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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5990-6011页

作者： Song, Xiaotian Xie, Xiangning Lv, Zeqiong Yen, Gary G. Ding, Weiping Lv, Jiancheng Sun, Yanan Sichuan University College of Computer Science Chengdu610065 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

Neural architecture search (NAS) has received increasing attention because of its exceptional merits in automating the design of deep neural network (DNN) architectures. However, the performance evaluation process, as a key part of NAS, often requires training a large number of DNNs. This inevitably makes NAS computationally expensive. In past years, many efficient evaluation methods (EEMs) have been proposed to address this critical issue. In this article, we comprehensively survey these EEMs published up to date, and provide a detailed analysis to motivate the further development of this research direction. Specifically, we divide the existing EEMs into four categories based on the number of DNNs trained for constructing these EEMs. The categorization can reflect the degree of efficiency in principle, which can in turn help quickly grasp the methodological features. In surveying each category, we further discuss the design principles and analyze the strengths and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs. Furthermore, we also discuss the current challenges and issues to identify future research directions in this emerging topic. In summary, this survey provides a convenient overview of EEM for interested users, and they can easily select the proper EEM method for the tasks at hand. In addition, the researchers in the NAS field could continue exploring the future directions suggested in the article. © 2020 IEEE.

关键词： Network architecture

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A data-driven architecture for adaptive decentralized charging and energy management

A data-driven architecture for adaptive decentralized chargi...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Ascher, Dominik Albayrak, Sahin Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin10587 Germany

ISBN: (纸本)9789531842976

Distributed energy resources (DER), renewable energy sources (RES) and electric vehicles (EV) pose considerable challenges with respect to their efficient integration within the power system. Operation and control strategies for energy management systems (EMS) and charging station management systems (CSMS) are required to be highly adaptable and able to rapidly incorporate information received about power system supplies and demands to maintain efficient system operation. Here, information integration and monitoring is particularly crucial in use cases such as smart charging of EV and vehicle-to-grid (V2G) interactions. For this, standardization is a key challenge, as communication standards such as OCPP and EEBus facilitate interoperability, as well as monitoring and controlling aligned devices using shared data models and protocols. In this work, a data-driven architecture for integrated and adaptive charging and energy management is presented, which allows for mapping of logical system states and actions to concrete protocol data models. Based on actual and predicted information received with respect to system state, control strategies can be derived and adapted during run-time to address changed system requirements, behavior or system topologies of DER and charging stations (CS). © 2024 IEEE.

关键词： data-driven architecture energy management ICT smart charging

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Attention-Guided CNN with LSTM for Temporal Gastrointestinal Disease Progression Prediction in Endoscopic Video Sequences 4

Attention-Guided CNN with LSTM for Temporal Gastrointestinal...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohamad, Fayazoddin Dhumpati, Raghu Reenadevi, R. Rao, P.S.V. Srinivasa Sutaria, Kamal Kulkarni, Shirish NY United States Bahrain Polytechnic Department of Computer Science & Engineering Bahrain City Bahrain Salem India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The detection and tracking of changes in the progression of GI disease using endoscopic video analysis remains difficult due to temporal changes and image complexity. Proper models of prediction are critical in diagnosing individuals at an early stage and offering them treatment. In the work presented in this paper, we introduce novel Attention-Guided Convolutional Neural Network (AG-CNN) combined with LSTM networks to analyze temporal endoscopic video sequences. It uses spatial attention to catch necessary areas and temporal attention to capture assertive illness transformation in video frames. For disease progression prediction the proposed model yield 94.8% accuracy with 93.2% sensitivity and 95.6% specificity. The attention mechanism increased the feature extraction speed by 22 percent when compared to traditional CNN-LSTM classes. Independent validation checks confirmed that the two models produced a prediction concordance of 92.7% based on different patient samples. The overall performance of temporal GI disease progression prediction by the AG-CNN-LSTM framework shows potential usage in clinical diagnosis by providing automated disease progression monitoring and treatment planning. © 2024 IEEE.

关键词： Video analysis

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A Review of Blockchain-Based E-Voting Systems: Comparative Analysis and Findings

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第23期17卷 49-67页

作者： Fatih, Rabia Arezki, Sara Gadi, Taoufiq Faculty of Sciences and Techniques Mathematics Computer Science and Engineering Sciences Laboratory Hassan first University of Settat Settat Morocco

The emergence of blockchain has ushered in a significant transformation in information systems research. Blockchain’s key pillars such as decentralization, immutability, and transparency have paved the path for extensive exploration in various research domains. This particular study is focused on electronic voting, aiming to improve voting procedures by making better use of the benefits offered by blockchain technology. Through a comprehensive review of existing literature, we highlight the potential benefits of blockchain-based electronic voting systems such as transparency, security, and efficiency. However, several challenges, such as scalability, personal data confidentiality, and ensuring robust identity verification, persist. Addressing these issues is necessary to unlock the full potential of blockchain-based electronic voting systems, thereby fostering the development of trustworthy election systems in the future. © 2023 by the authors of this article. Published under CC-BY. All Rights Reserved.

