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Automation of Programming for Promising High-Performance computing Systems 17th

Automation of Programming for Promising High-Performance C...

引用

parallel computing Technologies - 17th international conference, PaCT 2023, Proceedings

作者： Bakhtin, Vladimir Zakharov, Dmitry Kataev, Nikita Kolganov, Alexander Yakobovskiy, Mikhail Keldysh Institute of Applied Mathematics RAS Moscow Russia

ISBN: (纸本)9783031416729

Automation of parallel programming may focus on various tasks the programmer is burdened while developing a parallel program. Related tools assist the program profiling and aid the programmer with transforming the program to a form suitable for the efficient parallelization. Finally, these tools express an implicit program parallelism using a chosen programming model and optimize the parallel program for target architectures. However, the choice of the target Application Programming Interfaces (API) is of great importance in the development of interactive parallelization tools. On the one hand, the perfect choice of API should ensure the programming of the variety of modern and promising architectures. On the other hand, API must simplify the development of assistant tools and allow the programmer to explore the decisions made by the automated parallelization system. System FOR Automated parallelization (SAPFOR) is an umbrella of assistant tools designed to automate parallel programming. It accomplishes various tasks and allows the user to take an advantage of the interactive semi-automatic parallelization. SAPFOR expresses parallelism using the DVMH directive-based programming model, which aims at developing efficient parallel programs for heterogeneous and hybrid computing clusters. the paper presents an empirical study that examines the capability of SAPFOR to assist parallel programming on the example of development of a parallel program for numerical simulation of hydrodynamic instabilities. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Feature Extraction and Classification Stage on Facial Expression: A Review 6

Feature Extraction and Classification Stage on Facial Expres...

引用

6th international conference on Informatics and Computational Sciences, ICICoS 2022

作者： Shidiq, Muchamad Bachram Ernawan, Ferda Khubrani, Mousa Nugroho, Fajar Agung Universitas Nusa Mandiri Faculty of Computer Science Jakarta Indonesia Universiti Malaysia Pahang Faculty of Computing College of Computing & Applied Sciences Pahang Malaysia Jazan University Faculty of Computer Science & Information Technology Jazan Saudi Arabia Diponegoro University Department of Informatics Semarang Indonesia

ISBN: (数字)9781665460996

ISBN: (纸本)9781665460996

Human facial expression becomes an important technology in recent years. As information technology and networks have grown, identification and authentication have become more frequent in people's daily lives, especially using biometric technology. Human facial recognition involves face detection, feature extraction, and classification. A lot of experiments showed that there are various techniques for extracting facial features and classifying facial expressions. this paper reviews and analyze the various optimization techniques on extract feature and classification stage for human facial expression recognition. this review will compare two kinds of extract features methods and one classification method. the first technique of extracting features is the optimization technique using the K-Mean algorithm, which helps to increase recognition accuracy. the second extract feature is an optimization technique using improved Gradient Local Ternary Pattern (GLTP) which is beneficial for computational resources efficiency. Lastly, the optimization technique for image classification using a three-staged Support Vector Machine (SVM) is very helpful for increasing accuracy and eliminating error. the modified GLTP is able to obtain an accuracy of 97%. © 2022 IEEE.

关键词： Classification (of information)

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Admission Experts based on Android+VR, AR Technology and Voice Operation 6

Admission Experts based on Android+VR, AR Technology and Voi...

引用

6th international conference on Digital Technology in Education, ICDTE 2022

作者： Xiao, Juan Yang, Weichen Li, Faying School of Computer and Artificial Intelligence Xiangnan University Hunan Chenzhou China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Hunan Chenzhou China Xiangnan University Hunan Chenzhou China

ISBN: (纸本)9781450398091

With the improvement of people’s living standards and the increasing abundance of materials, acceptance has become an indispensable part of people’s life. Nowadays, the application of smartphone is more and more widely used. A mobile APP based on Android system with the theme of acceptance is developed. Augmented Reality (AR) and Virtual Reality (VR) technologies as well as voice operation functions are applied to bring users a technological, convenient and novel experience of acceptance. © 2022 Association for computing Machinery.

