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A Learning-Based Iterative Algorithm for AoI-Optimal Trajectory Planning in UAV-Assisted IoT Networks

IEEE Transactions on Wireless Communications 2025年第6期24卷 4598-4613页

作者： Huang, Zihao Chen, Hai Gu, Bo Gong, Shimin Su, Zhou Guizani, Mohsen Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518107 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China Qatar University Computer Science and Engineering Department Doha2713 Qatar

In this paper, we employ an unmanned aerial vehicle (UAV) to ensure the freshness of sensing data, as measured by the age of information (AoI), in Internet of Things (IoT) networks. Specifically, the UAV switches between flying and hovering modes to collect data from widely distributed IoT devices. UAV trajectory planning, which determines the times and moments of data collection, is vital for optimizing the system AoI. Considering the limited UAV onboard energy and mission duration, AoI-optimal trajectory planning is formulated as a mixed-integer nonlinear programming (MINLP) problem. We first decompose the MINLP problem into two subproblems: a UAV time scheduling subproblem and a UAV path planning subproblem. Then, we propose a learning-based iterative (LBI) algorithm that consists of two modules: a successive convex approximation (SCA)-based module for solving the time scheduling subproblem, and a hierarchical asynchronous advantage actor-critic (A3C) module for addressing the path planning subproblem. The numerical results verify that the proposed LBI algorithm outperforms typical baselines in terms of the AoI performance. © 2025 IEEE. All rights reserved,

关键词： Unmanned aerial vehicles (UAV)

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Mobile Application to Help Abandoned Pets 3

Mobile Application to Help Abandoned Pets

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3rd IEEE International Conference on Blockchain and Distributed systems Security, ICBDS 2024

作者： Rahajeng, Salicca Dewi Nurfitriyani, Siti Julianingsih Akbar, Zakin Izzani Leonardo, Calvin Warnars, Harco Leslie Hendric Spits Warnars, Laurens Spits Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350354348

As social beings, humans like to interact with each other, including other creatures of God, such as animals, and keep them as pets. Many pets, such as dogs, cats, birds, fish, rabbits, and so on, include unusual pets such as reptiles, pigs, insects, etc. As a result of the lack of human awareness of pets and the nature of keeping animals, which is only for fun, it is not soulful to keep animals. So when people are bored, they abandon their pets, which causes new problems in human life today. In this case, we created a mobile application that can be shared with pet lovers where they can share information about abandoned pets and the possibility of owning pets from these abandoned pets. Mobile applications were designed using use case diagrams to show business processes, class diagrams to show database models, and user interfaces to present a similar interface to users. This mobile application will become a platform that can gather all people who care about pets and make pets a part of humans. © 2024 IEEE.

关键词： Mobile applications

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Classification and Comprehension of Software Requirements Using Ensemble Learning

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computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad Ahmad Syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

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The optimization of deep learning classification through the comparison between different algorithm by using LABR 28th

The optimization of deep learning classification through the...

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28th International Conference on Knowledge Based and intelligent information and Engineering systems, KES 2024

作者： Kadhem, Rasha Mallat, Souheyl Zrigui, Mounir Department of Computer Science Faculty of Sciences of Monastir University of Monastir Tunisia Research Laboratory in Algebra Numbers theory and Intelligent Systems Monastir Tunisia Department of Computer Science University of ALQadisiyah Iraq

The deep learning model depends on the specific requirements of our recommender system and the characteristics of the Arabic textual data we are working with it Consider experimenting with multiple models to determine which one performs best for our particular use case, in our case we used Large Arabic Book Review (LABR) is a large sentiment analysis dataset for the Arabic language. It consists of over 63,000 book reviews and Other Datasets such as Amazon Products reviews that we can translate to Arabic and then use for the task, each dataset are tested by three models of deep learning (CNN, RNN,LSTM)and classified by three method (rating, polarity, balance polarity)classification for three type of feature extraction (Bow, TF-IDF, word embedding) after do train 70% and test 30% we get based on the results and the task at hand, if CNN (Convolutional Neural Network) has proven in both training and testing phases to be the most effective among LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network). © 2024 The Authors.

关键词： Convolutional neural networks

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Internet of Things Challenges and the Emerging Technology of TinyML 19

Internet of Things Challenges and the Emerging Technology of...

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19th Annual International Conference on Distributed Computing in Smart systems and the Internet of Things, DCOSS-IoT 2023

作者： Tsoukas, Vasileios Gkogkidis, Anargyros Kakarountas, Athanasios University of Thessaly Intelligent Systems Laboratory Department of Computer Science and Biomedical Informatics Lamia Greece

ISBN: (纸本)9798350346497

The merger of Internet of Things (IoT) and Machine Learning undoubtedly offers several benefits, but due to the nature of IoT devices and their underlying design, a number of difficulties and challenges exist. The most fundamental of these has to do with the security dangers posed by the transmission of voluminous amounts of data, as well as several networking obstacles, such as delay and bandwidth constraints. Moving all processing close to the network's edge and preventing the transmission of data to third parties, such as the cloud or other devices, is a potential solution. Embedded Machine Learning, and TinyML in particular, is a new technology that allows the implementation of Machine Learning on hardware with limited resources, such as Microcontrollers and FPGAs. In this paper, the fundamental issues of IoT devices are examined, the TinyML technology's advantages are outlined, and a summary of the most recent TinyML apps and systems is offered. © 2023 IEEE.

