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28th International Conference on Concurrency Theory, CONCUR 2017

作者： Chen, Taolue Song, Fu Wu, Zhilin Department of Computer Science and Information Systems Birkbeck United Kingdom University of London United Kingdom State Key Laboratory of Novel Software Technology Nanjing University China School of Information Science and Technology ShanghaiTech University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

ISBN: (纸本)9783959770484

In 2011, Cook et al. showed that the satisfiability and entailment can be checked in polynomial time for a fragment of separation logic that allows for reasoning about programs with pointers and linked lists. In this paper, we investigate whether the tractability results can be extended to more expressive fragments of separation logic that allow defining data structures beyond linked lists. To this end, we introduce separation logic with a simply-nonlinear compositional inductive predicate where source, destination, and static parameters are identified explicitly (SLIDSNC). We show that if the inductive predicate has more than one source (destination) parameter, the satisfiability problem for SLIDSNC becomes intractable in general. This is exemplified by an inductive predicate for doubly linked list segments. By contrast, if the inductive predicate has only one source (destination) parameter, the satisfiability and entailment problems for SLIDSNC are tractable. In particular, the tractability results hold for inductive predicates that define list segments with tail pointers and trees with one hole. © Taolue Chen, Fu Song, and Zhilin Wu.

关键词： Formal logic

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Combining Word Embedding and Knowledge-Based Topic Modeling for Entity Summarization

Combining Word Embedding and Knowledge-Based Topic Modeling ...

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International Conference on Semantic Computing

作者： Seyedamin Pouriyeh Mehdi Allahyari Krys Kochut Gong Cheng Hamid Reza Arabnia Computer Science Department University of Georgia Athens GA USA Computer Science Department Georgia Southern University Statesboro USA National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Word embedding is becoming more popular in the Semantic Web community as an effective approach for capturing semantics in various contexts. In this paper, we combine word embedding and topic modeling to model RDF data for the entity summarization task. In our model, ES-LDAext, which is the extended version of our previous model, we utilize the word embedding to supplement the RDF data before applying entity summarization. In addition, in the model presented here, we use RDF literals as a very good source of information to create more reliable and representative summaries for entities. To do that, we use the Named Entity Recognition approach to extract entities within literals before feeding them into the word embedding model to enrich the RDF data. Experimental results demonstrate the effectiveness of the proposed model.

关键词： Resource description framework Data models Mathematical model Semantics Computational modeling Task analysis Data mining

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Using Microservices for Rapid Creation of Remote Sensing Products

Using Microservices for Rapid Creation of Remote Sensing Pro...

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software Architecture Companion (ICSA-C)

作者： Bo Xiang Zheng Li Yan Liu He Zhang Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences China Department of Computer Engineering and Computer Science University of Concepción Chile Faculty of Engineering and Computer Science Concordia University Canada State Key Laboratory of Novel Software Technology Software Institute Nanjing University China

Driven by the demands of various sensing data products, remote sensing software systems have increasingly been developed and applied to a wide range of fields. Due to the legacy monolithic product line, we used to satisfy the requirements on a project-by-project basis, only with reuse of some fundamental source codes and tools. By employing microservices to refactor our product line, we are now able to follow a divide-and-conquer strategy to maximize the reusability of component business capacities and create satellite remote sensing products at a much faster pace. This paper mainly introduces our microservice-oriented product line, and the data-analytics-visualization (DAV) pattern for logically organizing and managing relevant microservices of different products. In particular, two projects (namely archaeological detection and cultural heritage protection & urban planning and land use) are used to demonstrate how we prevent to develop thematic products from scratch.

关键词： Sensors Cultural differences Remote sensing Satellites Monitoring Portals computer architecture

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Reducing uncertainty of schema matching via crowdsourcing with accuracy rates

arXiv

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arXiv 2018年

作者： Zhang, Chen Jason Chen, Lei Jagadish, H.V. Zhang, Mengchen Tong, Yongxin School of Computer Science and Technology Shandong University of Finance and Economics Jinan Shandong China Department of Computer Science and Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Department of Electrical Engineering and Computer Science University of Michigan United States State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China

Schema matching is a central challenge for data integration systems. Inspired by the popularity and the success of crowdsourcing platforms, we explore the use of crowdsourcing to reduce the uncertainty of schema matching. Since crowdsourcing platforms are most effective for simple questions, we assume that each Correspondence Correctness Question (CCQ) asks the crowd to decide whether a given correspondence should exist in the correct matching. Furthermore, members of a crowd may sometimes return incorrect answers with different probabilities. Accuracy rates of individual crowd workers are probabilities of returning correct answers which can be attributes of CCQs as well as evaluations of individual workers. We prove that uncertainty reduction equals to entropy of answers minus entropy of crowds and show how to obtain lower and upper bounds for it. We propose frameworks and efficient algorithms to dynamically manage the CCQs to maximize the uncertainty reduction within a limited budget of questions. We develop two novel approaches, namely "Single CCQ" and "Multiple CCQ", which adaptively select, publish and manage questions. We verify the value of our solutions with simulation and real implementation. Copyright © 2018, The Authors. All rights reserved.

