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IEEE International Conference on Big Data

作者： Katerina Kyriakou Apostolos Apostolaras Polychronis Velentzas Georgios Benos Konstantinos Koutsoukos Chrysostomos Symvoulidis Kaitai Liang Zeshun Shi Asterios Leonidis Kyriaki Miniadou Eleni Veroni Spyridon Evangelatos Georgios Th. Papadopoulos Thanasis Korakis Department of Electrical and Computer Engineering University of Thessaly Volos Greece CERTH The Centre for Research & Technology Hellas Volos Greece Research & Development Thridium Ltd. London United Kingdom Delft University of Technology Delft The Netherlands Institute of Computer Science (ICS) Foundation for Research and Technology - Hellas (FORTH) Crete Greece Research & Innovation Development Netcompany-Intrasoft S.A. Luxembourg Luxembourg Dept. of Electronic Engineering Hellenic Mediterranean University Crete Greece Department of Informatics and Telematics Harokopio University of Athens Athens Greece

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

This paper introduces the Biometrics Data Space framework, which is a secure ecosystem built on Data Spaces technology and it is designed to address the challenges of suspect identification during cross-border crime investigation. Apart from Data Spaces technology, the proposed framework innovates by leveraging also Privacy Enhancing Technologies (PETs) and blockchain to enable secure, trustworthy, and sovereign data exchange between Law Enforcement Agencies (LEAs) across borders. Specifically, it utilizes advanced PETs, including Large-Scale Biometric Data Indexing based on deep hashing techniques and Homomorphic Encryption to allow for suspect identification without disclosing sensitive information of personal biometric data. Thus, it enables LEAs to securely compare and exchange encrypted sensitive biometric data, including facial images, fingerprints and voiceprints, while maintaining data privacy and data sovereignty. LEAs define the usage rules for the biometic data they own and these rules are enforced to and respected by the other LEAs participating in the Biometrics Data Space. The proposed architecture is designed to be scalable, allowing the incorporation of additional biometric modalitiies and the easy expansion and integration with new participant LEAs.

关键词： Biometrics Law enforcement Space technology Terrorism Ecosystems Regulation Blockchains Resource management Spectrogram Indexing

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Efficient Classification for Neural Machines Interpretations Based on Mathematical Models

Efficient Classification for Neural Machines Interpretations...

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Innovative Practices in technology and Management (ICIPTM), International Conference on

作者： Rahul Kumar Monit Kapoor Neeraj Varshney Manish Saraswat Akhil Sankhyan Devendra Singh Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India Department of Computer Engineering and Applications GLA University Mathura India Lloyd Institute of Engineering & Technology Greater Noida India Lloyd Law College Greater Noida India Department of Computer Science & Engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

This study explores mathematical models to enhance our understanding of information generated by neural machines. We tested various models, including Statistical Machine Translation (SMT), Neural Machine Translation (NMT), Mathematical Transformation Recurrent Neural Networks (M.T RNN), and Simple Recurrent Units (SRU). Our research indicates that these models significantly improve the coherence and understanding of computer-generated writing, revolutionizing natural language processing and machine learning. Proper categorization facilitates the organization and utilization of interpreted data, opening avenues for diverse applications across academic fields. For more information, click on this link. These findings empower researchers and professionals to choose effective interpretation strategies for specific language combinations, enhancing machine translation intelligibility and accuracy.

关键词： Recurrent neural networks Accuracy Statistical analysis Computational modeling Organizations Machine learning Coherence

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Evolutionary Trends of Intelligent IoT Networking for Commercial and Industrial Use Cases

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中国通信（英文版） 2023年第8期20卷前插1-前插4页

作者： Shuai Han Rong Bo Nadra Guizani Sai Xu Ruoyu Su Liang Xiao Cheng Li Department of Electronics and Communication Engineering Harbin Institute of Technology Communications Research Centre Canada Engineering program and the computing Research Association School of Cybersecurity Northwestern Polytechnical UniversityXi'anChina Nanjing University of Posts and Telecommunications Department of Informatics and Communication Engineering Xiamen Univer- sityFujianChina Department of Electrical and Computer Engineering of Memorial University St.John'sCanada

The intelligent Internet of Things(IIoT)is one of the key enablers for the digital transformation of traditional industries towards Industry *** services have diversified requirements due to the nature of their commercial and industrial *** various devices in IIoT environments have their unique properties in terms of resource limita-tions,network lifetimes,and application Quali-ty-of-Service(QoS)requirements which affect the performance under such applications ***,IoT devices typically have constrained power,storage,computing,high latency,massive multiple access and communications resources which limit the performance of IIoT for commercial and industrial Use *** the other hand,devices used in IIoT were not initially designed with security in mind and they had been considered *** response to the above challenges,domestic and foreign scholars have gradually carried out research on network architecture,collaborative scheduling strategy,energy supply technology,resource man-agement,and ***,the research on IIoT is still in its infancy,and has not yet formed comprehen-sive and systematic theoretical methods and technical standards.

