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End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects

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arXiv 2021年

作者： Ahmad, Muhammad Shabbir, Sidrah Roy, Swalpa Kumar Hong, Danfeng Wu, Xin Yao, Jing Khan, Adil Mehmood Mazzara, Manuel Distefano, Salvatore Chanussot, Jocelyn Department of Computer Science National University of Computer and Emerging Sciences Islamabad Chiniot-Faisalabad Campus Chiniot35400 Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy Pakistan The Department of Computer Science and Engineering Jalpaiguri Government Engineering College West Bengal 735102 India The Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China The School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Fractional Signals and Systems Beijing100081 China The Institute of Data Science and Artificial Intelligence Innopolis University Innopolis 420500 Russia Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia The Univ. Grenoble Alpes CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. Notably, the complex characteristics i.e., the nonlinear relation among the captured spectral information and the corresponding object of HSI data make accurate classification challenging for traditional methods. In the last few years, Deep Learning (DL) has been substantiated as a powerful feature extractor that effectively addresses the nonlinear problems that appeared in a number of computer vision tasks. This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. This survey enlists a systematic overview of DL for HSIC and compared state-of-the-art strategies of the said topic. Primarily, we will encapsulate the main challenges of traditional machine learning for HSIC and then we will acquaint the superiority of DL to address these problems. This survey breakdown the state-of-the-art DL frameworks into spectral-features, spatial-features, and together spatial-spectral features to systematically analyze the achievements (future research directions as well) of these frameworks for HSIC. Moreover, we will consider the fact that DL requires a large number of labeled training examples whereas acquiring such a number for HSIC is challenging in terms of time and cost. Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines. © 2021, CC BY.

关键词： Surveys

Mutation testing for ethereum smart contract

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arXiv 2019年

作者： Wu, Haoran Wang, Xingya Xu, Jiehui Zou, Weiqin Zhang, Lingming Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University 210093 China Department of Computer Science University of Texas Dallas United States

Smart contract is a special program that manages digital assets on blockchain. It is difficult to recover the loss if users make transactions through buggy smart contracts, which cannot be directly fixed. Hence, it is important to ensure the correctness of smart contracts before deploying them. This paper proposes a systematic framework to mutation testing for smart contracts on Ethereum, which is currently the most popular open blockchain for deploying and running smart contracts. Fifteen novel mutation operators have been designed for Ethereum Smart Contracts (ESC), in terms of keyword, global variable/- function, variable unit, and error handling. An empirical study on 26 smart contracts in four Ethereum DApps has been conducted to evaluate the effectiveness of mutation testing. The experimental results show that our approach can outperform the coverage-based approach on defect detection rate (96.01% vs. 55.68%). The ESC mutation operators are effective to reveal real defects and we found 117 out of 729 real bug reports are related to our operators. These show the great potential of using mutation testing for quality assurance of ESC. Copyright © 2019, The Authors. All rights reserved.

关键词： Smart contract

A dynamic level interaction method based on trusted value

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Journal of computers (Taiwan) 2019年第1期30卷 142-152页

作者： Zhang, Yong-sheng Fan, Yue-qin Cui, Ran-ran Wei, Yu Department of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

With the rapid development of the cloud computing, there is a large amount of service providers with low quality in the marketplace. The mendacious service providers cause a serious loss for the users. In order to select an efficient cloud service, it can be trust and meet the user’s privacy protection requirements. In this paper, a dynamic level interaction method based on credible value is indicated. The cloud service providers and cloud users are divided into different dynamic levels according to their historical records. The security level of privacy data is decided according to the users’ trust level. This model introduces the center degree factor to filter the mendacious service and it can get a more accurate service evaluation by limiting the malicious users to make false evaluation for the service and it can control the providers’ access rights to protect the users’ information. The results of simulation experiment show that this model can improve the interaction successful rate and strengthen the safety of user privacy data. It demonstrates the efficiency and effectiveness of our approach in trusted interaction with other counterparts. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Grading

Multi-view knowledge graph embedding for entity alignment

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arXiv 2019年

作者： Zhang, Qingheng Sun, Zequn Hu, Wei Chen, Muhao Guo, Lingbing Qu, Yuzhong State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science University of California Los Angeles United States

We study the problem of embedding-based entity alignment between knowledge graphs (KGs). Previous works mainly focus on the relational structure of entities. Some further incorporate another type of features, such as attributes, for refinement. However, a vast of entity features are still unexplored or not equally treated together, which impairs the accuracy and robustness of embedding-based entity alignment. In this paper, we propose a novel framework that unifies multiple views of entities to learn embeddings for entity alignment. Specifically, we embed entities based on the views of entity names, relations and attributes, with several combination strategies. Furthermore, we design some cross-KG inference methods to enhance the alignment between two KGs. Our experiments on real-world datasets show that the proposed framework significantly outperforms the state-of-the-art embeddingbased entity alignment methods. The selected views, cross-KG inference and combination strategies all contribute to the performance improvement. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge graph

When Learning Joins Edge: Real-Time Proportional Computation Offloading via Deep Reinforcement Learning

