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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Martin Brown Dominic Thomas Md Abdullah Al Hafiz Khan Abm Adnan Azmee Monica Nandan Yang Pei Department of Data Science and Analytics Kennesaw State University Marietta USA Information Systems Kennesaw State University Marietta USA Computer Science Kennesaw State University Marietta USA Computer Science Social Work and Human Services Kennesaw State University Marietta USA

ISBN: (数字)9798350345018

ISBN: (纸本)9798350345025

This demonstration introduces the CaseFinder tool, a GUI designed to automate visualizations and quantitatively analyze 911 police reports. The tool allows for efficient data exploration for non-technical users integrating behavioral health-related keywords. By amalgamating diverse keyword category inputs, CaseFinder provides a holistic approach to analyzing reports by uncovering indications of behavioral health problems while including perspectives from multiple professional domains. Ultimately, CaseFinder aims to improve the understanding of the magnitude of the problem of behavioral health issues involved in 911 crises to mitigate reoccurrence.

关键词： Visualization Law enforcement Statistical analysis Data visualization Graphical user interfaces

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Triple Pseudo-Siamese Network with Cross-Contextual Attention for Medical Image Retrieval

Triple Pseudo-Siamese Network with Cross-Contextual Attentio...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： G Kavitha Yogesh Lohumi Kamal Kant H K Bhargav Harshal Patil Ramya Maranan DOS in CS&E University BDT College of Engineering Davangere India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering School of Engineering and Technology (formerly known as UIET) CSJM University Kanpur UP India Department of Computer Science and Engineering Industry Institute Interaction Cell (IIIC) Shridevi Institute of Engineering and Technology Tumakuru Karnataka India School of Computer Science & Engineering IILM University Greater Noida Uttar Pradesh India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Hospitals are now increasingly using digital imaging data, therefore medical image archives become larger and larger. Sizable databases of these sorts are difficult to organize and query; hence there is a need for content-based medical image retrieval (CBMIR) solutions. The CBMIR systems have a main challenge which is the semantic gap among the high level semantic detail obtained by human beings and low level visual detail rendered by imaging equipments. This research presents a novel approach to medical image retrieval that uses ImageCLEFMed 2009 dataset in order to address them. By pre-processing phase, we mean an improved square root Sage-Husa adaptive Kalman filter technique used for increasing the quality of images while reducing noise. For feature extraction, it combines Short-Time Fourier Transform and Continuous Wavelet Transform (STFT-CWT) together. The Triple Pseudo-Siamese Network with Cross-Contextual Attention (TPS-CCA), which captures complex patterns and relationships within the field of medicine, forms the core of this methodology. Salp Swarm Optimization has been employed for fine-tuning parameters of TPS-CCA model in order to improve its performance further. Therefore, this method shows significantly better accuracy, precision, sensitivity, specificity and F1-score compared to existing ones. This system provides a robust and efficient solution for retrieving relevant medical images, potentially aiding in improved diagnostics and patient care. The proposed method attains higher accuracy as (99.4%), precision as (99.5%), sensitivity as (99.6%), specificity as (99.8%), F1-score as (99.6%) and lower error rate as 3.

关键词： Adaptation models Sensitivity Accuracy Image retrieval Semantics Noise Kalman filters Reliability Particle swarm optimization Medical diagnostic imaging

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RSVP for VPSA: A Meta Design Study on Rapid Suggestive Visualization Prototyping for Visual Parameter Space Analysis

arXiv

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arXiv 2024年

作者： Klaffenboeck, Manfred Gleicher, Michael Sorger, Johannes Wimmer, Michael Möller, Torsten The Institute of Visual Computing and Human Centered Technology TU Wien Austria The Complexity Science Hub Vienna Austria The Department of Computer Sciences The University of Wisconsin - Madison United States The Faculty of Computer Science The University of Vienna The Research Network Data Science Uni Wien Austria

Visual Parameter Space Analysis (VPSA) enables domain scientists to explore input-output relationships of computational models. Existing VPSA applications often feature multi-view visualizations designed by visualization experts for a specific scenario, making it hard for domain scientists to adapt them to their problems without professional help. We present RSVP, the Rapid Suggestive Visualization Prototyping system encoding VPSA knowledge to enable domain scientists to prototype custom visualization dashboards tailored to their specific needs. The system implements a task-oriented, multi-view visualization recommendation strategy over a visualization design space optimized for VPSA to guide users in meeting their analytical demands. We derived the VPSA knowledge implemented in the system by conducting an extensive meta design study over the body of work on VPSA. We show how this process can be used to perform a data and task abstraction, extract a common visualization design space, and derive a task-oriented VisRec strategy. User studies indicate that the system is user-friendly and can uncover novel insights. © 2024, CC BY.

