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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

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FSRF:An Improved Random Forest for Classification

FSRF:An Improved Random Forest for Classification

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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

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

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

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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

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

arXiv

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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

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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

Human Observation-Inspired Trajectory Prediction for Autonom...

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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

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Stock market prediction based on machine learning and social sentiment analysis

TechRxiv

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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

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A Fair VNF assignment Algorithm for network functions virtualization

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

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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

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The Multi-Task Deep Model-Based Pneumonia Detection 2

The Multi-Task Deep Model-Based Pneumonia Detection

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2021 2nd International Conference on computer Information and Big Data Applications, CIBDA 2021

作者： Ai, Xingfang Li, Zhongliang Cao, Min Li, Xuechen Information Engineering College Jiangmen Polytechnic Jiangmen Guangdong529000 China College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong518000 China Radiology Department Guangzhou Panyu Sanatorium Guangzhou Guangdong510000 China

As a common lung disease, pneumonia affects millions of people worldwide each year and is often detected during physical examination using chest X-ray images (CXRs), which are usually diagnosed by radiologists. This time-consuming task often leads to fatigue-based diagnostic error and cannot be performed in countries or areas lack of radiologists. In this work, we proposed a multi-task learning model: Mt-pnet. We learn from the experience of imaging doctors in the diagnosis of pneumonia, let the network pay attention to the patient's gender and age information in the diagnosis of pneumonia. The combined information is helpful for the network to learn robust image features for pneumonia diagnosis. At the same time, we also found that using multi-task model is helpful to improve the recall of patients with pneumonia, which has important clinical significance. © Published under licence by IOP Publishing Ltd.

关键词： Diagnosis

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

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

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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

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