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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Ridha Ghayoula Wided Amara Amor Smida Jaouhar Fattahi Mohamed I. Waly Sahbi Bahroun Issam El Gmati Faculty of Engineering University of Moncton New Brunswick Canada SysCom Laboratory ENIT University of Tunis El Manar Tunis Tunisia College of Applied Medical Sciences Majmaah University Majmaah KSA Department of Computer Science && Software Engineering Laval University Quebec Canada Institut Superieur d’Informatique University of Tunis El Manar Tunis Tunisia College of Engineering Umm Al Qura University Umm Al Qura KSA

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

In the domain of medical imaging, ensuring the security of confidential patient data stands as a paramount concern. The imperative for robust encryption to safeguard medical images against unauthorized access has driven the widespread adoption of the RSA algorithm, celebrated for its efficacy in data security assurance. This study specifically centers on the deployment of RSA-1024 cryptography on an Artix-7 FPGA, targeting applications in medical imaging. Acknowledging the demand for heightened security and accelerated processing, we have engineered RSA crypto-accelerators as hardware implementations of the RSA algorithm. We present a comprehensive methodology for implementing the 1024-bit RSA encryption/decryption algorithm, leveraging the square and multiply technique for modular exponentiation. Our algorithms are meticulously instantiated through VHDL code, tailored for execution on the Artix-7 xc7a35tcsg324-1 FPGA manufactured by Xilinx. Rigorous testing using the Vivado 2022.2 tool validates the functionality and dependability of our design. This research underscores the pivotal role of advanced cryptographic methodologies in safeguarding medical images and underscores the strides made in FPGA-based security solutions.

关键词： VHDL Codes Scalability Refining Reliability engineering Encryption Security

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Topological superconductors from a materials perspective

arXiv

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arXiv 2023年

作者： Mandal, Manasi Drucker, Nathan C. Siriviboon, Phum Nguyen, Thanh Boonkird, Tiya Lamichhane, Tej Nath Okabe, Ryotaro Chotrattanapituk, Abhijatmedhi Li, Mingda Quantum Measurement Group MIT CambridgeMA02139 United States Department of Nuclear Science and Engineering MIT CambridgeMA02139 United States School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States Department of Physics MIT CambridgeMA02139 United States Department of Chemistry MIT CambridgeMA02139 United States Department of Electrical Engineering and Computer Science MIT CambridgeMA02139 United States

Topological superconductors (TSCs) have garnered significant research and industry attention in the past two decades. By hosting Majorana bound states which can be used as qubits that are robust against local perturbations, TSCs offer a promising platform toward (non-universal) topological quantum computation. However, there has been a scarcity of TSC candidates, and the experimental signatures that identify a TSC are often elusive. In this perspective, after a short review of the TSC basics and theories, we provide an overview of the TSC materials candidates, including natural compounds and synthetic material systems. We further introduce various experimental techniques to probe TSC, focusing on how a system is identified as a TSC candidate, and why a conclusive answer is often challenging to draw. We conclude by calling for new experimental signatures and stronger computational support to accelerate the search for new TSC candidates. © 2023, CC BY.

关键词： Quantum computers

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FEDCSD: A FEDERATED LEARNING BASED APPROACH FOR CODE-SMELL DETECTION

arXiv

引用

arXiv 2023年

作者： Alawadi, Sadi Alkharabsheh, Khalid Alkhabbas, Fahed Kebande, Victor Awaysheh, Feras M. Palomba, Fabio Awad, Mohammed Center for Applied Intelligent Systems Research School of Information Technology Halmstad University Halmstad Sweden Department of Software Engineering Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Jordan Internet of Things and People Research Center Malmö University Sweden Department of Computer Science Blekinge Institute of Technology Sweden Institute of Computer Science Delta Research Centre University of Tartu Estonia SeSa Lab University of Salerno Italy Arab American University Palestine Palestine

software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting "God Class," to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model’s highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： computer software selection and evaluation

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Future Backbone Optical Networks: Fiber Densification Versus Network Densification

Future Backbone Optical Networks: Fiber Densification Versus...

