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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Long, Yunfei Xu, Huosheng Qi, Pengyuan Zhang, Liguo Li, Jun College of Computer Science and Technology Harbin Engineering University Harbin150001 China Wuhan Digital Engineering Institute Wuhan430000 China China Industrial Control Systems Cyber Emergency Research Team Beijing100000 China

ISBN: (纸本)9783030786113

The word embeddings approaches have attracted extensive attention and widely used in many natural language processing (NLP) tasks. Relatedness between words can be reflected in vector space by word embeddings. However, the current word embeddings approaches commonly do not explore the context-specific information of word deeply in the overall corpus. In this paper, we propose to use graph attention network for word embeddings. We build a large single word graph for a corpus based on word order, then learn a word embeddings graph attention network (WEGAT) for the corpus. Our WEGAT is initialized with one-hot representation for word. We propose to use masked language model (MLM) as supervised task. In addition, through the text classification experiment, it is showed that accuracy of the word embeddings represented by WEGAT is higher than the current method for the same classification method. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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Investigating the Impact of Cognitive Load on Human Trust in Hybrid Human-Robot Collaboration

SSRN

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SSRN 2024年

作者： Guo, Hao Wu, Bangan Li, Qi Ding, Zhen Jiang, Feng Yi, Chunzhi School of Computer Science and Technology Harbin Institute of Technology Harbin China School of Management Harbin Institute of Technology Harbin China School of Mechatronics Engineering Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Artificial Intelligence Nanjing University of Information Science & Technology Nanjing China School of Medicine and Health Harbin Institute of Technology Harbin China

Human trust plays a crucial role in the effectiveness of human-robot collaboration. Despite its significance, the development and maintenance of an optimal trust level are obstructed by the complex nature of influencing factors and their mechanisms. This study investigates the effects of cognitive load on human trust within the context of a hybrid human-robot collaboration task. An experiment is conducted where the humans and the robot, acting as team members, collaboratively construct pyramids with differentiated levels of task complexity. Our findings reveal that cognitive load exerts diverse impacts on human trust in the robot. Notably, there is an increase in human trust under conditions of high cognitive load. Furthermore, the rewards for performance are substantially higher in tasks with high cognitive load compared to those with low cognitive load, and a significant correlation exists between human trust and the failure risk of performance in tasks with low and medium cognitive load. By integrating interdependent task steps, this research emphasizes the unique dynamics of hybrid human-robot collaboration scenarios. The insights gained not only contribute to understanding how cognitive load influences trust but also assist developers in optimizing collaborative target selection and designing more effective human-robot interfaces in such environments. © 2024, The Authors. All rights reserved.

关键词： Behavioral research

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Impact of Cognitive Load on Human Trust in Hybrid Human-Robot Collaboration

arXiv

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arXiv 2024年

关键词： Man machine systems

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An Efficient Hybrid Red Panda Optimization-Similarity-Navigated Graph Neural Network with Blockchain Technology for Shared Electric Vehicle Charging

An Efficient Hybrid Red Panda Optimization-Similarity-Naviga...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Sammy. F Rajeshwari S Prashanth G K Babu Thirumalaimuthu Hemant Singh Pokhariya Natrayan L Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Mathematics Bangalore Institute of Technology Bengaluru Karnataka India Department of MCA Siddaganga Institute Technology Tumkur Karnataka India Department of Instrumentation and Control Engineering St. Joseph's College of Engineering Chennai Tamil Nandu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Mechanical Engineering Saveetha School of EngineeringSIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Blockchain technology for shared electric vehicle (EV) charging makes use of a distributed ledger in handling different transactions which promotes authenticity in charging. However, this technology can be non-scalable at times which slows down the transaction rates as the scale increases in an organization and may also demand high computational as well as energy requirements which increases the inefficiency of the total system, the operating expenses of EV charging station, costs of energy utilized for charging, cost of maintenance and cost of station infrastructure may prove to be a weakness, impacting the feasibility of infrastructure deployment and upkeep expenses of the charging stations. To overcome these drawbacks, this paper proposes a hybrid method for blockchain technology with shared EV charging. The proposed approach combines the use of the Red Panda Optimization (RPO) with Similarity-Navigated Graph Neural Network (SNGNN). Hence, it is named as hybrid RPO-SNGNN approach. Reducing the operating cost of EV charging position and maximizes the security efficiency is the main aim of the proposed approach. To optimize scheduling and resource allocation by finding efficient paths by RPO and predict future demand and user behaviour patterns by SNGNN. By then, the proposed model has been put into practice on the MATLAB work platform and contrasted with other existing methods. The proposed technique outperforms all existing techniques, including Fuzzy-based Cross Hierarchical Reinforcement Q Learning (FCHRQL), Non-Dominated Sorting Genetic Algorithm-Long Short-Term Memory Network (NSGA-II-LSTMN), and Ant Colony Optimization-Proximal Policy Optimization (ACO-PPO). The proposed method shows the operating cost of $550 and the security rate is 97%. From the result, the proposed approach operating cost is low and the security rate is high when compared to the existing approaches.

