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9th International Conference on computer and Communications, ICCC 2023

作者： Lin, Qingzhuan Hu, Jinsong Yan, Shihao Zhou, Xiaobo Chen, Youjia Shu, Feng Fuzhou University College of Physics and Information Engineering Fuzhou China Edith Cowan University School of Science Security Research Institute Perth Australia Anhui Agricultural University Institute of Intelligent Agriculture School of Information and Computer Hefei China Hainan University School of Information and Communication Engineering Haikou China

ISBN: (纸本)9798350317251

This work proposes a covert transmission scheme for integrated sensing and communication (ISAC) scenario, where the radar with the aid of an intelligent reflecting surface (IRS) is employed to achieve covert communication and target detection simultaneously. Specifically, we jointly design the transmit beamforming vector of radar and the phase shifts of the IRS to maximize the covert rate subject to a covertness constraint, a total power constraint, and a detection mutual information constraint. To solve the non-convex optimization problem, we resort to alternating optimization and the semidefinite relaxation method. Numerical results illustrate the effectiveness of the covert transmission system aided by the IRS and demonstrate the performance trade-off between radar detection and covert communication. © 2023 IEEE.

关键词： Beamforming

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Network Embedding with Enhanced Feature Representations for Link Prediction 26

Network Embedding with Enhanced Feature Representations for ...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Li, Weisheng Chen, Wentao Pan, Zhihong Deng, Yuanfei Tang, Yong He, Chaobo South China Normal University School of Computer Science Guangzhou China Pazhou Lab Guangzhou China South China Normal University School of Artificial Intelligence Foshan China South China Normal University School of Physics and Telecommunication Engineering Guangzhou China

ISBN: (纸本)9798350331684

Link prediction is a hot research topic in graph data analytics, which aims to identify the likelihood of a future connection between two nodes in a network. Among the approaches to link prediction, network embedding has gained tremendous popularity in tackling link prediction tasks. Basically, there are several network embedding methods, such as matrix factorization, random walk, and deep learning. However, most of the existing methods for network embedding solely rely on structure information or fail to effectively combine network structure and feature data. To this end, this paper proposes a novel method that learns network embedding with enhanced feature representations for link prediction (EFRLP). Specifically, structure graphs, bipartite graphs, and co-feature graphs are generated from the attributed network, weighted bipartite graphs are then built over bipartite graphs and correlation weights between features. We further adopt a biased random walk to obtain more representative node sequences. With the help of enriched feature representations, not only can the higher-order semantic relationships be effectively captured but also network sparsity can be alleviated. Then, node sequences are selected into the Skip-Gram model for learning low-dimensional embeddings. Finally, the probability of the existence of edges between nodes is predicted by deep fusion. The effectiveness of the EFRLP method is demonstrated by extensive comparison experiments with several baselines on two real-world datasets. © 2023 IEEE.

关键词： Network embeddings

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Evaluation and Language Training of Multinational Enterprises Employees by Deep Learning in Cloud Manufacturing Resources 10th

Evaluation and Language Training of Multinational Enterprise...

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10th International Conference on Innovations in computer science and engineering, ICICSE 2022

作者： Karn, Arodh Lal Webber, Julian L. Mehbodniya, Abolfazl Stalin David, D. Subramaniam, Balu Rangasamy, Rajasekar Sengan, Sudhakar Department of Financial and Actuarial Mathematics School of Mathematics and Physics Xi’an Jiaotong-Liverpool University Jiangsu Suzhou215123 China Kuwait Kuwait Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600072 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India

ISBN: (纸本)9789811974540

In other growth trends and sustainable development of the economy’s globalization, low manufacturing utilization rates, production resource imbalances, and impaired coordination functions are growing. To tackle the difficulties mentioned above, organizations must securely determine how to share distant and heterogeneous production resources. Previses work is a network language learning framework for learning audio language clouds. The application offers a single computer user and Internet users economic and easy oral learning and large-scale oral assessment technologies. To select Cloud Manufacturing (CM) resources to fulfill service production enterprise assessment, five first-level metrics (time, cost, availability, reliability, security) and 13 s are used. Deep Learning (DL) is utilized to calculate the index weights, the ultimate total score, each indication, and membership at all levels. Degree determines it. It quantifies Cloud Manufacturing Resource Selection (CMRS). This model shows that manufacturing RS involves DL. Finally, it’s proven feasible to pick the resources to produce using a fuzzy comprehensive assessment. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Personnel training

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Managing Uncertainty in Production Planning for Fast-Moving Consumer Goods: A Linear Programming and Monte Carlo Simulation Framework

Managing Uncertainty in Production Planning for Fast-Moving ...