引用

International Symposium on Advanced Networks and Telecommunication Systems (ANTS)

作者： Anuj Agrawal Vimal Bhatia Department of Computer Science and Engineering Indian Institute of Technology Gandhinagar India Department of Electrical Engineering Signals and Software Group Indian Institute of Technology Indore India

ISBN: (数字)9781665448932

ISBN: (纸本)9781665448949

The increasing amount of traffic being aggregated to the terrestrial backbone optical networks (BONs) from the metro/access communication networks has caused capacity crunch in the BONs. Increasing coverage with rural and remote connectivity, wireless network densification, data hungry 5G applications, and increasing datacenters (DCs) are among the major factors that necessitated the deployment of huge amount of optical fibers around the world to connect cell sites, to interconnect DCs, to provide fiber to the home (FTTH) services, among others. Due to massive fiberization in the metro/access networks and hence to handle the increased traffic being aggregated to BONs through them, efforts are being made to increase the capacity of BONs via novel devices, fiber designs, and spectral/spatial flexibility and expansion along with the additional deployment of fibers (referred to as fiber densification) in BONs. In this paper, we focus on the topological aspect of BON and present BON densification as an approach to enable the realization of several advanced optical network technologies in BONs. We analyze the advantages of BON densification considering multicore fibers (MCFs) for which low transmission reach has been the main hindrance towards its deployment in BONs. Simulations indicate that through BON densification, MCF networks can achieve significantly low blocking, and high resiliency against multi-link failures. Furthermore, we highlight several other aspects of future communication networks which indicate that BON densification is a promising approach as compared to the conventional fiber densification.

关键词： Optical fibers Wireless networks Optical fiber networks Optical fiber communication Optical fiber devices Telecommunications Optical fiber subscriber loops

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Chance-Constrained Stochastic Mpc of Greenhouse Production Systems with Parametric Uncertainty

SSRN

引用

SSRN 2023年

作者： Svensen, Jan Lorenz Cheng, Xiaodong Boersma, Sjoerd Sun, Congcong Department of Applied Mathematics and Computer Science Technical University of Denmark Richard Petersens Plads 324 Kongens Lyngby2800 Denmark Wageningen University and Research Wageningen6700 AC Netherlands Agricultural Biosystems Engineering Group Wageningen University and Research Wageningen6700 AC Netherlands

Greenhouse climate control is important to provide sufficient fresh food for the growing population in an economical and sustainable manner. However, the developed crop-climate models are generally complex with parametric uncertainties and far from describing the real system accurately, which affects adversely the control system’s performance. To improve optimality and guarantee robustness during the control process, we develop and implement a stochastic model predictive control (MPC) scheme for greenhouse production systems using chance constraints considering parametric uncertainties. As chance-constrained MPC contains stochastic variables with probabilistic nature, the constraint formulation and uncertainty propagation of nonlinear models are computational expensive. By leveraging the advantages of model linearization, the proposed chance-constrained MPC method enables a more straightforward formulation of chance constraint and a computationally cheaper optimization in comparison to directly using the nonlinear model. Finally, the efficacy of the proposed approach is demonstrated on a greenhouse climate control case study. © 2023, The Authors. All rights reserved.

