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31st International Conference on Machine Learning, ICML 2014

作者： Yi, Jinfeng Zhang, Lijun Wang, Jun Jin, Rong Jain, Anil K. IBM Thomas J. Watson Research Center Yorktown HeightsNY10598 United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9781634393973

Learning a statistical model for high-dimensional data is an important topic in machine learning. Although this problem has been well studied in the supervised setting, little is known about its unsupervised counterpart. In this work, we focus on the problem of clustering high-dimensional data with sparse centers. In particular, we address the following open question in unsupervised learning: "is it possible to reliably cluster high-dimensional data when the number of samples is smaller than the data dimensionality?" We develop an efficient clustering algorith- m that is able to estimate sparse cluster centers with a single pass over the data. Our theoretical analysis shows that the proposed algorithm is able to accurately recover cluster centers with only 0(s log d) number of samples (data points), provided all the cluster centers are s-sparse vectors in a d dimensional space. Experimental results verify both the effectiveness and efficiency of the proposed clustering algorithm compared to the state-of-the-art algorithms on several benchmark datasets. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Clustering algorithms

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Efficient algorithms for robust one-bit compressive sensing 31

Efficient algorithms for robust one-bit compressive sensing

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31st International Conference on Machine Learning, ICML 2014

作者： Zhang, Lijun Yi, Jinfeng Jin, Rong National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China IBM Thomas J. Watson Research Center Yorktown HeightsNY10598 United States Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

ISBN: (纸本)9781634393973

While the conventional compressive sensing assumes measurements of infinite precision, one- bit compressive sensing considers an extreme setting where each measurement is quantized to just a single bit. In this paper, we study the vector recovery problem from noisy one-bit measurements, and develop two novel algorithms with formal theoretical guarantees. First, we propose a passive algorithm, which is very efficient in the sense it only needs to solve a convex optimization problem that has a closed-form solution. Despite the apparent simplicity, our theoret-ical analysis reveals that the proposed algorithm can recover both the exactly sparse and the ap-proximately sparse vectors. In particular, for a sparse vector with s nonzero elements, the sample complexity is O(s log n/Ε2), where n is the dimensionality and e is the recovery error. This result improves significantly over the previously best known sample complexity in the noisy setting, which is O(s log n/ Ε4). Second, in the case that the noise model is known, we devel-op an adaptive algorithm based on the principle of active learning. The key idea is to solicit the sign information only when it cannot be inferred from the current estimator. Compared with the passive algorithm, the adaptive one has a lower sample complexity if a high-precision solution is desired. Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.

关键词： Compressed sensing

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SHAP: Suppressing the Detection of Inconsistency Hazards by Pattern Learning

SHAP: Suppressing the Detection of Inconsistency Hazards by ...

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Asia-Pacific Conference on software Engineering

作者： Wang Xi Chang Xu Wenhua Yang Ping Yu Xiaoxing Ma Jiang Lu Department of Computer Science and Technology Nanling University Naniinz China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781479974276

Context-aware applications rely on contexts derived from sensory data to adapt their behavior. However, contexts can be inconsistent and cause application anomaly or crash. One popular solution is to detect and resolve context inconsistencies at runtime. However, we observe that many detected inconsistencies do not indicate real context problems. Instead, they are caused by improper inconsistency detection. These inconsistencies are harmless, and their resolution is unnecessary or may even cause new problems. We name them inconsistency hazards. Inconsistency hazards should be suppressed, but their occurrences resemble normal inconsistencies. In this paper, we present a pattern-learning based approach SHAP to suppressing the detection of inconsistency hazards. Our key insight is that the detection of such hazards is subject to certain patterns of context changes. These patterns, although difficult to specify manually, can be learned effectively from historical inconsistency detection data. We evaluated our SHAP experimentally through three context-aware applications. The results reported that SHAP can automatically suppress the detection of over 90% inconsistency hazards, while preserving the detection of over 98% normal inconsistencies, with only negligible overhead.

