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arXiv 2025年

作者： Xiao, Han Hu, Xiaoyan Wong, Kai-Kit Hong, Hanjiang Alexandropoulos, George C. Chae, Chan-Byoung School of Information and Communications Engineering Xi’an Jiaotong University Xi’an710049 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Department of Electronic and Electrical Engineering University College London London United Kingdom Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens16122 Greece Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States School of Integrated Technology Yonsei University Seoul03722 Korea Republic of

This letter proposes a fluid reconfigurable intelligent surface (FRIS) paradigm, extending the conventional reconfigurable intelligent surface (RIS) technology to incorporate position reconfigurability of the elements. In our model, a ‘fluid’ element is realized by a dense matrix of subelements over a given space and dynamically selecting specific elements for signal modulation based on channel conditions. Specifically, we consider a FRIS-assisted single-user single-input single-output (SU-SISO) system and formulate an optimization problem that can jointly optimize element selection and their discrete phase shifts to maximize the achievable rate. To address this problem efficiently, we propose an iterative algorithm based on the cross-entropy optimization (CEO) framework. Simulation results reveal that FRIS achieves significant performance gains over traditional RIS. Copyright © 2025, The Authors. All rights reserved.

关键词： Beam forming networks

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Retracted: Self-attention Bi-RNN for developer emotion recognition based on EEG

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IET Software 2022年第4期17卷 620-631页

作者： Yingdong Wang Yuhui Zheng Lu Cao Zhiling Zhang Qunsehng Ruan Qingfeng Wu School of Informatics Xiamen University Xiamen China School of Information Mechanical and Electrical Engineering Ningde Normal University Ningde China Department of Nature Science and Computer Ganzhou Teachers College Ganzhou China School of Film Xiamen University Xiamen China

Retraction: [Yingdong Wang, Yuhui Zheng, Lu Cao, Zhiling Zhang, Qunsehng Ruan, Qingfeng Wu, Self-attention Bi-RNN for developer emotion recognition based on EEG, IET Software 2022 ( https://***/10.1049/sfw2.12080 )]. The above article from IET Software , published online on 23 December 2022 in Wiley Online Library (***), has been retracted by agreement between the Editor-in-Chief, Hana Chockler, the Institution of engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Human Factors in Model-Driven engineering: Future Research Goals and Initiatives for MDE

arXiv

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arXiv 2024年

作者： Liebel, Grischa Klünder, Jil Hebig, Regina Lazik, Christopher Nunes, Inês Graßl, Isabella Steghöfer, Jan-Philipp Exelmans, Joeri Oertel, Julian Marquardt, Kai Juhnke, Katharina Schneider, Kurt Gren, Lucas Happe, Lucia Herrmann, Marc Wyrich, Marvin Tichy, Matthias Goulão, Miguel Wohlrab, Rebekka Kalantari, Reyhaneh Heinrich, Robert Greiner, Sandra Rukmono, Satrio Adi Chakraborty, Shalini Abrahão, Silvia Amaral, Vasco Department of Computer Science Reykjavik University Menntavegur 1 Reykjavik102 Iceland Software Engineering Group Leibniz University Hannover Welfengarten 1 Hannover30167 Germany Department of Software Engineering University of Rostock Albert-Einstein-Straße Rostock18057 Germany Department of Software Engineering Humboldt-Universität zu Berlin Unter den Linden 6 Berlin10099 Germany Frankfurt Germany Department of Software Engineering II University of Passau Innstraße 33 Passau94032 Germany XITASO GmbH Austraße 35 Augsburg86153 Germany Department of Computer Science University of Antwerp Middelheimlaan 1 Antwerp2020 Belgium Department of Informatics Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany SMT-EMI6 Carl Zeiss SMT GmbH Rudolf-Eber-Straße 2 Oberkochen73447 Germany Department of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg41127 Sweden Department of Interdisciplinary Didactics Karlsruhe Institute of Technology Engesserstr. 2 Karlsruhe75131 Germany Department of Computer Science Saarland University Saarland Informatics Campus Saarbrücken66123 Germany Institute of Software Engineering and Languages Ulm University James-Franck-Ring Ulm89081 Germany Department of Computer Science NOVA School of Science & Technology Campus de Caparica Caparica2829-516 Portugal Department of Engineering Design and Teaching Innovation University of Ottawa 800 King Edward Avenue OttawaK1N6N5 Canada KASTEL - Institute of Information Security and Dependability Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe75131 Germany Department of Computer Science University of Bern Bern3012 Switzerland Department of Mathematics and Computer Science Eindhoven University of Technology De Zaale Eindhoven5600 MB Netherlands Instituto Universitario Mixto de Tecnología Informática Universitat Politècnica de València Camino de Vera s/n Valencia46022 Spain

