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Workshop on Education for High Performance Computing (EduHPC)

作者： David P. bunde Kishwar Ahmed sridevi Ayloo Tisha brown-Gaines Joel Fuentes vishwesh Jatala Ruth Kurniawati Işıl Öz Apan Qasem Philip J. schielke Mary C. Tedeschi Thomas Y. Yeh Dept. Computer Science Knox College Galesburg IL USA Dept. Electrical Engineering and Computer Science The University of Toledo Toledo OH USA Dept. Computer Systems Technology CityTech City University of New York Brooklyn NY USA Computer Science Dept. Belmont University Nashville TN USA Computer Science Dept. Universidad del Bí o-Bío Chillán Chile Dept. of EECS Indian Institute of Technology Bhilai India Computer and Information Science Dept. Westfield State University Westfield MA USA Computer Engineering Dept. Izmir Institute of Technology Izmir Turkey Dept. Computer Science Texas State University San Marcos TX USA Dept. Computer Science Concordia University Texas Austin TX USA Dept. Computer Science St. John's University New York NY USA Dept. Information Systems and Statistics Baruch College New York NY USA Computer Science Dept. Pomona College Claremont CA USA

ISBN: (纸本)9781665473675

We present efforts to encourage the adoption of modules for teaching heterogeneous parallel computing through a faculty development workshop. The workshop was held remotely using a novel format to exploit the advantages of a virtual format and mitigate its disadvantages. Adoption at a wide variety of institutions showed module effectiveness and also gathered feedback leading to several module improvements. We also report on the adoptions themselves, which show the importance of supporting adaptation of the modules for diverse settings.

关键词： Technological innovation Conferences High performance computing Education Parallel processing Heterogeneous networks

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Fungal disease detection in maize leaves using haar wavelet features 3rd

Fungal disease detection in maize leaves using haar wavelet ...

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3rd International Conference on Information and Communication Technology for Intelligent systems, ICTIs 2018

作者： Deshapande, Anupama s. Giraddi, shantala G. Karibasappa, K.G. Desai, shrinivas D. Department of Computer Science and Engineering B.V. Bhoomaraddi College of Engineering and Technology Hubli India School of Computer Science and Engineering KLE Technological University Hubli India

ISBN: (纸本)9789811317415

Agriculture is the backbone of Indian economy. Diseases in crops are causing huge loss to the economy. Only early detection can reduce these losses. Manual detection of the diseases is not feasible. Automated detection of plants diseases using image processing techniques would help farmers in earlier detection and thus prevent huge losses. Maize is an important commercial cereal crop of the world. The aim of this study is the detection of common fungal diseases, common rust, and northern leaf blight in maize leaf. The proposed system aims at early detection and further classification of diseases into common rust, northern leaf blight, multiple diseases, or healthy using first-order histogram features and Haar wavelet features based on GLCM features. Two classifiers, namely, k-NN and svM are considered for the study. The highest accuracy of 85% is obtained with k-NN for k = 5 and accuracy obtained with svM-based classification is 88%. © 2019, springer Nature singapore Pte Ltd.

关键词： Classification (of information)

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securing Reconfigurable Intelligent surface-Aided Cell-Free Networks

arXiv

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arXiv 2022年

作者： Hao, Wanming Li, Junjie sun, Gangcan Zeng, Ming Dobre, Octavia A. Henan Institute of Advanced Technology School of Information Engineering Zhengzhou University Zhengzhou450001 China Henan Institute of Advanced Technology Zhengzhou University Zhengzhou450001 China School of Information Engineering Institute of Industrial Technology Zhengzhou University Zhengzhou450001 China Department of Electrical Engineering and Computer Engineering Universite Laval QuebecG1V 0A6 Canada Faculty of Engineering and Applied Science Memorial University St. John'sNLA1B 3X5 Canada

