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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule

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The Advanced Encryption Standard with a Dynamic S-Box Using ...

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Abdelrahman Altigani Muhammad Rukunuddin Ghalib Huwaida T. Elshoush Muawia A. Elsadig Nashat Alrefai Shiraz Naserelden Computer Information Science Higher Colleges of Technology Al Ain UAE School of Engineering & Computing De Monfort University Dubai UAE Computer Science Department Faculty of Mathematical Sciences and Informatics University of Khartoum Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam KSA Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam KSA Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption.

关键词： Ciphers Schedules Heuristic algorithms Dynamics Force Dynamic scheduling Encryption computer security Time complexity Standards

Improving Scalability with GPU-Aware Asynchronous Tasks

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Improving Scalability with GPU-Aware Asynchronous Tasks

IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Jaemin Choi David F. Richards Laxmikant V. Kale Department of Computer Science University of Illinois at Urbana-Champaign Urbana Illinois USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore California USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Asynchronous tasks, when created with over-decomposition, enable automatic computation-communication overlap which can substantially improve performance and scal-ability. This is not only applicable to traditional CPU-based systems, but also to modern GPU -accelerated platforms. While the ability to hide communication behind computation can be highly effective in weak scaling scenarios, performance begins to suffer with smaller problem sizes or in strong scaling due to fine-grained overheads and reduced room for overlap. In this work, we integrate G PU -aware communication into asynchronous tasks in addition to computation-communication overlap, with the goal of reducing time spent in communication and further increasing GPU utilization. We demonstrate the performance impact of our approach using a proxy application that performs the Jacobi iterative method, Jacobi3D. In addition to optimizations to minimize synchronizations between the host and GPU devices and increase the concurrency of GPU operations, we explore techniques such as kernel fusion and CUDA Graphs to mitigate fine-grained overheads at scale.

关键词： Jacobian matrices Performance evaluation Concurrent computing Scalability Graphics processing units Supercomputers Iterative methods

Cheesemap: A High-Performance Point-Indexing Data Structure for Neighbor Search in LiDAR Data

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arXiv 2025年

作者： Laso, Ruben Yermo, Miguel Research Group for Scientific Computing Faculty of Computer Science University of Vienna Währinger Straße 29 Vienna Vienna1090 Austria Universidad de Santiago de Compostela Rúa de Jenaro de la Fuente Domínguez Galicia Santiago de Compostela15782 Spain

Point cloud data, as the representation of three-dimensional spatial information, is a fundamental piece of information in various domains where indexing and querying these point clouds efficiently is crucial for tasks such as object recognition, autonomous navigation, and environmental modeling. In this paper, we present a comprehensive comparative analysis of various data structures combined with neighboring search methods across different types of point clouds. Additionally, we introduce a novel data structure, cheesemap, to handle 3D LiDAR point clouds. Exploring the sparsity and irregularity in the distribution of points, there are three flavors of the cheesemap: dense, sparse, and mixed. Results show that the cheesemap can outperform state-of-the-art data structures in terms of execution time per query, particularly for ALS (Aerial Laser Scanning) point clouds. Memory consumption is also minimal, especially in the sparse and mixed representations, making the cheesemap a suitable choice for applications involving three-dimensional point clouds. © 2025, CC BY.

关键词： Indexing (of information)

Handwritten chemical formulas classification model using deep transfer convolutional neural networks 2

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Handwritten chemical formulas classification model using dee...

2nd IEEE International Conference on Electronic Engineering, ICEEM 2021

作者： Hagag, Ahmed Omara, Ibrahim Alfarra, Ahmed N. K. Mekawy, Fahd Benha University Faculty of Computers and Artificial Intelligence Department of Scientific Computing Benha13518 Egypt University Menoufia Faculty of Science Menoufia Department of Mathematics and Computer Science Menoufia32511 Egypt Harbin Institute of Technology School of Management Harbin150001 China

