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largeNetVis: Visual Exploration of large Temporal Networks Based on Community Taxonomies

ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第1期29卷 203-213页

作者： Linhares, Claudio D. G. Ponciano, Jean R. Pedro, Diogenes S. Rocha, Luis E. C. Traina, Agma J. M. Poco, Jorge Univ Sao Paulo Inst Math & Comp Sci Sao Carlos Brazil Univ Ghent Dept Econ Ghent Belgium Univ Ghent Dept Phys & Astron Ghent Belgium Fdn Getulio Vargas Sch Appl Math Rio De Janeiro Brazil

Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose largeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map - TAM) shows the community- and node-level activity under a temporal perspective. We demonstrate the usefulness and effectiveness of largeNetVis through two usage scenarios and a user study with 14 participants.

关键词： temporal networks dynamic graphs network visualization visual scalability community taxonomy

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data analysis and visualization for Electrical Safety Tools and Equipment 5

Data Analysis and Visualization for Electrical Safety Tools ...

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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Xue, Liang Yang, Lei Suzhou City Univ Comp Sci & Artificial Intelligence Coll Suzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

Electric safety tool data has the characteristics of large data volume, high dimensionality, and strong effectiveness. Traditional data analysis still has problems such as insufficient accuracy, slow processing speed, and imprecise feature selection when processing this type of data. This paper proposes a method for predicting the loss of safety tools based on classification algorithms, which provides data support for procurement by predicting the loss. In addition, a supplier scoring model based on scoring indicators was constructed to explore the potential value of the data. First, the data is characterized by construction, elimination, fusion, and reconstruction, and correlation coefficients are used for feature selection and tested on different prediction models;Secondly, a data classification model based on scoring indicators is established to form a new data dimension, including after-sales service, product quality, scale indicators, and customer feedback;Finally, visualize the data set from the perspective of the overall table and sub-tables, and the changing trends of key evaluation indicators were analyzed. This method can not only provide data for predicting the loss of electric power tools but also solve the problem of supplier evaluation and realize multi-dimensional in-depth mining and comprehensive visualization of data.

关键词： component Machine learning Feature engineering Classification algorithm data analysis data visualization

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QEVIS: Multi-Grained visualization of Distributed Query Execution

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 153-163页

作者： Shen, Qiaomu You, Zhengxin Yan, Xiao Zhang, Chaozu Xu, Ke Zeng, Dan Qin, Jianbin Tang, Bo Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Huawei Technol Co Ltd Shenzhen Peoples R China Shenzhen Univ Shenzhen Inst Comp Sci Shenzhen Peoples R China

Distributed query processing systems such as Apache Hive and Spark are widely-used in many organizations for large-scale data analytics. Analyzing and understanding the query execution process of these systems are daily routines for engineers and crucial for identifying performance problems, optimizing system configurations, and rectifying errors. However, existing visualization tools for distributed query execution are insufficient because (i) most of them (if not all) do not provide fine-grained visualization (i.e., the atomic task level), which can be crucial for understanding query performance and reasoning about the underlying execution anomalies, and (ii) they do not support proper linkages between system status and query execution, which makes it difficult to identify the causes of execution problems. To tackle these limitations, we propose QEVIS, which visualizes distributed query execution process with multiple views that focus on different granularities and complement each other. Specifically, we first devise a query logical plan layout algorithm to visualize the overall query execution progress compactly and clearly. We then propose two novel scoring methods to summarize the anomaly degrees of the jobs and machines during query execution, and visualize the anomaly scores intuitively, which allow users to easily identify the components that are worth paying attention to. Moreover, we devise a scatter plot-based task view to show a massive number of atomic tasks, where task distribution patterns are informative for execution problems. We also equip QEVIS with a suite of auxiliary views and interaction methods to support easy and effective cross-view exploration, which makes it convenient to track the causes of execution problems. QEVIS has been used in the production environment of our industry partner, and we present three use cases from real-world applications and user interview to demonstrate its effectiveness. QEVIS is open-source at https://***/

关键词： visual analytics system distributed query execution performance analysis

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Incidents1M: A large-Scale dataset of Images With Natural Disasters, Damage, and Incidents

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ieee TRANSACTIONS ON PATTERN analysis AND MACHINE INTELLIGENCE 2023年第4期45卷 4768-4781页

