen, W.","1990","Physics and Applications of Pseudosparks [isbn:9781461366874 isbn:9781461537861]","","","197-217","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1088/0022-3727/13/1/002","Discharge current induced by the motion of charged particles","Sato, N","1980-1-14","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","13","1","3-6","journal article","IOP Publishing [crossref:266]","" "doi:10.1109/27.106800","Particle-in-cell charged-particle simulations, plus Monte Carlo collisions with neutral atoms, PIC-MCC","Birdsall, C.K.","1991-4","IEEE Transactions on Plasma Science [issn:0093-3813]","19","2","65-85","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1016/0021-9991(79)90051-2","Fully multidimensional flux-corrected transport algorithms for fluids","Zalesak, Steven T","1979-6","Journal of Computational Physics [issn:0021-9991]","31","3","335-362","journal article","Elsevier BV [crossref:78]","" "doi:10.1088/0022-3727/39/14/017","Diffusion correction to the Raether–Meek criterion for the avalanche-to-streamer transition","Montijn, Carolynne; Ebert, Ute [orcid:0000-0003-3891-6869]","2006-6-30","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","39","14","2979-2992","journal article","IOP Publishing [crossref:266]","" "doi:10.1007/978-3-663-14090-0 isbn:9783528085995 isbn:9783663140900","High-Voltage Insulation Technology","Kind, Dieter; Kärner, Hermann","1985","","","","","book","Springer Science and Business Media LLC [crossref:297]","" "","Space-charge effects in high-density plasmas","Morrow, R","1982-6","Journal of Computational Physics [issn:0021-9991]","46","3","454-461","journal article","Elsevier BV [crossref:78]","" "doi:10.1007/s42835-022-01029-y","Numerical Simulation of Gas Discharge Using SUPG-FEM-FCT Method with Adaptive Mesh Refinement","Choi, Chan Young; Park, Il Han [orcid:0000-0002-9383-6856]","2022-2-28","Journal of Electrical Engineering & Technology [issn:1975-0102 issn:2093-7423]","17","3","1873-1881","journal article","Springer Science and Business Media LLC [crossref:297]","" ===###===@@@=== "citing_id","citing_publication_date","cited_id","cited_publication_date" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-662-07918-8_3","2004" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(73)90147-2","1973-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/20.877674","2000-7" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tps.2003.815469","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tpwrd.2011.2172694","2012-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-1-4615-3786-1_11","1990" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/13/1/002","1980-1-14" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/27.106800","1991-4" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(79)90051-2","1979-6" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/39/14/017","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-663-14090-0","1985" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(82)90026-2",""

1.0

deposit localhost:330 doi:10.1007/978-3-030-00668-6_8 - "id","title","author","pub_date","venue","volume","issue","page","type","publisher","editor" "doi:10.1007/978-3-662-07918-8_3","Influence of Dielectric Properties, State, and Electrodes on Electric Strength","Ushakov, Vasily Y.","2004","Insulation of High-Voltage Equipment [isbn:9783642058530 isbn:9783662079188]","","","27-82","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1016/0021-9991(73)90147-2","Flux-corrected transport. I. SHASTA, a fluid transport algorithm that works","Boris, Jay P; Book, David L","1973-1","Journal of Computational Physics [issn:0021-9991]","11","1","38-69","journal article","Elsevier BV [crossref:78]","" "doi:10.1109/20.877674","An investigation of FEM-FCT method for streamer corona simulation","Woong-Gee Min, ; Hyeong-Seok Kim, ; Seok-Hyun Lee, ; Song-Yop Hahn, ","2000-7","IEEE Transactions on Magnetics [issn:0018-9464]","36","4","1280-1284","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1109/tps.2003.815469","Numerical study on influences of barrier arrangements on dielectric barrier discharge characteristics","Woo Seok Kang, ; Jin Myung Park, ; Yongho Kim, ; Sang Hee Hong, ","2003-8","IEEE Transactions on Plasma Science [issn:0093-3813]","31","4","504-510","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "","Spatial Distribution of Ion Current Around HVDC Bundle Conductors","Zhou, Xiangxian; Cui, Xiang; Lu, Tiebing; Fang, Chao; Zhen, Yongzan","2012-1","IEEE Transactions on Power Delivery [issn:0885-8977 issn:1937-4208]","27","1","380-390","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1007/978-1-4615-3786-1_11","The Solution of the Continuity Equations in Ionization and Plasma Growth","Davies, A. J.; Niessen, W.","1990","Physics and Applications of Pseudosparks [isbn:9781461366874 isbn:9781461537861]","","","197-217","book chapter","Springer Science and Business Media LLC [crossref:297]","" "doi:10.1088/0022-3727/13/1/002","Discharge current induced by the motion of charged particles","Sato, N","1980-1-14","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","13","1","3-6","journal article","IOP Publishing [crossref:266]","" "doi:10.1109/27.106800","Particle-in-cell charged-particle simulations, plus Monte Carlo collisions with neutral atoms, PIC-MCC","Birdsall, C.K.","1991-4","IEEE Transactions on Plasma Science [issn:0093-3813]","19","2","65-85","journal article","Institute of Electrical and Electronics Engineers (IEEE) [crossref:263]","" "doi:10.1016/0021-9991(79)90051-2","Fully multidimensional flux-corrected transport algorithms for fluids","Zalesak, Steven T","1979-6","Journal of Computational Physics [issn:0021-9991]","31","3","335-362","journal article","Elsevier BV [crossref:78]","" "doi:10.1088/0022-3727/39/14/017","Diffusion correction to the Raether–Meek criterion for the avalanche-to-streamer transition","Montijn, Carolynne; Ebert, Ute [orcid:0000-0003-3891-6869]","2006-6-30","Journal of Physics D: Applied Physics [issn:0022-3727 issn:1361-6463]","39","14","2979-2992","journal article","IOP Publishing [crossref:266]","" "doi:10.1007/978-3-663-14090-0 isbn:9783528085995 isbn:9783663140900","High-Voltage Insulation Technology","Kind, Dieter; Kärner, Hermann","1985","","","","","book","Springer Science and Business Media LLC [crossref:297]","" "","Space-charge effects in high-density plasmas","Morrow, R","1982-6","Journal of Computational Physics [issn:0021-9991]","46","3","454-461","journal article","Elsevier BV [crossref:78]","" "doi:10.1007/s42835-022-01029-y","Numerical Simulation of Gas Discharge Using SUPG-FEM-FCT Method with Adaptive Mesh Refinement","Choi, Chan Young; Park, Il Han [orcid:0000-0002-9383-6856]","2022-2-28","Journal of Electrical Engineering & Technology [issn:1975-0102 issn:2093-7423]","17","3","1873-1881","journal article","Springer Science and Business Media LLC [crossref:297]","" ===###===@@@=== "citing_id","citing_publication_date","cited_id","cited_publication_date" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-662-07918-8_3","2004" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(73)90147-2","1973-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/20.877674","2000-7" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tps.2003.815469","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/tpwrd.2011.2172694","2012-1" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-1-4615-3786-1_11","1990" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/13/1/002","1980-1-14" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1109/27.106800","1991-4" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(79)90051-2","1979-6" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1088/0022-3727/39/14/017","" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1007/978-3-663-14090-0","1985" "doi:10.1007/s42835-022-01029-y","2022-02-28","doi:10.1016/0021-9991(82)90026-2",""

process

deposit localhost doi id title author pub date venue volume issue page type publisher editor doi influence of dielectric properties state and electrodes on electric strength ushakov vasily y insulation of high voltage equipment book chapter springer science and business media llc doi flux corrected transport i shasta a fluid transport algorithm that works boris jay p book david l journal of computational physics journal article elsevier bv doi an investigation of fem fct method for streamer corona simulation woong gee min hyeong seok kim seok hyun lee song yop hahn ieee transactions on magnetics journal article institute of electrical and electronics engineers ieee doi tps numerical study on influences of barrier arrangements on dielectric barrier discharge characteristics woo seok kang jin myung park yongho kim sang hee hong ieee transactions on plasma science journal article institute of electrical and electronics engineers ieee spatial distribution of ion current around hvdc bundle conductors zhou xiangxian cui xiang lu tiebing fang chao zhen yongzan ieee transactions on power delivery journal article institute of electrical and electronics engineers ieee doi the solution of the continuity equations in ionization and plasma growth davies a j niessen w physics and applications of pseudosparks book chapter springer science and business media llc doi discharge current induced by the motion of charged particles sato n journal of physics d applied physics journal article iop publishing doi particle in cell charged particle simulations plus monte carlo collisions with neutral atoms pic mcc birdsall c k ieee transactions on plasma science journal article institute of electrical and electronics engineers ieee doi fully multidimensional flux corrected transport algorithms for fluids zalesak steven t journal of computational physics journal article elsevier bv doi diffusion correction to the raether–meek criterion for the avalanche to streamer transition montijn carolynne ebert ute journal of physics d applied physics journal article iop publishing doi isbn isbn high voltage insulation technology kind dieter kärner hermann book springer science and business media llc space charge effects in high density plasmas morrow r journal of computational physics journal article elsevier bv doi y numerical simulation of gas discharge using supg fem fct method with adaptive mesh refinement choi chan young park il han journal of electrical engineering technology journal article springer science and business media llc citing id citing publication date cited id cited publication date doi y doi doi y doi doi y doi doi y doi tps doi y doi tpwrd doi y doi doi y doi doi y doi doi y doi doi y doi doi y doi doi y doi

1

220,031

7,349,155,600

IssuesEvent

2018-03-08 09:41:34

xwikisas/application-licensing

https://api.github.com/repos/xwikisas/application-licensing

closed

The French translation page is not hidden

Priority: Major Type: Bug

This makes the Licenses page appear in the page tree in the navigation panel.

1.0

The French translation page is not hidden - This makes the Licenses page appear in the page tree in the navigation panel.

non_process

the french translation page is not hidden this makes the licenses page appear in the page tree in the navigation panel

0

4,642

7,482,419,393

IssuesEvent

2018-04-05 01:15:56

UnbFeelings/unb-feelings-GQA

https://api.github.com/repos/UnbFeelings/unb-feelings-GQA

closed

Reorganizar o subprocesso que lida com as não conformidades

process wiki

- Explicitar no processo que a não conformidade deve ser acompanhada por um prazo diferente das auditorias. Por exemplo, o prazo para resolver o problema X é dois dias, e não até a próxima auditoria - Colocar na descrição da atividade do processo: ao registrar a não conformidade, é necessário informar a complexidade dela

1.0

Reorganizar o subprocesso que lida com as não conformidades - - Explicitar no processo que a não conformidade deve ser acompanhada por um prazo diferente das auditorias. Por exemplo, o prazo para resolver o problema X é dois dias, e não até a próxima auditoria - Colocar na descrição da atividade do processo: ao registrar a não conformidade, é necessário informar a complexidade dela

process

reorganizar o subprocesso que lida com as não conformidades explicitar no processo que a não conformidade deve ser acompanhada por um prazo diferente das auditorias por exemplo o prazo para resolver o problema x é dois dias e não até a próxima auditoria colocar na descrição da atividade do processo ao registrar a não conformidade é necessário informar a complexidade dela

1

8,081

11,253,936,260

IssuesEvent

2020-01-11 19:45:49

GoogleCloudPlatform/wombat-dressing-room

https://api.github.com/repos/GoogleCloudPlatform/wombat-dressing-room

closed

add CI/CD

type: process

we should add our standard kokoro CI/CD setup to this project. This is blocked by #16 , which is preventing the build from passing in various environments.

1.0

add CI/CD - we should add our standard kokoro CI/CD setup to this project. This is blocked by #16 , which is preventing the build from passing in various environments.

process

add ci cd we should add our standard kokoro ci cd setup to this project this is blocked by which is preventing the build from passing in various environments

1

11,433

14,248,237,714

IssuesEvent

2020-11-19 12:39:15

tikv/tikv

https://api.github.com/repos/tikv/tikv

closed

Optimize Coprocessor Analyze performance

difficulty/medium sig/coprocessor status/help-wanted

## Feature Request The performance of Coprocessor Analyze might be improved. We can use FlameGraph to check the current hotspot and do optimization. We can at least change to use batch table scanners.

