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7th International Conference on computer-Human Interaction Research and Applications, CHIRA 2023

作者： Potur, Caner İnce, Gökhan MGEO Test and Verification Directorate ASELSAN Inc. Ankara Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

ISBN: (纸本)9783031493676

In the avionics industry, software test automation is a crucial component, as it plays a significant role in accelerating the development process. Since all automation systems are error-prone, the automation systems that affect flight safety should be supervised by humans as they must be reliable. In this study, an augmented reality based software verification system is designed to observe the visual verification of cockpit screens and detect possible automation errors. In the designed system, an observer can instantly see the test steps and automation results using an augmented reality headset as a hologram on the cockpit screen and can report the errors they observe in test automation. The performance of the proposed system was analyzed qualitatively and quantitatively. The experimental results demonstrated that the use of augmented reality in cockpit display verification systems accelerated the testing process, reduced users’ cognitive load, and improved the system’s usability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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DSLR-CNN: Efficient CNN Acceleration using Digit-Serial Left-to-Right Arithmetic

arXiv

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arXiv 2025年

作者： Nisar, Malik Zohaib Ibrahim, Muhammad Sohail Gorgin, Saeid Usman, Muhammad Lee, Jeong-A Department of Computer Engineering College of IT Convergence Chosun University Gwangju61452 Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg93053 Germany Tehran33535-111 Iran

Digit-serial arithmetic has emerged as a viable approach for designing hardware accelerators, reducing interconnections, area utilization, and power consumption. However, conventional methods suffer from performance and latency issues. To address these challenges, we propose an accelerator design using left-to-right (LR) arithmetic, which performs computations in a most-significant digit first (MSDF) manner, enabling digit-level pipelining. This leads to substantial performance improvements and reduced latency. The processing engine is designed for convolutional neural networks (CNNs), which includes low-latency LR multipliers and adders for digit-level parallelism. The proposed DSLR-CNN is implemented in Verilog and synthesized with Synopsys design compiler using GSCL 45nm technology, the DSLR-CNN accelerator was evaluated on AlexNet, VGG-16, and ResNet-18 networks. Results show significant improvements across key performance evaluation metrics, including response time, peak performance, power consumption, operational intensity, area efficiency, and energy efficiency. The peak performance measured in GOPS of the proposed design is 4.37× to 569.11× higher than contemporary designs, and it achieved 3.58× to 44.75× higher peak energy efficiency (TOPS/W), outperforming conventional bit-serial designs. © 2025, CC BY.

关键词： Convolutional neural networks

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Evaluating student levelling based on machine learning model’s performance

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Discover Internet of Things 2022年第1期2卷 1-25页

作者： Ghareeb, Shatha Hussain, Abir Jaafar Al-Jumeily, Dhiya Khan, Wasiq Al-Jumeily, Rawaa Baker, Thar Al Shammaa, Ahmed Khalaf, Mohammed Faculty of Engineering and Technology Liverpool John Moores University Byrom Street LiverpoolL3 3AF United Kingdom College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates College of Engineering University of Sharjah Sharjah27272 United Arab Emirates Department of Computer Science Al-Maarif University College Ramadi31001 Iraq

In this paper, a novel application of machine learning algorithms is presented for student levelling. In multicultural countries such as UAE, there are various education curriculums where the sector of private schools and quality assurance is supervising various private schools for many nationalities. As there are various education curriculums in United Arab Emirates, specifically Abu Dhabi, to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. Every curriculum follows different education methods such as assessment techniques, reassessment rules, and exam boards. Currently, students who transfer to other curriculums are not correctly placed to their appropriate year group as a result of the start and end dates of each academic year as well as due to their date of birth, in which students who are either younger or older for that year group can create gaps in their learning and performance. In addition, pupils’ academic journeys are not stored which create a gap for the schools to track their learning process. In this paper, we propose a computational framework applicable in multicultural countries such as United Arab Emirates in which multi-education systems are implemented. Machine Learning are used to provide the appropriate student’ level aiding schools to provide a smooth transition when assigning students to their year groups and provide levelling and differentiation information of pupils for a smooth transition between one education curriculums to another, in which retrieval of their progress is possible. For classification and discriminant analysis of pupils levelling, three machine learning classifiers are utilised including random forest classifier, Artificial Neural Network, and combined classifiers. The simulation results indicated that the proposed machine learning classifiers generated effective performance in terms of accuracy. © The Au

关键词： Students

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Risk Assessment of Linkedin Profiles

Risk Assessment of Linkedin Profiles

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International Conference on computer Science and engineering (UBMK)

作者： Arzum Aydın Şerif Bahtiyar Cyber Security and Privacy Research Lab SPFLab. Faculty of Computer and Informatics Engineering Istanbul Technical University Maslak Istanbul Türkiye

Fake or fraudulent accounts are a significant problem in social networking that is often addressed by the users of various social networking platforms involving Linkedin. Fraudulent accounts generally try to convince people about their identity that they are authentic. Adversaries use fake profiles or they impersonate a real identity on social networks to obtain more privileges. Therefore, it is critical to understand problems regarding fraudulent profiles. In this paper, we propose new sustainable framework to assess risks about profiles in social networking platforms. The framework is implemented as a web extension and it is analyzed with ITU alumni profiles in Linkedin. The analyses results show that there are different profiles within one community in Linkedin that may be significant threat for social network users.

