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An optimal algorithm for preemptive scheduling on non-simultaneously available uniform machines

Frontiers of Information Technology & Electronic engineering 2025年第3期26卷 472-478页

作者： Hao ZHOU Liping CAO Qi WEI Zhenyu SHU Yiwei JIANG College of Basic Science Zhejiang Shuren UniversityHangzhou 310015China School of Management and E-Business Zhejiang Gongshang UniversityHangzhou 310018China College of International Economics and Trade Ningbo University of Finance and EconomicsNingbo 315175China School of Computer and Data Engineering NingboTech UniversityNingbo 315100China

We study preemptive scheduling on m uniform machines with non-simultaneous available times to minimize the *** machine has a different speed and a different available *** first provide a lower bound on the optimal makespan of the problem by converting the real machines to virtual machines that guarantee a machine with an earlier available time having a greater speed at any time.

关键词： preemptive scheduling virtual machines converting real machines virtual machines lower bound non simultaneous available times uniform machines makespan

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Towards Responsible Decisions with Limited Training data Using Human-in-the-Loop 20th

Towards Responsible Decisions with Limited Training Data Us...

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20th Principle and Practice of data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Mahidadia, Ashesh Bain, Michael Suryanto, Hendra Kang, Byeong Guan, Charles Compton, Paul Rich Data Corporation Sydney Australia School of Computer Science and Engineering University of New South Wales Sydney Australia School of Information and Communication Technology University of Tasmania Hobart Australia

ISBN: (纸本)9789819600250

A fundamental issue with machine learning is that the training data must contain sufficient examples of every data pattern of interest for the essentially statistical techniques of machine learning to derive a model that accurately covers all data patterns. For example, in real-world situations, samples from minority populations may not be adequately represented in available training datasets, resulting in models that are biased in favour of majority populations. We present an approach to partially address this problem by using domain knowledge to construct rules to correctly classify, specifically, those cases representing rare patterns in training data that the machine learning model has incorrectly classified. We use Ripple-Down Rules (RDR) to add rules, as it is a proven approach to knowledge acquisition and incremental maintenance that enables rules to be very easily and quickly added to a knowledge base on a case-by-case basis. This paper extends previous work using a human expert by using both artificial and real-world data and a simulated expert to add rules in multiple controlled experiments. Results show that domain knowledge can be successfully used in our approach to mitigate performance issues arising from a limited representation of a subgroup in the available training dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (Science and Information Organization). All Rights Reserved.

关键词： Deep learning

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

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IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, Diep N. Linh Trung, Nguyen Nguyen, Van-Dinh Van Huynh, Nguyen Thai Hoang, Dinh Krunz, Marwan Dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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A leaded sine–cosine artificial protozoa optimizer algorithm for solving multi-objective optimization problems

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Cluster Computing 2025年第5期28卷 1-65页

作者： Liao, Junjie Melethil Sethumadhavan, Syam Ke, Rui Gao, Zheng-Ming Zhao, Juan College of Computer and Information Technology China Three Gorges University Yichang443002 China School of Computer Engineering Jingchu University of Technology Jingmen448000 China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang443002 China Big Data Research Center Jingchu University of Technology Jingmen448000 China School of Electronics and Information Engineering Jingchu University of Technology Jingmen448000 China

Many real-world engineering problems require simultaneous optimization of multiple objectives. The artificial protozoa optimizer (APO), a recently proposed and efficient optimization method, has demonstrated potential in solving multi-objective problems. In order to solve multi-objective problems, this paper proposes a novel algorithm based on the APO framework, the Leaded Sine Cosine Multi-objective APO (LSCMOAPO) algorithm. LSCMOAPO mainly integrates the sine–cosine algorithm (SCA) and a leader selection strategy to guide the population towards the true Pareto front. The algorithm was validated through extensive simulations involving 41 benchmark test functions (ZDT-series, UF-series, CF-series, and CEC2020-series) and five practical engineering problems. Performance was evaluated using the inverted generational distance, generational distance, Hypervolume, and spacing metrics. Comparative analysis with ten other multi-objective optimization algorithms (MOPSO, MOSSA, MOGWO, MODA, MOCryStAl, MOALO, MOGOA, MOSMA, MOGNDO, and MOAPO) also validated the better performance of LSCMOAPO in more than 86% occasions (The average of the evaluation indicators) in realizing high-quality solutions to all multi-objective problems, including linear, nonlinear, continuous, and discrete Pareto optimal front. It will set a new benchmark for new algorithm proposed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Multiobjective optimization