ISBN: (纸本)9781665418423

With the spread of the COVID19 pandemic, blended learning has become one of the most used methods in educational organizations such as universities, community colleges, and schools. In blended learning, the students' practical activities are done in more than one way, including simulation software and the place of study. For chemical experiment programs, the classification of handwritten chemical formulas plays an important role in determining the simulation software's efficiency. Accordingly, in this study, we propose a model for handwritten chemical formula classification. First, this paper describes a handwritten chemical formulas dataset that contains eight classes (HCFD8). Second, convolutional neural networks (CNNs) with pre-trained weights are used as a deep feature extractor to extract features from the images. Third, due to limited training images per class, the proposed model uses data augmentation techniques to expand the training images. Then, an enhanced multilayer perceptron (EMLP) strategy is used to classify the image. Finally, we provide a performance analysis of typical deep learning approaches on HCFD8, which shows that the proposed model performs good accuracy results. © 2021 IEEE.

关键词： Convolutional neural networks

AN END-TO-END DEEP LEARNING METHOD FOR SOLVING NONLOCAL ALLEN-CAHN AND CAHN-HILLIARD PHASE-FIELD MODELS

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arXiv 2024年

作者： Geng, Yuwei Burkovska, Olena Ju, Lili Zhang, Guannan Gunzburger, Max Department of Mathematics University of South Carolina United States Computer Science and Mathematics Division Oak Ridge National Laboratory United States Department of Scientific Computing Florida State University United States Oden Institute for Computational Engineering and Sciences University of Texas at Austin United States

We propose an efficient end-to-end deep learning method for solving nonlocal Allen-Cahn (AC) and Cahn-Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the vicinity of the true moving sharp interface whose width is determined by a grid-independent parameter that is substantially larger than the local grid size. In this work, we introduce non-mass conserving nonlocal AC or CH phase-field models with regular, logarithmic, or obstacle double-well potentials. Because of non-locality, some of these models feature totally sharp interfaces separating phases. The discretization of such models can lead to a transition between phases whose width is only a single grid cell wide. Another motivation is to use deep learning approaches to ameliorate the otherwise high cost of solving discretized nonlocal phase-field models. To this end, loss functions of the customized neural networks are defined using the residual of the fully discrete approximations of the AC or CH models, which results from applying a Fourier collocation method and a temporal semi-implicit approximation. To address the long-range interactions in the models, we tailor the architecture of the neural network by incorporating a nonlocal kernel as an input channel to the neural network model. We then provide the results of extensive computational experiments to illustrate the accuracy, structure-preserving properties, predictive capabilities, and cost reductions of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Partial differential equations

Pandemics in silico: Scaling Agent-based Simulations on Realistic Social Contact Networks

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arXiv 2024年

作者： Kitson, Joy Costello, Ian Chen, Jiangzhuo Jiménez, Diego Hoops, Stefan Mortveit, Henning Meneses, Esteban Yeom, Jae-Seung Marathe, Madhav V. Bhatele, Abhinav Department of Computer Science University of Maryland College Park United States Google Inc. Mountain View United States Biocomplexity Institute and Initiative University of Virginia Charlottesville United States Max Planck Computing and Data Facility Garching Germany National Advanced Computing Collaboratory National High Technology Center San José Costa Rica Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore United States

Preventing the spread of infectious diseases requires implementing interventions at various levels of government and evaluating the potential impact and efficacy of those preemptive measures. Agent-based modeling can be used for detailed studies of the spread of such diseases in the presence of possible interventions. The computational cost of modeling epidemic diffusion through large social contact networks necessitates the use of parallel algorithms and resources in order to achieve quick turnaround times. In this work, we present Loimos, a scalable parallel framework for simulating epidemic diffusion. Loimos uses a hybrid of time-stepping and discrete event simulation to model disease spread, and is implemented on top of Charm++, an asynchronous, many-task runtime that enables over-decomposition and adaptive overlap of computation and communication. We demonstrate that Loimos is able to achieve significant speedups while scaling to large core counts. In particular, Loimos is able to simulate 200 days of a COVID-19 outbreak on a digital twin of California in about 42 seconds, for an average of 4.6 billion traversed edges per second (TEPS), using 4096 cores on Perlmutter at NERSC. © 2024, CC BY.

