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arXiv 2021年

作者： Gong, Wenwen Wu, Huiping Wang, Xiaokang Zhang, Xuyun Wang, Yawei Chen, Yifei Khosravi, Mohammad R. Colleage of Information and Electrical Engineering China Agricultural University Beijing100000 China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang262700 China Department of Computer Science St. Francis Xavier University AntigonishNS Canada Department of Computing Macquarie University SydneyNSW2122 Australia Department of Computer Engineering Persian Gulf University Bushehr*** Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz71557-13876 Iran

With the ever-increasing popularity of Internet of Things (IoT), massive enterprises are attempting to encapsulate their developed outcomes into various lightweight web APIs (application programming interfaces) that can be accessible remotely. In this situation, finding and composing a list of existing web APIs that can corporately fulfill the software developers' functional needs have become a promising way to develop a successful mobile app, economically and conveniently. However, the big volume of candidate IoT web APIs put additional burden on the app developers' web APIs selection decision-makings, since it is often a challenging task to simultaneously guarantee the diversity and compatibility of the finally selected a set of web APIs. Considering this challenge and latest successful applications of game theory in IoT, a Diversified and Compatible web APIs Recommendation approach, namely DivCAR, is put forward in this paper. First of all, to achieve API diversity, DivCAR employs random walk sampling technique on a pre-built "API-API" correlation graph to generate diverse "API-API" correlation subgraphs. Afterwards, with the diverse "API-API" correlation subgraphs, we model the compatible web APIs recommendation problem to be a minimum group Steiner tree search problem. Through solving the minimum group Steiner tree search problem, manifold sets of compatible and diverse web APIs ranked are returned to the app developers. At last, we design and enact a set of experiments on a real-world dataset crawled from ***. Experimental results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the web APIs recommendation diversity and compatibility. Copyright © 2021, The Authors. All rights reserved.

关键词： Internet of things

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Ultrathin quantum light source enabled by a nonlinear van der Waals crystal with vanishing interlayer-electronic-coupling

arXiv

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arXiv 2022年

作者： Guo, Qiangbing Qi, Xiao-Zhuo Gao, Meng Hu, Sanlue Zhang, Lishu Zhou, Wenju Zang, Wenjie Zhao, Xiaoxu Wang, Junyong Yan, Bingmin Xu, Mingquan Wu, Yun-Kun Eda, Goki Xiao, Zewen Gou, Huiyang Feng, Yuan Ping Guo, Guang-Can Zhou, Wu Ren, Xi-Feng Qiu, Cheng-Wei Pennycook, Stephen J. Wee, Andrew T.S. Department of Materials Science and Engineering National University of Singapore Singapore117575 Singapore Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore Singapore117546 Singapore Key Laboratory of Quantum Information CAS University of Science and Technology of China Anhui Hefei230026 China Synergetic Innovation Center of Quantum Information & Quantum Physics University of Science and Technology of China Anhui Hefei230026 China School of Physical Sciences CAS Key Laboratory of Vacuum Physics University of Chinese Academy of Sciences Beijing100049 China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan430074 China Department of Physics National University of Singapore Singapore117542 Singapore Center for High Pressure Science and Technology Advanced Research Beijing100094 China School of Materials Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Interlayer electronic coupling in two-dimensional (2D) materials enables tunable and emergent properties by stacking engineering. However, it also brings significant evolution of electronic structures and attenuation of excitonic effects in 2D semiconductors as exemplified by quickly degrading excitonic photoluminescence and optical nonlinearities in transition metal dichalcogenides when monolayers are stacked into van der Waals structures. Here we report a novel van der Waals crystal, niobium oxide dichloride, featuring a vanishing interlayer electronic coupling and scalable second harmonic generation intensity of up to three orders higher than that of exciton-resonant monolayer WS2. Importantly, the strong second-order nonlinearity enables correlated parametric photon pair generation, via a spontaneous parametric down-conversion (SPDC) process, in flakes as thin as ~46 nm. To our knowledge, this is the first SPDC source unambiguously demonstrated in 2D layered materials, and the thinnest SPDC source ever reported. Our work opens an avenue towards developing van der Waals material-based ultracompact on-chip SPDC sources, and high-performance photon modulators in both classical and quantum optical technologies. © 2022, CC BY-NC-ND.

