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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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XTQA: Span-Level Explanations for Textbook Question Answering

arXiv

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arXiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

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Discriminative Additive Scale Loss for Deep Imbalanced Classification and Embedding

Discriminative Additive Scale Loss for Deep Imbalanced Class...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Weiming Jiang Yang Wang Qiaolin Ye Mingbo Zhao Mingliang Xu 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 AI team Shanghai Shizhuang Information Technology Co. Ltd Shanghai China College of Information Science and Technology Nanjing Forestry University Nanjing China School of Information Science and Technology Donghua University Shanghai China School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9781665423991

Real-world data in emerging applications may suffer from highly-skewed class imbalanced distribution, however how to deal with this kind of problem appropriately through deep learning needs further investigation. In this paper, we mainly propose a novel cross-entropy based loss function, referred to as Additive Scale Loss (ASL), for deep representation learning and imbalanced classification. To deal with the class imbalanced problem, ASL aims at increasing the loss in case of misclassification, which can avoid the superimposed loss values caused by the large amount of easily classified data in the unbalanced database to dominate the loss value of misclassified data. Moreover, in real-world applications, one data source may be used for multiple scenarios, such as classification and embedding learning, however training two separable models to handle these problems is costly, especially in deep learning area. To tackle this issue, we present and integrate a discriminative inter-class separation term into ASL, and propose a discriminative ASL (D-ASL), which can not only improve the classification performance, but also obtain discriminative representations simultaneously. The discriminative inter-class separation term is general, and can be easily integrated to other loss functions, such as CE and FL, as the byproducts. Finally, a new deep convolutional neural network equipped with D-ASL and a fully-connected (FC) layer is proposed, which can classify the imbalanced image data and obtain the discriminative representations at the same time. Extensive experimental results verified the superior performance of our method.

关键词： Deep learning Training Representation learning Additives databases Soft sensors Conferences

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Erratum to "Multi-View Face Synthesis via Progressive Face Flow"

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2021年 30卷 6700页

作者： Yangyang Xu Xuemiao Xu Jianbo Jiao Keke Li Cheng Xu Shengfeng He School of Computer Science and Engineering South China University of Technology Guangzhou China State Key Laboratory of Subtropical Building Science the Key Laboratory of Big Data and Intelligent Robot Ministry of Education and the Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information South China University of Technology Guangzhou China University of Oxford Oxford U.K.

In the above article [1], unfortunately, Fig. 5 was not displayed correctly with many empty images. The correct version is supplemented here.

关键词： Faces

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Collaborative Three-Tier Architecture Non-contact Respiratory Rate Monitoring using Target Tracking and False Peaks Eliminating Algorithms

arXiv

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arXiv 2020年

作者： Mo, Haimiao Ding, Shuai Yang, Shanlin Vasilakos, Athanasios V. Zheng, Xi The School of Management Hefei University of Technology Anhui Hefei23009 China The Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education China The National Engineering Laboratory of Big Data Distribution and Exchange Technologies China Grimstad Norway The College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China The School of Computing Macquarie University Sydney Australia

Monitoring the respiratory rate is crucial for helping us identify respiratory disorders. Devices for conventional respiratory monitoring are inconvenient and scarcely available. Recent research has demonstrated the ability of non-contact technologies, such as photoplethysmography and infrared thermography, to gather respiratory signals from the face and monitor breathing. However, the current non-contact respiratory monitoring techniques have poor accuracy because they are sensitive to environmental influences like lighting and motion artifacts. Furthermore, frequent contact between users and the cloud in real-world medical application settings might cause service request delays and potentially the loss of personal data. We proposed a non-contact respiratory rate monitoring system with a cooperative three-layer design to increase the precision of respiratory monitoring and decrease data transmission latency. To reduce data transmission and network latency, our three-tier architecture layer-by-layer decomposes the computing tasks of respiration monitoring. Moreover, we improved the accuracy of respiratory monitoring by designing a target tracking algorithm and an algorithm for eliminating false peaks to extract high-quality respiratory signals. By gathering the data and choosing several regions of interest on the face, we were able to extract the respiration signal and investigate how different regions affected the monitoring of respiration. The results of the experiment indicate that when the nasal region is used to extract the respiratory signal, it performs experimentally best. Our approach performs better than rival approaches while transferring fewer data. © 2020, CC BY.

关键词： Edge computing

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An IGS algorithm applied to DNA sequence design 17

An IGS algorithm applied to DNA sequence design

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17th IEEE International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and big data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019

作者： Liu, Kaiqiang Wang, Bin Liu, Yuan Wu, Ranfeng Zhang, Xun Liu, Xin Zhang, Qiang Yin, Zhixiang Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China School of Mathematics and Big Data Anhui University of Science and Technology Dalian China

ISBN: (纸本)9781728130255;9781728130248

American scientist Adelman first used DNA molecules to solve the NP-complete problem, thus creating a new research field. As an advanced interdisciplinary subject, it has attracted many scholars because of its high parallelism, great storage density and low energy consumption. DNA sequence design is critical in the field of DNA computing. The success of DNA computing depends on the quality of DNA sequence. In this paper, we proposed an improved gravitational search algorithm for DNA sequence design. We improved the gravitational coefficient and introduced chaotic system and Gaussian mutation to the position update of the particles. We compared the result from our improved algorithm with the result obtained by other algorithms. The result shows that our proposed algorithm can generate high quality DNA sequence for DNA computing. © 2019 IEEE.

关键词： DNA computing DNA sequence design Gravitational search algorithm NP-complete problem

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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Erratum to “Event-triggering-based H∞ load frequency control for multi-area cyber–physical power system under DoS attacks” [Franklin Open Volume 3, June 2023, 100012]

Franklin Open

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Franklin Open 2024年 9卷

作者： Xingyue Liu Kaibo Shi Kun Zhou Shiping Wen Yiqian Tang Lin Tang School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 PR China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 PR China Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 PR China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 PR China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

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Multiview feature selection combining latent space and similarity structure learning

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Pattern Recognition 2026年 169卷

作者： Zhuowen Li Hongmei Chen Tengyu Yin Zhong Yuan Chuan Luo Shi-Jinn Horng Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu 611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu 611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu 611756 PR China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu 611756 PR China College of Computer Science Sichuan University Chengdu 610065 China Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung 404327 Taiwan

Unsupervised multiview feature selection dependent on similar or clustering structures has dramatically progressed, but both ignore the mutually reinforcing relationship between structure learning. Firstly, the two methods of appeal ignore the possibility of shared joint learning, although they can learn coherent information. Secondly, they are inadequate for data structure learning and lack awareness of co-learning global and local structures. This paper proposes an unsupervised feature selection method (SCSF_FS) that integrates similarity and clustering structures learning to capture intrinsic information in data. Specifically, we use latent space learning to partition multiple data matrices into view-specific base matrices and clustering metrics. Structural learning is used to transform specific clustering metrics into coherent clustering structures. In addition, adaptive weights are used on each view’s similarity matrix to learn a consistent similarity structure, while laplacian graph learning is introduced to unify the similarity and clustering structures. Consequently, a unified framework is designed to unify multiview consistency learning, global structure, and local structure preservation. Moreover, an optimization iteration algorithm is designed to solve it. Comparison with eight algorithms shows the effectiveness of the proposed method.

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