检索结果-内蒙古大学图书馆

Simulating Bluetooth virus propagation on the real map via infectious attenuation algorithm and discrete dynamical system

引用

Iran Journal of Computer Science 2024年第4期7卷 755-776页

作者： Zhu, Qingyi Wan, Wentao Gan, Chengquan Fan, Ziqi Yang, Lu-Xing School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing 400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing 400065 China School of Information Technology Deakin University Melbourne 3125 VIC Australia

In recent years, mobile smart devices have gained popularity globally due to their convenience and efficiency. However, this popularity has also attracted the attention of virus creators, resulting in a significant cost to users. Current research on virus propagation primarily focuses on developing anti-virus software and macroscopic modeling. Software development often lags behind and only begins when viruses become a significant threat. In contrast, macroscopic modeling frequently deviates from reality as real networks do not completely conform to the characteristics of scale-free or small-world networks. Furthermore, special factors such as device mobility and signal attenuation must be considered when studying Bluetooth-transmitted viruses. Traditional research methods have shown limited performance in this field. To address these issues, this paper proposes a method that describes the propagation behavior of Bluetooth viruses in smart devices by utilizing an Infectious Attenuation Algorithm (IAA) and real maps. This method incorporates population density, map contours, and user security awareness to construct a unique discrete system. More importantly, we integrate this system into the map of Deyuan Street in Chengdu City and create a visualization system using the Simulation of Urban Mobility (SUMO) simulation software. In this system, the proposed IAA simulates the decay of virus infectivity based on the distance between devices and the attenuation of signal power between them. Our research approach considers the two core elements of infectivity attenuation and maps, making it more realistic and overcoming the lag in software development. Additionally, the introduction of SUMO for visualization simulation enhances the practical significance of our experiments. Finally, we propose feasible prevention strategies based on comparative experiments, data analysis, and theoretical analysis. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2

关键词： Bluetooth virus Discrete dynamical system Infectious attenuation algorithm Population density Real map security awareness SUMO simulation

来源：评论

学校读者我要写书评

暂无评论

Collaborative Resource Allocation for Blockchain-Enabled Internet of Things with Multi-Agent Deep Reinforcement Learning

Collaborative Resource Allocation for Blockchain-Enabled Int...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Xinyu Wan, Jianxiong Li, Leixiao Liu, Chuyi Si, Xiaowei School of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Ho-hhot China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People's Taiwan

ISBN: (纸本)9781665410205

Mobile Edge Computing (MEC) reduces service latency and enhances Quality of Service (QoS) by offloading tasks to the wireless network edge. However, the rapid growth of task offloading and the associated data transmission security challenges deserve further investigation. This study proposes a blockchain-MEC hybrid solution where mobile devices process tasks and engage in block mining to boost system utility. The objective is to maximize the accumulated reward in the blockchain-MEC system by optimizing off loading decisions, channel selection, transmission power, computing resources, and block intervals. A Markov Decision Process is formulated to model the optimization problem which is solved via a multi-agent deep reinforcement learning (MADRL) algorithm. The results of the simulation demonstrate that our approach is more effective than the baseline method. © 2024 IEEE.

关键词： Quality of service

来源：评论

学校读者我要写书评

暂无评论

Multimodal Image-Text Representation Learning for Sketch-Less Facial Image Retrieval

Multimodal Image-Text Representation Learning for Sketch-Les...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Dawei Dai Yingge Liu Shiyu Fu Guoyin Wang Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Sketch-less facial image retrieval (SLFIR) framework aims to break the barriers that drawing a high-quality facial sketch requires excellent skills and substantial time, it performs the retrieval using a partial sketch with as few strokes as possible. However, such early-stage sketches often contain only local details, resulting in poor retrieval performance. In this study, we propose learning of the representation by fusing the sketches with prior human semantic knowledge to improve the early retrieval performance. Specifically, (1) based on the LAION-Face dataset, a facial language-image pretraining (FLIP) model is constructed to learn the aligned representations of facial image and text; (2) subsequently, using FLIP as the backbone, multiscale features of sketch and text are extracted and fused to learn the efficient representation for the final retrieval. The proposed method achieves state-of-the-art early retrieval performance on all two public datasets and exhibits a good generalization ability in practical testing.