Purpose: Software modelling and Model-Driven engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. Methods: During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. Results: In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. Conclusion: We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values. © 2024, CC BY.

关键词： Software design

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QoE-Guaranteed Optimization in MEC-Enabled Metaverse: An Active Inference Deep Reinforcement Learning Approach

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Du, Jianbo Gong, Jie Chu, Xiaoli Xiong, Zehui Chen, Xianfu Dong, Mianxiong Yu, F. Richard Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security School of Communications and Information Engineering Xi'an710121 China The University of Sheffield Mappin Street Department of Electronic and Electrical Engineering SheffieldS1 3JD United Kingdom Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore Singapore Shenzhen CyberAray Network Technology Company Ltd Shenzhen518000 China Muroran Institute of Technology Department of Sciences and Informatics Muroran050-8585 Japan Carleton University Systems and Computer Engineering OttawaON Canada

In this paper, we consider a MEC-enabled metaverse scenario which consists of a remote metaverse server and an edge server that cooperates to provide services to mobile users. The edge server is deployed at the base station (BS), serves a dual role: augmenting computational capabilities for user equipment (UE) and pre-caching a portion of the metaverse service contents before each time slot. Moreover, the foreground information and the requested contents generated by the UEs can also be cached to the BS. We formulate a problem to maximize the cache hit number by jointly optimizing contents pre-caching and resource allocation at the BS while considering UEs preference and reducing the UEs total energy consumption, essential for the efficient delivery of services in dynamic MEC environments. To solve this problem, we reformulate it as a partially observable markov decision process and propose an active inference enabled deep reinforcement learning algorithm, which combines active inference with deep reinforcement learning to select the optimal strategy by minimizing the expected free energy. Simulations show that the proposed algorithm can effectively improve the total quality of experience and the cache hit number of UEs, while minimizing the UEs total energy consumption compared with other baseline algorithms © 2015 IEEE.

关键词： Active learning

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Sub-THz fiber wireless multi-IFoF fronthaul of a V-band massive MIMO antenna for multi-user 6G networks

Sub-THz fiber wireless multi-IFoF fronthaul of a V-band mass...

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49th European Conference on Optical Communications (ECOC 2023)

作者： M. Vargemidou P. Toumasis K. Kanta R. Maximidis G. Giannoulis Z. S. He Y. Leiba D. Apostolopoulos H. Avramopoulos A. Miliou N. Pleros C. Vagionas Department of Informatics Center for Interdisciplinary Research & Innovation Aristotle University of Thessaloniki Thessaloniki Greece School of Electrical and Computer Engineering National Technical University of Athens Greece Sinowave AB Hovås Greece Siklu Communications Ltd. Petah Tikva 4959501 Israel

Three D-band Fiber-Wireless Multi-IFoF links each with 0.5 Gb/s per beam are experimentally transmitted across a 7km fiber and a V-band MIMO antenna with 90°-degree RF-beamsteering, forming the first demonstratio...

