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arXiv 2024年

作者： Li, Hongkang Wang, Meng Ma, Tengfei Liu, Sijia Zhang, Zaixi Chen, Pin-Yu Department of Electrical Computer and System Engineering Rensselaer Polytechnic Institute TroyNY United States Department of Biomedical Informatics Stony Brook University Stony Brook NY United States Department of Computer Science and Engineering Michigan State University East LansingMI United States MIT-IBM Watson AI Lab IBM Research MA United States Department of Computer Science and Engineering University of Science and Technology of China Anhui Hefei China IBM Thomas J. Watson Research Center Yorktown Heights NY United States

Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-convex interactions across layers and the recursive graph structure have made it challenging to establish a theoretical foundation for learning and generalization. This study introduces the first theoretical investigation of a shallow Graph Transformer for semi-supervised node classification, comprising a self-attention layer with relative positional encoding and a two-layer perceptron. Focusing on a graph data model with discriminative nodes that determine node labels and non-discriminative nodes that are class-irrelevant, we characterize the sample complexity required to achieve a desirable generalization error by training with stochastic gradient descent (SGD). This paper provides the quantitative characterization of the sample complexity and number of iterations for convergence dependent on the fraction of discriminative nodes, the dominant patterns, and the initial model errors. Furthermore, we demonstrate that self-attention and positional encoding enhance generalization by making the attention map sparse and promoting the core neighborhood during training, which explains the superior feature representation of Graph Transformers. Our theoretical results are supported by empirical experiments on synthetic and real-world benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Enhancing Accuracy in WBANs Through Optimal Sensor Positioning and Signal Processing: A Systematic Methodology

Enhancing Accuracy in WBANs Through Optimal Sensor Positioni...

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International Conference on Electrical engineering, computer Science and informatics (EECSI)

作者： Israa Ibraheem Al Barazanchi Mayasa M. Abdulrahman Reema Thabit Wahidah Hashim Amol M. Dalavi ravi Sekhar College of Computing and Informatics Universiti Tenaga Nasional (UNITEN) Malaysia College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Computer Engineering Department College of Engineering University of Baghdad Baghdad Iraq Symbiosis Institute of Technology (SIT) Pune Campus Symbiosis International (Deemed University) (SIU) Pune India

ISBN: (数字)9798350355314

ISBN: (纸本)9798350355321

Wireless Body Area Networks (WBANs) have transformed fitness monitoring by providing continuous real-time data collection from small sensors placed on or inside the human body. Even though WBANs have important benefits, their accuracy and dependability are jeopardized by issues with sensor placement and signal processing. Incorrect sensor positioning can lead to inaccurate information, while noisy surroundings make signal processing more challenging. This focuses on using a single methodical approach to improve sensor placement and enhance signal processing in WBANs. We suggest a comprehensive solution that tackles placement sensitivity and noise reduction by combining anatomical modeling, real-time feedback mechanisms, and innovative machine learning algorithms. Experimental results show a 25% increase in signal quality and a 35% improvement in data precision when compared to conventional techniques. This method not only enhances the dependability of health monitoring systems but also validates their ability to be scaled and their effectiveness in various medical uses. Our results highlight the potential of WBANs to transform health care services, providing more accurate diagnostics and customized treatments.

关键词： Wireless communication Sensor placement Accuracy Machine learning algorithms Medical services Signal processing Real-time systems Sensors Reliability Monitoring

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Sparsity-Aware Intelligent Massive random Access Control in Open raN: A Reinforcement Learning Based Approach

arXiv

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arXiv 2023年

作者： Tang, Xiao Liu, Sicong Du, Xiaojiang Guizani, Mohsen Department of Information and Communication Engineering School of Informatics Xiamen University Xiamen361005 China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ07030 United States Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates

Massive random access of devices in the emerging Open radio Access Network (O-raN) brings great challenge to the access control and management. Exploiting the bursting nature of the access requests, sparse active user detection (SAUD) is an efficient enabler towards efficient access management, but the sparsity might be deteriorated in case of uncoordinated massive access requests. To dynamically preserve the sparsity of access requests, a reinforcement-learning (RL)-assisted scheme of closed-loop access control utilizing the access class barring technique is proposed, where the RL policy is determined through continuous interaction between the RL agent, i.e., a next generation node base (gNB), and the environment. The proposed scheme can be implemented by the near-real-time raN intelligent controller (near-RT RIC) in O-raN, supporting rapid switching between heterogeneous vertical applications, such as mMTC and uRLLC services. Moreover, a data-driven scheme of deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces, where a replay buffer is applied for automatic large-scale data collection. An actor-critic framework is formulated to incorporate the strategy-learning modules into the near-RT RIC. Simulation results show that the proposed schemes can achieve superior performance in both access efficiency and user detection accuracy over the benchmark scheme for different heterogeneous services with massive access requests. © 2023, CC BY.

关键词： Reinforcement learning

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Reconfigurable intelligent surfaces for wireless communications:Overview of hardware designs,channel models,and estimation techniques

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Intelligent and Converged Networks 2022年第1期3卷 1-32页

作者： Mengnan Jian George C.Alexandropoulos Ertugrul Basar Chongwen Huang Ruiqi Liu Yuanwei Liu Chau Yuen the Wireless Research Institute ZTE CorporationBeijing 100029China the State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518055China Department of Informatics and Telecommunications National and Kapodistrian University of AthensAthens 15784Greece Communications Research and Innovation Laboratory(CoreLab) Department of Electrical and Electronics EngineeringKoçUniversityIstanbul 34450Turkey College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310027China School of Electronic Engineering and Computer Science Queen Mary University of LondonLondonE14NSUK. the Engineering Product Development(EPD)Pillar Singapore University of Technology and DesignSingapore 487372Singapore

The demanding objectives for the future sixth generation(6G)of wireless communication networks have spurred recent research efforts on novel materials and radio-frequency front-end architectures for wireless connectivity,as well as revolutionary communication and computing *** the pioneering candidate technologies for 6G belong the reconfigurable intelligent surfaces(RISs),which are artificial planar structures with integrated electronic circuits that can be programmed to manipulate the incoming electromagnetic field in a wide variety of *** RISs in wireless networks have been recently advocated as a revolutionary means to transform any wireless signal propagation environment to a dynamically programmable one,intended for various networking objectives,such as coverage extension and capacity boosting,spatiotemporal focusing with benefits in energy efficiency and secrecy,and low electromagnetic field *** by the recent increasing interests in the field of RISs and the consequent pioneering concept of the RIS-enabled smart wireless environments,in this paper,we overview and taxonomize the latest advances in RIS hardware architectures as well as the most recent developments in the modeling of RIS unit elements and RIS-empowered wireless signal *** also present a thorough overview of the channel estimation approaches for RIS-empowered communications systems,which constitute a prerequisite step for the optimized incorporation of RISs in future wireless ***,we discuss the relevance of the RIS technology in the latest wireless communication standards,and highlight the current and future standardization activities for the RIS technology and the consequent RIS-empowered wireless networking approaches.

关键词： reconfigurable intelligent surfaces beyond 5G 6G channel modeling hardware architecture channel estimation smart wireless environment standardization

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Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

SSRN

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SSRN 2024年

作者： Wang, Zhiyuan Duan, Jinhao Yuan, Chenxi Chen, Qingyu Chen, Tianlong Yao, Huaxiu Zhang, Yue Wang, Ren Xu, Kaidi Shi, Xiaoshuang Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China Department of Computer Science Drexel University PhiladelphiaPA19104 United States Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104 United States Section of Biomedical Informatics & Data Science Yale School of Medicine Yale University New HavenCT06510 United States Department of Computer Science University of North Carolina at Chapel Hill Chapel HillNC27599 United States Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence interaction systems, particularly in the healthcare engineering domain. However, a general method for quantifying the uncertainty of free-form answers has yet to be established in open-ended medical question-answering (QA) tasks, where irrelevant words and sequences with limited semantic information can be the primary source of uncertainty due to the presence of generative inequality. In this paper, we propose the Word-Sequence Entropy (WSE ), which calibrates the uncertainty proportion at both the word and sequence levels according to the semantic relevance, with greater emphasis placed on keywords and more relevant sequences when performing uncertainty quantification. We compare WSE with 6 baseline methods on 5 free-form medical QA datasets, utilizing 7 "off-the-shelf" large language models (LLMs), and show that WSE exhibits superior performance on accurate uncertainty measurement under two standard criteria for correctness evaluation. Additionally, in terms of the potential for real-world medical QA applications, we achieve a significant enhancement (e.g., +6.36% improvement of model accuracy on the COVID-QA dataset) in the performance of LLMs when employing sequences with lower uncertainty that are identified by WSE as final answers, without requiring additional task-specific fine-tuning or architectural modifications. © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Out Domain Data Augmentation on Punjabi Children Speech Recognition using Tacotron 1

