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A data Analytics Methodology to Visually Analyze the Impact of Bias and Rebalancing

ieee ACCESS 2023年 11卷 56691-56702页

作者： Lavalle, Ana Mate, Alejandro Trujillo, Juan Teruel, Miguel A. Univ Alicante Dept Software & Comp Syst Lucentia Res Grp Alicante 03690 Spain

data Analytics have become a key component of many business processes which influence several aspects of our daily life. Indeed, any misinterpretation or flaw in the outputs of data Analytics results can cause significant damage, specialy when dealing with one of the most often overlooked issues, namely the unaware use of biased data. When data bias goes unadverted, it warps the meaning of data, having a devastating effect on data Analytics results. Although it is widely argued that the most common manner to deal with data bias is to rebalance biased datasets, it is not an aseptic transformation, leading to several potentially undesired side-effects that will probably harm the result of data analyses. Therefore, in order to analyze the underlying bias in datasets, in this work we present (i) a comprehensive methodology based on visualization techniques, which assists users in the definition of their analytical requirements to detect and visually represent the data bias automatically helping them to find out whether it is appropriate to artificially rebalance their dataset or not;(ii) a novel metamodel for visually representing bias;(iii) a motivating real-world running example used to analyze the impact of bias in data Analytics and (iv) an assessment of the improvements introduced by our proposal through a complete real-world case study by using a Fire Department Calls for Service dataset, thus demonstrating that rebalancing datasets is not always the best option. It is crucial to study the context where the decisions are going to be taken. Moreover, it is also important to do a pre-analysis with the aim of knowing the nature of the datasets and how biased they are.

关键词： data analytics data bias data visualization model-driven development requirements engineering artificial intelligence

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Fall Determinants in Older Adults

Fall Determinants in Older Adults

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2023 ieee World AI IoT Congress, AIIoT 2023

作者： Banerjee, Joyeeta Gao, Jinzhu Saxe, Jonathan M. Jacobson, Lewis E. Jensen, Courtney University of the Pacific School of Engineering and Computer Science Stockton United States St. Vincent Hospital Trauma Center IndianapolisIN United States University of the Pacific Health Education and Sports Medicine Stockton United States

ISBN: (纸本)9798350337617

Older adults commonly require special care and falling is a major reason for hospital admission. Identifying the cause of falls would help enable precautionary measures to be taken, which may reduce both injury and medical expenses. This study focuses on understanding underlying medical conditions or medicines associated with an increased risk of falls. Two datasets, one with trauma patient details and the other with fall-related admitted patient details, were analyzed to determine whether any relationship exists between falls and any underlying medical conditions and associated medications. data visualization techniques and machine learning algorithms (e.g., SVC, Logistic Regression, and Naïve Bayes) have been used to study the cause of falls and whether they can be anticipated prior to their occurrence. © 2023 ieee.

关键词： data visualization

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Dual-Uncertainty Guided Multimodal MRI-Based Visual Pathway Extraction

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ieee TRANSACTIONS ON BIOMEDICAL ENGINEERING 2025年第6期72卷 1993-2000页

作者： Diakite, Alou Li, Cheng Osman, Yousuf Babiker M. Chen, Zan Pan, Yiang Zhang, Jiawei Tan, Tao Zheng, Hairong Wang, Shanshan Chinese Acad Sci Paul C Lauterbur Res Ctr Biomed Imaging Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Zhejiang Univ Technol Hangzhou Peoples R China Macao Polytech Univ Fac Appl Sci Macau Peoples R China

Objective: This study aims to accurately extract the visual pathway (VP) from multimodal MR images while minimizing reliance on extensive labeled data and enhancing extraction performance. Method: We propose a novel approach that incorporates a Modality-Relevant Feature Extraction Module (MRFEM) to effectively extract essential features from T1-weighted and fractional anisotropy (FA) images. Additionally, we implement a mean-teacher model integrated with dual uncertainty-aware ambiguity identification (DUAI) to enhance the reliability of the VP extraction process. Results: Experiments conducted on the Human Connectome Project (HCP) and Multi-Shell Diffusion MRI (MDM) datasets demonstrate that our method reduces annotation efforts by at least one-third compared to fully supervised techniques while achieving superior extraction performance over six state-of-the-art semi-supervised methods. Conclusion: The proposed label-efficient approach alleviates the burdens of manual annotation and enhances the accuracy of multimodal MRI-based VP extraction. Significance: This work contributes to the field of medical imaging by facilitating more efficient and accurate visual pathway extraction, thereby improving the analysis and understanding of complex brain structures with reduced reliance on expert annotation.