In this paper, we investigate the physical layer security in the reconfigurable intelligent surface (RIs)-aided cell-free networks. A maximum weighted sum secrecy rate problem is formulated by jointly optimizing the active beamforming (bF) at the base stations and passive bF at the RIss. To handle this non-trivial problem, we adopt the alternating optimization to decouple the original problem into two sub-ones, which are solved using the semidefinite relaxation and continuous convex approximation theory. To decrease the complexity for obtaining overall channel state information (CsI), we extend the proposed framework to the case that only requires part of the RIs’ CsI. This is achieved via deliberately discarding the RIs that has a small contribution to the user’s secrecy rate. based on this, we formulate a mixed integer non-linear programming problem, and the linear conic relaxation is used to obtained the solutions. Finally, the simulation results show that the proposed schemes can obtain a higher secrecy rate than the existing ones. © 2022, CC bY.

关键词： Channel state information

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Computational pathology: A survey review and the way forward

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Journal of Pathology Informatics 2024年 15卷 100357-100357页

作者： Hosseini, Mahdi s. bejnordi, babak Ehteshami Trinh, vincent Quoc-Huy Chan, Lyndon Hasan, Danial Li, Xingwen Yang, stephen Kim, Taehyo Zhang, Haochen Wu, Theodore Chinniah, Kajanan Maghsoudlou, sina Zhang, Ryan Zhu, Jiadai Khaki, samir buin, Andrei Chaji, Fatemeh salehi, Ala Nguyen, bich Ngoc samaras, Dimitris Plataniotis, Konstantinos N. Department of Computer Science and Software Engineering (CSSE) Concordia Univeristy Montreal H3H 2R9 QC Canada Qualcomm AI Research Qualcomm Technologies Netherlands B.V. Amsterdam Netherlands Institute for Research in Immunology and Cancer of the University of Montreal Montreal H3T 1J4 QC Canada The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) University of Toronto Toronto M5S 3G4 ON Canada Huron Digitial Pathology St. Jacobs N0B 2N0 ON Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton E3B 5A3 NB Canada University of Montreal Hospital Center Montreal H2X 0C2 QC Canada Department of Computer Science Stony Brook University Stony Brook 11794 NY United States

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

关键词： Clinical pathology computer aided diagnosis (CAD) Deep learning Digital pathology survey Whole slide image (WsI)

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Advancements and challenges of deep learning architectures for aerial image analysis: A systematic review

Intelligent Systems with Applications

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Intelligent systems with Applications 2025年 27卷

作者： Hashibul Ahsan shoaib Hadiur Rahman Nabil Md Anisur Rahman Md Mohsin Kabir M.F. Mridha Jungpil shin Information Technology St. Francis College Brooklyn NY 11201 United States Department of Computer Science and Engineering American International University-Bangladesh Dhaka 1229 Bangladesh School of Engineering and Technology Western Illinois University Illinois 61455 United States Division of Computer Science and Software Engineering Mälardalens University 722 20 Västerås Sweden School of Computer Science and Engineering The University of Aizu Aizu-wakamatsu 965-8580 Japan

The rapid advancement of deep learning (DL) technologies has significantly transformed the domain of aerial image analysis. This systematic review explores the forefront of deep learning architectures specifically designed for the processing and analysis of aerial imagery. It offers a comprehensive examination of updated models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformers, highlighting their unique contributions and comparative effectiveness in aerial image analysis. This review critically compares these architectures through an extensive literature survey, focusing on their impact on enhancing accuracy, computational efficiency, and overall performance in critical aerial imaging tasks, such as classification, object detection, and semantic segmentation. Additionally, it sheds light on the innovative architectural improvements that have been crucial in overcoming traditional challenges associated with aerial image processing, such as handling high-resolution data, managing diverse and changing landscapes, and ensuring real-time analysis capabilities. by synthesizing current findings and identifying prevailing trends, this review not only charts the progress in the field but also outlines future research directions, emphasizing the need for more adaptable, robust, and efficient deep-learning solutions to meet the growing demands of aerial image analysis.