关键词： Linearization

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Machine Learning-Driven Insights for Wastewater Remediation: A Gaussian Process Regression Approach

SSRN

引用

SSRN 2025年

作者： Nadeem, Nimra Khaliq, Zubair Bentalib, Abdulaziz Qadir, Muhammad Bilal Saeed, Mehwish Ahmad, Fayyaz Shahzad, Muhammad Wakil Jumah, Abdulrahman Bin Department of Textile Engineering National Textile University Faisalabad37610 Pakistan Innovative Nanomaterials Textiles and AI Solutions Research Group National Textile University Faisalabad37610 Pakistan Department of Materials National Textile University Faisalabad37610 Pakistan Department of Chemical Engineering College of Engineering King Saud University P.O. Box 800 Riyadh11421 Saudi Arabia Department of Computer Science University of Agriculture Faisalabad37610 Pakistan Department of Applied Sciences National Textile University Faisalabad37610 Pakistan Department: Mechanical and Construction Engineering Northumbria University Newcastle United Kingdom

Here, Gaussian Process Regression (GPR) modeling has been implemented to better predict the degradation response for the wastewater treatment process. For this purpose, previously published data on detailed optimization of three crucial factors, namely catalyst (CFA-CuFe2O4), Oxidant (H2O2), and dye (MO) concentration, have been used. Compared to traditional polynomial regression, the AI-driven GPR model demonstrated superior prediction accuracy, effectively correlating critical operational parameters such as catalyst concentration, oxidant levels, and dye concentration with the degradation efficiency of pollutants. The GPR model indicated pairwise correlations of CFA-CuF (0.3877, p=0.0912) and H2O2 (0.1982, p=0.4022) with moderate positive degradation, while MO exhibited a weak negative correlation (-0.0837, p=0.7257). Partial correlations also indicate strong positive correlations with degradation for CFA-CuF (0.3970, p=0.1028) and H2O2 (0.2159, p=0.3895). The superiority of the GPR model has been validated owing to the Gaussian Process Regression Mean (RPAE value) of 0.99. Additionally, the GPR model allowed for the simultaneous interpretation of three predictors on the response variable, a task that is not feasible with the polynomial regression model. Consequently, the GPR provides enhanced modeling, deeper insights, and dependable predictions, demonstrating it is a more sustainable and effective approach for pollutant degradation in wastewater treatment compared to polynomial modeling. © 2025, The Authors. All rights reserved.

关键词： Regression analysis

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Multi-Resolution Localization of Individual Logs in Wooden Piles Utilizing YOLO with Tiling on Client/Server Architectures 33

Multi-Resolution Localization of Individual Logs in Wooden P...

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33rd European Modeling and Simulation Symposium, EMSS 2021

作者： Praschl, Christoph Auersperg-Castell, Philipp Forster-Heinlein, Brigitte Zwettler, Gerald Adam Research Group Advanced Information Systems and Technology Research and Development Department University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Bluedynamics Auersperg-Castell KG Kritzing 31 Freinberg4785 Austria Faculty of Computer Science and Mathematics University of Passau Innstraße 43 Passau94032 Germany

In industrial domains with time and cost intensive manual or semi-automated inspection the demand for automation is high. Utilizing state of the art deep learning models for localization in vision-based domains such as wood log analysis, the precision can be increased while reducing the demand for manual inspection. In this paper a YOLO network is trained on wood log images to allow for detection of single wood piles in images with hundreds and thousands instances. Due to the high variability in scale and large amount of wood logs within the images, common YOLO architectures are not applicable. Thus, tiling is necessitated to implicitly form a multi-resolution image pyramid. Due to lack in training data, besides common data augmentation modelling of different seasonal and weather conditions is applied. The wood log detection process can be run on a client/server architecture to allow for both, preview and refined results. Evaluation on real-world data sets shows an log detection accuracy of 82,9% utilizing a tiny YOLO model and 94,1% with a fully connected YOLO model, respectively. © 2021 The Authors.

