检索结果-内蒙古大学图书馆

IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Yezhen Wang Haobin Zheng Changjiang Mao Jing Zhang Xiao Ke College of Computer and Data Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou

ISBN: (纸本)9781728194202

With the economic and social development and the substantial improvement of material conditions, the generation of domestic waste has grown rapidly and has become a constraint factor for the development of new urbanization. In the past few years, research on the domestic waste industry has been limited to intelligent waste sorting, neglecting the role of intelligent management of waste storage sites. To relieve it, We propose a deep learning-based multi-task network for intelligent management of garbage deposit points, which combines algorithms such as YoloV5,Deepsort, Insightface, and Openpose to achieve waste bin detection, waste bin status recognition and analysis, face recognition, action recognition, and multiple object tracking based on real-time surveillance video. Besides, we propose a new dataset named Waste Bin Status, which provides a meaningful addition to the existing field of waste bin identification. Experiments on WBS dataset validate that our method is superior to other methods for garbage point status identification. Moreover, our network is trained to work with different scenarios of garbage deposits, demonstrating state-of-the-art performance in real-world tests.

关键词： Industries Face recognition Surveillance Education Streaming media Multitasking Real-time systems

来源：评论

学校读者我要写书评

暂无评论

Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zijian Zhu Yichi Zhang Hai Chen Yinpeng Dong Shu Zhao Wenbo Ding Jiachen Zhong Shibao Zheng Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Dept. of Comp. Sci. and Tech. THBI Lab Institute for AI Tsinghua University BNRist Center Zhongguancun Laboratory Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Computer Science and Technology Anhui University SAIC Motor AI Lab

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations; 2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features; 3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mathematical Programming for Piecewise Linear Representation of Discrete Time Series 15th

Mathematical Programming for Piecewise Linear Representation...

引用

15th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2019, co-located with the 5th International Conference on Harmony Search, Soft computing and Applications, ICHSA 2019

作者： Xiyang, Yang Jing, Zhang Fusheng, Yu Zhiwei, Li Key Laboratory of Intelligent Computing and Information Processing of Fujian Province Quanzhou Normal University Quanzhou China School of Mathematical Sciences Beijing Normal University Beijing100875 China

ISBN: (纸本)9783030325909

Piecewise linear representation (PLR) of a time series arises in variety of disciplines in data mining. Unlike most PLR methods who separate a discrete time series into a few discontinuous line segments, l 1trend filtering method is one of the few PLR methods who generates continuous line segment representations. However, the approximation errors of l 1 trend filtering seldom reach its minimum. In this paper, we propose a binary integer programming model to produce a continuous PLR of time series with the least approximation error, and therefore it is well suitable to analyzing time series with an underlying piecewise linear trend. We describe the motives of the proposed method and give some illustrative examples. The improvement in approximation error is demonstrated by some experiments on some real-world time series datasets. © 2020, Springer Nature Switzerland AG.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area networks

Research Square

引用

Research Square 2021年

作者： Zhang, Jianshan Li, Ming Zheng, Xianghan Hsu, Ching-Hsien College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory Network Computing and Intelligent Information Processing Fuzhou China Department of Computer Science and Information Engineering Asia University Taichung Taiwan

With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud centre is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific forms of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on Workflow Applications (WAs) in Wireless Metropolitan Area networks (WMANs). We devise a cloudlet placement strategy based on a Particle swarm optimization algorithm using Genetic algorithm operators with the Encoding Library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlets placement scheme, and compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04%-44.99%. © 2021, CC BY.

关键词： computation offloading

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Access Control with Privacy-Preserving Data Retrieval for Cloud-Assisted IoV

引用

IEEE Transactions on Vehicular Technology 2025年

作者： Li, Wenchao Xia, Chunhe Yang, Simiao Wang, Kangming Huang, Guotao Huang, Luqi Guo, Fuchun Susilo, Willy Wang, Tianbo Beihang University School of Cyber Science and Technology Beijing100191 China University of Wollongong School of Computing and Information Technology Institute of Cybersecurity and Cryptology WollongongNSW2522 Australia Beihang University Beijing Key Laboratory of Network Technology Beijing100191 China Guangxi Normal University Guangxi Collab. Innovation Center of Multi-source Information Integration and Intelligent Processing Guilin541004 China Beihang University Sino-French Engineer School Beijing100191 China Beihang University Computer Science and Engineering School Beijing100191 China

