检索结果-内蒙古大学图书馆

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Neural Visual-Inertial Odometry with Integrated Mechanical Choreography 5

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Accelerating Blockchain-Enabled Federated Learning with Clustered Clients

引用

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

来源：评论

学校读者我要写书评

暂无评论

A General Smart Contract Vulnerability Detection Framework with Self-attention Graph Pooling 5th

A General Smart Contract Vulnerability Detection Framework w...

引用

5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Zou, Lihan Gong, Changhao Wu, Zhen Tan, Jie Tang, Junnan Jiang, Zigui Li, Dan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China School of Software Engineering Sun Yat-Sen University Zhuhai519080 China

ISBN: (纸本)9789819981038

In recent years, the increasing development of Web 3.0 has generated growing attention toward blockchain and smart contracts. However, due to their immutability, smart contracts still exhibit various vulnerabilities that hackers can exploit, resulting in significant losses. Numerous smart contracts on various blockchains, including Ethereum, have been attacked due to various vulnerabilities. The inefficiency of detecting these vulnerabilities has become a major bottleneck in advancing blockchain and smart contracts. Although detecting smart contract vulnerabilities has attracted much attention, most existing machine learning-based methods rely on adequate expert knowledge and target only specific known vulnerabilities via binary classification models. To address this limitation, our proposed approach introduced a general vulnerability detection method that can be applied to identify various common vulnerabilities via a uniform framework. We leveraged the Abstract Syntax Trees (AST) and self-attention-based graph pooling models to generate topological graphs from smart contract code analysis. We adopted Graph Neural Networks for vulnerability detection. Experimental results demonstrated that the proposed approach exhibited satisfactory performance in detecting multiple and unseen vulnerabilities compared to traditional methods. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smart contract

来源：评论

学校读者我要写书评

暂无评论

Category-aware Filtering for Open-set Semi-Supervised Object Detection 5

Category-aware Filtering for Open-set Semi-Supervised Object...

引用

5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Zou, Yiqi Wang, Kuo Zhang, Dongyu Mao, Mingzhi Li, Guanbin Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Software Engineering Zhuhai519082 China

ISBN: (纸本)9798350373820

Semi-Supervised Object Detection (SSOD) has gained significant interest in recent years. However, the majority of existing methods are based on the close-set assumption. In this paper, we address a challenging but more practical scenario, Open-Set Semi-Supervised Object Detection (OSSOD), where out-of-distribution (OOD) samples are contained in unlabeled data. The previous approach employs an offline detector to eliminate OOD pseudo labels, which is intricate and time-consuming. And the limited availability of labeled data hampers the performance of the OOD detector. In contrast, we propose a simple yet effective approach that facilitates the detector's performance in open scenarios. Specifically, we employ soft-distribution on all identified instances as our training objective to enhance the model's feature extraction capability. And we further design a radius-aware matching approach to filter OOD samples according to categories with varying diversity in unlabeled data. Our category-aware design has been proven effective through extensive experiments, surpassing current state-of-the-art work. © 2024 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

引用

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

引用

IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：