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TechRxiv 2024年

作者： Zhang, Fan Wang, Zhaohan Lyu, Xin Zhao, Siyuan Li, Mengjian Geng, Weidong Ji, Naye Du, Hui Gao, Fuxing Wu, Hao Li, Shunman Faculty of Humanities and Arts Macau University of Science and Technology China The College of Media Engineering Communication University of Zhejiang China Research Center for Artificial Intelligence and Fine Arts Zhejiang Lab Zhejiang China School of Animation and Digital Arts Communication University of China Beijing China College of Computer Science and Technology Zhejiang University The Research Center for Artificial Intelligence and Fine Arts Zhejiang Lab Zhejiang China College of Media Engineering Communication University of Zhejiang China Zhejiang Institute of Economics and Trade Zhejiang China

Speech-driven gesture generation is an emerging field within virtual human creation. However, a significant challenge lies in accurately determining and processing the multitude of input features (such as acoustic, semantic, emotional, personality, and even subtle unknown features). Traditional approaches, reliant on various explicit feature inputs and complex multimodal processing, constrain the expressiveness of resulting gestures and limit their applicability. To address these challenges, we present Persona-Gestor, a novel end-to-end generative model designed to generate highly personalized 3D full-body gestures solely relying on raw speech audio. The model combines a fuzzy feature extractor and a non-autoregressive Adaptive Layer Normalization (AdaLN) transformer diffusion architecture. The fuzzy feature extractor harnesses a fuzzy inference strategy that automatically infers implicit, continuous fuzzy features. These fuzzy features, represented as a unified latent feature, are fed into the AdaLN transformer. The AdaLN transformer introduces a conditional mechanism that applies a uniform function across all tokens, thereby effectively modeling the correlation between the fuzzy features and the gesture sequence. This module ensures a high level of gesture-speech synchronization while preserving naturalness. Finally, we employ the diffusion model to train and infer various gestures. Extensive subjective and objective evaluations on the Trinity, ZEGGS, and BEAT datasets confirm our model’s superior performance to the current state-of-the-art approaches. Persona-Gestor improves the system’s usability and generalization capabilities, setting a new benchmark in speech-driven gesture synthesis and broadening the horizon for virtual human technology. Supplementary videos and code can be accessed at https://***/Diffmotion-v2-website/. © 2024, CC BY.

关键词： Semantics

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An efficient machine learning approach for extracting eSports players distinguishing features and classifying their skill levels using symbolic transfer entropy and consensus nested cross validation

arXiv

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arXiv 2024年

作者： Noroozi, Amin Hasan, Mohammad S. Ravan, Maryam Norouzi, Elham Law, Ying-Ying Department of Digital Technologies and Arts Staffordshire University England Staffordshire United Kingdom Department of Electrical and Computer Engineering New York Institute of Technology New YorkNY United States Department of Computer Engineering Azad University Astara Iran

Discovering features that set elite players apart is of great significance for eSports coaches as it enables them to arrange a more effective training program focused on improving those features. Moreover, finding such features results in a better evaluation of eSports players’ skills, which, besides coaches, is of interest for game developers to design games automatically adaptable to the players’ expertise. Sensor data combined with machine learning have already proved effective in classifying eSports players. However, the existing methods do not provide sufficient information about features that distinguish high-skilled players. In this paper, we propose an efficient method to find these features and then use them to classify players' skill levels. We first apply a time window to extract the players' sensor data, including heart rate, hand activities, etc., before and after game events in the League of Legends game. We use the extracted segments and symbolic transfer entropy to calculate connectivity features between sensors. The most relevant features are then selected using the newly developed consensus nested cross validation method. These features, representing the harmony between body parts, are finally used to find the optimum window size and classify players' skills. The classification results demonstrate a significant improvement by achieving 90.1% accuracy. Also, connectivity features between players’ gaze positions and keyboard, mouse, and hand activities were the most distinguishing features in classifying players' skills. The proposed method in this paper can be similarly applied to sportspeople’s data and potentially revolutionize the training programs in both eSports and sports industries. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Fog Computing in IoT: Architecture, Applications, and Research Challenges

