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arXiv 2023年

作者： Xu, Yiheng Sivaraman, Pranav Devarajan, Hariharan Mohror, Kathryn Bhatele, Abhinav Department of Computer Science University of Maryland United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory United States

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward even for storage experts. The relationship between an application’s I/O characteristics (such as I/O volume, processes involved, etc.) and the best storage sub-system for it can be complicated. As a result, adapting parallel applications to use burst buffers efficiently is a trial-and-error process. In this work, we present a Python-based tool called PrismIO that enables programmatic analysis of I/O traces. Using PrismIO, we identify bottlenecks on burst buffers and parallel file systems and explain why certain I/O patterns perform poorly. Further, we use machine learning to model the relationship between I/O characteristics and burst buffer selections. We run IOR (an I/O benchmark) with various I/O characteristics on different storage systems and collect performance data. We use the data as the input for training the model. Our model can predict if a file of an application should be placed on BBs for unseen IOR scenarios with an accuracy of 94.47% and for four real applications with an accuracy of 95.86%. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Optimizing a Distributed Graph Data Structure for K - Path Centrality Estimation on HPC

Optimizing a Distributed Graph Data Structure for K - Path C...

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IEEE Conference on High Performance Extreme computing (HPEC)

作者： Lance Fletcher Trevor Steil Roger Pearce Lawrence Livermore National Laboratory (LLNL) Center for Applied Scientific Computing Department of Computer Science and Engineering Texas A&M Univeristy

K-Path centrality is based on the flow of information in a graph along simple paths of length at most K. This work addresses the computational cost of estimating K-path centrality in large-scale graphs by introducing the random neighbor traversal graph (RaNT-Graph). The distributed graph data structure employs a combination of vertex delegation partitioning and rejection sampling, enabling it to sample massive amounts of random paths on large scale-free graphs. We evaluate our approach by running experiments which demonstrate weak scaling on R-MAT graphs and strong scaling on large real-world graphs. The RaNT-Graph approach achieved a 56,544x speedup over the baseline 1D partition implementation when estimating K-path centrality on a graph with 89 million vertices and 1.9 billion edges.

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Novel vision transformer and data augmentation technique for efficient detection of monkeypox disease

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Multimedia Tools and Applications 2024年 1-19页

作者： Alarfaj, Aisha Ahmed Ahmad, Salman Hakeem, Abeer M. Alabdulqader, Ebtisam Abdullah PERO, Chiara Alsubai, Shtwai Innab, Nisreen Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Post Graduate Resident Department of Urology Allama Iqbal Teaching Hospital Dera Ghazi Khan Pakistan Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Department of Information Technology College of Computer and Information Sciences King Saud University P. O. Box 800 Riyadh11421 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Via Giovanni Paolo II 132 Fisciano Salerno Italy Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Diriyah Riyadh13713 Saudi Arabia Department of Information and Communication Engineering Yeungnam University Gyeongsan Korea Republic of

Recent technological advancements have paved the way for the optimization of medical processes, particularly automated disease detection. Moreover, the adoption of machine learning (ML) has greatly helped in automating disease detection. Such approaches can detect various diseases early, enabling timely treatment to save countless lives. Early and accurate diagnosis is very important for diseases like monkeypox, to curb its spread. Monkeypox is a viral disease caused by double-stranded DNA and can be transmitted through close contact with infected humans or animals. It’s early identification and accurate lesion diagnosis are critical to contain the disease. This study proposes an automated approach to optimize the diagnosis of monkeypox disease using a novel vision transformer, which is utilized due to its effectiveness for feature extraction. The Proposed approach’s efficiency and accuracy are tested on a public benchmark dataset comprising a variety of skin lesions of different ages and genders. In addition, data augmentation involves rotation, scaling, and flipping thereby enhancing the density of the training data set for better generalization of ML models. Experiments involve binary, as well as, multi-class classification. For the binary class, the proposed model achieves an accuracy of 97.63%, outperforming traditional ML and deep learning (DL) techniques. In the case of multi-class classification with monkeypox, measles, normal, HFMD, cowpox, and chickenpox classes, the proposed model archives an accuracy of 90.61% while precision, recall, and F1 scores are 91.39%, 89.17%, and 90.28%, respectively. Furthermore, the proposed approach shows average accuracy, precision, recall, and F1 scores of 97.54%, 96.19%, 95.16%, and 95.67%, respectively for five-fold cross-validation. Experiments demonstrate that the combination of data augmentation techniques and the vision transformer model significantly optimizes diagnostic performance. In brief, advanced DL architectur