1.0

Optimize Coprocessor Analyze performance - ## Feature Request The performance of Coprocessor Analyze might be improved. We can use FlameGraph to check the current hotspot and do optimization. We can at least change to use batch table scanners.

process

optimize coprocessor analyze performance feature request the performance of coprocessor analyze might be improved we can use flamegraph to check the current hotspot and do optimization we can at least change to use batch table scanners

1

93,079

11,738,729,659

IssuesEvent

2020-03-11 16:31:36

department-of-veterans-affairs/caseflow

https://api.github.com/repos/department-of-veterans-affairs/caseflow

opened

User Research Study: Contested Claims | Attorney Fees or Full Contested Claims

Team: Foxtrot 🦊 Type: design 💅

## User story BVA Intake users need a way to intake contested claims in Caseflow, starting with attorney fees claims (which may be one of the more frequent claims we see coming in). ## Problem statement The team needs to establish an understanding of what contested claims are, the various types, and what the general process is for someone to submit such a claim. Via user research, we need to clarify how users are currently processing contested claims, what their workarounds in Caseflow are, and what their pain points and needs are. ## What is out of scope? - What happens after a user intakes a contested claim (this belongs in Team Echo's territory) ## Background/context - Contested claims occur when two or more people are fighting over the same benefit (they don't have to be related to the veteran e.g. an attorney, funeral home etc. - Some work might have been done on this by a previous Caseflow team, however the functionality does not fully exist in Caseflow and we are unclear what user needs look like ## What are the unknowns?  - Apportionments: issues that are covered, history of apportionments in the dept, more context information - Do we need to design for apportionments? - What does the process of filing such a claim look like? - How are issues impacted? - What use cases do we need to consider? - What is happening behind the scenes for each contested claim type? ## For research tickets only: What questions do we hope to answer? What are our learning goals?  - What are the various types of contested claims? What is the general process? - How are users currently processing contested claims? What is working well and what are they struggling with? What are their workarounds? - What does an ideal future look like? What does success look like to our users? ## Existing design and content  - Currently this functionality exists in VACOLS but the purpose of this research study is to clarify what this looks like - Users might have established their own workaround in Caseflow, that we need to learn more about ## Success criteria  - Users can successfully intake attorney fees claims - Users can successfully enter a claimant who isn't currently listed in Caseflow - Users can successfully intake all other contested claims ## Technical/logistical constraints (if known)  - Existing Intake bugs - Scheduling with intake users

1.0

User Research Study: Contested Claims | Attorney Fees or Full Contested Claims - ## User story BVA Intake users need a way to intake contested claims in Caseflow, starting with attorney fees claims (which may be one of the more frequent claims we see coming in). ## Problem statement The team needs to establish an understanding of what contested claims are, the various types, and what the general process is for someone to submit such a claim. Via user research, we need to clarify how users are currently processing contested claims, what their workarounds in Caseflow are, and what their pain points and needs are. ## What is out of scope? - What happens after a user intakes a contested claim (this belongs in Team Echo's territory) ## Background/context - Contested claims occur when two or more people are fighting over the same benefit (they don't have to be related to the veteran e.g. an attorney, funeral home etc. - Some work might have been done on this by a previous Caseflow team, however the functionality does not fully exist in Caseflow and we are unclear what user needs look like ## What are the unknowns?  - Apportionments: issues that are covered, history of apportionments in the dept, more context information - Do we need to design for apportionments? - What does the process of filing such a claim look like? - How are issues impacted? - What use cases do we need to consider? - What is happening behind the scenes for each contested claim type? ## For research tickets only: What questions do we hope to answer? What are our learning goals?  - What are the various types of contested claims? What is the general process? - How are users currently processing contested claims? What is working well and what are they struggling with? What are their workarounds? - What does an ideal future look like? What does success look like to our users? ## Existing design and content  - Currently this functionality exists in VACOLS but the purpose of this research study is to clarify what this looks like - Users might have established their own workaround in Caseflow, that we need to learn more about ## Success criteria  - Users can successfully intake attorney fees claims - Users can successfully enter a claimant who isn't currently listed in Caseflow - Users can successfully intake all other contested claims ## Technical/logistical constraints (if known)  - Existing Intake bugs - Scheduling with intake users

non_process

user research study contested claims attorney fees or full contested claims user story bva intake users need a way to intake contested claims in caseflow starting with attorney fees claims which may be one of the more frequent claims we see coming in problem statement the team needs to establish an understanding of what contested claims are the various types and what the general process is for someone to submit such a claim via user research we need to clarify how users are currently processing contested claims what their workarounds in caseflow are and what their pain points and needs are what is out of scope what happens after a user intakes a contested claim this belongs in team echo s territory background context contested claims occur when two or more people are fighting over the same benefit they don t have to be related to the veteran e g an attorney funeral home etc some work might have been done on this by a previous caseflow team however the functionality does not fully exist in caseflow and we are unclear what user needs look like what are the unknowns apportionments issues that are covered history of apportionments in the dept more context information do we need to design for apportionments what does the process of filing such a claim look like how are issues impacted what use cases do we need to consider what is happening behind the scenes for each contested claim type for research tickets only what questions do we hope to answer what are our learning goals what are the various types of contested claims what is the general process how are users currently processing contested claims what is working well and what are they struggling with what are their workarounds what does an ideal future look like what does success look like to our users existing design and content currently this functionality exists in vacols but the purpose of this research study is to clarify what this looks like users might have established their own workaround in caseflow that we need to learn more about success criteria users can successfully intake attorney fees claims users can successfully enter a claimant who isn t currently listed in caseflow users can successfully intake all other contested claims technical logistical constraints if known existing intake bugs scheduling with intake users

0

358,853

10,650,901,224

IssuesEvent

2019-10-17 09:16:38

wso2/product-apim

https://api.github.com/repos/wso2/product-apim

closed

[Publisher][UI] Exception thrown when selecting the user name twice

3.0.0-alpha Priority/High Severity/Major Type/React-UI

Select the user name in the publisher ui <img width="113" alt="Screen Shot 2019-09-23 at 10 35 36 AM" src="https://user-images.githubusercontent.com/4861150/65402759-fa6dd380-dded-11e9-845a-2f261383a6ad.png"> First time logout popup is shown. Instead of selecting any button, select the user name again and following <img width="1083" alt="Screen Shot 2019-09-23 at 10 32 53 AM" src="https://user-images.githubusercontent.com/4861150/65402760-fe015a80-dded-11e9-8beb-211fc40bae9f.png">

1.0

[Publisher][UI] Exception thrown when selecting the user name twice - Select the user name in the publisher ui <img width="113" alt="Screen Shot 2019-09-23 at 10 35 36 AM" src="https://user-images.githubusercontent.com/4861150/65402759-fa6dd380-dded-11e9-845a-2f261383a6ad.png"> First time logout popup is shown. Instead of selecting any button, select the user name again and following <img width="1083" alt="Screen Shot 2019-09-23 at 10 32 53 AM" src="https://user-images.githubusercontent.com/4861150/65402760-fe015a80-dded-11e9-8beb-211fc40bae9f.png">

non_process

exception thrown when selecting the user name twice select the user name in the publisher ui img width alt screen shot at am src first time logout popup is shown instead of selecting any button select the user name again and following img width alt screen shot at am src

0

7,158

10,308,215,462

IssuesEvent

2019-08-29 10:24:07

natario1/CameraView

https://api.github.com/repos/natario1/CameraView

closed

Frame.getSize returns null

about:frame processing is:bug status:has pr

See screenshot what code I had written. I got only one error like this **java.lang.IllegalStateException: frame.size must not be null** ``` FATAL EXCEPTION: FrameProcessorsWorker Process: com.royalbimrah.ai, PID: 12887 java.lang.IllegalStateException: frame.size must not be null at com.royalbimrah.ai.facedetection.FaceDetectionActivity.process(FaceDetectionActivity.kt:67) at com.otaliastudios.cameraview.CameraView$Callbacks$13.run(CameraView.java:1742) at android.os.Handler.handleCallback(Handler.java:751) at android.os.Handler.dispatchMessage(Handler.java:95) at android.os.Looper.loop(Looper.java:154) at android.os.HandlerThread.run(HandlerThread.java:61) ``` Also, you can see code and let me know how to solve this error soon. ![image](https://user-images.githubusercontent.com/39325207/62851469-c50c9b00-bd03-11e9-8fed-b14053b82165.png)

1.0

Frame.getSize returns null - See screenshot what code I had written. I got only one error like this **java.lang.IllegalStateException: frame.size must not be null** ``` FATAL EXCEPTION: FrameProcessorsWorker Process: com.royalbimrah.ai, PID: 12887 java.lang.IllegalStateException: frame.size must not be null at com.royalbimrah.ai.facedetection.FaceDetectionActivity.process(FaceDetectionActivity.kt:67) at com.otaliastudios.cameraview.CameraView$Callbacks$13.run(CameraView.java:1742) at android.os.Handler.handleCallback(Handler.java:751) at android.os.Handler.dispatchMessage(Handler.java:95) at android.os.Looper.loop(Looper.java:154) at android.os.HandlerThread.run(HandlerThread.java:61) ``` Also, you can see code and let me know how to solve this error soon. ![image](https://user-images.githubusercontent.com/39325207/62851469-c50c9b00-bd03-11e9-8fed-b14053b82165.png)

process

frame getsize returns null see screenshot what code i had written i got only one error like this java lang illegalstateexception frame size must not be null fatal exception frameprocessorsworker process com royalbimrah ai pid java lang illegalstateexception frame size must not be null at com royalbimrah ai facedetection facedetectionactivity process facedetectionactivity kt at com otaliastudios cameraview cameraview callbacks run cameraview java at android os handler handlecallback handler java at android os handler dispatchmessage handler java at android os looper loop looper java at android os handlerthread run handlerthread java also you can see code and let me know how to solve this error soon

1

149,807

11,924,462,219

IssuesEvent

2020-04-01 09:34:09

input-output-hk/ouroboros-network

https://api.github.com/repos/input-output-hk/ouroboros-network

closed

Chain DB: simulate VolatileDB corruption

byron chain db consensus priority medium testing

#1625 fixes #1624, but doesn't test it, because it's quite an edge case to test it: the VolatileDB has to be empty and the block we're adding must be an EBB with the same block as the block at the current tip. Suggested approach: add a `WipeVolDB` command to the ChainDB q-s-m tests that closes the ChainDB, wipes the entire VolatileDB, and reopens the ChainDB. This could trigger #1624. Doing partial truncation of the VolatileDB is much more complicated, as it will depend on implementation details of the VolatileDB.

1.0

Chain DB: simulate VolatileDB corruption - #1625 fixes #1624, but doesn't test it, because it's quite an edge case to test it: the VolatileDB has to be empty and the block we're adding must be an EBB with the same block as the block at the current tip. Suggested approach: add a `WipeVolDB` command to the ChainDB q-s-m tests that closes the ChainDB, wipes the entire VolatileDB, and reopens the ChainDB. This could trigger #1624. Doing partial truncation of the VolatileDB is much more complicated, as it will depend on implementation details of the VolatileDB.

non_process

chain db simulate volatiledb corruption fixes but doesn t test it because it s quite an edge case to test it the volatiledb has to be empty and the block we re adding must be an ebb with the same block as the block at the current tip suggested approach add a wipevoldb command to the chaindb q s m tests that closes the chaindb wipes the entire volatiledb and reopens the chaindb this could trigger doing partial truncation of the volatiledb is much more complicated as it will depend on implementation details of the volatiledb

0

175,015

27,774,137,917

IssuesEvent

2023-03-16 16:09:06

phetsims/calculus-grapher

https://api.github.com/repos/phetsims/calculus-grapher

closed

phetioFeatured overrides

status:ready-for-review design:phet-io

Use Studio to feature/unfeature elements, and generate `calculus-grapher-phet-io-overrides.js`. If you're clear on the process for this, contact @arouinfar. Because this sim does not yet have an API, you can safely commit changes to `calculus-grapher-phet-io-overrides.js` without having to regenerate the API. This should be completed after https://github.com/phetsims/calculus-grapher/issues/224 (PhET-iO design review).

1.0

phetioFeatured overrides - Use Studio to feature/unfeature elements, and generate `calculus-grapher-phet-io-overrides.js`. If you're clear on the process for this, contact @arouinfar. Because this sim does not yet have an API, you can safely commit changes to `calculus-grapher-phet-io-overrides.js` without having to regenerate the API. This should be completed after https://github.com/phetsims/calculus-grapher/issues/224 (PhET-iO design review).

non_process

phetiofeatured overrides use studio to feature unfeature elements and generate calculus grapher phet io overrides js if you re clear on the process for this contact arouinfar because this sim does not yet have an api you can safely commit changes to calculus grapher phet io overrides js without having to regenerate the api this should be completed after phet io design review

0

3,375

6,501,055,061

IssuesEvent

2017-08-23 08:06:11

gaocegege/Processing.R

https://api.github.com/repos/gaocegege/Processing.R

closed

Add tutorials

community/processing difficulty/low priority/p1 size/no-idea status/to-be-claimed type/enhancement

* Documentation is important, although it is so big that it could be done forever -- so maybe a few crucial improvements, then leave the rest for contributors.