关键词： Visualization

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Enhancing Emotional Experience by Building Emotional Virtual Characters in VR Volleyball Games

Enhancing Emotional Experience by Building Emotional Virtual...

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作者： Bai, Zechen Yao, Naiming Mishra, Nidhi Chen, Hui Wang, Hongan Magnenat Thalmann, Nadia Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute for Media Innovation Nanyang Technological University Singapore Singapore

Virtual reality (VR) volleyball games can provide an immersive entertainment experience and facilitate users to get familiar with game rules. In real world, players often experience emotions from scores, strokes, passes, and the influence from their teammates or opponents, and so forth. However, seldom studies are carried out for this emotional experience in a virtual environment, as most VR volleyball games mainly concentrate on the game playing, such as body movements. In this article, we propose to enhance emotional experience by building emotional virtual characters in VR volleyball games. The virtual characters cannot only arouse their emotions but also express their facial expressions according to the game situation. Emotion patterns are learned from real-world volleyball matches. The results demonstrate that our framework has great potential to enhance the user's emotional experience and engagement. © 2021 John Wiley & Sons, Ltd.

关键词： Sports

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Camera-Based HRV Prediction for Remote Learning Environments 10

Camera-Based HRV Prediction for Remote Learning Environments

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10th IEEE Smart World Congress, SWC 2024

作者： Wang, Kegang Wei, Yantao Tang, Jiankai Wang, Yuntao Li, Zhe Tong, Mingwen Gao, Jie Ma, Yujian Zhao, Zhongjin Central China Normal University Wuhan430079 China National Key Laboratory of Human Factors Engineering Beijing100094 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Ant Group Beijing100000 China East China Normal University Shanghai200241 China

ISBN: (纸本)9798331520861

In recent years, the widespread use of online facial videos has brought increasing attention to remote photoplethys-mography (rPPG) in affective computing. Recovering blood volume pulse (BVP) signals from facial videos is a complex task, involving multiple stages of preprocessing, image analysis, and postprocessing to accurately reconstruct waveforms. Not only is the heart rate metric utilized for affective computing, but the heart rate variability (HRV) metric is even more significant. The challenge in obtaining HRV indices through rPPG lies in the necessity for algorithms to precisely predict the BVP peak positions. In this paper, we collected the Remote Learning Affect and Physiology (RLAP) dataset, which includes over 32 hours of highly synchronized video and labels from 58 subjects. This is a public dataset whose BVP labels have been meticulously designed to better suit the training of HRV models. Using the RLAP dataset, we trained a new model called Seq-rPPG, it is a model based on one-dimensional convolution, and experimental results reveal that this structure is more suitable for handling HRV tasks, which outperformed all other baselines in HRV performance and also demonstrated significant advantages in computational efficiency. The RLAP dataset is open source: https://***/KegangWangCCNU/RLAP-dataset © 2024 IEEE.

关键词： Heart

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian-Fang Ma Xuan-Hao Liu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods.