关键词： Costs Q-learning Electric vehicle charging Graph neural networks Scheduling Blockchains Security Resource management Optimization Sorting

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Lunar Asset Messaging and on Orbit Navigation (LA MOON)

Lunar Asset Messaging and on Orbit Navigation (LA MOON)

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IAF Space Exploration Symposium 2021 at the 72nd International Astronautical Congress, IAC 2021

作者： Derewa, Chrishma Holt, Samuel Lally, Michael Trybula, Robert Yuan, Christine Coble, Kyle Huang, Calvin Jet Propulsion Laboratory California Institute of Technology United States University of Colorado Boulder Aerospace Engineering United States New York University Computer Science United States University of Southern California Computer Science United States Cornell University Mechanical Engineering United States Kth Royal Institute of Technology Systems Control and Robotics Pasadena City College Computer Science United States

ISBN: (纸本)9781713842965

NASA has titled its 2020 thrust for the Moon, Artemis. The increased focus on the Moon as a destination for future human and robotic expeditions necessitates general purpose navigational and communications infrastructure reducing their complexity to help establish a sustained presence. A framework through which Lunar missions can relay communications and localize their positions shifts the burden from the individual mission and enables resource allocation tailored to mission-specific goals. During the summer of 2020, student interns under the Innovation to Flight (i2F) program at the National Aeronautics and Space Administration s (NASA) Jet Propulsion Laboratory (JPL) in collaboration with the University of Colorado Boulder designed, built, and tested a prototype framework capable of providing surface assets with communication and positioning services. The team utilized the existing i2F CubeSat bus in addition to developing several CubeSat engineering development units (EDUs), a ground vehicle, and a ground station to simulate a scenario in which a lunar surface mission is supported by these services. A primary goal of the summer was to develop a method for localizing the ground vehicle through trilateration. Distances are inferred from the round-Trip time of flight (ToF) of radio signals between an asset and several elements. Signals were sent and received using LimeSDR software defined radios on-board both the ground vehicle and the EDUs;ToF and trilateration were calculated on a Qualcomm Snapdragon development board located within the LA MOON payload data system. The ModalAI chipset on the Qualcomm was instrumental in executing visual based position estimation. Communications was facilitated through a bent-pipe approach addressing the NASA requirement to provide solutions for in communication denied locations. The ground vehicle relayed information to other surface assets in addition to its ground station through the supporting constellation. This project demons

关键词： Orbits

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Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition

arXiv

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arXiv 2024年

作者： Yue, Hao Wu, Yingtao Wang, Min Hu, Hesuan Wu, Weimin Zhang, Jihui Shandong Qingdao266580 China College of Information Engineering Yangzhou University Jiangsu Yangzhou225127 China School of Electro-Mechanical Engineering Xidian University Shanxi Xi’an710071 China School of Computer Science and Engineering College of Engineering Nanyang Technological University 639798 Singapore State Key Laboratory of Industrial Control Technology Zhejiang University Zhejiang Hangzhou310027 China Institute of Cyber-Systems and Control Zhejiang University Zhejiang Hangzhou310027 China Institute of Complexity Science School of Automation Qingdao University Shandong Qingdao266071 China Shandong Key Laboratory of Industrial Control Technology Shandong Qingdao266071 China

Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve the efficiency and reduce the cost in CMCP, we propose a new framework which considers the collaboration among services with the same functionality. A mathematical evaluation formulation for the service composition and service usage scheme is constructed with the following critical indexes: completion time, cost, and number of selected services. Subsequently, a problem decomposition based genetic algorithm is designed to obtain the optimal service compositions with service usage schemes. A smart clothing customization case is illustrated so as to show the effectiveness and efficiency of the method proposed in this paper. Finally, the results of simulation experiments and comparisons show that these solutions obtained by our method are with the minimum time, a lower cost, and the fewer selected services. © 2024, CC BY-NC-ND.

关键词： Genetic algorithms

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Study on properties and composition of ancient glass based on PCA and gradient lifting tree model

Study on properties and composition of ancient glass based o...