1.0

Add tutorials - * Documentation is important, although it is so big that it could be done forever -- so maybe a few crucial improvements, then leave the rest for contributors.

process

add tutorials documentation is important although it is so big that it could be done forever so maybe a few crucial improvements then leave the rest for contributors

1

22,226

11,701,749,781

IssuesEvent

2020-03-06 20:23:33

kennyrkun/rmm

https://api.github.com/repos/kennyrkun/rmm

opened

windows service will not start

bug: unconfirmed m:windows-service p:windows

don't really know why. click the start button in the task manager and it says starting but goes back to stopped. don't really know why, we oughtta figure it out though.

1.0

windows service will not start - don't really know why. click the start button in the task manager and it says starting but goes back to stopped. don't really know why, we oughtta figure it out though.

non_process

windows service will not start don t really know why click the start button in the task manager and it says starting but goes back to stopped don t really know why we oughtta figure it out though

0

223,560

24,711,915,683

IssuesEvent

2022-10-20 01:59:22

alpersonalwebsite/react-todo-board

https://api.github.com/repos/alpersonalwebsite/react-todo-board

opened

CVE-2022-37601 (High) detected in loader-utils-1.2.3.tgz, loader-utils-1.4.0.tgz

security vulnerability

## CVE-2022-37601 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - loader-utils-1.2.3.tgz, loader-utils-1.4.0.tgz</summary> <details><summary>loader-utils-1.2.3.tgz</summary> utils for webpack loaders Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz</a> Path to dependency file: /package.json Path to vulnerable library: /node_modules/react-dev-utils/node_modules/loader-utils/package.json Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - react-dev-utils-9.1.0.tgz - :x: **loader-utils-1.2.3.tgz** (Vulnerable Library) </details> <details><summary>loader-utils-1.4.0.tgz</summary> utils for webpack loaders Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz</a> Path to dependency file: /package.json Path to vulnerable library: /node_modules/loader-utils/package.json Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - webpack-4.1.0.tgz - :x: **loader-utils-1.4.0.tgz** (Vulnerable Library) </details> Found in base branch: master </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> Prototype pollution vulnerability in function parseQuery in parseQuery.js in webpack loader-utils 2.0.0 via the name variable in parseQuery.js. Publish Date: 2022-10-12 URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2022-37601>CVE-2022-37601</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (9.8)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Release Date: 2022-10-12 Fix Resolution (loader-utils): 2.0.0 Direct dependency fix Resolution (react-scripts): 5.0.1Fix Resolution (loader-utils): 2.0.0 Direct dependency fix Resolution (react-scripts): 5.0.1 </details> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)

True

CVE-2022-37601 (High) detected in loader-utils-1.2.3.tgz, loader-utils-1.4.0.tgz - ## CVE-2022-37601 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - loader-utils-1.2.3.tgz, loader-utils-1.4.0.tgz</summary> <details><summary>loader-utils-1.2.3.tgz</summary> utils for webpack loaders Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.2.3.tgz</a> Path to dependency file: /package.json Path to vulnerable library: /node_modules/react-dev-utils/node_modules/loader-utils/package.json Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - react-dev-utils-9.1.0.tgz - :x: **loader-utils-1.2.3.tgz** (Vulnerable Library) </details> <details><summary>loader-utils-1.4.0.tgz</summary> utils for webpack loaders Library home page: <a href="https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz">https://registry.npmjs.org/loader-utils/-/loader-utils-1.4.0.tgz</a> Path to dependency file: /package.json Path to vulnerable library: /node_modules/loader-utils/package.json Dependency Hierarchy: - react-scripts-3.0.1.tgz (Root Library) - webpack-4.1.0.tgz - :x: **loader-utils-1.4.0.tgz** (Vulnerable Library) </details> Found in base branch: master </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> Prototype pollution vulnerability in function parseQuery in parseQuery.js in webpack loader-utils 2.0.0 via the name variable in parseQuery.js. Publish Date: 2022-10-12 URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2022-37601>CVE-2022-37601</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (9.8)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Release Date: 2022-10-12 Fix Resolution (loader-utils): 2.0.0 Direct dependency fix Resolution (react-scripts): 5.0.1Fix Resolution (loader-utils): 2.0.0 Direct dependency fix Resolution (react-scripts): 5.0.1 </details> *** Step up your Open Source Security Game with Mend [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)

non_process

cve high detected in loader utils tgz loader utils tgz cve high severity vulnerability vulnerable libraries loader utils tgz loader utils tgz loader utils tgz utils for webpack loaders library home page a href path to dependency file package json path to vulnerable library node modules react dev utils node modules loader utils package json dependency hierarchy react scripts tgz root library react dev utils tgz x loader utils tgz vulnerable library loader utils tgz utils for webpack loaders library home page a href path to dependency file package json path to vulnerable library node modules loader utils package json dependency hierarchy react scripts tgz root library webpack tgz x loader utils tgz vulnerable library found in base branch master vulnerability details prototype pollution vulnerability in function parsequery in parsequery js in webpack loader utils via the name variable in parsequery js publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction none scope unchanged impact metrics confidentiality impact high integrity impact high availability impact high for more information on scores click a href suggested fix type upgrade version release date fix resolution loader utils direct dependency fix resolution react scripts fix resolution loader utils direct dependency fix resolution react scripts step up your open source security game with mend

0

65,824

14,761,947,162

IssuesEvent

2021-01-09 01:06:29

rsoreq/zaproxy

https://api.github.com/repos/rsoreq/zaproxy

opened

CVE-2020-36184 (Medium) detected in jackson-databind-2.9.10.jar, jackson-databind-2.9.2.jar

security vulnerability

## CVE-2020-36184 - Medium Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - jackson-databind-2.9.10.jar, jackson-databind-2.9.2.jar</summary> <details><summary>jackson-databind-2.9.10.jar</summary> General data-binding functionality for Jackson: works on core streaming API Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a> Path to dependency file: zaproxy Path to vulnerable library: /tmp/ws-ua_20200729112444_WCAEYA/downloadResource_JMENZF/20200729112922/jackson-databind-2.9.10.jar Dependency Hierarchy: - wiremock-jre8-2.25.1.jar (Root Library) - zjsonpatch-0.4.4.jar - :x: **jackson-databind-2.9.10.jar** (Vulnerable Library) </details> <details><summary>jackson-databind-2.9.2.jar</summary> General data-binding functionality for Jackson: works on core streaming API Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a> Path to dependency file: zaproxy/buildSrc/build.gradle.kts Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.9.2/1d8d8cb7cf26920ba57fb61fa56da88cc123b21f/jackson-databind-2.9.2.jar Dependency Hierarchy: - kotlin-reflect-1.3.72.jar (Root Library) - :x: **jackson-databind-2.9.2.jar** (Vulnerable Library) </details> Found in base branch: develop </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/medium_vul.png' width=19 height=20> Vulnerability Details</summary> FasterXML jackson-databind 2.x before 2.9.10.8 mishandles the interaction between serialization gadgets and typing, related to org.apache.tomcat.dbcp.dbcp2.datasources.PerUserPoolDataSource. Publish Date: 2021-01-06 URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36184>CVE-2020-36184</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (4.2)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Local - Attack Complexity: High - Privileges Required: Low - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: Low - Integrity Impact: Low - Availability Impact: Low For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Origin: <a href="https://github.com/FasterXML/jackson-databind/issues/2998">https://github.com/FasterXML/jackson-databind/issues/2998</a> Release Date: 2021-01-06 Fix Resolution: com.fasterxml.jackson.core:jackson-databind:2.9.10.8 </details>

True

CVE-2020-36184 (Medium) detected in jackson-databind-2.9.10.jar, jackson-databind-2.9.2.jar - ## CVE-2020-36184 - Medium Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Libraries - jackson-databind-2.9.10.jar, jackson-databind-2.9.2.jar</summary> <details><summary>jackson-databind-2.9.10.jar</summary> General data-binding functionality for Jackson: works on core streaming API Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a> Path to dependency file: zaproxy Path to vulnerable library: /tmp/ws-ua_20200729112444_WCAEYA/downloadResource_JMENZF/20200729112922/jackson-databind-2.9.10.jar Dependency Hierarchy: - wiremock-jre8-2.25.1.jar (Root Library) - zjsonpatch-0.4.4.jar - :x: **jackson-databind-2.9.10.jar** (Vulnerable Library) </details> <details><summary>jackson-databind-2.9.2.jar</summary> General data-binding functionality for Jackson: works on core streaming API Library home page: <a href="http://github.com/FasterXML/jackson">http://github.com/FasterXML/jackson</a> Path to dependency file: zaproxy/buildSrc/build.gradle.kts Path to vulnerable library: /home/wss-scanner/.gradle/caches/modules-2/files-2.1/com.fasterxml.jackson.core/jackson-databind/2.9.2/1d8d8cb7cf26920ba57fb61fa56da88cc123b21f/jackson-databind-2.9.2.jar Dependency Hierarchy: - kotlin-reflect-1.3.72.jar (Root Library) - :x: **jackson-databind-2.9.2.jar** (Vulnerable Library) </details> Found in base branch: develop </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/medium_vul.png' width=19 height=20> Vulnerability Details</summary> FasterXML jackson-databind 2.x before 2.9.10.8 mishandles the interaction between serialization gadgets and typing, related to org.apache.tomcat.dbcp.dbcp2.datasources.PerUserPoolDataSource. Publish Date: 2021-01-06 URL: <a href=https://vuln.whitesourcesoftware.com/vulnerability/CVE-2020-36184>CVE-2020-36184</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (4.2)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Local - Attack Complexity: High - Privileges Required: Low - User Interaction: Required - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: Low - Integrity Impact: Low - Availability Impact: Low For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Upgrade version Origin: <a href="https://github.com/FasterXML/jackson-databind/issues/2998">https://github.com/FasterXML/jackson-databind/issues/2998</a> Release Date: 2021-01-06 Fix Resolution: com.fasterxml.jackson.core:jackson-databind:2.9.10.8 </details>

non_process

cve medium detected in jackson databind jar jackson databind jar cve medium severity vulnerability vulnerable libraries jackson databind jar jackson databind jar jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to dependency file zaproxy path to vulnerable library tmp ws ua wcaeya downloadresource jmenzf jackson databind jar dependency hierarchy wiremock jar root library zjsonpatch jar x jackson databind jar vulnerable library jackson databind jar general data binding functionality for jackson works on core streaming api library home page a href path to dependency file zaproxy buildsrc build gradle kts path to vulnerable library home wss scanner gradle caches modules files com fasterxml jackson core jackson databind jackson databind jar dependency hierarchy kotlin reflect jar root library x jackson databind jar vulnerable library found in base branch develop vulnerability details fasterxml jackson databind x before mishandles the interaction between serialization gadgets and typing related to org apache tomcat dbcp datasources peruserpooldatasource publish date url a href cvss score details base score metrics exploitability metrics attack vector local attack complexity high privileges required low user interaction required scope unchanged impact metrics confidentiality impact low integrity impact low availability impact low for more information on scores click a href suggested fix type upgrade version origin a href release date fix resolution com fasterxml jackson core jackson databind isopenpronvulnerability true ispackagebased true isdefaultbranch true packages vulnerabilityidentifier cve vulnerabilitydetails fasterxml jackson databind x before mishandles the interaction between serialization gadgets and typing related to org apache tomcat dbcp datasources peruserpooldatasource vulnerabilityurl

