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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： Adnan Ahmed Abi Sen Yaser Mohammed Aljohani Abdulelah Alhussain Hassan Hakami Nour Mahmoud Bahbouh Ahmad Alkhodre Mohammed S. Alghamdi Computing and Information Technology Islamic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Saudi Electronic University Al-Madinah Saudi Arabia Department of Information and Communication Sciences Granada University Granada Spain Faculty of Computer and Information Systems Islamic University Al-Madinah Saudi Arabia Faculty of the Holy Quran and Islamic Studies Islamic University Al-Madinah Saudi Arabia

The revolution of technologies and communications pushed for smarter applications and services. A smart university has become a target for educational institutions in many countries in the last few years. The smart-university provides many advantages in all aspects: academic, administrative, and research. Transformation to a smart university will save time, effort, and cost, in addition to increasing the quality of services, users' satisfaction, and sustainability. This research provided reviewing study for the most important standards, services, applications, and tools that must be present in smart universities. Then, the research proposed a framework to transform universities into smart ones. In recent years, the Kingdom of Saudi Arabia (KSA) is considered one of the leading countries in the field of digital transformation. So, in the next phase, we will try to apply the proposed framework in one of the KSA universities.

关键词： Heuristic algorithms Digital transformation Urban areas Education Transforms Quality of service Internet of Things

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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Any Bluetooth device can be hacked. Know how?

Cyber Security and Applications

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Cyber Security and Applications 2024年 2卷

作者： Muraleedhara, Prathibha Christo, Mary Subaja J, Jaya Yuvasini  D College of Technology – Information Security and Program Management University of Houston 77204 TX United States Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kantankulathur Chennai India Department of Electronics and Communication Engineering Hindustan College of Engineering and Technology India Assistant Professor Department of Computer Science and Business Systems Thiagarajar College of Engineering Madurai 625015 India

In today's world, Bluetooth technology is integrated into almost every device we use, from wireless headsets, mice, and keyboards to cars and smart home devices. But with the convenience of this technology comes the risk of privacy and security breaches. Each Bluetooth device has potential vulnerabilities that cybercriminals can exploit and take advantage of. It is important to create awareness about various Bluetooth vulnerabilities, exploits, and ways to prevent them. This article examines the dark side of Bluetooth technology by explaining how hackers can find ways to bypass the advanced security features of laptops, phones, cars, or smart home devices to compromise the devices and steal personal sensitive data. Whether it is a casual user of Bluetooth technology or a business owner with multiple devices, understanding these risks is crucial to protecting yourself and your information. This article begins by explaining the basics of Bluetooth technology and how it works. Followed by a list of security risks involved while using Bluetooth and finally, it highlights the best practices to protect the devices and the data they contain. © 2024

关键词： Ad hoc networks and security risk Bluetooth Exploit Threat Vulnerability

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Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning

arXiv

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arXiv 2023年

作者： Wang, Shengbo Li, Ke Yang, Yin Cao, Yuting Huang, Tingwen Wen, Shiping College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar Science Program Texas A & M University at Qatar Doha23874 Qatar Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) models, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： Multilayer neural networks

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The Role of Wannabes in the Digital Nomad Ecosystem in Times of Pandemic

The Role of Wannabes in the Digital Nomad Ecosystem in Times...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Antonio De Almeida, Marcos Moreira De Souza, Jano Correia, Antonio Schneider, Daniel Rio de Janeiro Brazil Inesc Tec and University of Trás-os-Montes e Alto Douro Vila Real Portugal University of Nebraska at Omaha College of Information Science & Technology United States Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The purpose of this paper is to reveal a set of new results obtained from an ongoing investigation focused on the way that the particular characteristics which are inherent to 'wannabe' digital nomads' activities contribute to the sustainability of the whole digital nomad ecosystem. In line with the premise of this research, we assume the importance of understanding the impacts that are being felt in the personal knowledge management ecology practices and routines of digital nomads as experienced by a specific online population (i.e., Reddit user base), together with a deep and wide examination of their preferences and expectations regarding the technology-mediated work-life issues that exert a direct influence on the digital nomad community. To this end, we gathered and further processed text posts and comments from users in the '/r/digitalnomad' subreddit. From a sociotechnical standpoint, the empirical data extracted from this sample population about the wannabe/how to be digital nomad symbiotic ecosystem can provide insightful information for researchers worldwide about future design-level interventions. © 2022 IEEE.

关键词： COVID-19

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Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models

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Scientific Reports 2025年第1期15卷 1-16页

作者： Ghoneim, Sherif S. M. Baz, Mohammed Alzaed, Ali Zewdie, Yohannes Tesfaye Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Technology Debre Markos University P. BOX 269 Debre Markos Ethiopia

Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955. © The Author(s) 2025.

关键词： Dissolved gas analysis Furnic compounds Insulating oil Insulating paper Power transformer XGBoost/LightGBM

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Adaptive Curriculum Learning for Semi-supervised Segmentation of 3D CT-Scans 28th

Adaptive Curriculum Learning for Semi-supervised Segmentati...