关键词： Deep learning Aerial image Object detection Image segmentation Image classification computer vision

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Refining adversarial attacks on machine-learning phishing webpage detectors through functionality-preserving HTML manipulations

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AIP Conference Proceedings 2024年第1期3193卷

作者： R. sonia s. Anila s. A. Karthik s. Meenakshi Eric Howard Kartikeya Parmar J. K. Periasamy v. vijayan Department of Computer Applications B S Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai Tamil Nadu 600048 India Department of Computer Science and Engineering Adi Shankara Institute of Engineering and Technology Ernakulam Kerala 683574 India Department of Information Science and Engineering BMS Institute of Technology and Management Bengaluru Karnataka 560064 India Department of S&H (General Engineering) R.M.K. Engineering College Kavarapettai Tamil Nadu 601 206 India Department of Physics and Astronomy Macquarie University Sydney Australia Department of Mechanical Engineering Nowgong Engineering College Madhya Pradesh 471201 India Department of Computer Science and Engineering Sri Sai Ram Engineering College Chennai Tamil Nadu 600044 India Department of Mechanical Engineering K.Ramakrishnan College of Technology Trichy Tamilnadu India

Research has demonstrated that Machine-Learning Phishing Webpage Detectors, which is vulnerable to malicious changes of the input webpage’s HTML code. Unfortunately, the newly suggested assaults have not been very effective so far because they target specific HTML components and don’t optimise the employment of the manipulations that have been accepted. To get over these limitations, we created a novel set of granular manipulations that can change input phishing webpage HTML code while keeping its aesthetic appeal and malevolent intent (alterations are rendered-preserving and functional by design). After that, we use a query-efficient black-box optimization technique to choose which alterations to apply in order to evade the target detector. In contrast to the weaker attacks created in earlier work, our results demonstrate that our attacks may completely demolish the performance of state-of-the-art ML-PWD with only 30 questions. This allows for a considerably more equitable evaluation of ML-robustness. PWD’s

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Predicting Dislocation Density in Martensite Ab-Initio

SSRN

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ssRN 2022年

作者： Andric, Predrag Echeverri Restrepo, sebastián Maresca, Francesco SKF B.V. Meidoornkade 14 Houten3992 AE Netherlands Department of Physics King’s College London Strand LondonWC2R 2LS United Kingdom Faculty of Science and Engineering University of Groningen Groningen9747AG Netherlands

The high dislocation density (ρd) in Fe-C and/or Fe-N martensite is known to be one of the main causes of its overall yield strength. Experiments indicate that the dislocation density and, therefore, the yield strength of martensite in low and medium C steels, follows a well defined behavior with respect to the background concentration of C (co). In the present work, we introduce a model based on the finding that the dislocation density after γ → α' is sufficiently high to accommodate/trap most of the available interstitials (C or N) within Cottrell atmospheres. We show that the number of interstitials trapped in Cottrell atmospheres scales with the interaction energy between the stress field of the dislocations and the C/N misfit tensor. The new model, which contains no free fitting parameters, shows that the dislocation density scales as ρd ∼ c01/4, leading to an extremely good match with experiments across a wide range of C and N concentrations. Therefore, this model sheds light on the mechanistic origin of dislocation strengthening in Fe-C and Fe-N martensite. © 2022, The Authors. All rights reserved.

关键词： Martensite

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A Novel Feature Extraction and siamese Zeiler and Fergus Forward Taylor Network-based Rice Plant Leaf Disease Detection

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Journal of Phytopathology 2025年第3期173卷

作者： Karthick Muthusamy Ramprasath Jayaprakash vivek Duraivelu satheesh Kumar sabapathy Department of Information Technology Nandha College of Technology Erode Tamilnadu India Department of Information Technology Dr Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Department of Information Technology Institute of Aeronautical Engineering Hyderabad Telangana India