0

16,126

9,693,785,412

IssuesEvent

2019-05-24 17:02:48

pcrane70/blackrock

https://api.github.com/repos/pcrane70/blackrock

opened

CVE-2018-8014 (High) detected in tomcat-embed-core-8.5.20.jar

security vulnerability

## CVE-2018-8014 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - tomcat-embed-core-8.5.20.jar</summary> Core Tomcat implementation Library home page: <a href="http://tomcat.apache.org/">http://tomcat.apache.org/</a> Path to dependency file: /blackrock/dataguard-gateway/pom.xml Path to vulnerable library: /root/.m2/repository/org/apache/tomcat/embed/tomcat-embed-core/8.5.20/tomcat-embed-core-8.5.20.jar Dependency Hierarchy: - spring-boot-starter-jersey-1.5.7.RELEASE.jar (Root Library) - spring-boot-starter-tomcat-1.5.7.RELEASE.jar - :x: **tomcat-embed-core-8.5.20.jar** (Vulnerable Library) </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> The defaults settings for the CORS filter provided in Apache Tomcat 9.0.0.M1 to 9.0.8, 8.5.0 to 8.5.31, 8.0.0.RC1 to 8.0.52, 7.0.41 to 7.0.88 are insecure and enable 'supportsCredentials' for all origins. It is expected that users of the CORS filter will have configured it appropriately for their environment rather than using it in the default configuration. Therefore, it is expected that most users will not be impacted by this issue. Publish Date: 2018-05-16 URL: <a href=https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-8014>CVE-2018-8014</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (9.8)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Change files Origin: <a href="https://nvd.nist.gov/vuln/detail/CVE-2018-8014">https://nvd.nist.gov/vuln/detail/CVE-2018-8014</a> Release Date: 2019-04-08 Fix Resolution: Replace or update the following files: 7.0.89, 8.0.53, 8.5.32, 9.0.9 </details> *** Step up your Open Source Security Game with WhiteSource [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)

True

CVE-2018-8014 (High) detected in tomcat-embed-core-8.5.20.jar - ## CVE-2018-8014 - High Severity Vulnerability <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/vulnerability_details.png' width=19 height=20> Vulnerable Library - tomcat-embed-core-8.5.20.jar</summary> Core Tomcat implementation Library home page: <a href="http://tomcat.apache.org/">http://tomcat.apache.org/</a> Path to dependency file: /blackrock/dataguard-gateway/pom.xml Path to vulnerable library: /root/.m2/repository/org/apache/tomcat/embed/tomcat-embed-core/8.5.20/tomcat-embed-core-8.5.20.jar Dependency Hierarchy: - spring-boot-starter-jersey-1.5.7.RELEASE.jar (Root Library) - spring-boot-starter-tomcat-1.5.7.RELEASE.jar - :x: **tomcat-embed-core-8.5.20.jar** (Vulnerable Library) </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/high_vul.png' width=19 height=20> Vulnerability Details</summary> The defaults settings for the CORS filter provided in Apache Tomcat 9.0.0.M1 to 9.0.8, 8.5.0 to 8.5.31, 8.0.0.RC1 to 8.0.52, 7.0.41 to 7.0.88 are insecure and enable 'supportsCredentials' for all origins. It is expected that users of the CORS filter will have configured it appropriately for their environment rather than using it in the default configuration. Therefore, it is expected that most users will not be impacted by this issue. Publish Date: 2018-05-16 URL: <a href=https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-8014>CVE-2018-8014</a> </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/cvss3.png' width=19 height=20> CVSS 3 Score Details (9.8)</summary> Base Score Metrics: - Exploitability Metrics: - Attack Vector: Network - Attack Complexity: Low - Privileges Required: None - User Interaction: None - Scope: Unchanged - Impact Metrics: - Confidentiality Impact: High - Integrity Impact: High - Availability Impact: High For more information on CVSS3 Scores, click <a href="https://www.first.org/cvss/calculator/3.0">here</a>. </details> <details><summary><img src='https://whitesource-resources.whitesourcesoftware.com/suggested_fix.png' width=19 height=20> Suggested Fix</summary> Type: Change files Origin: <a href="https://nvd.nist.gov/vuln/detail/CVE-2018-8014">https://nvd.nist.gov/vuln/detail/CVE-2018-8014</a> Release Date: 2019-04-08 Fix Resolution: Replace or update the following files: 7.0.89, 8.0.53, 8.5.32, 9.0.9 </details> *** Step up your Open Source Security Game with WhiteSource [here](https://www.whitesourcesoftware.com/full_solution_bolt_github)

non_process

cve high detected in tomcat embed core jar cve high severity vulnerability vulnerable library tomcat embed core jar core tomcat implementation library home page a href path to dependency file blackrock dataguard gateway pom xml path to vulnerable library root repository org apache tomcat embed tomcat embed core tomcat embed core jar dependency hierarchy spring boot starter jersey release jar root library spring boot starter tomcat release jar x tomcat embed core jar vulnerable library vulnerability details the defaults settings for the cors filter provided in apache tomcat to to to to are insecure and enable supportscredentials for all origins it is expected that users of the cors filter will have configured it appropriately for their environment rather than using it in the default configuration therefore it is expected that most users will not be impacted by this issue publish date url a href cvss score details base score metrics exploitability metrics attack vector network attack complexity low privileges required none user interaction none scope unchanged impact metrics confidentiality impact high integrity impact high availability impact high for more information on scores click a href suggested fix type change files origin a href release date fix resolution replace or update the following files step up your open source security game with whitesource

0

627,387

19,903,822,744

IssuesEvent

2022-01-25 10:40:40

iteles/blw-baby

https://api.github.com/repos/iteles/blw-baby

opened

Homemade baby puffs

priority-2

`Baby puffs` are sold everywhere in every country and are super popular because they are an easy grab-n-go snack that makes practically zero mess and the baby can serve themselves _and_ hold their snacks all by their lonesome. This kind of thing: ![image](https://user-images.githubusercontent.com/4185328/150883076-0b5bf2be-acae-420e-af54-edd4ca056cae.png) I'm not a fan of hidden ingredients but _especially_ not a fan of the mega packaging, so I though we would do well to try our own. A quick google has yielded two recipes we can try to start: 1. https://whatgreatgrandmaate.com/healthy-homemade-baby-puffs/ I'm thinking apple sauce (which we already have frozen), pear & a little kale and a starter. 2. https://feedingtinybellies.com/strawberry-kiwi-puffs/ Note: the _size_ of the puffs is _extremely_ important in order to guarantee proper cooking.

1.0

Homemade baby puffs - `Baby puffs` are sold everywhere in every country and are super popular because they are an easy grab-n-go snack that makes practically zero mess and the baby can serve themselves _and_ hold their snacks all by their lonesome. This kind of thing: ![image](https://user-images.githubusercontent.com/4185328/150883076-0b5bf2be-acae-420e-af54-edd4ca056cae.png) I'm not a fan of hidden ingredients but _especially_ not a fan of the mega packaging, so I though we would do well to try our own. A quick google has yielded two recipes we can try to start: 1. https://whatgreatgrandmaate.com/healthy-homemade-baby-puffs/ I'm thinking apple sauce (which we already have frozen), pear & a little kale and a starter. 2. https://feedingtinybellies.com/strawberry-kiwi-puffs/ Note: the _size_ of the puffs is _extremely_ important in order to guarantee proper cooking.

non_process

homemade baby puffs baby puffs are sold everywhere in every country and are super popular because they are an easy grab n go snack that makes practically zero mess and the baby can serve themselves and hold their snacks all by their lonesome this kind of thing i m not a fan of hidden ingredients but especially not a fan of the mega packaging so i though we would do well to try our own a quick google has yielded two recipes we can try to start i m thinking apple sauce which we already have frozen pear a little kale and a starter note the size of the puffs is extremely important in order to guarantee proper cooking

0

3,171

6,224,315,048

IssuesEvent

2017-07-10 14:01:12

nodejs/node

https://api.github.com/repos/nodejs/node

reopened

stdout/stderr buffering with TTY

process repro-exists tty

Recently I discovered that at least with the latest io.js (currently testing with the `next` branch in particular) and possibly older versions, stdout and stderr seem to be buffering when those streams refer to a TTY. Here's a simple example that replicates the issue for me: ``` javascript while (true) { console.error('stderr is tty? ' + process.stderr.isTTY); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); require('fs').writeFileSync('/tmp/stderr_buffer_len', 'stderr buffer length=' + process.stderr._writableState.length + '\n'); } ``` In one terminal do `tail -F /tmp/stderr_buffer_len` and then in another do `iojs test.js`. I also tested the same script with node v0.10.30 and the stderr buffer length is always 0.

1.0

stdout/stderr buffering with TTY - Recently I discovered that at least with the latest io.js (currently testing with the `next` branch in particular) and possibly older versions, stdout and stderr seem to be buffering when those streams refer to a TTY. Here's a simple example that replicates the issue for me: ``` javascript while (true) { console.error('stderr is tty? ' + process.stderr.isTTY); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); console.error('foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc'); require('fs').writeFileSync('/tmp/stderr_buffer_len', 'stderr buffer length=' + process.stderr._writableState.length + '\n'); } ``` In one terminal do `tail -F /tmp/stderr_buffer_len` and then in another do `iojs test.js`. I also tested the same script with node v0.10.30 and the stderr buffer length is always 0.

process

stdout stderr buffering with tty recently i discovered that at least with the latest io js currently testing with the next branch in particular and possibly older versions stdout and stderr seem to be buffering when those streams refer to a tty here s a simple example that replicates the issue for me javascript while true console error stderr is tty process stderr istty console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc console error foo bar baz quux quuz aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb cccccccccccccccccccccccccccccccc require fs writefilesync tmp stderr buffer len stderr buffer length process stderr writablestate length n in one terminal do tail f tmp stderr buffer len and then in another do iojs test js i also tested the same script with node and the stderr buffer length is always

1

8,503

11,685,550,937

IssuesEvent

2020-03-05 09:18:24

storm-fsv-cvut/smoderp2d

https://api.github.com/repos/storm-fsv-cvut/smoderp2d

opened

internal poit and stream to output

Output post-processing

Internal point (shp layer) save to core output stream - part in computed area (with added outputs for each segment - other issue from Dec 11) save also to core output output point data (.dat) save also to core output or subfolder of core

1.0

internal poit and stream to output - Internal point (shp layer) save to core output stream - part in computed area (with added outputs for each segment - other issue from Dec 11) save also to core output output point data (.dat) save also to core output or subfolder of core

process

internal poit and stream to output internal point shp layer save to core output stream part in computed area with added outputs for each segment other issue from dec save also to core output output point data dat save also to core output or subfolder of core

1

17,538

23,346,359,160

IssuesEvent

2022-08-09 18:22:34

GSA/EDX

https://api.github.com/repos/GSA/EDX

closed

Review Web Manager Position Descriptions for requirements (Skills, Competencies, Training)

process professional-development strategy

- [x] Develop a roster of website managers from the Touchpoints survey - [x] Identify PDs of each website manager - [x] [Fix Roster with lessons learned from survey and review new PDs](https://github.com/GSA/EDX/issues/113#issuecomment-1059581758) - [x] Determine the number of unique PDs (and standardized PDs vs. specific PDs) - [x] Pull a sample of PDs - [x] Review PDs for requirements (skills, competencies, training) - [x] Identify opportunities to include requirements How can we elevate the role of the website manager so 1) that role is clearly identified & delinated 2) site managers have the required skills & competencies 3) site managers are held accountable to be in compliance with all regulations? In Issue 55, we reviewed a sample of site manager PDs and found that none of them addressed skills, competencies, regulations, or any requirements pertaining to website management compliance. After consultation with Classification, we decided to review the PDs of every site manager to determine the 1) series 2) bargaining unit status 3) skills, competencies required 4) regulations. We'll analyze that information and make a recommendation if site manager PDs should include certain requirements. We'll continue to collaborate with Classification and consult with Labor Relations.

1.0

Review Web Manager Position Descriptions for requirements (Skills, Competencies, Training) - - [x] Develop a roster of website managers from the Touchpoints survey - [x] Identify PDs of each website manager - [x] [Fix Roster with lessons learned from survey and review new PDs](https://github.com/GSA/EDX/issues/113#issuecomment-1059581758) - [x] Determine the number of unique PDs (and standardized PDs vs. specific PDs) - [x] Pull a sample of PDs - [x] Review PDs for requirements (skills, competencies, training) - [x] Identify opportunities to include requirements How can we elevate the role of the website manager so 1) that role is clearly identified & delinated 2) site managers have the required skills & competencies 3) site managers are held accountable to be in compliance with all regulations? In Issue 55, we reviewed a sample of site manager PDs and found that none of them addressed skills, competencies, regulations, or any requirements pertaining to website management compliance. After consultation with Classification, we decided to review the PDs of every site manager to determine the 1) series 2) bargaining unit status 3) skills, competencies required 4) regulations. We'll analyze that information and make a recommendation if site manager PDs should include certain requirements. We'll continue to collaborate with Classification and consult with Labor Relations.

process

review web manager position descriptions for requirements skills competencies training develop a roster of website managers from the touchpoints survey identify pds of each website manager determine the number of unique pds and standardized pds vs specific pds pull a sample of pds review pds for requirements skills competencies training identify opportunities to include requirements how can we elevate the role of the website manager so that role is clearly identified delinated site managers have the required skills competencies site managers are held accountable to be in compliance with all regulations in issue we reviewed a sample of site manager pds and found that none of them addressed skills competencies regulations or any requirements pertaining to website management compliance after consultation with classification we decided to review the pds of every site manager to determine the series bargaining unit status skills competencies required regulations we ll analyze that information and make a recommendation if site manager pds should include certain requirements we ll continue to collaborate with classification and consult with labor relations

1

291,952

25,187,597,348

IssuesEvent

2022-11-11 19:46:06

ValveSoftware/Proton

https://api.github.com/repos/ValveSoftware/Proton

closed

The Ultimate Doom (2280) rerelease

Game compatibility Need Retest

# Compatibility Report - Name of the game with compatibility issues: The Ultimate Doom - Steam AppID of the game: 2280 ## System Information - GPU: AMD Radeon RX5700 - Driver/LLVM version: Mesa 20.1.7 llvm 10.0.1 - Kernel version: 5.8.5 - Link to full system information report as [Gist](https://gist.github.com/): https://gist.github.com/xDShot/8e9ae635b715265059e09bb21f592afa - Proton version: 5.0-9 ## I confirm: - [x] that I haven't found an existing compatibility report for this game. - [x] that I have checked whether there are updates for my system available.  ## Symptoms  Black screen when launched from Steam. Works if launched directly outside of Steam with system Wine.

