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"abstract": "Selecting meaningful features is central in the analysis of scientific data. Today's multivariate scientific datasets are often large and complex making it difficult to define general features of interest significant to scientific applications. To address this problem, we propose three general, spatiotemporal metrics to quantify the significant properties of data features-concentration, continuity and co-occurrence, named collectively as CO3. We implemented an interactive visualization system to investigate complex multivariate time-varying data from satellite remote sensing with great spatial resolutions, as well as from real-time continental-scale power grid monitoring with great temporal resolutions. The system integrates CO3 metrics with an elegant multi-space user interaction tool to provide various forms of quantitative user feedback. Through these, the system supports an iterative user-driven analysis process. Our findings demonstrate that the CO3 metrics are useful for simplifying the problem space and revealing potential unknown possibilities of scientific discoveries by assisting users to effectively select significant features and groups of features for visualization and analysis. Users can then comprehend the problem better and design future studies using newly discovered scientific hypotheses.",
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"normalizedAbstract": "Since Fall 2019, the spread of SARS-CoV-2 virus has changed everyday life routines globally. Public health confinement measures have been taken to contain the propagation of the pandemic. An international effort has been made to model and predict the spatiotemporal evolution of the pandemic. Today, a main question arises on how to communicate complex multivariate, geospatial and time dependent information efficiently. A further challenge consists in communicating these information without any bias or place for misinterpretation, and for the largest targeted audience. In this regard the following paper will first identify ergonomics criteria for efficient data visualization, and then present several visualizations in a pre/post fashion, reflecting how visualizations initially proposed by data scientists can be improved after the application of ergonomics guidelines.",
"fno": "264400a004",
"keywords": [
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"Geospatial Analysis",
"Spatiotemporal Phenomena",
"Data Visualization",
"Ergonomics",
"Infectious Dis Eases",
"Health Sciences",
"Epidemiology",
"SARS Co V 2",
"Public Health"
],
"authors": [
{
"affiliation": "University of Geneva,Medical Information Sciences Division,Department of Radiology and Medical Informatics,Switzerland",
"fullName": "Hugues Turbé",
"givenName": "Hugues",
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"affiliation": "University of Geneva,Medical Information Sciences Division,Department of Radiology and Medical Informatics,Switzerland",
"fullName": "Victor Garretas Ruiz",
"givenName": "Victor Garretas",
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"affiliation": "University of Geneva,Medical Information Sciences Division,Department of Radiology and Medical Informatics,Switzerland",
"fullName": "Mina Bjelogrlic",
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"affiliation": "University of Geneva,Medical Information Sciences Division,Department of Radiology and Medical Informatics,Switzerland",
"fullName": "Jessica Rochat",
"givenName": "Jessica",
"surname": "Rochat",
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"affiliation": "University of Geneva,Medical Information Sciences Division,Department of Radiology and Medical Informatics,Switzerland",
"fullName": "Christian Lovis",
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"affiliation": "Naval Surface Warfare Center, Dahlgren, USA, University of Louisiana at Lafayette, Lafayette, LA 70504, USA, e-mail: kecia.wright@navy.mil",
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"affiliation": "Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, USA, e-mail: gracanin@vt.edu",
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"title": "The effect of trails on first-time and subsequent navigation in a virtual environment",
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"abstract": "Trails are a little-researched type of aid that offers great potential benefits for navigation, especially in virtual environments (VEs). An experiment was performed in which participants repeatedly searched a virtual building for target objects assisted by: (1) a trail, (2) landmarks, (3) a trail and landmarks, or (4) neither. The trail was displayed as a white line that showed exactly where a participant had previously traveled. The trail halved the distance that participants traveled during first-time searches, indicating the immediate benefit to users if even a crude form of trail were implemented in a variety of VE applications. However, the general clutter or \"pollution\" produced by trails reduced the benefit during subsequent navigation and, in the later stages of these searches, caused participants to travel more than twice as far as they needed to, often accidentally bypassing targets even when a trail led directly to them. The proposed solution is to use gene alignment techniques to extract a participant's primary trail from the overall, polluted trail, and graphically emphasize the primary trail to aid navigation.