关键词： Context Hazards Medical services Dynamic scheduling Heuristic algorithms Sensors Context-aware services

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Multicategory large margin classification methods: Hinge losses vs. coherence functions

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Artificial Intelligence 2014年 215卷 55-78页

作者： Zhihua Zhang Cheng Chen Guang Dai Wu-Jun Li Dit-Yan Yeung Key Laboratory of Shanghai Education Commission for Intelligence Interaction & Cognition Engineering Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210023 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China

Generalization of large margin classification methods from the binary classification setting to the more general multicategory setting is often found to be non-trivial. In this paper, we study large margin classification methods that can be seamlessly applied to both settings, with the binary setting simply as a special case. In particular, we explore the Fisher consistency properties of multicategory majorization losses and present a construction framework of majorization losses of the 0–1 loss. Under this framework, we conduct an in-depth analysis about three widely used multicategory hinge losses. Corresponding to the three hinge losses, we propose three multicategory majorization losses based on a coherence function . The limits of the three coherence losses as the temperature approaches zero are the corresponding hinge losses, and the limits of the minimizers of their expected errors are the minimizers of the expected errors of the corresponding hinge losses. Finally, we develop multicategory large margin classification methods by using a so-called multiclass C -loss.

关键词： Multiclass margin classification Fisher consistency Multicategory hinge losses Coherence losses Multicategory boosting algorithm

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Mathematical modeling of CSTR bioreactor control for production of recombinant protein

Mathematical modeling of CSTR bioreactor control for product...

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2014 4th International Conference on Advanced Materials Research, ICAMR 2014

作者： Jia, Xiao Yi Lin, Yu Tian Lin, Hui Bin Gao, Ling Lin, Jian Qun Lin, Jian Qiang School of Administration Shandong University Jinan 250100 China Zhejiang University Hangzhou China Shandong Academy of Chinese Medicine Jinan 250014 China Shandong Provincial Key Lab for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University Jinan 250014 China State Key Lab of Microbial Technology Shandong University Jinan 250100 China

ISBN: (纸本)9783038350323

Fermentation process using recombinant strain for production of recombinant protein is widely used in commercialization of the biotechnologies. The continuous stirred tank reactor (CSTR) is a typical microbial cultivation method, has the major advantage of high productivity. Mathematical modeling and simulation is useful for analysis and optimization of the CSTR fermentation process. Most of the mathematical models developed for CSTR are black box models without information of the intracellular dynamics and regulations. In this research, a mathematical model is built based on gene regulation for recombinant protein production using CSTR, and simulation is made using this model. © (2014) Trans Tech Publications, Switzerland.

关键词： Recombinant proteins

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EqualVisor: Providing Memory Protection in an Untrusted Commodity Hypervisor

EqualVisor: Providing Memory Protection in an Untrusted Comm...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Liang Deng Qingkai Zeng Weiguang Wang Yao Liu Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology(Nanjing University) Nanjing China

ISBN: (纸本)9781479965144

In cloud computing, hypervisor is the all-powerful software running in the highest privilege layer, thus attackers who compromise a hypervisor may jeopardize the whole cloud, especially cause memory corruption of any sensitive workloads within the cloud. In this paper, we propose a novel architecture and approach to provide memory protection from an untrusted hypervisor on current x86 platforms. Unlike previous approaches such as nested virtualization, we do not place another higher privilege TCB below the hypervisor. Instead, our approach introduces a properly isolated tiny TCB running in the same privilege level and the same address space with the hypervisor, and uses this TCB to intercept and validate hypervisor's privilege actions for memory protection. In this way, we can enforce further memory security policies only relying on the TCB even if the hypervisor is fully compromised.

关键词： Virtual machine monitors Registers Hardware Logic gates software Security Virtualization

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Graphics and Scene Text Classification in Video

Graphics and Scene Text Classification in Video

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International Conference on Pattern Recognition

作者： Jiamin Xu Palaiahnakote Shivakumara Tong Lu Trung Quy Phan Chew Lim Tan National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia School of Computing National University of Singapore

ISBN: (纸本)9781479952106

Achieving good accuracy for text detection and recognition is a challenging and interesting problem in the field of video document analysis because of the presences of both graphics text that has good clarity and scene text that is unpredictable in video frames. Therefore, in this paper, we present a novel method for classifying graphics texts and scene texts by exploiting temporal information and finding the relationship between them in video. The method proposes an iterative procedure to identify Probable Graphics Text Candidates (PGTC) and Probable Scene Text Candidates (PSTC) in video based on the fact that graphics texts in general do not have large movements especially compared to scene texts which are usually embedded on background. In addition to PGTC and PSTC, the iterative process automatically identifies the number of video frames with the help of a converging criterion. The method further explores the symmetry between intra and inter character components to identify graphics text candidates and scene text candidates. Boundary growing method is employed to restore the complete text line. For each segmented text line, we finally introduce Eigen value analysis to classify graphics and scene text lines based on the distribution of respective Eigen values. Experimental results with the existing methods show that the proposed method is effective and useful to improve the accuracy of text detection and recognition.

