检索结果-内蒙古大学图书馆

International Conference on Data engineering

作者： Yu Chen Sheng Zhang Ziying Zhou Xiaokun Wang Yu Liang Ning Chen Yuting Yan Mingjun Xiao Jie Wu Zhuzhong Qian Harry Xu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Systems Science Beijing Normal University China School of Computer and Electronic Information / School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China P.R. China Center for Networked Computing Temple University USA Computer Science Department Samueli School of Engineering University of California Los Angeles USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy; meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches.

关键词： Crowdsourcing Accuracy Visual analytics Games Data engineering Outsourcing Task analysis

来源：评论

学校读者我要写书评

暂无评论

Distributionally Robust Control for Chance-Constrained Signal Temporal Logic Specifications

arXiv

引用

arXiv 2024年

作者： Kordabad, Arash Bahari Vlahakis, Eleftherios E. Lindemann, Lars Dimarogonas, Dimos V. Soudjani, Sadegh Max Planck Institute for Software Systems Kaiserslautern Germany Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science Viterbi School of Engineering University of Southern California Los Angeles United States

We consider distributionally robust optimal control of stochastic linear systems under signal temporal logic (STL) chance constraints when the disturbance distribution is unknown. By assuming that the underlying predicate functions are Lipschitz continuous and the noise realizations are drawn from a distribution having a concentration of measure property, we first formulate the underlying chance-constrained control problem as stochastic programming with constraints on expectations and propose a solution using a distributionally robust approach based on the Wasserstein metric. We show that by choosing a proper Wasserstein radius, the original chance-constrained optimization can be satisfied with a user-defined confidence level. A numerical example illustrates the efficacy of the method. © 2024, CC BY-NC-ND.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Method for Quantum Denoisers Using Convolutional Neural Network

引用

Computational Intelligence and Neuroscience 2022年 2022卷 4885897页

作者： Kim, Bong-Hyun Madhavi, S. School of Software Computer Engineering Major Seowon University 377-3 Musimseo-ro Seowon-gu Cheongju-si Chungcheongbuk-do28674 Korea Republic of Computer Science and Engineering Department PVP Siddhartha Institute of Technology Andhra Pradesh Kanuru India

In many applications of quantum information science, high-dimensional entanglement is needed. Quantum teleportation is used for transferring information from one place to another using Einstein-Podolsk-Rosen pairs (EPR) and two classical bits of communication in a channel. Since we cannot produce multiple copies of an unknown state for amplification, we will generate multiple EPR pairs. However, after the distribution of the EPR pairs, they will have decreased fidelity with the ideal EPR state. So, to maintain the quantum states and maximize the quantification of the entanglement without losing the strength of the states, we propose to denoise the channel for a few types of noise. We created a random noise source and filtered out the irrelevant information without affecting the relevant information encoded in the quantum states. The proposed model is used for successful denoising of GHZ states from spin flips and bit flip errors. Much of the research work is not carried out by using machine-language-based neural networks for noise-reduction in quantum channels. In this paper, we propose a denoiser called quantum denoiser CNQD, which uses a feedforward convolution neural network model. We tuned our model with highly entangled GHZ states with zero phases and phase between [0, ∏] mixed with different kinds of noise. Finally, the proposed model can be used for optimal quantum communication via noisy quantum channels using GHZ states. © 2022 Bong-Hyun Kim and S. Madhavi.

关键词： Quantum entanglement

来源：评论

学校读者我要写书评

暂无评论

FSRF:An Improved Random Forest for Classification

FSRF:An Improved Random Forest for Classification

引用

2020 IEEE International Conference on Advances in Electrical engineering and computer Applications, AEECA 2020

作者： Feng, Wenxian Ma, Chenkai Zhao, Guozhang Zhang, Rui Jilin University Department of Software Engineering Changchun China Jilin University Department of Computer Science Changchun China

ISBN: (纸本)9781728165202

Random forest algorithm is a flexible and easy-to-use machine learning algorithm, which is widely used in classification problems. However, the traditional random forest has some limitations. Because the randomness added by random forest to decision trees almost only occurs in the feature selection when the decision trees are generated, the fixity of decision trees generation rules will lead to relatively serious over fitting. At the same time, in the face of data with high and unbalanced feature dimensions, the performance of algorithm is seriously weakened because high-dimensional data usually contains many irrelevant and redundant features. To solve these problems, we propose an improved random forest algorithm FSRF. Based on the traditional random forest algorithm, we use the feature selection methods to preprocess the data and get the feature subset with the best classification performance to construct the random forest. At the same time, we introduce sparse matrix projection to improve the generation of the random forest. Experiments show that our method reduces the influence of redundant features on classification and improves the accuracy. © 2020 IEEE.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Research on Key Word Information Retrieval Based on Inverted Index 8th

Research on Key Word Information Retrieval Based on Inverted...

