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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Huang, Ching-Jung Gochoo, Munkhjargal Tan, Tan-Hsu National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan United Arab Emirates University Zayed Center for Health Sciences Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

Traditional methods for video recognition require hand-crafted features, which often involves offline pre-processing for real-world videos. In this study, we propose a conceptually simple framework that directly takes raw videos as an input source for activity recognition. Our framework consists of two streams, namely a spatial stream and a temporal stream. The spatial stream is trained on RepVGG-B0 ConvNet using cropped RGB features, while the temporal stream uses an attention-based Bi-directional Long Short-Term Memory (Bi-LSTM) network to learn posture vectors from human pose data obtained through Faster R-CNN pre-Trained model. Our proposed method is evaluated on a standard video action recognition benchmark, MSR Daily Activity3D, and proves to be competitive with state-of-The-Art action recognition methods. We achieve state-of-The-Art performance on MSR Daily Activity3D with a precision and recall rate of 99.01% and 98.91%, respectively. Our results demonstrate the effectiveness of our approach in recognizing video actions. © 2023 IEEE.

关键词： Long short-term memory

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A Red/Blue Video Game for Hide&Seek Analysis for Hardware Trojans and the Seeker's Dilemma

A Red/Blue Video Game for Hide&Seek Analysis for Hardware Tr...

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Annual IEEE Systems Conference

作者： Peter Jamieson Obed Amaning-Yeboah Jared Butler Deniz Misirlioglu Riley Taylor Suman Bhunia Department of Electrical and Computer Engineering Miami University Oxford Ohio USA Department of Computer Science and Software Engineering Miami University Oxford Ohio USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

This paper describes our work in creating a video game to analyze the human dynamics of a Hide&Seek game. By harnessing human computation, we use this approach to get insights into how humans in a game Hide&Seek help understand how this relates to other problems, such as how Hardware Trojans might be hidden in an Integrated Chip. Hide&Seek can be directly related to cybersecurity, and in recent work, this idea has been extended further for hardware trojans in the perspective of The Seeker's Dilemma (an extension of Hide&Seek on a graph). Within the cybersecurity context, Hide&Seek is analogous to a continuous game of cat-and-mouse: the red player (the cat) assumes the role of the seeker, while the blue player (the mouse) acts as the hider. This study aims to model these games with human players and mathematical representations of Hide&Seek on a graph to collect data to better understand the hiding and seeking problems for a given graph. We describe the video game and provide results for a small sample of players hiding and seeking on three levels/graphs.

关键词： Video games Aggregates Games Data collection Hardware Mathematical models Data models Trojan horses computer security

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Enhancing Information Freshness via Knowledge Graph-Aided Semantic Communication 11

Enhancing Information Freshness via Knowledge Graph-Aided Se...

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2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Chen, Jie Yang, Shuai Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong Hong Kong

ISBN: (纸本)9798350314014

This paper investigates semantic communication to enhance the information freshness of status update systems, using time division multiple access (TDMA) as an example. Age of information (AoI) is used to characterize information freshness, defined as the time elapsed since the generation of the last successfully received status update. Prior works on analyzing and optimizing AoI have focused on the conventional bit-oriented communication paradigm, aiming to efficiently and reliably deliver each bit of the update message. When a large number of users take turns transmitting their update messages over a wireless channel to a common access point (AP), conventional bit-oriented TDMA systems may result in a high average AoI because users have to wait a long time for the next update opportunity. To address this problem, we propose a knowledge graph-aided semantic communication for TDMA systems, referred to as KGSC-TDMA, to achieve higher information freshness. With the aid of the shared knowledge graph, KGSC-TDMA transmits the meaning of update messages in the form of triplets instead of exact bits, thus reducing the amount of information transmitted and the time to receive update messages. Furthermore, an update in KGSC-TDMA is successful, even with bit errors, as long as the AP can recover the meaning of the message received. Experimental results indicate that KGSC-TDMA outperforms conventional bit-oriented TDMA in average AoI, thanks to shorter packet durations and robust semantic recovery. © 2023 IEEE.

