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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Xianzhen Li Haijun Zhang Yi Yang Shuicheng Yan Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Computer Science and Technology Soochow University Suzhou China Harbin Institute of Technology (Shenzhen) Shenzhen China Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia Sea AI Lab (SAIL) & National University of Singapore Singapore

ISBN: (纸本)9781665423991

Deep subspace clustering (DSC) with the auto-encoder and self-expression layer is of great concern due to encouraging performance. However, existing methods usually adopt a “single-task” strategy based on a single dataset, without considering other related tasks or data. As such, they cannot discover other useful information to improve the clustering task. Besides, the local structure preservation of the latent codes in mapping is usually ignored. In this paper, we therefore present an effective “multi-task” strategy via the self-supervised data augmentation, and propose a new end-to-end trainable Triplet Deep Subspace Clustering Network (TDSC-net). Specifically, TDSC-net firstly generates triplet data (i.e., anchor, positive and negative data) from input data by a spectral clustering module and a self-supervised data augmentation module. This can enable it to inherit the merits of self-supervised learning and multitask learning implicitly. After that, TDSC-net builds a triplet deep autoencoder network with a self-expression layer, which takes the triplet data as input, where they share the common network layers (i.e., autoencoder and self-expression layers) over the triple tasks for complementary learning and mutual supervision. A triplet loss is also included to retain the local information of deep latent codes, which will also benefit the self-expression. Furthermore, TDSC-net separates the self-expression layer from decoding process to improve the efficiency of reconstruction. Extensive results on several public datasets demonstrate the effectiveness of our triplet-task DSC strategy.

关键词： Codes Conferences Multitasking Decoding data mining Task analysis

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Low-rank reduced biquaternion tensor ring decomposition and tensor completion

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Applied Mathematics and Computation 2025年 504卷

作者： Luo, Hui Liu, Xin Liu, Wei Zhang, Yang School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology China Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology China School of Artificial Intelligence at Sun Yat-sen University the Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Mathematics University of Manitoba MB Canada

We define the reduced biquaternion tensor ring (RBTR) decomposition and provide a detailed exposition of the corresponding algorithm RBTR-SVD. Leveraging RBTR decomposition, we propose a novel low-rank tensor completion algorithm RBTR-TV integrating RBTR ranks with total variation (TV) regularization to optimize the process. Numerical experiments on color image and video completion tasks indicate the advantages of our method. © 2025 Elsevier Inc.

关键词： Numerical methods

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Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI

arXiv

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arXiv 2024年

作者： Wang, Zi Xiao, Min Zhou, Yirong Wang, Chengyan Wu, Naiming Li, Yi Gong, Yiwen Chang, Shufu Chen, Yinyin Zhu, Liuhong Zhou, Jianjun Cai, Congbo Wang, He Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Institute of Artificial Intelligence Xiamen University China Human Phenome Institute Fudan University China Department of Imaging Xiamen Cardiovascular Hospital of Xiamen University School of Medicine Xiamen University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University Department of Medical Imaging Shanghai Medical School Shanghai Institute of Medical Imaging China Fujian Province Key Clinical Specialty Construction Project Medical Imaging Department Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering and Imperial-X Imperial College London United Kingdom Department of Bioengineering Imperial College London United Kingdom

Dynamic magnetic resonance imaging (MRI) plays an indispensable role in cardiac diagnosis. To enable fast imaging, the k-space data can be undersampled but the image reconstruction poses a great challenge of high-dimensional processing. This challenge necessitates extensive training data in deep learning reconstruction methods. In this work, we propose a novel and efficient approach, leveraging a dimension-reduced separable learning scheme that can perform exceptionally well even with highly limited training data. We design this new approach by incorporating spatiotemporal priors into the development of a Deep Separable Spatiotemporal Learning network (DeepSSL), which unrolls an iteration process of a 2D spatiotemporal reconstruction model with both temporal low-rankness and spatial sparsity. Intermediate outputs can also be visualized to provide insights into the network behavior and enhance interpretability. Extensive results on cardiac cine datasets demonstrate that the proposed DeepSSL surpasses state-of-the-art methods both visually and quantitatively, while reducing the demand for training cases by up to 75%. Additionally, its preliminary adaptability to unseen cardiac patients has been verified through a blind reader study conducted by experienced radiologists and cardiologists. Furthermore, DeepSSL enhances the accuracy of the downstream task of cardiac segmentation and exhibits robustness in prospectively undersampled real-time cardiac MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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An optimized capsule-LSTM model for facial expression recognition with video sequences

arXiv

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arXiv 2021年

作者： Liu, Siwei Long, Yuanpeng Xu, Gao Yang, Lijia Xu, Shimei Yao, Xiaoming Shu, Kunxian School of Computer Science and Technology Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China School of Economic Information Engineering Southwestern University of Finance and Economics Chengdu611130 China *** Hetie International Square Building 2 Room404 Sichuan Chengdu China

To overcome the limitations of convolutional neural network in the process of facial expression recognition, a facial expression recognition model Capsule-LSTM based on video frame sequence is proposed. This model is composed of three networks includingcapsule encoders, capsule decoders and LSTM network. The capsule encoder extracts the spatial information of facial expressions in video frames. Capsule decoder reconstructs the images to optimize the network. LSTM extracts the temporal information between video frames and analyzes the differences in expression changes between frames. The experimental results from the MMI dataset show that the Capsule-LSTM model proposed in this paper can effectively improve the accuracy of video expression recognition. © 2021, CC BY.

