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arXiv 2019年

作者： Chen, Min Zhou, Ping Wu, Di Hu, Long Hassan, Mohammad Mehedi Alamri, Atif School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China Chair of Pervasive and Mobile Computing Information Systems Department College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Chair of Pervasive and Mobile Computing Software Engineering Department CCIS King Saud University Riyadh11543 Saudi Arabia

There are a lot of hidden dangers in the change of human skin conditions, such as the sunburn caused by long-time exposure to ultraviolet radiation, which not only has aesthetic impact causing psychological depression and lack of selfconfidence, but also may even be life-threatening due to skin canceration. Current skin disease researches adopt the auto-classification system for improving the accuracy rate of skin disease classification. However, the excessive dependence on the image sample database is unable to provide individualized diagnosis service for different population groups. To overcome this problem, a medical AI framework based on data width evolution and self-learning is put forward in this paper to provide skin disease medical service meeting the requirement of real time, extendibility and individualization. First, the wide collection of data in the close-loop information flow of user and remote medical data center is discussed. Next, a data set filter algorithm based on information entropy is given, to lighten the load of edge node and meanwhile improve the learning ability of remote cloud analysis model. In addition, the framework provides an external algorithm load module, which can be compatible with the application requirements according to the model selected. Three kinds of deep learning model, i.e. LeNet-5, AlexNet and VGG16, are loaded and compared, which have verified the universality of the algorithm load module. The experiment platform for the proposed real-time, individualized and extensible skin disease recognition system is built. And the system's computation and communication delay under the interaction scenario between tester and remote data center are analyzed. It is demonstrated that the system we put forward is reliable and effective. Copyright © 2019, The Authors. All rights reserved.

关键词： data acquisition

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Assignment-preference ant colony optimization for the two-echelon vehicle routing problem

IPPTA: Quarterly Journal of Indian Pulp and Paper Technical ...

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IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association 2018年第2期30卷 497-507页

作者： Yan, Xueming Hao, Zhifeng Huang, Han Li, Bi Jiang, Shengyi School of Information Science and Technology and School of Cyber Security Guangdong University of Foreign Studies China School of Mathematics and Big Data Foshan University China School of Software Engineering South China University of Technology China

Ant colony optimization (ACO) can be used to find approximate solutions to difficult optimization problems. Many implementations of ACO have been presented for a variety of combinatorial optimization problems. In this paper, we propose assignment-preference ant colony optimization (AP-ACO) to deal with the two-echelon vehicle routing problem (2E-VRP). 2E-VRP is a combinatorial optimization problem, where the delivery from a depot to the customers is managed by routing and consolidating the freight through satellites, and the objective is to minimize the total cost. Due to the uncertainty of satellite-to-customer assignment, it might be difficult for ACO to search for different routing subproblems. We introduce assignment preferences to ACO to provide solutions for 2E-VRP. A multi-player game is used to analyze the mutual relationships among routing subproblems to obtain assignment preferences for satellites and customers. Assignment preferences estimation, the pheromone updating rule, and solution construction are exploited to improve the search efficiency of the proposed algorithm. The computational results from three test sets with sizes ranging from 20 to 50 customers indicate the effectiveness and usefulness of the proposed algorithm for solving 2E-VRPs. © 2018 Indian Pulp and Paper Technical Association. All rights reserved.

关键词： Ant colony optimization

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Controllability analysis of transcriptional regulatory networks for Saccharomyces cerevisiae 44

Controllability analysis of transcriptional regulatory netwo...

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44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

作者： Liu, Suling Wang, Pei Xu, Qiong Chen, Aimin Lü, Jinhu School of Mathematics and Statistics Henan University Kaifeng China School of Mathematics and Statistics Institute of Applied Mathematics Laboratory of Data Analysis Technology Henan University Kaifeng China School of Mathematics and Statistics Institute of Applied Mathematics Henan University Kaifeng China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data Brain Machine Intelligence Beihang University Beijing China

ISBN: (纸本)9781509066841

Structural controllability of complex networks has been a research focus in recent years. However, few works considered the structural controllability of biological networks, especially for dynamic biological networks. In this paper, structural controllability of one static and five dynamic transcriptional regulatory networks (TRNs) for Saccharomyces cerevisiae (S. cerevisiae) have been investigated. The five dynamic networks included two endogenous and three exogenous ones. We clarified the controllability properties of these networks, as well as explored the differences among different types of networks. Our results revealed that the structural properties of different types of networks are significantly different. We found that the TRNs are rather difficult to be fully controlled. However, the dynamic TRNs were relatively easier to be controlled than the static one, and one needs relatively more external inputs to control the exogenous TRNs than the endogenous ones. Based on the structural controllability analysis of subnetworks with the same sizes, as well as randomly perturbed networks by preserving degree distributions, we further illustrated that the structural controllability of the six networks may be mainly dominated by their degree distributions. Our investigations clarify the control properties of TRNs in S. cerevisiae, it provides some insights on real-world control of biological networks. © 2018 IEEE.

