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arXiv 2017年

作者： Zhang, Lijun Yang, Tianbao Jin, Rong Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science University of Iowa Iowa CityIA52242 United States Alibaba Group Seattle United States

To cope with changing environments, recent developments in online learning have introduced the concepts of adaptive regret and dynamic regret independently. In this paper, we illustrate an intrinsic connection between these two concepts by showing that the dynamic regret can be expressed in terms of the adaptive regret and the functional variation. This observation implies that strongly adaptive algorithms can be directly leveraged to minimize the dynamic regret. As a result, we present a series of strongly adaptive algorithms that have small dynamic regrets for convex functions, exponentially concave functions, and strongly convex functions, respectively. To the best of our knowledge, this is the first time that exponential concavity is utilized to upper bound the dynamic regret. Moreover, all of those adaptive algorithms do not need any prior knowledge of the functional variation, which is a significant advantage over previous specialized methods for minimizing dynamic regret. Copyright © 2017, The Authors. All rights reserved.

关键词： Adaptive algorithms

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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

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Improved dynamic regret for non-degenerate functions 17

Improved dynamic regret for non-degenerate functions

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Proceedings of the 31st International Conference on Neural Information Processing Systems

作者： Lijun Zhang Tianbao Yangt Jinfeng Yi Rong Jin Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science The University of Iowa Iowa City AI Foundations Lab IBM Thomas J. Watson Research Center Yorktown Heights NY Alibaba Group Seattle

ISBN: (纸本)9781510860964

Recently, there has been a growing research interest in the analysis of dynamic regret, which measures the performance of an online learner against a sequence of local minimizers. By exploiting the strong convexity, previous studies have shown that the dynamic regret can be upper bounded by the path-length of the comparator sequence. In this paper, we illustrate that the dynamic regret can be further improved by allowing the learner to query the gradient of the function multiple times, and meanwhile the strong convexity can be weakened to other non-degenerate conditions. Specifically, we introduce the squared path-length, which could be much smaller than the path-length, as a new regularity of the comparator sequence. When multiple gradients are accessible to the learner, we first demonstrate that the dynamic regret of strongly convex functions can be upper bounded by the minimum of the path-length and the squared path-length. We then extend our theoretical guarantee to functions that are semi-strongly convex or self-concordant. To the best of our knowledge, this is the first time that semi-strong convexity and self-concordance are utilized to tighten the dynamic regret.

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Correction to: A whitelist and blacklist-based co-evolutionary strategy for defensing against multifarious trust attacks

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Applied Intelligence 2018年第7期48卷 1891-1891页

作者： Shujuan Ji Haiyan Ma Yongquan Liang Hofung Leung Chunjin Zhang Shandong Provincial Key Laboratory of Wisdom Mine Information Technology Shandong University of Science and Technology Qingdao China Shandong Provincial Key Laboratory of Novel Distributed Computer Software Technology Shandong Normal University Jinan China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China Center of Network Shandong University of Science and Technology Qingdao China

The article A whitelist and blacklist-based co-evolutionary strategy for defensing against multifarious trust attacks, written by Shujuan Ji, Haiyan Ma, Yongquan Liang, Hofung Leung and Chunjin Zhang, was originally p...

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

arXiv

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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

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A Low Complexity SCMA Detector based on Stochastic Computation

A Low Complexity SCMA Detector based on Stochastic Computati...

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IEEE Pulsed Power Conference

作者： Kaining Han Jianhao Hu Jienan Chen Gerald E. Sobelman National key Lab. of Communications University of Electronic Science and Technology of China Department of Electronic and Computer Engineering The University of Minnesota

ISBN: (纸本)9781509063901

Sparse Code Multiple Access (SCMA) is a promising multiple access technology candidate for 5G wireless communication systems. The high detection complexity is its bottleneck. Stochastic computation is an ultra-low complexity digital signal processing technique in which probabilities are represented and processed with streams of random bits. In this paper, we propose a novel low complexity stochastic iterative detection approach for SCMA detection based on stochastic computation. We refer it as stochastic SCMA detector. Analysis and simulation results show that the proposed stochastic SCMA detector has only 36% hardware complexity compared to previous state-of-the-art soft input soft output (SISO) SCMA detector with satisfied BER performance.

关键词： Stochastic Computation Sparse Code Multiple Access 5G

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An efficient algorithm to identify minimal failure-causing schemas from exhaustive test suite 28

An efficient algorithm to identify minimal failure-causing s...

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28th International Conference on software Engineering and Knowledge Engineering, SEKE 2016

作者： Qi, Yuanchao Wang, Qi Xu, Chiya He, Tieke Wang, Ziyuan School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China State Key Lab for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)189170639X

Combinatorial testing is widely used to detect failures caused by interactions among parameters for its efficiency and effectiveness. Fault localization plays an important role in this testing technique. And minimal failure-causing schema is the root cause of failure. In this paper, an efficient algorithm, which identifies minimal failure-causing schemas from existing failed test cases and passed test cases, is proposed to replace the basic algorithm with worse time performance. Time complexity of basic and improved algorithms is calculated and compared. The result shows that the method that utilizes the differences between failed test cases and passed test cases is better than the method that only uses the sub-schemas of those test cases.

关键词： Testing

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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

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

arXiv

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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

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Density Based Self Organizing Incremental Neural Network For Data Stream Clustering

Density Based Self Organizing Incremental Neural Network For...

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International Joint Conference on Neural Networks

作者： Baile Xu Furao Shen Jinxi Zhao National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University

ISBN: (纸本)9781509006212

Clustering is an important technique widely used in many areas such as machine learning, pattern recognition, data analysis etc. Data stream clustering is a branch of clustering that draws much attention in recent years, where data objects are processed as an ordered sequence. In this paper, we propose an unsupervised learning neural network named Density Based Self Organizing Incremental Neural Network (DenSOINN) for data stream clustering tasks. DenSOINN is a self organizing competitive network that grows incrementally to learn suitable nodes to fit the distribution of learning data, combining on-line unsupervised learning and topology learning by means of competitive Hebbian learning rule [19]. By adopting a density-based clustering mechanism, DenSOINN can discover arbitrarily shaped clusters and diminish the negative effect of noise. In addition, we adopt a self-adaptive distance framework to obtain good performance for learning unnormalized input data. Experiments show that the DenSOINN can achieve high standard performance equally on both raw data and normalized data.

关键词： Cluster Analysis data flow Noise effects data objects organizing Self Neural network Unsupervised learning branches Incremental

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