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arXiv 2024年

作者： Xie, Yiping Yu, Zitong Wu, Bingjie Xie, Weicheng Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Singapore

Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://***/XieYiping66/SFDA-rPPG. Copyright © 2024, The Authors. All rights reserved.

关键词： Photoplethysmography

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Optimal Sensor Placement for Full-Set TDOA Localization Accounting for Sensor Location Errors

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IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Zhang, Chengjie Han, Xinyang Kong, He Ho, K.C. Southern University of Science and Technology Shenzhen Key Laboratory of Control Theory and Intelligent Systems Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology Shenzhen518055 China University of Missouri Electrical Engineering and Computer Science Department ColumbiaMO65211 United States

The positioning accuracy of a signal source is influenced by where the sensors are deployed. Studies in the literature for the optimal sensor placement (OSP) of localization often ignore the presence of sensor position errors. In practice, however, it is impossible to place the sensors precisely at the desired locations. This work designs OSP by accounting for the presence of Gaussian sensor position errors that are independent and identically distributed (IID), with the full set of Time Difference of Arrival measurements corrupted by Gaussian IID noise. The criterion is the A-optimality equal to the trace of the source position Cramer-Rao Bound (CRB). We show that the presence of sensor position errors introduces a multiplication factor to the same criterion for the case without sensor position errors. The latter factor is dependent on the ratio of the sensor position error power to the range difference noise power but independent of the sensor positions, implying that the A-optimality OSP in the presence of sensor position errors is the same as in the absence. To our knowledge, this is the first time such results have been derived. Simulation studies validate the derived OSP, show the minimal OSP performance gap with sensor positioning error and without, and illustrate the better source localization accuracy by OSP. © 1965-2011 IEEE.

关键词： Cramer-Rao bounds

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Balance Multi-Head Attention based on software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Study on over Minimization for Nonnegative Signal Recovery

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Journal of Scientific Computing 2023年第3期95卷 1-24页

作者： Tao, Min Zhang, Xiao-Ping Department of Mathematics National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Tsinghua-Berkeley Shenzhen Institute Tsinghua University Beijing China Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

In this paper, we carry out a comprehensive study for the unconstrained $$L_1$$ over $$L_2$$ sparsity promoting model, widely used in the regime of coherent dictionaries for recovering nonnegative sparse signals. First, we prove the existence of global solutions. Second, we analyze the sparse property of any local minimizer of the $$L_{1}/L_{2}$$ model. This property serves as a certificate to rule out the nonlocal minimizers. Third, we focus on algorithmic development on the unconstrained model with nonnegative constraint. We derive an analytical solution for the proximal operator of the $$L_{1} / L_{2}$$ with nonnegative constraint. Then, we apply the alternating direction method of multipliers in a particular splitting way, referred to as ADMM $$_p^+$$ . We establish its global convergence to a d-stationary solution (sharpest stationary) by verifying the Lyapunov function with the Kurdyka-Łojasiewicz property instead of imposing. Extensive numerical simulations confirm the superiority of ADMM $$_p^+$$ over the existing state-of-the-art methods in nonnegative sparse recovery.

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GroPipe: A Grouped Pipeline Hybrid Parallel Method for Accelerating DCNNs Training

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IEEE Transactions on computers 2025年第7期74卷 2487-2500页

作者： Liu, Bin Ma, Yongyao Hu, Zijian Ji, Zeyu He, Zhenli Li, Keqin Northwest A&F University College of Information Engineering Shaanxi Yangling712100 China Shaanxi Engineering Research Center of Agricultural Information Intelligent Perception and Analysis Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs China Yunnan University Yunnan Key Laboratory of Software Engineering Kunming650091 China Yunnan University School of Software Kunming650091 China State University of New York Department of Computer Science New PaltzNY12561 United States

Training large Deep Convolutional Neural Networks (DCNNs) with increasingly large datasets to improve model accuracy has become extremely time-consuming. Distributed training methods, such as data parallelism (DP) and pipeline model parallelism (PMP), offer potential solutions but face challenges like load imbalance and significant communication overhead. This paper introduces GroPipe, a novel architecture that synergistically integrates PMP and DP, markedly improving training speeds. GroPipe employs an automatic model partitioning algorithm based on a performance projection technique, ensuring load balance and facilitating quantitative performance evaluation in PMP. Additionally, it adopts a group-based delayed asynchronous communication strategy to efficiently reduce communication overhead in DP. Using the ResNet and VGG models with the ImageNet dataset, extensive experiments are performed on an 8-GPU server and demonstrate GroPipe’s effectiveness. GroPipe achieves substantial improvements in time to accuracy, showing an average improvement of 42.2% and 14.0% on the ResNet series, and 79.2% and 43.9% on the VGG series, without compromising Top-1 accuracy. © 1968-2012 IEEE.

关键词： Training Computational Modeling Parallel Processing Load Modeling Pipelines Data Models computers Memory Management Asynchronous Communication Resource Management Delayed Asynchronous Communication Deep Convolutional Neural Networks Hybrid Parallelism Pipelined Model Parallelism Deep Convolutional Neural Network Training Methods Training Speed Load Balancing Image Net Dataset Parallel Data Communication Overhead Partition Model Partitioning Algorithm Top 1 Accuracy Pipeline Model Load Imbalance Learning Rate Training Time Resource Utilization Parallelization Model Convergence Memory Usage Training Efficiency Balanced Distribution Backward Pass Multiple GP Us Forward Pass Multiple Partitions Vertical Dimension Parallel Efficiency VGG 19 Model Throughput Experiments Parallel Training Idle Time

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Prepartition: Load Balancing Approach for Virtual Machine Reservations in a Cloud Data Center

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Journal of computer Science & Technology 2023年第4期38卷 773-792页