关键词： Medical image processing

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Review the Challenges Associated with Steganography Using Artificial Intelligence Techniques

Review the Challenges Associated with Steganography Using Ar...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Rasmi Al-Mousa Sultan Mohammed Albilasi Malak Hamad Al-mashagbeh Mahmoud Asassfeh Mahmoud Odeh Khaled AlQawasmi Ghassan Samara Dena Abu Laila Department of Cybersecurity Zarqa University Zarqa Jordan Department of Cybersecurity and Cloud Computing Applied Science Private University Amman Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Cybersecurity Faculty Zarqa Technical Intermediate College Zarqa Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The importance of data masking techniques has increased due to the rapid expansion of covert communications and the mass sharing of multimedia content. Steganography is a technique that allows for the safe and covert sharing of data. It's the science of incorporating data that is hard to see with the unaided eye. File types that support Steganography include audio, video, and image formats. Artificial Intelligence (AI) can enhance Steganography while also complicating the discovery of hidden data and augmenting steganalysis. By looking into these issues, this study intends to compare the various uses and techniques of AI and investigate the difficulties involved in incorporating AI technologies into Steganography. The study used the comparative research approach, comparing the literature with works that addressed Traditional Steganography in general. According to this Paper, countries need to educate themselves on classical Steganography as soon as possible to enhance information security and protection in the future.

关键词： Steganography Technological innovation Reviews Information security Streaming media Media Multimedia communication Artificial intelligence Virtualization Protection

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A news image captioning approach based on multimodal pointer-generator network

A news image captioning approach based on multimodal pointer...

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作者： Chen, Jingqiang Zhuge, Hai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Network Engineering Guangzhou University Guangzhou China Department of Computer Science School of Engineering and Applied Science Aston University Birmingham United Kingdom Key Laboratory of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer-generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer-generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word-image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out-of-domain BBC test dataset show that the proposed model outperforms the original pointer-generator network, the generic image captioning method, the extractive news image captioning method, and the LDA-based method according BLEU, METEOR, and ROUGL-L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model. © 2020 John Wiley & Sons, Ltd.

关键词： Statistical tests

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Malaria Parasite Detection Using a Quantum-Convolutional Network

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computers, Materials & Continua 2022年第3期70卷 6023-6039页

作者： Javaria Amin Muhammad Almas Anjum Abida Sharif Mudassar Raza Seifedine Kadry Yunyoung Nam University of Wah Wah CanttPakistan National University of Technology(NUTECH) IslamabadPakistan COMSATS University Islamabad Vehari CampusVehariPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Malaria is a severe illness triggered by parasites that spreads via mosquito *** underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through ***-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected ***,in this study,we present a new idea based on the ensemble quantum-classical framework for malaria *** methods comprise three core steps:localization,segmentation,and *** the first core step,an improved FRCNN model is proposed for the localization of the infected malaria ***,the RGB localized images were converted into YCbCr channels to normalize the image intensity ***,the actual lesion region was segmented using a histogram-based color thresholding *** segmented images were employed for classification in two different *** the first method,a CNN model is developed by the selection of optimum layers after extensive experimentation,and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of themicroscopicmalaria ***,a quantum-convolutionalmodel is employed for informative feature extraction from microscopicmalaria images,and the extracted feature vectors are supplied to the softmax layer for ***,classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to *** proposed models attain a precision rate greater than 90%,thereby proving that these models performed better than the existing models.

关键词： Quantum convolutional RGB YCbCr histogram Malaria

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A Study of Optimized Proposed Model for Gateway Congestion Control 11

A Study of Optimized Proposed Model for Gateway Congestion C...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Quraishi, Suhail Javed Khan, Rubina Liyakat Hussein, Tanvir Mahmoud Saxena, Vineet Chaudhury, Sushovan Rastogi, Ajay Mriirs Department: Computer Applications Faridabad India The Applied College Imam Abdulrahman Bin Faisal University Computer Science Department Dammam Saudi Arabia Imam Abdulrahman Bin Faisal University Saudi Arabia University of Engineering and Management Department of Computer Science and Engineering West Bengal Kolkata India College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9781665487320

A congestion control mechanism is needed to balance the network load to prevent pack drops and avoid network deadlocks. The main purpose is to analyze efficient and reliable congestion control methods which will reduce the weaknesses in various existing congestion control methods and propose a framework to minimize the gateway congestion in the network. As the demand of heterogeneous users of computer network day by day dynamically and rapidly changing in many aspects, grounded on the application situations our congestion control mechanism functions well in the situations that occur in Internet. The maximum degree of dynamics can be handle efficiently by keeping the generality of its existing end-to-end solution in our congestion control design. © 2022 IEEE.