关键词： Monolayers

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Preparing a commercial quantum key distribution system for certification against implementation loopholes

arXiv

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arXiv 2023年

作者： Makarov, Vadim Abrikosov, Alexey Chaiwongkhot, Poompong Fedorov, Aleksey K. Huang, Anqi Kiktenko, Evgeny Petrov, Mikhail Ponosova, Anastasiya Ruzhitskaya, Daria Tayduganov, Andrey Trefilov, Daniil Zaitsev, Konstantin Russian Quantum Center Skolkovo Moscow121205 Russia Vigo Quantum Communication Center University of Vigo VigoE-36310 Spain NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Bangkok10110 Thailand QRate Skolkovo Moscow143026 Russia Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China Steklov Mathematical Institute Russian Academy of Sciences Moscow119991 Russia atlanTTic Research Center University of Vigo VigoE-36310 Spain School of Telecommunication Engineering Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain National Research University Higher School of Economics Moscow101000 Russia

A commercial quantum key distribution (QKD) system needs to be formally certified to enable its wide deployment. The certification should include the system’s robustness against known implementation loopholes and attacks that exploit them. Here we ready a fiber-optic QKD system for this procedure. The system has a prepare-and-measure scheme with decoy-state BB84 protocol, polarisation encoding, qubit source rate of 312.5 MHz, and is manufactured by QRate. We detail its hardware and post-processing. We analyse the hardware for known implementation loopholes, search for possible new loopholes, and discuss countermeasures. We then amend the system design to address the highest-risk loopholes identified. We also work out technical requirements on the certification lab and outline its possible structure. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum cryptography

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Diversified and compatible web APIs recommendation in IoT

arXiv

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arXiv 2021年

关键词： Internet of things

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Gas–solid interface charge characterisation techniques for HVDC GIS/GIL insulators

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high Voltage 2020年第2期5卷 95-109页

作者： Lei Zhang Chuanjie Lin Chuanyang Li Simone Vincenzo Suraci Geng Chen Uwe Riechert Tohid Shahsavarian Masayuki Hikita Youping Tu Zhousheng Zhang Davide Fabiani Jinliang He School of Electrical Engineering Shanghai University of Electric PowerChangyang Road#2588Shanghai200090People's Republic of China State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing100084People's Republic of China Department of Electrical Electronicand Information Engineering‘Guglielmo Marconi’University of BolognaViale Risorgimento 2Bologna40136Italy Beijing Area Major Laboratory of High Voltage and Electromagnetic Compatibility North China Electric Power UniversityBeijing 102206People's Republic of China ABB Switzerland Ltd High Voltage ProductsZurichSwitzerland Electrical Insulation Research Center Institute of Materials ScienceElectrical and Computer EngineeringUniversity of ConnecticutStorrsCT 06269USA Kyushu Institute of Technology KitakyushuJapan

Surface charge accumulation on the spacers is one of the key issues restraining the development of HVDC GIS/*** precise measurement of surface charge properties provides the basis for further study of the surface charge transport mechanism as well as the charge-induced flashover mechanism under DC *** this study,the authors discuss their perspective on the current status,development needs and potential developing orientation of surface charge characterisation *** surface potential measurement methods and charge inversion algorithms are reviewed regarding the previous studies and future research *** and outlooks of surface charge measurement techniques are also discussed with the background of laboratory experiment results and on-site *** is hopefully that this study can serve as a useful guide reference for researchers within the same research *** importantly,it is authors’hope that this study can inspire some novel ideas for readers into developing of more accurate and scientific interface charge characterisation techniques.

关键词： GIL charge interface

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Developing an intelligent virtual reality interactive system based on the ADDIE model for learning pour-over coffee brewing

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computers and Education: Artificial Intelligence 2021年 2卷

作者： Yu, Shih-Jou Hsueh, Yu-Ling Sun, Jerry Chih-Yuan Liu, Hao-Ze Institute of Education National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Education National Chiao Tung University Hsinchu30010 Taiwan Department of Computer Science & Information Engineering National Chung Cheng University Chiayi62102 Taiwan National Chung Cheng University Chiayi62102 Taiwan National Chung Cheng University Chiayi62102 Taiwan National Center for High-performance Computing National Applied Research Laboratories Hsinchu Science Park Hsinchu30076 Taiwan

This study aims to use the analysis, design, development, implementation, and evaluation (ADDIE) model to develop an intelligent virtual reality (VR) interactive system for learning pour-over coffee brewing. The operation of pour-over coffee brewing is complex. An intelligent system is a concrete actuator that realizes artificial intelligence (AI) decisions. When learners face the challenge of learning complex skills, an intelligent system can actively analyze their learning behaviors and provide corresponding intelligent feedback to correct them. Furthermore, head-mounted-display VR can structure an immersive environment to improve the learning experience and knowledge transfer. This research integrates these two techniques into an intelligent VR interactive system and utilizes the ADDIE model to enable the system to achieve the ideal educational framework. The participants were 103 students who experienced and evaluated this system. The results indicated that the intelligent VR interactive system matched the participants' learning requirements and enhanced their learning performance. In addition, the learners proposed some suggestions to optimize the system, such as adding detailed content to the intelligent feedback and note function. Overall, this system increases the effectiveness of learning. The study suggests that instructors can use an intelligent VR interactive system to strengthen learners’ manipulative ability and knowledge. © 2021 The Authors

