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"abstract": "A novel method for the interactive detail preserving deformation of 3D meshes is proposed in this paper. It is based on preserving the distances between each vertex and its adjacent vertices and the angles between edges formed by each vertex and its adjacent vertices. The distances and the angles are invariant over the rigid transformations. If preserving the distances and the angles it can preserve the local details and volumetric details of the mesh, which is important requirement of mesh deformation. The mesh is deformed by specifying constraints of vertices positions. These constraints are incorporated in the distances and the angles reconstruction equations, and their solution produces a deformed surface geometry that preserves the local detail in the least-squares sense. We demonstrate the effectiveness of our method with several detail-preserving deformation operators.",
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"content": "A novel method for the interactive detail preserving deformation of 3D meshes is proposed in this paper. It is based on preserving the distances between each vertex and its adjacent vertices and the angles between edges formed by each vertex and its adjacent vertices. The distances and the angles are invariant over the rigid transformations. If preserving the distances and the angles it can preserve the local details and volumetric details of the mesh, which is important requirement of mesh deformation. The mesh is deformed by specifying constraints of vertices positions. These constraints are incorporated in the distances and the angles reconstruction equations, and their solution produces a deformed surface geometry that preserves the local detail in the least-squares sense. We demonstrate the effectiveness of our method with several detail-preserving deformation operators.",
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"abstract": "Topology matters. Despite the recent success of point cloud processing with geometric deep learning, it remains arduous to capture the complex topologies of point cloud data with a learning model. Given a point cloud dataset containing objects with various genera, or scenes with multiple objects, we propose an autoencoder, TearingNet, which tackles the challenging task of representing the point clouds using a fixed-length descriptor. Unlike existing works directly deforming predefined primitives of genus zero (e.g., a 2D square patch) to an object-level point cloud, our TearingNet is characterized by a proposed Tearing network module and a Folding network module interacting with each other iteratively. Particularly, the Tearing network module learns the point cloud topology explicitly. By breaking the edges of a primitive graph, it tears the graph into patches or with holes to emulate the topology of a target point cloud, leading to faithful reconstructions. Experimentation shows the superiority of our proposal in terms of reconstructing point clouds as well as generating more topology-friendly representations than benchmarks.",
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"abstract": "Threats in cybersecurity come in a variety of forms, and combating such threats involves handling a huge amount of data from different sources. It is absolutely necessary to use algorithmic models to defend against these threats. However, all models are sensitive to deviation from the original contexts in which the models were developed. Hence, it is not really an overstatement to say that ‘all models are wrong’. In this paper, we propose a visual analytics loop for supporting the continuous development of models during their deployment. We describe the roles of three types of operators (monitors, analysts and modelers), present the visualization techniques used at different stages of model development, and demonstrate the utility of this approach in conjunction with a prototype software system for corporate insider threat detection. In many ways, our environment facilitates an agile approach to the development and deployment of models in cybersecurity.",
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"abstract": "Modeling complex systems is a time-consuming, difficult and fragmented task, often requiring the analyst to work with disparate data, a variety of models, and expert knowledge across a diverse set of domains. Applying a user-centered design process, we developed a mixed-initiative visual analytics approach, a subset of the Causemos platform, that allows analysts to rapidly assemble qualitative causal models of complex socio-natural systems. Our approach facilitates the construction, exploration, and curation of qualitative models bringing together data across disparate domains. Referencing a recent user evaluation, we demonstrate our approach’s ability to interactively enrich user mental models and accelerate qualitative model building.",
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"abstract": "In this paper, we propose a shape-guided segmentation algorithm for fine-grained visual classification(FGVC). First, edge information is extracted from the query image and compared with each sample of training set, which can help us retrieve a subset of candidate proposals. These proposals are used to learn prior shape knowledge by separately estimating the foreground probabilities of corresponding pixels in the query image. Then, a redefined energy function is introduced to translate the minimum of energy to a good segmentation, with which we can dynamically pick out the most preferable proposal. After that, we obtain the label map of the image at the pixel level. Finally, the high-quality segmentation is used to aid locating semantic parts. We fine-tune one global model and two part models on Caffe to extract deep features and use a learned SVM classifier for categorization. We test three aspects in our experiment, including foreground segmentation, part localization and final classification. The results show that our method outperforms the state-of-the-art approaches on the famous Caltech-UCSD Birds 200–2011 dataset.",
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"abstract": "We propose a novel adaptation method for generalizing road segmentation to novel weather, lighting or viewing geometries. The method assumes a source domain consisting of an ensemble of labeled training datasets and an unlabeled target test dataset that deviates substantially from the training ensemble. The training dataset is used to compile a geometry-anchored prior over the road pixel locations and to train a fully-convolutional network road segmentation system. At inference, a probabilistic Houghing method is used to detect line intersections in the test image and thereby estimate the vanishing point of the road, thus anchoring the learned geometric prior. This prior is then used to extract high confidence road and background regions which serve as surrogate ground truth to adapt the network to the target domain. Leave-one-out evaluation across five diverse road segmentation datasets demonstrates substantial improvement in generalization across changes in viewing geometry and weather conditions, yielding results that are on average comparable and in some cases superior to a more complex GAN-based domain adaptation approach. These results demonstrate the potential for classical computer vision methods to guide adaptation of supervised machine learning algorithms, leading to improved generalization across domains.",
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"abstract": "Drones are employed in a growing number of visual recognition applications. A recent development in cell tower inspection is drone-based asset surveillance, where the autonomous flight of a drone is guided by localizing objects of interest in successive aerial images. In this paper, we propose a method to train deep weakly-supervised object localization (WSOL) models based only on image-class labels to locate object with high confidence. To train our localizer, pseudo labels are efficiently harvested from a self-supervised vision transformers (SSTs). However, since SSTs decompose the scene into multiple maps containing various object parts, and do not rely on any explicit super-visory signal, they cannot distinguish between the object of interest and other objects, as required WSOL. To address this issue, we propose leveraging the multiple maps generated by the different transformer heads to acquire pseudo-labels for training a deep WSOL model. In particular, a new Discriminative Proposals Sampling (DiPS) method is introduced that relies on a CNN classifier to identify discriminative regions. Then, foreground and background pixels are sampled from these regions in order to train a WSOL model for generating activation maps that can accurately localize objects belonging to a specific class. Empirical results<sup>1</sup><sup>1</sup>Our code is available: https://github.com/shakeebmurtaza/dips on the challenging TelDrone dataset indicate that our proposed approach can outperform state-of-art methods over a wide range of threshold values over produced maps. We also computed results on CUB dataset, showing that our method can be adapted for other tasks.",
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"abstract": "With the fast development of hardware computation, visual object detection has achieved a prosperous development in both industrial application and academic researching. Beneficial from the great success in the field of artificial intelligence, mobile robot research has received much inspiration for autonomy path planning. Although state-of-art object detection algorithms, such as YOLO, Mask-RCNN, can detect and classify targets from robot view, complicated tasks require an autonomous robot that has the capability to adjust its viewpoint and self-decide anchor targets to get better detection results. In this paper, a method is proposed to help an agent learn what actions to take according to detected objects in its viewpoint. The agent is also able to search objects in different rooms and indoor environments only with prior knowledge of potential objects in a room. Experimental results indicate that our active searching approach can help agents learn active room selection and adjust viewpoint to receive better object detection results.",
