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International Conference on computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. The experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing software

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Enhancement of Mechanical Performance and Low-Temperature Strengthening Mechanism: High-Entropy Alloy/Graphene Composite Material

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SSRN 2024年

作者： Zhou, Shaocong Liang, Yongchao Pu, Yuanwei Zhou, Yu Zhou, Lili Tian, Zean Institute of Advanced Optoelectronic Materials and Technology School of Big Data and information engineering Guizhou University Guiyang550025 China Department of Information Engineering Gannan Medical University Ganzhou341000 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

High-entropy alloy (HEA) /graphene (Gr) composites hold vast potential for various applications owing to their exceptional mechanical properties. However, the elucidation of their micro-mechanical mechanisms remains elusive, impeding their advancement. Therefore, this study utilizes molecular dynamics (MD) simulations to explore the tensile behavior of CoCrFeMnNi HEA and HEA/Gr composites, aiming to investigate the strengthening mechanism of the reinforcing phase and deformation behavior across different temperatures. The insertion of Gr enhances the yield strength and elasticity modulus of HEA/Gr by 57.5% and 19%, respectively, compared to pure HEA. The hindering effect of the Gr layer interface on dislocation propagation fosters the generation of numerous immobile dislocations, leading to significant disparities in atomic stress and strain distribution, thereby markedly enhancing mechanical properties. Moreover, the hindrance posed by the Gr layer facilitates out-of-plane deformation, and the fracture of Gr significantly impacts material properties. Low-temperature stretching induces FCC-BCC phase transitions in the composites, with transformation-induced plasticity (TRIP) triggering a synergistic effect on material strength and plasticity. Throughout the plastic deformation phase, phase transition promotes dislocation slip and transfer, thereby extending dislocation plasticity and augmenting material plasticity and strength. The results provide important insights into the strengthening mechanism of HEA/Gr at low temperatures, illustrating the key role of reinforcement in mechanical properties from the dual perspectives of Gr insertion and fracture. © 2024, The Authors. All rights reserved.

关键词： High-entropy alloys

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MGRD: Multigranularity Reconstruction Deviation Modeling for Time Series Anomaly Detection

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Peng, Furong Ma, Rongxin Lu, Xuan Qian, Yuhua Xu, Yong Hu, Zhiguo Wu, Hongtao Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China Shanxi University College of Physics and Electronic Engineering Shanxi Taiyuan030006 China Shanxi Intelligent Transportation Institute Company Ltd. Shanxi Taiyuan030032 China Harbin Institute of Technology at Shenzhen School of Computer Science and Technology Peng Cheng Laboratory Shenzhen518055 China

In complex industrial environments, unsupervised time series anomaly detection (AD) with instrumentation and measurement data has significant applications in enhancing reliability and safety. Differentiating noise and anomalies has been a critical topic in AD. Indiscriminately fitting data with deep learning models is susceptible to overfitting. We assume that noise is ubiquitous, while anomaly is manifesting within small segments. To this end, we propose the hypothesis of multigranularity structural deviation in anomalous data from the perspective of suddenness in anomaly signals. Based on this hypothesis, we design a multigranularity time series AD model that builds global- and local-granularity reconstructions for the data and measures the anomalies through the deviation of these reconstructions. The global-granularity reconstruction is computed by all observed data that are equally used for reconstructions, while the local-granularity reconstruction is mainly computed on nearby points. Based on transformers, we propose a multigranularity attention (MGA) module with global and local attention to generate multigranularity outputs. The module employs the Gaussian kernel to control the weights of the nearby points in local attention and applies Kullback-Leibler (KL) divergence to regulate the attention matrices of the two reconstructions from degeneration. The deviation of the two reconstructions can not only detect anomaly data with suddenness changes but also provide intuitive interpretability. Results on five public datasets showcase that our model can obtain 11.38% improvement on average and obtain 38% improvement on the server machine dataset (SMD). © 1963-2012 IEEE.

关键词： Time series

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Traffic Aware Power Saving Communication Assisted By Double-Faced Active RIS

Traffic Aware Power Saving Communication Assisted By Double-...

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IEEE International Conference on Communications (ICC)

作者： Yuyan Zhou Yang Liu Qingqing Wu Qingjiang Shi Jun Zhao School of Information and Communication Engineering Dalian University of Technology Department of Electronic Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University and Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Nanyang Technological University

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

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PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction and Reconstruction of Multivariate Time Series 19

PRAD: Unsupervised KPI Anomaly Detection by Joint Prediction...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Xiong, Zhiying Fan, Qilin Wang, Kai Li, Xiuhua Zhang, Xu Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing China Chongqing University Chongqing Key Laboratory of Digital Cinema Art Theory and Technology Chongqing China Harbin Institute of Technology School of Computer Science and Technology Weihai China Harbin Institute of Technology Research Institute of Cyberspace Security Weihai China Haihe Laboratory of Information Technology Application Innovation Tianjin China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350346558

Detecting anomalies for key performance indicator (KPI) data is of paramount importance to ensure the quality and reliability of network services. However, building the anomaly detection system for KPI is challenging due to the scarcity of abnormal labels and highly dynamic or even unseen patterns. In this paper, we propose an unsupervised KPI anomaly detection framework named PRAD by jointly optimizing prediction-based and reconstruction-based modules. Specifically, PRAD employs two parallel graph attention networks (GATs) to learn metric-oriented and time-oriented relationships. A temporal convolutional attention network (TCAN) is used to capture complex long-term dependencies. Furthermore, to tackle the overfitting issue in the variational auto-encoder (VAE), PRAD designs a reconstruction-based module that combines VAE with a generative adversarial network (GAN). The experimental results on the two public datasets demonstrate that the proposed PRAD outperforms existing anomaly detection methods. © 2022 IEEE.

