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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yan, Guochen Xie, Luyuan Gao, Xinyi Zhang, Wentao Shen, Qingni Fang, Yuejian Wu, Zhonghai School of Computer Science Peking University Beijing China School of Software and Microelectronics Peking University Beijing China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China The University of Queensland Brisbane Australia Center for Machine Learning Research Peking University Beijing China

ISBN: (纸本)157735897X

Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distribution is always non-independent and identical distribution (non-IID), resulting in client drift and unsatisfactory performance. Despite existing federated learning methods attempting to solve the non-IID problems, they still show marginal advantages but rely on frequent communication which would incur high costs and privacy concerns. In this paper, we propose a novel federated learning method: Federated learning via Valuable Condensed Knowledge (FedVCK). We enhance the quality of condensed knowledge and select the most necessary knowledge guided by models, to tackle the non-IID problem within limited communication budgets effectively. Specifically, on the client side, we condense the knowledge of each client into a small dataset and further enhance the condensation procedure with latent distribution constraints, facilitating the effective capture of high-quality knowledge. During each round, we specifically target and condense knowledge that has not been assimilated by the current model, thereby preventing unnecessary repetition of homogeneous knowledge and minimizing the frequency of communications required. On the server side, we propose relational supervised contrastive learning to provide more supervision signals to aid the global model updating. Comprehensive experiments across various medical tasks show that FedVCK can outperform state-of-the-art methods, demonstrating that it’s non-IID robust and communication-efficient. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Identification and Visualization of Spatial and Temporal Trends in Textile Industry

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computers, Materials & Continua 2023年第2期74卷 4165-4181页

作者： Umair Yousaf Muhammad Asif Shahbaz Ahmed Noman Tahir Azeem Irshad Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Habib Hamam Department of Computer Science National Textile UniversityFaisalabad38000Pakistan Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea Faculty of Engineering Uni de MonctonMonctonNB E1A3E9Canada International Institute of Technology and Management Commune d’AkandaBPLibreville1989Gabon School of Electrical Engineering Department of Electrical and Electronic Eng.ScienceUniversity of JohannesburgJohannesburg2006South Africa Spectrum of Knowledge Production&Skills Development Sfax3027Tunisia

The research volume increases at the study rate,causing massive text *** to these enormous text corpora,we are drowning in data and starving for ***,recent research employed different text mining approaches to extract information from this text *** proposed approaches extract meaningful and precise phrases that effectively describe the text’s *** extracted phrases are commonly termed ***,these key phrases are employed to determine the different fields of study ***,these key phrases can also be used to determine the spatiotemporal trends in the various research *** this research,the progress of a research field can be better revealed through spatiotemporal bibliographic trend ***,an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific *** study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research *** data was collected from various open access ***,this research determined the spatiotemporal trends using quality *** research also focused on finding the research collaboration of different countries in a particular research *** research collaborations of other countries’researchers show the impact on import and export of those *** visualization approach is also incorporated to understand the results better.

关键词： Text mining spatiotemporal trend research collaboration

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Power in computer Security and Privacy: A Critical Lens

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IEEE Security and Privacy 2023年第2期21卷 48-52页

作者： Redmiles, Elissa M. Bennett, Mia M. Kohno, Tadayoshi Max Planck Institute for Software Systems Saarland66123 Germany University of Washington SeattleWA98195 United States Paul G. Allen School of Computer Science and Engineering University of Washington SeattleWA98195 United States

Critical theory is an approach to research focused on acknowledging and dismantling power structures. In this piece, we illustrate the ways in which security and privacy research already takes a critical approach and ... 详细信息