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"abstract": "Real-time RGB-D scanning technique has become widely used to progressively scan objects with a hand-held sensor. Existing online methods restore color information per voxel, and thus their quality is often limited by the tradeoff between spatial resolution and time performance. Also, such methods often suffer from blurred artifacts in the captured texture. Traditional offline texture mapping methods with non-rigid warping assume that the reconstructed geometry and all input views are obtained in advance, and the optimization takes a long time to compute mesh parameterization and warp parameters, which prevents them from being used in real-time applications. In this work, we propose a progressive texture-fusion method specially designed for real-time RGB-D scanning. To this end, we first devise a novel texture-tile voxel grid, where texture tiles are embedded in the voxel grid of the signed distance function, allowing for high-resolution texture mapping on the low-resolution geometry volume. Instead of using expensive mesh parameterization, we associate vertices of implicit geometry directly with texture coordinates. Second, we introduce real-time texture warping that applies a spatially-varying perspective mapping to input images so that texture warping efficiently mitigates the mismatch between the intermediate geometry and the current input view. It allows us to enhance the quality of texture over time while updating the geometry in real-time. The results demonstrate that the quality of our real-time texture mapping is highly competitive to that of exhaustive offline texture warping methods. Our method is also capable of being integrated into existing RGB-D scanning frameworks.",
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"abstract": "In this paper, the free vibrations of incomplete toroidal shells with different meridional boundary conditions are studied based on the linear Flugge thin shell theory. The cross-section of toroidal shells is assumed to be a closed circle. The circumferential modes are represented by a series of trigonometric functions and the meridional modes are expressed as a combination of beam functions which satisfy the boundary conditions at the ends of the shell. It is shown that both the natural frequencies and the mode shapes can be accurately predicted as long as the employed beam functions satisfy the boundary conditions. The dependence of the free vibration characteristics of elastic toroidal shell upon boundary conditions is also illustrated and explained by means of an example in this paper.",
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"doi": "10.1109/CISAI54367.2021.00041",
"title": "Finite element modelling and failure behaviour analysis of Carbon Fibre-reinforced Plastic thin-walled tube with cutouts under quasi-static loading",
"normalizedTitle": "Finite element modelling and failure behaviour analysis of Carbon Fibre-reinforced Plastic thin-walled tube with cutouts under quasi-static loading",
"abstract": "Due to cutouts that may lead to failure during the application process of CFRP, the study of impact performance and failure analysis can understand the failure mechanism, which can guide the design optimization of cutouts and realize the significant performance improvement of composite materials. This paper uses the finite element modelling method to simulate the failure behaviour of the carbon fibre-reinforced plastic thin-wall tubes. Two laminate stacking angles are used to investigate the effect of stacking angle on CFRP tubes under quasi-static loading. The effect of the cutouts, distribution and diameter size of the cutouts on the mises stress nephogram is investigated. It was obtained that as the stacking angle increased, the axial stiffness of the tube decreased, which reduced the initial peak load. For the effect of cutout distribution, the maximum stress appeared around the edge of cutout, which regularly showed at the end edge of the tube. It was shown that the cutout size was one of the essential factors on thin-walled CFRP tubes under quasi-static loading. The study deals with the buckling behaviour of thin-walled CFRP tubes with cutouts. Through finite element analysis, the conclusions of this study can be used to guide the cutouts of CFRP thin-walled tubes in different ways and provide conditions for the mechanical connection between CFRP thin-walled tubes.",
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"content": "Due to cutouts that may lead to failure during the application process of CFRP, the study of impact performance and failure analysis can understand the failure mechanism, which can guide the design optimization of cutouts and realize the significant performance improvement of composite materials. This paper uses the finite element modelling method to simulate the failure behaviour of the carbon fibre-reinforced plastic thin-wall tubes. Two laminate stacking angles are used to investigate the effect of stacking angle on CFRP tubes under quasi-static loading. The effect of the cutouts, distribution and diameter size of the cutouts on the mises stress nephogram is investigated. It was obtained that as the stacking angle increased, the axial stiffness of the tube decreased, which reduced the initial peak load. For the effect of cutout distribution, the maximum stress appeared around the edge of cutout, which regularly showed at the end edge of the tube. It was shown that the cutout size was one of the essential factors on thin-walled CFRP tubes under quasi-static loading. The study deals with the buckling behaviour of thin-walled CFRP tubes with cutouts. Through finite element analysis, the conclusions of this study can be used to guide the cutouts of CFRP thin-walled tubes in different ways and provide conditions for the mechanical connection between CFRP thin-walled tubes.",
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"fno": "069200a178",
"keywords": [
"Buckling",
"Carbon Fibre Reinforced Plastics",
"Composite Materials",
"Failure Mechanical",
"Failure Analysis",
"Finite Element Analysis",
"Laminates",
"Pipes",
"CFRP Thin Walled Tubes",
"Finite Element Modelling",
"Carbon Fibre Reinforced",
"Thin Walled Tube",
"Cutouts",
"Quasistatic Loading",
"Failure Analysis",
"Failure Mechanism",
"Failure Behaviour",
"Thin Wall Tubes",
"Laminate Stacking Angles",
"Stacking Angle",
"Diameter Size",
"Cutout Distribution",
"Cutout Size",
"Thin Walled CFRP Tubes",
"Finite Element Analysis",
"Information Science",
"Stacking",
"Loading",
"Failure Analysis",
"Electron Tubes",
"Finite Element Analysis",
"Plastics",
"Thin Walled Tube",
"Cutout Size",
"Laminate Stacking Angle",
"Buckling Behaviour",
"Carbon Fibre Reinforced Plastic"
],
"authors": [
{
"affiliation": "University of Science and Technology, Beijing,Advanced Institute of Engineers,Beijing,China",
"fullName": "Hansen Zeng",
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{
"affiliation": "China University of Mining and Technology,School of Materials Science and Physics,Xuzhou,China",
"fullName": "Xinyang Liu",
"givenName": "Xinyang",
"surname": "Liu",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Jilin University,College of Materials Science and Engineering,Changchun,China",
"fullName": "Chenghe Bai",
"givenName": "Chenghe",
"surname": "Bai",
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"abstract": "The work is devoted to the description of the visualization of the volume-spatial form of a thin-walled shell structure, which is represented in the stress-strained state, which occurs when the shell is fixed along the upper edge and is freely positioned below the fastening boundary in the field of gravity and elasticity of materials. Without gravity, the shell is a straight circular truncated cone. The developed software module can be used in design and calculation of thin-walled shell structures for their non-linear deformation, as well as their visualization. The spatial shape visualization of the shell structure can be used to simulate various products, for example, conical antennas or products of the textile industry, flexible elastic shells in hydraulic engineering, etc.",
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"proceeding": {
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"title": "2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)",
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"title": "Optimization of FHB5.10 Electrical Appliance Shell Injection Molding Process Based on Moldflow and BP Neural Network",
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"abstract": "The purpose of this article is to reduce the warpage deformation of thin-walled injection molded products and improve the prediction ability of their warpage deformation. Aiming at the thin-walled shell of FHB5.10 electrical apparatus with complex internal structure and many forming features such as porous, groove and scoliosis, the four-sided mosaic core-pulling mechanism is used to complete the forming of its side features in the die design. The key point is that the finite element simulation of each scheme in the orthogonal test is carried out by using Moldflow software, and the influence degree of each variable on the warping deformation of plastic parts is in the following order: holding time E > melt temperature A > mold temperature B > injection time C > cooling time F > holding pressure D. Finally, each process scheme of the orthogonal test is used as sample data to train the BP neural network. The results show that using BP neural network to predict the warping deformation of plastic parts has high prediction accuracy, which provides a reference for the production of injection molds.",