作者： Weber, Ethan Papadopoulos, Dim P. Lapedriza, Agata Ofli, Ferda Imran, Muhammad Torralba, Antonio BAIR Berkeley Berkeley CA 94720 USA Tech Univ Denmark DTU DK-2800 Lyngby Denmark Univ Oberta Catalunya Barcelona 08018 Spain MIT Medialab Cambridge MA 02139 USA Qatar Comp Res Inst Doha Qatar MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident will occur, so timely emergency response is critical to saving the lives of those endangered by destructive events. Fortunately, technology can play a role in these situations. Social media posts can be used as a low-latency data source to understand the progression and aftermath of a disaster, yet parsing this data is tedious without automated methods. Prior work has mostly focused on text-based filtering, yet image and video-based filtering remains largely unexplored. In this work, we present the Incidents1M dataset, a large-scale multi-label dataset which contains 977,088 images, with 43 incident and 49 place categories. We provide details of the dataset construction, statistics and potential biases;introduce and train a model for incident detection;and perform image-filtering experiments on millions of images on Flickr and Twitter. We also present some applications on incident analysis to encourage and enable future work in computer vision for humanitarian aid. Code, data, and models are available at http://***.

关键词： Social networking (online) Task analysis Satellites Computational modeling data models visualization Training Global warming Image analysis Disasters Visual recognition scene understanding image dataset social media disaster analysis incident detection

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Seeing the Shift: Keep an Eye on Semantic Changes in Times of LLMs

Seeing the Shift: Keep an Eye on Semantic Changes in Times o...

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2024 Conference on visualization in data Science-VDS-Annual

作者： Buchmueller, Raphael Koerte, Friedericke Keim, Daniel Univ Konstanz Constance Germany

ISBN: (纸本)9798331528423;9798331528430

This position paper discusses the profound impact of large Language Models (LLMs) on semantic change, emphasizing the need for comprehensive monitoring and visualization techniques. Building on linguistic concepts, we examine the interdependency between mental and language models, highlighting how LLMs and human cognition mutually influence each other within societal contexts. We introduce three primary theories to conceptualize such influences: (T1) Recontextualization, (T2) Standardization, and (T3) Semantic Dementia, illustrating how LLMs drive, standardize, and potentially degrade language semantics. Our subsequent review categorizes methods for visualizing semantic change into frequency-based, embedding-based, and context-based techniques, being first in assessing their effectiveness in capturing linguistic evolution: Embedding-based methods are highlighted as crucial for a detailed semantic analysis, reflecting both broad trends and specific linguistic changes. We underscore the need for novel visualization tools to explain LLM-induced semantic changes, ensuring the preservation of linguistic diversity and mitigating biases, while providing essential insights for the research on semantic change visualization and the dynamic nature of language evolution in the times of LLMs.

关键词： Computation and Language visualization Semantic Change Word Embeddings large Language Models

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WHATSNEXT: Guidance-enriched Exploratory data analysis with Interactive, Low-Code Notebooks

WHATSNEXT: Guidance-enriched Exploratory Data Analysis with ...

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ieee symposium on Visual Languages and Human-Centric Computing (VL/HCC)

作者： Chen, Chen Hoffswell, Jane Guo, Shunan Rossi, Ryan Chan, Yeuk-Yin Du, Fan Koh, Eunyee Liu, Zhicheng Univ Maryland College Pk MD 20742 USA Adobe Res Seattle WA USA Adobe Res San Jose CA USA

ISBN: (纸本)9798350329469

Computational notebooks such as Jupyter are popular for exploratory data analysis and insight finding. Despite the module-based structure, notebooks visually appear as a single thread of interleaved cells containing text, code, visualizations, and tables, which can be unorganized and obscure users' data analysis workflow. Furthermore, users with limited coding expertise may struggle to quickly engage in the analysis process. In this work, we design and implement an interactive notebook framework, WHATSNEXT, with the goal of supporting low-code visual data exploration with insight-based user guidance. In particular, we (1) re-design a standard notebook cell to include a recommendation panel that suggests possible next-step exploration questions or analysis actions to take, and (2) create an interactive, dynamic tree visualization that reflects the analytic dependencies between notebook cells to make it easy for users to see the structure of the data exploration threads and trace back to previous steps.

关键词： Visual analytics Interactive systems and tools

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Proceedings - 2024 Topological data analysis and visualization, TopoInVis 2024

Proceedings - 2024 Topological Data Analysis and Visualizati...