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Real-Time Road Damage Detection Using an Optimized YOLOv9s-Fusion in IoT Infrastructure

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IEEE Internet of Things Journal 2025年第11期12卷 17649-17660页

作者： Waseem Khan, Muhammad Obaidat, Mohammad S. Mahmood, Khalid Sadoun, Balqies Sanaullah Badar, Hafiz Muhammad Gao, Wu Center for Bio-inspired System-on-Chip School of Computer Science Northwestern Polytechnical University Xi’an China Sahiwal Campus Pakistan The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan The School of Communication and Computing Engineering University of Science and Technology Beijing Beijing100083 China The Amity School of Engineering and Technology Amity University Uttar Pradesh Noida201301 India The Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Douliu Yunlin64002 Taiwan School of Computer and Information Engineering Henan University Kaifeng475001 China The College of Engineering Al-Balqa’ Applied University Al-Salt19117 Jordan

In IoT-enabled smart infrastructure, accurate and real-time road damage detection is crucial for enhancing road safety and optimizing maintenance processes. However, detecting road damage in complex and dynamic environments presents significant challenges, such as varying lighting conditions, diverse damage types, and the need for fast processing to enable real-time decision-making. This study introduces an advanced approach utilizing the YOLOv9s-Fusion model to overcome these challenges. Leveraging the RDD2022 dataset, which comprises 1,976 annotated images of road damage from China, we employ comprehensive data preprocessing to create optimal conditions for model training. The YOLOv9s-Fusion model integrates innovative features, including a Transformer-based auxiliary module and enhanced feature extraction layers, specifically designed to detect fine-grained damage patterns accurately. Experimental results demonstrate that the model outperforms existing approaches, achieving notable improvements in mean Average Precision (mAP) and F1-score. Ablation studies further validate the impact of our modifications, highlighting the model’s robustness in real-time detection across diverse conditions. This IoT-centric approach sets a new standard for autonomous road damage detection, significantly advancing vehicle navigation and smart infrastructure management capabilities. © 2014 IEEE.

关键词： Intelligent systems

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Joint Near Field Uplink Communication and Localization Using Message Passing-Based Sparse Bayesian Learning

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 7666-7675页

作者： Liu, Fei Yuan, Zhengdao Guo, Qinghua Zhang, Yuanyuan Wang, Zhongyong Zhang, J. Andrew Zhengzhou University School of Geoscience and Techonology The School of Electrical and Information Engineering Zhengzhou450000 China Open University of Henan Artificial Intelligence Technology Engineering Research Center Zhengzhou450000 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia Zhengzhou University of Light Industry School of Electronics and Information Henan Zhengzhou450002 China University of Technology Sydney Global Big Data Technologies Centre SydneyNSW2007 Australia

This work deals with the problem of uplink communication and localization in an integrated sensing and communication system, where users are in the near field (NF) of antenna aperture due to the use of high carrier frequency and large antenna arrays at base stations. We formulate joint NF signal detection and localization as a problem of recovering signals with a sparse pattern, and we adopt differentiation modulations to avoid the frequent use of pilot signals. To solve the problem, we develop a message passing based sparse Bayesian learning (SBL) algorithm, where multiple unitary approximate message passing (UAMP)-based sparse signal estimators work jointly to recover the sparse signals with low complexity. Simulation results demonstrate the effectiveness of the proposed method. © 1967-2012 IEEE.

关键词： Carrier communication

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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