1.0

The Ultimate Doom (2280) rerelease - # Compatibility Report - Name of the game with compatibility issues: The Ultimate Doom - Steam AppID of the game: 2280 ## System Information - GPU: AMD Radeon RX5700 - Driver/LLVM version: Mesa 20.1.7 llvm 10.0.1 - Kernel version: 5.8.5 - Link to full system information report as [Gist](https://gist.github.com/): https://gist.github.com/xDShot/8e9ae635b715265059e09bb21f592afa - Proton version: 5.0-9 ## I confirm: - [x] that I haven't found an existing compatibility report for this game. - [x] that I have checked whether there are updates for my system available.  ## Symptoms  Black screen when launched from Steam. Works if launched directly outside of Steam with system Wine.

non_process

the ultimate doom rerelease compatibility report name of the game with compatibility issues the ultimate doom steam appid of the game system information gpu amd radeon driver llvm version mesa llvm kernel version link to full system information report as proton version i confirm that i haven t found an existing compatibility report for this game that i have checked whether there are updates for my system available please add proton log command to the game s launch options and drag and drop the generated home steam appid log into this issue report symptoms black screen when launched from steam works if launched directly outside of steam with system wine

0

19,450

25,732,054,973

IssuesEvent

2022-12-07 21:07:38

LLNL/maestrowf

https://api.github.com/repos/LLNL/maestrowf

opened

Version info syncing issues

Release Process Discussion

Currently we seem to have 3 sources of package version info which is not in sync: * git tags: develop's latest tag appears to be 1.1.7dev0, master is 1.1.4, and more concerning develop has inconsistent use of dev, dev0, and no dev tag at all * pyproject.toml: most up to date source since the switch to poetry * top level `__init__.py` is still on 1.1.9dev1 I propose switching this to a single source and automating it if possible and potentially a different versioning scheme (dev tags). Importlib appears able to read from the pyproject.toml now, so I think that single source could be the toml file now. Additionally, there's a few interesting options for at least semi-automated version bumping: * poetry's built in 'version' command: it has some potentially useful bump rules like prepatch/preminor/prerelease. There are some quirks though: repeated use of prerelease does tick the release (-alpha) tag numbers, but repeated use of prepatch doesn't? Also not clear if this can use the abbreviated form 'a0' instead of the default '-alpha.0' * bumpver package supports more customization on what the actual tag is, but still limited to alpha/beta/release/final/post, and no dev. dev could likely be added if we make a pr to that package and it gets accepted. Either one would likely be able to use hook into an automated tag creation since both have a version query on the cli. Potentially bigger changes to the versioning scheme beyond just possible switching away from dev if jumping on an existing solution right away: * have explicit version commits/tagging post pushing to master, which isn't really changing anything * drop pre-release/dev tagging/version bumps altogether on develop beyond an initial alpha/dev tag upon cutting a release? we don't currently release those anyway, so much as it's nice in principle, is it worth the effort of maintaining tags/version bumps on every PR/commit onto develop? Can always do some one off pre-release tags/bumps for dev version releases if needed

1.0

Version info syncing issues - Currently we seem to have 3 sources of package version info which is not in sync: * git tags: develop's latest tag appears to be 1.1.7dev0, master is 1.1.4, and more concerning develop has inconsistent use of dev, dev0, and no dev tag at all * pyproject.toml: most up to date source since the switch to poetry * top level `__init__.py` is still on 1.1.9dev1 I propose switching this to a single source and automating it if possible and potentially a different versioning scheme (dev tags). Importlib appears able to read from the pyproject.toml now, so I think that single source could be the toml file now. Additionally, there's a few interesting options for at least semi-automated version bumping: * poetry's built in 'version' command: it has some potentially useful bump rules like prepatch/preminor/prerelease. There are some quirks though: repeated use of prerelease does tick the release (-alpha) tag numbers, but repeated use of prepatch doesn't? Also not clear if this can use the abbreviated form 'a0' instead of the default '-alpha.0' * bumpver package supports more customization on what the actual tag is, but still limited to alpha/beta/release/final/post, and no dev. dev could likely be added if we make a pr to that package and it gets accepted. Either one would likely be able to use hook into an automated tag creation since both have a version query on the cli. Potentially bigger changes to the versioning scheme beyond just possible switching away from dev if jumping on an existing solution right away: * have explicit version commits/tagging post pushing to master, which isn't really changing anything * drop pre-release/dev tagging/version bumps altogether on develop beyond an initial alpha/dev tag upon cutting a release? we don't currently release those anyway, so much as it's nice in principle, is it worth the effort of maintaining tags/version bumps on every PR/commit onto develop? Can always do some one off pre-release tags/bumps for dev version releases if needed

process

version info syncing issues currently we seem to have sources of package version info which is not in sync git tags develop s latest tag appears to be master is and more concerning develop has inconsistent use of dev and no dev tag at all pyproject toml most up to date source since the switch to poetry top level init py is still on i propose switching this to a single source and automating it if possible and potentially a different versioning scheme dev tags importlib appears able to read from the pyproject toml now so i think that single source could be the toml file now additionally there s a few interesting options for at least semi automated version bumping poetry s built in version command it has some potentially useful bump rules like prepatch preminor prerelease there are some quirks though repeated use of prerelease does tick the release alpha tag numbers but repeated use of prepatch doesn t also not clear if this can use the abbreviated form instead of the default alpha bumpver package supports more customization on what the actual tag is but still limited to alpha beta release final post and no dev dev could likely be added if we make a pr to that package and it gets accepted either one would likely be able to use hook into an automated tag creation since both have a version query on the cli potentially bigger changes to the versioning scheme beyond just possible switching away from dev if jumping on an existing solution right away have explicit version commits tagging post pushing to master which isn t really changing anything drop pre release dev tagging version bumps altogether on develop beyond an initial alpha dev tag upon cutting a release we don t currently release those anyway so much as it s nice in principle is it worth the effort of maintaining tags version bumps on every pr commit onto develop can always do some one off pre release tags bumps for dev version releases if needed

1

21,629

30,033,459,318

IssuesEvent

2023-06-27 11:10:05

metabase/metabase

https://api.github.com/repos/metabase/metabase

closed

Join is removed with "Removing invalid MBQL clause" joining two columns together with the same field ID

Type:Bug Priority:P2 .Regression .metabase-lib .Team/QueryProcessor :hammer_and_wrench:

### Describe the bug If you join two tables together on a column with the same field ID in the notebook editor, the join gets removed on save. ### To Reproduce Following the first three steps from https://github.com/metabase/metabase/issues/12930: 1. Create question Q1 - Orders join with Products and People, summarized by Count, grouped by Category and Source ![image](https://user-images.githubusercontent.com/1447303/87708713-6e290480-c7a3-11ea-861a-72d09dac8c30.png) 2. Create question Q2 - Products, summarized by Average Rating, grouped by Category ![image](https://user-images.githubusercontent.com/1447303/87715536-c9f88b00-c7ad-11ea-8c81-4c57662acf5e.png) 3. Custom question > Saved Questions > Q1 and join Q2 on Category. <img width="593" alt="image" src="https://github.com/metabase/metabase/assets/39073188/31a3d51a-f550-45f9-8f18-d0c9e6f5235e"> 4. Click "Save". The Join should be removed with only Q1 remaining: <img width="666" alt="image" src="https://github.com/metabase/metabase/assets/39073188/38925aed-73ea-499a-aff8-c3bc84c7490c"> ### Expected behavior Join should work ### Logs The browser console shows this warning on save: ![image](https://github.com/metabase/metabase/assets/39073188/4faef6d5-4110-46a6-9cfd-1ee24245c1d9) ### Information about your Metabase installation ```JSON master, commit 7587d3cf1a6e35e593f900f98d266591a6160a31 ``` ### Severity High ### Additional context _No response_

1.0

Join is removed with "Removing invalid MBQL clause" joining two columns together with the same field ID - ### Describe the bug If you join two tables together on a column with the same field ID in the notebook editor, the join gets removed on save. ### To Reproduce Following the first three steps from https://github.com/metabase/metabase/issues/12930: 1. Create question Q1 - Orders join with Products and People, summarized by Count, grouped by Category and Source ![image](https://user-images.githubusercontent.com/1447303/87708713-6e290480-c7a3-11ea-861a-72d09dac8c30.png) 2. Create question Q2 - Products, summarized by Average Rating, grouped by Category ![image](https://user-images.githubusercontent.com/1447303/87715536-c9f88b00-c7ad-11ea-8c81-4c57662acf5e.png) 3. Custom question > Saved Questions > Q1 and join Q2 on Category. <img width="593" alt="image" src="https://github.com/metabase/metabase/assets/39073188/31a3d51a-f550-45f9-8f18-d0c9e6f5235e"> 4. Click "Save". The Join should be removed with only Q1 remaining: <img width="666" alt="image" src="https://github.com/metabase/metabase/assets/39073188/38925aed-73ea-499a-aff8-c3bc84c7490c"> ### Expected behavior Join should work ### Logs The browser console shows this warning on save: ![image](https://github.com/metabase/metabase/assets/39073188/4faef6d5-4110-46a6-9cfd-1ee24245c1d9) ### Information about your Metabase installation ```JSON master, commit 7587d3cf1a6e35e593f900f98d266591a6160a31 ``` ### Severity High ### Additional context _No response_

process

join is removed with removing invalid mbql clause joining two columns together with the same field id describe the bug if you join two tables together on a column with the same field id in the notebook editor the join gets removed on save to reproduce following the first three steps from create question orders join with products and people summarized by count grouped by category and source create question products summarized by average rating grouped by category custom question saved questions and join on category img width alt image src click save the join should be removed with only remaining img width alt image src expected behavior join should work logs the browser console shows this warning on save information about your metabase installation json master commit severity high additional context no response

1

22,010

6,228,039,713

IssuesEvent

2017-07-10 22:10:00

XceedBoucherS/TestImport5

https://api.github.com/repos/XceedBoucherS/TestImport5

closed

RichTextBox TextFormatter Not Supporting Flowdocument Property?