关键词： Augmented reality

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Chip-Firing Reservoir computing 2022

Chip-Firing Reservoir Computing

引用

6th international conference on Compute and Data Analysis, ICCDA 2022

作者： Liu, Liying Zheng, Yang Luo, Ye School of Informatics Xiamen University China State Key Laboratory of Cryptology P.O.Box 5159 Beijing100878 China

ISBN: (纸本)9781450395472

Spike neural networks (SNNs), as a network structure closer to biological neurons, have been applied to many machine learning fields because of their high computing capability. However, due to its high hardware requirements and difficulty in training, the application cost of SNNs in practice is higher than that of traditional artificial neural networks and the accuracy is usually lower. In order to build a brain-like neural network with lower hardware requirements and reduce training requirements, we propose a new computing network structure called chip-firing reservoir (CFR), which can be considered as a type of more discretized SNNs. CFR computing (CFRC) can extract spatiotemporal information to improve computing capability as SNNs, while only the output layer is trained as in conventional reservoir computing (RC), reducing the difficulty of training. Furthermore, since CFRC only use integer calculations, we hope that the computational cost can be significantly reduced compared to SNNs based on floating-point computations. In the experiment, we test and analyze the CFRC for image classification tasks. the results show that CFRC has functional performance on the MNIST image data set. © 2022 ACM.

关键词： Image classification

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Multi-layer parallelization in Transportation Management Software 6th

Multi-layer Parallelization in Transportation Management Sof...

引用

6th international conference on Information, Communication and computing Technology (ICICCT)

作者： Ivaschenko, Anton Maslennikov, Sergey Stolbova, Anastasia Golovnin, Oleg Samara State Tech Univ Molodogvardeyskaya 244 Samara Russia Samara Natl Res Univ Moskovskoye Shosse 34 Samara Russia

ISBN: (纸本)9783030883782;9783030883775

In this paper there is introduced a comparative analysis of multi-layer data model used for parallelization of transportation network objects processing in the process of logistics optimization. A concept of multi-layer parallelization is based on distribution of the objects and subjects of a complex transportation logistics system including the road network, pick-up and delivery points, drivers, tracks and trailers, transportation instructions and order flows to the logical layers established considering the requirements of parallel computing. this paper continues this research by study of parallel computing characteristics comparing to traditional approach. the proposed data model allows increasing the performance of parallel computing application in transportation logistics problem domain. the considered performance metrics include execution time, total parallel overhead, speedup, and efficiency. Research results are implemented and applied in practice as a part of a transportation management software solution.

关键词： parallel computing Transportation logistics Routing Multi-layer model

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Deep Reinforcement Learning Optimization Algorithm Designed for IRS-Assisted Edge computing

Deep Reinforcement Learning Optimization Algorithm Designed ...

引用

IEEE international conference on Electronic Information and Communication Technology (ICEICT)

作者： Kaixu Liu Fei Lin Yingxue Zhao Junrui Zhang School of Information and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

To reduce the computing delay of the terminals and improve the poor wireless channel environment, in this paper, a computing resource allocation algorithm for IRS-assisted edge computing is proposed. By introducing the intelligent reflecting surface (IRS) technique to reduce the edge computing latency, the computing offloading variables are optimized through an Alternating optimization approach, and a partial offloading strategy for MEC offloading is adopted. Deep reinforcement learning (DRL) is applied for coarse phase control of the IRS, and fine phase control of the IRS is achieved through fine phase optimization. In simulations, the proposed scheme is demonstrated to be effective and low latency.

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Prediction of COVID-19 from Lung Scans Using Deep Learning 6

Prediction of COVID-19 from Lung Scans Using Deep Learning

引用

6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Pramanik, Atreyi Sinha, Aashna Aluvala, Srinivas School of Applied and Life Sciences Division of Research and Innovation Uttaranchal University Dehradun India School of Computing Sciences S R University Telangana Hyderabad India

ISBN: (纸本)9798350304480

the COVID-19 pandemic has placed enormous demands on the world's healthcare systems, needing creative methods for the early and precise diagnosis of sickness. In this review study, we investigate the application of deep learning methods for COVID-19 prediction from lung scans. Deep learning methods, in particular convolutional neural networks (CNNs), have demonstrated remarkable promise in separating COVID-19 infections from other respiratory diseases by utilizing the power of artificial intelligence. We analyze the various approaches and datasets used in deep learning-based COVID-19 prediction studies through a thorough examination of the available literature. the paper examines the difficulties with deep learning models'interpretability as well as potential biases that could appear during training and inference. We also investigate the use of pre-training and transfer learning methods to enhance model convergence and overcome data scarcity issues. the importance of early detection and prognostic skills made possible by deep learning models is also highlighted in this research, underlining their potential to direct clinical decision-making and resource allocation. We look at the consequences of model robustness in controlling the ever-evolving pandemic as well as the adaptability of these models to new COVID-19 variants. Overall, this review paper summarizes the state of deep learning-based COVID-19 prediction from lung scans and discusses its accomplishments, difficulties, and potential future approaches. We want to contribute to the development of AI-driven diagnoses and support healthcare professionals in making well-informed decisions to successfully tackle the COVID-19 epidemic by critically reviewing the available studies. © 2023 IEEE.