关键词： Feature extraction Magnetic resonance imaging visualization data mining Convolutional codes Optical imaging Filters Dictionaries Biomedical optical imaging Uncertainty Multimodal MRI visual pathway extraction uncertainty

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Visual Navigation Based on Language Assistance and Memory

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ieee ACCESS 2023年 11卷 13996-14005页

作者： Xiao, Shouguan Fu, Weiping Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China Xian Int Univ Sch Engn Xian 710077 Peoples R China

In order to solve outdoor mobile robots' dependence on geographic information systems, and to realize automatic navigation in the face of complex and changeable scenes, we propose a method that selects landmark and adds prompt guidance so that the mobile robot can navigate relying on visual-language and memory. Visual-language can guide the direction of the mobile robot's movement, obeying the annotation of people and according to its memory of the scene, which refers to the strategy of selecting passed-by landmarks for the route and remembering the scene features. When passing it, the agent can ascertain the position and match it to carry out the action. Experiments showed that our proposed method can achieve the purpose of independent navigation without GIS, and is superior to existing methods.

关键词： visualization Feature extraction Navigation Semantics Mobile robots Nonhomogeneous media data mining Natural language processing visual-language memory mobile robot

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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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Design of Crawler and Visual Interactive Interface Based on Scrapy Framework 3

Design of Crawler and Visual Interactive Interface Based on ...

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3rd ieee International Conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Gao, Lili Meng, Qi Shandong Agriculture and Engineering University Department of Information Science and Engineering Jinan China

ISBN: (纸本)9798350398410

Nowadays, when studying and analyzing problems, the previous conventional analysis methods are obviously difficult to meet our needs in such a huge data scale environment. At the same time, the emergence of web crawlers solves the problem of data acquisition under big data. This paper completes a series of tasks such as data acquisition, download and storage in the designated website by studying the scrapy framework. At the same time, through the research of pyecharts library, JSON library and other visualization related technologies, we can realize the visual processing and display of the crawling data of the scrapy framework, so as to realize the more intuitive analysis of the data. © 2023 ieee.

关键词： data acquisition

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Vision Transformer With Relation Exploration for Pedestrian Attribute Recognition

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ieee TRANSACTIONS ON MULTIMEDIA 2025年 27卷 198-208页

作者： Tan, Hao Tan, Zichang Weng, Dunfang Liu, Ajian Wan, Jun Lei, Zhen Li, Stan Z. Inst Automat Chinese Acad Sci CASIA State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci UCAS Sch Artificial Intelligence Beijing 100049 Peoples R China Sangfor Technol Inc Shenzhen 518055 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol SIAT Shenzhen 518067 Peoples R China AI PRIME Shanghai 200082 Peoples R China Macau Univ Sci & Technol Sch Comp Sci & Engn Macau Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China Westlake Univ Hangzhou 310024 Zhejiang Peoples R China

Pedestrian attribute recognition has achieved high accuracy by exploring the relations between image regions and attributes. However, existing methods typically adopt features directly extracted from the backbone or utilize a single structure (e.g., transformer) to explore the relations, leading to inefficient and incomplete relation mining. To overcome these limitations, this paper proposes a comprehensive relationship framework called Vision Transformer with Relation Exploration (ViT-RE) for pedestrian attribute recognition, which includes two novel modules, namely Attribute and Contextual Feature Projection (ACFP) and Relation Exploration Module (REM). In ACFP, attribute-specific features and contextual-aware features are learned individually to capture discriminative information tailored for attributes and image regions, respectively. Then, REM employs Graph Convolutional Network (GCN) Blocks and Transformer Blocks to concurrently explore attribute, contextual, and attribute-contextual relations. To enable fine-grained relation mining, a Dynamic Adjacency Module (DAM) is further proposed to construct instance-wise adjacency matrix for the GCN Block. Equipped with comprehensive relation information, ViT-RE achieves promising performance on three popular benchmarks, including PETA, RAP, and PA-100 K datasets. Moreover, ViT-RE achieves the first place in the WACV 2023 UPAR Challenge.