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28th International Conference on Neural information Processing, ICONIP 2021

作者： Nartey, Obed Tettey Yang, Guowu Agyapong, Dorothy Araba Yakoba Wu, JinZhao Sarpong, Asare K. Frempong, Lady Nadia Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Electronic Information Guangxi University Nanning530004 China Biomedical Engineering Program Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Medicine Department of Radiology and Imaging Sciences Emory University 1364 Clitton Rd. AtlantaGA30322 United States

ISBN: (纸本)9783030921842

Semi-supervised learning algorithms make use of both labelled training data and unlabelled data. However, the visual domain gap between these sets poses a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of generating noisy pseudo-labels and placing over-confident labelling belief on incorrect classes leads to deviation from the solution. To solve this challenge, the study investigates a curriculum-styled approach for deep semi-supervised segmentation which relaxes and treats pseudo-labels as continuous hidden variables by developing an adaptive pseudo-label generation strategy to jointly optimized the pseudo-label generation and selection process. A regularization scheme is further proposed to smoothen the probability outputs and sharpen the less represented pseudo-label regions. The proposed method was evaluated on three publicly available computer Tomography (CT) scan benchmarks and extensive experiments on all modules have demonstrated the efficacy of the proposed method. © 2021, Springer Nature Switzerland AG.

关键词： Curricula

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CapST: An Enhanced and Lightweight Method for Deepfake Video Classification

arXiv

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arXiv 2023年

作者： Ahmad, Wasim Peng, Yan-Tsung Chang, Yuan-Hao Ganfure, Gaddisa Olani Khan, Sarwar Shahzad, Sahibzada Adil Institute of Information Science Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei116 Taiwan Social Networks Human-Centered Computing Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan Dire Dawa University Ethiopia Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

The proliferation of deepfake videos, synthetic media produced through advanced Artificial Intelligence techniques, has raised significant concerns across various sectors, encompassing realms such as politics, entertainment, and security. In response, this research introduces an innovative and streamlined model designed to classify deepfake videos generated by five distinct encoders adeptly. Our approach not only achieves state-of-the-art performance but also optimizes computational resources. At its core, our solution employs part of a VGG19_bn as a backbone to efficiently extract features, a strategy proven effective in image-related tasks. We integrate a Capsule Network coupled with a Spatial-Temporal attention mechanism to bolster the model’s classification capabilities while conserving resources. This combination captures intricate hierarchies among features, facilitating robust identification of deepfake attributes. Delving into the intricacies of our innovation, we introduce an existing video-level fusion technique that artfully capitalizes on temporal attention mechanisms. This mechanism serves to handle concatenated feature vectors, capitalizing on the intrinsic temporal dependencies embedded within deepfake videos. By aggregating insights across frames, our model gains a holistic comprehension of video content, resulting in more precise predictions. Experimental results on an extensive benchmark dataset of deepfake videos (DFDM) showcase the efficacy of our proposed method. Notably, our approach achieves up to a 4% improvement in accurately categorizing deepfake videos compared to baseline models, all while demanding fewer computational resources. Copyright © 2023, The Authors. All rights reserved.

关键词： Routing algorithms

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Estimating the 3D center point of an object with Kinect sensor RGB-D images

Estimating the 3D center point of an object with Kinect sens...

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IEEE Latin American Robotics Symposium, LARS

作者： Gustavo Fardo Armênio João Alberto Fabro Renzo De Rosa Tognella Felipe Pierre Conter Marlon Vaz De Oliveira Everson De Souza Silva Computer Engineering Course - UTFPR Curitiba PR Brazil PPGCA-Graduate Program on Applied Informatics UTFPR-Federal University of Technology - Paraná Curitiba PR Brazil Information System Course - UTFPR Curitiba PR Brazil UNILA - University of Latin American Integration Foz do Iguaçu PR Brazil CPGEI-Graduate Program on Electrical Engineering and Industrial Informatics/UTFPR IFPR- Federal Institute of Technology - Paraná Pinhais PR Brazil PPGCA-Graduate Program on Applied Informatics UTFPR Curitiba Brazil

This article describes the estimation of a 3D point using a Kinect sensor and the Robot Operating System (ROS) along with You Only Look Once (YOLO) for object detection. The Kinect sensor provides RGB-D images, which are used to create a Point Cloud representing the geometry of the environment. ROS is used as a robotics development framework, while YOLO is employed to identify objects in the scene. The article presents the packages used, the datasets used for measurement, and the configuration of ROS and YOLO. Additionally, the functionalities of RViz, a 3D visualization tool used in the tests, are explored. Furthermore, it covers the methods employed, the acquired data, and an analysis of the error margin in relation to the measurement of the distance between the Kinect and the object. The findings and techniques presented in this study contribute to addressing the challenges faced in the RoboCup@Home competition, specifically in the context of object manipulation tasks.

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AI-Based Feature Extraction and Cosine Similarity for Automation of Student Learning Assessment

AI-Based Feature Extraction and Cosine Similarity for Automa...

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information technology (ACIT)

作者： Bambang Dwi Wijanarko Bachtiar Rosilah Binti Hassan Dina Fitria Murad Ridho Bramulya Ihsan Yaya Heryadi Engkos Achmad Kuncoro Eka Julianti Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Master of English Education Graduate School Universitas Terbuka Jakarta Indonesia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Selangor Malaysia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Manajemen Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia Information System Department Universitas Terbuka Jakarta Indonesia

This research is ongoing research into the student learning process which aims to develop artificial intelligence-based technology to calculate essay exam scores automatically, based on the textual proximity of students' answers to the answer key. This research uses an approach based on natural language processing (NLP) and text analysis. Feature extraction was carried out using the BERT model and similarity comparison using the Cosine distance method. Model evaluation was carried out using sensitivity and specificity tests. The model evaluation results show a sensitivity of 98.21%, which means this assessment model is able to detect positive cases well.

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