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SSRN 2025年

作者： Zhang, Liang Lin, Jionghao Sabatini, John Zapata-Rivera, Diego Forsyth, Carol Jiang, Yang Hollander, John Hu, Xiangen Graesser, Arthur C. Institute for Intelligent Systems University of Memphis MemphisTN United States Department of Electrical and Computer Engineering University of Memphis MemphisTN United States Faculty of Education The University of Hong Kong Hong Kong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Centre for Learning Analytics Monash University MelbourneVIC Australia Department of Psychology University of Memphis MemphisTN United States Educational Testing Service PrincetonNJ United States Department of Psychology and Counseling Arkansas State University JonesboroAR United States Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

As human learners respond to questions in Intelligent Tutoring Systems (ITSs), their performance data (correct or incorrect answers) is crucial for assessing and predicting their knowledge states. However, data sparsity, often arising from skipped or incomplete responses, hampers accurate assessment and personalized instruction. To address this challenge, we propose a Generative Adversarial Imputation Networks (GAIN) method using a three-dimensional representation (learners, questions, and attempts) with Convolutional Neural Networks (CNNs) in the input and output layers. We evaluate our method on three ITS datasets, AutoTutor ARC, ASSISTments, and MATHia, where it outperforms baselines, including tensor factorization methods and other GAN variants, across multiple maximum attempt settings. To confirm imputation effectiveness for learner modeling, we applied Bayesian Knowledge Tracing (BKT) and observed significant reductions in RMSE compared to original sparse data, indicating substantial improvements in modeling accuracy. Moreover, analyzing divergence of key BKT parameters (initial knowledge $P(L_0)$, learning rate $P(T)$, guess rate $P(G)$, and slip rate $P(S)$) via Kullback-Leibler (KL) divergence shows that the imputed data preserves essential learning characteristics. Our findings highlight GAIN’s potential for mitigating data sparsity in ITSs, enabling more adaptive, individualized instruction and fostering more effective AI-driven education. © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Novel Lightweight Convolutional Neural Network for EEG-Based Miniature-Event-Related Potentials

A Novel Lightweight Convolutional Neural Network for EEG-Bas...

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IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications

作者： Chang Liu Wenxiao Zhong Yang Di Liang Zhao Xingwei An Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin P. R. China Tianjin Key Laboratory of Brain Science and Neuroengineering academy of medical engineering and translation of medicine Tianjin University Tianjin Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration State Key Laboratory of Advanced Medical Materials and Devices Tianjin University Tianjin Tianjin P. R. China

ISBN: (数字)9798350322996

ISBN: (纸本)9798350323009

Miniature asymmetric visual evoked potential (aVEP) induced by very small lateral visual stimuli is a comfortable stimulation paradigm. It could only induce a miniature potential about 0.5 μV in amplitude, which could be challenging to detect. Most decoding methods based on miniature aVEP are machine learning approaches, and deep learning methods have not yet been introduced. Therefore, this paper introduces deep learning techniques for the first time and designs a lightweight convolutional neural network. Specifically, depthwise convolution is employed to reduce the number of network parameters and extract temporal and spatial features from EEG signals. The model achieves an average classification accuracy of 78.48% on the miniature aVEP dataset, which is 1.14% higher than the previous state-of-the-art model, DCPM. Through comparison with other deep learning models in terms of parameter count, computational complexity, and classification accuracy, it is evident that the proposed model surpasses them in all these aspects The results suggest that the CNN model proposed in this study achieves high classification accuracy while demanding fewer computational resources. Although the network model we proposed is very simple, it can achieve good results in processing miniature aVEP. In future research, we will continue to improve this lightweight network to enhance its decoding performance for miniature aVEP.

关键词： Deep learning Visualization Accuracy Computational modeling Neural networks Virtual environments Brain modeling

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An evolutionary lion optimization algorithm-based image compression technique for biomedical applications

An evolutionary lion optimization algorithm-based image comp...

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作者： Geetha, Karuppaiah Anitha, Veerasamy Elhoseny, Mohamed Kathiresan, Shankar Shamsolmoali, Pourya Selim, Mahmoud M. Department of Computer Science & Engineering MIET Engineering College Tamilnadu India Department of Computer Science and Engineering Imayam College of Engineering Anna University ChennaiTamilnadu India Faculty of Computers and Information Mansoura University Mansoura Egypt Scientific Group for Research and Technology Cairo Egypt Department of Computer Applications Alagappa University Karaikudi India Huawei Technologies Corporate Shanghai China. & Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Mathematics Al-Aflaj College of Science and Human Studies Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2-LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search-based LBG (CS-LBG), Firefly-based LBG (FF-LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2-LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS-LBG, FA-LBG, and JPEG2000 methods. The experimental values revealed that the L2-LBG process yielded effective compression performance with a better-quality reconstructed image. © 2020 John Wiley & Sons Australia, Ltd

关键词： Vector quantization

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TEAM: Task-Clustering and Enhanced Adaptability for Meta-Reinforcement Learning on Robotics Through Multi-Task Diffusion and Optimization

TEAM: Task-Clustering and Enhanced Adaptability for Meta-Rei...

