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arXiv 2024年

作者： Fang, Yini Institute of Computing Systems Architecture School of Informatics University of Edinburgh United Kingdom

The global mobile traffic consumption has been growing sharply, driven by the surge of smart IoT devices and the adoption of 5G technology. Further, the COVID-19 pandemic has accelerated the digital transformation of work and study, leading to tremendous traffic demands. Due to these reasons, mobile operators are facing pressing resource management problems related to storage and overhead of transferring large amounts of metadata that serve for analysing user demands and planning the infrastructure needed to accommodate these. In this context, intelligent measurement collection, precise geo-spatial traffic analysis, and forecasting are becoming essential to assuring high performance and cost-efficiency of cellular network deployments. Modelling dynamic traffic patterns and especially the continuously changing dependencies between different base stations, which previous studies overlook, is challenging. Traditional algorithms struggle to process large volumes of data and to extract deep insights that help elucidate mobile traffic demands with fine granularity, as well as how these demands will evolve in the future. Therefore, in this thesis we harness the powerful hierarchical feature learning abilities of Deep Learning (DL) techniques in both spatial and temporal domains and develop solutions for precise city-scale mobile traffic analysis and forecasting. Firstly, we design Spider, a mobile traffic measurement collection and reconstruction framework with a view to reducing the cost of measurement collection and inferring traffic consumption with high accuracy, despite working with sparse information. In particular, we train a reinforcement learning agent to selectively sample subsets of target mobile coverage areas and tackle the large action space problem specific to this setting. We then introduce a lightweight neural network model to reconstruct the traffic consumption based on historical sparse measurements. Our proposed framework outperforms existing solutions

关键词： Volume measurement

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A Cross-Organizational Identity Proofing System for Seamless Online ID Re-Binding Leveraging Individual Number Cards

A Cross-Organizational Identity Proofing System for Seamless...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Sayako Shimizu Hiroyuki Sato Motonori Nakamura Hukofumi Suzuki Yasuo Okabe Information Systems Architecture Science Research National Institute of Informatics Tokyo Japan Institute for Information Management and Communication Kyoto University Kyoto Japan Cyber Science Infrastructure Development Department Academic Authentication Systems Office National Institute of Informatics Tokyo Japan Academic Center for Computing and Media Studies Kyoto University Kyoto Japan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This study focuses on the issue of ID management when researchers change their organizational affiliations, aiming at the effective utilization of research data in educational and research organizations. Although research data management systems are being developed at each organization, the continuous use of research data remains an issue for researchers. For example, when a researcher moves from one organization to another, the research data that was previously available to him or her becomes unavailable. In response, there is a need for a system that allows researchers to continue using their research data smoothly even if they move from one organization to another. To solve this problem, the authors propose a cross-organizational identity proofing system that ensures the continuity of IAL2/ AAL2 and completes the ID transfer online. The idea is to use researcher ID numbers, ORCID, and public personal authentication to verify the user's identity when linking the IDs before and after the transfer. To facilitate this, the study performs identity verification using an Individual Number Card (a Japanese governmental system) to enable easy ID linkage between IdPs with high identity confirmation and authentication strength. The implementation has been carried out in the development environment of Orthros, an ID linkage service provided by the National institute of informatics (NIl), utilizing the API service of xID Co., which is a solution for linking with the Individual Number Card. This approach is expected to overcome ID management issues associated with researcher transfers and improve the efficiency of research data use.

关键词： Couplings Digital transformation Employment Education Authentication Organizations Software

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Analysis and Design of Mobile Model for DTN

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Chinese Journal of Electronics 2023年第4期18卷 635-638页

作者： Zhiting Lin Yugui Qu Yusheng Ji Baohua Zhao Department of Computer Science University of Science and Technology of China Hefei China Province Key Laboratory of Software in Computing and Communication Hefei China Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Delay tolerant networks may become unexpectedly partitioned due to node mobility or variation in signal strength. However, most widely used models in some relative works are generally very simplistic. In order to exploit intelligent forwarding algorithms, a novel nodal mobile model of delay tolerant networks is presented to map reality with more accuracy. And several approaches are introduced to analyze the network structure, such as n-cliques, n-clans, degree, closeness and betweenness. Our research results showed that the centralizations became smaller when the wireless connections were concerned. It meant that quite a number of nodes became potential relays because of the new structure of networks.