关键词： Microcontrollers

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Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures

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Journal of Machine Learning Research 2023年第1期24卷 1-10页

作者： Heddes, Mike Nunes, Igor Vergés, Pere Kleyko, Denis Abraham, Danny Givargis, Tony Nicolau, Alexandru Veidenbaum, Alexander Department of Computer Science University of California IrvineCA92617 United States Intelligent Systems Lab Research Institutes of Sweden Kista164 40 Sweden

Hyperdimensional computing (HD), also known as vector symbolic architectures (VSA), is a framework for computing with distributed representations by exploiting properties of random high-dimensional vector spaces. The commitment of the scientific community to aggregate and disseminate research in this particularly multidisciplinary area has been fundamental for its advancement. Joining these efforts, we present Torchhd, a high-performance open source Python library for HD/VSA. Torchhd seeks to make HD/VSA more accessible and serves as an efficient foundation for further research and application development. The easy-to-use library builds on top of PyTorch and features state-of-the-art HD/VSA functionality, clear documentation, and implementation examples from well-known publications. Comparing publicly available code with their corresponding Torchhd implementation shows that experiments can run up to 100× faster. Torchhd is available at: https://***/hyperdimensional-computing/torchhd. ©2023 Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, and Alexander Veidenbaum.

关键词： Python

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Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

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computers, Materials & Continua 2024年第3期78卷 3283-3301页

作者： Rafi Ullah Mohd Hilmi bin Hasan Sultan Daud Khan Mussadiq Abdul Rahim Computer and Information Sciences Department Universiti Teknologi PETRONASBandar Seri Iskandar Perak32610Malaysia Department of Computer Science National University of TechnologyIslamabad46000Pakistan Interdisciplinary Research Center for Intelligent Secure Systems King Fahd University of Petroleum and MineralsDammam31261Saudi Arabia

Medical imaging plays a key role within modern hospital management systems for diagnostic *** methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image ***,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy ***,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression *** response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking *** watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion *** the process of watermark embedding,motion vectors are selected based on their magnitude and phase *** embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the *** embedding of watermark bits is dependent on motion *** reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking *** reason for this is that the use of reversible watermarks may impede the claims of ownership and legal *** restoration of original data or images may call into question ownership or other legal *** peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image *** all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds *** results substantiate the efficacy of the proposed technique in preserving data integrity.

关键词： Block matching algorithm(BMA) compression full-search algorithm motion vectors ultrasound image sequence watermarking

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JarviX: A LLM No code Platform for Tabular Data Analysis and Optimization

JarviX: A LLM No code Platform for Tabular Data Analysis and...

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2023 Conference on Empirical Methods in Natural Language Processing: Industry Track, EMNLP 2023

作者： Liu, Shang-Ching Wang, ShengKun Lin, Wenqi Hsiung, Chung-Wei Hsieh, Yi-Chen Cheng, Yu-Ping Luo, Sian-Hong Chang, Tsungyao Zhang, Jianwei Department of Computer Science University of Hamburg Germany Department of Computer Science Virginia Tech United States Synergies Intelligent Systems Inc. Taiwan

In this study, we introduce JarviX, a sophisticated data analytics framework. JarviX is designed to employ Large Language Models (LLMs) to facilitate an automated guide and execute high-precision data analyzes on tabular datasets. This framework emphasizes the significance of varying column types, capitalizing on state-of-the-art LLMs to generate concise data insight summaries, propose relevant analysis inquiries, visualize data effectively, and provide comprehensive explanations for results drawn from an extensive data analysis pipeline. Moreover, JarviX incorporates an automated machine learning (AutoML) pipeline for predictive modeling. This integration forms a comprehensive and automated optimization cycle, which proves particularly advantageous for optimizing machine configuration. The efficacy and adaptability of JarviX are substantiated through a series of practical use case studies. © 2023 Association for Computational Linguistics.

关键词： Pipelines

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Fast K-Medoids With the l1-Norm

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1769-1777页

作者： Capo, Marco Perez, Aritz Lozano, Jose A. Basque Center of Applied Mathematics Bilbao48009 Spain University of the Basque Country UPV/EHU Intelligent Systems Group Department of Computer Science and Artificial Intelligence San Sebastián20018 Spain

K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of the obtained solutions strongly depends upon their initialization phase. In this work, we propose an algorithm for the K-medoids problem on the l1-norm/Manhattan distance with a computational complexity of O(n max lbrace log n, Kd), along with theoretical guarantees in terms of the accuracy of the obtained approximation. In addition, we propose a cheap split-merge mechanism that can be used to restart the proposed algorithm after its convergence to a fixed point. Under some mild assumptions, we prove that such a restart procedure reduces the error of the given fixed point. The work also includes an extensive experimentation, in which we compare our method to the most popular approaches for the K-medoids problem: PAM, CLARA, and Park's K-medoids. The obtained empirical results show the proposed algorithm to consistently converge to the solutions with the lowest errors, up to two orders of magnitude of relative error lower than the previously mentioned methods, while also requiring the lowest computational running times among them: up to three orders of magnitude lower. © 2020 IEEE.

关键词： Clustering algorithms

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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