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Trails are a little-researched type of aid that offers great potential benefits for navigation, especially in virtual environments (VEs). An experiment was performed in which participants repeatedly searched a virtual building for target objects assisted by: (1) a trail, (2) landmarks, (3) a trail and landmarks, or (4) neither. The trail was displayed as a white line that showed exactly where a participant had previously traveled. The trail halved the distance that participants traveled during first-time searches, indicating the immediate benefit to users if even a crude form of trail were implemented in a variety of VE applications. However, the general clutter or \"pollution\" produced by trails reduced the benefit during subsequent navigation and, in the later stages of these searches, caused participants to travel more than twice as far as they needed to, often accidentally bypassing targets even when a trail led directly to them. The proposed solution is to use gene alignment techniques to extract a participant's primary trail from the overall, polluted trail, and graphically emphasize the primary trail to aid navigation.",
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"normalizedAbstract": "Trails are a little-researched type of aid that offers great potential benefits for navigation, especially in virtual environments (VEs). An experiment was performed in which participants repeatedly searched a virtual building for target objects assisted by: (1) a trail, (2) landmarks, (3) a trail and landmarks, or (4) neither. The trail was displayed as a white line that showed exactly where a participant had previously traveled. The trail halved the distance that participants traveled during first-time searches, indicating the immediate benefit to users if even a crude form of trail were implemented in a variety of VE applications. However, the general clutter or \"pollution\" produced by trails reduced the benefit during subsequent navigation and, in the later stages of these searches, caused participants to travel more than twice as far as they needed to, often accidentally bypassing targets even when a trail led directly to them. The proposed solution is to use gene alignment techniques to extract a participant's primary trail from the overall, polluted trail, and graphically emphasize the primary trail to aid navigation.",
"fno": "01492761",
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"Virtual Reality",
"Navigation",
"User Interfaces",
"Trails",
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"Virtual Building",
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"Clutter",
"Gene Alignment Technique",
"Polluted Trail",
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"Navigation",
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"Data Mining",
"Pollution",
"Chromium",
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"Human Computer Interaction",
"Displays",
"Performance Analysis"
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"authors": [
{
"affiliation": "Sch. of Comput., Leeds Univ., UK",
"fullName": "R.A. Ruddle",
"givenName": "R.A.",
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"id": "proceedings/vr/2020/5608/0/09089581",
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"doi": null,
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"proceeding": {
"id": "12OmNAYoKmw",
"title": "2013 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
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"id": "12OmNqFJhRx",
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"title": "User awareness of tracking uncertainties in AR navigation scenarios",
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"abstract": "Current Augmented Reality navigation applications for pedestrians usually do not visualize tracking errors. However, tracking uncertainties can accumulate so that the user is presented with a distorted impression of navigation accuracy. To increase the awareness of users about potential imperfections of the tracking at a given time, we alter the visualization of the navigation system. We developed four visualization and error visualization concepts and used a controlled Mixed Reality environment to conduct a pilot study. We found that, while error visualization has the potential to improve AR navigation systems, it is difficult to find suitable visualizations, which are correctly understood among the users.",
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"abstract": "Data Provenance has created an emerging requirement for technologies that enable end users to access, evaluate, and act on the provenance of data in recent years. In the era of Big Data, the amount of data created by corporations around the world has grown each year. As an example, both in the Social Media and e-Science domains, data is growing at an unprecedented rate. As the data has grown rapidly, information on the origin and lifecycle of the data has also grown. In turn, this requires technologies that enable the clarification and interpretation of data through the use of data provenance. This study proposes methodologies towards the visualization of W3C-PROV-O Specification compatible provenance data. The visualizations are done by summarization and comparison of the data provenance. We facilitated the testing of these methodologies by providing a prototype, extending an existing open source visualization tool. We discuss the usability of the proposed methodologies with an experimental study; our initial results show that the proposed approach is usable, and its processing overhead is negligible.",