引用

作者： Hengtian Zhao Junjia Du Yayue Xu Yujie Li Yingai Jin State Key Laboratory of Automotive Simulation and Control Jilin University College of Automotive Engineering Jilin University College of Computer Science and Technology Jilin University College of Journalism & Communication Jilin University

In order to help archaeologists better study ancient glass, uncover the mystery of the early Silk Road, and also better protect these cultural *** this paper, the properties and composition of ancient glass are *** types of glass samples were analyzed by PCA principal component analysis and weighted summation, and the type characteristic equation was *** calculating the candidate points iteratively in MATLAB, the optimal critical eigenvalue was set to 5, and the type judgment model was *** to get the type of the characteristic equation, the selection is related components as high potassium and the composition of class division, choose negative correlation components as lead, barium and the composition of class division, through the PCA principal component analysis of two types of glass data are reduced to 3d in the class we USES Kmeans clustering algorithm for two types of glass, For the sensitivity analysis of subclass partitioning, we used SOBOL global sensitivity analysis method, and obtained that principal component 1 would have the greatest impact on the model output.

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Tripartite entanglement measure under local operations and classical communication

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Physical Review A 2023年第3期107卷 032405-032405页

作者： Xiaozhen Ge Lijun Liu Shuming Cheng Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong China College of Mathematics and Computer Science Shanxi Normal University Linfen 041000 China The Department of Control Science and Engineering Tongji University Shanghai 201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 201804 China Institute for Advanced Study Tongji University Shanghai 200092 China

Multipartite entanglement is an indispensable resource in quantum communication and computation; however, it is a challenging task to faithfully quantify this global property of multipartite quantum systems. In this work we study the concurrence fill, which admits a geometric interpretation to measure genuine tripartite entanglement for the three-qubit system [Xie and Eberly, Phys. Rev. Lett. 127, 040403 (2021)]. First, we use the well-known three-tangle and bipartite concurrence to reformulate this quantifier for all pure states. We then construct an explicit example to conclusively show that the concurrence fill can be increased under local operations and classical communication (LOCC) on average, implying it is not an entanglement monotone. Moreover, we give a simple proof of the LOCC monotonicity of the three-tangle and find that the bipartite concurrence and the squared concurrence can have distinct performances under the same LOCC. Finally, we propose a reliable monotone to quantify genuine tripartite entanglement, which can also be easily generalized to the multipartite system. Our results shed light on the study of genuine entanglement and also reveal the complex structure of multipartite systems.

关键词： Quantum correlations in quantum information Quantum entanglement Quantum networks

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A Hybrid Genetic Algorithm to Enhance the Laser Speckle Blood Image 12th

A Hybrid Genetic Algorithm to Enhance the Laser Speckle Bloo...

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12th International Conference on Signal and Information Processing, Networking and computers, ICSINC 2024

作者： Gao, Xia Yuan, Mingbo Feng, Yuntong Li, Ran Yang, Hui School of Internet of Things and Intelligent Control Shandong College of Electronic Technology Jinan250200 China School of Integrated Circuits Shandong College of Electronic Technology Jinan250200 China Haide College Ocean University of China Shandong Qingdao266100 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China College of Medical Instruments Shanghai University of Medicine and Health Science Shanghai201318 China

ISBN: (纸本)9789819649662

This study introduces a hybrid genetic algorithm to enhance blood flow laser speckle contrast images under low coherence laser conditions. The algorithm addresses the challenges of laser speckle imaging by improving speckle contrast analysis accuracy, thereby enhancing blood flow detection. By optimizing image quality, the proposed method provides a cost-effective solution that offers flexibility in light source selection for Laser Speckle Blood Imaging (LSBI) technology. The results highlight the algorithm’s effectiveness in optimizing speckle contrast and improving overall imaging performance, leading to more precise blood flow analysis and potential reductions in operational costs for medical imaging practices. By employing the hybrid genetic algorithm in Laser Speckle Blood Imaging (LSBI) technology, the method effectively mitigates errors in speckle contrast analysis caused by light source coherence. This results in higher-quality blood flow images, offering significant advancements in non-invasive human experiments. The enhanced accuracy and improved imaging quality underscore the algorithm's potential to elevate LSBI technology, making it a valuable tool for both clinical and research applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coherence Hybrid genetic algorithm Laser speckle blood imaging

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Yang Shuxin Zhang Suyu Dong Long Xu Weihe Dong Xiaokun Li Pengzhong Sun Feng Jiang Xianyu Zhang Gongning Luo College of Computer and Control Engineering Northeast Forestry University Harbin China School of Medicine and Health Harbin Institute of Technology Harbin China School of Computer Science and Technology Heilongjiang University Harbin China School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Breast Surgery Harbin Medical University Cancer Hospital Harbin China Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level.

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