关键词： Time division multiple access

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Multi-Objective Binary Coordinate Search for Feature Selection

Multi-Objective Binary Coordinate Search for Feature Selecti...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Lab Canada Ontario Tech University Department of Electrical Computer and Software Engineering OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350337020

A supervised feature selection method selects an appropriate but concise set of features to differentiate classes, which is highly expensive for large-scale datasets. Therefore, feature selection should aim at both minimizing the number of selected features and maximizing the accuracy of classification, or any other task. However, this crucial task is computationally highly demanding on many real-world datasets and requires a very efficient algorithm to reach a set of optimal features with a limited number of fitness evaluations. For this purpose, we have proposed the binary multi-objective coordinate search (MOCS) algorithm to solve large-scale feature selection problems. To the best of our knowledge, the proposed algorithm in this paper is the first multi-objective coordinate search algorithm. In this method, we generate new individuals by flipping a variable of the candidate solutions on the Pareto front. This enables us to investigate the effectiveness of each feature in the corresponding subset. In fact, this strategy can play the role of crossover and mutation operators to generate distinct subsets of features. The reported results indicate the significant superiority of our method over NSGA-II, on five real-world large-scale datasets, particularly when the computing budget is limited. Moreover, this simple hyper-parameter-free algorithm can solve feature selection much faster and more efficiently than NSGA-II. © 2023 IEEE.

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Advancements in 5G and 6G Technologies Shaping the Future of Global Connectivity and Communication 5

Advancements in 5G and 6G Technologies Shaping the Future of...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Pachar, Sunita Reddy, Satti Sudha Mohan Kompally, Gouthami Srinivasagopalan, Lakshmi Narasimhan Sangore, Rohidas Balu Katta, Sivakoteswararao GLA University IBM Department India SRKR Engineering College Department of ECE India Department of CSE-IOT India Software Development Enterprise Architecture PlanoTX United States Computer Science and Engineering MIT Art Design Technology University India KIETW Department of Mechanical Kakinada India

ISBN: (纸本)9798331530013

The evolution of wireless communication is poised for a major shift with the transition from 5G to 6G, a leap characterized by breakthroughs in speed, ultra-reliable low-latency communication, and AI-driven network optimization. This paper examines these advancements, focusing on sub-millisecond latency, terahertz spectrum utilization, and enhanced network capabilities tailored to industries such as smart infrastructure, healthcare, and autonomous vehicles. While 6G promises revolutionary improvements, it also presents new challenges, including increased computational complexity and energy demands, data privacy concerns, and efficient spectrum allocation. The proposed model aims to address these issues by integrating AI and machine learning for optimized resource management, resulting in a high-performance, cost-effective solution for real-time, data-intensive applications. To assess the model's effectiveness, comprehensive simulations and real-world trials will be conducted, measuring critical metrics such as network throughput, latency, reliability, and energy efficiency. This study offers an all-encompassing guide to understanding and harnessing the transformative potential of 6G technology, laying the groundwork for future wireless communication systems. © 2024 IEEE.

关键词： Terahertz spectroscopy

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CD-FL:Cataract Images Based Disease Detection Using Federated Learning

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computer Systems science & engineering 2023年第11期47卷 1733-1750页

作者： Arfat Ahmad Khan Shtwai Alsubai Chitapong Wechtaisong Ahmad Almadhor Natalia Kryvinska Abdullah Al Hejaili Uzma Ghulam Mohammad Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over *** cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye *** recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been *** to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field *** address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this *** study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient *** significant features are extracted and normalized using the min-max normalization *** the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two *** transferring the attributes to the global model,the suggested method trains the local *** global model subsequently improves the technique after integrating the new *** client analyses the results in three rounds to decrease the over-fitting *** experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s *** experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

关键词： Privacy-preserving cataract disease federated learning fundus images healthcare smartphone applications machine learning