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IEEE International Conference on Robotic Computing (IRC)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Chi-Wei Feng Shih-Yun Sunkuan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan

ISBN: (数字)9798331521554

ISBN: (纸本)9798331521561

The ability of robots to imitate human learning strategies-rapidly adapting to new tasks without large datasets-has garnered significant attention in meta-learning. Meta-reinforcement learning seeks to enhance robotic agent flexibility across diverse tasks and contexts, offering promise where single-task learning often fails. Despite advancements like multi-task diffusion models and task-weighted optimization mechanisms, effectively training tasks with varying complexities simultaneously remains a major challenge. This paper introduces a novel meta-reinforcement learning method that addresses this issue by clustering the training tasks of robotic arms based on semantic and trajectory similarities, while leveraging adaptive learning rates and task-specific weights proposed by the multitask optimization techniques. Our approach, TEAM, emphasizes performance-driven semantic clustering, optimizing based on robotic task similarity, complexity, and convergence objectives. We also integrate fast adaptive and multi-task optimization of the diffusion model to enhance computational efficiency and adaptability. More specifically, we introduce a cluster-specific optimization technique, using specialized parameters for each group to allow more refined task handling. The experimental validation demonstrates the effectiveness of this scalable method in improving performance, adaptability, and efficiency in real-world, heterogeneous robotic tasks, further advancing robotic computing in meta-reinforcement learning.

关键词： Training Metalearning Program processors Semantics Multitasking Diffusion models Complexity theory Trajectory Robots Optimization

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Markerless Sagittal Gait Analysis of Cerebral Palsy in Children Using Pose Estimation Techniques

Markerless Sagittal Gait Analysis of Cerebral Palsy in Child...

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Innovation in Computing and Engineering (ICE), International Conference on

作者： Vinay Kumar Vats Chandra Prakash Amandeep Kaur Rajesh Kumar Computer Science and Engineering National Institute of Technology Delhi India Computational Intelligence and Smart Motion Research (CISMR) Lab SVNIT Surat India Malaviya National Institute of Technology Jaipur India Department of Human Anatomy and Physiology Faculty of Health Sciences University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

Cerebral Palsy (CP) is a neurological disorder affecting children’s motor function and posture. Early detection and monitoring of Cerebral palsy is crucial for effective intervention and improved quality of life. Gait analysis is a critical tool for assessing motor function in children with cerebral palsy, often requiring controlled environments and markers for accuracy. Markerless gait analysis has emerged as a promising tool for objectively evaluating gait abnormalities in children with CP. In this study, we propose a markerless methodology utilising pose estimation to analyse gait patterns from sagittal video recordings captured in unconstrained environments. The dataset consists of 49 children, including 10 typically developing children and 39 with cerebral palsy, focusing on side-view videos for the analysis. Given the motor disabilities of these children, conducting gait analysis in a constrained environment can alter their natural walking patterns, making our approach particularly advantageous. Then we identify the key gait events, heel strike and toe-off, to distinguish gait between cycles by tracing knee joint movements. This technique demonstrates the potential for practical, non-invasive gait assessment in natural settings, offering insights into the distinct gait characteristics of children with cerebral palsy compared to their typically developing peers.

关键词： Legged locomotion Neurological diseases Cerebral palsy Technological innovation Pose estimation Motors Motion control Gait recognition Video recording Monitoring

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ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting

ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse ...

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2022 IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022

作者： Sah, Ramesh Kumar McDonell, Michael Pendry, Patricia Parent, Sara Ghasemzadeh, Hassan Cleveland, Michael J Washington State University School of Electrical Engineering and Computer Science Pullman United States Washington State University Elson S. Floyd College of Medicine Pullman United States Washington State University Department of Human Development Pullman United States Arizona State University College of Health Solutions Tempe United States

ISBN: (纸本)9781665487917

Stress detection and classification from wearable sensor data is an emerging area of research with significant implications for individuals' physical and mental health. In this work, we introduce a new dataset, ADARP, which contains physiological data and self-report outcomes collected in real-world ambulatory settings involving individuals diagnosed with alcohol use disorders. We describe the user study, present details of the dataset, establish the significant correlation between physiological data and self-reported outcomes, demonstrate stress classification, and make our dataset public to facilitate research. © 2022 IEEE.

关键词： Wearable sensors

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ToP-ToM: Trust-aware Robot Policy with Theory of Mind

arXiv

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arXiv 2023年

作者： Yu, Chuang Serhan, Baris Cangelosi, Angelo UCL Interaction Centre Computer Science Department University College London LondonWC1E 6BT United Kingdom Cognitive Robotics Lab Manchester Centre for Robotics and AI The University of Manchester ManchesterM13 9PL United Kingdom

Theory of Mind (ToM) is a fundamental cognitive architecture that endows humans with the ability to attribute mental states to others. humans infer the desires, beliefs, and intentions of others by observing their behavior and, in turn, adjust their actions to facilitate better interpersonal communication and team collaboration. In this paper, we investigated trust-aware robot policy with the theory of mind in a multiagent setting where a human collaborates with a robot against another human opponent. We show that by only focusing on team performance, the robot may resort to the reverse psychology trick, which poses a significant threat to trust maintenance. The human's trust in the robot will collapse when they discover deceptive behavior by the robot. To mitigate this problem, we adopt the robot theory of mind model to infer the human's trust beliefs, including true belief and false belief (an essential element of ToM). We designed a dynamic trust-aware reward function based on different trust beliefs to guide the robot policy learning, which aims to balance between avoiding human trust collapse due to robot reverse psychology. The experimental results demonstrate the importance of the ToM-based robot policy for human-robot trust and the effectiveness of our robot ToM-based robot policy in multiagent interaction settings. © 2023, CC BY.

