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3rd IEEE International conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Wang, Shuhan Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350380989

This article presents a seabed sounding model that integrates geometric analysis with computational optimization, significantly enhancing the efficiency of multibeam survey line planning. We develop a mathematical model to precisely determine the coverage width and overlap rate for adjacent survey strips, taking into account the seabed slope and survey line direction, which is critical for accurate seabed mapping. Our proposed strategies for the shortest set of survey lines necessary for complete sea area coverage are innovative, with the optimal route from deep to shallow water requiring only 34 lines over 68 nautical miles, demonstrating a potential reduction in time and resource expenditure. The model's robustness is further evidenced by its ability to adapt to seabed undulations, ensuring thorough coverage. This research offers substantial improvements in seabed survey efficiency, with implications for maritime navigation, underwater construction, and environmental studies. © 2024 IEEE.

关键词： Geometric analysis Greedy strategy Multi-beam sounding surface subdivision visual analysis

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An Accuracy Assessment for Active data Physicalization 27

An Accuracy Assessment for Active Data Physicalization

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27th International conference on Information visualisation (iv) / 19th International conference Computer Graphics, Imaging and visualization (CGiv)

作者： Ramos Barbosa, Cleyton Luiz Soares de Sousa, Thiago Augusto Monteiro, Walbert Cunha dos Santos, Diego Hortencio Oliveira de Araujo, Tiago Davi Meiguins, Bianchi Serique Fed Univ Para Comp Sci Grad Program Belem Para Brazil Univ Aveiro IEETA DETI Aveiro Portugal

ISBN: (纸本)9798350341614

Recent literature reviews point out that active data physicalizations still lack evaluation scenarios. These evaluations mainly focus on the perception of visual and physical characteristics of physicalizations and users' performance of analysis tasks. This paper addresses a proposed accuracy assessment of data physicalization for data representation. The data physicalization used is a physical and dynamic bar chart using 3D printed models (3D bars) and LED strips inside the bars, both controlled by microcontrollers (Arduino), being possible to represent different datasets. The accuracy assessment of this physicalization is based on the height and color of the 3D bars. For this, distance sensors measure the actual height values of the bars, and a computer vision camera/algorithm collects and estimates the RGB value of the bars. From original and measured values datasets, bar charts images are built to perform a similarity comparison between the images. The similarity metric is used as an accuracy metric of the bar heights to represent data and obtained as initial results value higher than 96%. Furthermore, an evaluation scenario of the distance between the reference and measured colors (Delta E 2000) classifies most of them as having proper perception. Additionally, the accuracy loss was evaluated on data physicalization in a scenario of continuous use (ten configurations without any calibration), and the variation of errors was around 2%.

关键词： information visualization active data physicalization accuracy assessment

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10th International Workshop on Big data Management and Service, BDMS 2024, 9th International Workshop on Big data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph data Management and analysis, GDMA 2024 held in conjunction with 29th International conference on database Systems for Advanced Applications, DASFAA 2024

10th International Workshop on Big Data Management and Servi...

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10th International Workshop on Big data Management and Service, BDMS 2024, 9th International Workshop on Big data Quality Management, BDQM 2024, DASFAA 2024 Workshop on Emerging Results in data Science and Engineering, ERDSE 2024 and 8th International Workshop on Graph data Management and analysis, GDMA 2024 held in conjunction with 29th International conference on database Systems for Advanced Applications, DASFAA 2024

