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4th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2024

作者： Xia, Ding Yao, Peiyuan Liang, Junyuan Chen, Wuhui School of Computer Science Sun Yat-sen University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China

ISBN: (纸本)9798350359589

This paper addresses the continuously increasing storage demands challenge faced by blockchain networks, with a particular focus on Ethereum. We propose a novel framework that divides the network into consensus nodes, which inherit Ethereum characteristics, and storage nodes responsible for storing Merkle Patricia Trie (MPT) nodes. This design aims to reduce the storage load on individual nodes by distributing MPT nodes based on their key values. Our approach maintains network security and data integrity while easing the storage burden through a distributed storage mechanism. Key to our work is the dynamic adjustment of storage load across an expandable network of storage nodes. We validate our framework through practical experiments, involving modifications to the go-ethereum source code and testing with authentic Ethereum block data. The results confirm that our work not only mitigates storage issues but also enhances synchronization efficiency. © 2024 IEEE.

关键词： blockchain distributed system ethereum merkle patricia trie storage scalability

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Parallel software-Based Self-Testing with Bounded Model Checking for Kilo-Core Networks-on-Chip

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Journal of computer science & Technology 2023年第2期38卷 405-421页

作者：张颖季鹏飞朱潘玮 Zebo Peng 李华伟江建慧 School of Software Engineering Tongji UniversityShanghai 200092China Department of Computer and Information Science Linkoping UniversityLinkoping 58183Sweden Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Online testing is critical to ensuring reliable operations of the next generation of supercomputers based on a kilo-core network-on-chip(NoC)interconnection *** present a parallel software-based self-testing(SBST)solution that makes use of the bounded model checking(BMC)technique to generate test sequences and parallel *** this method,the parallel SBST with BMC derives the leading sequence for each router’s internal function and detects all functionally-testable faults related to the function.A Monte-Carlo simulation algorithm is then used to search for the approximately optimum configuration of the parallel packets,which guarantees the test quality and minimizes the test ***,a multi-threading technology is used to ensure that the Monte-Carlo simulation can reach the approximately optimum configuration in a large random space and reduce the generating time of the parallel *** results show that the proposed method achieves a high fault coverage with a reduced test ***,by performing online testing in the functional mode with SBST,it effectively avoids the over-testing problem caused by functionally untestable turns in kilo-core NoCs.

关键词： software-based self-testing(SBST) parallel test kilo-core networks-on-chip(NoCs) online testing

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Curriculum Learning for Self-Iterative Semi-Supervised Medical Image Segmentation

Curriculum Learning for Self-Iterative Semi-Supervised Medic...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Jiayu Xu, Dexuan Chen, Yanyuan Lou, Yiwei Huang, Yue Peking University School of Software and Microelectronics Beijing China Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education China Peking University School of Computer Science Beijing China Peking University School of Electronic Engineering and Computer Science Beijing China

ISBN: (纸本)9798350386226

Self-training with data augmentation emerges as an efficacious strategy for harnessing unlabeled data in the realm of semi-supervised medical image segmentation. Within the synthetic domain, existing models make a deliberate trade-off, sacrificing some of its absolute performance on labeled data to bolster its generalization capabilities on the predominantly abundant unlabeled data encompassed within the entire dataset. In this study, we find out the essence of employing data augmentation techniques to create a proxy data domain that serves as a bridge between labeled and unlabeled data. To this end, we optimize the aforementioned approach by incorporating the concept of curriculum learning, which encompasses two primary components: Dynamic Copy-Paste strategies and the Self-Iterative Segmentation Model. Concerning the former, the dynamic scaling of the copy-paste box guides the model in acquiring shared semantics, progressing from easier (labeled data) to more challenging (unlabeled data). In order to facilitate this incremental learning process, we have devised models that supports progressive iterative evolution throughout the training phase. Our approach has demonstrated remarkable efficacy through a comprehensive series of benchmarks, consistently outperforming existing methods and achieving state-of-the-art performance. © 2024 IEEE.

关键词： curriculum learning medical image segmentation neural networks semi-supervised

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Thermal Simulation Computing Network Based on Deep Learning

Thermal Simulation Computing Network Based on Deep Learning

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2024 International Symposium on Digital Home, ISDH 2024

作者： Wang, Ruomei Teng, Yi Zhang, Yaoquan Zhou, Fan School of Software and Engineering Guangzhou510303 China School of Computer Science Guangdong University of Education Guangzhou510303 China School of Computer Science Sun Yat-sen University National Engineering Research Center of Digital Life Guangzhou510006 China

ISBN: (纸本)9798331509873

The heat and moisture transfer simulation about human body and clothing is a method to simulate the regulation mechanism of human body, and heat and moisture performance of clothing in thermal environment by using computer technology. According to the simulation results, it can be used to the monitor and predicate the key physiological data of human body, and the functional clothing design. Some mathematical models about this area have been built. However, a large number of differential equations are involved to be used to the numerical computation in the simulation process, which leads to high computational complexity, large memo-ry consumption and long computing time. With the expansion of computing scale, these problems will become more obvious and directly affect the practical application of simulation models. The main work of this paper is to study the efficient solution method based on neural network, and proposed a deep learning-based differential equation computing network. The network uses differential equation as the constraint of the loss function of neural network, and integrates the differential equation into the training and prediction process of the network. The experimental results show that the proposed network can reduce the computational complexity of the simulation model and improve the computational efficiency. © 2024 IEEE.

