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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Chen, Xiang He, Si-Min Bu, Dong-Bo Chen, Run-Sheng Gao, Wen Key Lab. of Intelligent Information Processing Chinese Academy of Sciences Beijing 100080 China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Insitute of Biophysics Chinese Academy of Sciences Beijing 100101 China Graduate University Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)1424415098

We demonstrate a new algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Our approach is based on the free energy minimization criterion, and utilizes a sophisticated energy model that is more accurate and supports more types of pseudoknots. By using a "maximal stem" and "stem merging" strategy, the search space for RNA structure prediction is significantly reduced. We have also developed a greedy search algorithm with perturbation on stems. The FlexStem algorithm is applied to a large number of sequences taken from Sprinzl, Pseudobase and other reliable resource. We find that FlexStem outperforms the well-known optimal and heuristic algorithms such as Mfold, PKNOTS, HotKnots and ILM in overall sensitivity and has comparable results to those algorithms in overall specificity. Performance evaluation demonstrate that FlexStem requires significantly less time than the optimal algorithm PKNOTS. In addition, our algorithm has better prediction results on pseudoknot-free sequences compared to competing algorithms. ©2007 IEEE.

关键词： Heuristic algorithms

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Weighted ROC curve in cost space: extending AUC to cost-sensitive learning 23

Weighted ROC curve in cost space: extending AUC to cost-sens...

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Proceedings of the 37th International Conference on Neural information processing Systems

作者： Huiyang Shao Qianqian Xu Zhiyong Yang Peisong Wen Peifeng Gao Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS and BDKM University of Chinese Academy of Sciences

In this paper, we aim to tackle flexible cost requirements for long-tail datasets, where we need to construct a (1) cost-sensitive and (2) class-distribution robust learning framework. The misclassification cost and the area under the ROC curve (AUC) are popular metrics for (1) and (2), respectively. However, limited by their formulations, models trained with AUC are not well-suited for cost-sensitive decision problems, and models trained with fixed costs are sensitive to the class distribution shift. To address this issue, we present a new setting where costs are treated like a dataset to deal with arbitrarily unknown cost distributions. Moreover, we propose a novel weighted version of AUC where the cost distribution can be integrated into its calculation through decision thresholds. To formulate this setting, we propose a novel bilevel paradigm to bridge weighted AUC (WAUC) and cost. The inner-level problem approximates the optimal threshold from sampling costs, and the outer-level problem minimizes the WAUC loss over the optimal threshold distribution. To optimize this bilevel paradigm, we employ a stochastic optimization algorithm (SACCL) which enjoys the same convergence rate (O(ε-4)) with the SGD. Finally, experiment results show that our algorithm performs better than existing cost-sensitive learning methods and two-stage AUC decisions approach.

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Context-aware movie recommendations with factorization machines

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Journal of Computational information Systems 2014年第8期10卷 3313-3323页

作者： Jin, Ting Liu, Hui Li, Danqing Chen, Jing Shanghai Key Lab. of Intelligent Information Processing Fudan University Shanghai 200433 China School of Information Science and Technology Hainan University Haikou 570228 China Research Lab. of Information Management Changzhou University Changzhou 213164 China

Recommender Systems play an important role in handing large amounts of information and support users by recommending content considered as being particularly interesting for them. In this paper, Contextaware Movie Recommender System is implemented by using the Collab.rative Filtering framework that is integrated with a factorization machine approach as illustrated in the generic model. CARS makes use of multiple contextual information which provides useful details about user's current situation in order to give more relevant recommendations to users. In an offline experiment, it has been compared with other approaches (e.g. SVD++, KNN Item-based), with results that show significant improvement in the accuracy of recommendations. In addition, selecting suitable contextual features is important since only few of them are useful. This enables the error of rating prediction to be reduced. © 2014 Binary information Press.

关键词： Recommender systems

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Automatic adaptation of annotation standards: Chinese word segmentation and POS tagging: a case study 09

Automatic adaptation of annotation standards: Chinese word s...

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Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language processing of the AFNLP: Volume 1 - Volume 1

作者： Wenbin Jiang Liang Huang Qun Liu Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Google Research Charleston Rd. Mountain View CA

ISBN: (纸本)9781932432459

Manually annotated corpora are valuable but scarce resources, yet for many annotation tasks such as treebanking and sequence lab.ling there exist multiple corpora with different and incompatible annotation guidelines or standards. This seems to be a great waste of human efforts, and it would be nice to automatically adapt one annotation standard to another. We present a simple yet effective strategy that transfers knowledge from a differently annotated corpus to the corpus with desired annotation. We test the efficacy of this method in the context of Chinese word segmentation and part-of-speech tagging, where no segmentation and POS tagging standards are widely accepted due to the lack of morphology in Chinese. Experiments show that adaptation from the much larger People's Daily corpus to the smaller but more popular Penn Chinese Treebank results in significant improvements in both segmentation and tagging accuracies (with error reductions of 30.2% and 14%, respectively), which in turn helps improve Chinese parsing accuracy.

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research on network Chinese pornography characteristic and corresponding filtering system's model

Research on network Chinese pornography characteristic and c...

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International Conference on Internet Technology and Applications, ITAP 2010

作者： Peng, Yu-Zhong School of Computer and Information Engineering Key Lab. of Scientific Computing and Intelligent Information Processing in Universities of Guangxi Guangxi Teacher Education University Nanning 53001 China

ISBN: (纸本)9781424451432

At present,the internet pornographic text is in varied forms and changeful, although it is prohibited ever. It severely harms people's mental and physical health development and social stability. There are IP-based,keyword-based and intelligent content analysis filtering systems against it today. But they are difficult to deal with manifestations of diversity, changeful, and increasingly concealed porn. In this paper, a in-depth research has done for the appeared characteristics of such undesirable information on our statistical analysis. And based on this characteristics, a new text pre-processing algorithm PA-PTCI is proposed, and a effective model combined with content filtering technology to the current text information filtering pornography problem is designed. The experimental result shows the PA-PTCI algorithm and the proposed model are quite effective. ©2010 IEEE.