关键词： Complex networks

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On the Use of Q-Learning to Solve the Selectable Virtual Network Embedding Problem

On the Use of Q-Learning to Solve the Selectable Virtual Net...

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IEEE International Performance Computing and Communications Conference

作者： Sen Wang Biao Zhang School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9781538668092;9781538668085

I. INTRODUCTION The problem of embedding Virtual Networks (VN) in a Substrate Network (SN) is the main resource allocation challenge in network virtualization. In this paper, we try to solve the Selectable Virtual Network Embedding (SVNE) problem where the Infrastructure Provider (InP) can decide whether to accept an incoming Virtual Network Request (VNR) to increase its revenue and decrease Block Ratio. We argue that the major challenge of the SVNE problem lies in the contradiction between making online embedding decisions and pursuing a long-term objective. Therefore, we propose an SVNE algorithm based on Q-learning.

关键词： Neural networks Bandwidth Approximation algorithms big data software engineering Indium phosphide III-V semiconductor materials

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Strict pattern matching under non-overlapping condition

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Science China(Information Sciences) 2017年第1期60卷 5-20页

作者： Youxi WU Cong SHEN He JIANG Xindong WU School of Computer Science and Engineering Hebei University of Technology Hebei Province Key Laboratory of Big Data Calculation School of Computer Science and Technology Tianjin University School of Software Dalian University of Technology School of Computer Science and Information Engineering Hefei University of Technology School of Computing and Informatics University of Louisiana at Lafayette

Pattern matching(or string matching) is an essential task in computer science, especially in sequential pattern mining, since pattern matching methods can be used to calculate the support(or the number of occurrences) of a pattern and then to determine whether the pattern is frequent or not. A state-of-the-art sequential pattern mining with gap constraints(or flexible wildcards) uses the number of non-overlapping occurrences to denote the frequency of a pattern. Non-overlapping means that any two occurrences cannot use the same character of the sequence at the same position of the pattern. In this paper, we investigate strict pattern matching under the non-overlapping condition. We show that the problem is in P at first. Then we propose an algorithm, called NETLAP-Best, which uses Nettree structure. NETLAP-Best transforms the pattern matching problem into a Nettree and iterates to find the rightmost root-leaf path, to prune the useless nodes in the Nettree after removing the rightmost root-leaf path. We show that NETLAP-Best is a complete algorithm and analyse the time and space complexities of the algorithm. Extensive experimental results demonstrate the correctness and efficiency of NETLAP-Best.

关键词： pattern matching sequential pattern mining gap constraint flexible wildcard non-overlapping occurrence Nettree

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An efficient quantum circuits optimizing scheme compared with QISKit

arXiv

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arXiv 2018年

作者： Zhang, Xin Xiang, Hong Xiang, Tao Fu, Li Sang, Jun School of Big Data and Software Engineering Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Computer Science Chongqing University

Recently, the development of quantum chips has made great progress-the number of qubits is increasing and the fidelity is getting higher. However, qubits of these chips are not always fully connected, which sets additional barriers for implementing quantum algorithms and pro-gramming quantum programs. In this paper, we introduce a general circuit optimizing scheme, which can efficiently adjust and optimize quantum circuits according to arbitrary given qubits' layout by adding additional quantum gates, exchanging qubits and merging single-qubit gates. Compared with the optimizing algorithm of IBM's QISKit, the quantum gates consumed by our scheme is 74.7%, and the execution time is only 12.9% on average. Copyright © 2018, The Authors. All rights reserved.

关键词： Timing circuits

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Cross-Project Change-Proneness Prediction

Cross-Project Change-Proneness Prediction

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Chao Liu Dan Yang Xin Xia Meng Yan Xiaohong Zhang School of Big Data & Software Engineering Chongqing University Chongqing China Faculty of Information Technology Monash University Melbourne Australia College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9781538626672

software change-proneness prediction (whether or not class files in a project will be changed in the next release) can help software developers to focus on preventive actions to reduce maintenance costs, and managers to allocate resources more effectively. Prior studies found that change-proneness prediction works well if there is sufficient amount of training data to build a model. However, it is not feasible for projects with limited historical data especially for new projects. To address this issue, cross-project change-proneness prediction, which builds a prediction model by using data in another project (i.e., source project), and predicts the change-proneness in a target project, is proposed. Considering there are a large number of source projects, one challenge for cross-project change-proneness prediction is that given a target project, how to automatically select a source project which could show good prediction accuracy on it. In this paper, we propose a selective cross-project (SCP) model for change-proneness prediction. SCP automatically finds the source project which has the similar data distribution with the target project by measuring distribution similarity between source and target projects. We evaluate SCP by conducting an empirical study on 14 open source projects. We compare it with 2 most related change-proneness models, including RCP (Random Cross-Project prediction) proposed by Malhotra and Bansal, and CLAMI+ developed by Yan et al. Experiment results show that SCP improves RCP and CLAMI+ by 25.34% and 4.30% in terms of AUC respectively; and by 171.42% and 172.31% in terms of cost-effectiveness, respectively.