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Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning

arXiv

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arXiv 2024年

作者： Salazar, Teresa Gama, João Araujo, Helder Abreu, Pedro Henriques The Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal INESC TEC The Faculty of Economy University of Porto Porto4200-465 Portugal The Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3004-531 Portugal

In the evolving field of machine learning, ensuring fairness has become a critical concern, prompting the development of algorithms designed to mitigate bias in decision-making processes. However, achieving fairness in the presence of group-specific concept drift remains an unexplored frontier, and our research represents pioneering efforts in this regard. Group-specific concept drift refers to situations where one group experiences concept drift over time while another does not, leading to a decrease in fairness even if accuracy remains fairly stable. Within the framework of Federated Learning, where clients collaboratively train models, its distributed nature further amplifies these challenges since each client can experience group-specific concept drift independently while still sharing the same underlying concept, creating a complex and dynamic environment for maintaining fairness. The most significant contribution of our research is the formalization and introduction of the problem of group-specific concept drift and its distributed counterpart, shedding light on its critical importance in the realm of fairness. Additionally, leveraging insights from prior research, we adapt an existing distributed concept drift adaptation algorithm to tackle group-specific distributed concept drift which uses a multi-model approach, a local group-specific drift detection mechanism, and continuous clustering of models over time. The findings from our experiments highlight the importance of addressing group-specific concept drift and its distributed counterpart to advance fairness in machine *** Codes 68T01 © 2024, CC BY.

关键词： Machine learning

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George J. Boole: A Nineteenth Century Man for the Modern Digital Era 18th

George J. Boole: A Nineteenth Century Man for the Modern Dig...

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18th International Conference on computer Aided Systems Theory, EUROCAST 2022

作者： Stanković, Radomir S. Stanković, Milena Astola, Jaakko Moraga, Claudio Mathematical Institute of SASA Belgrade Serbia Faculty of Electronic Engineering Niš Serbia Tempere University of Technology Tampere Finland Faculty of Computer Science Technical University of Dortmund Dortmund Germany Department of Informatics Technical University "Federico Santa María" Valparaíso Chile

ISBN: (纸本)9783031253119

It is entirely justified to use the attribute digital in describing contemporary era due to omnipresence of digital technologies and devices based on them and their strong influence to almost all aspects of human activities. The present paper is a tribute to a man whose work in logic and mathematics, leading to the mathematical logic, set theoretical foundations for the development and establishing of digital era. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Digital devices

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Code Smell Detection Using Whale Optimization Algorithm

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computers, Materials & Continua 2021年第8期68卷 1919-1935页

作者： Moatasem M.Draz Marwa S.Farhan Sarah N.Abdulkader M.G.Gafar Department of Software Engineering Faculty of Computers and InformationKafrelsheikh UniversityKafr ElsheikhEgypt Faculty of Informatics and Computer Science British University in EgyptCairoEgypt Department of Information Systems Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceHelwan UniversityCairoEgypt Faculty of Computer Studies Arab Open UniversityCairoEgypt Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam bin Abdulaziz UniversityKharjSaudi Arabia Department of Machine Learning and Information Retrieval Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr ElsheikhEgypt

Software systems have been employed in many fields as a means to reduce human efforts;consequently,stakeholders are interested in more updates of their *** smells arise as one of the obstacles in the software *** are characteristics of software source code that indicate a deeper problem in *** smells appear not only in the design but also in software *** smells introduce bugs,affect software maintainability,and lead to higher maintenance *** code smells can be formulated as an optimization problem of finding the best detection *** researchers have recommended different techniques to improve the accuracy of code smell detection,these methods are still unstable and need to be *** research has sought only to discover a few at a time(three or five types)and did not set rules for detecting their *** research improves code smell detection by applying a search-based technique;we use the Whale Optimization Algorithm as a classifier to find ideal detection *** this algorithm,the Fisher criterion is utilized as a fitness function to maximize the between-class distance over the withinclass *** proposed framework adopts if-then detection rules during the software development life *** rules identify the types for both medium and large *** are conducted on five open-source software projects to discover nine smell types that mostly appear in *** proposed detection framework has an average of 94.24%precision and 93.4%*** accurate values are better than other search-based algorithms of the same *** proposed framework improves code smell detection,which increases software quality while minimizing maintenance effort,time,and ***,the resulting classification rules are analyzed to find the software metrics that differentiate the nine code smells.