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2024 ieee Topological data analysis and visualization, TopoInVis 2024

ISBN: (纸本)9798331528447

The proceedings contain 6 papers. The topics discussed include: efficient representation and analysis for a large tetrahedral mesh using Apache spark;critical point extraction from multivariate functional approximation;topological simplification of Jacobi sets for piecewise-linear bivariate 2D scalar fields with adjustment of the underlying data;revisiting accurate geometry for Morse-Smale complexes;multi-scale cycle tracking in dynamic planar graphs;and asymptotic topology of 3D linear symmetric tensor fields.

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Open-Ended Online Learning for Autonomous Visual Perception

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ieee TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第8期35卷 10178-10198页

作者： Yu, Haibin Cong, Yang Sun, Gan Hou, Dongdong Liu, Yuyang Dong, Jiahua Chinese Acad Sci Shenyang Inst Automation Inst Robot & Intelligent Mfg State Key Lab Robot Shenyang Peoples R China Natl Univ Def Technol Hefei Peoples R China

The visual perception systems aim to autonomously collect consecutive visual data and perceive the relevant information online like human beings. In comparison with the classical static visual systems focusing on fixed tasks (e.g., face recognition for visual surveillance), the real-world visual systems (e.g., the robot visual system) often need to handle unpredicted tasks and dynamically changed environments, which need to imitate human-like intelligence with open-ended online learning ability. Therefore, we provide a comprehensive analysis of open-ended online learning problems for autonomous visual perception in this survey. Based on "what to online learn " among visual perception scenarios, we classify the open-ended online learning methods into five categories: instance incremental learning to handle data attributes changing, feature evolution learning for incremental and decremental features with the feature dimension changed dynamically, class incremental learning and task incremental learning aiming at online adding new coming classes/tasks, and parallel and distributed learning for large-scale data to reveal the computational and storage advantages. We discuss the characteristic of each method and introduce several representative works as well. Finally, we introduce some representative visual perception applications to show the enhanced performance when using various open-ended online learning models, followed by a discussion of several future directions.

关键词： Task analysis Machine vision Learning systems Visual perception data models visualization Training Incremental learning lifelong learning online learning open-ended visual perception

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Research on the Application of visualization Methods in the Training Process of Neural Networks 4

Research on the Application of Visualization Methods in the ...

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4th International symposium on AI, ISAI 2024

作者： Liu, Zhiyi Zhang, Kai Zheng, Yejie Zheng, Likun Eastmoney Ai Research Institute Eastmoney Information Co. Ltd Shanghai China Eastmoney Information Co. Ltd Shanghai China

ISBN: (纸本)9798350342482

large language models are being increasingly applied across various fields, with uses ranging from predictive analysis to strategic decision-making processes. Despite their impressive predictive accuracy, the interpretability of these neural network-based models presents a challenge, a topic of significant interest in both industry and academia. This paper investigates the use of visualization methods to enhance the interpretability of these large models. Firstly, we will review and summarize the existing research on the interpretability of neural networks. Then, we will conduct experimental analyses to understand the specific characteristics and needs when dealing with these models. Finally, based on the results of the experimental research, we will explore effective application strategies for large-scale neural network models across various fields. Our studies are anticipated to provide valuable insights for decision-makers, researchers, and practitioners interested in the application and understanding of large language models. © 2024 ieee.

关键词： Neural network models

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An interactive visualization tool for circular economy and building materials recovery 30

An interactive visualization tool for circular economy and b...

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30th ieee International symposium for Design and Technology in Electronic Packaging, SIITME 2024

作者： Cosma, Ioan Petre, Ioana Stalidi, Cosmina Suciu, George Danila, Andrei Research and Development BEIA Consult International Bucharest Romania

ISBN: (纸本)9798331539511

Construction materials represent the largest material stock accumulated in modern society. The unprecedented growth of material stock is directly linked to high resource and energy consumption, demolition of waste discharges, and is correlated with the increased embodied energy and the associated CO2 emissions. Governments in Europe are looking for possibilities in the construction sector to replace raw materials with secondary resources through reuse and recycling by transitioning to a circular economy. But for cities, the practical aspects of the initiative remain challenging in multiple aspects. This work aims to introduce a visualization tool for built environment material stocks and flows to support circularity enabling recycling and reuse of these materials in existing buildings and infrastructures. The recovery of building materials in the circular economy requires map-based visualization tools through which statistical analyses can be made on buildings or groups of buildings. For this, it is necessary that the visualization tool contains API links to real building inventory data from local public authorities and advanced functions of real-Time analysis of the attribute table and geospatial data. © 2024 ieee.

关键词： Recycling

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