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arXiv 2025年

作者： Huang, Xiaohan Wang, Dongjie Ning, Zhiyuan Qiao, Ziyue Long, Qingqing Zhu, Haowei Du, Yi Wu, Min Zhou, Yuanchun Xiao, Meng Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences China Computer Network Information Center Chinese Academy of Sciences China Department of Electrical Engineering and Computer Science University of Kansas United States School of Computing and Information Technology Great Bay University Dongguan China School of Software Tsinghua University China Institute for Infocomm Research Agency for Science Technology and Research Singapore

Feature transformation methods aim to find an optimal mathematical feature-feature crossing process that generates high-value features and improves the performance of downstream machine learning tasks. Existing frameworks, though designed to mitigate manual costs, often treat feature transformations as isolated operations, ignoring dynamic dependencies between transformation steps. To address the limitations, we propose TCTO, a collaborative multi-agent reinforcement learning framework that automates feature engineering through graph-driven path optimization. The framework’s core innovation lies in an evolving interaction graph that models features as nodes and transformations as edges. Through graph pruning and backtracking, it dynamically eliminates low-impact edges, reduces redundant operation, and enhances exploration stability. This graph also provides full traceability to empower TCTO to reuse high-utility subgraphs from historical transformations. To demonstrate the efficacy and adaptability of our approach, we conduct comprehensive experiments and case studies, which show superior performance across a range of datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications

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IEEE Transactions on Network and Service Management 2024年

作者： Tang, Jun Guo, Bing Shen, Yan Garg, Sahil Kaddoum, Georges Hossain, M. Shamim Sichuan University College of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China École de Technologie Supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Lebanese American University Artificial Intelligence and Cyber Systems Research Center Beirut03797751 Lebanon King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy. © 2024 IEEE.

关键词： Data assimilation

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A high granularity state-space method for contaminant detection and isolation in intelligent buildings 16

A high granularity state-space method for contaminant detect...

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16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020

作者： Kyriacou, Alexis Michaelides, Michalis Panayiotou, Christos Polycarpou, Marios KIOS Research and Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9781713823605

Conservation of the Indoor Air Quality is essential in modern, energy efficient, intelligent buildings. However, accidents or malicious acts that often result in various airborne contaminant releases can endanger the occupants' wellbeing. In this work, an indoor air quality monitoring methodology is presented for detecting and localizing a contaminant source in the 3D indoor environment. Specifically, a state-space model is used to describe the contaminant dispersion in a zone which is discretised into multiple cuboid cells. The airflow exchange between the cells is computed based on a 3D discretized coarse-grid CFD analysis. A contaminant detection and localization methodology that considers modelling uncertainty and measurement noise, is adapted and applied to the CFD-based state-space model for detecting the existence of a possible contaminant source and estimating its location within the zone. The performance of the approach is illustrated through simulation examples. © 2020 16th Conference of the International Society of Indoor Air Quality and Climate: Creative and Smart Solutions for Better Built Environments, Indoor Air 2020. All rights reserved.

关键词： Uncertainty analysis

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Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service

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Artificial Intelligence in Medicine 2024年 149卷 102779-102779页

作者： Aminizadeh, Sarina Heidari, Arash Dehghan, Mahshid Toumaj, Shiva Rezaei, Mahsa Jafari Navimipour, Nima Stroppa, Fabio Unal, Mehmet Medical Faculty Tabriz Branch Islamic Azad University Tabriz Iran Department of Software Engineering Haliç University Istanbul34060 Turkey Tabriz University of Medical Sciences Faculty of Medicine Tabriz Iran Urmia University of Medical Sciences Urmia Iran Tabriz University of Medical Sciences Faculty of Surgery Tabriz Iran Future Technology Research Center National Yunlin University of Science and Technology Douliou64002 Taiwan Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Mathematics School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Traditional healthcare methods, often characterized by limitations in disease prevention, predominantly react to illnesses after their onset rather than proactively averting them. The advent of Artificial Intelligence (AI) has ushered in a wave of transformative applications designed to enhance healthcare services, with Machine Learning (ML) as a noteworthy subset of AI. ML empowers computers to analyze extensive datasets, while Deep Learning (DL), a specific ML methodology, excels at extracting meaningful patterns from these data troves. Despite notable technological advancements in recent years, the full potential of these applications within medical contexts remains largely untapped, primarily due to the medical community's cautious stance toward novel technologies. The motivation of this paper lies in recognizing the pivotal role of the healthcare sector in community well-being and the necessity for a shift toward proactive healthcare approaches. To our knowledge, there is a notable absence of a comprehensive published review that delves into ML, DL and distributed systems, all aimed at elevating the Quality of Service (QoS) in healthcare. This study seeks to bridge this gap by presenting a systematic and organized review of prevailing ML, DL, and distributed system algorithms as applied in healthcare settings. Within our work, we outline key challenges that both current and future developers may encounter, with a particular focus on aspects such as approach, data utilization, strategy, and development processes. Our study findings reveal that the Internet of Things (IoT) stands out as the most frequently utilized platform (44.3 %), with disease diagnosis emerging as the predominant healthcare application (47.8 %). Notably, discussions center significantly on the prevention and identification of cardiovascular diseases (29.