关键词： Physiological models

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Runtime Verification of Neural-Symbolic Systems 10th

Runtime Verification of Neural-Symbolic Systems

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10th International Symposium on Dependable software engineering: Theories, Tools and Applications, SETTA 2024

作者： Deng, Shaojun Liu, Wanwei Zhang, Miaomiao College of Computer Science and Technology National University of Defense Technology Changsha China School of Software Engineering Tongji Uniersity Shanghai China

ISBN: (纸本)9789819606016

Neural-symbolic systems (NSSs), which are typically cyber-physical systems integrated with artificial intelligence modules, have received much attention in both academic and industrial fields. However, thorough verification (such as model checking) of such systems in general leads to prohibitively high costs due to the scale of the state space, and this makes it barely accomplishable within desired time-span. Consequently, light-weight verification techniques are introduced to deal with such cases, e.g., runtime verification (RV, for short). In this paper, we investigate an RV framework for NSSs against signal temporal logic (STL) properties. To guarantee that the on-line monitoring could be accomplished in time, we utilize Euler-prediction to sample the system under scrutiny at discrete time-steps. Our framework supports both qualitative and quantitative on-line monitoring of STL specifications, and thus it terminates whenever a property is satisfied and/or violated by a finite prefix of the trajectory, and it can also provide the bounds of the quantitative satisfaction/violation w.r.t. the remaining execution. Notably, for the so-called piece-wise linear NSSs against affine STL formulas, our approach is exact. On top of it, a prototype tool is implemented, and is experimentally evaluated. The results demonstrate the feasibility of the presented runtime verification approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Model checking

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Diverse Co-saliency Feature Learning for Text-Based Person Retrieval

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5465-5477页

作者： You, Shuai Chen, Cuiqun Feng, Yujian Liu, Hai Ji, Yimu Ye, Mang Nanjing University of Posts and Telecommunications (NJUPT) School of Internet of Things Nanjing China Anhui University School of Computer Science and Technology Hefei China South China Normal University school of computer Guangdong China NJUPT School of Computer Science Nanjing China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan 430072 China

Text-based Person Retrieval (TPR) plays a pivotal role in video surveillance systems for safeguarding public safety. As a fine-grained retrieval task, TPR faces the significant challenge of precisely capturing highly discriminative features across image and text modalities. Existing methods primarily focus on establishing modality-shared feature spaces to bridge cross-modal discrepancies. However, these methods are prone to disturbances from irrelevant information, such as background noises in the visual modality, and often over-emphasize specific local regions while neglecting the capture of diverse discriminative modal features, thereby limiting the robustness of cross-modal matching. In this paper, we introduce a novel framework, termed the Diverse Co-saliency Feature Learning Network (DCFL), which mines the co-saliency information between image and text modalities and enhances the diversity of cross-modal discriminative features while mitigating the interference of noise. Specifically, to construct cross-modal co-saliency features, we devise the Intra-modal Saliency Feature Learning (ISFL) and Cross-modal Saliency Feature Matching (CSFM) modules. ISFL employs a weighted mask mechanism to guide the model in reducing the impact of noise information in both modalities. Complementing ISFL, CSFM establishes consistent relationships between saliency features across modalities, leveraging text descriptions to align pedestrian-relevant visual regions. Furthermore, we propose the Diverse Co-saliency Feature Mining (DCFM) to bolster the diversity of discriminative co-saliency features across both image and text modalities. This module integrates a diversity regularization term, enabling the extraction of varied visual cues and capturing comprehensive features of the target individual. Extensive benchmark experiments demonstrate a substantial superiority of our approach over the state-of-the-art methods. The code will be released publicly. © 2005-2012 IEEE.

关键词： Co-saliency features modeling Diverse Co-saliency features Mining Text-based person retrieval

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Neural Visual-Inertial Odometry with Integrated Mechanical Choreography 5

Neural Visual-Inertial Odometry with Integrated Mechanical C...