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2023 International Conference on science, engineering and Business for Sustainable Development Goals, SEB-SDG 2023

作者： Olanrele, Oladeji Oluniyi Ismaila, Salami Olasunkanmi Adeaga, Oyetunde Adeoye Adeyemi, Oluseyi Afolabi Akintaro, Akinwale Stephen First Technical University Department of Computer and Industrial & Production Engineering Ibadan Nigeria Federal University of Agriculture Department of Mechanical Engineering Abeokuta Nigeria First Technical University Department of Mechanical and Mechatronics Engineering Ibadan Nigeria School of Physics Engineering and Computer Science University of Hertfordshire Hertfordshire United Kingdom University of Ibadan Department of Mechanical Engineering Ibadan Nigeria

ISBN: (纸本)9798350324785

The production planning of Fast-Moving Consumer Goods (FMCG) industries is a challenging task due to uncertainty and risk. This study proposes a novel algorithm that combines linear programming and Monte Carlo simulation methods to manage uncertainty and minimize costs in FMCG production planning. The algorithm uses simulation to forecast the various possible production units and quantify the impact of risk and uncertainty on each production decision. The output is an annual production plan that satisfies minimum customer requirements while optimizing profits. The proposed model was tested on an FMCG factory problem, and it was found to improve the company's expected profit by 2.06%, reduce variable costs by 7.65%, and decrease the contribution of inventory holding costs to the total system cost from 4.43% to 1.39%. The study highlights the high cost incurred due to inventory holding costs and demonstrates that the proposed model provides a more efficient approach to determining production and inventory quantities. The combination of linear programming and Monte Carlo simulation methods provides an efficient way to manage uncertainty and minimize costs while meeting customer requirements. These findings have important implications for FMCG production planning, and the proposed model can help companies make more informed decisions. © 2023 IEEE.

关键词： Linear programming

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SM-LightNet: A Lightweight Ship Detection Algorithm Based on Improved YOLOv8

SM-LightNet: A Lightweight Ship Detection Algorithm Based on...

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Academic International Symposium on Optoelectronics and Microelectronics Technology (AISOMT)

作者： Zhiguang Gao Xianwei Rong Xiaoyan Yu School of Computer Science and Information Engineering Harbin Normal University Harbin China School of Physics and Electronic Engineering Harbin Normal University Harbin China

ISBN: (数字)9798331542283

ISBN: (纸本)9798331542290

This article presents a streamlined ship detection algorithm SM-LightNet based on YOLOv8 improvement, which is suitable for environments such as ports with dense ship traffic and complex maritime navigation. Firstly, we introduce a Slim-neck structure that includes a lightweight convolution called GSConv and VoVGSCSP structure. It replaces the traditional convolution in the Neck part of YOLOv8 with GSConv, achieving real-time detection of edge devices and effectively reducing the complexity of computation and parameters. Secondly, replacing the C2f structure in Neck with VoVGSCSP enables the model to have a more concise network structure, which can effectively fuse feature information and enhance the precision and stability of object detection. Finally, the loss function MPDIoU was introduced, which calculates IoU by minimizing the point distance between the estimated boundary box and the actual boundary box, simplifying the calculation process and improving detection accuracy. The outcomes of the experiments indicate that the accuracy of the lightweight algorithm reaches 98.2%, and the GFLOPs of the algorithm are 7.4, which is 9.5% lower than the performance of the original model. The model parameter size is 2.80M, which is 7.5 % lower than the original model.

关键词： Accuracy Convolution Computational modeling Object detection Feature extraction Stability analysis Real-time systems Neck Marine vehicles Detection algorithms

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Digital Twin Ancient Architecture: The Exploration of the Digital Protection of Ancient Architecture

Digital Twin Ancient Architecture: The Exploration of the Di...

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作者： Li, Shanglin Lin, Anping School of Computer Science and Artificial Intelligence Xiangnan University Hunan Chenzhou423000 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan Chenzhou China School of Physics and Electronic Electrical Engineering Xiangnan University Hunan Chenzhou China

The digital protection of ancient architectures has achieved important achievements. However, technology development has encountered a bottleneck. The main problem is that the digitization of ancient buildings is limited to building static and isolated three-dimensional models, which cannot meet the increasing professional requirements of digital protection. To solve the above problems, the concept of digital twin ancient architecture (DTAA) is proposed, focusing on the high-dimensional data integration method of the DTAA, as well as the construction method and technical route. Finally, the development and application of digital twin ancient architecture are discussed. The proposed method can bring new ideas to the field of digital protection of ancient architectures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： 3D modeling

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Computational Scientific Discovery in Cognitive science

