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检索条件"任意字段=IEEE Symposium on Large Data Analysis and Visualization"

共 15616 条记录，以下是1561-1570 订阅

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TimeSeriesMaker: Interactive Time Series Composition in No Time

TimeSeriesMaker: Interactive Time Series Composition in No T...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Franziska Becker Tanja Blascheck University of Stuttgart Germany

ISBN: (数字)9798350393804

ISBN: (纸本)9798350393811

TimeSeriesMaker is an open-source application to visually compose time series data in an intuitive and shareable manner. visualization researchers often use time series data in studies about perceptual or cognitive phenomena and many other contexts. However, finding or generating time series data that fits a given scenario is not always easy. Using a component-based architecture, TimeSeriesMaker allows analysts to compose time series data with complex patterns by combining different components, such as noise, a linear trend or a seasonal pattern. An interactive compositor tree of these components lets analysts explore their combinations using different operators. We support reproducibility and transparency by including functionalities that allow analysts to export and share their configuration, which others can use to reload and modify the same time series. In a qualitative online study with visualization researchers, we found that our approach enables them to create a time series based on an example image or their own requirements. However, system usability could be further improved when interacting with the compositor tree. TimeSeriesMaker can be found here: https://***/time-series-maker/.

关键词： Time series analysis Noise data visualization Switches Market research Software Reproducibility of results

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A Reflection on Leveraging Vision Language Model for Visual analysis in Image-Based Person Re-Identification

A Reflection on Leveraging Vision Language Model for Visual ...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Wang Xia Tianci Wang Jiawei Li Guodao Sun Ronghua Liang Zhejiang University of Technology

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

Image-based person re-identification (Re-ID) aims to identify and track individuals across multiple camera views using query images. While machine learning methods have made progress, their real-world performance remains limited. A key challenge lies in the nature of person retrieval, which requires users to perform fine-grained matching and filtering. This process often involves manually comparing and evaluating a large number of candidate images, resulting in low retrieval efficiency and being time-consuming. To address these issues, we introduce textual information to assist users in performing fine-grained retrieval tasks. Specifically, we utilize vision-language models fine-tuned with domain knowledge to generate hierarchical textual descriptions as retrieval cues. We also provide a visual analysis tool, which adopts multi-view and adjustable visual encodings to aggregate and present image data, supporting interactive browsing and retrieval. Finally, we conduct a case study and visual analysis experiments to evaluate the effectiveness of the textual retrieval cues. The evaluation results reveal the potential of textual information in optimizing person retrieval and offers insights for future work.

关键词： Image coding Filtering Visual analytics Image retrieval Semantics Machine learning User experience Reflection Prompt engineering Identification of persons

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Road Accidents dataset analysis through Attribute-oriented Induction

Road Accidents Dataset Analysis through Attribute-oriented I...

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International symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Anna Biceková Michal Michňák František Babič Department of Cybernetics and Artificial Inteligence Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

The road traffic accidents data represents an important source of knowledge interesting for various decision processes. Based on the target group of users, we can identify several tasks like the most accurate classification model as possible, potentially useful hidden patterns and relations between them, or an interactive descriptive visualization. Based on the performed state-of-the-art, we decided to apply non-traditional method of descriptive data mining called Attribute-oriented Induction in combination of typical classification methods. The results confirmed our expectation that this method has an interesting potential how to extract important patterns based on existing hierarchy of concepts.

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Effective Seed Scheduling for Fuzzing with Graph Centrality analysis 43

Effective Seed Scheduling for Fuzzing with Graph Centrality ...

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43rd ieee symposium on Security and Privacy (SP)

作者： She, Dongdong Shah, Abhishek Jana, Suman Columbia Univ New York NY 10027 USA

ISBN: (数字)9781665413169

ISBN: (纸本)9781665413169

Seed scheduling, the order in which seeds are selected, can greatly affect the performance of a fuzzer. Existing approaches schedule seeds based on their historical mutation data, but ignore the structure of the underlying Control Flow Graph (CFG). Examining the CFG can help seed scheduling by revealing the potential edge coverage gain from mutating a seed. An ideal strategy will schedule seeds based on a count of all reachable and feasible edges from a seed through mutations, but computing feasibility along all edges is prohibitively expensive. Therefore, a seed scheduling strategy must approximate this count. We observe that an approximate count should have 3 properties -(i) it should increase if there are more edges reachable from a seed;(ii) it should decrease if mutation history information suggests an edge is hard to reach or is located far away from currently visited edges;and (iii) it should be efficient to compute over large CFGs. We observe that centrality measures from graph analysis naturally provide these three properties and therefore can efficiently approximate the likelihood of reaching unvisited edges by mutating a seed. We therefore build a graph called the edge horizon graph that connects seeds to their closest unvisited nodes and compute the seed node's centrality to measure the potential edge coverage gain from mutating a seed. We implement our approach in K-Scheduler and compare with many popular seed scheduling strategies. We find that K-Scheduler increases feature coverage by 25.89% compared to Entropic and edge coverage by 4.21% compared to the nextbest AFL-based seed scheduler, in arithmetic mean on 12 Google FuzzBench programs. It also finds 3 more previously-unknown bugs than the next-best AFL-based seed scheduler.

