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Wearable Sensor-based Multimodal Physiological Responses of Socially Anxious Individuals in Social Contexts on Zoom

IEEE Transactions on Affective Computing 2025年

作者： Toner, Emma R. Rucker, Mark Wang, Zhiyuan Larrazabal, Maria A. Cai, Lihua Datta, Debajyoti Lone, Haroon Boukhechba, Mehdi Teachman, Bethany A. Barnes, Laura E. University of Virginia Department of Psychology CharlottesvilleVA United States University of Virginia Department of Systems and Information Engineering CharlottesvilleVA United States South China Normal University Aberdeen Institute of Data Science and Artificial Intelligence Guangzhou China Indian Institute of Science Education and Research Bhopal Department of Electrical Engineering and Computer Science Bhopal India Janssen Pharmaceutical Companies of Johnson & Johnson TitusvilleNJ United States

Correctly identifying an individual's social context from passively worn sensors holds promise for delivering justin-time adaptive interventions (JITAIs) to treat social anxiety. In this study, we present results using passively collected data from a within-subjects experiment that assessed physiological responses across different social contexts (i.e, alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. group interactions), and levels of social threat (i.e., implicit vs. explicit social evaluation). Participants in the study (N = 46) reported moderate to severe social anxiety symptoms as assessed by the Social Interaction Anxiety Scale (≥34 out of 80). Univariate paired difference tests, multivariate random forest models, and cluster analyses were used to explore physiological response patterns across different social and non-social contexts. Our results suggest that social context is more reliably distinguishable than social phase, group size, or level of social threat, and that there is considerable variability in physiological response patterns even among distinguishable contexts. Implications for real-world context detection and future deployment of JITAIs are discussed. © 2025 IEEE.

关键词： Wearable sensors

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Fdm: Effective and Efficient Incident Detection on Sparse Trajectory data

SSRN

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SSRN 2023年

作者： Han, Xiaolin Grubenmann, Tobias Ma, Chenhao Li, Xiaodong Sun, Wenya Wong, Sze Chun Shang, Xuequn Cheng, Reynold The Northwestern Polytechnical University Xi’an China University of Southampton Southampton United Kingdom The Chinese University of Hong Kong Shenzhen China The University of Hong Kong Hong Kong HKU Musketeers Foundation Institute of Data Science Hong Kong Guangdong–Hong Kong-Macau Joint Laboratory Program 2020 China Department of Computer Science and Engineering Hong Kong School of Computer Science and Engineering China

Incident detection (ID), or the automatic discovery of anomalies from road traffic data (e.g., road sensor and GPS data), enables emergency actions (e.g., rescuing injured people) to be carried out in a timely fashion. Existing ID solutions based on data mining or machine learning often rely on dense traffic data;for instance, sensors installed in highways provide frequent updates of road information. In this paper, we ask the question: can ID be performed on sparse traffic data (e.g., location data obtained from GPS devices equipped on vehicles)? As these data may not be enough to describe the state of the roads involved, they can undermine the effectiveness of existing ID solutions. To tackle this challenge, we borrow an important insight from the transportation area, which uses trajectories (i.e., moving histories of vehicles) to derive incident patterns. We study how to obtain incident patterns from trajectories and devise a new solution (called Filter-Discovery-Match (FDM)) to detect anomalies in sparse traffic data. We have also developed a fast algorithm to support FDM. Experiments on a taxi dataset in Hong Kong and a simulated dataset show that FDM is more effective than state-of-the-art ID solutions on sparse traffic data, and is also efficient. © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Frequency-Domain Refinement of Vision Transformers for Robust Medical Image Segmentation Under Degradation

Frequency-Domain Refinement of Vision Transformers for Robus...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Sanaz Karimijarbigloo Sina Ghorbani Kolahi Reza Azad Ulas Bagci Dorit Merhof Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Industrial and Systems Engineering Tarbiat Modares University Tehran Iran Department of Radiology Northwestern University Chicago USA Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Medical image segmentation is crucial for precise diagnosis, treatment planning, and disease monitoring in clinical settings. While convolutional neural networks (CNNs) have achieved remarkable success, they struggle with modeling long-range dependencies. Vision Transformers (ViTs) address this limitation by leveraging self-attention mechanisms to capture global contextual information. However, ViTs often fall short in local feature description, which is crucial for precise segmentation. To address this issue, we reformulate self-attention in the frequency domain to enhance both local and global feature representation. Our approach, the Enhanced Wave Vision Transformer (EW-ViT), incorporates wavelet decomposition within the self-attention block to adaptively refine feature representation in low and high-frequency components. We also introduce the Prompt-Guided High-Frequency Refiner (PGHFR) module to handle image degradation, which mainly affects high-frequency components. This module uses implicit prompts to encode degradation-specific information and adjust high-frequency representations accordingly. Additionally, we apply a contrastive learning strategy to maintain feature consistency and ensure robustness against noise, leading to state-of-the-art (SOTA) performance in medical image segmentation, especially under various conditions of degradation. Source code is available at GitHub.