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"title": "How, Where, and Why Data Provenance Improves Query Debugging: A Visual Demonstration of Fine–Grained Provenance Analysis for SQL",
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"abstract": "Data provenance is meta-information about the origin and processing history of data. We demonstrate the prove-nance analysis of SQL queries and use it for query debugging. How-provenance determines which query expressions have been relevant for evaluating selected pieces of output data. Likewise, Where- and Why-provenance determine relevant pieces of input data. The combined provenance notions can be explored visually and interactively. We support a feature-rich SQL dialect with correlated subqueries and focus on bag semantics. Our fine-grained provenance analysis derives individual data provenance for table cells and SQL expressions.",
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"affiliation": "University of Tübingen,Tübingen,Germany",
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"abstract": "Vivid facial expressions contribute greatly to the visual realism of 3D face models. However, it is often tedious and expensive to model facial details which are authentic and real-time. In this paper, a simple and functional approach for modeling dynamic wrinkles of face is introduced. Started with structured facial mesh, we divide face region into some wrinkle subregions, and create some wrinkle lines with our algorithm in these subregions. Then, some key nodes are labeled on the facial mesh, these key nodes affect wrinkle lines if wrinkle lines locate the subregions that abut on the key nodes. With our algorithm, motion of wrinkle lines is produced by movement of key nodes. Consequently, wrinkle lines drive facial mesh to model expressive wrinkles. In this way, we create dynamic and functional expressive wrinkles of individuals available to date and demonstrate their use in facial animation.",
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"affiliation": "Sun Yat-Sen University, China",
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"abstract": "Non-photorealistic Rendering (NPR) has inspired many researchers to investigate a variety of techniques to simulate the style of artists. Recent work has resulted in methods of crayon stroke generation and cartoon style rendering. In this paper, we propose a stylized form of a glass painting to be used in cartoon rendered scenes and animations. Sand blasted glass is used as the base (canvas) and the medium of coloring is crayons. The paper describes an algorithm to model a sand blasted glass based on \"fog-based height maps\". We have also introduced a new and faster method to model a crayon texture using height maps and pigment adhesion techniques. Finally the crayontextured image is mapped on the sand blasted glass generating a new and stylized form of glass painting. The image painted on glass is also highlighted with black outlines that are again crayon texture based.",
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"abstract": "Traditional manual detection methods have many drawbacks in detecting surface defects of ceramic glass materials. At present, a method is needed to replace manual electronic automatic detection to avoid the problems of time-consuming, laborious and large errors. The purpose of this paper is to study the design of a visual inspection and classification system for the production of ceramic glass materials based on computational vision techniques. A computer vision-based quality control system for ceramic glass materials is designed. The mechanical structure is equipped with a rotating mechanism for adjusting the angle of the ceramic glass material, and the servo motor controls the working distance of the camera to adjust the image capture of the ceramic glass material of different sizes. In the image preprocessing task, in order to avoid the influence of the background image, an improved media filtering method is used to solve it. The design of the program and the realization of the main operation of the system are introduced. The software is written in Matlab language under Windows7 operating system. The main functions of the system are realized by software one by one. After testing 100 glassware, the correct detection rate of scratch defects is 99%, which meets the test requirements.",
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"content": "Traditional manual detection methods have many drawbacks in detecting surface defects of ceramic glass materials. At present, a method is needed to replace manual electronic automatic detection to avoid the problems of time-consuming, laborious and large errors. The purpose of this paper is to study the design of a visual inspection and classification system for the production of ceramic glass materials based on computational vision techniques. A computer vision-based quality control system for ceramic glass materials is designed. The mechanical structure is equipped with a rotating mechanism for adjusting the angle of the ceramic glass material, and the servo motor controls the working distance of the camera to adjust the image capture of the ceramic glass material of different sizes. In the image preprocessing task, in order to avoid the influence of the background image, an improved media filtering method is used to solve it. The design of the program and the realization of the main operation of the system are introduced. The software is written in Matlab language under Windows7 operating system. The main functions of the system are realized by software one by one. After testing 100 glassware, the correct detection rate of scratch defects is 99%, which meets the test requirements.",