关键词： Representation learning Semantics Image retrieval Feature extraction Faces Testing

来源：评论

学校读者我要写书评

暂无评论

A Differential Privacy Protection Protocol Based on Location Entropy

引用

Tsinghua Science and Technology 2023年第3期28卷 452-463页

作者： Ping Guo Baopeng Ye Yuling Chen Tao Li Yixian Yang Xiaobin Qian Xiaomei Yu State Key Laboratory of Public Big Data School of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Information Technology Innovation Service Center of Guizhou Province Guiyang 550025China School of Cyberspace Security Beijing University of Posts and TelecommnuicationsBeijing 100000China Guizhou CoVision Science&Technology Co. Ltd.Guiyang 550025China School of Information Science and Engineering Shandong Normal UniversityJinan 250000China

A Location-Based Service(LBS)refers to geolocation-based services that bring both convenience and *** an increase in the scale and value of data,most existing location privacy protection protocols cannot balance privacy and *** solve the revealing problems in LBS,we propose a differential privacy protection protocol based on location ***,we design an algorithm of the best-assisted user selection for constructing anonymity ***,we employ smart contracts to evaluate the credibility of participants,which ensures the honesty of ***,we provide a comprehensive experiment;the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge ***,our protocol improves data ***,it realizes user-controllable privacy protection,which improves privacy protection and strengthens security.

关键词： Location-Based Services(LBS) smart contract location entropy differential privacy privacy protection

来源：评论

学校读者我要写书评

暂无评论

Violence Detection In Videos via Motion-Guided Global and Local Views 8

Violence Detection In Videos via Motion-Guided Global and Lo...

引用

8th International Conference on data Science in cyberspace, DSC 2023

作者： Su, Ning Sun, Lijuan Gao, Yutong Wu, Jingchen Wu, Xu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security Bupt China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing China Bupt China

ISBN: (纸本)9798350331035

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. This lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. Therefore, in this paper, we design a novel Global-and-Local Cross-Modal Network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, The enhanced features are simultaneously fed into two parallel branches of the network. The global branch fuses the visual and audio features into holistic representations. The local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset. © 2023 IEEE.

关键词： Attention Mechanism Multimodality Multiple Instance Learning Violence Detection

来源：评论

学校读者我要写书评

暂无评论

Prior Semantic-Embedding Representation Learning for On-the-Fly

SSRN

引用

SSRN 2024年

作者： Liu, Yingge Dai, Dawei Zhou, Kenan Tan, Xiufang Wu, Yiqiao Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

In the context of fine-grained sketch-based image retrieval (FG-SBIR) framework, sketch drawing is a time-consuming and skill-intensive process that greatly restricts its practical application. Our study aims to enhance the early-stage retrieval efficiency by utilizing partial sketches with a minimum number of strokes in the on-the-fly FG-SBIR framework. Nevertheless, partial sketches with few strokes usually lack global information and consist only of sparse local details. This lack of detail results in ambiguous semantic content and indistinct differences among images, which consequently impairs early-stage retrieval. To acquire an efficient representation for these sketches, we propose a multi-granularity feature representation model that utilizes prior knowledge embedding to deal with the sketch drawing process. In the first stage,we design a triplet network to learn the joint a coarse representation embedding space that is common between the complete sketch and the image. In the second stage, We enhanced the semantic representation of sketches by utilizing prior knowledge, which aids in supplementing missing fine-grained features. This was achieved by formalizing the prior knowledge and integrating it into the feature vector obtained in the initial stage, thereby acquiring the final representation of a subset of sketches. Experiments conducted on two public datasets indicate that our method outperforms the state-of-the-art baseline during the early retrieval phase. In practical applications, our approach has also achieved good results. © 2024, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

Model-Based Throughput Optimization for Blockchain Sharding System 22

Model-Based Throughput Optimization for Blockchain Sharding ...