CodePlex

LastDefense[CodePlex] I tried to Bind the RichTextBox to a FlowDocument Property Type using custom formatter. I actually used the one in your code example with no other changes. When the RTB tries to bind I get the following error: Cannot create default converter to perform 'two-way' conversions between types 'System.Windows.Documents.FlowDocument' and 'System.String'. nbsp I changed the property type to string and the format being returned is plain text event though I have the custom formatter applied. nbsp I used the code in your example for the RTB and the custom formatter so I am not sure what I am doing wrong. Can you explain in more detail how the custom formatter is supposed to work? I have a Flowdocument to bind so it does not make sense to convert it to a string or xaml just to be converted back to a Flowdocument. nbsp Thanks

1.0

RichTextBox TextFormatter Not Supporting Flowdocument Property? - LastDefense[CodePlex] I tried to Bind the RichTextBox to a FlowDocument Property Type using custom formatter. I actually used the one in your code example with no other changes. When the RTB tries to bind I get the following error: Cannot create default converter to perform 'two-way' conversions between types 'System.Windows.Documents.FlowDocument' and 'System.String'. nbsp I changed the property type to string and the format being returned is plain text event though I have the custom formatter applied. nbsp I used the code in your example for the RTB and the custom formatter so I am not sure what I am doing wrong. Can you explain in more detail how the custom formatter is supposed to work? I have a Flowdocument to bind so it does not make sense to convert it to a string or xaml just to be converted back to a Flowdocument. nbsp Thanks

non_process

richtextbox textformatter not supporting flowdocument property lastdefense i tried to bind the richtextbox to a flowdocument property type using custom formatter i actually used the one in your code example with no other changes when the rtb tries to bind i get the following error cannot create default converter to perform two way conversions between types system windows documents flowdocument and system string nbsp i changed the property type to string and the format being returned is plain text event though i have the custom formatter applied nbsp i used the code in your example for the rtb and the custom formatter so i am not sure what i am doing wrong can you explain in more detail how the custom formatter is supposed to work i have a flowdocument to bind so it does not make sense to convert it to a string or xaml just to be converted back to a flowdocument nbsp thanks

0

108,899

13,685,793,384

IssuesEvent

2020-09-30 07:43:57

mdn/mdn-minimalist

https://api.github.com/repos/mdn/mdn-minimalist

closed

Forms

design-system enhancement

A couple of tweaks to form styles: - [x] When a piece of text is wrapped in a `code` element inside a highlighted `legend` element, set the `background-color` to transparent - [x] Remove left padding from highlighted `legend` elements

1.0

Forms - A couple of tweaks to form styles: - [x] When a piece of text is wrapped in a `code` element inside a highlighted `legend` element, set the `background-color` to transparent - [x] Remove left padding from highlighted `legend` elements

non_process

forms a couple of tweaks to form styles when a piece of text is wrapped in a code element inside a highlighted legend element set the background color to transparent remove left padding from highlighted legend elements

0

223,875

7,461,529,594

IssuesEvent

2018-03-31 04:13:57

rivine/rivine

https://api.github.com/repos/rivine/rivine

closed

make it possible to send block stakes to multiple people at once using rivinec

priority_major state_inprogress type_feature

currently `rivinec wallet send blockstakes` only allows to send to one person at a time, would be great if we can send to multiple people at once

1.0

make it possible to send block stakes to multiple people at once using rivinec - currently `rivinec wallet send blockstakes` only allows to send to one person at a time, would be great if we can send to multiple people at once

non_process

make it possible to send block stakes to multiple people at once using rivinec currently rivinec wallet send blockstakes only allows to send to one person at a time would be great if we can send to multiple people at once

0

22,072

30,594,928,893

IssuesEvent

2023-07-21 20:50:19

USGS-WiM/StreamStats

https://api.github.com/repos/USGS-WiM/StreamStats

opened

Disable Reorder Queue button when there are no batches in the queue

Batch Processor

![image](https://github.com/USGS-WiM/StreamStats/assets/40237491/7f0b211b-ac79-456a-bf65-6056e0ad6b44) The Reorder Queue button should be disabled because there are no batches that are able to be reordered Low priority issue

1.0

Disable Reorder Queue button when there are no batches in the queue - ![image](https://github.com/USGS-WiM/StreamStats/assets/40237491/7f0b211b-ac79-456a-bf65-6056e0ad6b44) The Reorder Queue button should be disabled because there are no batches that are able to be reordered Low priority issue

process

disable reorder queue button when there are no batches in the queue the reorder queue button should be disabled because there are no batches that are able to be reordered low priority issue

1

1,999

4,818,389,008

IssuesEvent

2016-11-04 16:14:10

bbcarchdev/anansi

https://api.github.com/repos/bbcarchdev/anansi

opened

UTF8 resources can not be added to the queue

bug processor:rdf

They all result in a parsing error. These are the errors generated when adding dbpedia:Cardiff to the queue: `anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Cardiff_Blues_vs_Leicester_Tigers_(2008–09_Heineken_Cup)> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ja.dbpedia.org/resource/カーディフ> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ko.dbpedia.org/resource/카디프> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://el.dbpedia.org/resource/Κάρντιφ> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Tŷ_Pont_Haearn> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/6/6/00_–_Cardiff,_Wales> `

1.0

UTF8 resources can not be added to the queue - They all result in a parsing error. These are the errors generated when adding dbpedia:Cardiff to the queue: `anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Cardiff_Blues_vs_Leicester_Tigers_(2008–09_Heineken_Cup)> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ja.dbpedia.org/resource/カーディフ> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://ko.dbpedia.org/resource/카디프> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://el.dbpedia.org/resource/Κάρντιφ> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/Tŷ_Pont_Haearn> anansi_1 | crawld[1]: %ANANSI-E-2001: failed to parse URI <http://dbpedia.org/resource/6/6/00_–_Cardiff,_Wales> `

process

resources can not be added to the queue they all result in a parsing error these are the errors generated when adding dbpedia cardiff to the queue anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri anansi crawld anansi e failed to parse uri

1

285,139

24,645,098,398

IssuesEvent

2022-10-17 14:21:39

streamnative/pulsar

https://api.github.com/repos/streamnative/pulsar

opened

ISSUE-18069: Flaky-test: NamespacesTest.testSplitBundleForMultiTimes

component/test flaky-tests

Original Issue: apache/pulsar#18069 --- ### Search before asking - [X] I searched in the [issues](https://github.com/apache/pulsar/issues) and found nothing similar. ### Example failure https://github.com/apache/pulsar/actions/runs/3224912715/jobs/5276751731#step:10:787 ### Exception stacktrace ``` org.apache.pulsar.client.admin.PulsarAdminException$ServerSideErrorException: --- An unexpected error occurred in the server --- Message: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded Stacktrace: java.lang.IllegalStateException: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$9(NamespaceService.java:419) at java.base/java.util.concurrent.CompletableFuture.uniAcceptNow(CompletableFuture.java:757) at java.base/java.util.concurrent.CompletableFuture.uniAcceptStage(CompletableFuture.java:735) at java.base/java.util.concurrent.CompletableFuture.thenAccept(CompletableFuture.java:2182) at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$13(NamespaceService.java:404) at org.apache.pulsar.common.util.collections.ConcurrentOpenHashMap$Section.put(ConcurrentOpenHashMap.java:409) ``` ### Are you willing to submit a PR? - [ ] I'm willing to submit a PR!

2.0

ISSUE-18069: Flaky-test: NamespacesTest.testSplitBundleForMultiTimes - Original Issue: apache/pulsar#18069 --- ### Search before asking - [X] I searched in the [issues](https://github.com/apache/pulsar/issues) and found nothing similar. ### Example failure https://github.com/apache/pulsar/actions/runs/3224912715/jobs/5276751731#step:10:787 ### Exception stacktrace ``` org.apache.pulsar.client.admin.PulsarAdminException$ServerSideErrorException: --- An unexpected error occurred in the server --- Message: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded Stacktrace: java.lang.IllegalStateException: Namespace bundle my-tenant/namespace-f713b9e6-e63b-4b97-975b-c879a722ae40/0x00000000_0x02000000 is being unloaded at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$9(NamespaceService.java:419) at java.base/java.util.concurrent.CompletableFuture.uniAcceptNow(CompletableFuture.java:757) at java.base/java.util.concurrent.CompletableFuture.uniAcceptStage(CompletableFuture.java:735) at java.base/java.util.concurrent.CompletableFuture.thenAccept(CompletableFuture.java:2182) at org.apache.pulsar.broker.namespace.NamespaceService.lambda$findBrokerServiceUrl$13(NamespaceService.java:404) at org.apache.pulsar.common.util.collections.ConcurrentOpenHashMap$Section.put(ConcurrentOpenHashMap.java:409) ``` ### Are you willing to submit a PR? - [ ] I'm willing to submit a PR!

non_process

issue flaky test namespacestest testsplitbundleformultitimes original issue apache pulsar search before asking i searched in the and found nothing similar example failure exception stacktrace org apache pulsar client admin pulsaradminexception serversideerrorexception an unexpected error occurred in the server message namespace bundle my tenant namespace is being unloaded stacktrace java lang illegalstateexception namespace bundle my tenant namespace is being unloaded at org apache pulsar broker namespace namespaceservice lambda findbrokerserviceurl namespaceservice java at java base java util concurrent completablefuture uniacceptnow completablefuture java at java base java util concurrent completablefuture uniacceptstage completablefuture java at java base java util concurrent completablefuture thenaccept completablefuture java at org apache pulsar broker namespace namespaceservice lambda findbrokerserviceurl namespaceservice java at org apache pulsar common util collections concurrentopenhashmap section put concurrentopenhashmap java are you willing to submit a pr i m willing to submit a pr

0

414,160

27,979,700,282

IssuesEvent

2023-03-26 01:42:23

opentiny/tiny-vue

https://api.github.com/repos/opentiny/tiny-vue

opened

[官网] Steps组件中混合了多个Timeline组件的demo

documentation

以下两个 demo 应该是 Timeline 组件的 demo，却用在了 Steps 组件中，建议移到 Steps 组件的 demo 中。 - [普通步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#normal-steps) - [时间线步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#timeline-steps) <img width="1357" alt="image" src="https://user-images.githubusercontent.com/9566362/227750633-000ba3c1-a278-43c9-94d9-3a01902dc13f.png">

1.0

[官网] Steps组件中混合了多个Timeline组件的demo - 以下两个 demo 应该是 Timeline 组件的 demo，却用在了 Steps 组件中，建议移到 Steps 组件的 demo 中。 - [普通步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#normal-steps) - [时间线步骤条](https://opentiny.design/tiny-vue/zh-CN/os-theme/components/steps#timeline-steps) <img width="1357" alt="image" src="https://user-images.githubusercontent.com/9566362/227750633-000ba3c1-a278-43c9-94d9-3a01902dc13f.png">

non_process

steps组件中混合了多个timeline组件的demo 以下两个 demo 应该是 timeline 组件的 demo，却用在了 steps 组件中，建议移到 steps 组件的 demo 中。 img width alt image src

0

312,059

23,414,342,951

IssuesEvent

2022-08-12 21:38:48

UnBArqDsw2022-1/2022_1_G5_SerFit

https://api.github.com/repos/UnBArqDsw2022-1/2022_1_G5_SerFit

closed

GOF Estrutural - Bridge

documentation

### Contact Details (optional) _No response_ ### Summary Documentar GOF Estrutural Bridge ### Motivation Entrega 3 ### Alternatives _No response_ ### Additional Context _No response_ ### Code of Conduct - [X] I agree to follow this project's Code of Conduct

1.0

GOF Estrutural - Bridge - ### Contact Details (optional) _No response_ ### Summary Documentar GOF Estrutural Bridge ### Motivation Entrega 3 ### Alternatives _No response_ ### Additional Context _No response_ ### Code of Conduct - [X] I agree to follow this project's Code of Conduct

non_process

gof estrutural bridge contact details optional no response summary documentar gof estrutural bridge motivation entrega alternatives no response additional context no response code of conduct i agree to follow this project s code of conduct

0

73,310

9,659,491,929

IssuesEvent

2019-05-20 13:34:40

wc-duck/datalibrary

https://api.github.com/repos/wc-duck/datalibrary

opened

doc: document dl-json

documentation

DL uses json... isch ;) Document the difference. * Valid json IS valid dl-json * DL-json support comments // and /**/ * DL json can use '' or "" for strings/keys * in dl-json '' or "" is optional on keys * Keys need to be valid c-identifiers * , is accepted for last item in array and map * new-line in strings are valid, or 'my_str' : "whoo Whaaa Whiiii"

1.0

doc: document dl-json - DL uses json... isch ;) Document the difference. * Valid json IS valid dl-json * DL-json support comments // and /**/ * DL json can use '' or "" for strings/keys * in dl-json '' or "" is optional on keys * Keys need to be valid c-identifiers * , is accepted for last item in array and map * new-line in strings are valid, or 'my_str' : "whoo Whaaa Whiiii"

non_process

doc document dl json dl uses json isch document the difference valid json is valid dl json dl json support comments and dl json can use or for strings keys in dl json or is optional on keys keys need to be valid c identifiers is accepted for last item in array and map new line in strings are valid or my str whoo whaaa whiiii

0

1,301

3,840,864,220

IssuesEvent

2016-04-04 00:33:13

moxie-leean/ng-web-app

https://api.github.com/repos/moxie-leean/ng-web-app

closed

Config watch

Pending Deploy process

Add a config watch script to package.json and include it in the general watch job.