关键词： Predictive models Measurement Complexity theory software data models Object oriented modeling Estimation

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A secure rational quantum state sharing protocol

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Science China(Information Sciences) 2018年第2期61卷 204-215页

作者： Zhao DOU Gang XU Xiu-Bo CHEN Xin LIU Yi-Xian YANG Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and Telecommunications School of Software Engineering Beijing University of Posts and Telecommunications Gui Zhou University Guizhou Provincial Key Laboratory of Public Big Data School of Computer Science Shaanxi Normal University School of Information Engineering Inner Mongolia University of Science and Technology

A novel rational protocol to share two arbitrary qubits among multiple parties is investigated in this paper. First, the protocol is presented, which is learned from Li et al.'s protocol. Second, the utility,security, correctness, fairness, Nash equilibrium, and Pareto optimality of our scheme are discussed in detail,where the utility, correctness, and fairness of rational quantum state sharing protocols are creatively given because the agent who recovers the state plays a different and more important role. Another important point is that assumptions about our protocol are more practical and suitable than existing protocols.

关键词： rational quantum state sharing Nash equilibrium secure correct

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Integrating the pressure-state-response model with the extension catastrophe progression for flood risk and resilience assessment

Environmental and Sustainability Indicators

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Environmental and Sustainability Indicators 2025年 27卷

作者： Shahab S. Band Sultan Noman Qasem Sajad Eslami Zulkefli Mansor Brij B. Gupta Hao-Ting Pai Meghdad Biyari Amir Mosavi Farzaneh Sajedi Hosseini Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia School of Business Stevens Institute of Technology Hoboken 07030 NJ USA Research Center for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Bandar Baru Bangi Selangor Darul Ehsan Malaysia Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Cybersecurity Korea University Seoul South Korea Symbiosis Centre for Information Technology Symbiosis International University Pune India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan Department of Information Management National Yunlin University of Science and Technology Yunlin Taiwan John von Neumann Faculty of Informatics Obuda University 1034 Budapest Hungary Ludovika University of Public Service Budapest Hungary Abylkas Saginov Karaganda Technical University Karaganda Kazakhstan Reclamation of Arid and Mountainous Regions Department Faculty of Natural Resources University of Tehran Karaj Iran

Despite the devastating effects of floods, the concept of resilience is still not fully considered in the assessment and management of flood risk. To study how resilience can lower the risk of floods and further enhance disaster response, this research aims to close this knowledge gap. With a focus on the Kashkan watershed in Iran, the study combines the extended catastrophe progression method with the pressure-state-response model. Three catastrophe models, namely the cusp, swallowtail, and butterfly, are applied. According to the findings, southern regions, i.e., Pol-Dokhtar city, have the highest risk of floods and the lowest resilience. Resilience and flood risk have a complementary relationship, according to the analysis, and resilience is a helpful metric for risk assessment. The results emphasize the necessity to incorporate resilience-focused pre-disruption planning and post-disaster recovery into flood risk management strategy. This work offers a foundation to incorporate resilience into future flood policies and strategies.

关键词： Flood Risk Resilience Pressure-state-response model Extension catastrophe progression method

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Crossbar-Net: A novel convolutional network for kidney tumor segmentation in CT images

arXiv

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arXiv 2018年

作者： Yu, Qian Shi, Yinghuan Sun, Jinquan Gao, Yang Zhu, Jianbing Dai, Yakang State Key Laboratory for Novel Software Technology National Research Institute for Big Data Science in Health and Medicine Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University China School of Data and Computer Science Shandong Women’s University China Suzhou Science and Technology Town Hospital China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences China

Due to the unpredictable location, fuzzy texture and diverse shape, accurate segmentation of the kidney tumor in CT images is an important yet challenging task. To this end, we in this paper present a cascaded trainable segmentation model termed as Crossbar-Net. Our method combines two novel schemes: (1) we originally proposed the crossbar patches, which consists of two orthogonal non-squared patches (i.e., the vertical patch and horizontal patch). The crossbar patches are able to capture both the global and local appearance information of the kidney tumors from both the vertical and horizontal directions simultaneously. (2) With the obtained crossbar patches, we iteratively train two sub-models (i.e., horizontal sub-model and vertical sub-model) in a cascaded training manner. During the training, the trained sub-models are encouraged to become more focus on the difficult parts of the tumor automatically (i.e., mis-segmented regions). Specifically, the vertical (horizontal) sub-model is required to help segment the mis-segmented regions for the horizontal (vertical) sub-model. Thus, the two sub-models could complement each other to achieve the self-improvement until convergence. In the experiment, we evaluate our method on a real CT kidney tumor dataset which is collected from 94 different patients including 3,500 CT slices. Compared with the state-of-the-art segmentation methods, the results demonstrate the superior performance of our method on the Dice similarity coefficient, true positive fraction, centroid distance and Hausdorff distance. Moreover, to exploit the generalization to other segmentation tasks, we also extend our Crossbar-Net to two related segmentation tasks: (1) cardiac segmentation in MR images and (2) breast mass segmentation in X-ray images, showing the promising results for these two tasks. Our implementation is released at https: //***/Qianyu1226/*** Codes 68T45, 68T05 Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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