检索结果-内蒙古大学图书馆

Aggregation-based dual heterogeneous task allocation in spatial crowdsourcing

Frontiers of Computer Science 2024年第6期18卷 215-227页

作者： Xiaochuan LIN Kaimin WEI Zhetao LI Jinpeng CHEN Tingrui PEI College of Information Science and Technology&Cyberspace Security Jinan UniversityGuangzhou 510632China National&Local Joint Engineering Research Center of Network Security Detection and Protection Technology Guangzhou 510632China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou 510632China School of Computer Science Beijing University of Posts and TelecommunicationsBeijing 100876China

Spatial crowdsourcing(SC)is a popular data collection paradigm for numerous *** the increment of tasks and workers in SC,heterogeneity becomes an unavoidable difficulty in task *** researches only focus on the single-heterogeneous task ***,a variety of heterogeneous objects coexist in real-world SC *** dramatically expands the space for searching the optimal task allocation solution,affecting the quality and efficiency of data *** this paper,an aggregation-based dual heterogeneous task allocation algorithm is put *** investigates the impact of dual heterogeneous on the task allocation problem and seeks to maximize the quality of task completion and minimize the average travel *** problem is first proved to be ***,a task aggregation method based on locations and requirements is built to reduce task ***,a time-constrained shortest path planning is also developed to shorten the travel distance in a *** that,two evolutionary task allocation schemes are ***,extensive experiments are conducted based on real-world datasets in various *** with baseline algorithms,our proposed schemes enhance the quality of task completion by up to 25% and utilize 34% less average travel distance.

关键词： task allocation aggregation shortest path dual heterogeneous spatial crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 15557-15572页

作者： Cui, Song Duan, Lijuan Qiao, Yuanhua Xiao, Ying Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China College of Applied Sciences Beijing University of Technology Beijing100124 China

Epileptic seizure prediction has the potential to promote epilepsy care and treatment. However, the seizure prediction accuracy does not satisfy the application requirements. In this paper, a novel framework for seizure prediction is proposed by learning synchronization patterns. For better representation, bag-of-wave (BoWav) feature extraction is proposed for modeling synchronization pattern of electroencephalogram (EEG) signal. An interictal codebook and preictal codebook, representing the local segments, are constructed by a clustering algorithm. Within a period of EEG signal on all electrodes, local segments are projected onto the learned codebooks. The proposed feature expresses the synchronization pattern of EEG signal with the histogram feature. Moreover, extreme learning machine (ELM) is used to classify the sequence of features. Experiments are performed on the Kaggle seizure prediction challenge dataset and the CHB-MIT dataset. The experiment on the CHB-MIT achieves a sensitivity of 88.24% and a false prediction rate per hour of 0.25. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Spectrum fragmentation-aware dynamic network slicing deployment in computing power networks based on elastic optical networks

引用

Journal of Optical Communications 2025年第2期46卷 335-346页

作者： Wang, Laiming Zhang, Haojie Li, Lei Ren, Danping Hu, Jinhua Zhao, Jijun School of Information and Electrical Engineering Hebei University of Engineering Handan056038 China Hebei Key Laboratory of Security and Protection Information Sensing and Processing Handan056038 China Beijing KELIBANG Information Technology Corporation Limited Beijing10081 China

The widespread application of AI with high computing requirements has driven the rapid development of the computing field. Computing Power Networks (CPNs) have been recognized as solutions to providing on-demand computing services, and its service provisioning can be modeled as a network slicing deployment problem. Elastic Optical Networks (EONs) offer the flexibility to allocate spectrum resources, making them well-suited for network slicing technology. Consequently, EON-based CPNs have attracted considerable attention. However, the unbalanced distribution of computing resources leads to inefficient computing resource utilization. Meanwhile, spectrum resources may be isolated and difficult for other services. This phenomenon is known as spectrum fragmentation, leading to inefficient spectrum resource utilization. To achieve balanced and efficient resource utilization, this paper first analyzes the main reasons for load unbalance and spectrum fragmentation in CPNs: mismatched slicing deployment and inappropriate resource scheduling. Therefore, a dynamic network slicing scheme based on traffic prediction (DNS-TP) is designed. Its core highlight is cooperative optimization slicing deployment and resource scheduling based on spectrum fragmentation awareness. Simulation results show that the proposed scheme enhances the network slicing acceptance ratio, computing and spectrum resource utilization while exhibiting strong performance in resource balancing. © 2024 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Fiber optic networks

来源：评论

学校读者我要写书评

暂无评论

Epileptic Spike Detection Based on Deep Learning 23

Epileptic Spike Detection Based on Deep Learning

引用

9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： Duan, Lijuan Cui, Yuanqi Wang, Yao Wang, Zeyu Cao, Min Qiao, Yuanhua Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China College of Applied Science Beijing University of Technology University Beijing China

ISBN: (纸本)9781450399029

Epilepsy is a chronic disease with high prevalence and high disease burden. Electroencephalogram (EEG) is one of the most important tools for diagnosing epilepsy. Detection of epileptic spikes from EEG can effectively aid the diagnosis of epilepsy but it is also a time-consuming and laborious process for neurologists, which can only be addressed by automatic spike detection methods. However, existing approaches are limited by the simplistic nature of conventional machine learning algorithms. The work researched about how human experts identify spikes and over-parameterized the process into a deep neural network named temporal-frequential and multi-order difference fusion (TeFreDiA) model which can accurately detect epileptic *** model was evaluated on data collected from 7 subjects with ten-fold cross-validation. Given an average accuracy and sensitivity of 95.64% and 96.89%, the model can be utilized to aid the diagnosis of epilepsy. © 2023 Copyright held by the owner/author(s).

