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The Journal of Machine Learning Research 2023年第1期24卷 5157-5209页

作者： Chengzhuo Ni Yaqi Duan Munther Dahleh Mengdi Wang Anru R. Zhang Department of Electrical & Computer Engineering Princeton University Laboratory for Information & Decision Systems Massachusetts Institute of Technology Electrical Engineering & Computer Science Department Massachusetts Institute of Technology Department of Biostatistics & Bioinformatics Duke University

The transition kernel of a continuous-state-action Markov decision process (MDP) admits a natural tensor structure. This paper proposes a tensor-inspired unsupervised learning method to identify meaningful low-dimensional state and action representations from empirical trajectories. The method exploits the MDP's tensor structure by kernelization, importance sampling and low-Tucker-rank approximation. This method can be further used to cluster states and actions respectively and find the best discrete MDP abstraction. We provide sharp statistical error bounds for tensor concentration and the preservation of diffusion distance after embedding. We further prove that the learned state/action abstractions provide accurate approximations to latent block structures if they exist, enabling function approximation in downstream tasks such as policy evaluation.

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Deep Learning in Medical Image Registration: Magic or Mirage?

arXiv

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arXiv 2024年

作者： Jena, Rohit Sethi, Deeksha Chaudhari, Pratik Gee, James C. Computer and Information Science Electrical and Systems Engineering Radiology Penn Image Computing and Science Laboratory United States

Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak performance, incorporating weak supervision and amortized optimization. However, the exact conditions for either paradigm to perform well over the other are shrouded and not explicitly outlined in the existing literature. In this paper, we make an explicit correspondence between the mutual information of the distribution of per-pixel intensity and labels, and the performance of classical registration methods. This strong correlation hints to the fact that architectural designs in learning-based methods is unlikely to affect this correlation, and therefore, the performance of learning-based methods. This hypothesis is thoroughly validated with state-of-the-art classical and learning-based methods. However, learning-based methods with weak supervision can perform high-fidelity intensity and label registration, which is not possible with classical methods. Next, we show that this high-fidelity feature learning does not translate to invariance to domain shift, and learning-based methods are sensitive to such changes in the data distribution. Finally, we propose a general recipe to choose the best paradigm for a given registration problem, based on these observations. © 2024, CC BY.

关键词： Contrastive Learning

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Hybrid Multi-Strategy Aquila Optimization with Deep Learning Driven Crop Type Classification on Hyperspectral Images

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computer systems Science & engineering 2023年第10期47卷 375-391页

作者： Sultan Alahmari Saud Yonbawi Suneetha Racharla ELaxmi Lydia Mohamad Khairi Ishak Hend Khalid Alkahtani Ayman Aljarbouh Samih M.Mostafa King Abdulaziz City for Science and Technology P.O Box 6086Riyadh 11442Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of AIML Aditya Engineering CollegeSurempallemAndhra PradeshIndia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Computer Science University of Central AsiaNaryn722600Kyrgyzstan Faculty of Computers and Information South Valley UniversityQena83523Egypt

Hyperspectral imaging instruments could capture detailed spatial information and rich spectral signs of observed *** spatial information and spectral signatures of hyperspectral images(HSIs)present greater potential for detecting and classifying fine *** accurate classification of crop kinds utilizing hyperspectral remote sensing imaging(RSI)has become an indispensable application in the agricultural *** is significant for the prediction and growth monitoring of crop *** the deep learning(DL)techniques,Convolution Neural Network(CNN)was the best method for classifying HSI for their incredible local contextual modeling ability,enabling spectral and spatial feature *** article designs a Hybrid Multi-Strategy Aquila Optimization with a Deep Learning-Driven Crop Type Classification(HMAODL-CTC)algorithm *** proposed HMAODL-CTC model mainly intends to categorize different types of crops on *** accomplish this,the presented HMAODL-CTC model initially carries out image preprocessing to improve image *** addition,the presented HMAODL-CTC model develops dilated convolutional neural network(CNN)for feature *** hyperparameter tuning of the dilated CNN model,the HMAO algorithm is ***,the presented HMAODL-CTC model uses an extreme learning machine(ELM)model for crop type classification.A comprehensive set of simulations were performed to illustrate the enhanced performance of the presented HMAODL-CTC *** comparison studies reported the improved performance of the presented HMAODL-CTC algorithm over other compared methods.