关键词： Multi agent systems

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Multi-tasking Dialogue Comprehension with Discourse Parsing 35

Multi-tasking Dialogue Comprehension with Discourse Parsing

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35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021

作者： He, Yuchen Zhang, Zhuosheng Zhao, Hai 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 China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Multi-party dialogue machine reading comprehension (MRC) raises an even more challenging understanding goal on dialogue with more than two involved speakers, compared with the traditional plain passage style MRC. To accurately perform the question-answering (QA) task according to such multi-party dialogue, models have to handle fundamentally different discourse relationships from common non-dialogue plain text, where discourse relations are supposed to connect two far apart utterances in a linguistics-motivated way. To further explore the role of such unusual discourse structure on the correlated QA task in terms of MRC, we propose the first multi-task model for jointly performing QA and discourse parsing (DP) on the multi-party dialogue MRC task. Our proposed model is evaluated on the latest benchmark Molweni, whose results indicate that training with complementary tasks indeed benefits not only QA task, but also DP task itself. We further find that the joint model is distinctly stronger when handling longer dialogues which again verifies the necessity of DP in the related MRC. © Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation, PACLIC 2021.

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EmoBench: Evaluating the Emotional Intelligence of Large Language Models

arXiv

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arXiv 2024年

作者： Sabour, Sahand Liu, Siyang Zhang, Zheyuan Liu, June M. Zhou, Jinfeng Sunaryo, Alvionna S. Li, Juanzi Lee, Tatia M.C. Mihalcea, Rada Huang, Minlie The CoAI Group DCST Institute for Artificial Intelligence Tsinghua University Beijing China The LIT Group Department of Computer Science and Engineering University of Michigan Ann Arbor United States DCST Tsinghua University Beijing China The Laboratory of Neuropsychology and Human Neuroscience HKU Hong Kong

Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major shortcomings: first, they mainly focus on emotion recognition, neglecting essential EI capabilities such as emotion regulation and thought facilitation through emotion understanding;second, they are primarily constructed from existing datasets, which include frequent patterns, explicit information, and annotation errors, leading to unreliable evaluation. We propose EMOBENCH, a benchmark that draws upon established psychological theories and proposes a comprehensive definition for machine EI, including Emotional Understanding and Emotional Application. EMOBENCH includes a set of 400 hand-crafted questions in English and Chinese, which are meticulously designed to require thorough reasoning and understanding. Our findings reveal a considerable gap between the EI of existing LLMs and the average human, highlighting a promising direction for future research. Our code and data are publicly available at https://***/Sahandfer/EmoBench. © 2024, CC BY.

关键词： Emotional intelligence

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Automated Grading Hemifacial Spasm Using Smartphone Cameras

Automated Grading Hemifacial Spasm Using Smartphone Cameras

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Smart World Congress (SWC), IEEE

作者： Ka I Chan Bo Hei Linghao Meng Ruen Liu Yuntao Wang Chang Chen Qingpei Hao Yuanchun Shi Global Innovation Exchange Institute Tsinghua University Beijing China Department of Neurosurgery Peking University People’s Hospital Beijing China Department of Computer Science and Technology Tsinghua University Beijing China National Key Laboratory of Human Factors Engineering Beijing China Qinghai University Xining Qinghai China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Hemifacial spasm is a chronic neurological condition characterized by involuntary facial muscle contractions caused by nerve compression. While familiar to specialists, it is less known to the public and general practitioners, which can lead to difficulties in diagnosis and severity assessment, and even misdiagnosis. Consequently, patients are common to have a long medical history. However, long-term patients tend to have poorer outcomes following surgery, and one-third of patients experience a delayed cure during postoperative rehabilitation. Moreover, $4 \%$ of patients experience recurrence, highlighting the importance of early and accurate diagnosis as well as postoperative monitoring. In this paper, we collected a video dataset of 50 hemifacial spasm patients and 9 healthy adults. We identified three facial features from the videos to establish a novel grading system closely aligned with the medical standards, specifically the Cohen-Albert Grading System. We also developed algorithms capable of automatically grading hemifacial spasm using smartphone cameras based on facial keypoint detection. These algorithms were evaluated on the dataset, achieving an accuracy of $88 \%$ for detection and a mean absolute error of $\mathbf{0 . 4 2}$ for grading.

关键词： Accuracy Surgery Hafnium Cameras Facial muscles History Medical diagnostic imaging Facial features Standards Monitoring

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