关键词： Long short-term memory

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data placement for multi-tenant data federation on the cloud

arXiv

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arXiv 2021年

作者： Liu, Ji Mo, Lei Yang, Sijia Zhou, Jingbo Ji, Shilei Xiong, Haoyi Dou, Dejing Big Data Lab Baidu Inc. Beijing China School of Automation Southeast University Nanjing China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Business Intelligence Lab Baidu Inc. Beijing China Security department Baidu Inc. Beijing China

Due to privacy concerns of users and law enforcement in data security and privacy, it becomes more and more difficult to share data among organizations. data federation brings new opportunities to the data-related cooperation among organizations by providing abstract data interfaces. With the development of cloud computing, organizations store data on the cloud to achieve elasticity and scalability for data processing. The existing data placement approaches generally only consider one aspect, which is either execution time or monetary cost, and do not consider data partitioning for hard constraints. In this paper, we propose an approach to enable data processing on the cloud with the data from different organizations. The approach consists of a data federation platform named FedCube and a Lyapunov-based data placement algorithm. FedCube enables data processing on the cloud. We use the data placement algorithm to create a plan in order to partition and store data on the cloud so as to achieve multiple objectives while satisfying the constraints based on a multi-objective cost model. The cost model is composed of two objectives, i.e., reducing monetary cost and execution time. We present an experimental evaluation to show our proposed algorithm significantly reduces the total cost (up to 69.8%) compared with existing approaches. © 2021, CC BY.

关键词： Cloud computing

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Optimizing biomedical ontology alignment in lexical vector space

Optimizing biomedical ontology alignment in lexical vector s...

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作者： Xue, Xingsi Wu, Xiaojing Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian Minhou China College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian Minhou China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian Minhou China Institute of Artificial Intelligence Fujian University of Technology Fuzhou Fujian Minhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian Minhou China

Biomedical ontology matching dedicates to find two heterogeneous ontologies' alignment and address their heterogeneity problem. Typically, a biomedical ontology has various biomedical concepts that are described with various labels and datatype property names, which forms a lexical space where each label or datatype property represents one dimension. Therefore, it is an effective way to present two biomedical concepts in a vector space, and use the cosine distance to measure their similarity. In this work, we present two biomedical concepts in a lexical vector space which is constructed with their inner and context concepts' lexical information, and then utilize two vector's cosine distance to measure similarity value. Then, we propose a compact Evolutionary Algorithm (cEA) to find the concept correspondences. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)'s testing cases, and the expeirmental results with Vector space Based Ontology Matcher (VBOM), Genetic Algorithm based Ontology Matcher (GAOM) and OAEI's participants show the effectiveness of our proposal. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Vector spaces

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A deep learning approach to concrete water-cement ratio prediction

Results in Materials

引用

Results in Materials 2022年 15卷

作者： Bello, Sururah Apinke Oyedele, Lukumon Olaitan, Olakunle Kazeem Olonade, Kolawole Adisa Olajumoke, Akinropo Musiliu Ajayi, Anuoluwapo Akanbi, Lukman Akinade, Olugbenga Sanni, Mistura Laide Bello, Abdul-Lateef Big Data Enterprise and Artificial Intelligence Laboratory (Big-DEAL) Bristol Business School University of the West of England Bristol United Kingdom Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Department of Civil Engineering University of Lagos Akoka Nigeria Department of Civil Engineering Obafemi Awolowo University Ile-Ife Nigeria Faculty of Environment and Technology University of the West of England Bristol United Kingdom Department of Computing and Games School of Computing Engineering and Digital Technologies Teeside University Middlesbrough United Kingdom

Concrete is a versatile construction material, but the water content can greatly influence its quality. However, using the trials and error method to determine the optimum water for the concrete mix results in poor quality concrete structures, which often end up in landfills as construction wastes, thus threatening environmental safety. This paper develops deep neural networks to predict the required water for a normal concrete mix. Standard data samples obtained from certified/leading laboratories were fed into a deep learning model (multilayers feedforward neural network) to automate the calibration of mixing power of the concrete water content for improved water control accuracy. We randomly split the data into 70%, 15% and 15%, respectively, to train, validate and test the model. The developed DNN model was subjected to relevant statistical metrics and benchmarked against the random forest, gradient boosting machines, and support vector machines. The performance indices obtained by the DNN model have the highest reliability compared to other models for concrete water prediction. © 2022 The Authors

