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An OO-Based Approach of computing Offloading and Resource Allocation for Large-Scale Mobile Edge computing Systems 17th

An OO-Based Approach of Computing Offloading and Resource ...

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17th EAI international conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Tan, Yufu Ali, Sikandar Wang, Haotian Huang, Jiwei Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum - Beijing Beijing102249 China

ISBN: (纸本)9783030926373

Mobile edge computing (MEC) is an emerging paradigm to meet the increasing real-time performance demands for Internet of Things and mobile applications. By offloading the computationally intensive workloads to edge servers, the quality of service (QoS) could be greatly improved. However, with the growing popularity of MEC, the MEC systems grow extremely large, and thus the QoS optimization suffers from search space explosion problem, making it impractical in real-life scenarios. To attack this challenge, this paper studies the joint optimization of task offloading and computational resource allocation for large-scale MEC systems. We formulate this problem as a cost minimization problem and illustrate the NP-hardness of this problem. In order to solve this problem, we divide the original problem into two sub-problems and introduce the theory of Ordinal Optimization (OO) to search for a near-optimal computing offloading and resource allocation policy within a significantly reduced search space. Finally, the efficacy of our approach is validated by simulation experiments. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Mobile edge computing

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Model-Based Evaluation and Optimization of Dependability for Edge computing Systems 17th

Model-Based Evaluation and Optimization of Dependability for...

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17th EAI international conference on Collaborative computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Liang, Jingyu Ma, Bowen Ali, Sikandar Huang, Jiwei Beijing Key Laboratory of Petroleum Data Mining China University of Petroleum - Beijing Beijing102249 China

ISBN: (纸本)9783030926342

Edge computing moves part of the computing tasks to the edge of the network to improve service capabilities while reducing latency. It has been successfully applied in Internet of Things (IoT) and mobile computing systems. With the increasing popularity of edge computing, the ability of an edge computing system continuously providing services to users without interruptions and failures, which is also known as the dependability, has become an important issue. However, the evaluation and optimization of dependability attributes of an edge computing system still remains an largely unexplored problem. In this paper, we study this issue from a model-based viewpoint. We propose an atomic dependability model of a server and provide quantitative analyses of dependability attributes with Markov chain techniques. In order to facilitate the analyses of multiple attributes in large-scale environments, we adopt a state aggregation method for model simplification, and present its corresponding theoretical proof. Considering the edge-cloud collaboration, we put forward the dependability model of an edge computing system, and provide an evaluation approach using the state aggregation technique. Furthermore, taking task offloading as an example, we formulate the dependability optimization as a continuous-time Markov decision problem (CTMDP), and propose an efficient approach of solving the problem with reinforcement learning. Finally, we use a real-world dataset to conduct simulation experiments, and the experimental results validate the efficacy of our approach. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Markov processes

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Automated detection and forecasting of COVID-19 using deep learning techniques: A review

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NEUROcomputing 2024年 577卷

作者： Shoeibi, Afshin Khodatars, Marjane Jafari, Mahboobeh Ghassemi, Navid Sadeghi, Delaram Moridian, Parisa Khadem, Ali Alizadehsani, Roohallah Hussain, Sadiq Zare, Assef Sani, Zahra Alizadeh Khozeimeh, Fahime Nahavandi, Saeid Acharya, U. Rajendra Gorriz, Juan M. Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Ferdowsi Univ Mashhad Comp Engn Dept Mashhad Iran K N Toosi Univ Technol Fac Elect Engn Tehran Iran Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Melbourne Vic 3217 Australia Dibrugarh Univ Dibrugarh 786004 Assam India Islamic Azad Univ Fac Elect Engn Gonabad Branch Gonabad Iran Rajaie Cardiovasc Med & Res Ctr Tehran Iran Iran Univ Med Sci Tehran Iran Swinburne Univ Technol Associate Deputy Vice Chancellor Res Hawthorn Vic 3122 Australia Univ Southern Queensland Sch Math Phys & Comp Springfield QLD Australia Univ Southern Queensland Ctr Hlth Res Springfield QLD Australia Univ Cambridge Dept Pathol Cambridge England

In March 2020, the World Health Organization (WHO) declared COVID-19 a global epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real-time reverse transcription-polymerase chain reaction (RT-PCR) tests with a turnaround time of 2-3 days. To enhance diagnostic accuracy, medical professionals use medical imaging alongside RT-PCR. A positive result on both RT-PCR and medical imaging confirms a COVID-19 diagnosis. Imaging modalities like chest X-ray (CXR), computed tomography (CT) scans, and ultrasound are widely utilized for rapid and precise COVID-19 diagnoses. However, interpreting COVID-19 from these images is time-consuming and susceptible to human error. Therefore, leveraging artificial intelligence (AI) methods, particularly deep learning (DL) models, can deliver consistent, high-performance results. Unlike conventional machine learning (ML), DL models automate all stages of feature extraction, selection, and classification. This paper presents a comprehensive review of using DL techniques for diagnosing COVID-19 from medical imaging. The introduction provides an overview of diagnosing the coronavirus using medical imaging, highlighting associated challenges. Subsequently, the paper delves into key aspects of Computer-Aided Diagnosis Systems (CADS) based on DL methods for diagnosing COVID-19, covering segmentation, classification, explainable AI (XAI), and predictive research. Additionally, it reviews the rehabilitation systems such as the Internet of Medical Things (IoMT) in the context of COVID-19. In another section, uncertainty quantification (UQ) research is showcased, focusing on DL models for the diagnosis of Covid-19. Crucial challenges and future research directions are outlined in another section. Finally, discussion and conclusion sections are also provided at the end of the paper.

