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An Efficient Symbol Timing Scheme for OFDM systems Using Optimal Correlation-Based Circular-Shifted Preamble

IEEE Wireless Communications Letters 2024年 1-1页

作者： Yang, Fan Zhang, Xi University of Electronic Science and Technology of China Chengdu 611731 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA

We propose a coarse symbol timing scheme for wireless orthogonal frequency division multiplexing (OFDM) systems using an optimal correlation-based circular-shifted preamble (CSP). Our proposed scheme is implemented by off-line correlation-ranking and on-line processing. The on-line processing derives an optimal CSP for transmission according to the off-line correlation-ranking, and employs a delayed multiplication operation for symbol timing. The conducted simulation results show that our proposed scheme outperforms the other existing representative schemes over typical multi-path fading channels in terms of mean square error (MSE) while keeping the computational complexity low. IEEE

关键词： Orthogonal frequency division multiplexing

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The AI Companion in Education: Analyzing the Pedagogical Potential of ChatGPT in computer Science and Engineering

The AI Companion in Education: Analyzing the Pedagogical Pot...

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IEEE Education Engineering (EDUCON)

作者： Zhangying He Thomas Nguyen Tahereh Miari Mehrdad Aliasgari Setareh Rafatirad Hossein Sayadi Departmentof Computer Engineering and Computer Science California State University Long Beach CA USA Center for Information Systems and Technology Claremont Graduate University CA USA Department of Computer Science University of California Davis CA USA

ISBN: (数字)9798350394023

ISBN: (纸本)9798350394030

Artificial Intelligence (AI), with ChatGPT as a prominent example, has recently taken center stage in various domains including higher education, particularly in computer Science and Engineering (CSE). The AI revolution brings both convenience and controversy, offering substantial benefits while lacking formal guidance on their application. The primary objective of this work is to comprehensively analyze the pedagogical potential of ChatGPT in CSE education, understanding its strengths and limitations from the perspectives of educators and learners. We employ a systematic approach, creating a diverse range of educational practice problems within CSE field, focusing on various subjects such as data science, programming, AI, machine learning, networks, and more. According to our examinations, certain question types, like conceptual knowledge queries, typically do not pose significant challenges to ChatGPT, and thus, are excluded from our analysis. Alternatively, we focus our efforts on developing more in-depth and personalized questions and project-based tasks. These questions are presented to ChatGPT, followed by interactions to assess its effectiveness in delivering complete and meaningful responses. To this end, we propose a comprehensive five-factor reliability analysis framework to evaluate the responses. This assessment aims to identify when ChatGPT excels and when it faces challenges. Our study concludes with a correlation analysis, delving into the relationships among subjects, task types, and limiting factors. This analysis offers valuable insights to enhance ChatGPT's utility in CSE education, providing guidance to educators and students regarding its reliability and efficacy.

关键词： Systematics Limiting Focusing Machine learning Chatbots Reliability Task analysis

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The Methodology of Mask Detection Employing the YOLOv7 Framework

The Methodology of Mask Detection Employing the YOLOv7 Frame...

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Neural Networks, information and Communication Engineering (NNICE), International Conference on

作者： Fucheng Zhang Pinfang Tian School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-TimeIndustrial Systems Wuhan China

In the current field of object detection, the YOLOv7 model demonstrates significant advantages in terms of both detection speed and accuracy. However, the model shows deficiencies in focussing on key information, and its loss function's sensitivity to bounding boxes results in suboptimal performance in densely populated environments and small target detection scenarios. To address these issues, this study proposes a mask target detection method based on YOLOv7. By integrating a channel attention mechanism into the YOLOv7 model, this method enhances the model's focus on important information in images, effectively reducing interference from background factors. Additionally, the method incorporates Normalized Wasserstein Distance (NWD) into the loss function to optimize the model's evaluation of small target detection. Experimental verification shows that the improved model maintains its original detection speed while increasing the overall accuracy to 92.1%, a 2.9% improvement over the original YOLOv7 model. It achieves a 3.3% increase in the mAP.5 metric. This indicates that the optimised YOLOv7 model has superior detection capabilities, allowing it to perform mask-wearing detection tasks more rapidly and accurately in natural environments.

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Mobile Collaborator System Application for E-commerce Drugstore 8

Mobile Collaborator System Application for E-commerce Drugst...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Oktavia, Tanty Michael Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program-Computer Science Computer Science Department Jakarta11530 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665416979

Mostly Drugstores do not have any system that exclusively connect to access E-commerce system because the marketing concept of drugstores still use conventional store to sell their products. According to this situation, this study will propose e-commerce that accomodate drugstore service. For this study, the analysis methodology consists of seeing the most recent business processes in the e-commerce system system and the current situation in drugstore, interviewing the drugstore owner, and doing a literature research. Then, for the drugstore mobile application, create the Unified Modeling Language (UML). The system design is accomplished through the creation of Object-Oriented Detailed Designs in the form of diagrams, databases, and user interfaces. The drugstore mobile application, which includes features such as recommendation product menus, best-selling product menus, promo product menus, transaction cart menus, order tracking menus, order letter menus, and item receive confirmation menus, is the result of this study. © 2021 IEEE.

