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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xinyue Chen Yazhou Ren Jie Xu Fangfei Lin Xiaorong Pu Yang Yang School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China and Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9798331314385

Recently, federated multi-view clustering (FedMVC) has emerged to explore cluster structures in multi-view data distributed on multiple clients. Many existing approaches tend to assume that clients are isomorphic and all of them belong to either single-view clients or multi-view clients. While these methods have succeeded, they may encounter challenges in practical FedMVC scenarios involving heterogeneous hybrid views, where a mixture of single-view and multi-view clients exhibit varying degrees of heterogeneity. In this paper, we propose a novel Fed-MVC framework, which concurrently addresses two challenges associated with heterogeneous hybrid views, i.e., client gap and view gap. To address the client gap, we design a local-synergistic contrastive learning approach that helps single-view clients and multi-view clients achieve consistency for mitigating heterogeneity among all clients. To address the view gap, we develop a global-specific weighting aggregation method, which encourages global models to learn complementary features from hybrid views. The interplay between local-synergistic contrastive learning and global-specific weighting aggregation mutually enhances the exploration of the data cluster structures distributed on multiple clients. Theoretical analysis and extensive experiments demonstrate that our method can handle the heterogeneous hybrid views in FedMVC and outperforms state-of-the-art methods. The code is available at https://***/5Martina5/FMCSC.

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Transforming GP-CNN Tree Search Into Trainable Architectures for Image Classification

Transforming GP-CNN Tree Search Into Trainable Architectures...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Sun, Feng Ke, Yan Gong, Yue-Jiao Li, Yun Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China School of Computer Science and Engineering South China University of Technology Guangzhou China i4AI Ltd London WCIN3AX United Kingdom

ISBN: (纸本)9781665410205

Data-efficient image classification poses a challenge in achieving effectiveness with limited data, as evidenced by the current methods based on convolutional neural networks (CNNs) and genetic programming (GP). Existing works employing these two methods encounter limitations, such as a lack of flexibility and an inability to effectively explore the latent features of the data. To tackle these challenges, this paper introduces a genetic programming method for data-efficient image recognition, leveraging novel function sets, terminal sets, and program structures. This method transforms tree-based data structures in GP into trainable CNN architectures. Further, by employing block structures instead of single operations in the search space, the search space is reduced and the stability of the search structures enhanced. Comparative experiments with state-of-the-art neural network methods and GP-based methods on data-efficient classification datasets validate the GP-CNN method offering higher performance. © 2024 IEEE.

关键词： Genetic programming

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Interpretive Clinical Measurements Prediction and Diagnosis in ICU with Multi-Task Attention-Guided Sequential Feature Learning 7

Interpretive Clinical Measurements Prediction and Diagnosis ...

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7th Asian Conference on Artificial Intelligence technology, ACAIT 2023

作者： Zhao, Peng Xiao, Yan Lei, Yuqing Ren, Yazhou Xu, Zenglin Chongqing Medical University School of Basic Medical Sciences Chongqing China University of Electronic Science And Technology of China School of Computer Science And Engineering Chengdu China University of Electronic Science And Technology of China Shenzhen Institute For Advanced Study Shenzhen China Harbin Institute of Technology Shenzhen School of Computer Science And Technology Shenzhen China Peng Cheng National Lab Center of Artificial Intelligence Shenzhen China