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Mutually-Constrained Cross-Sectional and Longitudinal Non-Negative Matrix Factorization: Application to Modeling Brain Aging Trajectories

Mutually-Constrained Cross-Sectional and Longitudinal Non-Ne...

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IEEE International Symposium on Biomedical Imaging

作者： Ioanna Skampardoni Junhao Wen Erus Guray Haochang Shou Konstantina Nikita Christos Davatzikos Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Laboratory of AI and Biomedical Science (LABS) Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC University of Southern California Los Angeles CA USA Department of Biostatistics Epidemiology & Informatics Penn Statistics in Imaging and Visualization Center University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Brain aging is a multifaceted and highly heterogeneous process accompanied by several pathologies. Here, we propose a method for dissecting the heterogeneity of neuropathologic processes occurring with aging using machine learning and leveraging information from cross-sectional and longitudinal data. Specifically, we hypothesize that the heterogeneity observed in brain aging can be captured by a set of patterns consistent with longitudinal trajectories of brain change, the latter directly capturing evolving neuropathologic processes on an individual basis. Applying the method to structural magnetic resonance imaging data from the BLSA study, we derived five distinct, reproducible, and clinically informative components of neuroanatomical brain change, highlighting the method’s potential as a tool for precision medicine.

关键词： Pathology Magnetic resonance imaging Precision medicine Sociology Aging Hazards Trajectory

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Design and Implementation of Smart Street Lighting System Employing Esp-Mesh Network

SSRN

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SSRN 2023年

作者： Fuada, Syifaul Duttagupta, Subhasri Adiono, Trio Siregar, Lindawani Program Studi Sistem Telekomunikasi Universitas Pendidikan Indonesia West Java Province Bandung Indonesia Department of Computer Science and Engineering Amrita Viswa Vidyapeetham Keralam Amritapuri-Campus 690525 India Electrical Engineering Department School of Electrical Engineering and Informatics Institut Teknologi Bandung West Java Province Bandung Indonesia Teaching Factory of Manufacturing Electronics Politeknik Negeri Batam Kepulauan Riau Province Batam Indonesia

Street lighting is a public facility that gives highway users’ convenience during their nighttime trips, making it essential to be controlled and monitored using IoT technology. This study aimed to develop a smart lighting system connected through a Mesh network. The system monitors the street light and automatically controls it using the ESP8266 module, a microcontroller with Wi-Fi built-in. The options in selecting the lamp type include Halogen, Fluorescent, LED, or Incandescent. In general, LED is used because it has an advantage in energy-saving over other lamp types. Moreover, the system is equipped with automatic light intensity control through a dimming technique by Pulse Width Modulation (PWM), while BH1750 is used as a light sensor. Furthermore, it monitors the environmental temperature and humidity using DHT22. The communication between the lighting module is based on ESP-Mesh because each node is connected and forwarding data to the destination and the coverage area. ESP-Mesh allows the wireless network to have a scalable architecture in a broader area. It has features to self-organize and self-configure, implying the network could be built and maintained autonomously. Additionally, the system has a web server service with access to control each node and find out the mesh routing table. The results showed that each node could be configured as a mesh network using ESP8266, which sends dimming commands and reads the sensors. The dimming and intensity control was tested using real public street light called NL200D LED module and an off-the-self dimming control driver module, N-Channel MOSFET FQP30N06L. Furthermore, a Mesh connection was tested using five super bright 3mm LEDs representing real street light due to the limitation of the LED module in the laboratory. The street light has three control modes, including on/off-based, intensity control-based or dimming control manually through the website, and intensity control-based automatically using the two se

关键词： Mesh generation

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On the splitting dynamics of meetup social groups 14

On the splitting dynamics of meetup social groups

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14th International AAAI Conference on Web and Social Media, ICWSM 2020

作者： Bhowmick, Ayan Kumar Pramanik, Soumajit Pathak, Sayan Mitra, Bivas Department of Computer Science and Engineering IIT Kharagpur India MPI for Informatics Saarbrucken Germany Microsoft Research RedmondWA United States