Rice leaf disease affects the leaves of the rice plant that are caused by fungi, bacteria or viruses. Leaf disease leads to yellowing, wilting or lesions on the leaves, which affects photosynthesis and minimises crop production. General rice leaf diseases include rice blast, bacterial blight, and leaf smut, which reduce food production and the economic stability of farmers. Hence, rice plant leaf disease detection is an important aspect, which ensures healthy crop yields. Many methods have been proposed for rice plant leaf disease detection, but they did not fully handle the variability in disease symptoms. Therefore, siamese Zeiler and Fergus Forward Taylor Network (s-ZFFTNet) is developed for rice plant leaf disease detection. First, leaf disease images are collected from the rice leaf bacterial and fungal disease dataset and denoised by anisotropic diffusion. The plant leaf is segmented by conditional Generative Adversarial Network (cGAN). Then, the segmented image is augmented by rotation, colour change, and scaling factor. Then, Fuzzy Local binary Patterns (FLbP) with wavelet transform features are excerpted from an augmented image. In the rice plant leaf disease detection phase, a new hybrid s-ZFFTNet is utilised, which is the unification of the siamese Convolutional Neural Network (sCNN), Zeiler and Fergus Network (ZF-Net), and Taylor's series. The results acquired by s-ZFFT-Net are 92.654% of accuracy, 94.654% True Positive Rate (TPR), 91.757% True Negative Rate (TNR), 90.866% precision, and 92.721% F1-score for k fold value 8.

关键词： anisotropic diffusion conditional generative adversarial network deep learning rice plant leaf rice plant leaf disease detection

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The Emerging Role of the Knowledge Driven Applications of Wireless Networks for Next Generation Online stream Processing

The Emerging Role of the Knowledge Driven Applications of Wi...

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International Conference on system Modeling & Advancement in Research Trends (sMART)

作者： vijaya Gunturu P. Rajani Kumari s. M. Chithra bhargabjyoti saikia Rajesh singh Devesh Pratap singh Department of ECE SR University Warangal Telangana India D.V.R. & Dr. H.S. MIC College of Technology Department of Mathematics R. M. K. College of Engineering and Technology Department of ECE DUIET Dibrugarh University Assam Uttaranchal Institute of Technology Uttaranchal University Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (纸本)9781665487351

The present article discusses the use of stream processing to gather data from large-scale WIFI networks. Along with the foundational techniques for deliberate sampling, data collecting, likewise network monitoring in wireless networks, we also examine how understanding extraction may be viewed as an ML problem for applications for large-scale data streaming. We highlight the major This article discusses advancements in large data stream processing methods. We also look more closely at the database collection, edge detection, and methods for machine learning that may be used in the context of WIFI analytics. We discuss challenges, academic research, and the results of wireless network monitoring and stream analysis. Further research is anticipated into other dataflow improvements, such as pattern recognition and optimization algorithms.

关键词： Knowledge engineering Machine learning algorithms Wireless networks Image edge detection Machine learning Market research Wireless fidelity

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Malware Detection Using Machine Learning Models 23rd

Malware Detection Using Machine Learning Models

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23rd International Conference on Network-based Information systems, Nbis 2020

作者： Carvalho, Glaucio H. s. Woungang, Isaac Anpalagan, Alagan Traore, Issa barolli, Leonard Sheridan College 1430 Trafalgar Rd OakvilleONL6H 2L1 Canada Department of Computer Science Ryerson University 350 Victoria Street TorontoONM5B 2K3 Canada Department of Electrical and Computer Engineering Ryerson University TorontoONM5B 2K3 Canada Department of Electrical and Computer Engineering University of Victoria P.O. Box 1700 STN CSC VictoriaBCV8W 2Y2 Canada 3-30-1 Wajiro-higashi Higashi-kuFukuoka811-0295 Japan

ISBN: (纸本)9783030578107

The criminal mind of adversaries is the most dangerous threat to enterprises and organizations worldwide. The creation and dissemination of malware across the Internet have given attackers of all levels an edge when it comes to compromise modern computer systems. Among the most promising tools to defeat nefarious initiatives, machine learning algorithms, and more specifically classifiers, arise as key techniques to detect malware infection across computer systems and consequently immunize the system at early stages of the propagation. In this paper, we present a performance comparison between neural networks and logistic regression for the problem of malware detection. Results show that neural networks and logistic regression are able to detect a malware presence considering the memory usage and the CPU usage of healthy and infected computers with an accuracy of more than 90%. © 2021, springer Nature switzerland AG.

关键词： Malware

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