Computational Scientific Discovery in Cognitive Science

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Peter C. R. Lane Fernand Gobet School of Physics Engineering and Computer Science University of Hertfordshire Hatfield UK Centre for Philosophy of Natural and Social Science London School of Economics and Political Science London UK

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

AI-guided scientific discovery will become an indispensable tool for scientists in the not too distant future. This half-day tutorial begins by introducing the area of computational scientific discovery within the cognitive sciences along with an overview of recent models of cognitive behaviour developed using these tools. The second half of the tutorial presents a detailed walk-through of our methodology and computational system, where participants will learn how to design experiments, evolve and analyse candidate models using the GEMS (Genetically Evolving Models in science) system. Although specialised towards the cognitive sciences, many of the principles of model definition and discovery can be more broadly applied, and so the tutorial should be of interest to the wider Cognitive Machine Intelligence community.

关键词： Analytical models Computational modeling Tutorials Cognitive science Machine intelligence

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What limits limits?

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National science Review 2021年第1期8卷 11-14页

作者： Yong-Chun Liu Kun Huang Yun-Feng Xiao Lan Yang Cheng-Wei Qiu State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Frontier Science Center for Quantum Information Tsinghua University Department of Optics and Optical Engineering University of Science and Technology of China State Key Laboratory for Mesoscopic Physics and Frontiers Science Center for Nano-optoelectronics School of Physics Peking University Department of Electrical and Systems Engineering Washington University Department of Electrical and Computer Engineering National University of Singapore

There are fundamental rules and principles setting the limits of physical systems. It triggers an interesting thought—can we break the limits under specific circumstances? A realistic system can only provide limited functionalities because its performance is physically constrained by some fundamental principles.‘Breaking the limit’, which usually implies that the capability of a system could be enhanced significantly,

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ARI humanoid robot imitates human gaze behaviour using reinforcement learning in real-world environments

ARI humanoid robot imitates human gaze behaviour using reinf...

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IEEE-RAS International Conference on Humanoid Robots

作者： Khashayar Ghamati Abolfazl Zaraki Farshid Amirabdollahian School of Physics Engineering and Computer Science (SPECS) and Robotics Research Group of the University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

This paper presents a novel approach to enhance the social interaction capabilities of the ARI humanoid robot using reinforcement learning. We focus on enabling ARI to imitate human attention/gaze behaviour by identifying salient points in dynamic environments, employing the Zero-Shot Transfer technique combined with domain randomisation and generalisation. Our methodology uses the Proximal Policy Optimisation algorithm, training the reinforcement learning agent in a simulated environment to maximise robustness in real-world scenarios. We demonstrated the efficacy of our approach by deploying the trained agent on the ARI humanoid and validating its performance in human-robot interaction scenarios. The results indicated that using the developed model, ARI can successfully identify and respond to salient points, exhibiting human-like attention/gaze behaviours, which is an important step towards acceptability and efficiency in humanrobot interactions. This research contributes to advancing the capabilities of social robots in dynamic and unpredictable environments, highlighting the potential of combining ZeroShot Transfer with domain randomisation and generalisation for robust real-world applications.

关键词： Training Heuristic algorithms Social robots Humanoid robots Reinforcement learning Robustness Optimization

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Polarization-Entangled Photon-Pair Sources Based on 3R-MoS2

Polarization-Entangled Photon-Pair Sources Based on 3R-MoS2

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Weissflog, Maximilian A. Fedotova, Anna Tang, Yilin Santos, Elkin A. Laudert, Benjamin Shinde, Saniya Abtahi, Fatemeh Afsharnia, Mina Pérez, Inmaculada Pérez Ritter, Sebastian Qin, Hao Janousek, Jiri Shradha, Sai Staude, Isabelle Saravi, Sina Pertsch, Thomas Setzpfandt, Frank Lu, Yuerui Eilenberger, Falk Institute of Applied Physics Abbe Center of Photonics Friedrich Schiller University Jena Jena07745 Germany Institute of Solid State Physics Friedrich Schiller University Jena Helmholtzweg 3 Jena07743 Germany School of Engineering College of Science and Computer Science The Australian National University CanberraACT Australia Institute for Condensed Matter Physics Technical University of Darmstadt Darmstadt64289 Germany Fraunhofer Institute for Applied Optics and Precision Engineering IOF Jena07745 Germany

We demonstrate for the first time generation of polarization-entangled photon-pairs in a transition metal dichalcogenide. Using 3R-phase molybdenum disulfide, we experimentally show tunable, high-fidelity generation o... 详细信息

ISBN: (纸本)9781957171395

关键词： Polarization

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