ISBN: (纸本)9789819609130

The proceedings contain 26 papers. The special focus in this conference is on Big data Management and Service. The topics include: FAITH: A Fast, Accurate, and Lightweight database-Agnostic Learned Cost Model;fast Approximate Temporal Butterfly Counting on Bipartite Graphs via Edge Sampling;Financial-ICS: Identifying Peer Firms via LongBERT from 10K Reports;establishing a Decentralized Diamond Quality Management System: Advancing Towards Global Standardization;co-estimation of data Types and Their Positional Distribution;Enhancing Load Forecasting with VAE-GAN-Based data Cleaning for Electric Vehicle Charging Loads;audio-Guided visual Knowledge Representation;boundary Point Detection Combining Gravity and Outlier Detection Methods;A Meta-learning Approach for Category-Aware Sequential Recommendation on POIs;automatic Post-editing of Speech Recognition System Output Using Large Language Models;comparative analysis with Multiple Large-Scale Language Models for Automatic Generation of Funny Dialogues;effectiveness of the Programmed visual Contents Comparison Method for Two Phase Collaborative Learning in Computer Programming Education: A Case Study;generating Achievement Relationship Graph Between Actions for Alternative Solution Recommendation;generating News Headline Containing Specific Person Name;Investigating Evidence in Sentence Similarity Using MASK in BERT;acceleration of Synopsis Construction for Bounded Approximate Query Processing;Query Expansion in Food Review Search with Synonymous Phrase Generation by LLM;Question Answer Summary Generation from Unstructured Texts by Using LLMs;Real Estate Information exploration in VR with LoD Control by Physical Distance;voices of Asynchronous Learning Students: Revealing Learning Characteristics Through Vocabulary analysis of Notes Tagged in Videos;review Search Interface Based on Search Result Summarization Using Large Language Model;yes-No Flowchart Generation for Interactive exploration of Personalized Health Improve

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RILA: Reflective and Imaginative Language Agent for Zero-Shot Semantic Audio-visual Navigation

RILA: Reflective and Imaginative Language Agent for Zero-Sho...

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yang, Zeyuan Liu, Jiageng Chen, Peihao Cherian, Anoop Marks, Tim K. Le Roux, Jonathan Gan, Chuang Tsinghua Univ Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China South China Univ Technol Guangzhou Peoples R China Mitsubishi Elect Res Labs MERL Cambridge MA USA UMass Amherst Amherst MA USA MIT IBM AI Lab Cambridge MA USA

ISBN: (纸本)9798350353006

We leverage Large Language Models (LLM) for zero-shot Semantic Audio visual Navigation (SAVN). Existing methods utilize extensive training demonstrations for reinforcement learning, yet achieve relatively low success rates and lack generalizability. The intermittent nature of auditory signals further poses additional obstacles to inferring the goal information. To address this challenge, we present the Reflective and Imaginative Language Agent (RILA). By employing multi-modal models to process sensory data, we instruct an LLM-based planner to actively explore the environment. During the exploration, our agent adaptively evaluates and dismisses inaccurate perceptual descriptions. Additionally, we introduce an auxiliary LLM-based assistant to enhance global environmental comprehension by mapping room layouts and providing strategic insights. Through comprehensive experiments and analysis, we show that our method outperforms relevant baselines without training demonstrations from the environment and complementary semantic information.

关键词： large language models layout comprehension semantic audio-visual navigation sound source localization zero-shot navigation

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visual analysis of Spinels with General Line Coordinates

Visual Analysis of Spinels with General Line Coordinates

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International conference on Information visualisation (iv)

作者： Leandro Luque María Luján Ganuza Ernesto Bjerg Boris Kovalerchuk Department of Computer Science and Engineering (UNS) Inst. for Computer Science and Eng. (CONICET-UNS) Bahia Blanca Argentina Department of Geology (UNS) INGEOSUR (UNS-CONICET) Bahia Blanca Argentina Department of Computer Science Central Washington University Ellensburg United States

ISBN: (数字)9798350380163

ISBN: (纸本)9798350380170

In the geological context, the analysis of multidimensional data is a very common task and requires visualization techniques for exploration. General Line Coordinates (GLC) is a technique especially intended for lossless representation among the various techniques available for the visualization of multidimensional data. In this study, the application of GLC for pattern analysis and identification in mineral datasets is investigated. Furthermore, we develop a web-based tool to explore the possibility of employing GLC to leverage these approaches to derive visual discovery rules for pattern recognition.