关键词： Blockchain

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A low-cost buoyant force measurement device for monitoring sugar concentration in water solution

A low-cost buoyant force measurement device for monitoring s...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Kaučić, Luka Kovačević, Ivana Aleksi, Ivan Keser, Tomislav Zidar, Josip Rimac-Drlje, Snježana Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Computer Engineering and Automation Osijek Croatia Computer Science and Information Technology Osijek Faculty of Electrical Engineering Department of Communications Osijek Croatia

ISBN: (纸本)9798350322972

Monitoring sugar concentration during fermentation is crucial for producing high-quality alcoholic beverages. Traditional methods for measuring sugar concentration can be costly and time-consuming, especially for small-scale producers. In this study, we developed a low-cost buoyant force measurement device for monitoring sugar concentration in water solutions. The device consists of a buoyant object fully submerged in the solution and connected to a load-cell sensor. As the sugar concentration in the liquid increases, the buoyant force on the object increases, and the load-cell measures this force. Proposed device is calibrated using solutions of known sugar concentrations and its high accuracy and precision is presented. The device is tested in controlled environment to ensure accurate tracking changes in sugar concentration over time. Proposed device can be a valuable tool for small-scale producers looking to optimize their fermentation processes while minimizing costs. © 2023 IEEE.

关键词： Fermentation

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Improvement in Continuous Black-box Setting Search Performance by Tuning L-SHADE Differential Evolution Historical Memory Size Parameter 17

Improvement in Continuous Black-box Setting Search Performan...

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Ferš, Nataša Ošep Zamuda, Aleš University of Maribor Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9789616165617

In this work, using Modular Differential Evolution (Modular DE), we investigate the impact of parameter memory size (MS) on the search performance of Success-history based parameter adaptation (L-SHADE) for Differential Evolution. To benchmark it, we use the blackbox optimization benchmarking (BBOB) test suite from the COmparing Continuous Optimizers in a black-box setting (COCO) platform. We run the algorithm on all 24 functions in BBOB and observe results separately, on each of those function. Experiment and assessment performance are done with Iterative Optimization Heuristics Profiler (IOHprofiler). © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Iterative methods

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Analysis of the shortest path method application in social networks 32

Analysis of the shortest path method application in social n...

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32nd International Conference on Information Modeling and Knowledge Bases, EJC 2022

作者： Šumak, Boštjan Pušnik, Maja University of Maribor Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9781643683706

This paper analyzes the shortest path problem (SPP) in social networks, based on the investigation and implementation of different methods on a simulated example. The objectives of the paper include identification of the most commonly used methods for finding the shortest path in a social network as a strategic attempt to speed the search of network nodes, focusing on the application of the two most used SPP methods: The Dijkstra and Bellman-Ford algorithms. A comparative analysis is used as an investigation method for performance evaluation of different algorithms, based on their implementation and behavior, tested on a social network example. The research results indicate that the Dijkstra algorithm is faster, and therefore more suitable for searching the shortest connection in social networks. © 2023 The authors and IOS Press.

关键词： Graph theory

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Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking 17

Solving 100-Digit Challenge with Score 100 by Extended Runni...

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Zamuda, Aleš University of Maribor Faculty of Electrical Engineering and Computer Science Slovenia

ISBN: (纸本)9789616165617

This paper assesses Distance-based Success History Differential Evolution for 100-Digit Challenge and Numerical Optimization Scenarios (DISHchain3e+12). DISHchain3e+12 algorithm is based on DISH algorithm, using a Differential Evolution (DE) parameters control mechanism based on Euclidean distance between trial and parent in a search space to weigh Lehmer means in scaling and crossover historical parameter memories. In this paper, the maximum number of fitness evaluations and initial population size parameters are tailored in the DISHchain3e+12 algorithm per problem, since the benchmark used allows tuning up to two of the same parameters independently for each problem. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Population statistics

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Empowering Education through EERP: A Customizable Educational VR Escape Room Platform

Empowering Education through EERP: A Customizable Educationa...

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2023 IEEE International Symposium on Mixed and Augmented Reality Adjunct, ISMAR-Adjunct 2023

作者： Darejeh, Ali Faculty of Engineering Unsw School of Computer Science Sydney Australia

ISBN: (纸本)9798350328912

Efficient programming learning necessitates practical knowledge, creativity, and problem-solving skills. However, current methods often disregard these crucial elements, focusing solely on coding principles. As a result, student engagement is compromised. To mitigate this, game-based learning, especially through virtual reality, proves effective in enhancing outcomes and motivation. Addressing this gap, we have developed a pioneering VR-based escape room platform for learning programming. This innovation empowers educators to tailor rooms and puzzles to their curriculum, while its multiplayer feature fosters collaboration, intensifying the learning journey. By seamlessly integrating immersive learning with personalized content, our platform can revolutionize programming education. © 2023 IEEE.

关键词： Virtual reality

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Domination based graph neural networks

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International Journal of computers and Applications 2024年第11期46卷 998-1005页

作者： Meybodi, Mohsen Alambardar Safari, Mahdi Davoodijam, Ensieh Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics University of Isfahan Isfahan Iran Department of Software Engineering University of Kashan Kashan Iran

Graph Neural Networks (GNNs) have emerged as a widely used and effective method across various domains for learning from graph data. Despite the abundance of GNN variants, many struggle with effectively propagating messages over long distances. This paper introduces a novel hierarchical message passing framework for graph learning, specifically designed to address the challenge of long-distance message propagation in graphs. By constructing smaller graphs from the main graph using the concept of domination, a fundamental principle in graph theory, we facilitate more efficient message passing within each subgraph. Subsequently, we employ a Graph Attention Network (GAT) to aggregate these features and propagate them to distant nodes across the graph. Experimental results on standard node classification datasets validate that the proposed architecture achieves performance comparable to or better than conventional GNNs. Additionally, our model consistently performs better on graphs with missing edges. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Neural network models

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