Fog Computing in IoT: Architecture, Applications, and Resear...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Habelalmateen, Mohammed I. Khare, Neha Joshi, Abhishek Aravind, Ar Sapaev, B. Badhoutiya, Arti Siri, Dharmapuri The Islamic University Najaf Iraq IES University IES Insititute of Technology and Management Department of Computer Science & Engineering Madhya Pradesh Bhopal462044 India Uttaranchal University School of Liberal Arts Department of Liberal Arts Dehradun248007 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Alfraganus University Faculty of Medicine Department of Pharmaceuticals and Chemistry Tashkent Uzbekistan GLA University Department of Electrical Engineering Mathura India GRIET Telangana Hyderabad India

ISBN: (纸本)9798350336474

The union of Internet of Things (IoT) and Mist Figuring envoys a change in perspective in processing foundation. This paper gives an extensive investigation of Mist Figuring with regards to IoT, zeroing in on its design, applications, and the examination challenges that underlie this extraordinary innovation. Starting with an inside and out assessment of Haze Processing's calculated structure and its distinctive highlights, we clarify its basic differentiations from conventional Distributed computing models. Through a thorough examination of true applications, going from brilliant urban communities to medical care and modern IoT, this paper grandstands how Haze Figuring expands the capacities of IoT gadgets, giving restricted handling power and decreasing idleness. The conversation stretches out to the diverse design of Haze Registering, clarifying its progressive construction and hidden correspondence conventions. Also, the paper digs into safety efforts, asset allotment methodologies, and adaptability concerns, tending to the complicated difficulties that go with Haze Figuring executions. Through illustrative contextual investigations, we epitomize the common sense and viability of Mist Figuring, clarifying its effect across assorted areas. Besides, this paper offers bits of knowledge into arising patterns like Edge artificial intelligence joining and investigates expected cooperative energies with Blockchain innovation. The paper finishes up by summing up key discoveries and imagining future headings for Haze Registering in the IoT scene, accentuating its urgent job in molding the advanced biological system of tomorrow. © 2023 IEEE.

关键词： Internet of things

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Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

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computer Systems Science & engineering 2023年第5期45卷 1711-1726页

作者： Manar Ahmed Hamza Aisha Hassan Abdalla Hashim Dalia H.Elkamchouchi Nadhem Nemri Jaber S.Alzahrani Amira Sayed A.Aziz Mnahel Ahmed Ibrahim Abdelwahed Motwakel Department of Electrical and Computer Engineering International Islamic University MalaysiaKuala Lumpur53100Malaysia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt College of Science and Humanities Studies in Alquwayiyah Shaqra UniversitySaudi Arabia

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the *** the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the *** node clustering is a popular approach for ***,the sensor nodes are grouped to form clusters in a cluster-based WSN *** battery performance of the sensor nodes is likewise *** a result,the energy efficiency of WSNs is *** specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in *** the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was *** selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed *** provides the best routing path and increases the lifetime and energy efficiency of the ***-to-end delay and packet loss rate have also been *** proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor *** to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

关键词： Wireless sensor networks energy-efficient load balancing energy consumption network’s lifetime cluster heads grey wolf optimization tabu search particle swarm optimization

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Legato: Virtual Reality for Physical Rehabilitation of Patients in the Intensive Care Unit

Legato: Virtual Reality for Physical Rehabilitation of Patie...

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2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Eom, Sangjun Xu, Wenyi Zou, Liheng Frith, Ashley Escobar, Ernesto Streisfeld, Gabriel Mall, Anna Granger, Bradi Gorlatova, Maria Duke University Department of Electrical and Computer Engineering United States Duke University United States Georgia Institute of Technology School of Literature Media and Communication United States Duke University Department of Physical Therapy United States Duke University Hospital Duke Heart Center United States Duke University School of Nursing United States

ISBN: (纸本)9798331514846

Patients in intensive care units (ICUs) undergoing prolonged bed rest are at high risk of physical, cognitive, and psychological impairments due to immobility. While early mobilization protocols are critical for rehabilitation, the implementation is hindered by patients' restricted mobility and the complex ICU environment. To address these challenges, we present Legato, an immersive virtual reality (VR) game designed to enhance mobility activities for ICU patients. Legato offers a calming, customizable environment with thematic styles and music tracks, while providing real-time progress tracking and adaptive difficulty levels tailored to patients' hand movements. To evaluate its feasibility, we deployed Legato on Meta Quest 3 and conducted a pilot study with an ICU patient at Duke University hospital, demonstrating promising potential for improving rehabilitation outcomes. © 2025 IEEE.