关键词： Heating systems Emotion recognition Adaptation models Accuracy Psychology Gaze tracking Biomedical measurement Data models Physiology Trajectory

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A Passivity-Based Variable Impedance Controller for Incremental Learning of Periodic Interactive Tasks

A Passivity-Based Variable Impedance Controller for Incremen...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Matteo Dalle Vedove Edoardo Lamon Daniele Fontanelli Luigi Palopoli Matteo Saveriano Department of Industrial Engineering Università di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In intelligent manufacturing, robots are asked to dynamically adapt their behaviours without reducing productivity. human teaching, where an operator physically interacts with the robot to demonstrate a new task, is a promising strategy to quickly and intuitively reconfigure the production line. However, physical guidance during task execution poses challenges in terms of both operator safety and system usability. In this paper, we solve this issue by designing a variable impedance control strategy that regulates the interaction with the environment and the physical demonstrations, explicitly preventing at the same time passivity violations. We derive constraints to limit not only the exchanged energy with the environment but also the exchanged power, resulting in smoother interactions. By monitoring the energy flow between the robot and the environment, we are able to distinguish between disturbances (to be rejected) and physical guidance (to be accomplished), enabling smooth and controlled transitions from teaching to execution and vice versa. The effectiveness of the proposed approach is validated in wiping tasks with a real robotic manipulator.

关键词： Surface impedance Incremental learning Tracking Education End effectors Safety Impedance Usability Robots Oscillators

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FamiData Hub: A Speculative Design Exploration with Families on Smart Home Datafication 25

FamiData Hub: A Speculative Design Exploration with Families...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Wang, Ge Zhao, Jun Van Kleek, Max Pea, Roy Shadbolt, Nigel Institute for Human-Centered Artificial Intelligence (HAI) Stanford University Stanford CA United States Department of Computer Science University of Oxford Oxfordshire Oxford United Kingdom Graduate School of Education Stanford University Stanford CA United States

ISBN: (纸本)9798400713941

Smart home technologies are becoming increasingly common in households with children. While privacy and security concerns have been widely discussed, a critical issue often overlooked is the extensive data harvesting embedded in these smart homes and its manipulative impact on children through algorithmic decision-making. In this paper, we introduce FamiData Hub, a speculative prototype designed to empower families to navigate the datafication of smart homes. Through 17 study sessions - including speculative interviews followed by co-design activities - with 30 children and 25 parents, we found that families face challenges related to smart home datafication, such as the erosion of boundaries in family spaces, loss of control over family norms, and diminished autonomy in data-driven decision-making processes. Our findings offer key design recommendations for rethinking smart home technologies to better safeguard children's data, advocating for respectful, family-centered approaches that challenge the normalization of datafication in domestic life. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Children Datafication Smart Home

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Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models

Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Mode...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Shenglin Zhang Pengtian Zhu Minghua Ma Jiagang Wang Yongqian Sun Dongwen Li Jingyu Wang Qianying Guo Xiaolei Hua Lin Zhu Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Microsoft Tsinghua University Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Mobile Research Institute Beijing National Research Center for Information Science and Technology

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile’s daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution.

关键词： Privacy Filters Large language models Conferences Software reliability Maintenance Iterative methods Data mining Protection Faces

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TOWARD ADVERSARIAL TRAINING ON CONTEXTUALIZED LANGUAGE REPRESENTATION

arXiv

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arXiv 2023年

作者： Wu, Hongqiu Liu, Yongxiang Shi, Hanwen Zhao, Hai Zhang, Min Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Technology Soochow University Suzhou China

Beyond the success story of adversarial training (AT) in the recent text domain on top of pre-trained language models (PLMs), our empirical study showcases the inconsistent gains from AT on some tasks, e.g. commonsense reasoning, named entity recognition. This paper investigates AT from the perspective of the contextualized language representation outputted by PLM encoders. We find the current AT attacks lean to generate sub-optimal adversarial examples that can fool the decoder part but have a minor effect on the encoder. However, we find it necessary to effectively deviate the latter one to allow AT to gain. Based on the observation, we propose simple yet effective Contextualized representation-Adversarial Training (CreAT), in which the attack is explicitly optimized to deviate the contextualized representation of the encoder. It allows a global optimization of adversarial examples that can fool the entire model. We also find CreAT gives rise to a better direction to optimize the adversarial examples, to let them less sensitive to hyperparameters. Compared to AT, CreAT produces consistent performance gains on a wider range of tasks and is proven to be more effective for language pretraining where only the encoder part is kept for downstream tasks. We achieve the new state-of-the-art performances on a series of challenging benchmarks, e.g. AdvGLUE (59.1 → 61.1), HellaSWAG (93.0 → 94.9), ANLI (68.1 → 69.3). Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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