关键词： Crop type classification hyperspectral images agricultural monitoring deep learning metaheuristics

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Gupta, Deepti Elluri, Lavanya Jain, Avi Moni, Shafika Showkat Aslan, Omer Texas A&m University - Central Texas Dept. of Computer Information Systems TX United States College of Business University of Tampa FL United States Embry-Riddle Aeronautical University Dept. of Electrical Engineering and Computer Science FL United States Bandirma Onyedi Eylül University Department of Software Engineering Bandirma Turkey

ISBN: (纸本)9798350362480

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls - such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture - this approach strengthens an organization's defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth's transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, e

关键词： Smart contract

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Livestock Productivity with computer Vision-Based Cow Estrus Detection in Free Stall Barns using Various YOLOv8 Models 9

Optimizing Livestock Productivity with Computer Vision-Based...

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9th IEEE International Conference on Smart Instrumentation, Measurement and Applications, ICSIMA 2023

作者： Gunawan, Teddy Surya Kartiwi, Mira Saifudin, Ali Nur, Levy Olivia Nugroho, Bambang Setia International Islamic University Malaysia Electrical and Computer Engineering Department Kuala Lumpur53100 Malaysia International Islamic University Malaysia Information Systems Department Kuala Lumpur53100 Malaysia Airlangga University Department of Veterinary Sciences Surabaya Indonesia Telkom University School of Electrical Engineering Bandung Indonesia

ISBN: (纸本)9798350343380

In the domain of livestock management, the precise detection of estrus in cows is crucial for reproductive efficiency and enhanced livestock production. Traditional methods, primarily based on human observation, are labor-intensive and can be error-prone. This study leverages YOLOv8, a cutting-edge computer vision technology, for cow estrus detection. Our evaluation reveals that YOLOv8 achieved a remarkable accuracy rate, outperforming conventional methods in speed and reliability. Specifically, the model demonstrated a precision of 96%, a recall of 96.1%, and a mean average precision (mAP) of 98.35% for the 50% intersection over union (IoU) threshold. By integrating YOLOv8, we highlight the potential for substantial improvements in reproductive efficiency, labor cost savings, and increased profitability in the cattle sector. This work emphasizes the transformative impact of advanced technology in agriculture and paves the way for future innovations in livestock management. © 2023 IEEE.

关键词： computer vision

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Room-scale magnetoquasistatic wireless power transfer using a cavity-based multimode resonator

arXiv

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arXiv 2025年

作者： Sasatani, Takuya Sample, Alanson P. Kawahara, Yoshihiro Department of Electrical Engineering and Information Systems Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo Japan Electrical Engineering and Computer Science Department University of Michigan Ann ArborMI United States

Magnetoquasistatic wireless power transfer can be used to charge and power electronic devices such as smartphones and small home appliances. However, existing coil-based transmitters, which are composed of wire conductors, have a limited range. Here, we show that multimode-quasistatic cavity resonance can provide room-scale wireless power transfer. The approach uses multidirectional, widely distributed currents on conductive surfaces that are placed around the target volume. It generates multiple, mutually unique, three-dimensional magnetic field patterns, where each pattern is attributed to different eigenmodes of a single room scale resonator. Using these modes together, a power delivery efficiency exceeding 37.1% can be achieved throughout a 3 m × 3 m × 2 m test room. With this approach, power exceeding 50 W could potentially be delivered to mobile receivers, while in accordance with safety guidelines. Copyright © 2025, The Authors. All rights reserved.

