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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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Towards Federated learning Driving Technology for Privacy-Preserving Micro-Expression Recognition

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Tsinghua science and Technology 2025年第5期30卷 2169-2183页

作者： Mingpei Wang Ling Zhou Xiaohua Huang Wenming Zheng School of Computer Science and Engineering Macau University of Science and Technology Macau China Oulu School Nanjing Institute of Technology Nanjing China Key Laboratory of Child Development and Learning Science of Ministry of Education and Research Center for Learning Science Southeast University Nanjing China Key Laboratory of Child Development and Learning Science of Ministry of Education School of Biological Science and Medical Engineering Southeast University Nanjing China

As mobile devices and sensor technology advance, their role in communication becomes increasingly indispensable. Micro-expression recognition, an invaluable non-verbal communication method, has been extensively studied in human-computer interaction, sentiment analysis, and security fields. However, the sensitivity and privacy implications of micro-expression data pose significant challenges for centralized machine learning methods, raising concerns about serious privacy leakage and data sharing. To address these limitations, we investigate a federated learning scheme tailored specifically for this task. Our approach prioritizes user privacy by employing federated optimization techniques, enabling the aggregation of clients' knowledge in an encrypted space without compromising data privacy. By integrating established micro-expression recognition methods into our framework, we demonstrate that our approach not only ensures robust data protection but also maintains high recognition performance comparable to non-privacy-preserving mechanisms. To our knowledge, this marks the first application of federated learning to the micro-expression recognition task.

关键词： Human computer interaction Representation learning Data privacy Privacy Sentiment analysis Sensitivity Federated learning Data protection Protection Optimization

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A blockchain-oriented covert communication technology with controlled security level based on addressing confusion ciphertext

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Frontiers of computer science 2025年第2期19卷 133-135页

作者： Lejun ZHANG Bo ZHANG Ran GUO Zhujun WANG Guopeng WANG Jing QIU Shen SU Yuan LIU Guangxia XU Zhihong TIAN Sergey GATAULLIN Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China Research and Development Center for E-Learning Ministry of EducationBeijing 100039China College of Information Engineering Yangzhou UniversityYangzhou 225009China School Math and Computer Science Quanzhou Normal UniversityQuanzhou 362000China Pazhou Lab Guangzhou 510330China School of Physics and Materials Science Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of SciencesMoscow 117418Russia Industrial Programming Department MIREA-Russian Technological UniversityMoscow 119454Russia

1 Introduction Traditional covert communication[1]usually relies on centralized channels,which are efficient in transmission but susceptible to eavesdropping and attacks,leading to information ***,distributed methods also exist,such as peer-to-peer networks and distributed hash *** technology,which is decentralized,anonymous,non-tamperable and anti-attack,can be applied to covert communication to solve the problems of traditional methods and improve the quality of communication.

关键词： communication tables networks

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Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution

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IEEE Transactions on Multimedia 2025年

作者： Fu, Congrui Yuan, Hui Jiang, Shiqi Zhang, Guanghui Shen, Liquan Hamzaoui, Raouf Center of Experimental Teaching China Shandong University School of Control Science and Engineering China Ministry of Education Key Laboratory of Machine Intelligence and System Control China Shandong University School of software Shandong Jinan China Shandong University School of Computer Science and Technology China Shanghai University School of Communication and Information Engineering Shanghai China De Montfort University School of Engineering and Sustainable Development LeicesterLE1 9BH United Kingdom

By converting low-frame-rate, low-resolution videos into high-frame-rate, high-resolution ones, space-time video super-resolution techniques can enhance visual experiences and facilitate more efficient information dissemination. We propose a convolutional neural network (CNN) for space-time video super-resolution, namely GIRNet. Our method combines long-term global information and short-term local information from the video to better extract complete and accurate spatial-temporal information. To generate highly accurate features and thus improve performance, the proposed network integrates a feature-level temporal interpolation module with deformable convolutions and a global spatial-temporal information-based residual convolutional long short-term memory (convLSTM) module. In the feature-level temporal interpolation module, we leverage deformable convolution, which adapts to deformations and scale variations of objects across different scene locations. This provides a more efficient solution than conventional convolution for extracting features from moving objects. Our network effectively uses forward and backward feature information to determine inter-frame offsets, leading to the direct generation of interpolated frame features. In the global spatial-temporal information-based residual convLSTM module, the first convLSTM is used to derive global spatial-temporal information from the input features, and the second convLSTM uses the previously computed global spatial-temporal information feature as its initial cell state. This second convLSTM adopts residual connections to preserve spatial information, thereby enhancing the output features. Experiments on the Vimeo90K dataset show that the proposed method outperforms open source state-of-the-art techniques in peak signal-to-noise-ratio (by 1.45 dB, 1.14 dB, and 0.2 dB over STARnet, TMNet, and 3DAttGAN, respectively), structural similarity index(by 0.027, 0.023, and 0.006 over STARnet, TMNet, and 3DAttGAN, respectiv