关键词： Anomaly detection

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Hierarchical Relation Learning for Few-shot Semantic Segmentation in Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： He, Xin Liu, Yun Zhou, Yong Ding, Henghui Zhao, Jiaqi Liu, Bing Jiang, Xudong Tianjin University of Technology School of Computer Science and Engineering Tianjin300384 China China University of Mining and Technology School of Computer Science and Technology Xuzhou221116 China Nankai University College of Computer Science Tianjin300350 China Ministry of Education of the People’s Republic of China Engineering Research Center of Mine Digitization Xuzhou221116 China Fudan University Institute of Big Data Shanghai200433 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

Few-shot semantic segmentation (FSS) aims to segment specific semantic classes in a query image using only a few annotated support samples. While FSS has gained significant attention in natural image processing, it remains underexplored in the more challenging domain of remote sensing images (RSIs). Existing FSS approaches for RSIs primarily focus on enhancing feature representations of support or query images through hierarchical/multi-level feature fusion. However, unlike fully supervised segmentation that relies on feature extraction and optimization, FSS requires segmenting the query image based on its relations with annotated support images. To address this need, we propose the concept of Hierarchical Relation Learning (HRL) to explore the intrinsic support-query relations, allowing for the direct refinement of target object appearances in the query image. Specifically, we propose a Hierarchical Relation Network (HRNet), which performs single-scale relation extraction at each network hierarchy and multi-scale relation aggregation across hierarchies. In addition, we construct a Bidirectional Hierarchical Loss (BHLoss) to guide HRNet training, providing targeted supervision at each hierarchy in both top-down and bottom-up directions, thus facilitating robust multi-scale relation learning across hierarchies. Comprehensive experiments on the iSAID-5i, DLRSD-5i, and LoveDA-2i datasets demonstrate the superiority of the proposed HRL. © 1980-2012 IEEE.

关键词： Semantic Segmentation

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_data$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment

Dynamic resource provisioning for workflow scheduling under ...

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作者： Xu, Xiaolong Cao, Hao Geng, Qingfan Liu, Xihua Dai, Fei Wang, Chuanjian School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China College of Information Science and Technology Shihezi University Xinjiang China

Edge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software-defined network (SDN)-based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm-III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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DREAM: Decoupled Discriminative Learning with bigraph-aware Alignment for Semi-supervised 2D-3D Cross-modal Retrieval 39

DREAM: Decoupled Discriminative Learning with Bigraph-aware ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Fan Wang, Changhu Cheng, Zebang Peng, Xiaojiang Wang, Dongjie Xiao, Yijia Chen, Chong Hua, Xian-Sheng Luo, Xiao Department of Electrical and Computer Engineering Georgia Institute of Technology United States Department of Statistics University of California Los Angeles United States College of Big Data and Internet Shenzhen Technology University China Department of Electrical Engineering and Computer Science University of Kansas United States Department of Computer Science University of California Los Angeles United States Terminus Group China

ISBN: (纸本)157735897X

With the burst of big data, 2D-3D cross-modal retrieval has received increasing attention, which targets at retrieving relevant data of one modality using the query of the other modality. In this paper, we study an underexplored yet applicable problem of semi-supervised 2D-3D cross-modal retrieval, which could suffer from serious label scarcity in real-world applications. Moreover, the huge heterogeneous gap could deteriorate the process of learning from unlabeled data. In this work, we propose a novel approach named Decoupled Discriminative Learning with bigraph-aware Alignment (DREAM) for semi-supervised 2D-3D cross-modal retrieval. The core of our DREAM is to decouple the label prediction and reliability measurement processes to reduce overconfident samples in discriminative learning. In particular, we enhance a label prediction module with label propagation from labeled samples and additionally introduce a reliability measurement module to learn the scores of predicted labels. To reduce class-related bias, we compare reliability scores with class-specific adaptive thresholds to identify samples for additional learning. In addition, negative labels are estimated for unselected samples, which guides soft semantic learning to make the best use of all the information. To further minimize the heterogeneous gap, we build a bigraph graph that connects cross-modal similar examples and then conduct learning to cluster with most edges kept for alignment. Extensive experiments on several benchmark datasets validate the superiority of the proposed DREAM against many state-of-the-art baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semi-supervised learning

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Sparse Aggregation-Based Channel Estimation For Massive Mimo Systems With Decentralized Baseband Processing

Sparse Aggregation-Based Channel Estimation For Massive Mimo...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yanqing Xu Enbin Song Qingjiang Shi Tsung-Hui Chang School of Science & Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China College of Mathematics Sichuan University Chengdu China School of Software Engineering Tongji University Shanghai China

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm.

关键词： Baseband Signal processing algorithms Channel estimation computer architecture Mean square error methods Massive MIMO Signal processing

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