关键词： Deep learning

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The Role of Generative AI in Strengthening Secure software Coding Practices: A Systematic Perspective

arXiv

引用

arXiv 2025年

作者： Alwageed, Hathal S. Khan, Rafiq Ahmad College of Computer and Information Sciences Jouf University Sakaka42421 Saudi Arabia Software Engineering Research Group Department of Computer Science and IT University of Malakand Pakistan

As software security threats continue to evolve, the demand for innovative ways of securing coding has tremendously grown. The integration of Generative AI (GenAI) into software development holds significant potential for improving secure coding practices. This paper aims at systematically studying the impact of GenAI in enhancing secure coding practices from improving software security, setting forth its potential benefits, challenges, and implications. To outline the contribution of AI driven code generation tools, we analyze via a structured review of recent literature, application to the industry, and empirical studies on how these tools help to mitigate security risks, comply with the secure coding standards, and make software development efficient. We hope that our findings will benefit researchers, software engineers and cybersecurity professionals alike in integrating GenAI into a secure development workflow without losing the advantages GenAI provides. Finally, the state of the art advances and future directions of AI assisted in secure software engineering discussed in this study can contribute to the ongoing discourse on AI assisted in secure software engineering. © 2025, CC BY.

关键词： software design

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules

Cybersecurity Threats in the Era of AI: Detection of Phishin...

引用

Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Mohammad Aljaidi Ayoub Alsarhan Dimah Al-Fraihat Ahmed Al-Arjan Bashar Igried Subhieh M. El-Salhi Muhammad Khalid Qais Al-Na'amneh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology The Hashemite University Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince AL-Hussein bin Abdullah II of Information Technology The Hashemite University Jordan Department of Computer Science and Applications The Hashemite University Zarqa Jordan The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK Dep. of Cyber Security Applied Science Private University Amman Jordan

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing.

关键词： Adaptation models Accuracy Phishing Identity theft Refining Machine learning Passwords

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Classification of Awareness in Disorders of Consciousness Using Common Spatial Pattern 13

Classification of Awareness in Disorders of Consciousness Us...

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13th International Winter Conference on Brain-computer Interface, BCI 2025

作者： Lee, Chaewon Lee, Sunho Annen, Jitka Jeong, Ji-Hoon Massimini, Marcello Casarotto, Silvia Boly, Melanie Bodart, Olivier Thibaut, Aurore Rosanova, Mario Laureys, Steven Gosseries, Olivia Lee, Minji Dept. Data Science The Catholic University of Korea Bucheon Korea Republic of Dept. Biomedical Software Engineering The Catholic University of Korea Bucheon Korea Republic of Dept. GIGA-Research & Neurology University and University Hospital of Liege Liege Belgium Dept. Data Analysis University of Ghent GhentB9000 Belgium Dept. Computer Science Chungbuk National University Cheongju Korea Republic of Dept. Biomedical and Clinical Sciences "L. Sacco" University of Milan Milan Italy Wisconsin Institute for Sleep and Consciousness Dept. Psychiatry University of Wisconsin Madison United States Coma Science Group GIGA-Consciousness GIGA Research Center University of Liège Liège Belgium Centre du Cerveau Neurology department University Hospital of Liège Liège Belgium

ISBN: (纸本)9798331521929

Advances in intensive care have improved the survival rate of patients with severe acute brain injury, but diagnostic errors for patients with disorders of consciousness are still high. Accurate diagnosis of these patients is very important because effective treatment can vary depending on the diagnosis. In this study, we propose a framework for classifying unresponsive wakefulness syndrome and minimally conscious state, focusing on awareness. In particular, power spectral density and common spatial patterns were used together, considering that spatial information is a key feature in consciousness. The 16 patients with unresponsive wakefulness syndrome and 14 with minimally conscious state underwent resting-state electroencephalography measurements. In addition, we compared the performance by utilizing each frequency (delta, theta, alpha, beta, gamma bands) related to consciousness. As a result, the highest accuracy of 95.06% was achieved by the EEGNet classifier, especially in the beta frequency band. We demonstrated that spatial information is very important in consciousness, as we observed that classification performance improved when common spatial patterns were used. These results provide insight into various frameworks for diagnosing patients with disorders of consciousness and may help patients survive by increasing the diagnosis rate in the future. © 2025 IEEE.

关键词： Electroencephalography

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