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Self-Supervised Learning Using Noisy-Latent Augmentation

Self-Supervised Learning Using Noisy-Latent Augmentation

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Khosrowshahli, Rasa Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Engineering Department Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Lab Canada

ISBN: (纸本)9798350337020

Generally speaking, labeled data is difficult and expensive to provide for applications in machine learning and data mining. One of the earliest approaches to tackle this problem is semi-supervised self-training to take advantages of labeled and unlabeled data to create pseudo-labeled data. However, reaching a high level of confidence to predict the unseen data by a classifier with a limited number of labeled samples is a challenging task. Generating pseudo-labeled data can collaboratively improve the self-training to increase its confidence for further predictions. This paper proposes a collaborative framework between augmentation and self-training to accurately train a model with very limited labeled data. Our framework includes two components where the first component is an unsupervised technique to augment labeled data and feed to the second component which is self-training. The first component uses a custom variational autoencoder architecture (VAE) to generate new samples by adding randomly generated noise to encoded latent representation. As a result, the augmentation component can generate the unique and unexplored images with respect to limited input data distribution. We evaluated the proposed framework on handwritten MNIST image dataset. The conducted experiment shows that the generative component can be helpful in overcoming the problem of inaccurate self-training prediction when sufficient labeled data is not accessible. © 2023 IEEE.

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Exploiting the sequential nature of genomic data for improved analysis and identification

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computers in Biology and Medicine 2024年 183卷 109307-109307页

作者： Nawaz, M. Saqib Nawaz, M. Zohaib Junyi, Zhang Fournier-Viger, Philippe Qu, Jun-Feng College of Computer Science and Software Engineering Shenzhen University China Faculty of Computing and Information Technology Department of Computer Science University of Sargodha Pakistan School of Computer Engineering Hubei University of Arts and Science Hubei Xiangyang China

Genomic data is growing exponentially, posing new challenges for sequence analysis and classification, particularly for managing and understanding harmful new viruses that may later cause pandemics. Recent genome sequence classification models yield promising performance. However, the majority of them do not consider the sequential arrangement of nucleotides and amino acids, a critical aspect for uncovering their inherent structure and function. To overcome this, we introduce GenoAnaCla, a novel approach for analyzing and classifying genome sequences, based on sequential pattern mining (SPM). The proposed approach first constructs and preprocesses datasets comprising RNA virus genome sequences in three formats: nucleotide, coding region, and protein. Then, to capture sequential features for the analysis and classification of viruses, GenoAnaCla extracts frequent sequential patterns and rules in three forms and in codons. Eight classifiers are utilized, and their effectiveness is assessed by employing a variety of evaluation metrics. A performance comparison demonstrates that the suggested approach surpasses the current state-of-the-art genome sequence classification and detection techniques with a 3.18% performance increase in accuracy on average. © 2024 Elsevier Ltd

关键词： Genome

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

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A Survey on Vehicular Edge Computing Architectures 13

A Survey on Vehicular Edge Computing Architectures

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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Lee, Jaehwan Na, Woongsoo Kongju National University Department of Computer Science and Engineering Korea Republic of Kongju National University Department of Software Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

With the recent development of intelligent transportation system (ITS), vehicular applications are being actively studied including autonomous driving, vehicle platooning, that require real-time and high-performance processing resources. Accordingly, various system architectures are being studied to satisfy the high-performance computing requirements. Although the mobile cloud computing architecture provides large-scale computing resources, it is not suitable for vehicular environment that requires real-time processing due to the structure in which the edge node and the computing resources are geographically distant. The mobile edge computing (MEC) architecture places computing resources around edge devices. However, it does not consider edge devices moving at a high speed in a limited service area. Vehicular edge computing (VEC) and vehicular fog computing paradigm have emerged to address the latency requirement with the development of vehicular communications. In this paper, such a computation offloading architecture is presented and analyzed. © 2022 IEEE.

关键词： computer architecture

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