The article researches the problems of improving the education process through the use of the new state-of-The-Art learning technologies, in particular the combination of 3D modeling and virtual reality technologies, ...
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The Open-Source Software community has become the center of attention for many researchers, who are investigating various aspects of collaboration in this extremely large ecosystem. Due to its size, it is difficult to...
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ISBN:
(纸本)9781728187105
The Open-Source Software community has become the center of attention for many researchers, who are investigating various aspects of collaboration in this extremely large ecosystem. Due to its size, it is difficult to grasp whether or not it has structure, and if so, what it may be. Our hackathon project aims to facilitate the understanding of the developer collaboration structure and relationships among projects based on the bi-graph of what projects developers contribute to by providing an interactive collaboration graph of this ecosystem, using the data obtained from World of Code [1] infrastructure. Our attempts to visualize the entirety of projects and developers were stymied by the inability of the layout and visualization tools to process the exceedingly large scale of the full graph. We used WoC to filter the nodes (developers and projects) and edges (developer contributions to a project) to reduce the scale of the graph that made it amenable to an interactive visualization and published the resulting visualizations. We plan to apply hierarchical approaches to be able to incorporate the entire data in the interactive visualizations and also to evaluate the utility of such visualizations for several tasks.
The use of augmented reality technology to support humans with situated visualization in complex tasks such as navigation or assembly has gained increasing importance in research and industrial applications. One impor...
The use of augmented reality technology to support humans with situated visualization in complex tasks such as navigation or assembly has gained increasing importance in research and industrial applications. One important line of research regards supporting and understanding collaborative tasks. Analyzing collaboration patterns is usually done by conducting observations and interviews. To expand these methods, we argue that eye tracking can be used to extract further insights and quantify behavior. To this end, we contribute a study that uses eye tracking to investigate participant strategies for solving collaborative sorting and assembly tasks. We compare participants’ visual attention during situated instructions in AR and traditional paper-based instructions as a baseline. By investigating the performance and gaze behavior of the participants, different strategies for solving the provided tasks are revealed. Our results show that with situated visualization, participants focus more on task-relevant areas and require less discussion between collaboration partners to solve the task at hand.
This paper proposes an approach for deforestation detection using Geographical information system to manipulate and analyze geographical data. Here, the rate of deforestation is detected using a vegetation index measu...
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Millions of people worldwide work in jobs where assessing dynamic data presented visually to them is a key part of their tasks. Since the data is only represented in a visual format, these occupations are out of reach...
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Video super-resolution (VSR) aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Although some progress has been made, there are grand challenges to effectively utili...
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ISBN:
(数字)9781665469463
ISBN:
(纸本)9781665469463
Video super-resolution (VSR) aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Although some progress has been made, there are grand challenges to effectively utilize temporal dependency in entire video sequences. Existing approaches usually align and aggregate video frames from limited adjacent frames (e.g., 5 or 7 frames), which prevents these approaches from satisfactory results. In this paper, we take one step further to enable effective spatio-temporal learning in videos. We propose a novel Trajectory-aware Transformer for Video Super-Resolution (TTVSR). In particular, we formulate video frames into several pre-aligned trajectories which consist of continuous visual tokens. For a query token, self-attention is only learned on relevant visual tokens along spatio-temporal trajectories. Compared with vanilla vision Transformers, such a design significantly reduces the computational cost and enables Transformers to model long-range features. We further propose a cross-scale feature tokenization module to overcome scale-changing problems that often occur in longrange videos. Experimental results demonstrate the superiority of the proposed TTVSR over state-of-the-art models, by extensive quantitative and qualitative evaluations in four widely-used video super-resolution benchmarks. Both code and pre-trained models can be downloaded at https://***/researchmm/TTVSR.
Stealthy attacks make Intrusion Detection System (IDS) research perennial and notably important. From the literature, a few problems were identified in IDS studies particularly with regards to image generation. Image ...
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We propose a visual analysis method for the comparison and evaluation of structures in solid-state materials based on the electron density field using topological analysis. The work has been motivated by a material sc...
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Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate t...
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ISBN:
(数字)9781665469463
ISBN:
(纸本)9781665469463
Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requires sampling from two intractable distributions. In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs. Particularly, we lead a de coupled EBLVM consisting of a marginal energy-based distribution and a structural posterior to handle the difficulties when learning deep EBLVMs on images. By choosing a symmetric KL divergence in the lower level of our framework, a compact BiDVL for visual tasks can be obtained. Our model achieves impressive image generation performance over related works. It also demonstrates the significant capacity of testing image reconstruction and out-of-distribution detection.
SELinux policies used in practice are generally large and complex. As a result, it is difficult for the policy writers to completely understand the policy and ensure that the policy meets the intended security goals. ...
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ISBN:
(纸本)9781665433518
SELinux policies used in practice are generally large and complex. As a result, it is difficult for the policy writers to completely understand the policy and ensure that the policy meets the intended security goals. To remedy this, we have developed a tool called SEFlowViz that helps in visualizing the information flows of a policy and thereby helps in creating flow-secure policies. The tool uses the graph database Neo4j to visualize the policy. Along with visualization, the tool also supports extracting various information regarding the policy and its components through queries. Furthermore, the tool also supports the addition and deletion of rules which is useful in converting inconsistent policies into consistent policies.
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