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"content": "The purpose of this article is to reduce the warpage deformation of thin-walled injection molded products and improve the prediction ability of their warpage deformation. Aiming at the thin-walled shell of FHB5.10 electrical apparatus with complex internal structure and many forming features such as porous, groove and scoliosis, the four-sided mosaic core-pulling mechanism is used to complete the forming of its side features in the die design. The key point is that the finite element simulation of each scheme in the orthogonal test is carried out by using Moldflow software, and the influence degree of each variable on the warping deformation of plastic parts is in the following order: holding time E > melt temperature A > mold temperature B > injection time C > cooling time F > holding pressure D. Finally, each process scheme of the orthogonal test is used as sample data to train the BP neural network. The results show that using BP neural network to predict the warping deformation of plastic parts has high prediction accuracy, which provides a reference for the production of injection molds.",
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"content": "As stated by Mosaker (2001), Virtual Reality (VR) environments that portray the past can be considered modern-day time machines. A substantial variety of Virtual Heritage (VH) applications have been developed recently, with the mission of using VR technologies for cultural preservation purposes. However, few of these projects focused on properly assessing these applications' educational value and goodness of interaction. In light of these considerations, a VR application reproducing an ancient Roman Domus has been developed to assess Human Factors variables and learning ratio of users. Therefore, 161 participants have been divided into three conditions in a between-subjects design: a Virtual Reality Experience (VRE), a First Person Experience (FPE), and a Multi-media Presentation Experience (MPE). Results showed an overall appreciation of the topic in all conditions, with comparable learning performances. However, we discovered a higher engagement and enjoyment of users with the VRE. Therefore, the Domus Romana application has been proven to be an effective complementary educational tool in explaining Roman houses.",
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"fullName": "Paolo Boffi",
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"affiliation": "Politecnico di Milano",
"fullName": "Pier Luca Lanzi",
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{
"affiliation": "Università degli Studi di Milano-Bicocca",
"fullName": "Lilia Coppola",
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"affiliation": "Università degli Studi di Milano-Bicocca",
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{
"affiliation": "Università degli Studi di Milano-Bicocca",
"fullName": "Alberto Gallace",
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"abstract": "Virtual Reality (VR) has been used for training aircraft pilots, maritime seafarers, operators, etc as it provides an immersive environment with realistic lifelike quality. We developed and implemented a VR-based Liquefied Natural Gas (LNG) firefighting simulation system with head-mounted displays (HMD) and novel human factors evaluation that could train and assess both technical and non-technical skills in the firefighting scenarios. The proposed human factors evaluation is based on a competence model and the non-technical skills such as situation awareness, vigilance, and decision making of seafarers could be assessed. An experiment was carried out with 6 trainees and 2 trainers using the implemented LNG firefighting simulation system. The results show that that the maritime trainees felt the VR scene was realistic to them, evoked similar emotions (such as fear, stress) during the demanding events as in the real world and made them attentive during the experience.",
"abstracts": [
{
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"content": "Virtual Reality (VR) has been used for training aircraft pilots, maritime seafarers, operators, etc as it provides an immersive environment with realistic lifelike quality. We developed and implemented a VR-based Liquefied Natural Gas (LNG) firefighting simulation system with head-mounted displays (HMD) and novel human factors evaluation that could train and assess both technical and non-technical skills in the firefighting scenarios. The proposed human factors evaluation is based on a competence model and the non-technical skills such as situation awareness, vigilance, and decision making of seafarers could be assessed. An experiment was carried out with 6 trainees and 2 trainers using the implemented LNG firefighting simulation system. The results show that that the maritime trainees felt the VR scene was realistic to them, evoked similar emotions (such as fear, stress) during the demanding events as in the real world and made them attentive during the experience.",
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"affiliation": "Fraunhofer Singapore,Singapore",
"fullName": "Yisi Liu",
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"affiliation": "Nanyang Technological University,Fraunhofer Singapore,Singapore",
"fullName": "Jian Cui",
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{
"affiliation": "Nanyang Technological University,Fraunhofer Singapore,Singapore",
"fullName": "Fan Li",
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{
"affiliation": "Nanyang Technological University,Fraunhofer Singapore,Singapore",
"fullName": "Olga Sourina",
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{
"affiliation": "CEMS, Singapore Polytechnic,Singapore",
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"affiliation": "Singapore Polytechnic,Singapore",
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"abstract": "The maritime industry is switching to new types of fuel such as Liquefied Natural Gas (LNG). On one hand, these kinds of fuel are more sustainable to the environment, on the other hand, training on handling such fuel safely and dealing with emergency situation is necessary. Videos and lecture-based learning is commonly used to deliver such knowledge to the maritime trainees. In recent years, the advances in Virtual Reality (VR) have brought new opportunities to such training. It provides an immersive while safe environment for training on certain operations that are extraordinary or dangerous in real life. It also allows the learners to practice the tasks repeatedly. The VR-based training is mostly used for improving technical skills, however, to guarantee a more efficient and better assessment of trainee's performance, nontechnical skills such as decision making, situation awareness, vigilance are needed to be assessed and trained as well. In this paper, a VR-based LNG evacuation training system is presented. The system provides two training scenarios for learning the evacuation procedure. A novel human factors evaluation based on the behavioral data captured by VR was proposed and integrated with the training, which includes both technical and non-technical skills assessment. An experiment with 14 subjects was conducted to validate the human factors evaluation and to get feedback towards the VR-based training.",
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"content": "The maritime industry is switching to new types of fuel such as Liquefied Natural Gas (LNG). On one hand, these kinds of fuel are more sustainable to the environment, on the other hand, training on handling such fuel safely and dealing with emergency situation is necessary. Videos and lecture-based learning is commonly used to deliver such knowledge to the maritime trainees. In recent years, the advances in Virtual Reality (VR) have brought new opportunities to such training. It provides an immersive while safe environment for training on certain operations that are extraordinary or dangerous in real life. It also allows the learners to practice the tasks repeatedly. The VR-based training is mostly used for improving technical skills, however, to guarantee a more efficient and better assessment of trainee's performance, nontechnical skills such as decision making, situation awareness, vigilance are needed to be assessed and trained as well. In this paper, a VR-based LNG evacuation training system is presented. The system provides two training scenarios for learning the evacuation procedure. A novel human factors evaluation based on the behavioral data captured by VR was proposed and integrated with the training, which includes both technical and non-technical skills assessment. An experiment with 14 subjects was conducted to validate the human factors evaluation and to get feedback towards the VR-based training.",
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"normalizedAbstract": "The maritime industry is switching to new types of fuel such as Liquefied Natural Gas (LNG). On one hand, these kinds of fuel are more sustainable to the environment, on the other hand, training on handling such fuel safely and dealing with emergency situation is necessary. Videos and lecture-based learning is commonly used to deliver such knowledge to the maritime trainees. In recent years, the advances in Virtual Reality (VR) have brought new opportunities to such training. It provides an immersive while safe environment for training on certain operations that are extraordinary or dangerous in real life. It also allows the learners to practice the tasks repeatedly. The VR-based training is mostly used for improving technical skills, however, to guarantee a more efficient and better assessment of trainee's performance, nontechnical skills such as decision making, situation awareness, vigilance are needed to be assessed and trained as well. In this paper, a VR-based LNG evacuation training system is presented. The system provides two training scenarios for learning the evacuation procedure. A novel human factors evaluation based on the behavioral data captured by VR was proposed and integrated with the training, which includes both technical and non-technical skills assessment. An experiment with 14 subjects was conducted to validate the human factors evaluation and to get feedback towards the VR-based training.",