关键词： Long short-term memory

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An Empirical Study of Super-Resolution on Low-Resolution Micro-Expression Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Zhou, Ling Wang, Mingpei Huang, Xiaohua Zheng, Wenming Mao, Qirong Zhao, Guoying Macau University of Science and Technology School of Computer Science and Engineering 999078 China Nanjing Institute of Technology School of International Education Oulu School Jiangsu Nanjing211167 China Southeast University Key Laboratory of Child Development and Learning Science Ministry of Education Nanjing210096 China Southeast University Research Center for Learning Science China Southeast University School of Biological Science and Medical Engineering Jiangsu Nanjing210096 China Jiangsu University School of Computer Science and Communication Engineering Jiangsu Zhenjiang212013 China University of Oulu Center for Machine Vision and Signal Analysis 90014 Finland

Micro-expression recognition (MER) in low-resolution (LR) scenarios presents an important and complex challenge, particularly for practical applications such as group MER in crowded environments. Despite considerable advancements in super-resolution (SR) techniques for enhancing the quality of LR images and videos, few study has focused on investigate SR for improving LR MER. The scarcity of investigation can be attributed to the inherent difficulty in capturing the subtle motions of micro-expressions, even in original-resolution MER samples, which becomes even more challenging in LR samples due to the loss of distinctive features. Furthermore, a lack of systematic benchmarking and thorough analysis of SR-assisted MER methods has been noted. This paper tackles these issues by conducting a series of benchmark experiments that integrate both SR and MER methods, guided by an in-depth literature survey. Specifically, we employ seven cutting-edge state-of-the-art (SOTA) MER techniques and evaluate their performance on samples generated from 22 SOTA SR techniques, thereby addressing the problem of SR in MER. Through our empirical study, we uncover the primary challenges associated with SR-assisted MER and identify avenues to tackle these challenges by leveraging recent advancements in both SR and MER methodologies. Our analysis provides insights for progressing toward more efficient SR-assisted MER. © 2010-2012 IEEE.

关键词： Optical resolving power

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Evaluating the Impact of Special Characters on Password Security: a Comparative Analysis

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IEEE Network 2025年

作者： He, Daojing Liu, Zhiyong Chan, Sammy Zhou, Beibei Guizani, Mohsen Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Guizhou University State Key Laboratory of Public Big Data Guizhou China Research and Development Center Guangzhou Jingyuan Security Technology Co. Ltd Guangzhou China City University of Hong Kong Kowloon Department of Electrical Engineering Hong Kong Hong Kong Shanghai Urban Construction Vocational College School of Artificial Intelligence Application Shanghai China Machine Learning Department Abu Dhabi United Arab Emirates

Text-based password authentication remains essential for identity verification on internet platforms and blockchain systems. However, as computational power advances, security threats to user passwords have intensified, raising concerns about their effectiveness. While special characters (i.e., non-alphanumeric symbols, which include punctuation marks, mathematical symbols, and other non-letter/number characters like '.', '@', '#', '$', and similar) are often recommended to enhance password strength, the National Institute of Standards and Technology (NIST) prioritizes length over complexity, challenging their necessity. This study fills a critical gap in understanding user behavior regarding special character usage. By analyzing 15 datasets from Chinese, English, and German languages, we examined patterns of special character incorporation and their impact on password strength. Our findings indicate that users frequently employ predictable methods for including special characters, potentially compromising security, especially under stringent policies. We present three key contributions: (1) an analysis of special character usage patterns, (2) a quantitative assessment of strength differences using the PCFGv4.1 model, and (3) actionable recommendations for stakeholders to enhance password security practices. This research further advocates for aligning password policies with user behavior for more effective security. © 1986-2012 IEEE.