1.0

Config watch - Add a config watch script to package.json and include it in the general watch job.

process

config watch add a config watch script to package json and include it in the general watch job

1

55,347

11,425,209,385

IssuesEvent

2020-02-03 19:22:05

joomla/joomla-cms

https://api.github.com/repos/joomla/joomla-cms

closed

Wrong ACL behaviour with ‘modify state’ action in interaction of articles and categories

J3 Issue No Code Attached Yet

ACL !!! FRONTEND !!! For a given usergroup of article writers: Don’t grant any action rights on articles for this usergroup in System>Config>Articles. Then have a category and grant all action rights to the usergroup there. The writers will then be able, to create and edit articles, that are stored in that category (or a subcategory). This worked fine in the past. And it works fine currently: BUT: Does no longer work correct for ‘modify state’ action (although the computed inherited rights are shown correct in the category – but system in reality does not behave that way any longer). ### Steps to reproduce the issue Create a new top-level category ‘TestCatego’ Create a new usergroup ‘TestGroup’ as direct subgroup of ‘registerred’ Create a new accesslevel ‘TextAccLevel’ containing just ‘TestGroup’ Register a user ‘TestUser’ an attach it with ‘TestGroup’ Create a menuitem with TestAccLevel showing the articles in TestCatego as a blog System > Configuration > Articles > Rights: Ensure that you do not explicitly enable oder disable any rights to TestGroup. This shall lead to ‘disabled (inherited)’ for all article actions for TestGroup. Content > Categories > Select category ‘TestCatego’ > Rights: For TestGroup explicitly grant the following rights: create / edit / edit your own / modify status ### Expected result The calculated rights (after saving) will show, that a user of this group will be able to do the granted things in category TestCatego: Especially: Will be able to modify the status of any article in this group. That is the behavior to be expected. ### Actual result The system does NOT behave in this way: The user will not be able to modify the state of an article in this category IN THE FRONTEND. The expected behavior might be achieved by a workaround: Explicitly allow ‘modify status’ in ‘System > Configuration > Articles > Rights’ for TestGroup. BUT: From now on any user in TestGroup will be shown any hidden or no longer published article on the website – which is not the expected behavior again. In my eyes there must be a piece of code, dealing with ‘modify state’ in a wrong way? ### System information (as much as possible) Joomla 3.9.6

1.0

Wrong ACL behaviour with ‘modify state’ action in interaction of articles and categories - ACL !!! FRONTEND !!! For a given usergroup of article writers: Don’t grant any action rights on articles for this usergroup in System>Config>Articles. Then have a category and grant all action rights to the usergroup there. The writers will then be able, to create and edit articles, that are stored in that category (or a subcategory). This worked fine in the past. And it works fine currently: BUT: Does no longer work correct for ‘modify state’ action (although the computed inherited rights are shown correct in the category – but system in reality does not behave that way any longer). ### Steps to reproduce the issue Create a new top-level category ‘TestCatego’ Create a new usergroup ‘TestGroup’ as direct subgroup of ‘registerred’ Create a new accesslevel ‘TextAccLevel’ containing just ‘TestGroup’ Register a user ‘TestUser’ an attach it with ‘TestGroup’ Create a menuitem with TestAccLevel showing the articles in TestCatego as a blog System > Configuration > Articles > Rights: Ensure that you do not explicitly enable oder disable any rights to TestGroup. This shall lead to ‘disabled (inherited)’ for all article actions for TestGroup. Content > Categories > Select category ‘TestCatego’ > Rights: For TestGroup explicitly grant the following rights: create / edit / edit your own / modify status ### Expected result The calculated rights (after saving) will show, that a user of this group will be able to do the granted things in category TestCatego: Especially: Will be able to modify the status of any article in this group. That is the behavior to be expected. ### Actual result The system does NOT behave in this way: The user will not be able to modify the state of an article in this category IN THE FRONTEND. The expected behavior might be achieved by a workaround: Explicitly allow ‘modify status’ in ‘System > Configuration > Articles > Rights’ for TestGroup. BUT: From now on any user in TestGroup will be shown any hidden or no longer published article on the website – which is not the expected behavior again. In my eyes there must be a piece of code, dealing with ‘modify state’ in a wrong way? ### System information (as much as possible) Joomla 3.9.6

non_process

wrong acl behaviour with ‘modify state’ action in interaction of articles and categories acl frontend for a given usergroup of article writers don’t grant any action rights on articles for this usergroup in system config articles then have a category and grant all action rights to the usergroup there the writers will then be able to create and edit articles that are stored in that category or a subcategory this worked fine in the past and it works fine currently but does no longer work correct for ‘modify state’ action although the computed inherited rights are shown correct in the category – but system in reality does not behave that way any longer steps to reproduce the issue create a new top level category ‘testcatego’ create a new usergroup ‘testgroup’ as direct subgroup of ‘registerred’ create a new accesslevel ‘textacclevel’ containing just ‘testgroup’ register a user ‘testuser’ an attach it with ‘testgroup’ create a menuitem with testacclevel showing the articles in testcatego as a blog system configuration articles rights ensure that you do not explicitly enable oder disable any rights to testgroup this shall lead to ‘disabled inherited ’ for all article actions for testgroup content categories select category ‘testcatego’ rights for testgroup explicitly grant the following rights create edit edit your own modify status expected result the calculated rights after saving will show that a user of this group will be able to do the granted things in category testcatego especially will be able to modify the status of any article in this group that is the behavior to be expected actual result the system does not behave in this way the user will not be able to modify the state of an article in this category in the frontend the expected behavior might be achieved by a workaround explicitly allow ‘modify status’ in ‘system configuration articles rights’ for testgroup but from now on any user in testgroup will be shown any hidden or no longer published article on the website – which is not the expected behavior again in my eyes there must be a piece of code dealing with ‘modify state’ in a wrong way system information as much as possible joomla

0

841

3,309,449,918

IssuesEvent

2015-11-05 01:08:50

nodejs/node

https://api.github.com/repos/nodejs/node

closed

process.stdout/.stderr might loose data when calling process.exit()

duplicate process

this won't work reliable (but it should according to the docs): ```javascript var empty = new Buffer(0) process.stdout.write(empty, function() { process.stderr.write(empty, function() { process.exit(code); }); }); ``` Not all data is flushed when node.js ends and thus another process using its output will not get all written data. It also doesn't help to use something like ```javascript stream.pipe(process.stdout); stream.push(null); ``` That .end() throws and an finish is never emitted makes using process.stdout/.stderr something like lottery. Also trying to workaround via ```javascript fs.createWritableStream(null, {fd:1}); ``` leads to another bunch of problems like EAGAIN errors. I would suggest to implement .end() and finish events so that both process.stdout and .stderr behave like any other stream.

1.0

process.stdout/.stderr might loose data when calling process.exit() - this won't work reliable (but it should according to the docs): ```javascript var empty = new Buffer(0) process.stdout.write(empty, function() { process.stderr.write(empty, function() { process.exit(code); }); }); ``` Not all data is flushed when node.js ends and thus another process using its output will not get all written data. It also doesn't help to use something like ```javascript stream.pipe(process.stdout); stream.push(null); ``` That .end() throws and an finish is never emitted makes using process.stdout/.stderr something like lottery. Also trying to workaround via ```javascript fs.createWritableStream(null, {fd:1}); ``` leads to another bunch of problems like EAGAIN errors. I would suggest to implement .end() and finish events so that both process.stdout and .stderr behave like any other stream.

process

process stdout stderr might loose data when calling process exit this won t work reliable but it should according to the docs javascript var empty new buffer process stdout write empty function process stderr write empty function process exit code not all data is flushed when node js ends and thus another process using its output will not get all written data it also doesn t help to use something like javascript stream pipe process stdout stream push null that end throws and an finish is never emitted makes using process stdout stderr something like lottery also trying to workaround via javascript fs createwritablestream null fd leads to another bunch of problems like eagain errors i would suggest to implement end and finish events so that both process stdout and stderr behave like any other stream

1

20,034

26,518,407,169

IssuesEvent

2023-01-18 23:09:07

dotnet/runtime

https://api.github.com/repos/dotnet/runtime

closed

System.Diagnostics.Process.OutputDataReceived - determine final newline

area-System.Diagnostics.Process untriaged

### Description When using System.Diagnostics.Process.OutputDataReceived there is no way to determine if output has or doesn't have final newline ### Reproduction Steps Test that shows the issue: ```cs using System; using System.Collections.Generic; using System.Diagnostics; using System.Linq; using System.Text; using Xunit; namespace ProcessRedirectOutputApiIssue; public static class ProcessRedirectOutputTests { private static Process EchoProcess(string output) => new() { StartInfo = { FileName = "echo", ArgumentList = { "-n", output }, RedirectStandardOutput = true } }; // Capture output from Process.StandardOutput for the reference private static string Output_From_Stream(Process process) { process.WaitForExit(); return process.StandardOutput.ReadToEnd(); } // Capture output via Process.OutputDataReceived the way it is stated in the official documentation private static string Output_From_Event(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { output.Append(e.Data); output.Append('\n'); } }; process.WaitForExit(); return output.ToString(); } // Alternative way to capture output via Process.OutputDataReceived - skip final newline private static string Output_From_Event_Skip_Final_Newline(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { if (output.Length > 0) output.Append('\n'); output.Append(e.Data); } }; process.WaitForExit(); return output.ToString(); } private static readonly string[] Outputs = { "", "\n", "ab", "ab\n", "ab\ncd", "ab\ncd\n", }; private static readonly Func<Process, string>[] OutputCapturers = { Output_From_Stream, Output_From_Event, Output_From_Event_Skip_Final_Newline }; public static IEnumerable<object[]> TestData => from o in Outputs from oc in OutputCapturers select new object[] {o, oc}; [Theory] [MemberData(nameof(TestData))] public static void Process_Redirect_Output_Test(string output, Func<Process, string> outputCapturer) { using var process = EchoProcess(output); process.Start(); var capturedOutput = outputCapturer(process); Assert.Equal(output, capturedOutput); } } ``` csproj: ```csproj <Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <TargetFramework>net6.0</TargetFramework> </PropertyGroup> <ItemGroup> <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.4.0" /> <PackageReference Include="xunit.runner.visualstudio" Version="2.4.5" /> <PackageReference Include="xunit" Version="2.4.2" /> </ItemGroup> </Project> ``` ### Expected behavior There should be some way to determine if there is a final new line or not. Maybe the case above where final newline is skipped should succeed ### Actual behavior There is no way to determine from OutputDataReceived event if final newline exists or doesn't exist in the standard output: ![image](https://user-images.githubusercontent.com/4021496/202765092-2cd2b0b4-284c-4bca-8414-5bae9e5207e8.png) ### Regression? _No response_ ### Known Workarounds _No response_ ### Configuration Latest .NET 6 (or 7) SDK on Ubuntu 18.04 ### Other information _No response_

1.0

System.Diagnostics.Process.OutputDataReceived - determine final newline - ### Description When using System.Diagnostics.Process.OutputDataReceived there is no way to determine if output has or doesn't have final newline ### Reproduction Steps Test that shows the issue: ```cs using System; using System.Collections.Generic; using System.Diagnostics; using System.Linq; using System.Text; using Xunit; namespace ProcessRedirectOutputApiIssue; public static class ProcessRedirectOutputTests { private static Process EchoProcess(string output) => new() { StartInfo = { FileName = "echo", ArgumentList = { "-n", output }, RedirectStandardOutput = true } }; // Capture output from Process.StandardOutput for the reference private static string Output_From_Stream(Process process) { process.WaitForExit(); return process.StandardOutput.ReadToEnd(); } // Capture output via Process.OutputDataReceived the way it is stated in the official documentation private static string Output_From_Event(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { output.Append(e.Data); output.Append('\n'); } }; process.WaitForExit(); return output.ToString(); } // Alternative way to capture output via Process.OutputDataReceived - skip final newline private static string Output_From_Event_Skip_Final_Newline(Process process) { process.BeginOutputReadLine(); var output = new StringBuilder(); process.OutputDataReceived += (_, e) => { if (e.Data != null) { if (output.Length > 0) output.Append('\n'); output.Append(e.Data); } }; process.WaitForExit(); return output.ToString(); } private static readonly string[] Outputs = { "", "\n", "ab", "ab\n", "ab\ncd", "ab\ncd\n", }; private static readonly Func<Process, string>[] OutputCapturers = { Output_From_Stream, Output_From_Event, Output_From_Event_Skip_Final_Newline }; public static IEnumerable<object[]> TestData => from o in Outputs from oc in OutputCapturers select new object[] {o, oc}; [Theory] [MemberData(nameof(TestData))] public static void Process_Redirect_Output_Test(string output, Func<Process, string> outputCapturer) { using var process = EchoProcess(output); process.Start(); var capturedOutput = outputCapturer(process); Assert.Equal(output, capturedOutput); } } ``` csproj: ```csproj <Project Sdk="Microsoft.NET.Sdk"> <PropertyGroup> <TargetFramework>net6.0</TargetFramework> </PropertyGroup> <ItemGroup> <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.4.0" /> <PackageReference Include="xunit.runner.visualstudio" Version="2.4.5" /> <PackageReference Include="xunit" Version="2.4.2" /> </ItemGroup> </Project> ``` ### Expected behavior There should be some way to determine if there is a final new line or not. Maybe the case above where final newline is skipped should succeed ### Actual behavior There is no way to determine from OutputDataReceived event if final newline exists or doesn't exist in the standard output: ![image](https://user-images.githubusercontent.com/4021496/202765092-2cd2b0b4-284c-4bca-8414-5bae9e5207e8.png) ### Regression? _No response_ ### Known Workarounds _No response_ ### Configuration Latest .NET 6 (or 7) SDK on Ubuntu 18.04 ### Other information _No response_

