检索结果-内蒙古大学图书馆

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Qu, Y. Neil Ji, Zonghao Cui, Lei Zhang, Chong Liu, Lei Tian, Zhiyi Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250031 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250031 China Deakin University School of IT VIC3125 Australia Xidian University Guangzhou Institute of Technology Guangzhou510555 China University of Technology School of Computer Science SydneyNSW2007 Australia

ISBN: (纸本)9798350391367

Throughout the process of machine unlearning, in order to fulfill the requirements of machine unlearning and to comply with the rules of the right to be forgotten, it is natural to verify whether the unlearning model actually removes data points. Therefore, machine unlearning verification plays a crucial role. However, existing validation methods only perform one validation, and it is still challenging to determine whether subsequent updates of the model still maintain the unlearning effect. To address this challenge, we propose a novel continuous verification of catastrophic recalling in machine unlearning framework that aims to continuously evaluate whether a machine learning model effectively forgets specified data after an update. In this work, we first introduce adversarial testing as a crucial part of the overall framework. Verification then continues as the unlearned model is updated (adding or continuing to forget data points). We also analyzed the possibility that learning new data after unlearning may diminish the effect. Overall, our continuous unlearning verification framework provides a proactive approach to protecting individual privacy in the machine learning domain, thus facilitating the generation of more robust unlearning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

A Deep Reinforcement Learning Approach for Online Taxi Dispatching

A Deep Reinforcement Learning Approach for Online Taxi Dispa...

引用

International Conference on networking and network Applications (NaNA)

作者： YuBo Wang TaoYi Zhang ZhiCheng Wei Key Lab of Network and Information Security College of Computer and Cyber Security Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Security Hebei Normal University Shijiazhuang Hebei Province China

With the development of smart city transportation systems, developing reasonable dispatching strategies for idle ride-hailing vehicles has become an urgent research problem. In this paper, we address the short-sightedness issue of existing dispatching strategies and design a ride-hailing dispatching model based on deep reinforcement learning in the Markov decision process(MDP) framework to obtain long-term dispatching strategies with foresight. Firstly, we define the basic framework of agent and optimize the dispatching strategy of ride-hailing services by defining a metric that combines instant revenue with the demand situation of the next state in the action space as a reward function. Then, we construct an order-matching system simulator based on historical order data sets to facilitate interaction between the intelligent agent and the environment. Finally, based on the Bellman optimality equation, we approximate the value function using the Eval Net and Target Net, and train the model using Temporal Difference Learning. Simulation experiments demonstrate that this approach can increase driver income, provide more services to passengers, and effectively improve the operating efficiency of the transportation network.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on the Design of Government Data Governance Evaluation System under the Perspective of Digital Government 9

Research on the Design of Government Data Governance Evaluat...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Xu, Zirui Sun, Jinyang Lu, Zhe Han, Delong Qilu University of Technology Shandong Academy of Sciences Information Research Institute Shandong Academy of Sciences Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

In the context of the digital era, government data governance is a key factor in promoting the modernization of government governance, but there is a lack of a complete set of evaluation index system to standardize the content and scope of government data governance. This paper adopts the policy tool theory to code and analyze the 12 policies from the three policy tool dimensions of supply layer, demand layer, and environment layer, and proposes three first-level evaluation indicators of basic environment, actual effectiveness, and safeguard mechanism, on the basis of which the evaluation content is selected in a focused manner based on the integration of policy documents related to data governance. Meanwhile, a comprehensive, scientific and objective evaluation system of government data governance is constructed by adopting assessment means combining subjectivity and objectivity, and quantitative and qualitative. The system is designed to assess and guide the data governance practices of the domestic government, point out the direction for government departments at all levels to carry out data governance, and play an important role in promoting the transparency and efficiency of data management. © 2024 IEEE.

关键词： data governance digital government government information indicator system policy tools

来源：评论

学校读者我要写书评

暂无评论

Distributed Deep Learning for Cooperative Computation Offloading in Low Earth Orbit Satellite networks

引用

China Communications 2022年第4期19卷 230-243页

作者： Qingqing Tang Zesong Fei Bin Li School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Key Lab of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and TelecommunicationsMinistry of EducationNanjing 210003China

Low earth orbit(LEO) satellite network is an important development trend for future mobile communication systems, which can truly realize the“ubiquitous connection” of the whole world. In this paper, we present a cooperative computation offloading in the LEO satellite network with a three-tier computation architecture by leveraging the vertical cooperation among ground users, LEO satellites, and the cloud server, and the horizontal cooperation between LEO satellites. To improve the quality of service for ground users, we optimize the computation offloading decisions to minimize the total execution delay for ground users subject to the limited battery capacity of ground users and the computation capability of each LEO satellite. However, the formulated problem is a large-scale nonlinear integer programming problem as the number of ground users and LEO satellites increases, which is difficult to solve with general optimization algorithms. To address this challenging problem, we propose a distributed deep learning-based cooperative computation offloading(DDLCCO) algorithm, where multiple parallel deep neural networks(DNNs) are adopted to learn the computation offloading strategy dynamically. Simulation results show that the proposed algorithm can achieve near-optimal performance with low computational complexity compared with other computation offloading strategies.