关键词： Inductive power transmission

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1 Lyapunov Stability Analysis for Controlling UAV Trajectory Over 6G Wireless Networks

1 Lyapunov Stability Analysis for Controlling UAV Trajectory...

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2022 IEEE Military Communications Conference, MILCOM 2022

作者： Zhang, Xi Zhu, Qixuan Texas A&m University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States

ISBN: (纸本)9781665485340

With the commercial realization of the fifth generation (5G) wireless networks, the development of the sixth generation (6G) wireless networks has attracted considerable research attention. To meet the requirement of providing ubiquitous coverage, unmanned aerial vehicle (UAV) plays the key role in extending the line-of-sight area, and thus, has been proposed as a promising candidate technique in 6G wireless networks However, how to design an optimal UAV trajectory when it is moving and communicating with a base station or a mobile user in the network and how to guarantee the stability of the UAV's trajectory remain open problems. To address these issues, in this paper we design an optimal UAV trajectory, obtain the expression of the UAV's trajectory error, and propose a stability analysis mechanism to show that the UAV trajectory error is globally and asymptotically stable. First, we design the UAV optimal trajectory to maximize the communication performance while minimizing the energy consumption. Then, we derive a closed-form expression for the UAV trajectory in the transient state, which results in major trajectory errors, including the critical damping, underdamping, and overdamping. Third, we employ the control Lyapunov function to show that our developed UAV trajectory error correction scheme is globally and asymptotically stable under the feedback control law. Finally, we use numerical analyses to validate and evaluate our proposed UAV trajectory error control scheme. © 2022 IEEE.

关键词： Wireless networks

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Reducing Barriers in Analyzing Eye Tracking Data: The Development of a GUI to Preprocess Eye Tracking Data

Reducing Barriers in Analyzing Eye Tracking Data: The Develo...

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IEEE Symposium on systems and information engineering Design, SIEDS

作者： Jad A. Atweh Ihab Tabbara Ghady Nasrallah Sara L. Riggs Department of Systems and Information Engineering University of Virginia Charlottesville VA USA Department of Electrical and Computer Engineering American University of Beirut Beirut Lebanon

ISBN: (数字)9798350385144

ISBN: (纸本)9798350385151

By capturing gaze points and eye movement patterns, eye tracking technology enables researchers to gain insights into visual attention, cognitive processes, and human behavior. Raw eye tracking data is inherently noisy, containing coordinates with occasional invalid entries. Researchers commonly face the challenge of cleaning and refining this data before proceeding to calculate essential eye tracking metrics. Critical preprocessing steps involve filtering out noise, detecting events such as fixations and saccades, and deriving metrics such as fixation duration, saccade amplitude, and more. This paper introduces a tool that provides users with the ability to preprocess data from both Gazepoint and FOVIO eye trackers. The methodology adopted for this line of work is based on Nyström and Holmqvist’s (2010) data and velocity-driven algorithm. Preliminary results show the methodology developed as part of this line of work is promising in terms of improved accuracy and reliability in detecting fixations and saccades. Furthermore, this code and its versatile Graphical User Interface (GUI) has been successfully employed in multiple studies. Finally, we present supplementary code for calculating various eye tracking metrics and percent gaze overlap, extending the utility of our tool for in-depth analysis of visual attention and collaborative gaze behavior.

关键词： Visualization Codes Refining Noise Gaze tracking Manuals Reproducibility of results

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Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks

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computers, Materials & Continua 2022年第7期72卷 227-242页

作者： Murad A.A.Almekhlafi Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a University61101Yemen Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be *** the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their ***,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’*** have designed MATLAB simulation and programming programs by Visual *** for a semi-realistic communication *** includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving *** we verified the correctness of the system’s ***,we begin by analyzing and studying multiple and branching cases to achieve the *** get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.

关键词： Line of sight obstacle margin wireless networks communication link radio wave propagation

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