作者：田文洪徐敏贤周光耀吴逵须成忠 Rajkumar Buyya School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313001China Institute of Advanced Computing and Digital Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China Department of Computer Science University of VictoriaVictoriaBCV8W 3P6Canada State Key Laboratory of Internet of Things for Smart City University of MacaoMacao 999078China School of Computing and Information Systems University of MelbourneMelbourne 3010Australia

Load balancing is vital for the efficient and long-term operation of cloud data *** virtualization,post(reactive)migration of virtual machines(VMs)after allocation is the traditional way for load balancing and consoli***,it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring ***,we provide a new approach,called Prepartition,for load *** partitions a VM request into a few sub-requests sequentially with start time,end time and capacity demands,and treats each sub-request as a regular VM *** this way,it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal,which supports the resource allocation in a fine-grained *** with real-world trace and synthetic data show that our proposed approach with offline version(PrepartitionOff)scheduling has 10%–20%better performance than the existing load balancing baselines under several metrics,including average utilization,imbalance degree,makespan and Capacity_*** also extend Prepartition to online load *** results show that our proposed approach also outperforms state-of-the-art online algorithms.

关键词： cloud computing physical machine(PM) virtual machine(VM) reservation load balancing Prepartition

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An Adaptive Peridynamics with Correspondence Material Model for Coupled Creep-Plastic Fracture Problems

SSRN

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SSRN 2024年

作者： Zhang, Hanbo Li, Chengxuan Liu, Zhenhai Ye, Hongfei Li, Hui Zheng, Yonggang State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics School of Mechanics and Aerospace Engineering Dalian University of Technology Dalian116024 China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu610200 China

This paper introduces an adaptive peridynamics with a correspondence material model (A-PD-CMM) to effectively simulate the coupled creep-plastic behavior and the creep crack propagation in metal materials. In this method, to describe the coupled creep-plastic deformation with considering the creep damage, the multiaxial coupled constitutive model that integrates the Wen-Tu creep damage model and the J2 plastic model is developed. Moreover, a damage associate criterion is incorporated into the peridynamics framework to effectively capture the influence of damage on the force state. To detail the rapid damage evolution during the tertiary creep stage, a time-adaptive solving strategy is proposed. Additionally, the constitutive integration algorithm of the multiaxial coupled model is efficiently realized by decoupling it into two parts, that is, an implicit computation of creep-plasticity and an explicit evolution of creep damage. The efficacy and engineering applicability of the proposed A-PD-CMM are validated through several representative numerical examples, showcasing its potential in addressing complex material behavior. © 2024, The Authors. All rights reserved.

关键词： Crack propagation

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Multi-Hierarchy Metamorphic Testing for Hyphenated Words in Machine Translation

Multi-Hierarchy Metamorphic Testing for Hyphenated Words in ...

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Asia-Pacific Conference on software engineering

作者： Rui Zhu Chuanqi Tao Jerry Gao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Computer Engineering Department San Jose State University San Jose USA

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

With the advancement of deep neural networks, machine translation has seen rapid progress in recent years. Individuals often rely on machine translation software to facil-itate various tasks. However, the intricacies of neural networks can lead to translation errors, resulting in misunderstandings or conflicts. The most common method for testing machine translation is metamorphic testing. However, metamorphic testing at the phrase or sentence hierarchy may result in some test cases being incorrectly identified as failures. To mitigate this issue, we added the word hierarchy. We proposed a multi-hierarchy metamorphic testing method, MHT, to test machine translation. Hyphenated words as a specific format prone to translation errors, which are chosen as our research object. Based on the common notion that translations of words within the same sentence should be similar, we extract contents from different hierarchies within sentences containing hyphenated words and compare the similarity of their corresponding translations for these specific words. We conducted the experiments on 881 sentences leveraging Google Translate, Microsoft Bing Translator, and Baidu Translate, which detected 111, 91, and 111 suspicious errors with high precision (78.4 %, 82.4 %, and 81.1 %). Translation errors mainly include mis-translation, under-translation, over-translation, and non-translation.

关键词： Translation Web and internet services Artificial neural networks software Internet Machine translation Usability Testing software engineering

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Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases

Domain Adaptive Code Completion via Language Models and Deco...

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IEEE International Conference on Automated software engineering (ASE)

作者： Ze Tang Jidong Ge Shangqing Liu Tingwei Zhu Tongtong Xu Liguo Huang Bin Luo National Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore Huawei Software Engineering Application Technology China Department of Computer Science Southern Methodist University USA

Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, due to the lack of domain-specific knowledge, they may not be optimal in completing code that requires intensive domain knowledge for example completing the library names. Although there are several works that have confirmed the effectiveness of fine-tuning techniques to adapt language models for code completion in specific domains. They are limited by the need for constant fine-tuning of the model when the project is in constant iteration. To address this limitation, in this paper, we propose $k$ NM-LM, a retrieval-augmented language model (R-LM), that integrates domain knowledge into language models without fine-tuning. Different from previous techniques, our approach is able to automatically adapt to different language models and domains. Specifically, it utilizes the in-domain code to build the retrieval-based database decoupled from LM, and then combines it with LM through Bayesian inference to complete the code. The extensive experiments on the completion of intra-project and intra-scenario have confirmed that $k$ NM-LM brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A deep analysis of our tool including the responding speed, storage usage, specific type code completion, and API invocation completion has confirmed that $k$ NM-LM provides satisfactory performance, which renders it highly appropriate for domain adaptive code completion. Furthermore, our approach operates without the requirement for direct access to the language model's parameters. As a result, it can seamlessly integrate with black-box code completion models, making it easy to integrate our approach as a plugin to further enhance the performance of these models.

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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