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5th International Academic Exchange Conference on science and Technology Innovation, IAECST 2023

作者： Liu, Jiaxing Liu, Chengyu Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350357738

With the extensive application of deep learning, neural network-based Visual Inertial Odometry (VIO) has demonstrated significantly better performance than traditional geometric VIO. Deep learning-based VIO fuses information from vision and inertial measurement unit (IMU) to learn positional changes in an end-to-end data-driven manner. However, existing studies have generally focused only on the extraction of representations from raw data, ignoring the correlations and interactions between modalities. Moreover, these end-to-end networks are often criticized for their limited interpretability. This study proposes an innovative deep learning VIO model that incorporates mechanical choreography. The model enhances the network with prior positional information through the integration of results from IMU mechanical choreography. It leverages multimodal feature learning concepts and employs a cross-modal co-attention mechanism to effectively merge features extracted from images and IMUs. This approach aims to achieve highly accurate, efficient, and interpretable position estimation tasks. Finally, the trained model is tested on a public dataset and the accuracy is compared with traditional methods and classical neural VIO to verify the effectiveness and feasibility of the proposed method in this study. © 2023 IEEE.

关键词： Deep neural networks

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A Privacy-Preserving and Research-Utilizable Trajectory Generator via Deep Generative Approach 6

A Privacy-Preserving and Research-Utilizable Trajectory Gene...

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6th International Conference on Electronics Technology, ICET 2023

作者： Sun, Xinyue Wo, Tianyu Beihang University School of Computer Science and Engineering Beijing China Beihang University School of Software Beijing China

ISBN: (纸本)9798350337693

Large-scale trajectory data is critical for a smart city to improve the efficiency of a transportation system. However, the release of original trajectory data violates privacy protection principles if no privacy-preserving approach is adopted, that's why the public trajectory data for research is so limited. To enable extensive available trajectory data publishing, we propose a Privacy-Preserving and research-utilizable Trajectory Generator (PPTG), which uses a deep generative model to provide utilizable synthetic trajectories. Specifically, PPTG can not only extract the intrinsic and spatial features, but also get rid of possible privacy information from the real trajectories. In experiments, we show that the privacy-preserving trajectory data generated by PPTG can achieve superior performance in terms of privacy protection and data utility against the existing approaches. © 2023 IEEE.

关键词： Trajectories

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Monte Carlo Simulation-Based Robust Workflow Scheduling for Spot Instances in Cloud Environments

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Tsinghua science and Technology 2024年第1期29卷 112-126页

作者： Quanwang Wu Jianzhao Fang Jie Zeng Junhao Wen Fengji Luo College of Computer Science Chongqing UniversityChongqing 400044China National Experimental Teaching Demonstration Center Chongqing UniversityChongqing 400044China College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering The University of SydneySydney 2006Australia

When deploying workflows in cloud environments,the use of Spot Instances(SIs)is intriguing as they are much cheaper than on-demand ***,Sls are volatile and may be revoked at any time,which results in a more challenging scheduling problem involving execution interruption and hence hinders the successful handling of conventional cloud workflow scheduling *** some scheduling methods for Sls have been proposed,most of them are no more applicable to the latest Sls,as they have evolved by eliminating bidding and simplifying the pricing *** study focuses on how to minimize the execution cost with a deadline constraint when deploying a workflow on volatile Sls in cloud *** on Monte Carlo simulation and list scheduling,a stochastic scheduling method called MCLS is devised to optimize a utility function introduced for this *** the Monte Carlo simulation framework,MCLS employs sampled task execution time to build solutions via deadline distribution and list scheduling,and then returns the most robust solution from all the candidates with a specific evaluation mechanism and selection *** results show that the performance of MCLS is more competitive comparedwithtraditionalalgorithms.

关键词： constrained optimization Monte Carlo simulation robustness Spot Instances(Sls) workflow scheduling

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Accelerating Blockchain-Enabled Federated Learning with Clustered Clients

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IEEE Transactions on Big Data 2024年 1-14页

作者： Cui, Laizhong Li, Yinghao Zhou, Yipeng Qu, Youyang Liu, Jiangchuan College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing FSE Macquarie University Macquare Park NSW Australia Data61 CSIRO VIC Australia School of Computing Science Simon Fraser University Burnaby BC Canada

With the rapid development of big data, Federated learning (FL) has found numerous applications, enabling machine learning (ML) on edge devices while preserving privacy. However, FL still faces crucial challenges, such as single point of failure and poisoning attacks, which motivate the integration of blockchain-enabled FL (BeFL). Beyond that, the efficiency issue still limits the further application of BeFL. To address these issues, we propose a novel decentralized framework: Accelerating Blockchain-Enabled Federated Learning with Clustered Clients (ABFLCC), who utilize actual training time for clustering clients to achieve hierarchical FL and solve the single point of failure problem through blockchain. Additionally, the framework clusters edge devices considering their actual training times, which allows for synchronous FL within clusters and asynchronous FL across clusters simultaneously. This approach guarantees that devices with a similar training time have a consistent global model version, improving the stability of the converging process, while the asynchronous learning between clusters enhances the efficiency of convergence. The proposed framework is evaluated through simulations on three real-world public datasets, demonstrating a training efficiency improvement of 30% to 70% in terms of convergence time compared to existing BeFL systems. IEEE

关键词： Big data

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