关键词： Augmented reality

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User-Resource Associations for Delay Minimization in THz-Enabled Mobile Edge Computing Systems

User-Resource Associations for Delay Minimization in THz-Ena...

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International Conference on Information and Communication technology Convergence (ICTC)

作者： Heekang Song Wan Choi School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon South Korea Institute of New Media and Communications Seoul National University Seoul South Korea Dept. of Electrical and Computer Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Mobile edge computing (MEC) and terahertz (THz)-enabled communication systems are gaining significant attention for their potential to reduce user service delays in future mobile networks. This paper leverages user offloading and resource management in MEC systems operating at THz frequencies to minimize expected user service delays. Numerical results demon-strate that our approach outperforms the baseline algorithm.

关键词： Multi-access edge computing Benchmark testing Minimization Delays Information and communication technology Resource management Optimization Terahertz communications Convergence

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BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

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computers, Materials & Continua 2022年第1期70卷 557-579页

作者： R.Manjula Devi M.Premkumar Pradeep Jangir B.Santhosh Kumar Dalal Alrowaili Kottakkaran Sooppy Nisar Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060Tamil NaduIndia Department of Electrical and Electronics Engineering Dayananda Sagar College of EngineeringBengaluru560078KarnatakaIndia Rajasthan Rajya Vidyut Prasaran Nigam Sikar332025RajasthanIndia Department of Computer Science and Engineering Guru Nanak Institute of TechnologyHyderabad501506TelanganaIndia Mathematics Department College of ScienceJouf UniversitySakakaP.O.Box:2014Saudi Arabia Department of Mathematics College of Arts and SciencesPrince Sattam bin Abdulaziz UniversityWadi Aldawaser11991Saudi Arabia

In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization *** the run time increases exponentially,FS is treated as an NP-hard *** researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization *** paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large *** proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary *** findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art *** results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification *** proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 *** low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

关键词： Binary optimization feature selection machine learning hunger games search optimization

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Nonlinear Dynamics of Nervous Stomach Model Using Supervised Neural Networks

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computers, Materials & Continua 2022年第7期72卷 1627-1644页

作者： Zulqurnain Sabir Manoj Gupta Muhammad Asif Zahoor Raja N.Seshagiri Rao Muhammad Mubashar Hussain Faisal Alanazi Orawit Thinnukool Pattaraporn Khuwuthyakorn Department of Mathematics and Statistics Hazara UniversityMansehraPakistan Department of Electronics and Communication Engineering JECRC UniversityJaipur(Rajasthan)303905India Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan Department of Applied Mathematics School of Applied Natural SciencesAdama Science and Technology UniversityAdamaEthiopia Department of Mathematics University of PunjabJhelum CampusPakistan Department of Electrical Engineering Prince Sattam Bin Abdulaziz UniversityCollege of EngineeringAl Kharj16278Saudi Arabia Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

The purpose of the current investigations is to solve the nonlinear dynamics based on the nervous stomach model(NSM)using the supervised neural networks(SNNs)along with the novel features of Levenberg-Marquardt backpropagation technique(LMBT),i.e.,*** SNNs-LMBT is implemented with three different types of sample data,authentication,testing and *** ratios for these statistics to solve three different variants of the nonlinear dynamics of the NSM are designated 75%for training,15%for validation and 10%for testing,*** the numerical measures of the nonlinear dynamics of the NSM,the Runge-Kutta scheme is implemented to form the reference *** attained numerical form of the nonlinear dynamics of the NSM through the SNNs-LMBT is implemented in the reduction of the mean square error(MSE).For the exactness,competence,reliability and efficiency of the proposed SNNs-LMBT,the numerical actions are capable using the proportional arrangements through the features of the MSE results,error histograms(EHs),regression and correlation.