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"title": "Deep Learning for Seeing Through Window With Raindrops",
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"abstract": "When taking pictures through glass window in rainy day, the images are comprised and corrupted by the raindrops adhered to glass surfaces. It is a challenging problem to remove the effect of raindrops from an image. The key task is how to accurately and robustly identify the raindrop regions in an image. This paper develops a convolutional neural network (CNN) for removing the effect of raindrops from an image. In the proposed CNN, we introduce a double attention mechanism that concurrently guides the CNN using shape-driven attention and channel re-calibration. The shape-driven attention exploits physical shape priors of raindrops, i.e. convexness and contour closedness, to accurately locate raindrops, and the channel re-calibration improves the robustness when processing raindrops with varying appearances. The experimental results show that the proposed CNN outperforms the state-of-the-art approaches in terms of both quantitative metrics and visual quality.",
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"content": "When taking pictures through glass window in rainy day, the images are comprised and corrupted by the raindrops adhered to glass surfaces. It is a challenging problem to remove the effect of raindrops from an image. The key task is how to accurately and robustly identify the raindrop regions in an image. This paper develops a convolutional neural network (CNN) for removing the effect of raindrops from an image. In the proposed CNN, we introduce a double attention mechanism that concurrently guides the CNN using shape-driven attention and channel re-calibration. The shape-driven attention exploits physical shape priors of raindrops, i.e. convexness and contour closedness, to accurately locate raindrops, and the channel re-calibration improves the robustness when processing raindrops with varying appearances. The experimental results show that the proposed CNN outperforms the state-of-the-art approaches in terms of both quantitative metrics and visual quality.",
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"affiliation": "South China Univ Tech",
"fullName": "Yuhui Quan",
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"affiliation": "South China Univ Tech",
"fullName": "Shijie Deng",
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"affiliation": "South China Univ Tech",
"fullName": "Yixin Chen",
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"affiliation": "National University of Singapore",
"fullName": "Hui Ji",
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"Image Representation",
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"Computer Vision",
"Three Dimensional Displays",
"Multilayer Perceptrons",
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"affiliation": "Harvard University",
"fullName": "Todd Zickler",
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"content": "Neural Radiance Fields (NeRF) has achieved unprece-dented view synthesis quality using coordinate-based neu-ral scene representations. However, NeRF's view depen-dency can only handle simple reflections like highlights but cannot deal with complex reflections such as those from glass and mirrors. In these scenarios, NeRF models the virtual image as real geometries which leads to inaccurate depth estimation, and produces blurry renderings when the multi-view consistency is violated as the reflected objects may only be seen under some of the viewpoints. To over-come these issues, we introduce NeRFReN, which is built upon NeRF to model scenes with reflections. Specifically, we propose to split a scene into transmitted and reflected components, and model the two components with separate neural radiance fields. Considering that this decomposition is highly under-constrained, we exploit geometric priors and apply carefully-designed training strategies to achieve reasonable decomposition results. Experiments on various self-captured scenes show that our method achieves high-quality novel view synthesis and physically sound depth es-timation results while enabling scene editing applications.",
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"content": "We present a personalized, comprehensive eye-tracking solution based on tracking higher-order Purkinje images, suited explicitly for eyeglasses-style AR and VR displays. Existing eye-tracking systems for near-eye applications are typically designed to work for an on-axis configuration and rely on pupil center and corneal reflections (PCCR) to estimate gaze with an accuracy of only about 0.5°to 1°. These are often expensive, bulky in form factor, and fail to estimate monocular accommodation, which is crucial for focus adjustment within the AR glasses.Our system independently measures the binocular vergence and monocular accommodation using higher-order Purkinje reflections from the eye, extending the PCCR based methods. We demonstrate that these reflections are sensitive to both gaze rotation and lens accommodation and model the Purkinje images' behavior in simulation. We also design and fabricate a user-customized eye tracker using cheap off-the-shelf cameras and LEDs. We use an end-to-end convolutional neural network (CNN) for calibrating the eye tracker for the individual user, allowing for robust and simultaneous estimation of vergence and accommodation. Experimental results show that our solution, specifically catering to individual users, outperforms state-of-the-art methods for vergence and depth estimation, achieving an accuracy of 0.3782° and 1.108cm respectively.",