引用

8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Qi, Meihan Fang, Wei Zhao, Yongming Sha, Yu Sheng, Victor S. School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Beijing100081 China China Meteorological Administration Training Center Beijing China Department of Computer Texas Tech University LubbockTX79409 United States

ISBN: (纸本)9783031067600

With the advent of the era of big data, data has penetrated into every aspect of social life and become an important production factor in various industries. However, while providing convenience to our life, massive information also brings difficulties to information retrieval, which has problems of low retrieval efficiency and poor retrieval accuracy. Therefore, how to improve the efficiency and accuracy of information retrieval has become a key technical problem to be solved. In this paper, we propose a key word information retrieval scheme based on inverted index, which uses the inverted index segmentation algorithm based on keywords to realize the block retrieval of information, improves the efficiency of information retrieval, and improves the accuracy of retrieval content through reasonable error correction of keywords. The results of evaluation indicate that compared with the mainstream information retrieval methods, the keyword information retrieval scheme based on inverted index can carry out faster and more accurate information retrieval. Experimental results show that the keyword information retrieval scheme with inverted index can further improve the efficiency and accuracy of information retrieval. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

Quantum Machine Learning for software Supply Chain Attacks: How Far Can We Go?

arXiv

引用

arXiv 2022年

作者： Masum, Mohammad Nazim, Mohammad Jobair Hossain Faruk, Md. Shahriar, Hossain Valero, Maria Abdullah Hafiz Khan, Md. Uddin, Gias Barzanjeh, Shabir Saglamyurek, Erhan Rahman, Akond Ahamed, Sheikh Iqbal Analytics and Data Science Institute Kennesaw State University Kennesaw United States Department of Computer Science Kennesaw State University MariettaGA United States Department of Software Engineering Kennesaw State University MariettaGA United States Department of Information Technology Kennesaw State University Marietta United States Electrical and Software Engineering Institute for Quantum Science and Technology University of Calgary Canada Department of Computer Science Tennessee Tech University Tennessee United States Department of Computer Science Marquette University Wisconsin United States

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises- quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks. © 2022, CC BY.

关键词： Supply chains

来源：评论

学校读者我要写书评

暂无评论

Stock market prediction based on machine learning and social sentiment analysis

TechRxiv

引用

TechRxiv 2023年

作者： Ghazanfar, Mustansar Ali Anwar, Madiha Lee, Sin Wee Qazi, Nadeem Karimi, Amin Jhanjhi, N.Z. Javed, Ali School of Architecture Computing and Engineering University of East London United Kingdom Deputy Head of School of Computing Arden University United Kingdom School of Computer Science SCS Taylor’s University Malaysia Department of Software Engineering UET Taxila Pakistan

Precise stock market prediction is crucial for investors, but the volatility of the stock market is influenced by multiple factors such as public sentiments, business news, and related product volatility. While several algorithms have been proposed to predict the stock exchange index based on historical data, they are not ideal as external factors play a critical role in market volatility. To address this issue, we proposed a machine learning model that incorporates historical data with external factors such as social media sentiments, oil and gold trends, and financial news data to enhance prediction accuracy. Our study used HPQ, IBM, ORCL, and MSFT stock market datasets to validate the effectiveness of the proposed model, including an analysis of the impact of Covid19 on companies. Our experimental results showed the highest accuracy of 87.2% using oil and sentiment datasets. Additionally, we identified that social media significantly affects IBM stocks, and the GBM (Gradient Boosting Classifier) classifier produced consistent results. © 2023, CC BY.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

A Fair VNF assignment Algorithm for network functions virtualization

A Fair VNF assignment Algorithm for network functions virtua...