关键词： Crowdsourcing

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A real-time unsupervised background extraction-based target detection method for hyperspectral imagery

A real-time unsupervised background extraction-based target ...

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作者： Li, Cong Gao, Lianru Wu, Yuanfeng Zhang, Bing Plaza, Javier Plaza, Antonio Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing100049 China The College of Computer Science and Software Engineering Computer Vision Research Institute Shenzhen University Shenzhen518060 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica de Cáceres University of Extremadura Cáceres Spain

Target detection is an important technique in hyperspectral image analysis. The high dimensionality of hyperspectral data provides the possibility of deeply mining the information hiding in spectra, and many targets that cannot be visualized by inspection can be detected. But this also brings some problems such as unknown background interferences at the same time. In this way, extracting and taking advantage of the background information in the region of interest becomes a task of great significance. In this paper, we present an unsupervised background extraction-based target detection method, which is called UBETD for short. The proposed UBETD takes advantage of the method of endmember extraction in hyperspectral unmixing, another important technique that can extract representative material signatures from the images. These endmembers represent most of the image information, so they can be reasonably seen as the combination of targets and background signatures. Since the background information is known, algorithm like target-constrained interference-minimized filter could then be introduced to detect the targets while inhibiting the interferences. To meet the rapidly rising demand of real-time processing capabilities, the proposed algorithm is further simplified in computation and implemented on a FPGA board. Experiments with synthetic and real hyperspectral images have been conducted comparing with constrained energy minimization, adaptive coherence/cosine estimator and adaptive matched filter to evaluate the detection and computational performance of our proposed method. The results indicate that UBETD and its hardware implementation RT-UBETD can achieve better performance and are particularly prominent in inhibiting interferences in the background. On the other hand, the hardware implementation of RT-UBETD can complete the target detection processing in far less time than the data acquisition time of hyperspectral sensor like HyMap, which confirms strict real-ti

关键词： Field programmable gate arrays (FPGA)

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Fast rates of ERM and stochastic approximation: adaptive to error bound conditions 18

Fast rates of ERM and stochastic approximation: adaptive to ...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Mingrui Liu Xiaoxuan Zhang Lijun Zhang Rong Jin Tianbao Yang Department of Computer Science The University of Iowa Iowa City IA National Key Laboratory for Novel Software Technology Nanjing University China Machine Intelligence Technology Alibaba Group Bellevue WA

Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received increasing attention for developing optimization algorithms with fast convergence. However, the studies of EBC in statistical learning are hitherto still limited. The main contributions of this paper are two-fold. First, we develop fast and intermediate rates of empirical risk minimization (ERM) under EBC for risk minimization with Lipschitz continuous, and smooth convex random functions. Second, we establish fast and intermediate rates of an efficient stochastic approximation (SA) algorithm for risk minimization with Lipschitz continuous random functions, which requires only one pass of n samples and adapts to EBC. For both approaches, the convergence rates span a full spectrum between Õ(1/√n) and Õ(1/√n) depending on the power constant in EBC, and could be even faster than O(1/√n) in special cases for ERM. Moreover, these convergence rates are automatically adaptive without using any knowledge of EBC.

关键词：

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A confidence-aware top-k query processing toolkit on crowdsourcing 43

A confidence-aware top-k query processing toolkit on crowdso...

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43rd International Conference on Very Large Data Bases, VLDB 2017

作者： Li, Yan Kou, Ngai Meng Wangy, Hao Hou, U. Leong Gong, Zhiguo Department of Computer and Information Science University of Macau Macau SAR China State Key Laboratory for Novel Software Technology Nanjing University China

Ranking techniques have been widely used in ubiquitous applications like recommendation, information retrieval, etc. For ranking computation hostile but human friendly items, crowdsourcing is considered as an emerging technique to process the ranking by human power. However, there is a lack of an easy-to-use toolkit for answering crowdsourced top-k query with minimal effort. In this work, we demonstrate an interactive programming toolkit that is a unified solution for answering the crowdsourced top-k queries. The toolkit employs a new confidenceaware crowdsourced top-k algorithm, SPR. The whole progress of the algorithm is monitored and visualized to end users in a timely manner. Besides the visualized result and the statistics, the system also reports the estimation of the monetary cost and the breakdown of each phase. Based on the estimation, end users can strike a balance between the budget and the quality through the interface of this toolkit. © 2017 VLDB.