关键词： COVID-19 Diagnosis Deep Learning Explainable AI Rehabilitation Systems Uncertainty Quantification

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Overall quality optimization for DQM stage in High Energy Physics experiments 19

Overall quality optimization for DQM stage in High Energy Ph...

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19th international Workshop on advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Benekos, N. Parra-Royon, M. Benitez, J. M. CERN Dept Expt Phys EP NU Geneva Switzerland Univ Granada ETS Ingn Informat & Telecomunicac Dept Comp Sci & Artificial Intelligence DiCITSDaSCIIMUDS Granada 18014 Spain

data Acquisition (DAQ) and data Quality Monitoring (DQM) are key parts in the HEP data chain, where the data are processed and analyzed to obtain accurate monitoring quality indicators. Such stages are complex, including an intense processing work-flow and requiring a high degree of interoperability between software and hardware facilities. data recorded by DAQ sensors and devices are sampled to perform live (and offline) DQM of the status of the detector during data collection providing to the system and scientists the ability to identify problems with extremely low latency, minimizing the amount of data that would otherwise be unsuitable for physical analysis. DQM stage performs a large set of operations (Fast Fourier Transform (FFT), clustering, classification algorithms, Region of Interest, particles tracking, etc.) involving the use of computing resources and time, depending on the number of events of the experiment, sampling data, complexity of the tasks or the quality performance. The objective of our work is to show a proposal with aim of developing a general optimization of the DQM stage considering all these elements. Techniques based on computational intelligence like EA can help improve the performance and therefore achieve an optimization of task scheduling in DQM.

关键词： data acquisition

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End States Guided Matching Network for Retrieval-based Multi-turn Conversation 3

End States Guided Matching Network for Retrieval-based Multi...

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2021年第三届先进计算机技术、信息科学与通信国际会议(CTISC2021)

作者： Weixin Tan Dandan Song Yujin Gao Lab of High Volume language Information Processing & Cloud Computing Beijing Lab of Intelligent Information TechnologyBeijing Institute of Technology

ISBN: (纸本)9781665418683

Multi-turn conversation response selection aims to choose the best response from multiple candidates based on matching it with the dialogue context. Mostly, a response full of context-related information tends to be a proper ***, in some cases, a brief response like "ok" could be the more appropriate one. We find that it is a semantically ended conversation that a brief response usually comes after,so there is no need to provide any context-related information after that. Thus, in addition to match the response with context,it is also critical to recognize the state of whether a dialogue has ended, and learn how to get necessary information from context of different end states separately. To achieve this, we propose an end states guided matching network to determine and incorporate the end states by jointly consider the length of response and the local similarity between the response and last few utterances. In addition, we adopt multiple descriptive sequence representations for a more reliable matching *** results demonstrate that our model outperforms the state-of-the-art methods in multiple datasets.

关键词： Information science Computational modeling Computer network reliability Reliability data mining

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Features Explaining Malnutrition in India: A Machine Learning Approach to Demographic and Health Survey data 10th

Features Explaining Malnutrition in India: A Machine Learnin...

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10th international advanced computing conference, IACC 2020

作者： Vasu, Sunny Rajendrasingh Khare, Sangita Gupta, Deepa Jyotishi, Amalendu Department of Computer Science and Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham Bengaluru India School of Development Azim Premji University Bengaluru Karnataka India

ISBN: (纸本)9789811604003

India is one of the severely malnourished countries in the world. Under-nutrition is the reason for death among two-third of the 1.04 million deaths among the children under the age of five in the year 2019. Several strategies have been adopted by the Government of India and state governments to minimize the incidents of malnutrition. However, to make the policies effective, it is important to understand the key features explaining malnutrition. Analyzing the Indian Demographic Health Survey data (IDHS) of the year 2015–2016, this paper attempts to identify causes of four dimensions of malnutrition namely, Height Age Z-score (HAZ), Weight Age Z-score (WAZ), Weight Height Z-score (WHZ) and Body Mass Index (BMI). Using machine learning approach of feature reduction, the paper identifies ten most important features out of available 1341 features in the database for each of the four anthropometric parameters of malnutrition. The features are reduced and ranked using WEKA tool. Results and finding of this research would provide key policy inputs to address malnutrition and related mortality among the children under the age five. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Nutrition

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Efficient Binary Static Code data Flow Analysis Using Unsupervised Learning 4

Efficient Binary Static Code Data Flow Analysis Using Unsupe...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

作者： Obert, James Loffredo, Tim Sandia National Labs AlbuquerqueNM United States

ISBN: (纸本)9781665434102

The ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. © 2021 IEEE

关键词： data flow analysis

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Investigating the Optimal Parameterization of Deep Neural Network and Synthetic data Workflow for Imbalance Liver Disorder dataset Classification 4th

Investigating the Optimal Parameterization of Deep Neural Ne...