Out Domain Data Augmentation on Punjabi Children Speech Reco...

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1st International Conference on Mechatronics and Artificial Intelligence, ICMAI 2021

作者： Hasija, Taniya Kadyan, Virender Guleria, Kalpna Centre of Excellence for Speech and Multimodal Laboratory Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The performance of Automatic Speech Recognition (ASR) is directly proportional to the quality of the corpus used and the training data quantity. Data scarcity and more children's speech variability degrades the performance of ASR systems. As Punjabi is a tonal language and low resource language, less data is available for Punjabi children's speech. It leads to poor ASR performance for Punjabi children speech recognition. To overcome limited data conditions, in this paper, two corpora of different domains are evaluated for testing the feasibility of ASR performance. We have implemented Tacotron as an artificial speech synthesis system for Punjabi Language. The speech audios synthesized by Tacotron are merged with available speech corpus and tested on Punjabi children ASR using Mel Frequency Cepstral Coefficients (MFCC) + pitch feature extraction, and Deep Neural Network (DNN) acoustic modeling. It is noticed that the merged data corpus has shown reduced Word Error rate (WER) of the ASR system with a Relative Improvement (RI) of 9-12%. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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CNN segmentation of skin melanoma in pre-processed dermoscopy images

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Procedia computer Science 2024年 235卷 2775-2782页

作者： Seifedine Kadry Elena Verdú Robertas Damasevicius Laith Abualigah Vijendra Singh Venkatesan rajinikanth Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Higher School of Engineering and Technology Universidad Internacional de La Rioja Logroño 26006 Spain Department of Applied Informatics Vytautas Magnus University 53361 Kaunas Lithuania Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia School of Computer Science University of Petroleum and Energy Studies Dehradun 248007 India Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India

Using medical data to improve diagnosis accuracy has recently become common practice in hospitals. A modern computing environment has enabled real-time diagnosis of medical data using Convolutional Neural Networks (CNNs). To extract and evaluate skin melanoma recorded with digital dermatoscopy images (DDI), we developed a CNN segmentation framework. In this proposal, four phases are proposed: (i) DDI collection and resizing, (ii) DDI enhancement using pre-processing techniques, (iii) CNN segmentation for lesion extraction, (v) Comparing the extracted sections to the ground truth images, and (v) Verifying whether the framework is valid. Using DDI pre-processed with (i) Traditional procedures, (ii) Otsu’s thresholding, (iii) Kapur’s thresholding, and (iv) Fuzzy-Tsallis thresholding, this proposal examines the different CNN segmentation schemes presented in the literature. For mining skin lesions, the Moth-Flame Algorithm (MFA) combined with tri-level thresholding achieves an optimal threshold for the DDI. With Fuzzy-Tsallis thresholding images, the VGG-UNet performs better than the alternatives. This framework helps to achieve better values of Jaccard (88.47±2.13%), Dice (93.08±1.17%), and Accuracy (98.64±0.71%) on the chosen DDI database.

关键词： Watershed-algorithm skin melanoma segmentation Fuzzy-Tsallis entropy Moth-Flame algorithm evaluation

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Modeling Prototypical Preference Behavior and Diversity using rank Score Characteristic Functions 21

Modeling Prototypical Preference Behavior and Diversity usin...

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21st IEEE International Conference on Cognitive informatics and Cognitive Computing, ICCI*CC 2022

作者： Schweikert, Christina Shimojo, Shinsuke Glasser, Hannah Hendsey, Rebecca Alsaber, rami Hsu, D. Frank St. John's University QueensNY United States California Institute of Technology Division of Biology and Biological Engineering / Computation and Neural Systems PasadenaCA United States Fordham University Laboratory of Informatics and Data Mining Department of Computer and Information Science New YorkNY United States

ISBN: (纸本)9781665490849

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling. © 2022 IEEE.