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"Computer Based Training",
"Decision Making",
"Emergency Management",
"Human Factors",
"Marine Engineering",
"Natural Gas Technology",
"Production Engineering Computing",
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"abstract": "2D images are observations of the 3D physical world depicted with the geometry, material, and illumination components. Recovering these underlying intrinsic components from 2D images, also known as inverse rendering, usually requires a supervised setting with paired images collected from multiple viewpoints and lighting conditions, which is resource-demanding. In this work, we present GAN2X, a new method for unsupervised inverse rendering that only uses unpaired images for training. Unlike previous Shape-from-GAN approaches that mainly focus on 3D shapes, we take the first attempt to also recover non-Lambertian material properties by exploiting the pseudo paired data generated by a GAN. To achieve precise inverse rendering, we devise a specularity-aware neural surface representation that continuously models the geometry and material properties. A shading-based refinement technique is adopted to further distill information in the target image and recover more fine details. Experiments demonstrate that GAN2X can accurately decompose 2D images to 3D shape, albedo, and specular properties for different object categories, and achieves state-of-the-art performance for unsupervised single-view 3D face reconstruction. We also show its applications in downstream tasks including real image editing and lifting 2D GANs to decomposed 3D GANs.",
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"title": "Haptic, visual and visuo-haptic softness judgments for objects with deformable surfaces",
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"abstract": "The purpose of this study is to investigate multisensory visual-haptic softness perception using deformable objects. We created a set of rubber specimens, whose compliance varied in a controlled fashion (0.11 to 0.96 mm/N), but which were otherwise indistinguishable. Participants judged the magnitude of the stimuli according to their softness under haptic-only, vision-only and visuo-haptic conditions. In haptic and visuo-haptic conditions participants explored the stimuli without and with vision of their exploratory movements, respectively. In visual conditions, participants watched how another person explored the stimuli. Participants were well able to differentiate between the different stimuli under all three modality conditions. Stimuli were judged to be slightly softer under vision-only conditions than under haptic-only conditions; visuo-haptic judgments were in-between (average visual weight: 55%). These findings demonstrate that a) participants can reliably infer softness from indirect visual information alone-that is from watching corresponding exploratory movements and stimulus deformations-, and that b) such visual information has a major contribution to visuo-haptic softness judgments. We further observed that judgments were more variable under visual as compared to haptic conditions; the variability of visuo-haptic judgments was similar to that of haptic ones. The lack of benefit from adding visual to haptic information, and the contrast between the relatively high visual weight in visuo-haptic judgments on the one hand and the low reliability of visual relative to haptic-only information on the other hand, suggest that the integration of visual and haptic judgments was not optimal, but biased towards vision.",
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"abstract": "A bipartite graph models the relation between two different types of entities. It is applicable, for example, to describe persons' affiliations to different social groups or their association with subjects such as topics of interest. In these applications, it is important to understand the connectivity patterns among the entities in the bipartite graph. For the example of a bipartite relation between persons and their topics of interest, people may form groups based on their common interests, and the topics also can be grouped or categorized based on the interested audiences. Co-clustering methods can identify such connectivity patterns and find clusters within the two types of entities simultaneously. In this paper, we propose an interactive visualization design that incorporates co-clustering methods to facilitate the identification of node clusters formed by their common connections in a bipartite graph. Besides highlighting the automatically detected node clusters and the connections among them, the visual interface also provides visual cues for evaluating the homogeneity of the bipartite connections in a cluster, identifying potential outliers, and analyzing the correlation of node attributes with the cluster structure. The interactive visual interface allows users to flexibly adjust the node grouping to incorporate their prior knowledge of the domain, either by direct manipulation (i.e., splitting and merging the clusters), or by providing explicit feedback on the cluster quality, based on which the system will learn a parametrization of the co-clustering algorithm to better align with the users' notion of node similarity. To demonstrate the utility of the system, we present two example usage scenarios on real world datasets.",
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"abstract": "Clustering is a widely used to discover underlying patterns and groups in data and there is a need to validate the quality of clusters generated by the numerous clustering algorithms in use. The need for cluster validitation arises from the fundamental definition of unsupervised learning. As clustering is an unsupervised learning process, the prediction of correct number of clusters is a hurdle which can be cleared by using cluster validity indices to assess the quality of the clusters. We have developed a tool for cluster validation as a part of GOAPhAR, a web based tool that integrates from disparate sources, information regarding gene annotations, protein annotations, identifiers associated with probe sets, functional pathways, protein interactions, gene Ontology and publicly available microarray datasets. Our cluster validity tool calculates three indices to indicate clustering quality viz. the Silhouette, Dunn's and Davies-Bouldin indices and outputs them to the user. The values of these indices can be used to judge the quality of clustering and to optimize the process of selecting an appropriate clustering algorithm and number of clusters.",
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"content": "Clustering is a widely used to discover underlying patterns and groups in data and there is a need to validate the quality of clusters generated by the numerous clustering algorithms in use. The need for cluster validitation arises from the fundamental definition of unsupervised learning. As clustering is an unsupervised learning process, the prediction of correct number of clusters is a hurdle which can be cleared by using cluster validity indices to assess the quality of the clusters. We have developed a tool for cluster validation as a part of GOAPhAR, a web based tool that integrates from disparate sources, information regarding gene annotations, protein annotations, identifiers associated with probe sets, functional pathways, protein interactions, gene Ontology and publicly available microarray datasets. Our cluster validity tool calculates three indices to indicate clustering quality viz. the Silhouette, Dunn's and Davies-Bouldin indices and outputs them to the user. The values of these indices can be used to judge the quality of clustering and to optimize the process of selecting an appropriate clustering algorithm and number of clusters.",
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"abstract": "Cluster validity index (CVI) is an important method for evaluating the effect of clustering results generated by clustering algorithms. Currently, many CVIs have proposed, but they are suffering from issues of unstable and narrow range of applications. Therefore, a new clustering validity index-NCVI, is proposed in this paper. Firstly, the NCVI index combines the idea of maximum spanning tree and Euclidean distance formula. The clustering results are obtained by using the average of the sum of the weights of the maximum spanning trees of each cluster and the minimum distance between the clusters using the Euclidean distance between each cluster center point. At the same time, based on the underlying algorithm (average link hierarchical clustering algorithm) to determine the optimal cluster number, combined with the new cluster validity index NCVI designed a new K value optimization algorithm (KVOA). Finally, the paper evaluates the validity of the newly proposed index (NCVI) through four simulation data sets and two UCI real data sets, and compares it with other six classical indicators. The experimental results show that the proposed index has a good performance advantage over other indexes in the tested data set.",
