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The Original Method of Controlling a computer Using Distance Sensors 16

The Original Method of Controlling a Computer Using Distance...

16th IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and computer engineering, TCSET 2022

作者： Savaryn, Pavlo Strekha, Vadym Brych, Mykola Brych, Lesia Kabak, Vitalii Polishchuk, Mykola Lutsk National Technical University Department of Digital Education Technologies Lutsk Ukraine Lutsk National Technical University Department of Computer Science Lutsk Ukraine Internet of Things and Software Defined Networks Lab Lviv Polytechnic National University Lviv Ukraine Centre for International Education Lviv Polytechnic National University Lviv Ukraine Lutsk National Technical University Department of Computer Engineering and Cybersecurity Lutsk Ukraine

ISBN: (纸本)9781665468619

The article provides a brief overview of human-computer interaction methods and systems, and the research made in this area. We have designed the architecture of the system and tested a method of controlling a computer using ultrasonic distance sensors. In the course of the experiment, the following problems have been solved: obtaining and processing data from several distance sensors, data transfer to gesture recognition algorithm, uncontrolled oscillations of sensor indicators. The suggested method of controlling a computer can be used in ubiquitous computing. © 2022 IEEE.

关键词： Human computer interaction

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Revisiting Knowledge Transfer for Success Enterprise System Project

Revisiting Knowledge Transfer for Success Enterprise System ...

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2021 International Conference on Information Technology, ICIT 2021

作者： Hussien, Jamal Abdullateef, Mansoor Kahtan, Hasan Sulaiman, Riza Bangi Malaysia Abdulgabber Abdulhak Consultant Researcher Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665428705

This paper re-examines the relationship between the Knowledge Transfer (KT) and the project management process groups. Knowledge Transfer as one of the knowledge management life-cycle is supposed to have a remarkable impact on the enterprise system success. However, project failure is one of the main problems in enterprise systems management and lack of proper documentation on the results of previous projects and knowledge transfer are the main reasons. In this paper, we have found that enterprise system project divided into three stages, with (6) process groups in the Pre-Implementation stage, (6) process groups during-Implementation stage, and (3) process groups in the post-Implementation, however, there are eleven (11) roles who are evolving to anticipate the fifth results (Project Proposal, Evaluation Ratings, Project Charter, Project Plan, Project Documents), to increase the understanding of knowledge in each project stages to improve the enterprise system project success. © 2021 IEEE.

关键词： Project management

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A Blockchain-Based Distributed Collaborative Sensing and Spectrum Access Approach for Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Peng, Yuhuai Li, Yuan Guo, Yu Zhang, Dawei Khan, Fazlullah Alturki, Ryan Alshawi, Bandar Northeastern University College of Computer Science and Engineering Shenyang110819 China Zhiyuan Research Institute Xihu District Hangzhou China Zhengzhou University School of Computer and Artificial Intelligence Henan Zhengzhou450001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China KKR and KSR Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh Guntur India Division of Research and Development Lovely Professional University Phagwara India University Centre of Research and Development Chandigarh University Mohali India Umm Al-Qura University Department of Software Engineering College of Computing Makkah Saudi Arabia Umm Al-Qura University College of Computing Department of Computer and Network Engineering Makkah Saudi Arabia

Cognitive radio (CR) provides key technical support for the seamless communication of consumer electronic (CE) devices through distributed cooperative sensing and spectrum access. However, its openness and dynamic nature introduce potential security risks and challenges. Due to the openness of wireless communication, spectrum sensing and access are vulnerable to malicious attacks, and resource-constrained devices cannot effectively implement artificial intelligence (AI)-driven encryption and access control strategies. To address these limitations, this study proposes a blockchain-based distributed collaborative sensing method for CE devices. In the proposed method, after sensing, the smart contracts verify node legitimacy using the ring signature method. The malicious users are rejected by the outlier detection method, which improves the collaborative anti-interference ability. In addition, by using a centralized training and distributed execution architecture, a secure spectrum sensing strategy based on multi-agent reinforcement learning is designed. The proposed strategy ensures that each node makes independent decisions, which enhances the adaptability of the CE ecosystem. The proposed method is verified by extensive simulations, and the results show that it can maintain spectrum awareness of more than 95.72% and an access success rate of 91.19% under malicious attacks. Moreover, communication overhead is significantly reduced by approximately 93.53%. © 1975-2011 IEEE.