关键词： Quality of service

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Cyber-Physical Interplay for Data Offloading in Vehicular Networks

IEEE Networking Letters

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IEEE Networking Letters 2020年第2期2卷 54-57页

作者： Kolios, Panayiotis Ellinas, Georgios Panayiotou, Christos KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia1678 Cyprus Department of Electrical and Computer Engineering University of Cyprus Nicosia1678 Cyprus

This letter investigates the interplay of the speed-density relationship in transportation systems and the density-congestion relationship in communications systems, in terms of the data traffic volume offloaded. It also demonstrates that relaying, when this interplay is also taken into account, greatly enhances the forwarding potentials of vehicular terminals. © 2019 IEEE.

关键词： Cyber Physical System

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Periodicity Analysis of Most Time Series Methods: A Review

Periodicity Analysis of Most Time Series Methods: A Review

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Muneera Yousif Musab Ahmed Mohammed Mete Celik Asmaa E. Hamdoon Riham Jassim Noor M. Abdullah Software Department College of Information Technology Ninevah University Mosul Iraq College of Education for Humanities Sciences Kirkuk University Kirkuk Iraq Computer Engineering Department Engineering Faculty Erciyes University Kayseri Turkey Water Resources Studies Center for Northern Region Projects Ministry of Water Resources of Iraq Water Resources Studies Center for Northern Region Projects Ministry of Water Resources of Iraq Mosul Iraq

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

One of the most important methods of predicting the future is through past events and data repeated over time, as time series are those data indexed using time sequentially on data points distributed according to time, and periodic time series analysis is the analysis of a series of recorded measurements of a phenomenon that are repeated more than once during specific times periods. Periodicity detection has an impact on many areas in the world, such as climate, motion, education et cetera. It is important to consider in solving problems, moreover, there are many data sets in the world, the most prominent of which is time series data, and this type of data is constantly increasing. In the study, several data periodicity analysis methods (such as autocorrelation, wavelet transform, DTW, RNN, LSTM) were inspected.

关键词： Wavelet transforms Reviews Time series analysis Education Distributed databases Wavelet analysis Time measurement Autocorrelation Long short term memory Meteorology

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Number plate recognition smart parking management system using IoT

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Measurement: Sensors 2025年 37卷

作者： Ditta, Allah Ahmed, Muhammad Maroof Mazhar, Tehseen Shahzad, Tariq Alahmed, Yazan Hamam, Habib Department of Information Sciences Division of Science and Technology University of Education Lahore54000 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Software Engineering Al Ain University Al Ain University Abu Dhabi United Arab Emirates Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management. © 2024 The Authors

关键词： Digital storage

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Dynamic Pricing based Near-Optimal Resource Allocation for Elastic Edge Offloading

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IEEE Transactions on Mobile Computing 2025年

作者： Xia, Yun Xue, Hai Zhang, Di Mumtaz, Shahid Xu, Xiaolong Rodrigues, Joel J. P. C. University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Zhengzhou University School of Electrical and Information Engineering Henan International Joint Laboratory of Intelligent Health Information System National Telemedicine Center National Engineering Laboratory for Internet Medical Systems and Applications Zhengzhou450001 China Nottingham Trent University Department of Engineering NottinghamNG1 4FQ United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of Nanjing University of Information Science and Technology School of Software Nanjing210044 China Amazonas State University Manaus Brazil

In mobile edge computing (MEC), task offloading can significantly reduce task execution latency and energy consumption of end user (EU). However, edge server (ES) resources are limited, necessitating efficient allocation to ensure the sustainable and healthy development for MEC system. In this paper, we propose a dynamic pricing mechanism based near-optimal resource allocation for elastic edge offloading. First, we construct a resource pricing model and accordingly develop the utility functions for both EU and ES, the optimal pricing model parameters are derived by optimizing the utility functions. In the meantime, our theoretical analysis reveals that the EU's utility function reaches a local maximum within the search range, but exhibits barely growth with increased resource allocation beyond this point. To this end, we further propose the Dynamic Inertia and Speed-Constrained particle swarm optimization (DISC-PSO) algorithm, which efficiently identifies the near-optimal resource allocation. Comprehensive simulation results validate the effectiveness of DISC-PSO algorithm, demonstrating that it significantly outperforms existing schemes by reducing the average number of iterations to reach a near-optimal solution by 86.88%, increasing the EU utility function value by 0.13%, and decreasing the variance of results by 96.78%. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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Distributed formation control of networked mechanical systems

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IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

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GLoT: A Novel Gated-Logarithmic Transformer for Efficient Sign Language Translation

GLoT: A Novel Gated-Logarithmic Transformer for Efficient Si...

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IEEE Future Networks World Forum (FNWF)

作者： Nada Shahin Leila Ismail Department of Computer Science and Software Engineering Intelligent Distributed Computing and Systems (INDUCE) Lab College of Information Technology UAE University Al-Ain United Arab Emirates Emirates Center for Mobility Research UAE University Al-Ain United Arab Emirates

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Machine Translation has played a critical role in reducing language barriers, but its adaptation for Sign Language Machine Translation (SLMT) has been less explored. Existing works on SLMT mostly use the Transformer neural network which exhibits low performance due to the dynamic nature of the sign language. In this paper, we propose a novel Gated-Logarithmic Transformer (GLoT) that captures the long-term temporal dependencies of the sign language as a time-series data. We perform a comprehensive evaluation of GloT with the transformer and transformer-fusion models as a baseline, for Sign-to-Gloss-to-Text translation. Our results demonstrate that GLoT consistently outperforms the other models across all metrics. These findings underscore its potential to address the communication challenges faced by the Deaf and Hard of Hearing community.

关键词： Sign language Adaptation models Translation Accuracy Terminology Neural networks Semantics Logic gates Transformers Testing

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