关键词： computer networks

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A Novel Approach for Automated Design Information Mining from Issue Logs

arXiv

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arXiv 2024年

作者： Zhao, Jiuang Yang, Zitian Zhang, Li Lian, Xiaoli Yang, Donghao School of Computer Science and Engineering Beihang University Beijing China School of Software Engineering Beihang University Beijing China

software architectures are usually meticulously designed to address multiple quality concerns and support long-term maintenance. However, due to the imbalance between the cost and value for developers to document design rationales (i.e., the design alternatives and the underlying arguments for making or rejecting decisions), these rationales are often obsolete or even missing. The lack of design knowledge has motivated a number of studies to extract design information from various platforms in recent years. Unfortunately, despite the wealth of discussion records related to design information provided by platforms like open-source communities, existing research often overlooks the underlying arguments behind alternatives due to challenges such as the intricate semantics of discussions and the lack of benchmarks for design rationale extraction. In this paper, we propose a novel method, named by DRMiner, to automatically mine latent design rationales from developers’ live discussion in open-source community (i.e., issue logs in Jira). To better identify solutions and the arguments supporting them, DRMiner skillfully decomposes the problem into multiple text classification tasks and tackles them using prompt tuning of language models and customized text-related features. To evaluate DRMiner, we acquire issue logs from Cassandra, Flink, and Solr repositories in Jira, and then annotate and process them under a rigorous scheme, ultimately forming a dataset for design rationale mining. Experimental results show that DRMiner achieves an F1 score of 65% for mining design rationales, outperforming all baselines with a 7% improvement over GPT-4.0. Furthermore, we investigate the usefulness of the design rationales mined by DRMiner for automated program repair (APR) and find that the design rationales significantly enhance APR, achieving 14× higher full-match repairs on average. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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Online Joint Power Allocation and Task Scheduling for LEO Satellite Networks

Online Joint Power Allocation and Task Scheduling for LEO Sa...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Li, Zheyuan He, Lijun Wang, Juncheng Jia, Ziye Wang, Yanting Yuen, Chau School of Software Northwestern Polytechnical University China School of Information and Control Engineering China University of Mining and Technology China Hong Kong Baptist University Department of Computer Science Hong Kong Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology China School of Electrical and Electronics Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350368369

The excessive proliferation of Low Earth Orbit (LEO) satellites inescapably bring the explosive growth of space data in LEO Satellite Networks (LSNs). Meanwhile, the stochastic arrivals of space data together with the time-varying satellite-ground links in LSNs pose significant challenges for offloading a large volume of space data from LSNs to ground stations. To circumvent these challenges, we systematically study the energy-constrained online data offloading problem to jointly optimize power allocation and task scheduling for LSNs. First, we leverage Lyapunov optimization to decouple our formulated long-term stochastic joint optimization problem into a set of per-time-slot subproblems. Then, each subproblem is decoupled into a task scheduling problem and a power allocation problem. Next, we derive the optimal solution to the power allocation problem and propose a multi-armed bandit based quasi-optimal solution to the task scheduling problem. Finally, extensive simulation results show that our proposed algorithm has superior performance over the state-of-the-art solutions. © 2025 IEEE.

关键词： Stochastic systems

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Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuning 39

Improving Complex Reasoning over Knowledge Graph with Logic-...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xia, Tianle Ding, Liang Wan, Guojia Zhan, Yibing Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia JD Explore Academy College of Computing and Data Science Nanyang Technological University 639798 Singapore

ISBN: (纸本)157735897X

Answering complex queries over incomplete knowledge graphs (KGs) is a challenging task. Most previous works have focused on learning entity/relation embeddings and simulating first-order logic operators with various neural networks. However, they are bottlenecked by the inability to share world knowledge to improve logical reasoning, thus resulting in suboptimal performance. In this paper, we propose a complex reasoning schema over KG upon large language models (LLMs), containing a curriculum-based logical-aware instruction tuning framework, named LACT. Specifically, we augment the arbitrary first-order logical queries via binary tree decomposition, to stimulate the reasoning capability of LLMs. To address the difficulty gap among different types of complex queries, we design a simple and flexible logic-aware curriculum learning framework. Experiments across widely used datasets demonstrate that LACT has substantial improvements (brings an average +5.5% MRR score) over advanced methods, achieving the new state-of-the-art. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Curricula

