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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Miao, Haonan Zhao, Peizheng Sun, Yuqi Nan, Fang Zhang, Xiaolong Wu, Yaqiang Tian, Feng School of Computer Science and Technology Xi'an Jiaotong University Xi'an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China School of Advanced Technology Xi'an Jiaotong-Liverpool University Suzhou215123 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798350368741

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students' gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods. © 2025 IEEE.

关键词： classroom depth-enhanced 2D space dual-view gaze target estimation spatial transformation

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FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees 41

FedLMT: Tackling System Heterogeneity of Federated Learning ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Jiahao Zhou, Yipeng Wu, Di Hu, Miao Guizani, Mohsen Sheng, Quan Z. School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Abu Dhabi United Arab Emirates

Federated learning (FL) is an emerging machine learning paradigm for preserving data privacy. However, diverse client hardware often has varying computation resources. Such system heterogeneity limits the participation of resource-constrained clients in FL, and hence degrades the global model accuracy. To enable heterogeneous clients to participate in and contribute to FL training, previous works tackle this problem by assigning customized sub-models to individual clients with model pruning, distillation, or low-rank based techniques. Unfortunately, the global model trained by these methods still encounters performance degradation due to heterogeneous sub-model aggregation. Besides, most methods are heuristic-based and lack convergence analysis. In this work, we propose the FedLMT framework to bridge the performance gap, by assigning clients with a homogeneous pre-factorized low-rank model to substantially reduce resource consumption without conducting heterogeneous aggregation. We theoretically prove that the convergence of the low-rank model can guarantee the convergence of the original full model. To further meet clients' personalized resource needs, we extend FedLMT to pFedLMT, by separating model parameters into common and custom ones. Finally, extensive experiments are conducted to verify our theoretical analysis and show that FedLMT and pFedLMT outperform other baselines with much less communication and computation costs. Copyright 2024 by the author(s)

关键词： Federated learning

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples 4

A multi-target UAV fault diagnosis method based on feature d...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： An, Xue Zhang, Yizong Li, Shaobo Xiong, Xuan Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350354560

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data. © 2024 IEEE.

关键词： discriminant dimensionality reduction fault diagnosis multi-task learning small samples

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Realizing n-type CdSb with promising thermoelectric performance

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Journal of Materials Science & Technology 2023年第13期144卷 54-61页

作者： Peng Zhao Honghao Yao Shizhen Zhi Xiaojing Ma Zuoxu Wu Yijie Liu Xinyu Wang Li Yin Zongwei Zhang Shuaihang Hou Xiaodong Wang Siliang Chen Chen Chen Xi Lin Haoliang Liu Xingjun Liu Feng Cao Qian Zhang Jun Mao School of Materials Science and Engineering and Institute of Materials Genome&Big DataHarbin Institute of Technology(Shenzhen)Shenzhen 518055China School of Science Harbin Institute of Technology(Shenzhen)Shenzhen 518055China State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

Realizing high performance in both n-type and p-type materials is essential for designing efficient ther-moelectric ***,the doping bottleneck is often encountered,i.e.,only one type of conduction can be *** one example,p-type CdSb with high thermoelectric performance has been discovered for several decades,while its n-type counterpart has rarely been *** this work,the calculated band structure of CdSb demonstrates that the valley degeneracy is as large as ten for the conduction band,and it is only two for the valence ***,the n-type CdSb can potentially realize an ex-ceptional thermoelectric ***,the n-type conduction has been successfully real-ized by tuning the stoichiometry of *** further doping indium at the Cd site,an improved room-temperature electron concentration has been *** modeling predicts an optimal electron con-centration of∼2.0×1019 cm−3,which is higher than the current experimental ***,future optimization of the n-type CdSb should mainly focus on identifying practical approaches to optimize the electron concentration.

关键词： Thermoelectric materials n-type CdSb Indium doping Band degeneracy

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knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

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data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain knowledge embedding knowledge attention Mutual attention

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Discriminative latent semantics-preserving similarity embedding hashing for cross-modal retrieval

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Neural Computing and Applications 2024年第18期36卷 10655-10680页

作者： Chen, Yongfeng Tan, Junpeng Yang, Zhijing Cheng, Yongqiang Chen, Ruihan School of Information Engineering Guangdong University of Technology Guangdong Guangzhou510006 China School of Electronic and Information Engineering South China University of Technology Guangdong Guangzhou510641 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Guangzhou510665 China Department of Computer Science and Technology University of Hull HullHU6 7RX United Kingdom

Recently, there has been a significant increase in interest in cross-modal hashing technology. For hash code learning, most previous supervision methods use label information to create a similarity matrix in a straightforward manner. However, there are still the following challenges: (1) The asymmetric similarity matrix method only considers the similarity of labels, and the discriminant constraint in Hamming space is ignored;(2) there are optimization errors between the cross-modal semantic correlation of the Hamming space and the nonlinearity of the feature space;and (3) the cross-modal hash matrix is in a dynamic state during the optimization process, and the hash code is easily disturbed and there is bit uncertainty. To this end, we propose the Discriminative Latent Semantics-preserving Similarity Embedding Cross-modal Hashing (DLSSECH) method. Specifically, to reduce the quantization error, we introduce a non-asymmetric similarity decomposition based on orthogonal rotation bias. It can decompose the bitwise correlation of the learned hash code to capture more discriminative semantic information and compact hash code. In addition, to capture the cross-modal semantic correlation of nonlinear feature transformation and reduce quantization error, we propose the latent correlation error matrices based on orthogonal rotation decomposition. The model maintains the maximum difference in semantic dependencies between the projected data and its semantic representation. Finally, we use a sparse common hash matrix and non-asymmetric similarity decomposition factors to solve the uncertainty of the dynamic changes of the hash code. The effectiveness of DLSSECH has demonstrated through experiments on four cross-modal retrieval datasets, where it outperformed some state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Semantics

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Spatiotemporal Pattern Evolution for Vegetation Using Remote Sensing Technology: A case study of Zhoushan Island, China

Spatiotemporal Pattern Evolution for Vegetation Using Remote...

