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Performance evaluation of a patient-centric blockchain-based healthcare records management framework 20

Performance evaluation of a patient-centric blockchain-based...

2nd International Electronics Communication Conference, IECC 2020 and the 2nd Blockchain and Internet of Things Conference, BIOTC 2020

作者： Ismail, Leila Materwala, Huned Khan, Moien A.B. Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates Department of Family Medicine College of Medicine and Health Sciences United Arab University Al Ain United Arab Emirates Primary Care NHS North West London United Kingdom

ISBN: (纸本)9781450377706

Healthcare records management system has been revolutionized over the last decade aiming to provide accurate, efficient and enhanced patient care. The existing management system is either based on a client/server approach where each hospital maintains its own database or on a cloud approach where the health records are stored in a cloud server and managed by a third-party cloud service provider. However, these approaches suffer from the issues of security, privacy, data vulnerability and fragmentation. Furthermore, healthcare providers and patients are unable to have a unified view of a patient's medical history from all visited medical care centers. This results in additional treatment costs, repeated medical tests and increased time to diagnosis. The data traceability, immutability, transparency, replication, security and privacy traits of the emerging blockchain technology have a promising potential in the healthcare domain addressing these issues. In this paper, we propose BlockHR, a patient-centric healthcare records management system for efficient medical care at an optimal cost. The system enables healthcare providers to enter the patients' medical record data to the blockchain network and allows patients to enter their social data such as sleeping habits, physical activities, and current location. Consequently, BlockHR provides support to doctors for better diagnosis and prognosis. We evaluate the performance of BlockHR in terms of execution time and the total amount of data transferred for ledger update with an increasing number of hospitals and blocks in the network. © 2020 ACM.

关键词： Blockchain

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Firepower Distribution Method in Wargame System Based on Machine Learning and Wavelet Analysis 4

Firepower Distribution Method in Wargame System Based on Mac...

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2019 4th Annual International Conference on information System and Artificial Intelligence, ISAI 2019

作者： Shang, Tongfei Wang, Tianqi Ma, Jianfeng College of Information and Communication National University of Defense Technology Xi'an China Department of Computer Science and Technology School of Electronic and Information Engineering Xi'An Jiaotong University Xi'an China School of Software Engineering Xi'An Jiaotong University Xi'an China

It is the key to the success or failure of military operations to formulate practical and feasible operational plans quickly and in a timely manner. Aiming at the development and analysis of the auxiliary action plan of the computer chess system, the machine learning and wavelet analysis are used to analyze the research of the wargsme system from the planning and drafting process, which provides a reference for the fire distribution of the wargame system. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Wavelet analysis

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Computation Offloading and Content Caching with Traffic Flow Prediction for Internet of Vehicles in Edge Computing

Computation Offloading and Content Caching with Traffic Flow...

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IEEE International Conference on Web Services (ICWS)

作者： Zijie Fang Xiaolong Xu Fei Dai Lianyong Qi Xuyun Zhang Wanchun Dou School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China School of Information Science and Engineering Qufu Normal University China Macquarie University Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

The development of the Internet of Vehicles (IoV) enables numerous emerging in-vehicle applications to accommodate users with various contents, thus enhancing their traveling experiences. In IoV, content decoding tasks are typically offloaded to edge servers for implementation, as edge computing is an admirable paradigm to provide low-latency services. However, as different vehicular users may request the same contents, processing these contents repeatedly leads to the waste of storage, computation and bandwidth resources. Therefore, fine-grained computation offloading and content caching are demanded in IoV. In this paper, a joint optimization method for computation offloading and content caching based on traffic flow prediction, named COC, is proposed. Firstly, traffic flow covered by each edge server is predicted by a modified deep spatiotemporal residual network (ST-ResNet). Secondly, the non-dominated sorting genetic algorithm III (NSGA-III) is leveraged to realize the many-objective optimization to shorten the execution time and reduce the energy consumption of computation and transmission in IoV. Finally, evaluated by real-world big data from Nanjing China, COC shows a great reduction in execution time and energy consumption of transmission and computation compared to other methods.