关键词： LEO satellite networks computation offloading deep neural networks

来源：评论

学校读者我要写书评

暂无评论

NeuVSA: A Unified and Efficient Accelerator for Neural Vector Search 31

NeuVSA: A Unified and Efficient Accelerator for Neural Vecto...

引用

31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Yuan, Ziming Dai, Lei Li, Wen Zhang, Jie Liang, Shengwen Wang, Ying Liu, Cheng Li, Huawei Li, Xiaowei Guo, Jiafeng Wang, Peng Chen, Renhai Zhang, Gong Shanxi University School of Computer and Information Technology China Peking University School of Computer Science China Institute of Computing Technology CAS State Key Lab of Processors China University of Chinese Academy of Sciences China Zhongguancun National Laboratory Beijing China Institute of Computing Technology CAS Key Lab of Network Data Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798331506476

Neural Vector Search (NVS) has exhibited superior search quality over traditional key-based strategies for information retrieval tasks. An effective NVS architecture requires high recall, low latency, and high throughput to enhance user experience and cost-efficiency. However, implementing NVS on existing neural network accelerators and vector search accelerators is sub-optimal due to the separation between the embedding stage and vector search stage at both algorithm and architecture levels. Fortunately, we unveil that Product Quantization (PQ) opens up an opportunity to break separation. However, existing PQ algorithms and accelerators still focus on either the embedding stage or the vector search stage, rather than both simultaneously. Simply combining existing solutions still follows the beaten track of separation and suffers from insufficient parallelization, frequent data access conflicts, and the absence of scheduling, thus failing to reach optimal recall, latency, and throughput. To this end, we propose a unified and efficient NVS accelerator dubbed NeuVSA based on algorithm and architecture co-design philosophy. Specifically, on the algorithm level, we propose a learned PQ-based unified NVS algorithm that consolidates two separate stages into the same computing and memory access paradigm. It integrates an end-to-end joint training strategy to learn the optimal codebook and index for enhanced recall and reduced PQ complexity, thus achieving smoother acceleration. On the architecture level, we customize a homogeneous NVS accelerator based on the unified NVS algorithm. Each sub-accelerator is optimized to exploit all parallelism exposed by unified NVS, incorporating a structured index assignment strategy and an elastic on-chip buffer to alleviate buffer conflicts for reduced latency. All sub-accelerators are coordinated using a hardware-aware scheduling strategy for boosted throughput. Experimental results show that the joint training strategy improves recall

关键词： network embeddings

来源：评论

学校读者我要写书评

暂无评论

Multi-Relational Variational Contrastive Learning for Next POI Recommendation

Multi-Relational Variational Contrastive Learning for Next P...

引用

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

作者： Lin Chen Peipei Wang Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Next point-of-interest (POI) recommendation aims to predict the next interested POI to the user based on their historical check-in data in location-based social services. Most existing studies have attempted to model user visiting behaviors via sequence-based and graph-based models, and have achieved impressive performance. However, there is still room to explore the implicit transition preferences and contextual multiple semantic relationships among various POIs. To this end, we propose a novel graph-based Multi-Relational Variational Contrastive Learning (MRVCL) method for next POI recommendation, which captures local similarity associations and global contextual dependencies among POIs. Specifically, an order-free local-relational adaptive weighting module is designed to alleviate the problem of insufficient utilization of multi-hop neighbor information caused by the limit of neighbor order of nodes. We then develop a contextaware global-relational encoding module to capture implicit semantic sequential relationships. Finally, generative variational-contrastive learning is employed to construct a continuous representation of the latent features and reinforce the quality of representation learning. Extensive experiments on two real-world datasets validate that our MRVCL outperforms existing state-of-the-art methods on various evaluation metrics. To facilitate future research, our code and data are open-sourced at https://***/LinCH-en/MRVCL.