关键词： Discrete event simulation

A hybrid healthy diet recommender system based on machine learning techniques

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computers in Biology and Medicine 2025年 184卷 109389-109389页

作者： Sweidan, Sara Askar, S.S. Abouhawwash, Mohamed Badr, Elsayed Department of Artificial Intelligence Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura Egypt Department of Statistics and Operations Research College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia College of Engineering Michigan State University East LansingMI48824 United States Department of Mathematics Faculty of Science Mansoura University Mansoura35516 Egypt Department of Scientific Computing Faculty of Computer and Artificial Intelligence Benha University Benha Egypt Department of Information Systems College of Information Technology Misr University for Science & Technology Giza Egypt

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect all body functions but also increase the chances of developing chronic diseases and the associated morbidity and mortality rates. This study proposes a novel system that bridges the gap between healthcare providers and patients by offering both parties some tools for navigating the intricacies of dietary planning. In this system, machine learning techniques are used to determine the required calories before starting an obesity treatment. A hybrid precision model with minimal parameters is also developed to estimate the appropriate number of calories for losing weight and to formulate a healthy diet plan. A real dataset of 15 anthropometric measurements is analyzed using SVR, LR, and DTR regression models, and all the data are preprocessed before analysis to enhance model performance. Results show that the required calories can be estimated with a high correlation (R = 0.985) from independent measurements. The proposed model also calculates the healthy daily percentages of fats, proteins, and carbohydrates based on a knowledge base of medical rules and functions, thus facilitating the sequential treatment of obese patients. In sum, this study applies different models to design a practical, cost-effective approach for accurately determining the required calories and formulating valuable diet plans for obesity treatment and management. © 2024 Elsevier Ltd

关键词： Calorie Machine learning Regression Obesity treatment Expert system

A 3D Implementation of Convolutional Neural Network for Fast Inference

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A 3D Implementation of Convolutional Neural Network for Fast...

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Narasinga Rao Miniskar Pruek Vanna-Iampikul Aaron Young Sung Kyu Lim Frank Liu Jieun Yoo Corrinne Mills Nhan Tran Farah Fahim Jeffrey S Vetter Computer Science and Mathematic Division Oak Ridge National Laboratory Oak Ridge USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Physics University of Illinois Chicago Chicago USA Scientific Computing Division Fermi National Accelerator Laboratory Chicago USA

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.

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Kilometer-Scale E3SM Land Model Simulation over North America

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arXiv 2025年

作者： Wang, Dali Wang, Chen Cao, Qinglei Schwartz, Peter Yuan, Fengming Krishna, Jayesh Wu, Danqing Ricciuto, Danial Thornton, Peter Kao, Shih-Chieh Thornton, Michele Mohror, Kathryn Environmental Sciences Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA United States Department of Computer Science Saint Louis University Saint LouisMO United States Mathematics and Computer Sciences Argonne National Laboratory ArgonneIL United States

The development of a kilometer-scale E3SM Land Model (km-scale ELM) is an integral part of the E3SM project, which seeks to advance energy-related Earth system science research with state-of-the-art modeling and simulation capabilities on exascale computing systems. Through the utilization of high-fidelity data products, such as atmospheric forcing and soil properties, the km-scale ELM plays a critical role in accurately modeling geographical characteristics and extreme weather occurrences. The model is vital for enhancing our comprehension and prediction of climate patterns, as well as their effects on ecosystems and human activities. This study showcases the first set of full-capability, km-scale ELM simulations over various computational domains, including simulations encompassing 21.6 million land gridcells, reflecting approximately 21.5 million square kilometers of North America at a 1 km × 1 km resolution. We present the largest km-scale ELM simulation using up to 100,800 CPU cores across 2,400 nodes. This continental-scale simulation is 300 times larger than any previous studies, and the computational resources used are about 400 times larger than those used in prior efforts. Both strong and weak scaling tests have been conducted, revealing exceptional performance efficiency and resource utilization. The km-scale ELM uses the common E3SM modeling infrastructure and a general data toolkit known as KiloCraft. Consequently, it can be readily adapted for both fully-coupled E3SM simulations and data-driven simulations over specific areas, ranging from a single gridcell to the entire North America. © 2025, CC BY.

关键词： Digital elevation model