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arXiv 2023年

作者： Sharif, Omar Basak, Madhusudan Parvin, Tanzia Scharfstein, Ava Bradham, Alphonso Borodovsky, Jacob T. Lord, Sarah E. Preum, Sarah M. Department of Computer Science Dartmouth College United States Department of Computer Science and Engineering CUET Bangladesh Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College United States Department of Biomedical Data Science Geisel School of Medicine Dartmouth College United States Department of Psychiatry Dartmouth Health United States

Social media sites have become a popular platform for individuals to seek and share health information. Despite the progress in natural language processing for social media mining, a gap remains in analyzing health-related texts on social discourse in the context of events. Event-driven analysis can offer insights into different facets of healthcare at an individual and collective level, including treatment options, misconceptions, knowledge gaps, etc. This paper presents a paradigm to characterize health-related information-seeking in social discourse through the lens of events. Events here are board categories defined with domain experts that capture the trajectory of the treatment/medication. To illustrate the value of this approach, we analyze Reddit posts regarding medications for OPIOID USE DISORDER (OUD), a critical global health concern. To the best of our knowledge, this is the first attempt to define event categories for characterizing information-seeking in OUD social discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel treatment information-seeking event dataset to analyze online discourse on an event-based framework. This dataset contains Reddit posts on information-seeking events related to recovery from OUD, where each post is annotated based on the type of events. We also establish a strong performance benchmark (77.4% F1 score) for the task by employing several machine learning and deep learning classifiers. Finally, we thoroughly investigate the performance and errors of ChatGPT on this task, providing valuable insights into the LLM’s capabilities and ongoing characterization efforts. © 2023, CC BY-NC-SA.

关键词： Benchmarking

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A novel augmented reality-based simulator for enhancing orthopedic surgical training

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computers in Biology and Medicine 2025年 185卷 109536-109536页

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for instructors. Methods: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann–Whitney U tests. Results: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22±1.32mm with LoA −2.37 to 2.81 mm for distance deviation, 0.94±2.41 degrees with LoA −3.78 to 5.66 degrees for yaw deviation, −0.34±1.30 degrees with LoA −2.90 to 2.22 degrees for pitch deviation) and p-values of Mann–Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of 0.05. Conclusion: This work sh

关键词： Augmented reality

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EAViT: External Attention Vision Transformer for Audio Classification

EAViT: External Attention Vision Transformer for Audio Class...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Aquib Iqbal Abid Hasan Zim Md Asaduzzaman Tonmoy Limengnan Zhou Asad Malik Minoru Kuribayashi Department of Computer Science University of Massachusetts Amherst MA USA Department of Mechanical Engineering Aligarh Muslim University Aligarh India Department of Electrical Engineering Aligarh Muslim University Aligarh India School of Electronic and Information Engineering UESTC China School of Information Technology Monash University Malaysia Malaysia Center for Data-Driven Science and Artificial Intelligence Tohoku University Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper presents the External Attention Vision Transformer (EAViT) model, a novel approach designed to enhance audio classification accuracy. As digital audio resources proliferate, the demand for precise and efficient audio classification systems has intensified, driven by the need for improved recommendation systems, and user personalization in various applications, including music streaming platforms and environmental sound recognition. Accurate audio classification is crucial for organizing vast audio libraries into coherent categories, enabling users to find and interact with their preferred audio content more effectively. In this study, we utilize the GTZAN dataset, which comprises 1,000 music excerpts spanning ten diverse genres. Each 30-second audio clip is segmented into 3-second excerpts to enhance dataset robustness and mitigate overfitting risks, allowing for more granular feature analysis. The EAViT model integrates multi-head external attention (MEA) mechanisms into the Vision Transformer (ViT) framework, effectively capturing long-range dependencies and potential correlations between samples. This external attention (EA) mechanism employs learnable memory units that enhance the network’s capacity to process complex audio features efficiently. The study demonstrates that EAViT achieves a remarkable overall accuracy of 93.99%, surpassing state-of-the-art models.