关键词： Wireless communication Analytical models Wireless sensor networks Protocols Social networking (online) Sensitivity analysis Social network theory

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Communication and Control Co-Design in 6G: Sequential Decision-Making with LLMs

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IEEE Network 2024年

作者： Chen, Xianfu Wu, Celimuge Shen, Yi Ji, Yusheng Yoshinaga, Tsutomu Ni, Qiang Zarakovitis, Charilaos C. Zhang, Honggang Shenzhen CyberAray Network Technology Co. Ltd China Chinese Academy of Sciences Shanghai Advanced Research Institute China Meta-Networking Research Center China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan National Institute of Informatics Information Systems Architecture Research Division Tokyo Japan Lancaster University School of Computing and Communications Ukraine AXON Logic Greece City University of Macau Faculty of Data Science Macau China China

This article investigates a control system within the context of sixth-generation wireless networks. The remote control performance optimization confronts the technical challenges that arise from the intricate interactions between communication and control sub-systems, asking for a co-design. Considering the system dynamics, we formulate the sequential co-design decision-makings of communication and control over a discrete time horizon as a Markov decision process, for which a practical offline learning framework is proposed. Our proposed framework integrates large language models into the elements of reinforcement learning. We present a case study on the age of semantics-aware communication and control co-design to showcase the potential of our proposed learning framework. Furthermore, we discuss the open issues remaining to make our offline learning framework feasible for real-world implementations and highlight the research directions for future explorations. © 1986-2012 IEEE.

关键词： Markov processes

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mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations

arXiv

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arXiv 2022年

作者： Suzumura, Toyotaro Sugiki, Akiyoshi Takizawa, Hiroyuki Imakura, Akira Nakamura, Hiroshi Taura, Kenjiro Kudoh, Tomohiro Hanawa, Toshihiro Sekiya, Yuji Kobayashi, Hiroki Matsushima, Shin Kuga, Yohei Nakamura, Ryo Jiang, Renhe Kawase, Junya Hanai, Masatoshi Miyazaki, Hiroshi Ishizaki, Tsutomu Shimotoku, Daisuke Miyamoto, Daisuke Aida, Kento Takefusa, Atsuko Kurimoto, Takashi Sasayama, Koji Kitagawa, Naoya Fujiwara, Ikki Tanimura, Yusuke Aoki, Takayuki Endo, Toshio Ohshima, Satoshi Fukazawa, Keiichiro Date, Susumu Uchibayashi, Toshihiro Information Technology Center The University of Tokyo Japan Information Initiative Center Hokkaido University Japan Cyberscience Center Tohoku University Japan Faculty of Engineering Information and Systems University of Tsukuba Japan Division for Information and Communication Systems The University of Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Japan Center for Cloud Research and Development National Institute of Informatics Japan Research and Development Center for Academic Networks National Institute of Informatics Japan Research Center for Open Science and Data Platform National Institute of Informatics Japan Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Japan Global Scientific Information and Computing Center Tokyo Institute of Technology Japan Information Technology Center Nagoya University Japan Academic Center for Computing and Media Studies Kyoto University Japan Cybermedia Center Osaka University Japan Research Institute for Information Technology Kyushu University Japan

The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform’s virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an the overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans. © 2022, CC BY.

关键词： Internet of things

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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Addressing Variability in Reuse Prediction for Last-Level Caches

arXiv

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arXiv 2020年

作者： Faldu, Priyank Philosophy Institute of Computing Systems Architecture School of Informatics University of Edinburgh United Kingdom

Last-Level Cache (LLC) represents the bulk of a modern CPU processor’s transistor budget and is essential for application performance as LLC enables fast access to data in contrast to much slower main memory. Problematically, technology constraints make it infeasible to scale LLC capacity to meet the ever-increasing working set size of the applications. Thus, future processors will rely on effective cache management mechanisms and policies to get more performance out of the scarce LLC capacity. Applications with large working set size often exhibit streaming and/or thrashing access patterns at LLC. As a result, a large fraction of the LLC capacity is occupied by dead blocks that will not be referenced again, leading to inefficient utilization of the LLC capacity. To improve cache efficiency, the state-of-the-art cache management techniques employ prediction mechanisms that learn from the past access patterns with an aim to accurately identify as many dead blocks as possible. Once identified, dead blocks are evicted from LLC to make space for potentially high reuse cache blocks. In this thesis, we identify variability in the reuse behavior of cache blocks as the key limiting factor in maximizing cache efficiency for state-of-the-art predictive techniques. Variability in reuse prediction is inevitable due to numerous factors that are outside the control of LLC. The sources of variability include control-flow variation, speculative execution and contention from cores sharing the cache, among others. Variability in reuse prediction challenges existing techniques in reliably identifying the end of a block’s useful lifetime, thus causing lower prediction accuracy, coverage, or both. To address this challenge, this thesis aims to design robust cache management mechanisms and policies for LLC in the face of variability in reuse prediction to minimize cache misses, while keeping the cost and complexity of the hardware implementation low. To that end, we propose two cache man