关键词： Authentication Protocol

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H-TERF: A hybrid approach combining fuzzy multi-criteria decision-making techniques and enhanced random forest to improve WBAN-IoT

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Internet of Things (The Netherlands) 2025年 32卷

作者： Khoshvaght, Parisa Tanveer, Jawad Rahmani, Amir Masoud Mohammadi, Mohammad Mehranzadeh, Amin Lansky, Jan Hosseinzadeh, Mehdi Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam South Korea

The Internet of Things (IoT) technology today has grown rapidly compared to the last few years, and the use of this technology has increased the quality of service to users day by day. The various applications of IoT have caused the attention of this innovation to enhance among different organizations. One of the important challenges of the IoT is routing, which can affect having a stable network. In this research, a hybrid approach called H-TERF (Hybrid TOPSIS and Enhanced Random Forest) is proposed for achieving efficient routing in IoT networks, specifically in Wireless Body Area Networks (WBAN). This method initially cluster nodes by using the DBSCAN clustering algorithm to optimize intra-cluster communication. Then, for routing, the nodes are ranked using the Fuzzy TOPSIS and Fuzzy AHP. This ranking is determined by several criteria, including the remaining energy of nodes, node memory, and throughput. Additionally, to manage more complex criteria such as node historical records and traffic rate, the initial ranking by the TOPSIS approach, along with the other mentioned criteria, is fed into an enhanced random forest model to identify the optimal path. This hybrid method enhances network performance in terms of lifespan, efficiency, delay, and packet delivery ratio. The outcomes of the simulation show that the suggested method surpasses existing approaches and is highly effective for application in IoT and WBAN networks. For example, the performance improvement of the proposed approach over the F-EVM, DECR, and DHH-EFO approaches in energy consumption was 20.62%, 25.85%, and 32.57%, respectively. © 2025 Elsevier B.V.

关键词： DBSCAN clustering Enhanced random forest Fuzzy AHP Fuzzy TOPSIS Internet of Things

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Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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Study on the Fundamentals of Electrical engineering Based on Grey Correlation Analysis 18th

Study on the Fundamentals of Electrical Engineering Based on...

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18th International Conference on computer science and Education, ICCSE 2023

作者： Tang, Fei Xu, Jian Qi, Huamei Liao, Qingfen Qi, Junfeng School of Electrical Engineering and Automation Wuhan University Wuhan China School of Computer Science and Engineering Center for Teaching and learning development Central South University Changsha China

ISBN: (纸本)9789819707294

The fundamentals of electrical engineering is a compulsory course set up by the school of electrical and automation of Wuhan University for undergraduate junior students. As an important platform course of electrical engineering, it has high requirements for students to learn the pre sequence basic courses. Firstly, this paper uses grey correlation analysis to analyze the correlation between the courses of advanced mathematics, circuit theory and electrical engineering of undergraduate students in recent three years and this course. Then the correlation coefficient is obtained and normalized. Finally, it is used as the analysis of new students’ learning situation. In this way, we can effectively have a scientific and objective understanding of the basic knowledge of new students and the learning situation of preamble courses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Investigation and Study of Teachers’ teaching development centers in Higher Institutions in Hunan Province 17th

Investigation and Study of Teachers’ Teaching Development C...

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17th International Conference on computer science and Education, ICCSE 2022

作者： Jin, Yisu Cao, Dan Qi, Huamei Center for Teaching and Learning Development Central South University Changsha China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9789819924455

In recent years, colleges and universities in Hunan Province have set up teachers’ teaching development centers. In order to deeply understand their organizational structure and work situation, a questionnaire survey was conducted on the staff of teaching development institutions of ordinary colleges and universities in Hunan Province. According to the survey, the work of teaching development centers in Hunan Province has been widely recognized by the majority of teachers, but there are still problems such as imperfect organization, inadequate condition construction and lacking of academic professionalism. The solution of these problems requires the university to put forward the understanding and top-level design at the school level, strengthen the centers’ cooperation and exchanges between different colleges and universities, and jointly promote the level of teaching quality and faculty development. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： higher education questionnaire survey teachers’ teaching development center

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