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Fine-Grained Zero-Shot Object Detection via Semantic Decoupling 24

Fine-Grained Zero-Shot Object Detection via Semantic Decoupl...

引用

8th International Conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Duan, Lijuan Zhang, Zichen Gong, Zhi Beijing Key Laboratory of Trusted Computing Beijing University of Technology Beijing China School of Computer Science Beijing University of Technology Beijing China National Engineering Laboratory for Key Technologies of Information Security Level Protection Beijing China

ISBN: (纸本)9798400718182

Recent deep learning models have advanced object detection capabilities, but their reliance on labeled data limits scalability. Fine-grained zero-shot detection remains challenging due to the need to generalize to unseen classes and distinguish subtle features. To address this, we propose Fine-Grained Zero-Shot Object Detection via Semantic Decoupling (ZSD-SD). Our approach introduces a semantic decoupling module to separate visual features into semantic-related and semantic-unrelated components, facilitating alignment between visual and semantic spaces. A semantic-visual learning module further decouples these features, enhancing visual representations relevant to external semantic knowledge. This semantic-related representation significantly improves zero-shot performance. Experiments on two fine-grained object detection datasets demonstrate that ZSD-SD achieves state-of-the-art results. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

来源：评论

学校读者我要写书评

暂无评论

NDFuzz:a non-intrusive coverage-guided fuzzing framework for virtualized network devices

引用

Cybersecurity 2023年第1期6卷 54-74页

作者： Yu Zhang Nanyu Zhong Wei You Yanyan Zou Kunpeng Jian Jiahuan Xu Jian Sun Baoxu Liu Wei Huo Institute of Information Engineering Chinese Academy of SciencesBejingChina School of Cyber Security University of Chinese Academy of SciencesBejingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Renmin University of China BeijingChina

Network function virtualization provides programmable in-network middlewares by leveraging virtualization tech-nologies and commodity hardware and has gained popularity among all mainstream network device *** it is challenging to apply coverage-guided fuzzing,one of the state-of-the-art vulnerability discovery approaches,to those virtualized network devices,due to inevitable integrity protection adopted by those *** this paper,we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protec-tion bypassing method,which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference *** implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols,SNMP,DHCP and NTP,on nine popular virtualized network *** obtains an average 36%coverage improvement in comparison with its black-box *** discovers 2 O-Day vulnerabilities and 11-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of *** discovered vulnerabilities are confirmed by corresponding vendors.

关键词： Coverage-guided fuzzing Network devices Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Semantic-Enhanced Static Vulnerability Detection in Baseband Firmware 24

Semantic-Enhanced Static Vulnerability Detection in Baseband...

引用

44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Liu, Yiming Zhang, Cen Li, Feng Li, Yeting Zhou, Jianhua Wang, Jian Zhan, Lanlan Liu, Yang Huo, Wei Institute of Information Engineering Cas School of Cyber Security Ucas Beijing China Key Laboratory of Network Assessment Technology Cas China Beijing Key Laboratory of Network Security and Protection Technology China Nanyang Technological University Singapore

ISBN: (纸本)9798400702174

Cellular network is the infrastructure of mobile communication. Baseband firmware, which carries the implementation of cellular network, has critical security impact on its vulnerabilities. To handle the inherent complexity in cellular communication, cellular protocols are usually implemented as message-centric systems, containing the common message processing phase and message specific handling phase. Though the latter takes most of the code (99.67%) and exposed vulnerabilities (74%), it is rather under-studied: existing detectors either cannot sufficiently analyze it or focused on analyzing the former phase. To fill this gap, we proposed a novel semantic-enhanced static vulnerability detector named BVFinder focusing on message specific phase vulnerability detection. Generally, it identifies a vulnerability by locating whether a predefined sensitive memory operation is tainted by any attacker-controllable input. Specifically, to reach high automation and preciseness, it made two key improvements: a semantic-based taint source identification and an enhanced taint propagation. The former employs semantic search techniques to identify registers and memory offsets that carry attacker-controllable inputs. This is achieved by matching the inputs to their corresponding message and data types using textual features and addressing patterns within the assemblies. On the other hand, the latter technology guarantees effective taint propagation by employing additional indirect call resolution algorithms. The evaluation shows that BVFinder outperforms the state-of-the-art detectors by detecting three to four times of amount of vulnerabilities in the dataset. Till now, BVFinder has found four zero-day vulnerabilities, with four CVEs and 12,410 USD bounty assigned. These vulnerabilities can potentially cause remote code execution to phones using Samsung shannon baseband, affecting hundreds of millions of end devices. © 2024 ACM.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