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"Cameras",
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"affiliation": "University of North Carolina at Chapel Hill",
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"doi": "10.1109/ICKG55886.2022.00057",
"title": "Time Series Anomaly Detection for Smart Grids via Multiple Self-Supervised Tasks Learning",
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"abstract": "Smart grids have achieved tremendous success in energy infrastructure and also play an vital role in our daily lives. As an important auxiliary service, anomaly detection in smart grids, e.g., detecting abnormal activities and anomalous events from the electricity data, is very crucial to enhance the reliability and practicability for smart grids. In this paper, we mainly focus on the anomaly detection for time series data in smart grids. Although lots of anomaly detection approaches have been developed for time series data, most of them can not capture the mutual anomalous patterns dynamically among different sensors. In other words, dynamic correlations between different time series are always ignored, which consequently results in high false positive rates. To tackle the problem, we propose a self-supervised framework consisting of multiple tasks, i.e., inter-sample comtrastive task, intra-sample prediction task and reconstruction task, without using groundtruth labels. Technically, in the first task, we construct contrastive pairs from different samples (inter-sample pairs) and propose to use the multi-headed attention model to capture the dynamic mutual anomalous patterns. Furthermore, the prediction task with multiple fully connected (MLP) layers stacked with dropouts is proposed to capture the temporal intra-sample trends in time series data. In the final task, we use Gate Recurrent Unit (GRU) based autoencoders to compute the reconstruction error. The final loss is combined with inter-sample contrastive loss, reconstruction loss and intra-sample prediction loss. Compared with several baselines on a real-world dataset, the experimental results demonstrate that our proposed approach can effectively detect anomalies and significantly outperforms the state-of-the-art.",
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"content": "Smart grids have achieved tremendous success in energy infrastructure and also play an vital role in our daily lives. As an important auxiliary service, anomaly detection in smart grids, e.g., detecting abnormal activities and anomalous events from the electricity data, is very crucial to enhance the reliability and practicability for smart grids. In this paper, we mainly focus on the anomaly detection for time series data in smart grids. Although lots of anomaly detection approaches have been developed for time series data, most of them can not capture the mutual anomalous patterns dynamically among different sensors. In other words, dynamic correlations between different time series are always ignored, which consequently results in high false positive rates. To tackle the problem, we propose a self-supervised framework consisting of multiple tasks, i.e., inter-sample comtrastive task, intra-sample prediction task and reconstruction task, without using groundtruth labels. Technically, in the first task, we construct contrastive pairs from different samples (inter-sample pairs) and propose to use the multi-headed attention model to capture the dynamic mutual anomalous patterns. Furthermore, the prediction task with multiple fully connected (MLP) layers stacked with dropouts is proposed to capture the temporal intra-sample trends in time series data. In the final task, we use Gate Recurrent Unit (GRU) based autoencoders to compute the reconstruction error. The final loss is combined with inter-sample contrastive loss, reconstruction loss and intra-sample prediction loss. Compared with several baselines on a real-world dataset, the experimental results demonstrate that our proposed approach can effectively detect anomalies and significantly outperforms the state-of-the-art.",
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"keywords": [
"Data Handling",
"Deep Learning Artificial Intelligence",
"Learning Artificial Intelligence",
"Recurrent Neural Nets",
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"Unsupervised Learning",
"Anomaly Detection Approaches",
"Different Time Series",
"Dynamic Mutual Anomalous Patterns",
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"Inter Sample Comtrastive Task",
"Inter Sample Contrastive Loss",
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"Multiple Self Supervised Tasks Learning",
"Reconstruction Loss",
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"Smart Grids",
"Temporal Intra Sample Trends",
"Time Series Anomaly Detection",
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"Smart Grids",
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"Anomaly Detection",
"Multi Headed Attention",
"Self Supervised Learning"
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"affiliation": "Jiangsu Electric Power Information Technology Company,Nanjing,China",