关键词： visualization data visualization Pattern recognition Minerals Pattern analysis

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SHEVA: A visual Analytics System for Statistical Hypothesis exploration 49th

SHEVA: A Visual Analytics System for Statistical Hypothesis ...

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49th International conference on Very Large data Bases, VLDB 2023

作者： de Almeida, Vicente Nejar Ribeiro, Eduardo Bouarour, Nassim Comba, João Luiz Dihl Amer-Yahia, Sihem Instituto de Informática UFRGS Porto Alegre Brazil Universidade Federal do Tocantins Palmas Brazil Univ. Grenoble Alpes Grenoble France CNRS Univ. Grenoble Alpes Grenoble France

We demonstrate SHEVA, a System for Hypothesis exploration with visual Analytics. SHEVA adopts an Exploratory data analysis (EDA) approach to discovering statistically-sound insights from large datasets. The system addresses three longstanding challenges in Multiple Hypothesis Testing: (i) the likelihood of rejecting the null hypothesis by chance, (ii) the pitfall of not being representative of the input data, and (iii) the ability to navigate among many data regions while preserving the user’s train of thought. To address (i) & (ii), SHEVA implements significance adjustment methods that account for data-informed properties such as coverage and novelty. To address (iii), SHEVA proposes to guide users by recommending one-sample and two-sample hypotheses in a stepwise fashion following a data hierarchy. Users may choose from a collection of pre-trained hypothesis exploration policies and let SHEVA guide them through the most significant hypotheses in the data, or intervene to override suggested hypotheses. Furthermore, SHEVA relies on data-to-visual element mappings to convey hypothesis testing results in an interpretable fashion, and allows hypothesis pipelines to be stored and retrieved later to be tested on new datasets. © 2023, VLDB Endowment. All rights reserved.

关键词： Large dataset

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Exploratory data analysis of Population Level Smartphone-Sensed data 16th

Exploratory Data Analysis of Population Level Smartphone-Sen...

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16th International Joint conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International conference on Computer Vision Theory and Applications (VISAPP)

作者： Mansoor, Hamid Gerych, Walter Alajaji, Abdulaziz Buquicchio, Luke Chandrasekaran, Kavin Agu, Emmanuel Rundensteiner, Elke Worcester Polytech Inst Worcester MA 01609 USA

ISBN: (纸本)9783031254765;9783031254772

Mobile health involves gathering smartphone-sensor data passively from user's phones, as they live their lives 'In-the-wild", periodically annotating data with health labels. Such data is used by machine learning models to predict health. Purely Computational approaches generally do not support interpretability of the results produced from such models. In addition, the interpretability of such results may become difficult with larger study cohorts which make population-level insights desirable. We propose Population Level exploration and analysis of smartphone DEtected Symptoms (PLEADES), an interactive visual analytics framework to present smartphone-sensed data. Our approach uses clustering and dimension reduction to discover similar days based on objective smartphone sensor data, across participants for population level analyses. PLEADES enables analysts to apply various clustering and projection algorithms to several smartphonesensed datasets. PLEADES overlays human-labelled symptom and contextual information from in-the-wild collected smartphone-sensed data, to empower the analyst to interpret findings. Such views enable the contextualization of the symptoms that can manifest in smartphone sensor data. We used PLEADES to visualize two real world in-the-wild collected datasets with objective sensor data and human-provided health labels. We validate our approach through evaluations with data visualization and human context recognition experts.

关键词： Interactive visual analytics In-the-wild smartphone-sensed data Exploratory data analysis

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AirLens: Multi-Level visual exploration of Air Quality Evolution in Urban Agglomerations

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COMPUTER GRAPHICS FORUM 2022年第3期41卷 223-234页

作者： Qu, Dezhan Lv, Cheng Lin, Yiming Zhang, Huijie Wang, Rong Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China Jilin Univ Key Lab Intelligent Informat Proc Changchun Peoples R China