关键词： Nonlinear dynamics nervous stomach system reference dataset levenberg-marquardt backpropagation technique numerical outcomes

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A Comprehensive Evaluation of Secured Electronic Voting System Design based on Face Biometric Authentication Policy 8

A Comprehensive Evaluation of Secured Electronic Voting Syst...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Potluri, Pandarinath Jayakarthik, R. Agarwal, Shivam Shobana, S. Venkata Padmavathi, S. Aarthi, R. Swarnandhra College of Engineering and Technology Cse Department Sitarampuram West Godavari district Narsapuram534275 India Saveetha College of Liberal Arts and Sciences Simats Dept of Computer Science India Gl Bajaj Institute of Technology and Management Department of Information Technology Gr. Noida India R.M.K Engineering College Department of It Chennai India Gst Gitam Deemed to be University Electrical and Electronics Engineering Hyderabad campus Telangana India R.M.D Engineering College Department of Ece R. S. M Nagar Gummidipoondi601206 India

ISBN: (纸本)9798350376425

In the field of electronic voting systems, ensuring secure and reliable authentication mechanisms is paramount to safeguarding the integrity and fairness of the democratic process. Face biometric authentication has emerged as a promising approach, integrating advanced deep learning algorithms for accurate and efficient verification of voters based on their facial features. This paper presents a comprehensive overview of the system architecture and design considerations for integrating face biometric authentication into electronic voting systems. It describes the hardware and software components essential for system functionality, discusses the selection of appropriate technologies for face recognition and encryption, and elaborates on the design of user interfaces, database structures, and ballot generation systems. Furthermore, the paper delves into the implementation of face detection and recognition algorithms, emphasizing the importance of anti-spoofing measures and multi-factor authentication mechanisms to enhance security. It also addresses voter registration processes, ballot generation, the voting process, and vote counting and verification procedures within the context of face biometric authentication. Additionally, the paper highlights the significance of robust security measures, including encryption techniques, access controls, and regular security audits, to mitigate cyber threats and ensure the integrity of the electoral process. By integrating face biometric authentication into electronic voting systems, this paper aims to contribute to the development of secure, transparent, and inclusive electoral systems, fostering trust and confidence in democratic governance, with an accuracy rate of 96%. © 2024 IEEE.

关键词： Voting machines

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Hybrid Metaheuristics Feature Selection with Stacked Deep Learning-Enabled Cyber-Attack Detection Model

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computer Systems Science & engineering 2023年第5期45卷 1679-1694页

作者： Mashael M Asiri Heba G.Mohamed Mohamed K Nour Mesfer Al Duhayyim Amira Sayed A.Aziz Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Science&Art atMahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Due to exponential increase in smart resource limited devices and high speed communication technologies,Internet of Things(IoT)have received significant attention in different application ***,IoT environment is highly susceptible to cyber-attacks because of memory,processing,and communication *** traditional models are not adequate for accomplishing security in the IoT environment,the recent developments of deep learning(DL)models find *** study introduces novel hybrid metaheuristics feature selection with stacked deep learning enabled cyber-attack detection(HMFS-SDLCAD)*** major intention of the HMFS-SDLCAD model is to recognize the occurrence of cyberattacks in the IoT *** the preliminary stage,data pre-processing is carried out to transform the input data into useful *** addition,salp swarm optimization based on particle swarm optimization(SSOPSO)algorithm is used for feature selection ***,stacked bidirectional gated recurrent unit(SBiGRU)model is utilized for the identification and classification of ***,whale optimization algorithm(WOA)is employed for optimal hyperparameter optimization *** experimental analysis of the HMFS-SDLCAD model is validated using benchmark dataset and the results are assessed under several *** simulation outcomes pointed out the improvements of the HMFS-SDLCAD model over recent approaches.

关键词： Cyberattacks security deep learning internet of things feature selection data classification

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