关键词： Degradation Wavelet transforms Image segmentation Computer vision Frequency-domain analysis Noise Contrastive learning Transformers Noise measurement Medical diagnostic imaging

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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SF-DQN: provable knowledge transfer using successor feature for deep reinforcement learning 24

SF-DQN: provable knowledge transfer using successor feature ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Shuai Zhang Heshan Devaka Fernando Miao Liu Keerthiram Murugesan Songtao Lu Pin-Yu Chen Tianyi Chen Meng Wang Department of Data Science New Jersey Institute of Technology Newark NJ Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY IBM Thomas J. Watson Research Center IBM Research Yorktown Heights NY

This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics. In this setting, the Q-function of each RL problem (task) can be decomposed into a successor feature (SF) and a reward mapping: the former characterizes the transition dynamics, and the latter characterizes the task-specific reward function. This Q-function decomposition, coupled with a policy improvement operator known as generalized policy improvement (GPI), reduces the sample complexity of finding the optimal Q-function, and thus the SF & GPI framework exhibits promising empirical performance compared to traditional RL methods like Q-learning. However, its theoretical foundations remain largely unestablished, especially when learning the successor features using deep neural networks (SF-DQN). This paper studies the provable knowledge transfer using SFs-DQN in transfer RL problems. We establish the first convergence analysis with provable generalization guarantees for SF-DQN with GPI. The theory reveals that SF-DQN with GPI outperforms conventional RL approaches, such as deep Q-network, in terms of both faster convergence rate and better generalization. Numerical experiments on real and synthetic RL tasks support the superior performance of SF-DQN & GPI, aligning with our theoretical findings.

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Enhancing Internet of Things Safety with AI-Powered Intrusion Avoidance systems

Enhancing Internet of Things Safety with AI-Powered Intrusio...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Sachin Ramchandra Misal M. Monica Bhavani Chandra Rao Merla Ganieva Shodiya Azizovna Jumisree Sarmah Pathak Praveen Kumar International Institute of Management Science Chinchwad Pune India Data Science & Business Systems SRM University Kattankulathur Department of Computer Science & Engineering Malla Reddy Engineering College for Women Hyderabad Department of Methodology of Primary education Fergana State University Department of Physics Indian Institute of Teacher Education Gandhinagar Gujarat Astana IT University Astana Kazakhstan

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

The rapid proliferation of Internet of Things (IoT) devices has ushered in unprecedented levels of connectivity, offering enhanced automation and data sharing across diverse domains. However, this surge in interconnectivity has brought about a significant challenge regarding cyber security threats and intrusions. To overcome this problem, we propose a Hybrid Bayesian Tree Fusion (HBTF) for predicting the detection system. These systems leverage the capabilities of Artificial Intelligence to predict and proactively mitigate potential security breaches. We gather the NSL-KDD data set for evaluating the proposed method for forecasting intrusion detection. The collected data are preprocessed using Z score normalization to eliminate the noise. Extract the feature from the preprocessed data using kernel-based independent component analysis (KICA). The experimental findings of the proposed method are evaluated by recall, precision, f1-score, and accuracy from the result. The HBTF is greater than the existing method. The integration of AI augments the capability of IoT networks to counter emerging threats, ultimately fostering a safer and more secure interconnected world.

关键词： Automation Intrusion detection Independent component analysis Feature extraction Bayes methods Internet of Things Security data mining Artificial intelligence Computer crime

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A Multi-DOF Robot System Based on LightGBM-Driven EEG Decoding Model for BCI Human-Machine Interaction 42

A Multi-DOF Robot System Based on LightGBM-Driven EEG Decodi...

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42nd Chinese Control Conference, CCC 2023

作者： Yan, Weidong Xu, Zhaoliang Li, Yang Beihang University The Department of Automation Science and Electrical Engineering Beijing100191 China The Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Advanced Institute of Information Technology Peking University Hangzhou310000 China

ISBN: (纸本)9789887581543

In this study, a multi-degree-of-freedom (Multi-DOF) robot (MDR) system based on a LightGBM-driven electroencephalogram (EEG) decoding model is designed and developed to assist subjects with hand motor dysfunction in their daily activities and neurorehabilitation. The system mainly consists of a motor imagery electroencephalogram (MI-EEG) evoking layer, an intention decoding layer, and an interaction executive layer. The MI-EEG evoking layer initially displays a virtual reality (VR) motion imagination scenario, instructing the subjects to imagine a real hand gripping movement, simultaneously collecting the electrical EEG signals and preprocessing the EEG signals. Secondly, in the intention decoding layer, a network combining temporal-spectral feature fusion and LightGBM (TSFF-LightGBM) for MI-BCI classification is used to more effectively boost brain decoding accuracy and decrease decoding time. Finally, in the interaction executive layer, the Multi-DOF wearable robot is developed to offer hand grasp motion kinesthetic feedback and visual feedback synced with MI. The following are the key benefits of the proposed MDR system: (1) We propose a new lightweight network structure more suitable for brain computer interface (BCI) interaction systems, achieving more accurate decoding and shorter identification time in different data sets, which helps to improve the practicability of the system and promote the practical clinical application of BCI rehabilitation technology. (2) We integrate BCI, VR, a wearable Multi-DOF robot, motion kinesthetic feedback, and visual feedback to improve human-machine interaction. Compared to the most recent investigations, the average accuracy of the MDR system on the publicly accessible datasets BCI IV 2a and HGD reached 75.89% and 93.53%, respectively. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Brain computer interface

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Optimal Multi-Keyword Ranked Search Matching in Public Cloud with Enhanced Secure Search Scheme Over Encrypted data

Optimal Multi-Keyword Ranked Search Matching in Public Cloud...