引用

2020 International Symposium on Networks, computers and Communications, ISNCC 2020

作者： Singh Ghai, Karanbir Yassine, Abdulsalam Choudhury, Salimur Lakehead University Department of Computer Science Thunderbay Canada Lakehead University Department of Software Engineering Thunderbay Canada

ISBN: (纸本)9781728156286

On-demand resource management is a challenging prospect, especially in communication networks where the users' requirements of network resources are volatile. This challenge is magnified by the current shift of hardware-dependent network technologies into virtual Network Functions (vNFs) in a cloud-based platform and the proliferation of the Internet of Things (IoT) networks. vNFs are an integral part of edge devices that aims to improve service providers' response time, eliminate redundancy, and end to end latency results in operators' lower operating costs significantly. The efficiency of such a virtualized system largely depends on the optimization of resource allocation between users and Virtual Machines (VM) or hosting devices. One approach to addressed this issue is to minimize the latency of the entire communication network, however, the concern is that this mechanism does not ensure fairness of resource allocation because some of the users may receive enhanced services than others. In the previous study [1], a Stable Matching Algorithm is proposed to minimize the latency between vNFs and VMs but the algorithm failed to ensure the fairness of the requested services and server elements. In this study, we further extend the previous model to ensure the fairness. Our proposed solution minimizes the maximum latency in the network. This problem is an NP-hard and hence we provide a local search based solution. The experimental result shows improved fairness from other approaches and the fairness index is close to the optimal, which in fact represents the elimination of imbalances to a great extent. © 2020 IEEE.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

OAuth-Based Access Control Scheme Against Replay Attacks in IoT Environment 10th

OAuth-Based Access Control Scheme Against Replay Attacks in ...

引用

10th International Conference on computer science and its Applications, CSA 2018 and the 13th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2018

作者： Lee, Dae-Hwi Lee, Im-Yeong Department of Computer Science Engineering Soonchunhyang University Asan-si Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan-si Korea Republic of

ISBN: (纸本)9789811393402

The OAuth protocol is a standard that authorizes not only the IoT environment but also the client services connected to the Internet to access the resource of the user stored in the resource server without sharing the account information of the resource server. Each IoT service uses OAuth to reduce the exposure of user privacy and provides scalability and flexibility because it can take advantage of any standard-compliant service. However, in recent OAuth 2.0 protocols, security vulnerabilities such as replay attacks and CSRF are already announced. To prevent this, a variety of OAuth-based access control protocols are being researched. In this paper, we design an OAuth-based access control protocol that is safe for replay attacks in IoT environment, so that IoT service can securely access the user resource of external resource server. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Authorization

来源：评论

学校读者我要写书评

暂无评论

Human Observation-Inspired Trajectory Prediction for Autonomous Driving in Mixed-Autonomy Traffic Environments

Human Observation-Inspired Trajectory Prediction for Autonom...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Haicheng Liao Shangqian Liu Yongkang Li Zhenning Li Chengyue Wang Yunjian Li Shengbo Eben Li Chengzhong Xu State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of Macau Macau SAR China Department of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Departments of Civil and Environmental Engineering and Computer and Information Science State Key Laboratory of Internet of Things for Smart City University of Macau Macau SAR China State Key Laboratory of Internet of Things for Smart City and Departments of Civil and Environmental Engineering University of Macau Macau SAR China Faculty of Innovation Engineering Macau University of Science and Technology Macau SAR China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the burgeoning field of autonomous vehicles (AVs), trajectory prediction remains a formidable challenge, especially in mixed autonomy environments. Traditional approaches often rely on computational methods such as time-series analysis. Our research diverges significantly by adopting an interdisciplinary approach that integrates principles of human cognition and observational behavior into trajectory prediction models for AVs. We introduce a novel “adaptive visual sector” mechanism that mimics the dynamic allocation of attention human drivers exhibit based on factors like spatial orientation, proximity, and driving speed. Additionally, we develop a “dynamic traffic graph” using Convolutional Neural Networks (CNN) and Graph Attention Networks (GAT) to capture spatio-temporal dependencies among agents. Benchmark tests on the NGSIM, HighD, and MoCAD datasets reveal that our model (GAVA) outperforms state-of-the-art baselines by at least 15.2%, 19.4%, and 12.0%, respectively. Our findings underscore the potential of leveraging human cognition principles to enhance the proficiency and adaptability of trajectory prediction algorithms in AVs.

关键词： Adaptation models Visualization Time series analysis Predictive models Cognition Trajectory Convolutional neural networks

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：