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Domain generalization with semi-supervised learning for people-centric activity recognition

Science China(information Sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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Temperature-dependent wakeup behavior in back-end-of-line compatible ultra-thin HfxZr1-xO2 ferroelectric film

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Science China(information Sciences) 2025年第6期68卷 216-217页

作者： Xiaopeng LI Lu TAI Xiaoyu DOU Yang FENG Dong ZHANG Xuepeng ZHAN Jixuan WU Xiao GONG Jiezhi CHEN School of Information Science and Engineering Shandong University Beijing Superstring Academy of Memory Technology Department of Electrical and Computer Engineering National University of Singapore

HfO2-based ferroelectric（FE） devices have emerged as promising candidates for next-generation non-volatile memories（NVMs）, owing to their excellent CMOS compatibility, robust scalability, and low operating voltage requirements [1]. Among them, ultra-thin HfxZr1-xO2（HZO） FE films are particularly attractive for back-end-of-line（BEOL）integration in monolithic 3D memory architectures.

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Distribution-Based Time-Varying Ensemble Scientific Data Reduction for Uncertainty Visualization and Analysis

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Journal of information Science and engineering 2024年第2期40卷 397-419页

作者： Lin, Zhi-Rong Hsiao, An-Lung Li, Guan Wang, Ko-Chih Department of Computer Science and Information Engineering National Taiwan Normal University Taipei106 Taiwan Computer Network Information Center Chinese Academy of Sciences Beijing100083 China

Scientists often study physical phenomena using computer simulation models. The same simulation can generate different datasets because of different input parameter configurations or internal random variables. Therefore, each grid point is represented by multiple data values from simulation runs, and we call this type of data ensemble dataset. To gain insight into the physical phenomenon, scientists often have to visualize and analyze the ensemble datasets with uncertainty. Distribution-based data representation is a popular approach to handling the ensemble dataset and supporting uncertainty visualization. However, storing a time-varying ensemble dataset needs hundreds or even thousands of times storage size. Given the size of the time-varying ensemble dataset, it is natural to develop storage-reduced data representation to facilitate the time-varying ensemble data exploration. We propose a novel data representation to compactly represent the time-varying scientific data for uncertainty visualization and analysis. Our approach decouples data on the temporal domain into two types of distributions and stores. One distribution summarizes the data values on the temporal domain, and the other distribution describes the occurrence probability of a data value on the temporal domain. Our approach can provide time-varying ensemble scientific data analysis with uncertainty quantification and detailed temporal feature evolution with less storage requirement. © 2024 Institute of information Science. All rights reserved.

关键词： Probability distributions

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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A dual hashing-based authentication and secure data transmission scheme for vehicular cloud environment using MECC with optimal resource allocation mechanism

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Soft Computing 2024年第17-18期28卷 10423-10437页

作者： Varalakshmi, J. Dhanasekaran, S. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil India Department of Information Technology Kalasalingam Academy of Research and Education Krishnankoil India

Vehicle cloud computing (VCC) is a recent area of study that blends vehicular networks with cloud computing, offering networking and sensor capabilities to vehicles for interaction with other vehicles and roadside infrastructure. Integrating unidentified vehicles and infrastructure through a public network has raised concerns about security and privacy in VCC. Several researchers have proposed secure authentication mechanisms to address these issues and ensure secure data transmission in VCC. However, exchanging vehicle data in a vehicular cloud (VC) system presents specific challenges, including maintaining data privacy and implementing lightweight authentication procedures. Therefore, this study proposes an efficient authentication and secure data transfer mechanism for VC systems. The proposed approach utilizes Dual Hashing-based Secure Hashing Algorithm-384 (DHS384) and Modified Elliptical Curve Cryptography (MECC). An optimal resource allocation (RA) scheme is introduced to allocate VC resources optimally to a suitable virtual machine using the Improved Harris Hawks Optimization (IHHO) algorithm. Simulation results demonstrate that the proposed method outperforms existing approaches and enhances security. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Resource allocation

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Cross domain recommendation using dual inductive transfer learning

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Multimedia Tools and Applications 2024年 1-25页

作者： Zaman, Nilufar Jana, Angshuman Computer Engineering Trinity Academy of Engineering Kondhwa-Saswad Road Maharashtra Pune411048 India Computer Science Indian Institute of Information Technology Guwahati Bongora Assam Guwahati781015 India