关键词： convolutional neural networks COVID-19 CT scans deep learning prediction prognosis X-ray lung images

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Distributed computing of R Applications Using RBOINC Package with Applications to parallel Discrete Event Simulation 24th

Distributed Computing of R Applications Using RBOINC Packag...

引用

24th international conference on Distributed Computer and Communication Networks, DCCN 2021

作者： Astafiev, S.N. Rumyantsev, A.S. Petrozavodsk State University 33 Lenina Street Petrozavodsk Russia Institute of Applied Mathematical Research Karelian Research Centre of Russian Academy of Sciences 11 Pushkinskaya Street Petrozavodsk Russia

ISBN: (纸本)9783030971090

R programming language is commonly used for statistical computing, data science and stochastic simulation. Existing packages for R allow to run parallel code on various parallel architectures, however, the support for distributed (volunteer) computing is rather weak. this article describes a new R package RBOINC that allows to run parallel code on desktop grid systems via the BOINC open source system for grid computing, which is a promising approach for parallel stochastic simulation. Among the possible ways to utilize the new package, an approach to parallel regenerative stochastic simulation within the generalized semi-Markov processes framework is suggested. © 2022, Springer Nature Switzerland AG.

关键词： Stochastic systems

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Role and Challenges of Emerging Technologies in Healthcare Application 6th

Role and Challenges of Emerging Technologies in Healthcare A...

引用

6th international conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Vedanayagam, Sumathy Cyriloose, Pretty Diana Cyril Perumal, Govindaraj Department of Computing Technologies College of Engineering and Technology SRM Institute of Science and Technology TN Kattankulathur India Department of Biomedical Engineering Rajalakshmi Engineering College TN Chennai India

ISBN: (纸本)9789819780891

In the healthcare domain revolution, emerging technologies provide innovative solutions to enhance patient care and medical research. Each new concept has potential for specific problems, and they also have some challenges. Many technologies are introduced to improve service with security and qualities in healthcare applications like Blockchain, Digital Healthcare Systems, Nano Technology, Augmented Reality, Virtual Reality, and Artificial Intelligence. Blockchain technology yields security and transparency for electronic health records. We elaborate on the benefits and challenges of blockchain in healthcare applications. Powerful technology can be applied to various healthcare domains for the betterment and contribution of innovative solutions to healthcare. Blockchain has multiple features, including distributed ledger, decentralized storage, authentication, security, and immutability, which greatly solve complex healthcare problems. Blockchain plays an essential role in healthcare applications such as preserving and sharing patient data, diagnostic labs, and pharmacy stores to effectively handle narcotic and other prescribed drugs for safe use by clinicians, handling the patient medical records and clinical trial data. Technology also improves the security of patient medical records and the efficient operation of healthcare organizations. One of the recent applications of blockchain in the healthcare system is customized function to personalized medicine in the health information technology by providing shareable private keys by the healthcare providers. In addition, blockchain technology can maintain patients’ health information confidentially, which improves remote access to the individual. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Novel Graph-based Transient Analysis Method for Bulk Power System

Novel Graph-based Transient Analysis Method for Bulk Power S...

引用

Energy, Power and Grid (ICEPG), international conference on

作者： Zhen Dai Jun Tan Wei Zhang Xinglang Xie Xueqing Song Yuanfa Deng China Southern Power Grid Digital Grid Research Institute Co. LTD Guangzhou> China Shanghai Envision Digital Co. Ltd Shanghai China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

As the modern power system is undergoing a major transition to a self-aware and self-autonomous system to address the more and more complex system dynamics and operation conditions, the transient analysis of power system is also facing challenges to fast response to transient disturbances and take corresponding preventive and control measures to increase system security. However, traditional transient analysis methods rely on time-domain simulations are very time consuming. this paper proposed a graph computing based power system transient analysis approach to achieve efficient and accurate solution for power system stability analysis. To overcome the data management challenges, a spatiotemporal graph model is constructed for efficient data management in power system transient analysis. then, a graph computing based transient analysis method is proposed to leverage the parallel computing power to speed up the computational process. the case study shows that the proposed graph computing based parallel computing approach can achieve 5.16 times speed improvement rate over the traditional serial computing method.

关键词： Analytical models Computational modeling Power system stability parallel processing Spatiotemporal phenomena Velocity measurement Security Transient analysis Time-domain analysis Self-aware

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