关键词： Feature extraction Transformers Pedestrians visualization Periodic structures Correlation Semantics data mining Recurrent neural networks Computer vision Pedestrian attribute recognition vision transformer feature projection relation exploration

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SDRQuerier: A Visual Querying Framework for Cross-National Survey data Recycling

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第6期29卷 2862-2874页

作者： Tu, Yamei Li, Olga Wang, Junpeng Shen, Han-Wei Powalko, Przemek Tomescu-Dubrow, Irina Slomczynski, Kazimierz M. Blanas, Spyros Jenkins, J. Craig Ohio State Univ Columbus OH 43210 USA Polish Acad Sci PL-01224 Warsaw Poland Visa Res Palo Alto CA 94306 USA

Public opinion surveys constitute a widespread, powerful tool to study peoples' attitudes and behaviors from comparative perspectives. However, even global surveys can have limited geographic and temporal coverage, which can hinder the production of comprehensive knowledge. To expand the scope of comparison, social scientists turn to ex-post harmonization of variables from datasets that cover similar topics but in different populations and/or at different times. These harmonized datasets can be analyzed as a single source and accessed through various data portals. However, the Survey data Recycling (SDR) research project has identified three challenges faced by social scientists when using data portals: the lack of capability to explore data in-depth or query data based on customized needs, the difficulty in efficiently identifying related data for studies, and the incapability to evaluate theoretical models using sliced data. To address these issues, the SDR research project has developed the SDRQuerier, which is applied to the harmonized SDR database. The SDRQuerier includes a BERT-based model that allows for customized data queries through research questions or keywords (Query-by-Question), a visual design that helps users determine the availability of harmonized data for a given research question (Query-by-Condition), and the ability to reveal the underlying relational patterns among substantive and methodological variables in the database (Query-by-Relation), aiding in the rigorous evaluation or improvement of regression models. Case studies with multiple social scientists have demonstrated the usefulness and effectiveness of the SDRQuerier in addressing daily challenges.

关键词： data visualization data models Biological system modeling Rivers Portals Bit error rate Sociology data harmonization social science survey data recycling visual analytics visual data query

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TDLNet: Transfer data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG

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ieee TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 3958-3967页

作者： Bi, Jingfeng Chu, Ming Beijing Univ Posts & Telecommun Sch Automat Beijing 100876 Peoples R China

The limited number of brain-computer interface based on motor imagery (MI-BCI) instruction sets for different movements of single limbs makes it difficult to meet practical application requirements. Therefore, designing a single-limb, multi-category motor imagery (MI) paradigm and effectively decoding it is one of the important research directions in the future development of MI-BCI. Furthermore, one of the major challenges in MI-BCI is the difficulty of classifying brain activity across different individuals. In this article, the transfer data learning network (TDLNet) is proposed to achieve the cross-subject intention recognition for multiclass upper limb motor imagery. In TDLNet, the Transfer data Module (TDM) is used to process cross-subject electroencephalogram (EEG) signals in groups and then fuse cross-subject channel features through two one-dimensional convolutions. The Residual Attention Mechanism Module (RAMM) assigns weights to each EEG signal channel and dynamically focuses on the EEG signal channels most relevant to a specific task. Additionally, a feature visualization algorithm based on occlusion signal frequency is proposed to qualitatively analyze the proposed TDLNet. The experimental results show that TDLNet achieves the best classification results on two datasets compared to CNN-based reference methods and transfer learning method. In the 6-class scenario, TDLNet obtained an accuracy of 65%+/- 0.05 on the UML6 dataset and 63%+/- 0.06 on the GRAZ dataset. The visualization results demonstrate that the proposed framework can produce distinct classifier patterns for multiple categories of upper limb motor imagery through signals of different frequencies.

关键词： Brain-computer interface deep learning electroencephalogram motor imagery

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A Calibrated Force-Based Model for Mixed Traffic Simulation

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2023年第3期29卷 1664-1677页

作者： Chao, Qianwen Liu, Pengfei Han, Yi Lin, Yingying Li, Chaoneng Miao, Qiguang Jin, Xiaogang Xidian Univ Dept Comp Sci Xian 710038 Shaanxi Peoples R China Zhejiang Univ State Key Lab CAD&CG Hangzhou 310058 Zhejiang Peoples R China

Virtual traffic benefits a variety of applications, including video games, traffic engineering, autonomous driving, and virtual reality. To date, traffic visualization via different simulation models can reconstruct detailed traffic flows. However, each specific behavior of vehicles is always described by establishing an independent control model. Moreover, mutual interactions between vehicles and other road users are rarely modeled in existing simulators. An all-in-one simulator that considers the complex behaviors of all potential road users in a realistic urban environment is urgently needed. In this work, we propose a novel, extensible, and microscopic method to build heterogeneous traffic simulation using the force-based concept. This force-based approach can accurately replicate the sophisticated behaviors of various road users and their interactions in a simple and unified manner. We calibrate the model parameters using real-world traffic trajectory data. The effectiveness of this approach is demonstrated through many simulation experiments, as well as comparisons to real-world traffic data and popular microscopic simulators for traffic animation.

关键词： Computational modeling Roads Force Solid modeling Microscopy data models Trajectory Traffic simulation simulator detailed traffic flow heterogeneous social force

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