ISBN: (纸本)9781577357889

Groups in online social networks witness continuous evolution by loss of existing members and gain of new members. In this paper, we present a study of group split in Meetup, where a major fraction of members leave the existing group together and join a newly formed group. We identify pivotal group members, called splitters, playing key roles in group split by influencing the existing members to leave the group. We provide an in-depth analysis of the empirical data to reveal key motivating factors leading to a group split and its subsequent impact. Finally, we develop a prediction model for early detection of splitters, as well as the group members likely to be influenced by the splitter to leave the group. Copyright © 2020, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Social networking (online)

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Brain MR Image Classification for ADHD Diagnosis Using Deep Neural Networks 1

Brain MR Image Classification for ADHD Diagnosis Using Deep ...

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1st International Conference on Machine Vision and Image Processing, MVIP 2020

作者： Abdolmaleki, Sahar Abadeh, Mohammad Saniee Tarbiat Modares University Medical Informatics Department Tehran Iran Tarbiat Modares University Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (数字)9781728168326

ISBN: (纸本)9781728168326

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorder in childhood and adolescence. ADHD diagnosis currently includes psychological tests and depends on ratings of behavioral symptoms, which can be unreliable. Thus, an objective diagnostic tool based on non-invasive imaging can improve the understanding and diagnosis of ADHD. The purpose of this study is classifying brain images by using Artificial Intelligence methods such as clinical decision support system for the diagnosis of ADHD. For this purpose and according to a medical imaging classification system, firstly, image pre-processing is done. Then, a deep multi-modal 3D CNN is trained on GM from structural and fALFF from functional MRI using ADHD-200 training dataset. Finally, with the intention of classifying the extracted features, early and late fusion schemes are employed, and the output scores are classified with the SVM, KNN and LDA algorithms. The evaluation of the proposed approach on the ADHD-200 testing dataset revealed that the presence of personal characteristics alone increased the classification accuracy by 3.79%. In addition, using a combination of early, late fusion and personal characteristics together improved the accuracy of the classification by 5.84%. Among the three classifiers LDA showed better results and achieved a classification accuracy of 74.93%. The comparison of results showed that the combination of early and late fusion as well as considering personal characteristics has a significant effect on enhancing classification accuracy. As a result of this, the reliability of this medical decision support system is increased. © 2020 IEEE.

关键词： Magnetic resonance imaging

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Reinforcement Learning in Traffic Prediction of Core Optical Networks using Learning Automata

Reinforcement Learning in Traffic Prediction of Core Optical...

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2020 IEEE International Conference on Communications, Computing, Cybersecurity, and informatics, CCCI 2020

作者： Valkanis, Anastasios Beletsioti, Georgia A. Nicopolitidis, Petros Papadimitriou, Georgios Varvarigos, Emmanouel Aristotle University of Thessaloniki Department of Informatics Thessaloniki Greece School of Electrical and Computer Engineering National Technical University of Athens Athens Greece

ISBN: (纸本)9781728120355

The development of technology related to optical core networks provides the appropriate infrastructure for the reliable flow of information, that is constantly increasing with the increase of interconnected users and devices and the evolution of the applications used. Network engineering following traffic prediction is one of the most important factors determining the effectiveness of core networks. Because of its importance, the research community has dealt extensively with traffic forecasting by proposing prediction mechanisms that are mainly based on time series and machine learning models. The objective of traffic forecasting mechanisms is to achieve the maximum possible accuracy while maintaining a low complexity in their operation. In this paper, we propose a novel traffic prediction mechanism that is based on reinforcement learning using Learning Automata. The evaluation of the proposed mechanism is carried out using traffic matrices derived from real measurements in a core network. Our performance simulation results show the effectiveness of the proposed mechanism, which achieves satisfactory forecast accuracy with low operational complexity. © 2020 IEEE.

关键词： Reinforcement learning

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