ISBN: (纸本)9798350359145

During the outbreak of COVID-19, it is particularly important to improve clinical decision making for critically ill patients in intensive care units (ICU). Machine learning has become increasingly important in healthcare analysis with the wide adoption of Electronic Health Records (EHR). Many deep learning algorithms, such as Recurrent Neural Networks (RNNs), have been developed to deal with clinical time series data to predict the health status of patients, such as mortality, ventilation, and phenotype, etc. Despite RNN being successful in medical tasks, the special traits of data, such as long temporality, high dimensionality, existence of missing values, and multi-labels, have brought challenges to either shallow or deep learning algorithms. Motivated by the fact that the progress of diseases may vary over time, to address these challenges, we proposed a multi-task attention-guided recurrent neural network for learning sequential features (MASFL) model, which employs recurrent units and an attention mechanism. The recurrent units can memorize the past progressive information of patients after entering ICU, while the attention mechanism can selectively utilize this information for different prediction tasks. Meanwhile, the attention also helps to interpret the relationship between the clinical time series data and the prediction result. Furthermore, in order to solve undesirable local solutions for the patient's length-of-stay task, we develop an ordinal regression loss to address the issue. According to the experiments on MIMIC-III, we demonstrate that the proposed model can improve the prediction performance over state-of-the-art prediction approaches. © 2023 IEEE.

关键词： Recurrent neural networks

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Resource Optimization-Oriented Method for Generating Cooperative Interference Strategies

Resource Optimization-Oriented Method for Generating Coopera...

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2023 International Conference on Intelligent Communication and computer engineering, ICICCE 2023

作者： Zeng, Yue Liu, Qiao Dai, Tingting Huang, Yanwen Hou, Rui Gan, Yanglei Lang, Junjie School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang China

ISBN: (纸本)9798350395617

The generation of effective jamming strategies stands as a critical imperative in the realm of communication countermeasures. A comparative analysis of extant jamming strategy generation methods reveals a scarcity of research that simultaneously addresses the optimization of cooperative jamming effectiveness and the judicious allocation of resources, with a focus on resource minimization. In light of these observations, this study employs a multifaceted approach, harnessing machine learning and allied methodologies, to discern potential behavioral patterns within communication stations. Specifically, we construct a scenario of communication confrontation, featuring multiple jammers and multiple communication stations. Subsequently, we establish a model for the generation of cooperative jamming strategies, leveraging a double deep Q-network. Moreover, we introduce a dual-dynamic penalty function that synergizes with the double deep Q-network. This combined approach aids the model in searching for optimal solutions under specific constraints throughout the interference process's various stages. In doing so, the model strives to optimize the utilization of interference resources while ensuring effective cooperative interference. Simulation results substantiate the efficacy of the proposed approach. The double deep Q-network algorithm, when integrated with the dual dynamic penalty function, significantly enhances interference effectiveness. Furthermore, it achieves a noteworthy 25% reduction in interference resource consumption compared to an unconstrained algorithm. © 2023 IEEE.

关键词： Decision making

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Insurance Plan for Service Assurance in Cloud Computing Market with Incomplete Information

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Journal of Communications and Information Networks 2022年第1期7卷 11-22页

作者： Yanru Zhang Chunxiao Jiang Nguyen H.Tran Shengrong Bu FeiRichard Yu Zhu Han College of Computer Science and Engineering the University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study UESTCLonghuaShenzhen 518110China Tsinghua Space Center Tsinghua UniversityBeijing 10084China School of Computer Science the University of SydneySydneyNew South WalesAustralia Department of Engineering Brock UniversityONCanada School of Information Technology Depart.of Systems and Computer EngineeringCarleton UniversityOttawa K1S 5B6ONCanada Department of Electrical and Computer Engineering the University of HoustonHouston 77004TXUSA

Nowadays,cloud computing has been identified as new opportunities for migrating to the expected agility,reuse,and adaptive capabilities that can support the ever changing IT trends and ***,the rapid evolution of those technologies also comes with open issues such as security,privacy,integrity,quality of services,and their possible detrimental *** this work,the concept of insurance is introduced to compensate the cloud computing customers when encountering those failures if service providers(SPs)have insurance ***,we consider the situation when the insurer is unable to see the system failure risk levels of the SPs,which is usually seen as an incomplete information market,in contrast with the optimal situation in a complete information ***,an insurance-based cloud computing architecture is proposed to build a monetary credit system in which the cloud computing SP pays a premium for a certain coverage to the ***,problem is formulated to solve the optimal insurance plan in complete and incomplete information markets,together with detail analysis of insurance policies in both cases are ***,simulation results show the properties of the two insurance plans and parameters that affect the design of the insurance plan.

