Frontiers of Computer Science

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Computer Graphics 2.0: towards end-user-generated contents
Kun ZHOU
Front. Comput. Sci.    2017, 11 (5): 743-745.   DOI: 10.1007/s11704-017-7901-z
Abstract   PDF (278KB)
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A survey on Lyapunov-based methods for stability of linear time-delay systems
Jian SUN, Jie CHEN
Front. Comput. Sci.    2017, 11 (4): 555-567.   DOI: 10.1007/s11704-016-6120-3
Abstract   PDF (314KB)

Recently, stability analysis of time-delay systems has received much attention. Rich results have been obtained on this topic using various approaches and techniques. Most of those results are based on Lyapunov stability theories. The purpose of this article is to give a broad overview of stability of linear time-delay systems with emphasis on the more recent progress. Methods and techniques for the choice of an appropriate Lyapunov functional and the estimation of the derivative of the Lyapunov functional are reported in this article, and special attention is paid to reduce the conservatism of stability conditions using as few as possible decision variables. Several future research directions on this topic are also discussed.

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Lifelong machine learning: a paradigm for continuous learning
Bing LIU
Front. Comput. Sci.    2017, 11 (3): 359-361.   DOI: 10.1007/s11704-016-6903-6
Abstract   PDF (195KB)
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The role of prior in image based 3D modeling: a survey
Hao ZHU,Yongming NIE,Tao YUE,Xun CAO
Front. Comput. Sci.    2017, 11 (2): 175-191.   DOI: 10.1007/s11704-016-5520-8
Abstract   PDF (950KB)

The prior knowledge is the significant supplement to image-based 3D modeling algorithms for refining the fragile consistency-based stereo. In this paper, we review the image-based 3D modeling problem according to prior categories, i.e., classical priors and specific priors. The classical priors including smoothness, silhouette and illumination are well studied for improving the accuracy and robustness of the 3D reconstruction. In recent years, various specific priors which take advantage of Manhattan rule, geometry template and trained category features have been proposed to enhance the modeling performance. The advantages and limitations of both kinds of priors are discussed and evaluated in the paper. Finally, we discuss the trend and challenges of the prior studies in the future.

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Urban computing: enabling urban intelligence with big data
Yu ZHENG
Front. Comput. Sci.    2017, 11 (1): 1-3.   DOI: 10.1007/s11704-016-6907-2
Abstract   PDF (397KB)
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Rethinking big data in a networked world
Lionel M. NI,Haoyu TAN,Jiang XIAO
Front. Comput. Sci.    2016, 10 (6): 965-967.   DOI: 10.1007/s11704-016-6902-7
Abstract   PDF (200KB)
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A peep at knowledge science in a categorical prospect
Ruqian LU
Front. Comput. Sci.    2016, 10 (5): 767-768.   DOI: 10.1007/s11704-016-6905-4
Abstract   PDF (185KB)
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Learnware: on the future of machine learning
Zhi-Hua ZHOU
Front. Comput. Sci.    2016, 10 (4): 589-590.   DOI: 10.1007/s11704-016-6906-3
Abstract   PDF (264KB)
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Big graph search: challenges and techniques
Shuai MA,Jia LI,Chunming HU,Xuelian LIN,Jinpeng HUAI
Front. Comput. Sci.    2016, 10 (3): 387-398.   DOI: 10.1007/s11704-015-4515-1
Abstract   PDF (484KB)

On one hand, compared with traditional relational and XML models, graphs have more expressive power and are widely used today. On the other hand, various applications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. We first introduce the application of graph search in various scenarios. We then formalize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.

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Evolvable dialogue state tracking for statistical dialogue management
Kai YU,Lu CHEN,Kai SUN,Qizhe XIE,Su ZHU
Front. Comput. Sci.    2016, 10 (2): 201-215.   DOI: 10.1007/s11704-015-5209-4
Abstract   PDF (684KB)

Statistical dialogue management is the core of cognitive spoken dialogue systems (SDS) and has attracted great research interest. In recent years, SDS with the ability of evolution is of particular interest and becomes the cuttingedge of SDS research. Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states at each dialogue turn, given the previous interaction history. It plays an important role in statistical dialogue management. To provide a common testbed for advancing the research of DST,international DST challenges (DSTC) have been organised and well-attended by major SDS groups in the world. This paper reviews recent progresses on rule-based and statistical approaches during the challenges. In particular, this paper is focused on evolvable DST approaches for dialogue domain extension. The two primary aspects for evolution, semantic parsing and tracker, are discussed. Semantic enhancement and a DST framework which bridges rule-based and statistical models are introduced in detail. By effectively incorporating prior knowledge of dialogue state transition and the ability of being data-driven, the new framework supports reliable domain extension with little data and can continuously improve with more data available. Thismakes it excellent candidate for DST evolution. Experiments show that the evolvable DST approaches can achieve the state-of-the-art performance and outperform all previously submitted trackers in the third DSTC.

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