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"content": "Cluster validity index (CVI) is an important method for evaluating the effect of clustering results generated by clustering algorithms. Currently, many CVIs have proposed, but they are suffering from issues of unstable and narrow range of applications. Therefore, a new clustering validity index-NCVI, is proposed in this paper. Firstly, the NCVI index combines the idea of maximum spanning tree and Euclidean distance formula. The clustering results are obtained by using the average of the sum of the weights of the maximum spanning trees of each cluster and the minimum distance between the clusters using the Euclidean distance between each cluster center point. At the same time, based on the underlying algorithm (average link hierarchical clustering algorithm) to determine the optimal cluster number, combined with the new cluster validity index NCVI designed a new K value optimization algorithm (KVOA). Finally, the paper evaluates the validity of the newly proposed index (NCVI) through four simulation data sets and two UCI real data sets, and compares it with other six classical indicators. The experimental results show that the proposed index has a good performance advantage over other indexes in the tested data set.",
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"abstract": "Recent work in computer graphics has explored the synthesis of indoor spaces with furniture, accessories, and other layout items. In this work, we bridge the gap between the physical and virtual worlds: Given an input image of an interior or exterior space, and a general user specification of the desired furnishings and layout constraints, our method automatically furnishes the scene with a realistic arrangement and displays it to the user by augmenting the original image. Our method can deal with varying layouts and target arrangements at interactive rates, which affords the user a sense of collaboration with the design program, enabling the rapid visual assessment of various layout designs, a process which would typically be time consuming if done manually. Our method is suitable for smartphones and other camera-enabled mobile devices.",
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"content": "We present a human-centric method to sample and synthesize 3D room layouts and 2D images thereof, to obtain large-scale 2D/3D image data with the perfect per-pixel ground truth. An attributed spatial And-Or graph (S-AOG) is proposed to represent indoor scenes. The S-AOG is a probabilistic grammar model, in which the terminal nodes are object entities including room, furniture, and supported objects. Human contexts as contextual relations are encoded by Markov Random Fields (MRF) on the terminal nodes. We learn the distributions from an indoor scene dataset and sample new layouts using Monte Carlo Markov Chain. Experiments demonstrate that the proposed method can robustly sample a large variety of realistic room layouts based on three criteria: (i) visual realism comparing to a state-of-the-art room arrangement method, (ii) accuracy of the affordance maps with respect to ground-truth, and (ii) the functionality and naturalness of synthesized rooms evaluated by human subjects.",
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"fullName": "Siyuan Qi",
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"abstract": "Layout design is ubiquitous in many applications, e.g. architecture/urban planning, etc, which involves a lengthy iterative design process. Recently, deep learning has been leveraged to automatically generate layouts via image generation, showing a huge potential to free designers from laborious routines. While automatic generation can greatly boost productivity, designer input is undoubtedly crucial. An ideal AI-aided design tool should automate repetitive routines, and meanwhile accept human guidance and provide smart/proactive suggestions. However, the capability of involving humans into the loop has been largely ignored in existing methods which are mostly end-to-end approaches. To this end, we propose a new human-in-the-loop generative model, iPLAN, which is capable of automatically generating layouts, but also interacting with designers throughout the whole procedure, enabling humans and AI to co-evolve a sketchy idea gradually into the final design. iPLAN is evaluated on diverse datasets and compared with existing methods. The results show that iPLAN has high fidelity in producing similar layouts to those from human designers, great flexibility in accepting designer inputs and providing design suggestions accordingly, and strong generalizability when facing unseen design tasks and limited training data.",
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"abstract": "Flexible indoor scene synthesis is a popular topic in computer graphics and virtual reality research due to its wide-ranging applications in home design, games and automated robotics training. We propose a novel approach to automatic and flexible indoor scene synthesis using an energy-based method. We regard indoor scene synthesis as a multiple-object optimization problem with furniture location and orientation according to the user's intention, as a constraint on the energy of the optimization problem. Based on the relationship of objects, the embedded aesthetic criterion, the design criterion for proper placement and human movement in a scene, we design five energy functions, the overlap constraint, pairwise constraint, wall constraint, aisle constraint, angle constraint and penalty item, are proposed. We use a multi-object particle swarm intelligence optimization method with a Markov chain Monte Carlo algorithm to solve this optimization problem and obtain a Pareto-optimal solution. 3D gestures are used as the medium of interaction between the user and the system. Our method significantly enhances the existing weighted energy optimization method by allowing a joint optimization of various energy functions. The experiments confirm that all the energy functions can converge at the same time and that the proposed method obtains results superior to those of the weighted methods. The proposed method is general which can be used to obtain layouts for various kind of rooms with different furniture.",
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"content": "Flexible indoor scene synthesis is a popular topic in computer graphics and virtual reality research due to its wide-ranging applications in home design, games and automated robotics training. We propose a novel approach to automatic and flexible indoor scene synthesis using an energy-based method. We regard indoor scene synthesis as a multiple-object optimization problem with furniture location and orientation according to the user's intention, as a constraint on the energy of the optimization problem. Based on the relationship of objects, the embedded aesthetic criterion, the design criterion for proper placement and human movement in a scene, we design five energy functions, the overlap constraint, pairwise constraint, wall constraint, aisle constraint, angle constraint and penalty item, are proposed. We use a multi-object particle swarm intelligence optimization method with a Markov chain Monte Carlo algorithm to solve this optimization problem and obtain a Pareto-optimal solution. 3D gestures are used as the medium of interaction between the user and the system. Our method significantly enhances the existing weighted energy optimization method by allowing a joint optimization of various energy functions. The experiments confirm that all the energy functions can converge at the same time and that the proposed method obtains results superior to those of the weighted methods. The proposed method is general which can be used to obtain layouts for various kind of rooms with different furniture.",
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"affiliation": "Beijing Normal University",
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"affiliation": "Beijing Normal University",
"fullName": "Xingce Wang",
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"affiliation": "Beijing Normal University",
"fullName": "Zhongke Wu",
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"affiliation": "Beijing Normal University",
"fullName": "Shaolong Liu",
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"affiliation": "Beijing Normal University",
"fullName": "Mingquan Zhou",
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"abstract": "Computing statistical measures for large databases of time series is a fundamental primitive for querying and mining time-series data [1]-[6]. This primitive is gaining importance with the increasing number and rapid growth of time series databases. In this paper, we introduce a framework for efficient computation of statistical measures by exploiting the concept of affine relationships. Affine relationships can be used to infer statistical measures for time series, from other related time series, instead of computing them directly; thus, reducing the overall computational cost significantly. The resulting methods exhibit at least one order of magnitude improvement over the best known methods. To the best of our knowledge, this is the first work that presents an unified approach for computing and querying several statistical measures at once. Our approach exploits affine relationships using three key components. First, the AFCLST algorithm clusters the time-series data, such that high-quality affine relationships could be easily found. Second, the SYMEX algorithm uses the clustered time series and efficiently computes the desired affine relationships. Third, the SCAPE index structure produces a many-fold improvement in the performance of processing several statistical queries by seamlessly indexing the affine relationships. Finally, we establish the effectiveness of our approaches by performing comprehensive experimental evaluation on real datasets.",