关键词： service assurance cloud computing market incomplete information game theory insurance plan

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Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques

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computers, Materials & Continua 2025年第3期82卷 5115-5134页

作者： Abdul Ahad Ira Puspitasari Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires Department Information System Study Program Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Software Northwestern Polytechnical UniversityXi’an710072China Research Center for Quantum Engineering Design Faculty of Science and TechnologyUniversitas AirlanggaSurabaya60286Indonesia School of Computer Science and Software Engineering Shenzhen UniversityShenzhen518061China Cybersecurity Center Prince Mohammad Bin Fahd University617Al JawharahKhobarDhahran34754Saudi Arabia Cardiff School of Technologies Cardiff Metropolitan UniversityWestern AvenueCardiffCF52YBUK Instituto de Telecomunicacoes Escola Superior de Tecnologia e Gestao de AguedaUniversidade de Aveiroágueda3750-127Portugal

This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.

关键词： Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke

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Sampling from the Mean-field Stationary Distribution 37

Sampling from the Mean-Field Stationary Distribution

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37th Annual Conference on Learning Theory, COLT 2024

作者： Kook, Yunbum Zhang, Matthew S. Chewi, Sinho Erdogdu, Murat A. Li, Mufan Georgia Institute of Technology United States Department of Computer Science University of Toronto Vector Institute Canada School of Mathematics Institute for Advanced Study United States Department of Operations Research and Financial Engineering Princeton University United States

We study the complexity of sampling from the stationary distribution of a mean-field SDE, or equivalently, the complexity of minimizing a functional over the space of probability measures which includes an interaction term. Our main insight is to decouple the two key aspects of this problem: (1) approximation of the mean-field SDE via a finite-particle system, via uniform-in-time propagation of chaos, and (2) sampling from the finite-particle stationary distribution, via standard log-concave samplers. Our approach is conceptually simpler and its flexibility allows for incorporating the state-of-the-art for both algorithms and theory. This leads to improved guarantees in numerous settings, including better guarantees for optimizing certain two-layer neural networks in the mean-field regime. A key technical contribution is to establish a new uniform-in-N log-Sobolev inequality for the stationary distribution of the mean-field Langevin dynamics. © 2024 Y. Kook, M. S. Zhang, S. Chewi, M. A. Erdogdu & M.(. Li.

关键词： Probability distributions

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Online Detection of Anomalies in Temporal Knowledge Graphs with Interpretability

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Proceedings of the ACM on Management of Data 2024年第6期2卷 1-26页

作者： Jiasheng Zhang Rex Ying Jie Shao School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu CN Department of Computer Science Yale University New Haven CT USA Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen CN

Temporal knowledge graphs (TKGs) are valuable resources for capturing evolving relationships among entities, yet they are often plagued by noise, necessitating robust anomaly detection mechanisms. Existing dynamic graph anomaly detection approaches struggle to capture the rich semantics introduced by node and edge categories within TKGs, while TKG embedding methods lack interpretability, undermining the credibility of anomaly detection. Moreover, these methods falter in adapting to pattern changes and semantic drifts resulting from knowledge updates. To tackle these challenges, we introduce AnoT, an efficient TKG summarization method tailored for interpretable online anomaly detection in TKGs. AnoT begins by summarizing a TKG into a novel rule graph, enabling flexible inference of complex patterns in TKGs. When new knowledge emerges, AnoT maps it onto a node in the rule graph and traverses the rule graph recursively to derive the anomaly score of the knowledge. The traversal yields reachable nodes that furnish interpretable evidence for the validity or the anomalous of the new knowledge. Overall, AnoT embodies a detector-updater-monitor architecture, encompassing a detector for offline TKG summarization and online scoring, an updater for real-time rule graph updates based on emerging knowledge, and a monitor for estimating the approximation error of the rule graph. Experimental results on four real-world datasets demonstrate that AnoT surpasses existing methods significantly in terms of accuracy and interoperability. All of the raw datasets and the implementation of AnoT are provided in https://***/zjs123/ANoT.