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When Learning Joins Edge: Real-Time Proportional Computation...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Ning Chen Sheng Zhang Zhuzhong Qian Jie Wu Sanglu Lu State Key Lab. for Novel Software Technology Nanjing University CN Department of Computer and Information Sciences Temple University Philadelphia USA

ISBN: (纸本)9781728125848

Computation offloading makes sense to the interaction between users and compute-intensive applications. Current researches focused on deciding locally or remotely executing an application, but ignored the specific offloading proportion of application. A full offloading cannot make the best use of client and server resources. In this paper, we propose an innovative reinforcement learning (RL) method to solve the proportional computation problem. We consider a common offloading scenario with time-variant bandwidth and heterogeneous devices, and the device generates applications constantly. For each application, the client has to choose locally or remotely executing this application, and determines the proportion to be offloaded. We formalize the problem as a long-term optimization problem, and then propose a RL-based algorithm to solve it. The basic idea is to estimate the benefit of posible decisions, of wihch the decision with the maximum benefit is selected. Instead of adopting the original Deep Q Network (DQN), we propose Advanced DQN (ADQN) by adding Priority Buffer Mechanism and Expert Buffer Mechanism, which improves the utilization of samples and overcomes the cold start problem, respectively. The experimental results show ADQN's high feasibility and efficiency compared with several traditional policies, such as None Offloading Policy, Random Offloading Policy, Link Capacity Optimal Policy, and Computing Capability Optimal Policy. At last, we analyse the effect of expert buffer size and learning rate on ADQN's performance.

关键词： Computation offloading Advanced deep q network Expert buffer mechanism UNLOADING Learning buffer mechanism learning rate Clients customers learning (artificial intelligence) arithmetic

MALP: A More Effective Meta-Paths Based Link Prediction Method in Partially Aligned Heterogeneous Social Networks

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MALP: A More Effective Meta-Paths Based Link Prediction Meth...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Kai Zhu Meng Cao Heng-yang Lu National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Internet of Things Engineering Jiangnan University Wuxi China

In general, online social networks include different types of nodes and edges, which means that online social networks are a type of heterogeneous information network. Link prediction is a very important research problem in heterogeneous social networks. The solution to this problem is generally to predict the possibility of a link between two nodes by extracting the characteristics of the nodes in the network. However, the information provided by a single network may not be sufficient, so useful information can be passed from other networks to assist in link prediction in the target network. This is called a partially aligned heterogeneous social network link prediction problem. In this paper, a method, called Meta-path and AUC optimization based Link Predictor(MALP), is proposed to predict the social links in the partially aligned social networks at the same time with a semi-supervised AUC optimization technology. Experimental results on real social network data show that our approach exhibits better predictive performance than other state-of-the-art methods.

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Navy Droid: an efficient tool of energy inefficiency problem diagnosis for Android applications

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science China(Information sciences) 2018年第5期61卷 35-54页

作者： Yi LIU Jue WANG Chang XU Xiaoxing MA Jian L State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University

Energy inefficiency is an influential non-functional issue for smartphone applications, causing increased concerns from users. Locating these problems is labor-intensive, thus automated diagnosis tools are in demand. Some existing approaches detect energy inefficiency problems by exploring application states with the JPF framework, and get favorable results. However, the effects of these approaches are restricted because of their imprecise application execution models and incomplete energy inefficiency patterns. This paper introduces Navy Droid, an effective and efficient tool of energy inefficiency problem diagnosis for Android applications. We constructed a comprehensive application execution model in the form of a state machine,which accurately simulates the runtime behavior of Android applications. We designed a parallel algorithm to systematically explore an application's state space. Our approach supports more energy inefficiency patterns, and is able to detect complicated wake lock misuses. We implemented our approach as a prototype tool and applied it to real-world applications. We evaluated Navy Droid with 19 real-world Android applications, and Navy Droid located more energy inefficiency bugs in these applications than the existing work E-Green Droid did. Also, Navy Droid reduced the analysis time with its parallel state exploration *** experimental results demonstrated the effectiveness and efficiency of our approach for detecting energy inefficiency bugs in Android applications.

关键词： energy inefficiency smartphone application wake lock

FLBench: A Benchmark suite for federated learning

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arXiv 2020年

作者： Liang, Yuan Guo, Yange Gong, Yanxia Luo, Chunjie Zhan, Jianfeng Huang, Yunyou Guangxi Key Lab of Multi-Source Information Mining & Security Department of Computer Science Guangxi Normal University Guilin541004 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China BenchCouncil R&D Lab - Guilin Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices-so-called an isolated data island-while keeping their data secure and private. Most existing federated learning benchmarks work manually splits commonly-used public datasets into partitions to simulate real-world isolated data island scenarios. Still, this simulation fails to capture real-world isolated data island's intrinsic characteristics. This paper presents a federated learning (FL) benchmark suite named FLBench. FLBench contains three domains: medical, financial, and AIoT. By configuring various domains, FLBench is qualified to evaluate federated learning systems and algorithms' essential aspects, like communication, scenario transformation, privacy-preserving, data distribution heterogeneity, and cooperation strategy. Hence, it becomes a promising platform for developing novel federated learning algorithms. Currently, FLBench is open-sourced and in fast-evolution. We package it as an automated deployment tool. The benchmark suite is available from https://***/*** Codes 68-04 Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms