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A utility-driven scheduling algorithms in cloud computing

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Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 2016年第2期39卷 282-287页

作者： Chen, Tao School of Management Science and Engineering Anhui University of Finance and Economics Bengbu Anhui233030 China Department of Computer Science and Technology Tongji University Shanghai201804 China State Key Laboratoryfor Novel Software Technology Nanjing University China

Cloud computing is a successful business model and utility paradigm which enables people to use computing power via Internet at anytime anywhere. How to schedule a task to cloud computing is an important issue. In cloud computing some users focus execution time the others maybe expense on tasks execution, so how to satisfy the users and achieve the balance between execution time and expense is very vital. Based on classic Min-Min algorithm, an efficient task scheduling algorithm was proposed where Cobb-Douglas utility function was employed to express user.s preference to time and cost, and maximized user.s utility. Multi-objective optimal solution has been obtained via the utility function. Experimental results evaluating such a mechanism show that the algorithm validate vividly.

关键词： Distributed computer systems

software fault prediction based on one-class SVM

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Software fault prediction based on one-class SVM

2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Chen, Lin Fang, Bin Shang, Zhaowei Department of Computer Science Chongqing University Chongqing400030 China Chongqing Key Laboratory of Software Theory and Technology Department of Computer Science Chongqing University China

ISBN: (纸本)9781509003891

software fault prediction (SFP) is useful for helping the software engineer to locate potential faulty modules in software testing more easily, so that it can save a lot of time and budgets to improve the software quality. In this paper, aiming at solving the problem that the faulty samples are too rare to train a classifier, an one-class SFP model is proposed by using only non-faulty samples based on one-class SVM. The empirical validation is conducted on 6 extremely imbalanced datasets collected from real-world software containing only small amounts of faulty instances. The test results suggest that the proposed model can achieve a reasonable fault prediction performance when using only a small proportion of training samples, and performs much better than conventional and class imbalanced learning based SFP models in terms of G-mean measure. Thus the proposed model provided a considerable solution for SFP with a few faulty modules in early life of software testing. © 2016 IEEE.

关键词： software testing

An attention-based syntax-tree and tree-LSTM model for sentence summarization

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International Journal of Performability Engineering 2017年第5期13卷 775-782页

作者： Liu, Wenfeng Liu, Peiyu Yang, Yuzhen Gao, Yaling Yi, Jing School of Information Science and Engineering Shandong Normal University Jinan250014 China Department of Computer and Information Engineering Heze University Heze274015 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China Ruipu Peony Industrial Technology Development Co. Ltd 274015 China School of Computer Science and Technology Shandong Jianzhu University Jinan250101 China

Generative Summarization is of great importance in understanding large-scale textual data. In this work, we propose an attention-based Tree-LSTM model for sentence summarization, which utilizes an attention-based syntactic structure as auxiliary information. Thereinto, block-alignment is used to align the input and output syntax blocks, while inter-alignment is used for alignment of words within that of block pairs. To some extent, block-alignment can prevent structural deviations on the long sentences and inter-alignment is capable of increasing the flexibility of the generation in the blocks. This model can be easily trained to end-to-end mode and deal with any length of the input sentences. Compared with several relatively strong baselines, our model has achieved the state-of-art on DUC-2004 shared task. © 2017 Totem Publisher, Inc. All rights reserved.

关键词： Alignment

Relay navigation strategy study on intelligent drive on urban roads

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The Journal of China Universities of Posts and Telecommunications 2016年第2期23卷 79-90页

作者： Gao Hongbo Zhang Xinyu An Lifeng Liu Yuchao Li Deyi State Key Laboratory of Software Development Environment Beihang University Beijing 100191 China Information Technology Center Tsinghua University Beijing 100083 China Department of Computer Science and Technology Tsinghua University Beijing 100083 China The 61th Institute of Electronic System Engineering Beijing 100039 China

In order to solve navigation problem of intelligent vehicle driving on urban roads and to achieve the navigation in intersection area, intersection transition area and section area. The relay navigation strategy and algorithm can solve the navigation problem of intelligent vehicle driving in typical urban roads such as intersection area, intersection transition area and section area, realizing seamless handover among different typical areas. Bezier curve function model was introduced to different typical areas, which solved the self-adaption recognition problem in different typical areas and revised positional accuracy with the help of cloud computing positioning service. In order to explain the strategy implement, an instance based on the strategy was adopted. Instance analysis indicates that as for the navigation problem in intersection area, intersection transition area and section area, if the relay navigation strategy is utilized, the self-adaption recognition problem in different typical areas can be handled. Based on the relay navigation strategy, the drive of intelligent vehicle on urban roads can effectively solve the self-adaption recognition problem in different typical areas in urban and further solve driving problems of intelligent vehicle of the same category in urban roads.

关键词： intelligent vehicle intelligent drive urban roads relay navigation strategy

Register automata with linear arithmetic

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arXiv 2017年

作者： Chen, Yu-Fang Lengal, Ondrej Tan, Tony Wu, Zhilin Institute of Information Science Academia Sinica Taiwan FIT Brno University of Technology IT4Innovations Centre of Excellence Czech Republic Department of Computer Science and Information Engineering National Taiwan University Taiwan State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

We propose a novel automata model over the alphabet of rational numbers, which we call register automata over the rationals (RAQ). It reads a sequence of rational numbers and outputs another rational number. RAQ is an extension of the well-known register automata (RA) over infinite alphabets, which are finite automata equipped with a finite number of registers/variables for storing values. Like in the standard RA, the RAQ model allows both equality and ordering tests between values. It, moreover, allows to perform linear arithmetic between certain variables. The model is quite expressive: In addition to the standard RA, it also generalizes other well-known models such as affine programs and arithmetic circuits. The main feature of RAQ is that despite the use of linear arithmetic, the so-called invariant problem-a generalization of the standard non-emptiness problem-is decidable. We also investigate other natural decision problems, namely, commutativity, equivalence, and reachability. For deterministic RAQ, commutativity and equivalence are polynomial-time inter-reducible with the invariant problem. Copyright © 2017, The Authors. All rights reserved.

关键词： Polynomial approximation

Robust frequent directions with application in online learning

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arXiv 2017年

作者： Luo, Luo Chen, Cheng Zhang, Zhihua Li, Wu-Jun Zhang, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University 5 Yiheyuan Road Beijing100871 China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Nanjing210023 China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Heuristic methods

Learning with only multiple instance positive bags

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Learning with only multiple instance positive bags

2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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Game-theoretic design of optimal two-sided rating protocols for service exchange dilemma in crowdsourcing

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arXiv 2017年

作者： Lu, Jianfeng Xin, Yun Zhang, Zhao Liu, Xinwang Li, Kenli Department of Computer Science and Engineering Zhejiang Normal University Jinhua Zhejiang China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China school of of Computer National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China National Supercomputing Center in Changsha Changsha Hunan410082 China

Despite the increasing popularity and successful examples of crowdsourcing, it is stripped of aureole when collective efforts are derailed or severely hindered by elaborate sabotage. A service exchange dilemma arises when non-cooperation among self-interested users, and zero social welfare is obtained at myopic equilibrium. Traditional rating protocols are not effective to overcome the inefficiency of the socially undesirable equilibrium due to specific features of crowdsourcing: a large number of anonymous users having asymmetric service requirements, different service capabilities, and dynamically joining/leaving a crowdsourcing platform with imperfect monitoring. In this paper, we develop the first game-theoretic design of the two-sided rating protocol to stimulate cooperation among self-interested users, which consists of a recommended strategy and a rating update rule. The recommended strategy recommends a desirable behavior from three predefined plans according to intrinsic parameters, while the rating update rule involves the update of ratings of both users, and uses differential punishments that punish users with different ratings differently. By quantifying necessary and sufficient conditions for a sustainable social norm, we formulate the problem of designing an optimal two-sided rating protocol that maximizes the social welfare among all sustainable protocols, provide design guidelines for optimal two-sided rating protocols and a low-complexity algorithm to select optimal design parameters in an alternate manner. Finally, evaluation results show the validity and effectiveness of our protocol designed for service exchange dilemma in crowdsourcing. Copyright © 2017, The Authors. All rights reserved.

关键词： Game theory

Rating protocol design for extortion and cooperation in the crowdsourcing contest dilemma

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arXiv 2017年

作者： Lu, Jianfeng Xin, Yun Zhang, Zhao Tang, Shaojie Yan, Songyuan Tang, Changbing Department of Computer Science and Engineering Zhejiang Normal University Jinhua Zhejiang China State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Information Systems University of Texas at Dallas RichardsonTX75080 United States

Crowdsourcing has emerged as a paradigm for leveraging human intelligence and activity to solve a wide range of tasks. However, strategic workers will find enticement in their self-interest to free-ride and attack in a crowdsourcing contest dilemma game. Hence, incentive mechanisms are of great importance to overcome the inefficiency of the socially undesirable equilibrium. Existing incentive mechanisms are not effective in providing incentives for cooperation in crowdsourcing competitions due to the following features: heterogeneous workers compete against each other in a crowdsourcing platform with imperfect monitoring. In this paper, we take these features into consideration, and develop a novel game-theoretic design of rating protocols, which integrates binary rating labels with differential pricing to maximize the requester's utility, by extorting selfish workers and enforcing cooperation among them. By quantifying necessary and sufficient conditions for the sustainable social norm, we formulate the problem of maximizing the revenue of the requester among all sustainable rating protocols, provide design guidelines for optimal rating protocols, and design a low-complexity algorithm to select optimal design parameters which are related to differential punishments and pricing schemes. Simulation results demonstrate how intrinsic parameters impact on design parameters, as well as the performance gain of the proposed rating protocol. Copyright © 2017, The Authors. All rights reserved.

关键词： Crowdsourcing