关键词： Forecasting

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning architecture

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Journal of Computer Science & Technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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Inverse learning of black-box aggregator for robust Nash equilibrium

arXiv

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arXiv 2024年

作者： Chen, Guanpu Xu, Gehui He, Fengxiang Tao, Dacheng Parisini, Thomas Johansson, Karl Henrik Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden Digital Futures Stockholm100 44 Sweden Artificial Intelligence and its Applications Institute School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom College of Computing & Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Engineering and Architecture University of Trieste Trieste34127 Italy

In this note, we investigate the robustness of Nash equilibria (NE) in multi-player aggregative games with coupling constraints. There are many algorithms for computing an NE of an aggregative game given a known aggregator. When the coupling parameters are affected by uncertainty, robust NE need to be computed. We consider a scenario where players' weight in the aggregator is unknown, making the aggregator kind of "a black box". We pursue a suitable learning approach to estimate the unknown aggregator by proposing an inverse variational inequality-based relationship. We then utilize the counterpart to reconstruct the game and obtain first-order conditions for robust NE in the worst case. Furthermore, we characterize the generalization property of the learning methodology via an upper bound on the violation probability. Simulation experiments show the effectiveness of the proposed inverse learning approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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Reporting of Scope2 Co2-Emissions and Electricity Consumption in 111 Indian Ocean Cities between 2015-2022

SSRN

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SSRN 2024年

作者： Nematchoua, Modeste Kameni Buratti, Cinza Orosa, Jose A. Attia, Shady Teller, Jacques Bemanana, Muriel Akinola, Olatunji Bernard, Andrianirina Charles Sendrahasina, Rakotomalala Minoson Roy, Sambatra Eric Jean Falisoa, Rafanotsimiva Liva Jean-Pierre, Messina Reiter, Sigrid School of Informatics Computing and Cyber Systems Northern Arizona University 1295 S. Knoles Dr. Building 90 Room 320 FlagstaffAZ86011 United States Department of Engineering University of Perugia Via G. Duranti 63 Perugia06125 Italy Department of N. S. and Marine Engineering Escuela Técnica Superior de N. y M University of A Coruña Paseo de Ronda 51 A Coruna15011 Spain Sustainable Building Design Lab Dept. UEE Applied Sciences University of Liege Belgium LEMA ArGEnCo Department University of Liege Belgium Higher Polytechnic School University of Antsiranana Antsiranana Madagascar Science and Technology’s Higher Institute University of Mahajanga Mahajanga Madagascar University of Antananarivo Po Box 566 Antananarivo101 Madagascar Department of Industrial Engineering Higher Institute of Technology Antsiranana Antsiranana201 Madagascar Faculty of Architecture and Construction Notre-Dame du Kasaï University Congo

Several sources related that the electricity sector emits almost a quarter of greenhouse gases each year in the world. It is therefore one of the important sectors to take into account to limit global warming. Indian Ocean cities produce significant CO2 emissions during electricity consumption. Their volume and accuracy remain practically unknown and untested. Indeed, until now, there is no methodology suggested by the researchers to evaluate Fossil Fuel carbon dioxide (FFCO2) emission, and electricity consumption in this region. Aware of these crucial problems, this study was carried out to assess and analyse CO2 emissions coming from Electricity consumption (called Scope2) in 111 cities located in the Indian Ocean from 55 Power plants between 2015-2022 (08 years) and in four sectors (Residential, Commercial, Industrial, and On-road). To carry out a good comparison, all the data were grouped into three categories, Before COVID (2015-2018);During COVID (2019-2020);and After COVID (2021-2022)). The results showed that the CO2 emission difference is significant in the residential and commercial sectors. It was observed that CO2 emissions increased in 2019-2022 in the residential, industrial, and on-road sectors whereas, simultaneously during the same period, it decreased in the commercial sector. During the three periods, the CO2 emissions rate was the highest in the residential sector (around 52%), and the least on-road (around 1%). The significant difference in the commercial sector suggests a decrease in electricity consumption during the peak of the pandemic due to reduced business activities. Businesses adapted to new operating conditions, such as reduced hours or enhanced energy efficiency measures, which also contributed to the change in consumption patterns. © 2024, The Authors. All rights reserved.

关键词： Low emission

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