关键词： Representation learning Measurement Correlation Semantics Neural networks Contrastive learning Signal processing Transformers Encoding Speech processing

来源：评论

学校读者我要写书评

暂无评论

Research on liquid crystal display technology based on regional dynamic dimming algorithm 9

Research on liquid crystal display technology based on regio...

引用

9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yu, Zhitao Sun, Peng Zhou, Mingle Liu, Qianlong Zhao, Shilong Hisense Video Technology Co. Ltd. Qing Dao China Hisense Visual Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption. © 2024 IEEE.

关键词： Liquid crystal displays

来源：评论

学校读者我要写书评

暂无评论

Precise sensitivity recognizing,privacy preserving,knowledge graph-based method for trajectory data publication

引用

Frontiers of computer Science 2022年第4期16卷 219-221页

作者： Xianxian Li Bing Cai Li-e Wang Lei Lei Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Normal UniversityGuilin541004China College of Computer Science and Information Engineering Guangxi Normal UniversityGuilin541004China Nanning Tiancheng Zhiyuan Intellectual Property Service Co.Ltd. Nanning530000China

1Introduction The existing privacy protection methods of trajectory data publication have two ***,existing methods[1-3]cannot resist attribute link attacks *** practical scenarios,it is necessary to integrate data from a variety of fields for data analysis,such as cross-domain *** usually need to obtain the user attributes outside of the trajectory data as background knowledge.

关键词： knowledge outside preserving

来源：评论

学校读者我要写书评

暂无评论

A Multi-Channel Composite Recommendation Method in Federated Learning

A Multi-Channel Composite Recommendation Method in Federated...

引用

2023 IEEE International Conference on Dependable, Autonomic and Secure Computing, 2023 International Conference on Pervasive Intelligence and Computing, 2023 International Conference on Cloud and Big Data Computing, 2023 International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2023

作者： Chen, Shi Li, Qianmu Wang, Qi Zhao, Xinjian Ge, Hao Li, Nianzhe State Grid Jiangsu Information & Telecommunication Company Nanjing China School of Computer Science Ana Engineering Nanjing University of Science and Technology Nanjing China State Grid Smart Grid Research Institute Co. Ltd. State Grid Key Laboratory of Information & Network Security Nanjing China

ISBN: (纸本)9798350304602

There are various data security risks in power grid systems, and recommendation systems are a good solution to the selection of security strategies. Federated learning is a machine learning framework that can protect the data privacy of each participant and can effectively solve the data security external collaborative training modeling challenges. This paper proposed a flexible implantable recommendation algorithm based on knowledge graph and multi-channel composite to enhance federated learning on demand. The algorithm designs three different feature extraction modules to learn to construct multi-dimensional features on multi-attribute collaborative knowledge graphs. Among them, the distance translation module utilizes the Trans R recommendation algorithm to learn the internal semantic structure information in knowledge graph triples and terminal-security policy selection history;the random walk module uses the Node2Vec algorithm to learn the homogeneous and Structured information in the multi-attribute collaborative knowledge graph;the graph convolutional network module learns the high-order neighborhood multi-attribute collaborative information of terminal/security policy nodes through multi-layer feature propagation and aggregation. The composite network module adopts a convolutional neural network-like operation to first locally optimize the three modules, and then extracts the highly informative elements to obtain the final feature representation of the terminal/security policy. Finally, the algorithm merges the vectors of terminal-security policy pairs into the multi-layer perceptron to learn and calculate the prediction score for recommendation. © 2023 IEEE.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Secure and Efficient Blockchain-Based Access Control Scheme with Attribute Update

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Zuo, Yuting Xu, Li Li, Jiguo Li, Jie Wang, Xiaoding Piran, Md. Jalil Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Lab Shanghai200240 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

Secure data access control is widely concerned in Cyber Physical Systems (CPSs) to protect consumer rights. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is an ideal solution that supports fine-grained access control. It is a pity that they fail to consider the attribute control and the verifiable legitimacy when consumer's attributes are updated. Additionally, the existing revocable CP-ABE schemes are unsuitable for resource-constrained consumer electronic devices due to their high computational and storage overhead. This paper proposes a blockchain-based access control scheme with attribute update. A new revocation mechanism is designed to guarantee the forward/backward security of the attribute update. The lightweight ciphertext update protocol is proposed to optimize the computation cost. To realize secure and efficient attribute management, this paper designs the on-chain/off-chain separation storage mode, and proposes the traceability-based attribute set generation and validation algorithm. This paper performs the formal security proof. The experimental results demonstrate that the scheme is efficient in terms of encryption and decryption. © 1975-2011 IEEE.

关键词： Ciphertext

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：