关键词： information filtering

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From Use Case Model to Service Model An Environment Ontology Based Approach

From Use Case Model to Service Model An Environment Ontology...

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The Ninth International Conference on Grid and Cloud computing(第九届网格与云计算国际学术会议 GCC 2010)

作者： Guangjun Cai Bin Zhao Zhi Jin Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Graduate Unive Software Engineering Institute Peking University Beijing China

One fundamental problem in services computing is how to bridge the gap between business requirements and various heterogeneous IT services. This involves eliciting business requirements and building a solution accordingly by reusing availab.e services. While the business requirements are commonly elicited through use cases and scenarios, it is not straightforward to transform the use case model into a service model, and the existing manual approach is cumbersome and error-prone. In this paper, the environment ontology, which is used to model the problem space, is utilized to facilitate the model transformation process. The environment ontology provides a common understanding between business analysts and software engineers. The required software functionalities as well as the availab.e services’ capabilities are described using this ontology. By semi-automatically matching the required capability of each use case to the availab.e capabilities provides by services, a use case is realized by that set of services. At the end of this paper, a fictitious case study was used to illustrate how this approach works.

关键词： services computing requirements engineering use case environment ontology service model

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Automatic sports genre categorization and view-type classification over large-scale dataset 09

Automatic sports genre categorization and view-type classifi...

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17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums

作者： Li, Lingfang Zhang, Ning Duan, Ling-Yu Huang, Qingming Du, Jun Guan, Ling Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Ryerson Multimedia Research Laboratory Ryerson University Toronto ON Canada Institute of Digital Media Peking University Beijing 100871 China Graduate University of Chinese Academy of Sciences Beijing 100190 China NEC Research Labs. China Beijing 100084 China

ISBN: (纸本)9781605586083

This paper presents a framework with two automatic tasks targeting large-scale and low quality sports video archives collected from online video streams. The framework is based on the bag of visual-words model using speeded-up robust features (SURF). The first task is sports genre categorization based on hierarchical structure. Following on the second task which is based on automatically obtained genre, views are classified using support vector machines (SVMs). As a consequence, the views classification result can be used in video parsing and highlight extraction. As compared with state-of-the-art methods, our approach is fully automatic as well as domain knowledge free and thus provides a better extensibility. Furthermore, our dataset consists of 14 sport genres with 6850 minutes in total. Both sport genre categorization and view type classification have more than 80% accuracy rates, which validate this framework's robustness and potential in web-based applications. Copyright 2009 ACM.

关键词： Support vector machines

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Method for constructing G2 continuous splines via matrices decomposition

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Journal of information and Computational Science 2005年第4期2卷 661-669页

作者： Diao, Luhong Li, Hua Li, Zongmin Key Lab. of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate University Chinese Academy of Sciences Beijing 100080 China

Semi-G2 basis functions are introduced, the degree of which is larger than three. These basis functions are expressed explicitly via matrices decomposition. Based on them, equations for constructing G2 splines can be presented independently of geometric shape parameters' values. It makes the equation's solving easier. Analysis shows that this method may be extended to be applicable for constructing Gn splines.

关键词： Computer aided design

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Category aggregation among region proposals for object detection 17th

Category aggregation among region proposals for object detec...

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17th Pacific-Rim Conference on Multimedia, PCM 2016

作者： Li, Linghui Tang, Sheng Zhou, Jianshe Wang, Bin Tian, Qi Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing100190 China Beijing Advanced Innovation Center for Imaging Technology Capital Normal University Beijing100048 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249-1604 United States

ISBN: (纸本)9783319488950

Recently, an overwhelming majority of object detection methods have focused on how to reduce the number of region proposals while keeping high object recall without consideration of category information. It may lead to a lot of false positives due to the interferences between categories especially when the number of categories is very large. To eliminate such interferences, we propose a novel category aggregation approach based upon our observation that more frequently detected categories around an object have the higher probabilities to be present in an image. After further exploiting the co-occurrence relationship between categories, we can determine the most possible categories for an image in advance. Thus, many false positives can be greatly filtered out before subsequent classification process. Our extensive experiments on the well-known ILSVRC 2015 detection dataset show that our approach can achieve 49.0% of mAP in the validation dataset and 45.36% of mAP in the test dataset ranked 5th in the ILSVRC 2015 detection task. © Springer International Publishing AG 2016.

关键词： Object detection

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A HSC-based sample selection method for Support Vector Machine

A HSC-based sample selection method for Support Vector Machi...

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International Conference on Machine Learning and Cybernetics

作者： He, Qing Li, Ning Shi, Zhong-Zhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Hebei University Baoding 071002 Hebei China Graduate University of Chinese Academy of Sciences Hebei University Baoding 071002 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Support Vector Machine (SVM) is a classification technique of machine learning based on statistical learning theory. A quadratic optimization problem needs to be solved in the algorithm, and with the increase of the samples, the time complexity will also increase. So it is necessary to shrink training sets to reduce the time complexity. A sample selection method for SVM is proposed in this paper. It is inspired from the Hyper surface classification (HSC), which is a universal classification method based on Jordan Curve Theorem, and there is no need for mapping from lower-dimensional space to higher-dimensional space. The experiments show that the algorithm shrinks training sets keeping the accuracy for unseen vectors high. © 2010 IEEE.

关键词： Support vector machines

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