关键词： Special issues and sections Internet of Things Intelligent systems Commercialization Requirements engineering Industrial engineering

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An Improved Hierarchical Control Strategy for Power Management in Islanded Hybrid Microgrids

An Improved Hierarchical Control Strategy for Power Manageme...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Jeba Singh O K. Anish Pon Yamini Rajesh Sharma R Sheila Mahapathra Centre for Academic Research Alliance University Bengaluru India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Alliance University Bengaluru India Department of Electrical and Electronics Engineering Alliance University Bengaluru India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Power management and control through a hierarchical control strategy in a Hybrid Micro Grid (HMG) consisting of multiple AC and DC small Micro Grids (MG) with different voltage ratings are focussed of this research. The ultimate aim of the proposed power management strategy is to limit the continuous operation of Interlink (IL)in different load conditions. Hence, the power loss at the IL converters are reduced, orelse it may affect the operational stability of the HMG. The hierarchical control strategy for power management and coordination for islanded mode of HMG is implemented and analysed. The designed system consists of primary and secondary control levels, that provides an accurate power transfer among the small MGs. The new methodology ensure a continuous power flow within the small MGs with minimal operation of IL converter.

关键词： Accuracy Microgrids Power system stability Frequency conversion Hybrid power systems Stability analysis Information and communication technology Voltage control Frequency control Load flow

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Towards a Theoretical Framework of Autonomous Systems Underpinned by Intelligence and Systems Sciences

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 52-63页

作者： Yingxu Wang Ming Hou Konstantinos NPlataniotis Sam Kwong Henry Leung Edward Tunstel Imre JRudas Ljiljana Trajkovic International Institute of Cognitive Informatics and Cognitive Computing Department of Electrical and Computer EngineeringSchulich School of Engineering and Hotchkiss Brain InstituteUniversity of CalgaryCalgary T2N 1N4Canada Toronto Research Centre Defence Research&Development CanadaToronto M3K 2C9Canada Department of Electrical&Computer Engineering University of TorontoToronto M5S3G4Canada Department of Computer Science City University of Hong KongHong KongChina Department of Electrical and Computer Engineering Schulich School of EngineeringUniversity of CalgaryCalgary T2N 1N4Canada Autonomous&Intelligent Systems Department United Technologies Research CenterCT 06108 USA University Research&Innovation Center(EKIK) Óbuda UniversityBudapest H-1034Hungary School of Engineering Science Simon Fraser UniversityBurnaby V5A 1S6Canada

Autonomous systems are an emerging AI technology functioning without human intervention underpinned by the latest advances in intelligence,cognition,computer,and systems *** paper explores the intelligent and mathematical foundations of autonomous *** focuses on structural and behavioral properties that constitute the intelligent power of autonomous *** explains how system intelligence aggregates from reflexive,imperative,adaptive intelligence to autonomous and cognitive intelligence.A hierarchical intelligence model(HIM)is introduced to elaborate the evolution of human and system intelligence as an inductive *** properties of system autonomy are formally analyzed towards a wide range of applications in computational intelligence and systems *** paradigms of autonomous systems including brain-inspired systems,cognitive robots,and autonomous knowledge learning systems are *** in autonomous systems will pave a way towards highly intelligent machines for augmenting human capabilities.

关键词： Autonomous systems(AS) cognitive systems computational intelligence engineering paradigms intelligence science intelligent mathematics

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Editorial: Urban multi-energy system networks with high proportion of renewable energy

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Frontiers in Energy research 2025年 13卷

作者： Ding, Xiaoyi Xiang, Yue Morais, Hugo School of Water Resources and Hydroelectric Engineering Xi’an University of Technology Xi’an China Department of Electrical Engineering Sichuan University Chengdu China Institute of Systems and Computer Engineering: Research and Development Higher Technical Institute University of Lisbon Lisbon Lisbon Portugal

Faced with environment pollution and climate change, it is essential to develop advanced low-carbon energy system based on renewable sources. Micro-energy networks with a high proportion of renewable energy are one of the most important methods to achieve the complementary use of renewable energy and fossil energy with high efficiency of energy utilization. research on urban-level energy networks would be an important step for applying and integrating renewable sources. Faced with high penetration rate of renewable energy, the robust ability and safety operation are challenging from both sides of energy generation and the electrical grid. Meanwhile, the increasing number of electrical and fuel cell powered vehicles put additional difficulties for optimization and scheduling. Therefore, research on urban energy systems with a high proportion of renewable energy is extremely *** topic (“Urban Multi-energy System Networks with High Proportion of Renewable Energy”) aims to look into the key problems existing in the integrated multi-energy network system for the urban level and provide a detailed theoretical basis and experimental data for the design and operation of integrated micro-energy networks at the international academic level. The topic received several submissions within the field of renewable energy systems, including modeling and optimization, grid supply, the interaction between grid and EV/fuel cell cars, and advanced controlling strategy of off-grid/grid-connected microgrid systems, ***-carbon development of energyFeng et al. [1] modeled the natural gas system, power system, and their coupling components, and proposed a low-carbon economic control framework based on MASAC. The objective was to minimize both operational costs and carbon emissions. Simulation results using a nodal system demonstrated that this method significantly reduces costs and emissions compared to traditional approaches. Hu et al. [2] developed a low-carbon scheduling mod