关键词： Eye movements

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Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms

arXiv

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arXiv 2023年

作者： Saifullah, Shoffan Drezewski, rafal Yudhana, Anton Pranolo, Andri Kaswijanti, Wilis Suryotomo, Andiko Putro Putra, Seno Aji Khaliduzzaman, Alin Prabuwono, Anton Satria Japkowicz, Nathalie Institute of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Center of Knowledge Engineering and Data Science Department of Electrical Engineering and Informatics Universitas Negeri Malang Malang65145 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia College of Computer and Information Hohai University Nanjing211100 China Department of Informatics Faculty of Industrial Technology Universitas Ahmad Dahlan Yogyakarta55166 Indonesia Faculty of Agricultural Engineering and Technology Sylhet Agricultural University Sylhet3100 Bangladesh Faculty of Computing and Information Technology in Rabigh King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science American University WashingtonDC20016 United States

This study explored the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection for precise poultry hatchery practices. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset (200 single egg images) using augmented images (rotation, flip, scale, translation, and reflection). The training results demonstrated that all models achieved high accuracy, indicating their ability to learn and classify chicken eggs’ fertility. However, variations in accuracy and performance were observed when these models were evaluated on the testing datasets. The InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. It demonstrated excellent performance in all parameters of the evaluation metrics for both training and testing datasets. When evaluated on the testing datasets, it achieved an accuracy of 0.98, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.96 for identifying non-fertile eggs. The higher performance is attributed to its unique architecture, efficiently capturing features at different scales, which leads to improved accuracy and robustness. Further optimization and fine-tuning of the models might be necessary to address the limitations in accurately detecting fertile and non-fertile eggs using other models. This study highlighted the potential of CNN-transfer learning for nondestructive fertility detection and emphasized the need for further research to enhance the models’ capabilities and to ensure accurate *** Codes CS-Class: 68T07, 68T45, 68U10, ICT-Class: 94A08 © 2023, CC BY-SA.

关键词： Image classification

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The future of E-commerce in the publishing industry 2

The future of E-commerce in the publishing industry

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Aseh, Khairi Al-Qudah, Omar Mohammed Ali Abdullah Yusoff, M. Hafiz Pathmanathan, P. ravindran Latiff, Nurul Adilah Abdul Jusoh, Julaily Aida Abu-Ulbeh, Waheeb El-Ebiary, Yousef A. Baker Al Moaiad, Yazeed International Institute of Applied Science Swiss School of Management Netherlands Irbid National University Faculty of Financial and Business Science Head of E-Marketing Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia UMT Faculty of Ocean Engineering Technology and Informatics Malaysia UniSZA Faculty of Informatics and Computing Malaysia Faculty of Computer Science and Informatics Cybersecurity Department Jordan MEDIU Faculty of Computer and Information Technology Malaysia

ISBN: (纸本)9781665432221

In comparison to conventional book-printed content, electronic "publishing"has allowed users to import files to either a personal computer or an e-book reader to read text in a digital format. Reduced printing costs and reduced time to market by streamlined drafting, proofing, and printing methods are the benefits of the publishing industry over the conventional printed book (p-book). It prevents binding, distribution and shipping. The intent of this paper is to explore the strategic use of e-commerce in the transformation of the publishing industry. The first part of the paper discusses the strategic use of e-commerce/Web-Based Business in the transition from a single distribution channel mass producer of printed books to a multiple distribution channel that will include e-books and print on demand technology in the publishing industry. Emphasis is put on management techniques in the global marketplace for the use of emerging information technologies. The second section of this paper addresses the transfer in competitive influence between the different levels of the supply chain of the publishing industry, including the latest trends in electronic publishing from the stakeholders' point of view. Expected to serve as the base for development within the digitized print industry. As e-publishing becomes popular and moves towards maturity, to allow a "mainstream"status, common standards must emerge and combine. © 2021 IEEE.

关键词： Electronic publishing

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