引用

22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Yifan Wan, Jianxiong Liu, Chuyi Li, Leixiao Inner Mongolia University of Technology School of Data Science and Application Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Hohhot China Ministry of Education of the People's Republic of China Research Center of Large-Scale Energy Storage Technologies China

ISBN: (纸本)9798331509712

The throughput issue has become an urgent problem for large-scale application of blockchain technology. Sharding technology can improve system throughput and scalability. Current research on the optimal blockchain sharding problem uses Model-Free Reinforcement Learning (MFRL) to maximize system throughput. However, these methods learn directly from interactions with the system, resulting in extremely low sampling efficiency. To bridge this gap, this paper presents a Model-based Policy Optimization Blockchain Sharding algorithm (MBPOBS), which utilizes the Gaussian Process Regression (GPR) to precisely predict the future states, based on which the best sharding strategy is learned through imitation learning from the Cross-Entropy Method (CEM) demonstrator. Simulation results show that compared to traditional MRFL algorithms, MBPOBS accelerates the learning by up to 1.8x. © 2024 IEEE.

关键词： Blockchain modelbased reinforcement learning sample efficiency sharding

来源：评论

学校读者我要写书评

暂无评论

Granular-ball Representation Learning for Deep CNN on Learning with Label Noise

arXiv

引用

arXiv 2024年

作者： Dai, Dawei Zhu, Hao Xia, Shuyin Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

In actual scenarios, whether manually or automatically annotated, label noise is inevitably generated in the training data, which can affect the effectiveness of deep CNN models. The popular solutions require data cleaning or designing additional optimizations to punish the data with mislabeled data, thereby enhancing the robustness of models. However, these methods come at the cost of weakening or even losing some data during the training process. As we know, content is the inherent attribute of an image that does not change with changes in annotations. In this study, we propose a general granular-ball computing (GBC) module that can be embedded into a CNN model, where the classifier finally predicts the label of granular-ball (gb) samples instead of each individual samples. Specifically, considering the classification task: (1) in forward process, we split the input samples as gb samples at feature-level, each of which can correspond to multiple samples with varying numbers and share one single label;(2) during the backpropagation process, we modify the gradient allocation strategy of the GBC module to enable it to propagate normally;and (3) we develop an experience replay policy to ensure the stability of the training process. Experiments demonstrate that the proposed method can improve the robustness of CNN models with no additional data or optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Image annotation

来源：评论

学校读者我要写书评

暂无评论

GBCT: An Efficient and Adaptive Granular-Ball Clustering Algorithm for Complex data

arXiv

引用

arXiv 2024年

作者： Xia, Shuyin Shi, Bolun Wang, Yifan Xie, Jiang Wang, Guoyin Gao, Xinbo Chongqing Key Laboratory of Computational Intelligence The Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education The Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Traditional clustering algorithms often focus on the most fine-grained information and achieve clustering by calculating the distance between each pair of data points or implementing other calculations based on points. This way is not inconsistent with the cognitive mechanism of "global precedence" in human brain, resulting in those methods’ bad performance in efficiency, generalization ability and robustness. To address this problem, we propose a new clustering algorithm called granular-ball clustering (GBCT) via granular-ball computing. Firstly, GBCT generates a smaller number of granular-balls to represent the original data, and forms clusters according to the relationship between granular-balls, instead of the traditional point relationship. At the same time, its coarse-grained characteristics are not susceptible to noise, and the algorithm is efficient and robust;besides, as granular-balls can fit various complex data, GBCT performs much better in non-spherical data sets than other traditional clustering methods. The completely new coarse granularity representation method of GBCT and cluster formation mode can also used to improve other traditional methods. All codes can be available from the following link. Copyright © 2024, The Authors. All rights reserved.

关键词： Granular computing

来源：评论

学校读者我要写书评

暂无评论

GBSVM: Granular-ball Support Vector Machine

arXiv

引用

arXiv 2022年

作者： Xia, Shuyin Lian, Xiaoyu Wang, Guoyin Gao, Xinbo Chen, Jiancu Peng, Xiaoli The Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

GBSVM (Granular-ball Support Vector Machine) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular-ball as input, rather than a single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, this paper has fixed the errors of the original model of the existing GBSVM, and derived its dual model. Furthermore, a particle swarm optimization algorithm is designed to solve the dual model. The sequential minimal optimization algorithm is also carefully designed to solve the dual model. The solution is faster and more stable than the particle swarm optimization based version. The experimental results on the UCI benchmark datasets demonstrate that GBSVM has good robustness and efficiency. All codes have been released in the open source library at http://***/*** or https://***/syxiaa/GBSVM. Copyright © 2022, The Authors. All rights reserved.

关键词： Support vector machines

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：