关键词： Schedules Privacy Processor scheduling Gain measurement Fuzzing Internet Security

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NcoVis: A Visual analysis Framework for Exploring Academic Collaboration Networks under New Collaborative Relationships 25

NcoVis: A Visual Analysis Framework for Exploring Academic C...

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25th ieee International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 ieee.

关键词： visualization

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How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems

How Multimodal Integration Boost the Performance of LLM for ...

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Multidisciplinary Computational Intelligence Incubators (MCII), ieee symposium for

作者： Yuxiao Huang Wenjie Zhang Liang Feng Xingyu Wu Kay Chen Tan Dept. of Data Science and Artificial Intelligence (DSAI) The Hong Kong Polytechnic University Hong Kong SAR Dept. of Electrical and Elctronics Engineering The Hong Kong Polytechnic University Hong Kong SAR College of Computer Science Chongqing University Chongqing China Dept. of DSAI The Hong Kong Polytechnic University Hong Kong SAR

ISBN: (数字)9798331508395

ISBN: (纸本)9798331508401

Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges. Despite this recognition, a predominant limitation of existing LLM-based optimization methods is their struggle to capture the relationships among decision variables when relying exclusively on numerical text prompts, especially in high-dimensional problems. Keeping this in mind, we first propose to enhance the optimization performance using multimodal LLM capable of processing both textual and visual prompts for deeper insights of the processed optimization problem. This integration allows for a more comprehensive understanding of optimization problems, akin to human cognitive processes. We have developed a multimodal LLM-based optimization framework that simulates human problem-solving workflows, thereby offering a more nuanced and effective analysis. The efficacy of this method is evaluated through extensive empirical studies focused on a well-known combinatorial optimization problem, i.e., capacitated vehicle routing problem. The results are compared against those obtained from the LLM-based optimization algorithms that rely solely on textual prompts, demonstrating the significant advantages of our multimodal approach.

关键词： visualization Cognitive processes large language models Vehicle routing Optimization methods Multisensory integration Problem-solving Optimization Computational intelligence

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Optimizing Huffman Decoding for Error-Bounded Lossy Compression on GPUs 36

Optimizing Huffman Decoding for Error-Bounded Lossy Compress...

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36th ieee International Parallel and Distributed Processing symposium (ieee IPDPS)

作者： Rivera, Cody Di, Sheng Tian, Jiannan Yu, Xiaodong Tao, Dingwen Cappello, Franck Univ Alabama Dept Comp Sci Tuscaloosa AL 35487 USA Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA Argonne Natl Lab Math & Comp Sci Div Lemont IL USA

ISBN: (纸本)9781665481069

More and more HPC applications require fast and effective compression techniques to handle large volumes of data in storage and transmission. Not only do these applications need to compress the data effectively during simulation, but they also need to perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor for scientific data, and cuSZ is a version of SZ designed to take advantage of the GPU's power. At present, cuSZ's compression performance has been optimized significantly while its decompression still suffers considerably lower performance because of its sophisticated loss-less compression step-a customized Huffman decoding. In this work, we aim to significantly improve the Huffman decoding performance for cuSZ, thus improving the overall decompression performance in turn. To this end, we first investigate two state-of-the-art GPU Huffman decoders in depth. Then, we propose a deep architectural optimization for both algorithms. Specifically, we take full advantage of CUDA GPU architectures by using shared memory on decoding/writing phases, online tuning the amount of shared memory to use, improving memory access patterns, and reducing warp divergence. Finally, we evaluate our optimized decoders on an Nvidia V100 GPU using eight representative scientific datasets. Our new decoding solution obtains an average speedup of 3.64x over cuSZ's Huffman decoder and improves its overall decompression performance by 2.43x on average.

关键词： Distributed processing Analytical models Memory management Graphics processing units Distributed databases Throughput data models

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Effect of Weight Quantization on Learning Models by Typical Case analysis

Effect of Weight Quantization on Learning Models by Typical ...