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"abstract": "Neural Networks are one of many data mining analytical tools that can be utilized to make predictions for demographic sequences. In this paper, we presented the application of a hybrid model that integrates Long-Short Term Memory-Recurrent Neural Network (LSTM-RNN), time series analysis and clustering techniques where time series analysis and clustering methods provide augmentation of sequences data for the training of RNN. Comprehensive characteristics of nations from UN database are used as input to the hybrid model to predict the nations future population. The results prove that, RNN combined with time series and clustering methods has outperformed mere RNN approach without time series and clustering analysis. In addition, the hybrid Time Series and Clustering-RNN with relevant inputs lead to 20% higher predictive accuracy, measured by Root-Mean-Squared Error, compared to results produced by RNN alone.",
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"title": "Dynamic Binning for the Unknown Transient Patterns Analysis in Astronomical Time Series",
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"abstract": "In recent years, there arises a new opportunity for discovering transient phenomena such as supernovae, solar flares, and bursty events from detecting unknown transient patterns in astronomical time-series data. However, since these transient phenomena usually happen with unpredictable characteristics in shapes, sizes, and durations, scientists might lose some significant information due to the huge volume of astronomical data to be analyzed. Data sketching is useful to deal with such huge time-series data. A simple sketching technique is known as binning that captures the statistical summary of each bin of data points. In this paper, we attempt to provide a novel framework of data sketching for a statistical hypothesis testing and apply it for unknown transient pattern detection. The principal idea of statistical hypothesis testing lies in that two short-term and similar bins are mergeable into a long-term bin. By applying our proposed method, we suppress the unnecessary data while keeping the primary information without setting the bin size in advance. We evaluate our proposed method through experiments on the light curves in real-world data from telescopes with synthetic mixed-type transient patterns. Experimental results demonstrate that our proposed method outperforms several frameworks of transient pattern detection in astronomy.",
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"content": "In recent years, there arises a new opportunity for discovering transient phenomena such as supernovae, solar flares, and bursty events from detecting unknown transient patterns in astronomical time-series data. However, since these transient phenomena usually happen with unpredictable characteristics in shapes, sizes, and durations, scientists might lose some significant information due to the huge volume of astronomical data to be analyzed. Data sketching is useful to deal with such huge time-series data. A simple sketching technique is known as binning that captures the statistical summary of each bin of data points. In this paper, we attempt to provide a novel framework of data sketching for a statistical hypothesis testing and apply it for unknown transient pattern detection. The principal idea of statistical hypothesis testing lies in that two short-term and similar bins are mergeable into a long-term bin. By applying our proposed method, we suppress the unnecessary data while keeping the primary information without setting the bin size in advance. We evaluate our proposed method through experiments on the light curves in real-world data from telescopes with synthetic mixed-type transient patterns. Experimental results demonstrate that our proposed method outperforms several frameworks of transient pattern detection in astronomy.",
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"fno": "09671917",
"keywords": [
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"Dynamic Binning",
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"Transient Phenomena",
"Supernovae Flares",
"Solar Flares",
"Bursty Events",
"Astronomical Time Series Data",
"Astronomical Data",
"Data Sketching",
"Huge Time Series Data",
"Simple Sketching Technique",
"Data Points",
"Statistical Hypothesis Testing",
"Unknown Transient Pattern Detection",
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"Bin Size",
"Real World Data",
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"Shape",
"Conferences",
"Time Series Analysis",
"Big Data",
"Telescopes",
"Transient Analysis",
"Pattern Analysis",
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],
"authors": [
{
"affiliation": "Shizuoka University,Department of Informatics,Shizuoka,Japan",
"fullName": "Thanapol Phungtua-Eng",
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"affiliation": "Shizuoka University,Department of Informatics,Shizuoka,Japan",
"fullName": "Yoshitaka Yamamoto",
"givenName": "Yoshitaka",
"surname": "Yamamoto",
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{
"affiliation": "University of Tokyo,Institute of Astronomy,Tokyo,Japan",
"fullName": "Shigeyuki Sako",
"givenName": "Shigeyuki",
"surname": "Sako",
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"abstract": "Time series similarity search is the main subroutine of time series data mining algorithm. The efficiency of time series similarity research has become an obstacle to the development of time series mining algorithms. The representation of time series and the measurement of similarity are the basis of time series similarity research and play a crucial role in completing the similarity search task of time series. As a method of measuring similarity, the dynamic distortion of time can be effectively dealt with by deforming the time series over time, and it has good stability. However, time series data is usually an ever-increasing data stream, and the direct study of similarity will cause considerable storage space consumption, and may affect the accuracy and reliability of the algorithm. Therefore, it is necessary to determine the time series in advance, express the main self of the original time series in a concise and abstract form, and carry out similarity search on the developed sequence to improve the similarity search efficiency of the sequence.",
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{
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"content": "Time series similarity search is the main subroutine of time series data mining algorithm. The efficiency of time series similarity research has become an obstacle to the development of time series mining algorithms. The representation of time series and the measurement of similarity are the basis of time series similarity research and play a crucial role in completing the similarity search task of time series. As a method of measuring similarity, the dynamic distortion of time can be effectively dealt with by deforming the time series over time, and it has good stability. However, time series data is usually an ever-increasing data stream, and the direct study of similarity will cause considerable storage space consumption, and may affect the accuracy and reliability of the algorithm. Therefore, it is necessary to determine the time series in advance, express the main self of the original time series in a concise and abstract form, and carry out similarity search on the developed sequence to improve the similarity search efficiency of the sequence.",
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"title": "Deep Time Series Sketching and Its Application on Industrial Time Series Clustering",
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"abstract": "Today, voluminous multivariate time series data collected from sensors provides tremendous benefit for understanding of modern industrial systems such as power plants, wind turbines and aircrafts. However, the dynamic and complex nature of these systems, as well as the lack of prior knowledge impose challenges in perceiving different system behaviors from the time series data. To handle these issues, time series clustering has become one of the key analysis techniques. Nevertheless, the data nonlinearity, varying lengths and high dimensions of industrial time series could hinder the quality of clustering. To deal with these challenges, we propose Deep Time Series Sketching (DTSS) model. This model is a representation learning model based on temporal convolutional networks that perform on a sliding window basis along time series to learn the windows’ embeddings. The sequence of embeddings is then fed into embedding sketching to obtain its sketch. Such sketch is a descriptor of the whole time series and will be fed into K-means for clustering. Our model is a novel end-to-end hybrid model that incorporates both local and global contextual features. It is able to project multivariate time series with varying lengths into the same latent space. Moreover, we show that our model is able to perform early clustering as it can assign real-time label without seeing the whole time series. We test our model on both benchmark and real world industrial datasets, and experiments show that our proposed method outperforms popular time series clustering baselines.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Today, voluminous multivariate time series data collected from sensors provides tremendous benefit for understanding of modern industrial systems such as power plants, wind turbines and aircrafts. However, the dynamic and complex nature of these systems, as well as the lack of prior knowledge impose challenges in perceiving different system behaviors from the time series data. To handle these issues, time series clustering has become one of the key analysis techniques. Nevertheless, the data nonlinearity, varying lengths and high dimensions of industrial time series could hinder the quality of clustering. To deal with these challenges, we propose Deep Time Series Sketching (DTSS) model. This model is a representation learning model based on temporal convolutional networks that perform on a sliding window basis along time series to learn the windows’ embeddings. The sequence of embeddings is then fed into embedding sketching to obtain its sketch. Such sketch is a descriptor of the whole time series and will be fed into K-means for clustering. Our model is a novel end-to-end hybrid model that incorporates both local and global contextual features. It is able to project multivariate time series with varying lengths into the same latent space. Moreover, we show that our model is able to perform early clustering as it can assign real-time label without seeing the whole time series. We test our model on both benchmark and real world industrial datasets, and experiments show that our proposed method outperforms popular time series clustering baselines.",