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2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Chen, Yankun Chen, Chao Liu, Zhisong School of Information Engineering Zhejiang Ocean University Zhoushan316022 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9781510680012

For ecological environment protection research and regional sustainable development, vegetation, as a crucial land cover type, necessitates long-term, large-scale, and highly accurate monitoring. This paper formulates an enduring vegetation monitoring approach using Landsat satellite remote sensing imagery on the Google Earth Engine (GEE) cloud platform. By computing the Normalized Difference Vegetation Index (NDVI), we formulate a method for prolonged remote sensing monitoring of vegetation, unveiling its temporal and spatial pattern evolution. Firstly, based on Landsat remote satellite imagery and GEE, we calculated the NDVI. By leveraging the Normalized Difference Water Index (NDWI), we removed inland water bodies and outliers within the study area, enabling the precise generation of spatiotemporal NDVI distribution maps. Subsequently, the NDVI was analyzed for time trends in conjunction with grade transfer matrices. Finally, combined with Moran's I, high/low clusters, clustering and outlier, cold/hot spots, etc., the NDVI spatial pattern characteristics were analyzed. Using the Zhoushan Island in China as an example, the results indicated that: (1) The most significant changes were observed in the low NDVI area (0-0.2), which expanded from 28.84 km2 to 67.29 km2. (2) Concentrated mainly in the inland areas are the NDVI High-High clusters and the significant hot spots, while the coastal areas primarily feature NDVI Low-Low clusters and significant cold spots. © 2024 SPIE.

关键词： Vegetation

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Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

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Digital Communications and Networks 2022年第6期8卷 885-899页

作者： Liping Tao Yang Lu Xu Ding Yuqi Fan Jung Yoon Kim School of Computer Science and Information Engineering Hefei University of TechnologyHefei230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei University of TechnologyHefei230009China Institute of Industry&Equipment Technology Hefei University of TechnologyHefei230009China Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230009China Graduate School of Game Gachon UniversitySeongnam-Si461-701South Korea

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

关键词： IoT social data Blockchain Shar ding Associated transactions Transaction assi gnment

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Recent Advances in Asymmetric Structural Composites for Excellent Electromagnetic Interference Shielding: A Review

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Chinese Journal of Polymer Science 2024年第6期42卷 693-710页

作者： Ying Zhou Bai Xue Lan Xie Chang-Mei Wu Qiang Zheng Department of Polymer Materials and Engineering College of Materials and Metallurgy Guizhou University National Engineering Research Center for Compounding and Modification of Polymer Materials National and Local Joint Engineering Research Center for Functional Polymer Membrane Materials and Membrane Processes State Key Laboratory of Public Big Data Guizhou University Department of Polymer Science and Engineering Zhejiang University

Since electromagnetic pollution is detrimental to human health and the environment, numerous efforts have been successively made to achieve excellent electromagnetic interference shielding effectiveness(EMI SE) via designing the hierarchical structures for electromagnetic interference(EMI) shielding polymer composites. Among the plentiful structures, the asymmetric structures are currently a hot spot, principally categorizing into multi-layered, porous, fibrous, and segregated asymmetric structures, which endows the high EMI shielding performance for polymer composites incorporated with magnetic, conductive, and/or dielectric micro/nano-fillers, due to the “absorption-reflection-reabsorption” shielding mechanism. Therefore, this review provides the retrospection and summary of the efforts with respect to abundant asymmetric structures and multifunctional micro/nano-fillers for enhancing EMI shielding properties, which is conducive to the booming development of polymeric EMI shielding materials for the promising prospect in modern electronics and 5-generation(5G) technology.

关键词： Asymmetric structure Micro/nano functional filler Polymer composite Electromagnetic interference shielding

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A Dynamic Cross-Scale Transformer with Dual-Compound Representation for 3D Medical Image Segmentation 48

A Dynamic Cross-Scale Transformer with Dual-Compound Represe...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhang, Ruixia Wang, Zhiqiong Wang, Zhongyang Xin, Junchang Northeastern University College of Medicine and Biological Information Engineering China Northeastern University School of Computer Science & Engineering China Northeastern University Key Laboratory of Big Data Management and Analytics Liaoning Province Shenyang China

ISBN: (纸本)9781728163277

Transformer models exploit multi-head self-attention to capture long-range information. Further, window-based self-attention solves the problem of quadratic computational complexity and provides a new solution for dense prediction of 3D images. However, Transformers miss structural information due to the naive tokenization scheme. Furthermore, single-scale attention fails to achieve a balance between feature representation and semantic information. Aiming at the above problems, we propose a window-based dynamic crossscale cross-attention transformer (DCS-Former) for precise representation of the diversity features. DCS-former first constructs dual-compound feature representations through Antehoc Structure-aware Module and Post-hoc Class-aware Module. Then, the bidirectional attention structure is designed to interactively fuse structural features with class representations. The experimental results show that our method outperforms various competing segmentation models on three different public datasets. © 2023 IEEE.

关键词： Deep Learning Medical Image Segmentation Self-attention Vision Transformer

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