关键词： anomaly detection graph summarization temporal knowledge graph

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Smart Home Security System Using Face Recognition Based on IoT- CNN 2

Smart Home Security System Using Face Recognition Based on I...

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2nd International Conference on Information technology Research and Innovation, ICITRI 2023

作者： Febriantono, M. Aldiki Zuhair, Alvin Khaeruddin School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Islam Blitar Electrical Engineering Department Blitar Indonesia University of Muhammadiyah Malang Diploma III Electronic Technology Vocational Study Program Malang Indonesia

ISBN: (纸本)9798350324945

Home security is a crucial aspect that requires careful attention, particularly when it comes to addressing theft concerns. Hence, implementing smart door technology equipped with facial recognition holds promising potential for enhancing home security. This study aims to develop a more secure and regulated home entry system by leveraging Internet of Things (IoT) technology and Machine Learning computer Vision for facial recognition. The system integrates IoT devices, such as cameras and automatic doors, wherein facial image data is captured by the camera and processed using the Convolutional Neural Network (CNN) algorithm to identify individuals. Once an individual is recognized, the system grants access to the home through an automated door. By relying on facial features, the system effectively restricts unauthorized access and safeguards homes against theft risks. Therefore, the advancement of a safer and more controlled home entry system utilizing IoT technology and Machine Learning computer Vision holds tremendous benefits for homeowners. © 2023 IEEE.

关键词： Face recognition

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A nano-sheet graphene-based enhanced thermal radiation composite for passive heat dissipation from vehicle batteries

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Nano Materials science 2024年第4期6卷 443-455页

作者： Ji-Xiang Wang Christopher Salmean Jiaxin Li Chaojie Lei Jun Li Mingliang Zhong Bo Qi Yufeng Mao Hebei Key Laboratory of Man-machine Environmental Thermal Control Technology and Equipment Hebei Vocational University of Technology and EngineeringHebei 054000PR China College of Electrical Energy and Power EngineeringYangzhou UniversityYangzhou225009PR China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and TechnologyKowloonHong Kong SARPR China China Academy of Launch Vehicle Technology Beijing100076PR China Beijing Sino-Spark Technology Co. Ltd.Beijing100191PR China School of Environmental Science and Engineering Suzhou University of Science and TechnologySuzhou215009PR China Institute of Optics and Electronics Chinese Academy of SciencesChengdu610209PR China National Key Laboratory of Optical Field Manipulation Science and Technology Chinese Academy of SciencesChengdu610209PR China Taizhou Wavexploration Energy Ltd. Taizhou225513PR China Department of Electrical and Computer Engineering University of TorontoTorontoM5S3G4Canada

In response to thermal runaway(TR)of electric vehicles,recent attention has been focused on mitigation strategies such as efficient heat dredging in battery thermal *** management with particular focus on battery cooling has been becoming increasingly *** usually happened when an electric vehicle is unpowered and *** this state,traditional active battery cooling schemes are disabled,which can easily lead to dangerous incidents due to loss of cooling ability,and advanced passive cooling strategies are therefore gaining ***,we developed an enhanced thermal radiation material,consisting of~1μm thick multilayered nano-sheet graphene film coated upon the heat dissipation surface,thereby enhancing thermal radiation in the *** surface was characterized on the nanoscale,and tested in a battery-cooling *** found that the graphene-based coating's spectral emissivity is between 91% and 95% in the mid-infrared region,and thermal experiments consequently illustrated that graphene-based radiative cooling yielded up to15.1% temperature reduction when compared to the uncoated *** the novel graphene surface to augment a heat pipe,the temperature reduction can be further enlarged to 25.6%.The new material may contribute to transportation safety,global warming mitigation and carbon neutralization.

关键词： Battery thermal management Nano-sheet graphene Functional material Passive cooling Thermal radiation

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