In addition to the autonomous driving technology of single vehicles, the inter-group control algorithm serving the data sharing of multi-vehicle cooperative driving has also attracted widespread attention. To ensure secure communication, many encryption schemes have been proposed to protect the interaction data between vehicles. Nevertheless, traditional public key encryption schemes hinder the sharing of encrypted data. Based on the premise of ensuring the confidentiality of encrypted information, in order to facilitate efficient data sharing, conduct data searches across extensive cloud-based datasets, and authorize access under specified conditions, we introduce Fine-Grained Access Control with Privacy-Preserving Data Retrieval (FGAC-PPDR) for the Internet of Vehicles. This scheme offers a secure, flexible, and privacy-centric approach to data sharing for groups of vehicles in the IoV. Our proposed scheme enables encrypted data to be retrieved at the group level by the cloud server, and prevents vehicles outside the group from performing equality tests on the ciphertext. Furthermore, the data owner can create an authorization token with defined conditions to specify how the data is shared. During the process of data search and sharing, intensive computing tasks are undertaken by cloud servers with abundant computational resources. We also demonstrate that our scheme is secure against chosen ciphertext attacks (CCA). Finally, we provide security and performance analyses that verify the feasibility and effectiveness of our proposal. © 2025 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

A Novel Dynamic Group Signature with Membership Privacy

A Novel Dynamic Group Signature with Membership Privacy

引用

IEEE Conference on Dependable and Secure computing

作者： Junqing Lu Rongxin Qi Jian Shen School of Computer and Software Nanjing University of Information Science & Technology Nanjing China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

Group signature is a cryptography primitive that has been widely researched. It strikes a balance between digital signature and the user's demand for anonymity. A valid member in the group can generate a signature on behalf of the whole group. The public can only know that it was provided by a valid group member and learn nothing about the actual identity of the signer when verifying a group signature. Backes et al pointed out that the existing dynamic group signature schemes implicitly assume that the membership of everyone in the group is open to the public. Thus, they put forward a property called membership privacy for dynamic group signature. In this paper, we design a dynamic group signature scheme with membership privacy on top of Signature Proofs of Knowledge (SPK) and BBS+ signature. Further more, dynamic accumulator mechanism is adopted to revoke a group member's authority to sign. Then, a security analysis demonstrates that the proposed group signature scheme satisfies join-leave privacy. Finally, quantitative analysis and experimental results show that the proposed group signature scheme achieves the fewer signature size and less computation overhead compared with Backes's scheme.

关键词： Privacy Statistical analysis Conferences Cryptography Digital signatures

来源：评论

学校读者我要写书评

暂无评论

Understanding the Robustness of 3D Object Detection with Bird’s-Eye-View Representations in Autonomous Driving

arXiv

引用

arXiv 2023年

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua University China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Computer Science and Technology Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province China SAIC Motor AI Lab China Zhongguancun Laboratory China

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird’s-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDAR fusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. Code available at https: //***/zzj403/BEV_Robust. Copyright © 2023, The Authors. All rights reserved.

关键词： Birds

来源：评论

学校读者我要写书评

暂无评论

Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

引用

IEEE Transactions on Mobile computing 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

来源：评论

学校读者我要写书评

暂无评论

Fine-grained pseudo-code generation method via code feature extraction and transformer

arXiv

引用

arXiv 2021年

作者： Yang, Guang Zhou, Yanlin Chen, Xiang Yu, Chi School of Information Science and Technology Nantong University China Ministry of Industry and Information Technology China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University China

Pseudo-code written by natural language is helpful for novice developers’ program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

DRLO: Optimizing edge server placement in dynamic MEC scenarios using deep reinforcement learning

引用

Computer networks 2025年 268卷

作者： Yingya Guo Cen Chen College of Computer and Data Science Fuzhou University PR China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University PR China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian PR China

As an emerging computing paradigm, Mobile Edge computing (MEC) significantly enhances user experience and alleviates network congestion by strategically deploying edge servers in close proximity to mobile users. However, the effectiveness of MEC hinges on the precise placement of these edge servers, a critical factor in determining the Quality of Experience (QoE) for mobile users. While existing studies predominantly focus on optimizing edge server placement in static scenarios, they often fall short when faced with user mobility, resulting in a degradation of QoE. To address this challenge, we propose an adaptive edge server placement approach that leverages Deep Reinforcement Learning (DRL) to select the base stations for placing edge servers in a dynamic MEC environment. Our objective is to minimize access delay by optimizing edge server placement for adapting to dynamic environment. To tackle the vast action space associated with edge server placement, we introduce a novel activation function in the actor neural network for efficient exploration. Furthermore, to enhance the adaptability of the derived edge server placement strategy, we meticulously design a new reward function, which takes into account the minimization of total access delay within dynamic MEC scenarios. Finally, to validate the effectiveness of our proposed method, extensive experiments were conducted using the Shanghai Telecom dataset. The results demonstrate that our approach outperforms baseline methods in minimizing access delay for users in dynamic MEC scenarios.

关键词：

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：