"fullName": "Zhenyu Zhang",
"givenName": "Zhenyu",
"surname": "Zhang",
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{
"affiliation": "Jiangsu Electric Power Information Technology Company,Nanjing,China",
"fullName": "Lin Zhao",
"givenName": "Lin",
"surname": "Zhao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "State Grid Jiangsu Electric Power Company,Nanjing,China",
"fullName": "Dongyang Cai",
"givenName": "Dongyang",
"surname": "Cai",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Jiangsu Electric Power Information Technology Company,Nanjing,China",
"fullName": "Shuming Feng",
"givenName": "Shuming",
"surname": "Feng",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Information Engineering, Nanjing University of Finance & Economic,Nanjing,China",
"fullName": "Jiawei Miao",
"givenName": "Jiawei",
"surname": "Miao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Accounting, Nanjing University of Finance & Economic,Nanjing,China",
"fullName": "Yirun Guan",
"givenName": "Yirun",
"surname": "Guan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "College of Information Engineering, Nanjing University of Finance & Economic,Nanjing,China",
"fullName": "Haicheng Tao",
"givenName": "Haicheng",
"surname": "Tao",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "School of Management, Hefei University of Technology,Heifei,China",
"fullName": "Jie Cao",
"givenName": "Jie",
"surname": "Cao",
"__typename": "ArticleAuthorType"
}
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"abstract": "SimRank is a significant metric to measure the similarity of nodes in graph data analysis. The problem of SimRank computation has been studied extensively, however there is no existing work that can provide one unified algorithm to support the SimRank computation both on static and temporal graphs. In this work, we first propose CrashSim, an index-free algorithm for single-source SimRank computation in static graphs. CrashSim can provide provable approximation guarantees for the computational results in an efficient way. In addition, as the reallife graphs are often represented as temporal graphs, CrashSim enables efficient computation of SimRank in temporal graphs. We formally define two typical SimRank queries in temporal graphs, and then solve them by developing an efficient algorithm based on CrashSim, called CrashSim-T. From the extensive experimental evaluation using five real-life and synthetic datasets, it can be seen that the CrashSim algorithm and CrashSim-T algorithm substantially improve the efficiency of the state-of-the-art SimRank algorithms by about 30%, while achieving the precision of the result set with about 97%.",
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"content": "SimRank is a significant metric to measure the similarity of nodes in graph data analysis. The problem of SimRank computation has been studied extensively, however there is no existing work that can provide one unified algorithm to support the SimRank computation both on static and temporal graphs. In this work, we first propose CrashSim, an index-free algorithm for single-source SimRank computation in static graphs. CrashSim can provide provable approximation guarantees for the computational results in an efficient way. In addition, as the reallife graphs are often represented as temporal graphs, CrashSim enables efficient computation of SimRank in temporal graphs. We formally define two typical SimRank queries in temporal graphs, and then solve them by developing an efficient algorithm based on CrashSim, called CrashSim-T. From the extensive experimental evaluation using five real-life and synthetic datasets, it can be seen that the CrashSim algorithm and CrashSim-T algorithm substantially improve the efficiency of the state-of-the-art SimRank algorithms by about 30%, while achieving the precision of the result set with about 97%.",
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"abstract": "Nowadays Unmanned Aerial Vehicles (UAVs) pose an increasing threat to public areas such as parks, schools, hospitals and official buildings. Different methods of dealing with UAV detection are developing more and more actively. This paper primarily focuses on two key aims: the first aim is to perform a multi-label classification system and the second aim is to develop Stacked Bidirectional Long Short-Term Memory (LSTM) with two hidden layers to categorize multiple UAVs sounds. Frame-wise spectral-domain features are applied as inputs of the proposed system. Overall, the results of the study show that the sound of UAVs can be classified into multiple labels. This study has been one of the first attempts to thoroughly examine Stacked Bidirectional LSTM for UAV sound classification task.",
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"content": "Nowadays Unmanned Aerial Vehicles (UAVs) pose an increasing threat to public areas such as parks, schools, hospitals and official buildings. Different methods of dealing with UAV detection are developing more and more actively. This paper primarily focuses on two key aims: the first aim is to perform a multi-label classification system and the second aim is to develop Stacked Bidirectional Long Short-Term Memory (LSTM) with two hidden layers to categorize multiple UAVs sounds. Frame-wise spectral-domain features are applied as inputs of the proposed system. Overall, the results of the study show that the sound of UAVs can be classified into multiple labels. This study has been one of the first attempts to thoroughly examine Stacked Bidirectional LSTM for UAV sound classification task.",