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"affiliation": "GE Global Research,San Ramon,CA",
"fullName": "Hao Huang",
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"affiliation": "Brookhaven National Laboratory,Upton,NY",
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"abstract": "This paper proposes a new invertible two-dimensional map for image encryption in network. The map is a process of stretch-and-fold. Firstly a square image is divide two parts along the diagonal direction. Then they are stretched respectively and joins a array of pixels. Lastly the array is fold over to a new image. The process shuffles the positions of pixels. The map includes two sub maps: the left map and the right map. The security key is used to represent a sequence of number of iterations of the sub maps. Compared with the prevalent map, baker map, the new map has simpler formulation, bigger keys space, faster speed which is fit for image encryption. A symmetric image encryption approach based on the map is developed. The experimental tests are carried out and the??results proved the effectiveness of the new approach.",
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"content": "This paper proposes a new invertible two-dimensional map for image encryption in network. The map is a process of stretch-and-fold. Firstly a square image is divide two parts along the diagonal direction. Then they are stretched respectively and joins a array of pixels. Lastly the array is fold over to a new image. The process shuffles the positions of pixels. The map includes two sub maps: the left map and the right map. The security key is used to represent a sequence of number of iterations of the sub maps. Compared with the prevalent map, baker map, the new map has simpler formulation, bigger keys space, faster speed which is fit for image encryption. A symmetric image encryption approach based on the map is developed. The experimental tests are carried out and the??results proved the effectiveness of the new approach.",
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"title": "UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition",
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"abstract": "Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, 94.05%, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, 94.05%, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes.",
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"normalizedAbstract": "Recently proposed robust 3D face alignment methods establish either dense or sparse correspondence between a 3D face model and a 2D facial image. The use of these methods presents new challenges as well as opportunities for facial texture analysis. In particular, by sampling the image using the fitted model, a facial UV can be created. Unfortunately, due to self-occlusion, such a UV map is always incomplete. In this paper, we propose a framework for training Deep Convolutional Neural Network (DCNN) to complete the facial UV map extracted from in-the-wild images. To this end, we first gather complete UV maps by fitting a 3D Morphable Model (3DMM) to various multiview image and video datasets, as well as leveraging on a new 3D dataset with over 3,000 identities. Second, we devise a meticulously designed architecture that combines local and global adversarial DCNNs to learn an identity-preserving facial UV completion model. We demonstrate that by attaching the completed UV to the fitted mesh and generating instances of arbitrary poses, we can increase pose variations for training deep face recognition/verification models, and minimise pose discrepancy during testing, which lead to better performance. Experiments on both controlled and in-the-wild UV datasets prove the effectiveness of our adversarial UV completion model. We achieve state-of-the-art verification accuracy, 94.05%, under the CFP frontal-profile protocol only by combining pose augmentation during training and pose discrepancy reduction during testing. We will release the first in-the-wild UV dataset (we refer as WildUV) that comprises of complete facial UV maps from 1,892 identities for research purposes.",
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"keywords": [
"Convolution",
"Face Recognition",
"Feedforward Neural Nets",
"Image Texture",
"UV GAN",
"Adversarial Facial UV Map Completion",
"Pose Invariant Face Recognition",
"Robust 3 D Face Alignment Methods",
"Dense Correspondence",
"Sparse Correspondence",
"3 D Face Model",
"2 D Facial Image",
"Facial Texture Analysis",
"3 DMM",
"Multiview Image",
"Video Datasets",
"Local Adversarial DCN Ns",
"Global Adversarial DCN Ns",
"Identity Preserving Facial UV Completion Model",
"Fitted Mesh",
"Arbitrary Poses",
"In The Wild UV Dataset",
"Deep Convolutional Neural Network",
"3 D Morphable Model",
"Facial UV Maps",
"Deep Face Recognition Verification Models",
"Face",
"Three Dimensional Displays",
"Face Recognition",
"Two Dimensional Displays",
"Shape",
"Training",
"Generators"
],
"authors": [
{
"affiliation": null,
"fullName": "Jiankang Deng",
"givenName": "Jiankang",
"surname": "Deng",
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{
"affiliation": null,
"fullName": "Shiyang Cheng",
"givenName": "Shiyang",
"surname": "Cheng",
"__typename": "ArticleAuthorType"
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{
"affiliation": null,
"fullName": "Niannan Xue",
"givenName": "Niannan",
"surname": "Xue",
"__typename": "ArticleAuthorType"
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{
"affiliation": null,
"fullName": "Yuxiang Zhou",
"givenName": "Yuxiang",
"surname": "Zhou",
"__typename": "ArticleAuthorType"
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{
"affiliation": null,
"fullName": "Stefanos Zafeiriou",
"givenName": "Stefanos",
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"title": "2022 8th International Conference on Mechanical Engineering and Automation Science (ICMEAS)",
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"title": "Research on 3D modeling of UAV tilt photogrammetry",
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"abstract": "UAV oblique photogrammetry technology has been continuously developed and applied in 3D modeling in my country, and has played an important role in the fields of road surveying and building surveying. Its development and promotion have far-reaching significance for China's construction. This paper mainly introduces the principle of 3D modeling technology of UAV tilt photogrammetry, expounds the practical application of current tilt UAV photogrammetry technology, and proposes the application of static ground tilt UAV photogrammetry technology in the actual production process. Application technology analysis, the application trend of tilt UAV photogrammetry technology in 3D modeling is proposed, and the advantages and disadvantages of UAV tilt measurement technology are summarized for readers' reference.",
"abstracts": [
{
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"content": "UAV oblique photogrammetry technology has been continuously developed and applied in 3D modeling in my country, and has played an important role in the fields of road surveying and building surveying. Its development and promotion have far-reaching significance for China's construction. This paper mainly introduces the principle of 3D modeling technology of UAV tilt photogrammetry, expounds the practical application of current tilt UAV photogrammetry technology, and proposes the application of static ground tilt UAV photogrammetry technology in the actual production process. Application technology analysis, the application trend of tilt UAV photogrammetry technology in 3D modeling is proposed, and the advantages and disadvantages of UAV tilt measurement technology are summarized for readers' reference.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "UAV oblique photogrammetry technology has been continuously developed and applied in 3D modeling in my country, and has played an important role in the fields of road surveying and building surveying. Its development and promotion have far-reaching significance for China's construction. This paper mainly introduces the principle of 3D modeling technology of UAV tilt photogrammetry, expounds the practical application of current tilt UAV photogrammetry technology, and proposes the application of static ground tilt UAV photogrammetry technology in the actual production process. Application technology analysis, the application trend of tilt UAV photogrammetry technology in 3D modeling is proposed, and the advantages and disadvantages of UAV tilt measurement technology are summarized for readers' reference.",
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"Roads",
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"title": "2D Henon-Chebyshev Chaotic Map for Image Encryption",