process

system diagnostics process outputdatareceived determine final newline description when using system diagnostics process outputdatareceived there is no way to determine if output has or doesn t have final newline reproduction steps test that shows the issue cs using system using system collections generic using system diagnostics using system linq using system text using xunit namespace processredirectoutputapiissue public static class processredirectoutputtests private static process echoprocess string output new startinfo filename echo argumentlist n output redirectstandardoutput true capture output from process standardoutput for the reference private static string output from stream process process process waitforexit return process standardoutput readtoend capture output via process outputdatareceived the way it is stated in the official documentation private static string output from event process process process beginoutputreadline var output new stringbuilder process outputdatareceived e if e data null output append e data output append n process waitforexit return output tostring alternative way to capture output via process outputdatareceived skip final newline private static string output from event skip final newline process process process beginoutputreadline var output new stringbuilder process outputdatareceived e if e data null if output length output append n output append e data process waitforexit return output tostring private static readonly string outputs n ab ab n ab ncd ab ncd n private static readonly func outputcapturers output from stream output from event output from event skip final newline public static ienumerable testdata from o in outputs from oc in outputcapturers select new object o oc public static void process redirect output test string output func outputcapturer using var process echoprocess output process start var capturedoutput outputcapturer process assert equal output capturedoutput csproj csproj expected behavior there should be some way to determine if there is a final new line or not maybe the case above where final newline is skipped should succeed actual behavior there is no way to determine from outputdatareceived event if final newline exists or doesn t exist in the standard output regression no response known workarounds no response configuration latest net or sdk on ubuntu other information no response

1

466,253

13,399,058,488

IssuesEvent

2020-09-03 13:59:54

jules008/CDCTracker

https://api.github.com/repos/jules008/CDCTracker

opened

Change link button to copy data button

1 - High Priority enhancement

Copies data into clipboard AU23 - Mohr's username CDSAR - Members full SSN

1.0

Change link button to copy data button - Copies data into clipboard AU23 - Mohr's username CDSAR - Members full SSN

non_process

change link button to copy data button copies data into clipboard mohr s username cdsar members full ssn

0

7,549

10,674,679,330

IssuesEvent

2019-10-21 09:56:05

prisma/prisma2

https://api.github.com/repos/prisma/prisma2

opened

Remove panics

area/binaries kind/improvement process/candidate

Everytime I see one of these my eyes glaze over & I feel helpless: ``` ➜ prisma prisma2 lift save Error: Error in migration engine: thread 'tokio-runtime-worker-1' panicked at 'get data_type', src/libcore/option.rs:1034:5 stack backtrace: 0: std::panicking::default_hook::{{closure}} 1: std::panicking::default_hook 2: std::panicking::rust_panic_with_hook 3: std::panicking::continue_panic_fmt 4: rust_begin_unwind 5: core::panicking::panic_fmt 6: core::option::expect_failed 7: core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &mut F>::call_once 8: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 9: sql_schema_describer::mysql::SqlSchemaDescriber::get_table 10: <core::iter::adapters::Map<I,F> as core::iter::traits::iterator::Iterator>::fold 11: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 12: <sql_schema_describer::mysql::SqlSchemaDescriber as sql_schema_describer::SqlSchemaDescriberBackend>::describe 13: <sql_migration_connector::sql_database_migration_inferrer::SqlDatabaseMigrationInferrer as migration_connector::database_migration_inferrer::DatabaseMigrationInferrer<sql_migration_connector::sql_migration::SqlMigration>>::infer 14: <migration_core::api::MigrationApi<C,D> as migration_core::api::GenericApi>::infer_migration_steps 15: migration_core::api::rpc::RpcApi::create_sync_handler 16: tokio_executor::enter::exit 17: tokio_threadpool::blocking::blocking 18: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 19: futures::future::chain::Chain<A,B,C>::poll 20: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 21: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 22: futures::future::chain::Chain<A,B,C>::poll 23: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 24: <futures::future::map::Map<A,F> as futures::future::Future>::poll 25: <futures::future::either::Either<A,B> as futures::future::Future>::poll 26: <futures::future::map::Map<A,F> as futures::future::Future>::poll 27: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 28: <futures::stream::and_then::AndThen<S,F,U> as futures::stream::Stream>::poll 29: <futures::stream::forward::Forward<T,U> as futures::future::Future>::poll 30: <futures::future::map::Map<A,F> as futures::future::Future>::poll 31: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 32: futures::task_impl::std::set 33: std::panicking::try::do_call 34: __rust_maybe_catch_panic 35: tokio_threadpool::task::Task::run 36: tokio_threadpool::worker::Worker::run_task 37: tokio_threadpool::worker::Worker::run 38: std::thread::local::LocalKey<T>::with 39: std::thread::local::LocalKey<T>::with 40: tokio_reactor::with_default 41: tokio::runtime::threadpool::builder::Builder::build::{{closure}} 42: std::thread::local::LocalKey<T>::with 43: std::thread::local::LocalKey<T>::with note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace. ``` I'd us to return errors for these cases, rather than panic. Concretely this means turning code fragments like `.expect(...)` to match statements. Here's an example: https://github.com/prisma/prisma-engine/blob/a348799e88f99b9d287bedf9149abb764b0382f4/libs/sql-schema-describer/src/mysql.rs#L93 Does rust have a linter that will catch possible panic-able code?

1.0

Remove panics - Everytime I see one of these my eyes glaze over & I feel helpless: ``` ➜ prisma prisma2 lift save Error: Error in migration engine: thread 'tokio-runtime-worker-1' panicked at 'get data_type', src/libcore/option.rs:1034:5 stack backtrace: 0: std::panicking::default_hook::{{closure}} 1: std::panicking::default_hook 2: std::panicking::rust_panic_with_hook 3: std::panicking::continue_panic_fmt 4: rust_begin_unwind 5: core::panicking::panic_fmt 6: core::option::expect_failed 7: core::ops::function::impls::<impl core::ops::function::FnOnce<A> for &mut F>::call_once 8: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 9: sql_schema_describer::mysql::SqlSchemaDescriber::get_table 10: <core::iter::adapters::Map<I,F> as core::iter::traits::iterator::Iterator>::fold 11: <alloc::vec::Vec<T> as alloc::vec::SpecExtend<T,I>>::from_iter 12: <sql_schema_describer::mysql::SqlSchemaDescriber as sql_schema_describer::SqlSchemaDescriberBackend>::describe 13: <sql_migration_connector::sql_database_migration_inferrer::SqlDatabaseMigrationInferrer as migration_connector::database_migration_inferrer::DatabaseMigrationInferrer<sql_migration_connector::sql_migration::SqlMigration>>::infer 14: <migration_core::api::MigrationApi<C,D> as migration_core::api::GenericApi>::infer_migration_steps 15: migration_core::api::rpc::RpcApi::create_sync_handler 16: tokio_executor::enter::exit 17: tokio_threadpool::blocking::blocking 18: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 19: futures::future::chain::Chain<A,B,C>::poll 20: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 21: <futures::future::lazy::Lazy<F,R> as futures::future::Future>::poll 22: futures::future::chain::Chain<A,B,C>::poll 23: <futures::future::then::Then<A,B,F> as futures::future::Future>::poll 24: <futures::future::map::Map<A,F> as futures::future::Future>::poll 25: <futures::future::either::Either<A,B> as futures::future::Future>::poll 26: <futures::future::map::Map<A,F> as futures::future::Future>::poll 27: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 28: <futures::stream::and_then::AndThen<S,F,U> as futures::stream::Stream>::poll 29: <futures::stream::forward::Forward<T,U> as futures::future::Future>::poll 30: <futures::future::map::Map<A,F> as futures::future::Future>::poll 31: <futures::future::map_err::MapErr<A,F> as futures::future::Future>::poll 32: futures::task_impl::std::set 33: std::panicking::try::do_call 34: __rust_maybe_catch_panic 35: tokio_threadpool::task::Task::run 36: tokio_threadpool::worker::Worker::run_task 37: tokio_threadpool::worker::Worker::run 38: std::thread::local::LocalKey<T>::with 39: std::thread::local::LocalKey<T>::with 40: tokio_reactor::with_default 41: tokio::runtime::threadpool::builder::Builder::build::{{closure}} 42: std::thread::local::LocalKey<T>::with 43: std::thread::local::LocalKey<T>::with note: Some details are omitted, run with `RUST_BACKTRACE=full` for a verbose backtrace. ``` I'd us to return errors for these cases, rather than panic. Concretely this means turning code fragments like `.expect(...)` to match statements. Here's an example: https://github.com/prisma/prisma-engine/blob/a348799e88f99b9d287bedf9149abb764b0382f4/libs/sql-schema-describer/src/mysql.rs#L93 Does rust have a linter that will catch possible panic-able code?

process

remove panics everytime i see one of these my eyes glaze over i feel helpless ➜ prisma lift save error error in migration engine thread tokio runtime worker panicked at get data type src libcore option rs stack backtrace std panicking default hook closure std panicking default hook std panicking rust panic with hook std panicking continue panic fmt rust begin unwind core panicking panic fmt core option expect failed core ops function impls for mut f call once as alloc vec specextend from iter sql schema describer mysql sqlschemadescriber get table as core iter traits iterator iterator fold as alloc vec specextend from iter describe infer as migration core api genericapi infer migration steps migration core api rpc rpcapi create sync handler tokio executor enter exit tokio threadpool blocking blocking as futures future future poll futures future chain chain poll as futures future future poll as futures future future poll futures future chain chain poll as futures future future poll as futures future future poll as futures future future poll as futures future future poll as futures future future poll as futures stream stream poll as futures future future poll as futures future future poll as futures future future poll futures task impl std set std panicking try do call rust maybe catch panic tokio threadpool task task run tokio threadpool worker worker run task tokio threadpool worker worker run std thread local localkey with std thread local localkey with tokio reactor with default tokio runtime threadpool builder builder build closure std thread local localkey with std thread local localkey with note some details are omitted run with rust backtrace full for a verbose backtrace i d us to return errors for these cases rather than panic concretely this means turning code fragments like expect to match statements here s an example does rust have a linter that will catch possible panic able code

1

9,138

12,203,186,487

IssuesEvent

2020-04-30 10:10:13

MHRA/products

https://api.github.com/repos/MHRA/products

closed

Observability | Prometheus config

EPIC - Auto Batch Process :oncoming_automobile:

## User want As a technical user I want set Prometheus to collect and digest doc index updater logs So that I can enable monitoring and alerting tools ## Acceptance criteria - [ ] Prometheus should collect and digest logs from doc index updater - [ ] [Docs should explain how to see these logs ](https://github.com/MHRA/products/blob/master/infrastructure/docs/logs.md) ## Data - Potential impact **Size** **Value** **Effort** ### Exit Criteria met - [x] Backlog - [x] Discovery - [x] DUXD - [x] Development - [ ] Quality Assurance - [ ] Release and Validate

1.0

Observability | Prometheus config - ## User want As a technical user I want set Prometheus to collect and digest doc index updater logs So that I can enable monitoring and alerting tools ## Acceptance criteria - [ ] Prometheus should collect and digest logs from doc index updater - [ ] [Docs should explain how to see these logs ](https://github.com/MHRA/products/blob/master/infrastructure/docs/logs.md) ## Data - Potential impact **Size** **Value** **Effort** ### Exit Criteria met - [x] Backlog - [x] Discovery - [x] DUXD - [x] Development - [ ] Quality Assurance - [ ] Release and Validate

process

observability prometheus config user want as a technical user i want set prometheus to collect and digest doc index updater logs so that i can enable monitoring and alerting tools acceptance criteria prometheus should collect and digest logs from doc index updater docs should explain how to see these logs data potential impact size value effort exit criteria met backlog discovery duxd development quality assurance release and validate

1