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"abstract": "We present PuLP, a parallel and memory-efficient graph partitioning method specifically designed to partition low-diameter networks with skewed degree distributions. Graph partitioning is an important Big Data problem because it impacts the execution time and energy efficiency of graph analytics on distributed-memory platforms. Partitioning determines the in-memory layout of a graph, which affects locality, intertask load balance, communication time, and overall memory utilization of graph analytics. A novel feature of our method PuLP (Partitioning using Label Propagation) is that it optimizes for multiple objective metrics simultaneously, while satisfying multiple partitioning constraints. Using our method, we are able to partition a web crawl with billions of edges on a single compute server in under a minute. For a collection of test graphs, we show that PuLP uses 8–39× less memory than state-of-the-art partitioners and is up to 14.5× faster, on average, than alternate approaches (with 16-way parallelism). We also achieve better partitioning quality results for the multi-objective scenario.",
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"abstract": "Political Polarization is the divergence of attitudes toward ideological extremes and it is more likely to happen in groups of like-minded individuals. We measured polarization on Twitter during the 2016 United States Presidential elections, analyzing Twitter backchanneling conversations occurring during the three Presidential debates. The polarization metric we use is based on an existing metaphor of the electric dipole. In each debate the polarization dynamic followed a U-shaped pattern, with polarization being high at the beginning of the debate, decreasing over time, and finally bouncing back to a value that was higher than the initial one. The temporary decline of polarization is due to the increase in interaction with participants holding opposite opinions, but apparently this interaction is more conducive to confrontation than to revision of existing beliefs. We argue that the characteristics of Twitter are generally conducive to polarized and manipulable online debate.",
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"abstract": "A new method for air quality evaluation using only visible image analysis is introduced in this paper. Based on the fact that suspended particles in air are visible, we attempted to use visible images to develop an appropriate measure which can be closely related to the density of suspended particles (namely the values of PM2.5 and PM10). Furthermore, using this measure, we can evaluate the values of PM2.5 and PM10 via digital image processing. Combined with water droplets, suspended particles in air can form fog or haze. Based on the monochrome atmospheric scattering model, which has been widely used to describe the formation of a haze image, we propose a measure, normalized first-order absolute sum of high-frequency spectrum (NFAS) and attempt to investigate its relationship with the values of PM2.5 and PM10. The experimental results showed the proposed measure is closely related to PM2.5 and has a relation with PM10.",
"abstracts": [
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"content": "A new method for air quality evaluation using only visible image analysis is introduced in this paper. Based on the fact that suspended particles in air are visible, we attempted to use visible images to develop an appropriate measure which can be closely related to the density of suspended particles (namely the values of PM2.5 and PM10). Furthermore, using this measure, we can evaluate the values of PM2.5 and PM10 via digital image processing. Combined with water droplets, suspended particles in air can form fog or haze. Based on the monochrome atmospheric scattering model, which has been widely used to describe the formation of a haze image, we propose a measure, normalized first-order absolute sum of high-frequency spectrum (NFAS) and attempt to investigate its relationship with the values of PM2.5 and PM10. The experimental results showed the proposed measure is closely related to PM2.5 and has a relation with PM10.",
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"normalizedAbstract": "A new method for air quality evaluation using only visible image analysis is introduced in this paper. Based on the fact that suspended particles in air are visible, we attempted to use visible images to develop an appropriate measure which can be closely related to the density of suspended particles (namely the values of PM2.5 and PM10). Furthermore, using this measure, we can evaluate the values of PM2.5 and PM10 via digital image processing. Combined with water droplets, suspended particles in air can form fog or haze. Based on the monochrome atmospheric scattering model, which has been widely used to describe the formation of a haze image, we propose a measure, normalized first-order absolute sum of high-frequency spectrum (NFAS) and attempt to investigate its relationship with the values of PM2.5 and PM10. The experimental results showed the proposed measure is closely related to PM2.5 and has a relation with PM10.",
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"affiliation": "Tsinghua University, China",
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"affiliation": "Tsinghua University, China",