关键词： Energy consumption Intelligent vehicles Web services Optimization methods Bandwidth Servers Edge computing

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Computing the Rate-Distortion Function of Gray-Wyner System

arXiv

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arXiv 2020年

作者： Chen, Guojun Xu, Yinfei Liu, Ling Song, Tiecheng Hu, Jing School of Information Science and Engineering Southeast University Nanjing210096 China National Mobile Communication Research Laboratory Southeast University Nanjing210096 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Consider that lossy source coding is a very important technique in contemporary communications to provide a greater transmission rate. The rate-distortion (RD) region of lossy source coding remains elusive after decades of research. Gray and Wyner proposed the Gray-Wyner lossy source coding system in [1], shown in Fig. 1. However, the RD region was calculated and determined only for the case of some simple discrete sources with equal distortion constraints. Most of the research on Gray-Wyner lossy source coding systems focused on Common information (CI), which is a special corner point in the RD region. The two most popular CI are Wyner’s CI and Gásc-Körner CI, presented in [2] and [3], respectively. Xu et al. provided a converse proof of Wyner’s CI, which is equal to the minimum common message rate when the total rate is arbitrarily close to the RD function under suitable conditions in [4]. Moreover, Viswanatha et al. was more interested in the achievability part of CI and studied both Wyner’s CI and Gásc-Körner CI in [5]. [6] solved the rate region by constraining the sum of the two private rates, and calculated the Wyner’s CI based on the proposed method and previous work on Wyner’s CI in the Gray-Wyner system [7], [8]. However, due to the constrains on private rates, the solution was just part of rate region of Gray-Wyner system. Our results are more general and able to calculate the whole achievable rate region. Furthermore, we solve the expression for Wyner’s CI of two correlated Gaussian sources, which differs from the method applied in [5]. Some partial results were studied including Wyner’s CI characterization for bivariate Gaussian sources in [9], and total correlation characterization for vector Gaussian sources in [10]. Besides the Gray-Wyner lossy source coding system, other multi-terminal RD problems are studied in different distributed scenarios. In [11], Xiao et al. considered the problem of centralized/distributed data compression via one/two encode

关键词： Electric distortion

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A Novel Method to against Quantum Noises in Quantum Teleportation

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Journal of Quantum Computing 2020年第1期2卷 33-55页

作者： Shengyao Wu Wenjie Liu Zhiguo Qu School of Computer&Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China

In order to improve the anti-noise performance of quantum teleportation,this paper proposes a novel dynamic quantum anti-noise scheme based on the quantum teleportation which transmits single qubit state using Bell *** that quantum noise only acts on the transmitted qubit,i.e.,the entangled state that Alice and Bob share in advance is affected by the noise,thus affecting the final transmission *** this paper,a method for dynamically adjusting the shared entangled state according to the noise environment is *** calculating the maximum fidelity of the output state to determine the shared entangled state,which makes the quantum teleportation be affected by the noise as little as *** paper calculates the fidelity of teleportation under four kinds of channel noise(amplitude damping,phase damping,bit flip and depolarizing noise).The results show that the scheme has a suppression effect on phase damping,bit flip and depolarizing noise under certain *** the noise intensity is larger,the optimized efficiency is better.

关键词： Quantum noise quantum teleportation entangled channel fidelity

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Modeling Neural Networks Training Process with Markov Decision Process

Modeling Neural Networks Training Process with Markov Decisi...

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Artificial Intelligence and computer engineering (ICAICE), International Conference on

作者： Yantao Bai Wanwei Liu Xinjun Mao Zhen Liang Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science National University of Defense Technology Changsha China State Key Laboratory of High Performance Computing Institute for Quantum Information &#x0026 National University of Defense Technology Changsha China

ISBN: (纸本)9781665421874

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as “black boxes”. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks.

关键词： Training Machine learning algorithms Computational modeling Neural networks Machine learning Markov processes Controllability

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Android malware family classification and characterization using CFG and DFG 13

Android malware family classification and characterization u...

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13th International Symposium on Theoretical Aspects of software engineering, TASE 2019

作者： Xu, Zhiwu Ren, Kerong Song, Fu College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (纸本)9781728133423

Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have been proposed to protect legitimate users from the threat. However, most approaches focus on malware detection, while only a few of them consider malware classification or malware characterization. In this paper, we propose an extension of CDGDroid to classifying and characterizing Android malware families automatically. We first perform static analysis used in CDGDroid to extract control-flow graphs and data-flow graphs on the instruction level. Then we encode the graphs into matrices, and use them to build the family classification models via deep learning. For family characterization, we extract the n-gram sequences from the graphs, which are filtered according to the weights of the classification model built for the target family. And then we construct a vector space model and select the top-k sequences as a characterization of the target family. We have conducted some experiments to evaluate our approach and have identified that the family classification model taking the horizontal combination of CFG and DFG as features offers the best performance in terms of accuracy among all the models. Compared with CDGDroid, Drebin and many antivirus tools gathered in VirusTotal, our family classification model gives a better performance. Finally, We have also conducted experiments on family characterization, and the experimental results have shown that our characterization can capture the malicious behaviors of the testing families. © 2019 IEEE.