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ieee International symposium on Information Theory

作者： Shuhei Kashiwamura Ayaka Sakata Masaaki Imaizumi The University of Tokyo Tokyo Japan The Institute of Statistical Mathematics Tokyo Japan RIKEN AIP Tokyo Japan

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This paper examines the quantization methods used in large-scale data analysis models and their hyperparameter choices. The recent surge in data analysis scale has significantly increased computational resource requirements. To address this, quantizing model weights has become a prevalent practice in data analysis applications such as deep learning. Quantization is particularly vital for deploying large models on devices with limited computational resources. However, the selection of quantization hyperparameters, like the number of bits and value range for weight quantization, remains an underexplored area. In this study, we employ the typical case analysis from statistical physics, specifically the replica method, to explore the impact of hyperparameters on the quantization of simple learning models. Our analysis yields three key findings: (i) an unstable hyperparameter phase, known as replica symmetry breaking, occurs with a small number of bits and a large quantization width; (ii) there is an optimal quantization width that minimizes error; and (iii) quantization delays the onset of overparameterization, which mitigate overfitting as indicated by the double descent phenomenon. We also discover that non-uniform quantization can enhance stability. Additionally, we develop an approximate message-passing algorithm to validate our theoretical results.

关键词： Analytical models Quantization (signal) data analysis Computational modeling data models Stability analysis Delays

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GroupRugs: Visual Summaries for Groups in Collective Movement data

GroupRugs: Visual Summaries for Groups in Collective Movemen...

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Pacific (formerly Asia-Pacific APVIS) visualization symposium

作者： Marie Stolk Jules Wulms Kevin Verbeek TU Eindhoven

ISBN: (数字)9798331505813

ISBN: (纸本)9798331505820

As more and more moving objects are tracked, the amount and variety in trajectory data is ever increasing. Visual summaries provide an at-a-glance overview of such trajectories and are therefore a useful tool for exploring large trajectory collections. Typically, such a visual summary visualizes the spatial positions of moving entities using a one-dimensional representation and combines such representations by placing them in temporal order along a time line. However, existing summaries are generally not tailored to specific patterns that arise in the trajectory data. The formation of groups is a quintessential pattern that emerges in the collective motion of many types of tracked objects, such as humans, birds, and other animals. Our main contribution is GroupRugs, a visual summary technique for collective movement data that highlights the structure of the emerging groups and their evolution over time, while still summarizing the spatial relations in the data. Specifically, we introduce two methods to produce GroupRugs: a naive baseline approach, and a pipeline that optimizes several aspects of GroupRugs. The quality of a visual summary is usually assessed via two main criteria: spatial quality, which measures how well the one-dimensional representations capture the structure of the data points at each time step, and stability, which captures the coherence of consecutive one-dimensional representations over time. For GroupRugs, we additionally care for how well the structure of the emerging groups is expressed in the visualization. In extensive computational experiments, we quantitatively evaluate GroupRugs against state-of-the-art techniques for summarizing trajectories, using well-established metrics for the three important quality criteria. Our evaluation shows that GroupRugs greatly outperform existing techniques in expressing the structure of emerging groups, while sacrificing only little spatial quality or stability.

关键词： Human computer interaction Dimensionality reduction visualization Tracking Stability criteria Pipelines data visualization Probability Time measurement Trajectory

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MCI Detection Using FMRI Time Series Embeddings of Recurrence Plots

MCI Detection Using FMRI Time Series Embeddings of Recurrenc...

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ieee International symposium on Biomedical Imaging

作者： Ninad Aithal Chakka Sai Pradeep Neelam Sinha Centre for Brain Research Indian Institute of Science Bengaluru International Institute of Information Technology Bengaluru

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The human brain can be conceptualized as a dynamical system. Utilizing resting state fMRI time series imaging, we can study the underlying dynamics at ear-marked Regions of Interest (ROIs) to understand structure or lack thereof. This differential behavior could be key to understanding the neurodegeneration and also to classify between healthy and Mild Cognitive Impairment (MCI) subjects. In this study, we consider 6 brain networks spanning over 160 ROIs derived from Dosenbach template, where each network consists of 25-30 ROIs. Recurrence plot, extensively used to understand evolution of time series, is employed. Representative time series at each ROI is converted to its corresponding recurrence plot visualization, which is subsequently condensed to low-dimensional feature embeddings through Autoencoders. The performance of the proposed method is shown on fMRI volumes of 100 subjects (balanced data), taken from publicly available ADNI dataset. Results obtained show peak classification accuracy of 93% among the 6 brain networks, mean accuracy of 89.3% thereby illustrating promise in the proposed approach.

关键词： Accuracy Time series analysis data visualization Functional magnetic resonance imaging Feature extraction Task analysis Dynamical systems

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