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"abstract": "Chaotic systems is widely employed in image encryption on account of its momentous properties including nonperiodicity, ergodicity, randomness and initial state sensitivity. However, some chaos-based cryptosystems still exhibit security problems due to the weak performance of their applied chaotic maps. To address those problems, a new two-dimensional HenonChebyshev map (2D-HCM) is first proposed in this paper. It is generated by connecting Henon map and Chebyshev map where the output of one map is used to adapt the input of the others. It results in a better chaotic performance in 2D-HCM, including better chaotic behaviors, higher chaotic index, wider chaotic range and finer ergodicity than other extant chaotic maps. Using the 2D-HCM, we further design an image encryption algorithm. The proposed algorithm disorganize the pixel positions of the input plain image with random sequence generated by 2D-HCM, and then convert it into DNA-planes using DNA operation rules. Finally, the cipher image is acquired by employing the newly defined DNA-level 2D cellular automata (DNA-CA) to update the DNA-planes in each iteration where 2D-HCM produces the rule number sequences applied in the update. Our scheme has proven to be highly efficient and secure by the experimental results and security analysis.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Chaotic systems is widely employed in image encryption on account of its momentous properties including nonperiodicity, ergodicity, randomness and initial state sensitivity. However, some chaos-based cryptosystems still exhibit security problems due to the weak performance of their applied chaotic maps. To address those problems, a new two-dimensional HenonChebyshev map (2D-HCM) is first proposed in this paper. It is generated by connecting Henon map and Chebyshev map where the output of one map is used to adapt the input of the others. It results in a better chaotic performance in 2D-HCM, including better chaotic behaviors, higher chaotic index, wider chaotic range and finer ergodicity than other extant chaotic maps. Using the 2D-HCM, we further design an image encryption algorithm. The proposed algorithm disorganize the pixel positions of the input plain image with random sequence generated by 2D-HCM, and then convert it into DNA-planes using DNA operation rules. Finally, the cipher image is acquired by employing the newly defined DNA-level 2D cellular automata (DNA-CA) to update the DNA-planes in each iteration where 2D-HCM produces the rule number sequences applied in the update. Our scheme has proven to be highly efficient and secure by the experimental results and security analysis.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Chaotic systems is widely employed in image encryption on account of its momentous properties including nonperiodicity, ergodicity, randomness and initial state sensitivity. However, some chaos-based cryptosystems still exhibit security problems due to the weak performance of their applied chaotic maps. To address those problems, a new two-dimensional HenonChebyshev map (2D-HCM) is first proposed in this paper. It is generated by connecting Henon map and Chebyshev map where the output of one map is used to adapt the input of the others. It results in a better chaotic performance in 2D-HCM, including better chaotic behaviors, higher chaotic index, wider chaotic range and finer ergodicity than other extant chaotic maps. Using the 2D-HCM, we further design an image encryption algorithm. The proposed algorithm disorganize the pixel positions of the input plain image with random sequence generated by 2D-HCM, and then convert it into DNA-planes using DNA operation rules. Finally, the cipher image is acquired by employing the newly defined DNA-level 2D cellular automata (DNA-CA) to update the DNA-planes in each iteration where 2D-HCM produces the rule number sequences applied in the update. Our scheme has proven to be highly efficient and secure by the experimental results and security analysis.",
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"Cryptography",
"Henon Mapping",
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"Random Sequences",
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"abstract": "Shape skeleton extraction is a fundamental pre-processing task in shape-based pattern recognition. This paper presents a new algorithm for fast and precise extraction of kinematic skeletons of 3D dynamic surface meshes. Unlike previous approaches, surface motions are characterized by the mesh edge-length deviation induced by its transformation through time. Then a static skeleton extraction algorithm based on Reeb graphs exploits this latter information to extract the kinematic skeleton. This hybrid static and dynamic shape analysis enables the precise detection of objects¿ articulations as well as shape topological transitions corresponding to possibly-articulated immobile objects¿ features. Experiments show that the proposed algorithm is faster than previous techniques and still achieves better accuracy.",
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"abstract": "Severe weather conditions cause enormous amount of damages around the globe. Bow echo patterns in radar images are associated with a number of these destructive thunderstorm conditions such as damaging winds, hail and tornadoes. They are detected manually by meteorologists. In this paper, we propose an automatic framework to detect these patterns with high accuracy by introducing novel skeletonization and shape matching approaches. In this framework, first we extract regions with high probability of occurring bow echo from radar images, and apply our skeletonization method to extract the skeleton of those regions. Next, we prune these skeletons using our innovative pruning scheme with fuzzy logic. Then, using our proposed shape descriptor, Skeleton Context, we can extract bow echo features from these skeletons in order to use them in shape matching algorithm and classification step. The output of classification indicates whether these regions include a bow echo with over 97% accuracy.",
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"affiliation": "The Pennsylvania State University",
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"normalizedAbstract": "Building reliable object detectors that are robust to domain shifts, such as various changes in context, viewpoint, and object appearances, is critical for real-world applications. In this work, we study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf), which operates on the region features of the object detectors. For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision. CycConf encourages the object detector to explore invariant structures across instances under various motions, which leads to improved model robustness in unseen domains at test time. We observe consistent out-of-domain performance improvements when training object detectors in tandem with self-supervised tasks on various do-main adaptation benchmarks with static images (Cityscapes, Foggy Cityscapes, Sim10K) and large-scale video datasets (BDD100K and Waymo open data)1.",
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"fullName": "Thomas E. Huang",
"givenName": "Thomas E.",
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{
"affiliation": "University of Washington",
"fullName": "Benlin Liu",
"givenName": "Benlin",
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"affiliation": "ETH Zürich",
"fullName": "Fisher Yu",
"givenName": "Fisher",
"surname": "Yu",
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"affiliation": "UC San Diego",
"fullName": "Xiaolong Wang",
"givenName": "Xiaolong",
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"affiliation": "UC Berkeley",
"fullName": "Joseph E. Gonzalez",
"givenName": "Joseph E.",
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{
"affiliation": "UC Berkeley",
"fullName": "Trevor Darrell",
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"abstract": "Educational data mining (EDM) using enhanced research methods are allowing researchers to effectively model a spectrum of paradigms affecting students learning, including various epistemic emotions like confusion. confusion plays a vital role in learning, and some amount of confusion is constructive in learning new knowledge. However, when confusion is left unattended for long, it may lead the student to lose interest or feel frustrated and eventually drop out of the course. In this paper, we investigate student’s performance to detect the level of confusion in the exercises they attempt online. We investigate the performance of feedforward neural network algorithm, MLP (Multi-Layer Perceptron), and report the results and comparison of various algorithms and how the same methodology can be extended to any Learning Management System (LMS) on various digital learning platforms, including MOOCs especially because they suffer from high drop-out rates. We also discuss how we plan to extend our research to include more features to make it appropriate for cross-domain implementation.",
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"content": "Educational data mining (EDM) using enhanced research methods are allowing researchers to effectively model a spectrum of paradigms affecting students learning, including various epistemic emotions like confusion. confusion plays a vital role in learning, and some amount of confusion is constructive in learning new knowledge. However, when confusion is left unattended for long, it may lead the student to lose interest or feel frustrated and eventually drop out of the course. In this paper, we investigate student’s performance to detect the level of confusion in the exercises they attempt online. We investigate the performance of feedforward neural network algorithm, MLP (Multi-Layer Perceptron), and report the results and comparison of various algorithms and how the same methodology can be extended to any Learning Management System (LMS) on various digital learning platforms, including MOOCs especially because they suffer from high drop-out rates. We also discuss how we plan to extend our research to include more features to make it appropriate for cross-domain implementation.",
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