Cross Domain Recommendations have made a great impact on the field of online services. It helps the service provider of one domain to understand their users from the information of other domains to recommend the correct product to them. Unlike other traditional approaches, our approach finds both way latent relations between users and items considering the interactions between users and items. Traditional model normally avoids considering any implicit information about the user or item for calculating the latent relation between them. But our model Dual Inductive Transfer Learning(DITL) uses all possible user and item features and the ratings provided for recommendations. Our model learns the user’s preferences across the source and target domain and thus preserves the user’s taste across different but related domains. Our model auto-encodes the features in a feature vector to extract the user’s taste and thus transfers this across domains for efficient recommendation. The proposed model is tested on three huge benchmark datasets, the Douban dataset, the Amazon 5-crore dataset for the domain of movies, books and music whereas Tenrec dataset for the domain of articles and videos and it proves to outperform the classical cross-domain recommendation systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recommender systems

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Integrated feature selection and ensemble learning for heart disease detection: a 2-tier approach with ALAN and ET-ABDF machine learning model

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International Journal of information Technology (Singapore) 2024年第7期16卷 4489-4503页

作者： Mandula, Aruna Vijaya Kumar, Baby Shalini Department of Computer Science and Engineering Kalasalingam Academy of Research Education Department of Information Technology Kalasalingam Academy of Research Education

The findings of this investigation give a novel approach to the forecasting of heart disease. For the purpose of determining significant features, it is a 2-tier procedure that uses a combination of the analysis of variance (ANOVA) and Least Absolute Shrinkage and Selection Operator (lasso) regression known as ALAN. In addition to this, it uses the Ensemble Technique with Adaptive Boosted Decision Fusion (ET-ABDF) in order to enhance its accuracy. An improvement in the detection of critical properties for predictive modelling is achieved through the use of the ALAN technique. With an accuracy of 88.0%, precision of 89.81%, recall of 89.80%, and an F1 Score of 89.00%, the ET-ABDF model shows outstanding results in the identification of cardiac disease. The outstanding result of 96.21% for the area under the curve (AUC) shows its strong ability to differentiate across several groups. It has been established through the comparative analysis that the model that has been provided displays better results in comparison to other algorithms. It is possible that in the future, undertaking more research on new techniques for choosing characteristics and expanding studies to encompass a larger spectrum of cardiovascular problems will have the potential to increase the accuracy and practicability of heart disease prediction models in real-world healthcare settings. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adaptive Boosted Decision Fusion (ET-ABDF) ALAN ANOVA Lasso regression

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Track correlation algorithm based on CNN-LSTM for swarm targets

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Journal of Systems engineering and Electronics 2024年第2期35卷 417-429页

作者： CHEN Jinyang WANG Xuhua CHEN Xian Research Institute for National Defense Engineering Academy of Military SciencesLuoyang 471023China Information Engineering University Zhengzhou 450001China School of Computer Science and Technology Xidian UniversityXi’an 710071China

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for *** this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm ***, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.

关键词： track correlation correlation accuracy rate swarm target convolutional neural network(CNN) long short-term memory(LSTM)neural network

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Fuzzy clustering for electric field characterization and its application to thunderstorm interpretability

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Digital Communications and Networks 2025年第2期11卷 299-307页

作者： Xu Yang Hongyan Xing Xinyuan Ji Wei Xu Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye University34010 IstanbulTurkey

Changes in the Atmospheric Electric Field Signal(AEFS)are highly correlated with weather changes,especially with thunderstorm ***,little attention has been paid to the ambiguous weather information implicit in AEFS *** this paper,a Fuzzy C-Means(FCM)clustering method is used for the first time to develop an innovative approach to characterize the weather attributes carried by ***,a time series dataset is created in the time domain using AEFS *** AEFS-based weather is evaluated according to the time-series Membership Degree(MD)changes obtained by inputting this dataset into the ***,thunderstorm intensities are reflected by the change in distance from a thunderstorm cloud point charge to an AEF ***,a matching relationship is established between the normalized distance and the thunderstorm dominant MD in the space ***,the rationality and reliability of the proposed method are verified by combining radar charts and expert *** results confirm that this method accurately characterizes the weather attributes and changes in the AEFS,and a negative distance-MD correlation is obtained for the first *** detection of thunderstorm activity by AEF from the perspective of fuzzy set technology provides a meaningful guidance for interpretable thunderstorms.

关键词： Atmospheric electric field(AEF) Thunderstorm Fuzzy C-means(FCM) Attribute

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Committed-programming reductions: formalizations,implications and relations

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Science China(information Sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted Computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

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