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4th international conference on Soft computing and data mining (SCDM)

作者： Diana, Nova Eka Ahmad, Andi Batari Mahardika, Zwasta Pribadi Univ YARSI Fac Informat Technol Informat Dept Jakarta 10510 Indonesia Univ YARSI Fac Med Jakarta 10510 Indonesia

ISBN: (纸本)9783030360566;9783030360559

DNN (Deep neural network) has emerged as one of the standard methods to create a classification model. The most common issue affecting DNN performance is the class-imbalanced distribution dataset. This research designed two workflows for generating synthetic dataset using SMOTE algorithm, SDS-1, and SDS-2 dataset. We further investigated the optimal DNN parameters that generate the best optimum performance over those datasets. We used Indian Liver Patient dataset (ILPD) from the oldest source, UCI Machine Learning Repository, with a total of 583 records, consist of 416 positives and 167 negatives data. We measured the DNN performance using sensitivity and F-score metric following the nature of the medical domain that mainly focused on identifying a particular disease. The experiment results revealed that DNN model with the learning rate of 1E-1, TanH activation function, Xavier weighting, the epoch of 40, and the hidden layers of 10, delivered the best sensitivity and F-score value, 98.40% and 99.18%, respectively. The results suggested that the workflow for generating the class-balanced dataset will leverage the DNN performance.

关键词： Deep neural network Imbalanced dataset SMOTE Sensitivity F-score

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data mining and Association Analysis of Irrational Use of Antibiotics in Outpatient data of New Cooperative Medical System

Data Mining and Association Analysis of Irrational Use of An...

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international conference on Applications and Techniques in Cyber Intelligence, ATCI 2019

作者： Hu, Qingshun Ren, Xiaoqiang Qilu University of Technology No. 3501 Changqing University Road Changqing Campus Jinan CityShandong Province China

ISBN: (纸本)9783030251277

With the widespread use of antibiotics, bacterial resistance has become a focus of widespread attention in the world’s medical system. China is one of the largest use of antibiotics in the world. With the popularization of new cooperative medical system (NCMS), there are serious problems of excessive use of antibiotics in small rural medical institutions. Therefore, based on the NCMS data in a certain area, this paper analyzes the use of antibiotics in new rural cooperative clinics by using data mining algorithms. By correlating commonly used antibiotics with disease, the proportion of antibiotics found in this paper was 55.98%, much higher than the international average of 30%, and there was serious excess usage. Based on this paper, a series of improvement strategies were proposed to improve the management level of the medical system. © 2020, Springer Nature Switzerland AG.

关键词： data mining

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Research and Design of Square Kilometer Array Astronomical data Management Model Based on Fabric 13

Research and Design of Square Kilometer Array Astronomical D...

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2020 IEEE Congress on Cybermatics: 13th IEEE international conferences on Internet of Things, iThings 2020, 16th IEEE international conference on Green computing and Communications, GreenCom 2020, 13th IEEE international conference on Cyber, Physical and Social computing, CPSCom 2020 and 6th IEEE international conference on Smart data, Smartdata 2020

作者： Fu, Jinhua Xu, Jie Zhang, Shulin Zhang, Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou University of Light Industry Zhengzhou China Zhengzhou University of Light Industry Zhengzhou China Qinghai University Department of Computer Technology and Applications Xining China

ISBN: (纸本)9781728176475

At present, how to safely share data among different participants has become a huge challenge. The reliable traceability of SKA data is a powerful technical tool for controlling its security and sharing. In order to solve the problem of high management cost and low data credibility of traditional centralized big data management system, SKA data management and credible traceability model design based on Hyperledger Fabric were studied. In this paper, the overall framework and architecture of SKA data management are studied and designed, and the key technologies such as big data storage scheme, big data transmission, big data processing, blockchain metadata storage, alliance chain member allocation, traceability query, contribution mechanism and data sharing are discussed. Distributed architecture, Hyperledger Fabric alliance chain technology, database technology, Golang network programming, Rust asynchronous programming, Java Web and *** are comprehensively adopted. The functions of SKA data transmission, processing, storage, traceability query and data sharing are designed. The functions of transmission, processing, storage, management, sharing and credible traceability of SKA data management system are realized, which provides a reference for the design and implementation of SKA data management system based on blockchain. © 2020 IEEE.

关键词： Metadata

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