关键词： User interfaces

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Healthcare Professionals’ Perceptions Towards Ai Integration in Jordan's Health Sector

SSRN

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SSRN 2024年

作者： Klaib, Ahmad F. Alhosanie, Tasneem N. Mahdin, Ladees W. Altatar, Enas J. Software Engineering Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Information Systems Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan

PurposeThe impact of AI on healthcare is widely recognized there remains a scarcity of studies examining how doctors perceive and approach its use in medicine. This study aims to gather insights from healthcare providers in Jordan concerning the advantages of integrating AI into practices, their perspectives on AI applications in healthcare, and their views on the future role of AI in replacing key tasks within health *** survey was conducted among healthcare professionals working at facilities in Jordan. An online questionnaire was used to collect data on demographics, attitudes toward using AI for tasks, and opinions on the benefits of AI adoption. Categorical variables were presented as counts and percentages, while the continuous variables were interpreted as mean and standard deviation. The associations between the determinants and the outcomes were done using one-way ANOVA. Any test with a P-value 0.05 was considered *** total of 612 healthcare professionals participated in the survey with females comprising a majority of respondents (52.8%). The majority of respondents showed optimism about AI’s potential to improve and revolutionize the field, although there were concerns about AI replacing human roles. Generally, physical therapists, medical researchers, and pharmacists displayed openness to incorporating AI into their work routines. Younger individuals aged between 18 and 40 seemed accepting of AI in the domain. A significant portion of participants believed that AI could negatively impact job opportunities and reduce the time needed for diagnosing conditions, but did not find any correlation, between responses and *** conclude, the results of this study suggest that healthcare professionals, in Jordan, hold receptive views on incorporating artificial intelligence in the medical field similar to their counterparts in developed nations. However, there is a concern about the implications of AI, on job stability a

关键词： Artificial intelligence

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Slow Inter-area Electro-mechanical Oscillations Revisited: Structural Property of Complex Multi-area Electric Power systems

Slow Inter-area Electro-mechanical Oscillations Revisited: S...

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International Conference on Control, Decision and information Technologies (CoDIT)

作者： Hiya Akhil Gada Marija D. Ilic Laboratory for Information and Decision Systems (LIDS) of Massachusetts Institute of Technology Cambridge MA USA Department of Carnegie Electrical and Computer Engineering (ECE) Mellon University Pittsburgh PA

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper introduces a physically-intuitive notion of inter-area dynamics in systems comprising multiple interconnected energy conversion modules. The idea builds on an earlier general approach of setting their structural properties by modeling internal dynamics in stand-alone modules (components, areas) using the fundamental conservation laws between energy stored and generated, and then constraining explicitly their Tellegen’s quantities (power and rate of change of power). In this paper we derive, by following the same principles, a transformed state-space model for a general nonlinear system. Using this model we show the existence of an area-level interaction variable, intVar, whose rate of change depends solely on the area internal power imbalance and is independent of the model complexity used for representing individual module dynamics in the area. Given these structural properties of stand-alone modules, we define in this paper for the first time an inter-area variable as the difference of power wave incident to tie-line from Area I and the power reflected into tie-lie from Area II. Notably, these power waves represent the interaction variables associated with the two respective interconnected areas. We illustrate these notions using a linearized case of two lossless inter-connected areas, and show the existence of a new inter-area mode when the areas get connected. We suggest that lessons learned in this paper open possibilities for computationally-efficient modeling and control of inter-area oscillations, and offer further the basis for modeling and control of dynamics in changing systems comprising faster energy conversion processes.

关键词： Computational modeling Power system dynamics Process control Energy conversion Nonlinear dynamical systems State-space methods Complexity theory information technology Oscillators

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Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence

arXiv

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arXiv 2024年

作者： Le, Thao Miller, Tim Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO’s Data61 Australia

Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines. Copyright © 2024, The Authors. All rights reserved.

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models

Improving Fake News Detection with a Mixture of Experts: Lev...

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Advancements in Computing (ICAC), International Conference on

作者： Tharindu Gunasekara Disira Thihan Sansika Kodithuwakku Dinuka Dissanayake Harinda Fernando Samitha Vidhanaarachchi Archchana Sindhujan dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Science and Electronic Engineering University of Surrey United Kingdom

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods.

关键词： Sentiment analysis Analytical models Accuracy Bidirectional control Logic gates Linguistics Transformers Encoding Vectors Fake news

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An Evaluation of Different Deep Feature Extraction Approaches for MRI-Based Brain Tumor Detection

An Evaluation of Different Deep Feature Extraction Approache...

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Artificial Intelligence Science and Applications in Industry and Society (CAISAIS), International Conference on

作者： Nehal Sakr Fatma El-Zahraa El Gamal Reem El-Deeb Sara El-Metwally Sara Elhishi Information Technology Deptarment Mansoura University Mansoura Egypt Computer Science Department Mansoura University Mansoura Egypt Information Systems Department Mansoura University Mansoura Egypt

Brain tumor is a leading cause of death globally and Magnetic Resonance Imaging (MRI) is a powerful tool for its diagnosis. However, the ability to extract more representative features for efficient characterization of brain tumors is still a highly targeted topic. This paper aims to study the power of different deep learning approaches such as traditional Convolutional Neural Network (CNN), transfer learning via three well-known models (VGG16, InceptionV3, and ResNet50), and convolutional auto-encoder in extracting deep representative features for efficient characterization of brain tumors. The performance evaluation was done using two public datasets of MRI images and various benchmarking evaluation metrics. Both traditional CNN and convolutional auto-encoder achieved 100% accuracy, with the auto-encoder producing the lowest loss and highly productive with balanced datasets. Transfer learning models achieved an overall performance of 99% in different evaluation metrics and were found to be preferred with unbalanced datasets.

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