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： B. Radha Jaya Bharathi R R. Vidhya Muthulakshmi K. Vidhya R. Vijaya Kumar Reddy Ruchika Rondon Department of ICT and Cognitive Systems Sri Krishna Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai Tamil Nadu India Department of Computer Science Engineering KPR Institute of Engineering and technology Arasur Tamil Nadu India Department of Computer Science Engineering Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Department of Petroleum Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In this research work, the authors present an improved secure search algorithm that will allow for optimum multi-keyword ranked search matching in public cloud storage that uses encrypted data. The goal of the plan is to provide a method that is both safe and effective for searching for and retrieving important data stored in cloud settings, all while preserving the data's privacy and confidentiality. Experiments and simulations are carried out in order to assess the viability of the suggested plan, and mathematical expressions are used to depict the algorithms and assessment criteria. According to the findings of the research, the strategy that was presented is capable of achieving a high level of accuracy, recall, and F1-score while simultaneously reducing the number of false positives and false negatives. The research underlines how important it is to address security problems in cloud computing settings, and it gives significant insights that can be used to design cloud computing services that are both safe and efficient. In the future, possible topics of study might include further refining and optimizing of the scheme that was described, as well as the application of the method in real cloud computing systems.

关键词： Cloud computing data privacy Accuracy Refining Mathematical models Cryptography STEM

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Using Graph Neural Networks and Frequency Domain data for Automated Operational Modal Analysis of Populations of Structures

arXiv

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arXiv 2024年

作者： Jian, Xudong Xia, Yutong Duthé, Gregory Bacsa, Kiran Liu, Wei Chatzi, Eleni Future Resilient Systems Singapore-ETH Centre 138602 Singapore Institute of Data Science National University of Singapore 117602 Singapore Department of Civil Environmental and Geomatic Engineering ETH Zurich Zurich8049 Switzerland Department of Industrial Systems Engineering and Management National University of Singapore 117576 Singapore

The Population-Based Structural Health Monitoring (PBSHM) paradigm has recently emerged as a promising approach to enhance data-driven assessment of engineering structures by facilitating transfer learning between structures with some degree of similarity. In this work, we apply this concept to the automated modal identification of structural systems. We introduce a Graph Neural Network (GNN)-based deep learning scheme to identify modal properties, including natural frequencies, damping ratios, and mode shapes of engineering structures based on the Power Spectral Density (PSD) of spatially-sparse vibration measurements. Systematic numerical experiments are conducted to evaluate the proposed model, employing two distinct truss populations that possess similar topological characteristics but varying geometric (size, shape) and material (stiffness) properties. The results demonstrate that, once trained, the proposed GNN-based model can identify modal properties of unseen structures within the same structural population with good efficiency and acceptable accuracy, even in the presence of measurement noise and sparse measurement locations. The GNN-based model exhibits advantages over the classic Frequency Domain Decomposition (FDD) method in terms of identification speed, as well as against an alternate Multi-Layer Perceptron (MLP) architecture in terms of identification accuracy, rendering this a promising tool for PBSHM purposes. © 2024, CC BY.

关键词： Graph neural networks

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Performance Evaluation of Optical Wireless Communication System in Fog Weather Conditions

Performance Evaluation of Optical Wireless Communication Sys...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Kanagaraj Venusamy S. Kannadhasan E. Prakash Priyadarsini K Department of Mechatronics Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Study World College of Engineering Coimbatore Chennai India Department of Data Science and Business Systems School of Computing SRM Institute of Science & Technology Kattankulathur Chennai India

Free space optical communication being an emerging technology, has Fog as a primary challenge on its way which makes fog modelling necessary to manage wireless networks. In this paper, an analytical study was made on the performance of five mathematical fog models employing the NRZ-OOK modulation scheme for transmission and a Si PIN photodiode as the photo receptor. This article focuses on numerous performance specifications including Signal to noise ratio [SNR], Bit error rate [BER] and channel capacity all against visibility under fog for propagations wavelengths of 850 nm and 1550 nm and simulations were carried out in MATLAB. The results indicate that the wavelength of 1550nm as more advantageous option over the others for FSO wireless links and an investigation on different models and their characteristics define their suitability for implementation.

关键词： PIN photodiodes Wireless networks Bit error rate Mathematical models Silicon Optical fiber communication Optical transmitters

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