PosFuzz:augmenting greybox fuzzing with effective position distribution

引用

Cybersecurity 2023年第4期6卷 123-143页

作者： Yanyan Zou Wei Zou JiaCheng Zhao Nanyu Zhong Yu Zhang Ji Shi Wei Huo Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingChina Key Laboratory of Network Assessment Technology Chinese Academy of SciencesBeijingChina Beijing Key Laboratory of Network Security and Protection Technology BeijingChina Zhongguancun Laboratory BeijingChina

Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and ***-based greybox fuzzing generates input cases by mutating the input seed,i.e.,applying a sequence of mutation operators to randomly selected mutation positions of the ***,existing fruitful research work focuses on scheduling mutation operators,leaving the schedule of mutation positions as an overlooked aspect of fuzzing *** paper proposes a novel greybox fuzzing method,PosFuzz,that statistically schedules mutation positions based on their historical *** makes use of a concept of effective position distribution to represent the semantics of the input and to guide the *** first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation *** then leverages two sampling methods in different mutating stages to select the positions from the *** have implemented PosFuzz on top of AFL,AFLFast and MOPT,called Pos-AFL,-AFLFast and-MOPT respectively,and evaluated them on the UNIFUZZ benchmark(20 widely used open source programs)and LAVA-M *** result shows that,under the same testing time budget,the Pos-AFL,-AFLFast and-MOPT outperform their counterparts in code coverage and vulnerability discovery *** with AFL,AFLFast,and MOPT,PosFuzz gets 21%more edge coverage and finds 133%more paths on *** also triggers 275%more unique bugs on average.

关键词： Greybox fuzzing Mutation position Mutation operator Code coverage Vulnerability discovery

来源：评论

学校读者我要写书评

暂无评论

A Survey of security protection Methods for Deep Learning Model

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1533-1553页

作者： Peng, Haipeng Bao, Shuang Li, Lixiang Beijing University of Posts and Telecommunications Information Security Center State Key Laboratory of Networking and Switching Technology Beijing100876 China Beijing University of Posts and Telecommunications National Engineering Laboratory for Disaster Backup and Recovery Beijing100876 China

In recent years, deep learning (DL) models have attracted widespread concern. Due to its own characteristics, DL has been successfully applied in the fields of object detection, superresolution reconstruction, speech recognition, natural language processing, etc., bringing high efficiency to industrial production and daily life. With the Internet of Things, 6G and other new technologies have been proposed, leading to an exponential growth in data volume. DL models currently suffer from some security issues, such as privacy issues during data collection, defense issues during model training and deployment, etc. The sensitive data of users and special institutions that are directly used as training data of DL models may lead to information leakage and serious privacy problems. In addition, DL models have encountered many malicious attacks in the real world, such as poisoning attack, exploratory attack, adversarial attack, etc., which caused model security problems. Therefore, this article discusses ways of ensuring the security and data privacy of DL models under diversified attack methods and the ways of ensuring the privacy security of edge mobile devices equipped with pretrained deep neural networks. Alternatively, this article analyzes the privacy security of DL models for typical deployment platforms such as server/cloud, edge mobile device, and web browser and, then, summarizes future research direction. © 2020 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

Knowledge Graph Embedding Method Based on Entity Metric Learning 23

Knowledge Graph Embedding Method Based on Entity Metric Lear...

引用

9th International Conference on Computing and Artificial Intelligence, ICCAI 2023

作者： He, Meng Duan, Lijuan Zhang, Baochang Han, Shengwen Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Key Technologies of Information Security Level Protection China Institute of Artificial Intelligence Beihang University China Faculty of Information Technology Beijing University of Technology China

ISBN: (纸本)9781450399029

A knowledge graph is a knowledge base of graph structure that stores massive facts and their relations in the form of triplets. Embedding entities and relations in knowledge graph into low-dimensional continuous vector space is of great significance for downstream tasks, whereas most existing methods give an end-to-end fixed representation. Based on deep metric learning, we give different embeddings for the same entity in different relations and positions, thus dynamically representing entities in knowledge graph and effectively modeling complex relations. In addition, a path-based negative sampling method is proposed to widen the distance between similar entities to better deal with the aggregation problem of embedding entities in the representation space under one-to-many relation. Experiments show that the MRR and Hit@1 of this method on the linked prediction dataset FB15k-237 reach 0.360 and 0.267 respectively, which are higher than the mainstream distance models and semantic matching models. Finally, the MRR-Dimension curves demonstrates that our model can perform well in both low and high embedding dimensions and has the potential to be applied to large-scale knowledge graph. © 2023 Copyright held by the owner/author(s).

关键词： Knowledge graph

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：