关键词： Graphics Text recognition Image segmentation Image edge detection Accuracy Text categorization Iterative methods

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Text Detection Using Delaunay Triangulation in Video Sequence

Text Detection Using Delaunay Triangulation in Video Sequenc...

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IAPR International Workshop on Document Analysis Systems, DAS

作者： Liang Wu Palaiahnakote Shivakumara Tong Lu Chew Lim Tan National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia School of Computing National University of Singapore

Text detection and tracking in video sequence is gaining interest due to the challenges posed by low resolution and complex background. This paper proposes a new method for text detection by estimating trajectories between the corners of texts in video sequence over time. Each trajectory is considered as one node to form a graph for all trajectories and Delaunay triangulation is used to obtain edges to connect nodes of the graph. In order to identify the edges that represent text regions, we propose four pruning criteria based on spatial proximity, motion coherence, local appearance and canny rate. This results in several sub-graphs. Then we use depth first search to collect corner points, which essentially represent text candidates. False positives are eliminated using heuristics and missing trajectories will be obtained by tracking the corners in temporal frames. We test the method on different videos and evaluate the method in terms of recall, precision, f-measure with existing results. Experimental result shows that the proposed method is superior to existing method.

关键词： Trajectory Image edge detection Video sequences Feature extraction Image color analysis Joining processes

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2D and 3D Video Scene Text Classification

2D and 3D Video Scene Text Classification

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International Conference on Pattern Recognition

作者： Jiamin Xu Palaiahnakote Shivakumara Tong Lu Chew Lim Tan National Key Lab for Novel Software Technology Nanjing University Nanjing China Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia School of Computing National University of Singapore

ISBN: (纸本)9781479952106

Text detection and recognition is a challenging problem in document analysis due to the presence of the unpredictable nature of video texts, such as the variations of orientation, font and size, illumination effects, and even different 2D/3D text shadows. In this paper, we propose a novel horizontal and vertical symmetry feature by calculating the gradient direction and the gradient magnitude of each text candidate, which results in Potential Text Candidates (PTCs) after applying the k-means clustering algorithm on the gradient image of each input frame. To verify PTCs, we explore temporal information of video by proposing an iterative process that continuously verifies the PTCs of the first frame and the successive frames, until the process meets the converging criterion. This outputs Stable Potential Text Candidates (SPTCs). For each SPTC, the method obtains text representatives with the help of the edge image of the input frame. Then for each text representative, we divide it into four quadrants and check a new Mutual Nearest Neighbor Symmetry (MNNS) based on the dominant stroke width distances of the four quadrants. A voting method is finally proposed to classify each text block as either 2D or 3D by counting the text representatives that satisfy MNNS. Experimental results on classifying 2D and 3D text images are promising, and the results are further validated by text detection and recognition before classification and after classification with the exiting methods, respectively.

关键词： Three-dimensional displays Image edge detection Multi-layer neural network Text recognition Equations Accuracy Standards

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Predicting employee relations in enterprises by bi-directional link mining 11

Predicting employee relations in enterprises by bi-direction...

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2014 11th World Congress on Intelligent Control and Automation, WCICA 2014

作者： Hong, Wang Sun, Yanshen Institute of Information Science and Engineering Shandong Normal University Jinan Shandong Province China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan Shandong Province China Department of Earth Sciences ZheJiang University Hangzhou Zhejiang Province China

ISBN: (纸本)9781479958252

By using link mining method, we can understand associations among persons in enterprises. In this paper, we propose a link mining model called a bi-directional link mining, which is capable of predicting edges that do not exist but may appear in the future in dynamic multi-dimensional networks. Firstly, we construct a dynamic multi-dimensional networks from an E-mail network and a telephone network, which makes us correctly describe a Group's staff relationship. Then we present some algorithms to build a reduction dimensional network and a dynamic network. After that, we give bi-directional link mining methods in a dynamic multi-dimensional weighted network and apply these methods in the enterprise's employee relational network. Experimental results show that these methods can effectively predict associations among persons in a large scale employee networks. © 2014 IEEE.

关键词： Electronic mail

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