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"abstract": "Isosurfaces are commonly used to visualize scalar fields. Critical isovalues indicate isosurface topology changes: the creation of new surface components, merging of surface components or the formation of holes in a surface component. Therefore, they highlight interesting isosurface behavior and are helpful in exploration of large trivariate data sets. We present a method that detects critical isovalues in a scalar field defined by piecewise trilinear interpolation over a rectilinear grid and describe how to use them when examining volume data. We further review varieties of the Marching Cubes (MC) algorithm, with the intention to preserve topology of the trilinear interpolant when extracting an isosurface. We combine and extend two approaches in such a way that it is possible to extract meaningful isosurfaces even when a critical value is chosen as isovalue.",
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"abstract": "A description is given of a software system, TOPO, that numerically analyzes and graphically displays topological aspects of a three-dimensional vector field, v, to produce a single, relatively simple picture that characterizes v. The topology of v considered consists of its critical points (where v=0), their invariant manifolds, and the integral curves connecting these invariant manifolds. The field in the neighborhood of each critical point is approximated by the Taylor expansion. The coefficients of the first nonzero term of the Taylor expansion around a critical point are the 3*3 matrix Delta v. Critical points are classified by examining Delta v's eigenvalues. The eigenvectors of Delta v span the invariant manifolds of the linearized field around a critical point. Curves integrated from initial points on the eigenvectors a small distance from a critical point connect with other critical points (or the boundary) to complete the topology. One class of critical surfaces that is important in computational fluid dynamics is analyzed.<>",
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"content": "A description is given of a software system, TOPO, that numerically analyzes and graphically displays topological aspects of a three-dimensional vector field, v, to produce a single, relatively simple picture that characterizes v. The topology of v considered consists of its critical points (where v=0), their invariant manifolds, and the integral curves connecting these invariant manifolds. The field in the neighborhood of each critical point is approximated by the Taylor expansion. The coefficients of the first nonzero term of the Taylor expansion around a critical point are the 3*3 matrix Delta v. Critical points are classified by examining Delta v's eigenvalues. The eigenvectors of Delta v span the invariant manifolds of the linearized field around a critical point. Curves integrated from initial points on the eigenvectors a small distance from a critical point connect with other critical points (or the boundary) to complete the topology. One class of critical surfaces that is important in computational fluid dynamics is analyzed.<>",
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"Eigenvalues And Eigenfunctions",
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"Topology",
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"title": "Visualization of higher order singularities in vector fields",
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"abstract": "Presents an algorithm for the visualization of vector field topology based on Clifford algebra. It allows the detection of higher-order singularities. This is accomplished by first analysing the possible critical points and then choosing a suitable polynomial approximation, because conventional methods based on piecewise linear or bilinear approximation do not allow higher-order critical points and destroy the topology in such cases. The algorithm is still very fast, because of using linear approximation outside the areas with several critical points.",
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"content": "Presents an algorithm for the visualization of vector field topology based on Clifford algebra. It allows the detection of higher-order singularities. This is accomplished by first analysing the possible critical points and then choosing a suitable polynomial approximation, because conventional methods based on piecewise linear or bilinear approximation do not allow higher-order critical points and destroy the topology in such cases. The algorithm is still very fast, because of using linear approximation outside the areas with several critical points.",
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"abstract": "Topology analysis of plane, turbulent vector fields results in visual clutter caused by critical points indicating vortices of finer and finer scales. A simplification can be achieved by merging critical points within a prescribed radius into higher order critical points. After building clusters containing the singularities to merge, the method generates a piecewise linear representation of the vector field in each cluster containing only one (higher order) singularity. Any visualization method can be applied to the result after this process. Using different maximal distances for the critical points to be merged results in a hierarchy of simplified vector fields that can be used for analysis on different scales.",
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