The precise prevention and control of air pollution is a great challenge faced by environmental experts in recent years. Understanding the air quality evolution in the urban agglomeration is important for coordinated control of air pollution. However, the complex pollutant interactions between different cities lead to the collaborative evolution of air quality. The existing statistical and machine learning methods cannot well support the comprehensive analysis of the dynamic air quality evolution. In this study, we propose AirLens, an interactive visual analytics system that can help domain experts explore and understand the air quality evolution in the urban agglomeration from multiple levels and multiple aspects. To facilitate the cognition of the complex multivariate spatiotemporal data, we first propose a multi-run clustering strategy with a novel glyph design for summarizing and understanding the typical pollutant patterns effectively. On this basis, the system supports the multi-level exploration of air quality evolution, namely, the overall level, stage level and detail level. Frequent pattern mining, city community extraction and useful filters are integrated into the system for discovering significant information comprehensively. The case study and positive feedback from domain experts demonstrate the effectiveness and usability of AirLens.

关键词： CCS Concepts • Human-centered computing → visual analytics Geographic visualization Information visualization

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Space-Time Urban Explorer: A visual Tool for Exploring Spatiotemporal Crime and Patrolling data 37

Space-Time Urban Explorer: A Visual Tool for Exploring Spati...

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37th SIBGRAPI conference on Graphics, Patterns and Images (SIBGRAPI)

作者： Santos, Tiago Paulino Souza, Joao Matheus Siqueira Vieira, Thales Nonato, Luis Gustavo Fed Univ Alagoas UFAL Inst Comp Maceio Alagoas Brazil Univ Sao Paulo ICMC USP Inst Math & Comp Sci Sao Carlos Brazil

ISBN: (纸本)9798350376043;9798350376036

Spatiotemporal urban data gathered by public security departments holds immense potential for in-depth analysis, enhancing decision-making in areas such as crime prevention and patrolling strategies. However, extracting meaningful patterns from vast and complex spatiotemporal datasets presents a considerable challenge in the field of big data analytics. We introduce a novel visualization-assisted tool tailored for handling massive spatiotemporal urban datasets, with a specific focus on public security data. At the core of this tool are spatial graph vertex ordering algorithms that perform dimensionality reduction on the vertices' locations. To make this tool practical for handling massive spatiotemporal datasets, we present efficient preprocessing techniques. These techniques are carefully crafted to distill and represent urban spatiotemporal datasets, ensuring efficient data exploration. The effectiveness of the proposed solution is validated through case studies using real datasets from the Military Police of the State of Alagoas - Brazil. We demonstrate the effectiveness of the proposed solutions, showcasing the versatility of the visual tool in accomplishing various relevant analytical tasks.

关键词： Spatio-temporal data

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Towards Contextual Glyph Design: visualizing Hearing Screenings 27

Towards Contextual Glyph Design: Visualizing Hearing Screeni...

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27th International conference on Information visualisation (iv) / 19th International conference Computer Graphics, Imaging and visualization (CGiv)

作者： Ramos, Barbara Nascimento Macas, Catarina Lourenco, Nuno Polisciuc, Evgheni Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9798350341614

data is everywhere, our society shapes itself through it and, with the years passing by, it is becoming greater in dimension. A lot of this data now available is multivariate in nature. The bigger the volume and complexity are, the harder tasks to detect, classify, and measure characteristics and relations within data. Glyph-based visualization is one of the possible techniques commonly adopted in data science to address the representation of multivariate data. Multiple varieties of glyph design have been developed and studied over the past decades. However, little research was done to compare the effectiveness of glyphs designs developed for a specific context of the application. This paper aims to study data glyphs and their applications. More specifically, three visual explorations are produced as glyph design alternatives to represent a dataset related to audiological tests carried out in the population of Portugal. These glyphs were evaluated through controlled semi-structured experiments with users, and a crowdsourced experiment, and then an analysis of the performance result of each of the glyphs is presented, evaluating them in terms of learning and memorization. The results show how the use of metaphors and semantic relations to represent the attributes helps in understanding the glyph and its memorization. Additionally, we identified that the redundancy in encoding data might be beneficial.

关键词： data glyphs design Glyph-based visualization Multivariate data visualization

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