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Decoding Musical Neural Activity in Patients With Disorders of Consciousness Through Self-Supervised Contrastive Domain Generalization

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IEEE Transactions on Affective Computing 2025年第2期16卷 726-743页

作者： Cai, Honghua Pan, Jiahui Xiao, Qiuyi Jin, Jiarui Li, Yuanqing Xie, Qiuyou South China Normal University School of Software Foshan528225 China South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southern Medical University Zhujiang Hospital Guangzhou510260 China South China University of Technology Center for Brain-Computer Interfaces and Brain Information Processing Guangzhou510640 China Pazhou Lab Guangzhou510330 China

Identifying the brain responses of patients with disorders of consciousness (DOCs), which include comas, vegetative states (VSs, also called unresponsive wakefulness syndrome (UWS)) and minimally conscious states (MCSs), based on electroencephalography (EEG) has important clinical diagnosis implications. However, due to impaired motor and cognitive abilities, patients with DOCs may not be able to express their feelings and their brain responses to different stimuli, making it difficult to correctly label data. EEG classification algorithms trained with these data cannot make reliable classifications and predictions for clinical diagnosis purposes. To identify the brain responses produced for different types of stimuli in patients with DOCs, we proposed a self-supervised contrastive domain generalization framework (SSCDG) for cross-subject EEG classification. The model was first trained with healthy-subject EEG data induced by different stimuli to learn their corresponding unsupervised representations. Then, we used these representations to train a classifier to predict the emotional states of patients with DOCs under the corresponding stimuli. SSCDG was first evaluated on the SEED dataset, and it achieved an accuracy of 87.6%, which was 1.1% higher than that of the state-of-the-art (SOTA) approaches. Moreover, the SSCDG method was utilized to categorize EEG data acquired from seventeen DOC patients, including eleven in a UWS state and six in an MCS state, with seven patients demonstrating notable accuracy in three-class EEG classification tasks. The SSCDG results indicated that the seven patients with DOCs may have shown classifiable EEG responses to the presented stimuli. © 2010-2012 IEEE.

关键词： Electroencephalography

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Personalized Model Partitioning and Aggregation for Federated Human Activity Recognition

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhang, Weidong Wu, Xuangou Zhang, Tao Wang, Xiujun Wang, Jiacheng Zhang, Ruichen Wu, Tong Kang, Jiawen Niyato, Dusit Anhui University of Technology School of Computer Science and Technology Anhui China Beijing Jiaotong University School of Software Engineering Beijing China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore Singapore University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Guangdong University of Technology School of Automation Guangdong China

Federated learning-based human activity recognition (FL-HAR) has emerged as a significant research topic due to its extensive applications, enabling the collective integration of local client data and knowledge without compromising user privacy. A major challenge within FL-HAR is addressing the heterogeneity of data across distributed clients, which leads to varying feature distributions for the same activity across different clients. To overcome this limitation, we propose a novel algorithm for FL-HAR, termed FedPMP, with Adaptive Model Partitioning and Personalized Model Aggregation. This approach customizes individual models for each client, aligned with their individual data distribution. Specifically, our partitioning strategy categorizes the model into globally shared components and locally personalized components, based on layer-wise feature drift. The global components capture general features across the majority of user groups, while the local components extract user-specific personalized features. Furthermore, our personalized model aggregation approach separately consolidates the shared and personalized components, facilitating collaboration among clients in accordance with feature relevance. Notably, we prove that FedPMP converges under feature drift conditions, providing a solid theoretical foundation for its practical deployment. Extensive experiments on various real-world HAR benchmarks demonstrate that FedPMP can effectively address the issue of feature drift in HAR, boosting recognition accuracy for clients and reducing the variance in accuracy among them. Our method enhances recognition accuracy by an average factor of 5%, proving its efficacy and robustness under feature drift conditions. © 2015 IEEE.

关键词： Federated learning

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Exploiting the sequential nature of genomic data for improved analysis and identification

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computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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