关键词： renewable energy electric vehicle (EV) dynamic control multi-energy system synthetic fuel

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Transref: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting

SSRN

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SSRN 2024年

作者： Liu, Taorong Liao, Liang Chen, Delin Xiao, Jing Wang, Zheng Lin, Chia-Wen Satoh, Shin'ichi School of Computer Science Wuhan University Hubei Wuhan430072 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan

Image inpainting for completing complicated semantic environments and diverse hole patterns of corrupted images is challenging even for state-of-the-art learning-based inpainting methods trained on large-scale data. A reference image capturing the same scene of a corrupted image offers informative guidance for completing the corrupted image as it shares similar texture and structure priors to that of the holes of the corrupted image. In this work, we propose a Transformer-based encoder-decoder network for Reference-guided image inpainting, named TransRef. Specifically, the guidance is conducted progressively through a reference embedding procedure, in which the referencing features are subsequently aligned and fused with the features of the corrupted image. For precise utilization of the reference features for guidance, a reference-patch alignment (Ref-PA) module is proposed to align the patch features of the reference and corrupted images and harmonize their style differences, while a reference-patch transformer (Ref-PT) module is proposed to refine the embedded reference feature. Moreover, to facilitate the research of reference-guided image restoration tasks, we construct a publicly accessible benchmark dataset containing 50K pairs of input and reference images. Both quantitative and qualitative evaluations demonstrate the efficacy of the reference information and the proposed method over the state-of-the-art methods in completing complex holes. Code and dataset can be accessed at: https://***/Cameltr/TransRef. © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

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Multi-Parameter Analysis of Biosignals in Long-Term Control Medical Devices Management

Multi-Parameter Analysis of Biosignals in Long-Term Control ...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Serhii Holdobin Natalia Brynza Olha Tiutiunyk Olena Serhiienko Ihor Sosnov Pavlo Samus Industrial and Biomedical Electronics Department Kharkiv polytechnic institute National Technical University Kharkiv Ukraine Department of Informatics and Computer Technology Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Department of Applied Mathematics Kharkiv National University of Radio Electronics Kharkiv Ukraine Department of Informatics and Computer Engineering Simon Kuznets Kharkiv National University of Economics Kharkiv Ukraine Business Trade and Logistics Department Kharkiv Polytechnic Institute National Technical University Kharkiv Ukraine

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

The development of data processing technologies, microelectronics and sensor systems allows for high-precision multiparametric analysis of biosignals in real time. The paper considers the problem of automating medical processes to reduce the influence of the human factor and increase the accuracy of diagnostics. An improved method of multiparametric analysis of biosignals is proposed for long-term monitoring of the state of the cardiovascular system using modern sensor devices, data processing algorithms and artificial intelligence technologies. The research is aimed at improving the methods of collecting, transmitting and analyzing biosignals, which contributes to the creation of personalized medical devices and effective prediction of cardiovascular pathologies. The issues of classification of devices and biosignals, as well as their mathematical modeling to increase the accuracy of diagnostics, are considered.

关键词： Cardiovascular system Wireless sensor networks Accuracy Medical devices Heuristic algorithms Robot sensing systems Prediction algorithms Real-time systems Classification algorithms Monitoring

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Multi-Agent Reinforcement Learning for Energy Networks: Computational Challenges, Progress and Open Problems

arXiv

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arXiv 2024年

作者： Keren, Sarah Essayeh, Chaimaa Albrecht, Stefano V. Morstyn, Thomas Taub Faculty of Computer Science Technion - Israel Institute of Technology Israel Departement of Engineering Nottingham Trent University United Kingdom School of Informatics University of Edinburgh United Kingdom Department of Engineering Science University of Oxford United Kingdom

The rapidly changing architecture and functionality of electrical networks and the increasing penetration of renewable and distributed energy resources have resulted in various technological and managerial challenges. These have rendered traditional centralized energy-market paradigms insufficient due to their inability to support the dynamic and evolving nature of the network. This survey explores how multi-agent reinforcement learning (MARL) can support the decentralization and decarbonization of energy networks and mitigate the associated challenges. This is achieved by specifying key computational challenges in managing energy networks, reviewing recent research progress on addressing them, and highlighting open challenges that may be addressed using MARL. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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