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VIS4SL: A visual analytic approach for interpreting and diagnosing shortcut learning

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Knowledge-Based Systems 2025年 320卷

作者： Meng, Xiyu Tang, Tan Zhou, Yuhua Yan, Zihan Deng, Dazhen Wang, Yongheng Wu, Yuhan Wu, Yingcai College of Computer Science and Technology Zhejiang University No. 38 Zheda Road Hangzhou310027 China The State Key Lab of CAD&CG Zhejiang University Hangzhou310012 China Laboratory of Art and Archaeology Image Zhejiang University Hangzhou310007 China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou311100 China

Shortcut learning, a phenomenon where deep neural networks inadvertently learn irrelevant features, has been extensively discussed due to its impact on model generalization and unexpected failures. Interpreting and diagnosing shortcut learning is challenging due to its diverse manifestations and multiple influencing factors. To assist data scientists in these tasks, we introduce VIS4SL, an interactive visual analytics approach that harnesses both human intelligence and computational power. VIS4SL integrates a perturbation-based method with comprehensive visualizations to facilitate an understandable analysis of learned features. We also present a set of comparative visualizations that allow for the evaluation of model explanations against robust proxies, particularly human explanations, to quantify the degree of shortcut learning and assess model components. Two case studies, involving natural image classification and visualization classification, demonstrate the efficacy of VIS4SL in practical applications. Our findings reveal that the model uses the orientation of bars to differentiate between bar charts and Pareto charts. Furthermore, we explore how interactive visualizations enhance data scientists’ understanding of shortcut learning, enabling the development of more precise deep learning models. © 2025 Elsevier B.V.

关键词： Video analysis

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Tumor Classification using MS Spectra Based on Deep Learning

Tumor Classification using MS Spectra Based on Deep Learning

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Advanced Electronic Materials, Computers and Software Engineering (AEMCSE), International Conference on

作者： Hao Dong Kunxian Shu School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Chongqing China Chongqing Key Laboratory on Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9781728181431

ISBN: (纸本)9781728181448

Deep learning models plays a significant role in bioinformatics research, such as prediction of incidence, classification of disease samples, identification and detection of tumor areas. Mass spectrometry (MS) has been widely applied to protein research due to its high throughput and sensitivity. Tumor protein mass spectrometry data has high sample dimensions and low signal-to-noise ratio, which is difficult to extract features for classification. Here, we aim to develop a new method to extract features from tumor protein mass spectrometry data and classify proteomics data using deep learning models. Our results demonstrated that the deep learning models we proposed has a good performance and may provide ideas for researchers to classify other protein mass spectral data or similar data.

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Evolution of cooperation in spatial heterogeneous social dilemmas with dynamic self-adaptive matching

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Applied Mathematics and Computation 2026年 508卷

作者： Xiaopeng Li Xuexue Yang Xiuli Zhang Weiwei Han Yan Xu College of Big Data Statistics Guizhou University of Finance and Economics Guiyang Guizhou 550025 PR China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology Taiyuan Normal University Taiyuan Shanxi 030619 PR China School of Information Engineering Tianjin University of Commerce Tianjin 300134 PR China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 PR China

From the perspective of real-world interaction scenarios, the assumption of monotypic social dilemma engagement or fixed role allocations within heterogeneous social dilemmas contexts fundamentally misrepresents the dynamics of human social cognition. Empirical evidence consistently suggests that behavioral plasticity is achieved through adaptive identity switching across heterogeneous social dilemma environments. Herein, we develop an evolutionary game model simultaneously encompassing multiple social dilemmas (prisoner's dilemma game and snowdrift game), wherein agents engage in polymorphic game-environment governed by their own adaptive characteristic. Specifically, during the strategy update phase, if the agent x ′ s current strategy is retained, its strategy stickiness θ x increases one unit; otherwise, the metric reverts to the initial value of 0. Additionally, if θ x reaches the maximum value of 100, it signifies the termination of the old agent and the initialization of a new one, with the metric also reverting to 0. Moreover, agent whose strategy adherence is beyond predefined threshold θ t h is classified as possessing strong social dilemma resolution capacity, while that is below the threshold is allocated to mild-conflict scenarios. Subsequently, through sufficient Monte Carlo simulation, we systematically investigate the evolutionary trajectories and stable-state characteristics of cooperation in dynamically adaptive multi-social dilemma systems, yielding several insightful findings.

关键词： Evolution of cooperation Evolutionary dynamics Dynamic self-adaptive Heterogeneous social dilemmas

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