关键词： Static analysis

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Optimizing large-scale hyperparameters via automated learning algorithm

arXiv

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arXiv 2021年

作者： Gu, Bin Liu, Guodong Zhang, Yanfu Geng, Xiang Huang, Heng Department of machine learning Mohamed bin ZayedUniversity of Artificial Intelligence United Arab Emirates JD Finance America Corporation Mountain ViewCA94043 United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States School of Computer & Software Nanjing University of Information Science & Technology Nanjing China

Modern machine learning algorithms usually involve tuning multiple (from one to thousands) hyperparameters which play a pivotal role in terms of model generalizability. Black-box optimization and gradient-based algorithms are two dominant approaches to hyperparameter optimization while they have totally distinct advantages. How to design a new hyperparameter optimization technique inheriting all benefits from both approaches is still an open problem. To address this challenging problem, in this paper, we propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG). Specifically, we first exactly formulate hyperparameter optimization as an A-based constrained optimization problem, where A is a black-box optimization algorithm (such as deep neural network). Then, we use the average zeroth-order hyper-gradients to update hyperparameters. We provide the feasibility analysis of using HOZOG to achieve hyperparameter optimization. Finally, the experimental results on three representative hyperparameter (the size is from 1 to 1250) optimization tasks demonstrate the benefits of HOZOG in terms of simplicity, scalability, flexibility, effectiveness and efficiency compared with the state-of-the-art hyperparameter optimization methods. © 2021, CC0.

关键词： Deep neural networks

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Cross Project Defect Prediction via Balanced Distribution Adaptation Based Transfer Learning

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Journal of computer science & Technology 2019年第5期34卷 1039-1062页

作者： Zhou Xu Shuai Pang Tao Zhang Xia-Pu Luo Jin Liu Yu-Tian Tang Xiao Yu Lei Xue College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science Wuhan UniversityWuhan 430072China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China Department of ComputingThe Hong Kong Polytechnic UniversityHong Kong 999077China Key Laboratory of Network Assessment Technology Institute of Information EngineeringChinese Academy of Sciences Beijing 100190China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Department of Computer Science City University of Hong KongHong Kong 999077China

Defect prediction assists the rational allocation of testing resources by detecting the potentially defective software modules before releasing products. When a project has no historical labeled defect data, cross project defect prediction (CPDP) is an alternative technique for this scenario. CPDP utilizes labeled defect data of an external project to construct a classification model to predict the module labels of the current project. Transfer learning based CPDP methods are the current mainstream. In general, such methods aim to minimize the distribution differences between the data of the two projects. However, previous methods mainly focus on the marginal distribution difference but ignore the conditional distribution difference, which will lead to unsatisfactory performance. In this work, we use a novel balanced distribution adaptation (BDA) based transfer learning method to narrow this gap. BDA simultaneously considers the two kinds of distribution differences and adaptively assigns different weights to them. To evaluate the effectiveness of BDA for CPDP performance, we conduct experiments on 18 projects from four datasets using six indicators (i.e., F-measure, g-means, Balance, AUC, EARecall, and EAF-measure). Compared with 12 baseline methods, BDA achieves average improvements of 23.8%, 12.5%, 11.5%, 4.7%, 34.2%, and 33.7% in terms of the six indicators respectively over four datasets.

关键词： cross-project defect prediction transfer learning balancing distribution effort-aware indicator

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Shifu2: A network representation learning based model for advisor-advisee relationship mining

arXiv

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arXiv 2020年

作者： Liu, Jiaying Xia, Feng Wang, Lei Xu, Bo Kong, Xiangjie Tong, Hanghang King, Irwin Key Laboratory for Ubiquitous Network Service Software of Liaoning Province School of Software Dalian University of Technology China School of Science Engineering and Information Technology Federation University Australia Australia School of Software Dalian University of Technology China Department of Computer Science University of Illinois at Urbana-Champaign United States Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. Copyright © 2020, The Authors. All rights reserved.

关键词： Search engines

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