关键词： computer vision Accuracy Correlation Memory management Music Transformers Robustness Recommender systems Sound recognition Overfitting

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Demographic-Guided Behavior Patterns Contrast for Personality Prediction

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IEEE Transactions on Affective Computing 2024年

作者： Ji, Yu Wu, Wen Lin, Hui Hu, Wenxin Hu, Yi Kang, Liang Chen, Xi He, Liang East China Normal University School of Computer Science and Technology Shanghai China East China Normal University Institute of AI for Education Shanghai China East China Normal University Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention School of Psychology and Cognitive Science Shanghai China Shanghai Liulishuo Information Technology Co. Ltd. Shanghai China School of Data Science and Engineering Shanghai China

In recent years, personality has been considered as a valuable personal factor being incorporated into the provision of personalized learning. Although some studies have endeavored to obtain learners' personalities implicitly from their learning behaviors, they failed to achieve satisfactory prediction performance. On the one hand, most existing approaches ignore the imbalanced distribution of personality classes, which causes the personality classifiers to be biased toward the non-extreme personality class. On the other hand, the related methods normally focus on constructing statistical behavior features, while the sequence information of learning behaviors is ignored, but actually it can reflect learners' behavior patterns more finely. In this paper, inspired by the human learning strategy in the face of small samples, we propose an effective Demographic-Guided Behavior Patterns Contrast (DGBPC) model to classify learners' personalities through the demographic-guided contrast of learners' coarse behavior patterns. Besides, we construct and publish the Personality and Learning Behavior dataset (PLBD), which should be one of the largest public datasets regarding Big-Five personality and learning behavior sequence according to our knowledge. The experimental results on PLBD demonstrate that our DGBPC model could generate learner representations with higher discrimination and outperform the related methods in terms of balanced accuracy. © 2010-2012 IEEE.

关键词： Contrastive Learning

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Fairness-aware Multiobjective Evolutionary Learning

arXiv

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arXiv 2024年

作者： Zhang, Qingquan Liu, Jialin Yao, Xin The Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen518055 China The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The School of Data Science Lingnan University Hong Kong

Multiobjective evolutionary learning (MOEL) has demonstrated its advantages of training fairer machine learning models considering a predefined set of conflicting objectives, including accuracy and different fairness measures. Recent works propose to construct a representative subset of fairness measures as optimisation objectives of MOEL throughout model training. However, the determination of a representative measure set relies on dataset, prior knowledge and requires substantial computational costs. What's more, those representative measures may differ across different model training processes. Instead of using a static predefined set determined before model training, this paper proposes to dynamically and adaptively determine a representative measure set online during model training. The dynamically determined representative set is then used as optimising objectives of the MOEL framework and can vary with time. Extensive experimental results on 12 well-known benchmark datasets demonstrate that our proposed framework achieves outstanding performance compared to state-of-the-art approaches for mitigating unfairness in terms of accuracy as well as 25 fairness measures although only a few of them were dynamically selected and used as optimisation objectives. The results indicate the importance of setting optimisation objectives dynamically during training. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Analysis and Detection of Fake News Using Machine Learning

Analysis and Detection of Fake News Using Machine Learning

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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： P Sathish Kumar P. Suthanthiradevi C Arul Stephen B Ebenezer Abishek S. Sivakumar M Mathiyarasu Computer and Communication Engineering Rajalakshmi institute of technology Chennai Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Electronics and Communication Engineering BIHER Chennai

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society.

关键词： Support vector machines Logistic regression Accuracy Machine learning algorithms Tokenization Reliability Internet of Things

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Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization

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IEEE Transactions on Evolutionary Computation 2024年

作者： Xue, Xiaoming Hu, Yao Feng, Liang Zhang, Kai Song, Linqi Tan, Kay Chen City University of Hong Kong Department of Computer Science Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China Chongqing University College of Computer Science Chongqing400044 China Qingdao University of Technology Civil Engineering School Qingdao266520 China The Hong Kong Polytechnic University Department of Data Science and Artificial Intelligence Hong Kong Hong Kong

Expensive optimization problems (EOPs) have attracted increasing research attention over the decades due to their ubiquity in a variety of practical applications. Despite many sophisticated surrogate-assisted evolutionary algorithms (SAEAs) that have been developed for solving such problems, most of them lack the ability to transfer knowledge from previously-solved tasks and always start their search from scratch, making them troubled by the notorious cold-start issue. A few preliminary studies that integrate transfer learning into SAEAs still face some issues, such as defective similarity quantification that is prone to underestimate promising knowledge, surrogate-dependency that makes the transfer methods not coherent with the state-of-the-art in SAEAs, etc. In light of the above, a plug and play competitive knowledge transfer method is proposed to boost various SAEAs in this paper. Specifically, both the optimized solutions from the source tasks and the promising solutions acquired by the target surrogate are treated as task-solving knowledge, enabling them to compete with each other to elect the winner for expensive evaluation, thus boosting the search speed on the target task. Moreover, the lower bound of the convergence gain brought by the knowledge competition is mathematically analyzed, which is expected to strengthen the theoretical foundation of sequential transfer optimization. Experimental studies conducted on a series of benchmark problems and a practical application from the petroleum industry verify the efficacy of the proposed method. The source code of the competitive knowledge transfer is available at https://***/XmingHsueh/SAS-CKT. © 1997-2012 IEEE.

关键词： Petroleum industry

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Generative AI-aided Reinforcement Learning for Computation Offloading and Privacy Protection in VR-based Multi-Access Edge Computing

Generative AI-aided Reinforcement Learning for Computation O...

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Smart World Congress (SWC), IEEE

作者： Feiran You Hongyang Du Jiawen Kang Wei Ni Dusit Niyato Abbas Jamalipour School of Electrical and Computer Engineering The University of Sydney Sydney NSW Australia Data61 Commonwealth Scientific and Industrial Research Organization (CSIRO) Sydney NSW Australia Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Automation Guangdong University of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users’ privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users’ privacy within the PPO centralized management framework.

关键词： Privacy Solid modeling Simulation Noise reduction Noise Virtual reality Reinforcement learning Diffusion models Protection Optimization

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Sample-Driven Adaptive Feature Selection with Weighted Fuzzy Rough Sets

SSRN

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SSRN 2024年

作者： Yang, Xiaoling Chen, Hongmei Li, Tianrui Wan, Jihong Yao, Yiyu School of Computing and Artificial Intelligence Southwest Jiaotong University China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University China Department of Computer Science University of Regina Regina Canada

The noise in data may debase the performance of the classification model. An intuitive assumption suggests that data samples with a higher local density tend to have a greater probability of being correctly classified. Moreover, there are differences in the importance of features, which should further increase the influence of important features on the classification task. Motivated by these issues, we propose a sample-driven adaptive feature selection method to mitigate the impact of noise and obtain the importance of features. Firstly, we propose a Weighted Fuzzy Rough Sets model (WFRS) by using the more reliable samples with high local density. Inspired by the large margin theory, we further obtain a specific fuzzy relation with the goal of maximizing the within-class fuzzy relation while minimizing the between-class fuzzy relation. The approximation margin is derived by applying the WFRS model within the original feature space. Feature weights are further obtained by utilizing the information derived from the approximation margin. On this basis, the weighted feature space is obtained that compresses the features with the lower weight and stretches the features with higher weight. An evaluation function of features is constructed by capturing the uncertainty of fuzzy positive and non-negative regions in the weighted feature space. A fuzzy rough feature selection algorithm (FSW) is designed by capturing the local structure of data. The weighted features are subsequently selected to facilitate the downstream classification tasks. Experimental results show the WFRS model's robustness, the effectiveness of FSW, and its superiority compared to alternative methods. © 2024, The Authors. All rights reserved.

关键词： Rough set theory

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CroAuth: A Cross-domain Authentication Scheme based on Blockchain and Decentralized Identity

CroAuth: A Cross-domain Authentication Scheme based on Block...

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International Conference on computer Supported Cooperative Work in Design

作者： Zinuo Zhang Ruoting Xiong Xinyu Di Wei Ren School of Computer Science China University of Geosciences Wuhan China School of Computer Science University of East Anglia Norwich UK International College Chongqing University of Technology Chongqing China Ministry of Education Key Laboratory of Data Protection and Intelligent Management Sichuan University Chengdu China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Current solutions on cross-domain access authentication based on blockchain are largely using digital certificate authentication, where users cannot control their identity, and some operations, such as certificate authentication and update, are inefficient. In this paper, we propose a Self-Sovereign Identity (SSI) based cross-domain authentication scheme implemented on a consortium chain. SSI is introduced as a decentralized digital identity solution to enable user-controlled identity management. Besides, blockchain, as an infrastructure for distributed digital identity, provides a storage place for decentralized identity identifiers and related data. In our scheme, users only need to register in one domain, and can use verifiable credentials (VC) to access other domains in the same consortium chain, without repeated registration. We also propose key method for quickly changing accounts in case of account loss. The logic, performance and security analysis justified that our scheme can protect the privacy of users and can remain feasible in case of a large scale of users.

关键词： Privacy Federated learning Computational modeling Buildings Authentication Blockchains Registers

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