关键词： Software engineering intelligence search-based software engineering code smell detection software metrics whale optimization algorithm fisher criterion

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The Effect of Preprocessing on Skin Lesion Segmentation

The Effect of Preprocessing on Skin Lesion Segmentation

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International Conference on Information Technology (INCIT)

作者： Arief Kelik Nugroho Afiahayati Moh Edi Wibowo Hardyanto Soebono Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Jenderal Soedirman Purwokerto Indonesia Department of Dermatology and Venereology Faculty of Medicine Public Health and Nursing Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Image preprocessing is a crucial step in enhancing the quality of skin lesion images before segmentation, with the primary objective of removing noise and artifacts that could interfere with the segmentation process and reduce accuracy. This process involves various techniques designed to address artifacts such as hair, black frames, skin lines, and vignettes, which may arise due to variables in image acquisition, such as lighting conditions and skin characteristics. Several preprocessing techniques used include artifact removal using Gaussian filter, bottom hat filter, and median filter, as well as hair removal methods like DullRazor and short line detector. Image augmentation techniques, such as rotation and transformation, are also employed to ensure that key features in the image are well preserved and accurately reflected in the data, helping to prevent overfitting during the classification stage. Additionally, contrast enhancement through methods like Contrast-Limited Adaptive Histogram Equalization (CLAHE) and gamma correction is used to improve lesion visibility and enhance the accuracy of lesion boundary detection. Findings from various studies indicate that appropriate preprocessing can significantly improve image quality, leading to more accurate segmentation. Descriptive statistical testing can be employed to analyze the extent to which these preprocessing steps affect segmentation outcomes, focusing on accuracy improvements and the reduction of segmentation errors caused by artifacts and noise in the images. Thus, effective preprocessing not only enhances image quality but also provides a stronger foundation for more accurate segmentation and classification in skin lesion diagnosis.

关键词： Hair Image quality Image segmentation Accuracy Statistical analysis Noise Lighting Skin Lesions Overfitting

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Data Driven Methods to Predict Vertical Profiles of the Rails: A Case Study

Data Driven Methods to Predict Vertical Profiles of the Rail...

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International Conference on ICT For Smart Society (ICISS)

作者： Ade Chandra Nugraha Suhono Harso Supangkat IGB Baskara Nugraha Yunendar Aryo Handoko Awan Hermawan Purwadinata Dimas Wisnu Saputro Department of Computer Engineering and Informatics Bandung State of Polytechnic School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Smart City and Community Innovation Center School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Bandung Institute of Technology Bandung Indonesia Managing Director of Infrastructure PT Kereta Api Indonesia Bandung Indonesia Department of Computer Engineering and Informatics Bandung State of Polytechnic Bandung Indonesia

Predicting the value of railroad track geometry is needed to reduce the cost and time of rail condition measurements that have been carried out periodically every three months by railroad operators in Indonesia (PT Kereta Api Indonesia/PT KAI). Four track geometry parameters - vertical profile, horizontal alignment, twist, and gauge - are the reference measurements that will be mapped with alternative measurement results using accelerometers installed on regular trains (in-service trains). In this paper, eight prediction algorithms from the machine learning model are applied to map the vertical profile with 73 kilometers of measurement data in the Jakarta to Padalarang railroad segment, precisely on the Jatinegara-Cikampek measuring trip conducted from April 2019 to June 2022. The evaluation performance of the prediction results has been measured with the GadientBoosting Regressor algorithm is the best algorithm with MSE = 0.791, R^2 = 0.507 and feature importance on the Train speed and Az attributes. The research can be continued to detect the correlation of three other track geometry parameters referring to the dataset of rail geometry condition measurement results through accelerometers on regular trains (in-service trains).

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Digital Twins and Intelligent IoT for a Better World (Panel Discussion Report-I of IEEE AIoTSys2023)

Digital Twins and Intelligent IoT for a Better World (Panel ...

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2023 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2023

作者： Huang, Runhe Zhang, Daqing Yu, Zhiwen Nugent, Chris Jia, Weijia Yang, Kun Zhang, Yan Sekimoto, Yoshihide Guo, Bin Faculty of Computer and Information Sciences Hosei University Tokyo Japan Peking University Ip Paris France Harbin Engineering University Northwestern Polytechnical University China School of Computing Ulster University Ireland BNU-UIC Joint Ai Research Institute Beijing Normal University China School of Computer Science and Electronic Engineering University of Essex United Kingdom Department of Informatics University of Oslo Norway Institute of Industrial Science University of Tokyo Japan School of Computer Science Northwestern Polytechnical University China

ISBN: (纸本)9798350312270

This position paper provides a summary report of the panel discussion on "Digital Twins and Intelligent IoT for a Better World"at the International Conference on Artificial Intelligence of Things and Systems (AIoTSys'23). The panel featured seven distinguished invited panelists, including four IEEE Fellows from Peking University, Beijing Normal University, University of Essex, and the University of Oslo. Additionally, there was one vice president from Harbin engineering University, one school head from Ulster University, and one center vice director from the University of Tokyo. The panel covered seven topics: (1) The cost-effectiveness of IoTenabled digital twin design, (2) Ubiquitous crowd computing systems, (3) The vision for the fusion of the Intelligent IoT with the Digital Twin, (4) Intelligent networking in relation to Chinese culture and education, (5) Data-driven digital twin wireless networks, (6) Digital twins edge networking, and (7) The automatic generation of urban digital twins from local government s open data. The panel generated lively and comprehensive discussions, and this report represents the enriched outcome of our panelists insights, visions, and the interactive discussions with the audience. © 2023 IEEE.

关键词： Internet of things

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