Authors - Ezhilmathi Krishnasamy, Pascal Bouvry Abstract - The ongoing development in architecture and programming models, particularly regarding GPUs, significantly influences the landscape of high-performance computing. Presently, nearly all supercomputers worldwide are equipped with GPU compute nodes, promoting advancements in architecture that emphasize heterogeneity. Concurrently, there is a parallel evolution in programming models designed to effectively harness the potential of this diverse architecture for executing scientific applications. Notable architectures, longing trends in Nvidia GPUs, such as Grace Hopper and the AMD Mi300 series, alongside programming paradigms like SYCL, OpenCL, and various library-based models, including Kokos and StarPU, illustrate this trend. The primary motivation is to establish an appropriate programming model that targets modern architectures, essential for maximizing computational efficiency across specific applications. A multitude of programming models exist, each offering the potential for optimal utilization of advanced architectural capabilities. However, a critical question arises regarding the ease of adoption and the user-friendliness of these models for existing large-scale scientific codes. This study focuses on OpenMP Offloading, examining its application on Nvidia to ensure a unified source code compatible with various GPU architectures. Identifying an optimal programming model for effective OpenMP Offloading usage is of paramount importance. This paper conducts a comparative analysis of the OpenMP Offloading programming model against CUDA for Nvidia GPUs, facilitating a comprehensive performance evaluation. The analysis employs key BLAS operations to assess the performance characteristics of OpenMP Offloading in relation to CUDA, thereby elucidating the advantages and limitations associated with leveraging OpenMP Offloading.
Authors - Yury Zontov, Alexander Derendyaev, Daniil Tkachev, Arkady Vaynshtok, Vyacheslav Yakuba Abstract - A consulting system for development and adoption of high-quality decisions by territorial governments should provide tools for accessing relevant and reliable information about processes and results of population's life, as well as tools for analyzing socio-economic indicators and infrastructure functioning. Analysis tools should help identify problem situations ("bottlenecks") in ensuring the quality of life of population and to form a "tree" of management goals for the development of territory and improvement of life of population. The solution of these problems is possible by creating a human-machine platform for scenario analysis of the development of territories for numerous aspects of management. The decision-making process based on the representation, calculation and interpretation of current observed and predicted parameters is effective for both operational management and strategic planning. The information and analytical system under development is intended to become a convenient and powerful tool for analyzing the level of development of a region, scenario analysis, planning support and making high-quality management decisions.
Authors - Sunday Omotoye, Weifeng Chen Abstract - The escalation of insider threats poses significant harm to organizations and businesses, necessitating a thorough understanding of their nature and the associated threat landscape to develop effective Insider Threat Programs (ITP). This paper delves into the pervasive issue of insider threats within the retail industry, examining the applicability of the Cyber Kill Chain (CKC) to comprehend these threats. The retail sector, with its diverse and dispersed workforce, faces unique vulnerabilities that require organized strategies and proactive measures. By strategically integrating the CKC framework, the ITP systematically identifies and prevents insider threats at every stage of their lifecycle. This comprehensive approach serves as an indispensable element in fortifying the security framework of both the retail industry and broader economic landscape.
Authors - Sarmad K.D. Alkhafaji, Shahab Abdulla, Haydar Abdulameer Marhoon, Mohammed Diykh, Mustafa Ali Majed, Jafar Sadiq, Ali Aqeel Saleh, Aqeel Sahi, Hussein Alabdally Abstract - Chest radiography is a technique based on medical imaging that is employed to detect thoracic diseases. In this paper, we designed an intelligent method to diagnose thorax disease from chest X-ray (CXR) images. A novel empirical curvelet transform, coupled with a deep learning model, is proposed. The collected images are analysed using the proposed empirical curvelet transform (ECT) model. Then, the outputs of ECT model are sent to DenseNet. The proposed model is assessed using several statistical metrics. The proposed model achieves an accuracy of 98%. The results demonstrated the ability of the proposed model to detect Thoracic Disease.
Authors - Elias Tabane, Zenghui Wang, Ernest Mnkandla Abstract - In this paper, we present a comprehensive analysis of ensemble deep learning models for DNA sequence classification. We explore the performance of three standalone models: Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Units (GRU), along with an ensemble approach that combines all three. Our study evaluates the models based on four performance metrics: accuracy, precision, recall, and F1 score. The ensemble model achieved an accuracy of 90.6%, with precision, recall, and F1 score all at 0.91. We compare these results to the standalone models and demonstrate that ensemble learning significantly improves classification performance in the context of DNA sequence data. Additionally, we review relevant studies that have applied deep learning models to similar tasks and discuss the advantages of combining CNN, BiLSTM, and GRU for sequence classification tasks.
Authors - Indibar Debnath, Om Prakash Abstract - For a prime p and ℓ ∈ N with gcd(ℓ, p) = 1, this work explores the hulls of ℓ-length cyclic codes over Zp3 . We establish the form of the generators of the hulls and provide a characterization of the hulls. Further, we present a condition for these codes to be LCD. To formulate the p-dimensions of the hulls, first, we find the types of the hulls and then use them to come up with an expression for the p-dimensions of the hulls. Moreover, for a fixed p-dimension of the hull, we count all the cyclic codes.
Authors - Shahriar Sultan Ramit, Nayeem Ahmed, Md Fatin Ishrak, Md Ruhul Amin, Alaya Parven Alo, Md. Sadekur Rahman Abstract - Oral Squamous Cell Carcinoma (OSCC) is among the most frequent cancer death causes, and early detection plays a vital role in improving patient survival. The traditional histopathological diagnosis is subjective and labor-intensive which necessitates an automated and standardized classification methods. This study has used a publicly available dataset comprising a total of 10,000 histopathological images. This paper evaluates four CNN architectures ResNet101, InceptionV3, MobileNetV2 and Xception to classify OSCC and normal cells. For better accuracy Hyperparameter Tuning was done on MobileNetV2. Tuned MobileNetV2 achieved the best performance with accuracy, recall, and F1-score of 0.99, demonstrating its efficacy in classifying malignant vs. normal tissues. To further enhance interpretability Explainable AI techniques were employed, including LIME and Saliency Maps, enabling visual comprehension of model predictions. Our results demonstrate the importance of deep learning for OSCC detection overcoming the "black-box" issue of CNNs by explain ability. This study contributes to AI-driven diagnostic innovation through a more accurate and interpretable approach to OSCC classification.
Authors - Nelson Salgado-Reyes, Jorge Rivera-Guaman Abstract - This proposal outlines the establishment of a Computer Security Incident Response Team (CSIRT) at the Central University of Ecuador (UCE) with a focus on training, awareness, and a hybrid model adapted to the academic environment. The study utilizes a mixed qualitative-quantitative approach to analyze international best practices, diagnose cybersecurity vulnerabilities at UCE, and design the proposed model. The findings emphasize the importance of strengthening security culture, reducing incidents caused by human error, and integrating cybersecurity into the university curriculum. The model includes an organizational structure, educational services, and assessment metrics that adhere to standards such as ISO/IEC 27035 and NIST SP 800-61. This research contributes to the field of CSIRTs in educational institutions with limited resources, providing a replicable framework specifically tailored for Latin American contexts.