关键词： Crowdsourcing

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Design of gear reducer based on FOA optimization algorithm 1st

Design of gear reducer based on FOA optimization algorithm

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Lin, Xiaojia Zhang, Fuquan Xu, Lin Department of Computer Science Fujian Business University Fuzhou350012 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang Univeristy Fuzhou350121 China School of Software Beijing Institute of Technology Beijing100081 China Innovative Information Industry Research Institute Fujian Normal University Fuzhou350300 China

ISBN: (纸本)9783319707297

In order to optimize the design of gear reducer, gear reducer optimal design to improve reliability and security, slow convergence and local optimum for FOA algorithm is proposed based on the improved type FOA gear reducer optimization design model. To avoid falling into local optimum Drosophila optimization algorithms, improved FOA algorithm by introducing a correction factor. Superior to the gear reducer seven variables optimization design model for the study, to ensure the safety and reliability of the premise, the FOA has the advantage of improved convergence speed and avoid local optimization problems, in order to verify the proposed method and reliability. © Springer International Publishing AG 2018.

关键词： Reliability

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Opinion target extraction for the Chinese formal text based on dependency relations 1st

Opinion target extraction for the Chinese formal text based ...

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1st International Conference on Smart Vehicular technology, Transportation, Communication and Applications, VTCA 2017

作者： Yang, Xiao-Yan Xu, Ge Zhang, Fu-Quan Liao, Xiang-Wen Xu, Lin Department of Computer Science Minjiang University FuzhouFujian350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control FuzhouFujian350121 China School of Software Beijing Institute of Technology Beijing10081 China College of Mathematics and Computer Science Fuzhou University FuzhouFujian350108 China Innovative Information Industry Research Institute Fujian Normal University FuzhouFujian350300 China

ISBN: (纸本)9783319707297

Due to the increasing amount of opinion data on the internet, opinion mining has become a hot topic, in which extracting opinion targets is a key step. The state-of-the-art approaches only use direct dependency relation patterns to extract opinion targets and the indirect dependency relation patterns have not been used. In this paper, the dependency relations between opinion target and opinion word are defined, and direct and indirect dependency relation patterns are designed. Then, a bootstrapping approach is used to extract and evaluate both candidate patterns and opinion targets. The experimental results show that in formal text, the approach improves the performance compared with the state-of-the-art approaches for opinion target extraction. © Springer International Publishing AG 2018.

关键词： Sentiment analysis

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A Multilayer Framework for Online Metric Learning

arXiv

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arXiv 2018年

作者： Li, Wenbin Liu, Yanfang Huo, Jing Shi, Yinghuan Gao, Yang Wang, Lei Luo, Jiebo The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China The College of Mathematics and Information Engineering Longyan University 364012 China The State Key Laboratory for Novel Software Technology National Institute of Healthcare Data Science Nanjing University Nanjing210023 China The School of Computing and Information Technology University of Wollongong Australia The Department of Computer Science University of Rochester RochesterNY14611 United States

Online metric learning has been widely applied in classification and retrieval. It can automatically learn a suitable metric from data by restricting similar instances to be separated from dissimilar instances with a given margin. However, the existing online metric learning algorithms have limited performance in real-world classifications, especially when data distributions are complex. To this end, this paper proposes a multilayer framework for online metric learning to capture the nonlinear similarities among instances. Different from the traditional online metric learning, which can only learn one metric space, the proposed Multi-Layer Online Metric Learning (MLOML) takes an online metric learning algorithm as a metric layer and learns multiple hierarchical metric spaces, where each metric layer follows a nonlinear layers for the complicated data distribution. Moreover, the forward propagation (FP) strategy and backward propagation (BP) strategy are employed to train the hierarchical metric layers. To build a metric layer of the proposed MLOML, a new Mahalanobis-based Online Metric Learning (MOML) algorithm is presented based on the passive-aggressive strategy and one-pass triplet construction strategy. Furthermore, in a progressively and nonlinearly learning way, MLOML has a stronger learning ability than traditional online metric learning in the case of limited available training data. To make the learning process more explainable and theoretically guaranteed, theoretical analysis is provided. The proposed MLOML enjoys several nice properties, indeed learns a metric progressively, and performs better on the benchmark datasets. Extensive experiments with different settings have been conducted to verify these properties of the proposed MLOML. Copyright © 2018, The Authors. All rights reserved.

关键词： Multilayers

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