关键词： Intelligent systems

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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Random Occlusion Recovery with Noise Channel for Person Re-identification 16th

Random Occlusion Recovery with Noise Channel for Person Re-i...

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16th International Conference on Intelligent computing, ICIC 2020

作者： Zhang, Kun Wu, Di Yuan, Changan Qin, Xiao Wu, Hongjie Zhao, Xingming Zhang, Lijun Du, Yuchuan Wang, Hanli Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Guangxi Academy of Science Nanning530025 China School of Computer and Information Engineering Nanning Normal University Nanning530299 China School of Computer Science and Technology Soochow University Suzhou215006 China School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China Fudan University Shanghai200433 China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Ministry of Education Shanghai China Collaborative Innovation Center of Intelligent New Energy Vehicle and School of Automotive Studies Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Department of Transportation Engineering Tongji University Shanghai201804 China Department of Computer Science and Technology the Key Laboratory of Embedded System and Service Computing and Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9783030607982

Person re-identification, as the basic task of a multi-camera surveillance system, plays an important role in a variety of surveillance applications. However, the current mainstream person re-identification model based on deep learning requires a lot of labeled data, which takes a lot of time and manpower. In this study, we proposed a person re-identification method based on random occlusion recovery with noise channel. We add random occlusion blocks to the original image, use the GAN model for repair, and use the repaired image to expand the original training set. After that, the generated image is adjusted through the noise channel. Finally, we use the enhanced data set to train the baseline model. Our model achieves the state-of-the-art on Market-1501 dataset, proving that the method is effective. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Learning parallel dense correspondence from spatio-temporal descriptors for efficient and robust 4D reconstruction

arXiv

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arXiv 2021年

作者： Tang, Jiapeng Xu, Dan Jia, Kui Zhang, Lei School of Electronic and Information Engineering South China University of Technology China Department of Computer Science and Engineering HKUST Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Alibaba Group Damo Academy China Pazhou Lab Guangzhou China Peng Cheng Laboratory Shenzhen China

This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds. Despite the recent success achieved by extending deep implicit representations into 4D space [32], it is still a great challenge in two respects, i.e. how to design a flexible framework for learning robust spatio-temporal shape representations from 4D point clouds, and develop an efficient mechanism for capturing shape dynamics. In this work, we present a novel pipeline to learn a temporal evolution of the 3D human shape through spatially continuous transformation functions among cross-frame occupancy fields. The key idea is to parallelly establish the dense correspondence between predicted occupancy fields at different time steps via explicitly learning continuous displacement vector fields from robust spatio-temporal shape representations. Extensive comparisons against previous state-of-the-arts show the superior accuracy of our approach for 4D human reconstruction in the problems of 4D shape auto-encoding and completion, and a much faster network inference with about 8 times speedup demonstrates the significant efficiency of our approach. The trained models and implementation code are available at https://***/tangjiapeng/LPDC-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Machine Learning-based Energy-Aware Offloading in Edge-Cloud Vehicular Networks

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Procedia computer science 2021年 191卷 328-336页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi 15551 United Arab Emirates

A vehicular network underpinned by the 3-tier vehicle-edge-cloud infrastructure enables an efficient and safer travel experience. The compute-intensive vehicular applications are often offloaded to the edge and/or cloud servers to enhance the applications’ Quality of Services (QoS). The underlying edge-cloud servers consume a high among of energy. Consequently, it becomes crucial to optimizing energy consumption in the offloading process. Current energy-efficient offloading strategies in 2-tier vehicle-edge infrastructure, do not account for cloud computing energy consumption. In this paper, we address this void by proposing a machine learning-based energy-aware offloading algorithm, which optimizes the energy of the edge-cloud computing platform. The offloading strategy is enabled by the Support Vector Machine (SVM) regression model machine learning algorithm used for the edge-cloud power prediction. The experimental results show that the proposed algorithm is a promising approach in energy savings.

关键词： Cloud computing Computation offloading Edge computing Energy-efficiency Machine learning Queuing theory Support Vector Machine (SVM) Vehicular network

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