关键词： Wireless sensor networks

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Analysing Student Depression through Cyber-Activity Patterns Using Heterogeneous Graph Attention Networks 1

Analysing Student Depression through Cyber-Activity Patterns...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Shukla, Tushar Dhar Vanyan, Diana Rizvi, Qaim Mehdi George, Geogen Bhosale, Aryaraj Dattatraya Bhoopathy, V. University Institute of Physical Education & Sports Chandigarh University Department of Physical Education Mohali India Kuban State University Krasnodar Russia Kolkata India University of Technology and Applied Sciences College of Computing and Information Sciences Middle East Ibri Oman Indian Institute of Information Technology Pune India Sree Rama Engineering College Department of Computer Science and Engineering Tirupathi India

ISBN: (纸本)9798350352931

This study focusses on the crucial matter of identifying student sadness by analysing patterns of cyberactivity. The technology utilizes Heterogeneous Graph Attention Networks (HGATs) to represent and examine intricate connections within the data. The dataset used for training and testing the model was obtained from Kaggle. The model achieved an accuracy of 9 6. 8 %, a precision of 9 5. 4 %, a recall of 94.7 %, and an AUC-ROC of 98.3 %. The findings of this study demonstrate a substantial improvement over current models, indicating the high efficacy of HGATs in accurately capturing complex patterns in cyber-activity data. The results indicate that this method can function as a potent instrument for identifying depression at an early stage, with wide-ranging implications for monitoring the mental health of students and implementing intervention techniques. © 2024 IEEE.

关键词： Graph neural networks

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Supervised Machine Learning-Based Prediction of COVID-19

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computers, Materials & Continua 2021年第10期69卷 21-34页

作者： Atta-ur-Rahman Kiran Sultan Iftikhar Naseer Rizwan Majeed Dhiaa Musleh Mohammed Abdul Salam Gollapalli Sghaier Chabani Nehad Ibrahim Shahan Yamin Siddiqui Muhammad Adnan Khan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Information System College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of CIT Faculty of Applied StudiesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Directorate of IT The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan

COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the *** yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are *** around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely *** this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new *** situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this *** state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection *** study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential *** proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation *** experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation *** comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.

关键词： COVID-19 CSDC-SVM artificial intelligence machine learning cloud computing support vector machine

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On the Instance Optimality of Detecting Collisions and Subgraphs

arXiv

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arXiv 2023年

作者： Ben-Eliezer, Omri Grossman, Tomer Naor, Moni Simons Institute for the Theory of Computing University of California Berkeley United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot76100 Israel

Suppose you are given a function f : [n] → [n] via (black-box) query access to the function. You are looking to find something local, like a collision (a pair x ≠ y s.t. f(x) = f(y)). The question is whether knowing the ‘shape’ of the function helps you or not (by shape we mean that some permutation of the function is known). Formally, we investigate the unlabeled instance optimality of substructure detection problems in graphs and functions. A problem is g(n)-instance optimal if it admits an algorithm A satisfying that for any possible input, the (randomized) query complexity of A is at most g(n) times larger than the query complexity of any algorithm A′ which solves the same problem while holding an unlabeled copy of the input (i.e., any A′ that "knows the structure of the input"). Our results point to a trichotomy of unlabeled instance optimality among substructure detection problems in graphs and functions: • A few very simple properties have an O(1)-instance optimal algorithm. • Most properties of graphs and functions, with examples such as containing a fixed point or a 3-collision in functions, or a triangle in graphs, are nΩ(1)-far from instance optimality. • The problems of collision detection in functions and finding a claw in a graph serve as a middle ground between the two regimes. We show that these two properties are Ω(log n)far from instance optimality, and conjecture that this bound is tight. We provide evidence towards this conjecture, by proving that finding a claw in a graph is O(log(n))-instance optimal among all input graphs for which the query complexity of an algorithm holding an unlabeled certificate is (formula presanted) . Copyright © 2023, The Authors. All rights reserved.

关键词： Optimal detection

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