关键词： Blockchain

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities

The Reliability of Information Sources: Student Perceptions ...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Anita Mihalovicsne Kollar Jozsef Katona Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources; social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape.

关键词： Economics Power engineering Accuracy Social networking (online) engineering profession Education Reliability engineering Information filters Information age Reliability

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Neural Networks and Deep Learning: Innovations and Applications in Soft Computing

Neural Networks and Deep Learning: Innovations and Applicati...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Jahagirdar Imran Abdulkadar Chinnem Rama Mohan P. Vijaya Bharati Shamik Palit Gunda Srinivasa Rao Pankaj Mudholkar Computer Application Savitribai Phule Pune University Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Department of CSE Vignans Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computing Science and Software Engineering University of Stirling RAK Campus UAE School of Management and Commerce Mallareddy University Kompally Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing.

关键词： Deep learning Technological innovation Data privacy Privacy Natural languages Process control Real-time systems Informatics Biological neural networks Robots

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Concealing Sensitive Samples against Gradient Leakage in Federated Learning

arXiv

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arXiv 2022年

作者： Wu, Jing Hayat, Munawar Zhou, Mingyi Harandi, Mehrtash Department of Electrical and Computer Systems Engineering Monash University Australia Department of Data Science and AI Monash University Australia Department of Software Systems and Cybersecurity Monash University Australia

Federated Learning (FL) is a distributed learning paradigm that enhances users’ privacy by eliminating the need for clients to share raw, private data with the server. Despite the success, recent studies expose the vulnerability of FL to model inversion attacks, where adversaries reconstruct users’ private data via eavesdropping on the shared gradient information. We hypothesize that a key factor in the success of such attacks is the low entanglement among gradients per data within the batch during stochastic optimization. This creates a vulnerability that an adversary can exploit to reconstruct the sensitive data. Building upon this insight, we present a simple, yet effective defense strategy that obfuscates the gradients of the sensitive data with concealed samples. To achieve this, we propose synthesizing concealed samples to mimic the sensitive data at the gradient level while ensuring their visual dissimilarity from the actual sensitive data. Compared to the previous art, our empirical evaluations suggest that the proposed technique provides the strongest protection while simultaneously maintaining the FL performance. Code is located at https://***/JingWu321/DCS-2. Copyright © 2022, The Authors. All rights reserved.

关键词： Sensitive data

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Segmentation and Classification of Stomach Abnormalities Using Deep Learning

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computers, Materials & Continua 2021年第10期69卷 607-625页

作者： Javeria Naz Muhammad Attique Khan Majed Alhaisoni Oh-Young Song Usman Tariq Seifedine Kadry Department of Computer Science HITEC University TaxilaTaxilaPakistanl 2College of Computer Science and EngineeringUniversity of Ha’ilHa’ilSaudi Arabial 3Department of SoftwareSejong UniversitySeoulKoreaGwangjin-guKoreal 4College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabial 5Faculty of Applied Computing and TechnologyNoroff University CollegeKristiansandNorway

An automated system is proposed for the detection and classification of GI *** proposed method operates under two pipeline procedures:(a)segmentation of the bleeding infection region and(b)classification of GI abnormalities by deep *** first bleeding region is segmented using a hybrid *** threshold is applied to each channel extracted from the original RGB ***,all channels are merged through mutual information and pixel-based *** a result,the image is *** and deep learning features are extracted in the proposed classification *** transfer learning(TL)approach is used for the extraction of deep *** Local Binary Pattern(LBP)method is used for texture ***,an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture *** selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning *** experimental process is evaluated on the basis of two datasets:Private and *** accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data *** can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

关键词： Gastrointestinal tract contrast stretching segmentation deep learning features selection

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Utilizing Gyroscope and Accelerometer for Self-Managed Crystal Methamphetamine Recovery and Private Monitoring

Utilizing Gyroscope and Accelerometer for Self-Managed Cryst...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Ali Ahmed Omar Amr Ayman Atia Software Engineering Department October University for Modern Sciences and Arts (MSA) Cairo Egypt HCI-LAB Faculty of Computers and Artificial Intelligence Faculty of Computer Science Helwan University October University for Modern Sciences and Arts (MSA) Giza Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

Recovery from substance abuse is a continuous struggle as almost 60% of the addicts at any given instance in time during the recovery process experiences a relapse. The situation worsens during withdrawal, where the process, more so for those recovering from crystal methamphetamine addiction, often presents with intense symptoms that are really fatiguing. Mobile sensors offer a practical solution to continuously monitor these symptoms and provide near real-time insights into an individual's progress towards recovery. This paper explores two deep learning approaches, CNNs and LSTMs(Long Short Term Memory), to identify patterns related to withdrawal symptoms using accelerometer data. It will also address key limitations in current state-of-the-art methods, such as unbiased data collection, feature extraction, and mobile device implementation. Finally, the paper presents personalized reports designed to aid in therapy, helping individuals stay on track and reducing the likelihood of relapse.

关键词： Accelerometers Addiction Medical treatment Crystals Ubiquitous computing Real-time systems Mobile handsets Sensors Gyroscopes Monitoring

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FAIR Data Point:A FAIR-Oriented Approach for Metadata Publication

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Data Intelligence 2023年第1期5卷 163-183页

作者： Luiz Olavo Bonino da Silva Santos Kees Burger Rajaram Kaliyaperumal Mark D.Wilkinson Services and Cybersecurity group Faculty of Electrical EngineeringMathematics and Computer ScienceUniversity of Twente-Enschede7513GBthe Netherlands Biosemantics group Department of Human GeneticsLeiden University Medical Center-LeidenNL 2333 ZCthe Netherlands Escuela Técnica Superior de Ingenieria Agronomica Alimentaria y de BiosistemasCentro de Biotecnologia y Genomica de Plantas UPM-INIAUniversidad Politecnica de Madrid(UPM)-Instituto Nacional de Investigacion y Tecnologia Agraria y Alimentaria(INIA)MadridES 28223Spain

Metadata,data about other digital objects,play an important role in FAIR with a direct relation to all FAIR *** this paper we present and discuss the FAIR Data Point(FDP),a software architecture aiming to define a common approach to publish semantically-rich and machine-actionable metadata according to the FAIR *** present the core components and features of the FDP,its approach to metadata provision,the criteria to evaluate whether an application adheres to the FDP specifications and the service to register,index and allow users to search for metadata content of available FDPs.

关键词： FAIR FAIR data point FAIR principles Metadata Interoperability Linked data Semantic interoperability

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Dynamic knowledge graph completion through time-aware relational message passing

Dynamic knowledge graph completion through time-aware relati...

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International CSI computer Conference, CSICC

作者： Amirhossein Baqinejadqazvini Saedeh Tahery Saeed Farzi Department of Software Engineering Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran Department of Artificial Intelligence Faculty of Computer Engineering K. N. Toosi University of Technology Tehran Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran Iran

As the structure of knowledge graphs may vary over time, static knowledge graph completion methods do not deal with time-varying knowledge graphs. However, examining the paths between entities and entities' context information can lead to more accurate completion methods. This paper attempts to complete dynamic (time-varying) knowledge graphs by combining time-aware relational paths and relational context. The proposed model can improve dynamic knowledge graph completion methods by leveraging neural networks. Experimental results conducted on two standard datasets, ICEWS14 and ICEWS05-15, indicate our model's superiority in terms of Mean Reciprocal Rank (MRR) and Hit@k over its well-known counterparts, such as DE-TransE and DE-DistMult.

关键词： Computational modeling Message passing Neural networks Knowledge graphs Time factors Standards Context modeling

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