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Genetic optimization of BP neural network in the application of suspicious financial transactions pattern recognition

Genetic optimization of BP neural network in the application...

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6th international conference on Management of e-Commerce and e-Government, ICMeCG 2012

作者： Tang, Jun He, Lei School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

ISBN: (纸本)9781467329439

We explore and analyze the chaotic properties of the financial data, conduct classification learning in the financial transaction data. In this way, we are able to excavate the pattern and rule of customer transaction behavior, and isolate suspicious financial transactions. Using MATLAB to implement the programming of BP, we propose a genetic optimization of BP neural network to improve the defect of BP, which includes slow convergence and falling into local optimum easily. By using genetic algorithm to optimize the BP network, we are able to select the weight coefficient in a better way. Our experiments show that the optimized BP neural network function has a better predictive output. © 2012 IEEE.

关键词： MATLAB

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Towards Scalable Optimal Sequence Homology Detection

Towards Scalable Optimal Sequence Homology Detection

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19th international conference on High Performance Computing (HiPC)

作者： Daily, Jeff Krishnamoorthy, Sriram Kalyanaraman, Ananth Pacific NW Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781467323703;9781467323727

the field of bioinformatics and computational biology is experiencing a data revolution - experimental techniques to procure data have increased in throughput, improved in accuracy and reduced in costs. this has spurred an array of high profile sequencing and data generation projects. While the data repositories represent untapped reservoirs of rich information critical for scientific breakthroughs, the analytical software tools that are needed to analyze large volumes of such sequence data have significantly lagged behind in their capacity to scale. In this paper, we address homology detection, which is a fundamental problem in large-scale sequence analysis with numerous applications. We present a scalable framework to conduct large-scale optimal homology detection on massively parallel super-computing platforms. Our approach employs distributed memory work stealing to effectively parallelize optimal pairwise alignment computation tasks. Results on 120,000 cores of the Hopper Cray XE6 supercomputer demonstrate strong scaling and up to 2.42 x 10(7) optimal pairwise sequence alignments computed per second (PSAPS), the highest reported in the literature.

关键词： Cray computers DNA bioinformatics distributed memory systems mainframes parallel machines pattern recognition

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Motion Local Ternary pattern for Distributed Multi-View Human Action recognition

Motion Local Ternary Pattern for Distributed Multi-View Huma...

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6th international conference on Distributed Smart Cameras (ICDSC)

作者： Luo, Jiajia Qi, Hairong Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9781450317726

Multi-view human action recognition has gained a lot of attention in recent years for its superior performance as compared to the single view recognition. In this paper, we propose algorithms for the real-time realization of human action recognition in distributed camera networks (DCNs). We first present a new method for fast calculation of motion information by Motion Local Ternary pattern (Mltp) that is tolerant to illumination change, robust in homogeneous region and computationally efficient. Next, we combine the local interest point detector with Mltp to generate 3D patches containing motion information and introduce two feature descriptors for the extracted 3D patches. Taking advantage of the proposed Mltp, 3D patches generated from background can be further removed automatically and thus the foreground patches can be highlighted. Finally, the histogram representations based on Bag-of-Words modeling, are transmitted from local cameras to the base station for classification. At the base station, a probability model is produced to fuse the information from various views and a class label is assigned accordingly. Compared to the existing algorithms, the proposed methods have three advantages: 1) no preprocessing is required;2) communication among cameras is unnecessary;and 3) positions and orientations of cameras do not need to be fixed. We further evaluate both descriptors on the most popular multi-view action dataset IXMAS. Experimental results indicate that our approaches repeatedly achieve state-of-the-art results when various numbers of views are tested. In addition, our approaches are tolerant to the various combination of views and benefit from introducing more views at the testing stage.

关键词： Cameras

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Rules on the Web: Research and Applications - 6th international Symposium, RuleML 2012, Proceedings

Rules on the Web: Research and Applications - 6th Internatio...

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6th international Symposium on Rules on the Web: Research and Applications, RuleML 2012, collocated with the 20th biennial European conference on Artificial Intelligence (ECAI 2012)

ISBN: (纸本)9783642326882

the proceedings contain 25 papers. the topics discussed include: ontology-based query answering with existential rules;formalizing both refraction-based and sequential executions of production rule programs;bringing OWL ontologies to the business rules users;a rule based approach for business rule generation from business process models;a production rule-based framework for causal and epistemic reasoning;a rule-based calculus and processing of complex events;HARM: a hybrid rule-based agent reputation model based on temporal defeasible logic;imposing restrictions over temporal properties in OWL: a rule-based approach;using data-to-knowledge exchange for transforming relational databases to knowledge bases;PSOA2TPTP: a reference translator for interoperating PSOA RuleML with TPTP reasoners;PSOA RuleML API: a tool for processing abstract and concrete syntaxes;and rule-based high-level situation recognition from incomplete tracking data.

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Approximate regular expressions and their derivatives

Approximate regular expressions and their derivatives

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6th international conference on Language and Automata theory and Applications, LATA 2012

作者： Champarnaud, Jean-Marc Jeanne, Hadrien Mignot, Ludovic LITIS Université de Rouen 76801 Saint-Étienne du Rouvray Cedex France

ISBN: (纸本)9783642283314

Several studies have been achieved to construct a finite automaton that recognizes the set of words that are at a bounded distance from some word of a given language. In this paper, we introduce a new family of regular operators based on a generalization of the notion of distance and we define a new family of expressions, the approximate regular expressions. We compute Brzozowski derivatives and Antimirov derivatives of such operators, which allows us to provide two recognizers for the language denoted by any approximate regular expression. © 2012 Springer-Verlag.

关键词： pattern matching

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A self-training with active example selection criterion for biomedical named entity recognition

A self-training with active example selection criterion for ...

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6th international conference on Convergence and Hybrid Information Technology, ICHIT 2012

作者： Shin, Eonseok Munkhdalai, Tsendsuren Li, Meijing Paik, Incheon Ryu, Keun Ho Information Technology Planning Department of Korea Army Korea Army Korea Republic of Database/Bioinformatics Laboratory Chungbuk National University Cheongju Korea Republic of Computer Industry Laboratory School of Computer Sceince and Engineering University of Aizu Japan

ISBN: (纸本)9783642326448

Biomedical named entity recognition is an essential prerequisite task before effective text mining of biomedical literature can begin. Exploiting unlabeled text data with a relatively small labeled corpus to build an accurate classification model has been an active and challenging research topic in text mining, due to the recent growth of the amount of biomedical literature. In this work, we proposed a new semi-supervised learning method based on self-training for biomedical named entity recognition. In this method, one classifier iteratively labels informative examples queried from the unlabeled data and learns on the most confident ones of them. Performance of the classifier is therefore improved. the proposed method outperforms the traditional self-training algorithm in terms of f-measure as well as, the number of training iterations performed to build a good classification model. © 2012 Springer-Verlag.

关键词： Text processing

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Automatic facial expression recognition using extended AR-LBP

Automatic facial expression recognition using extended AR-LB...

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6th international conference on Information Processing, ICIP 2012

作者： Shrinivasa Naika, C.L. Shekhar, Jha Shashi Das Pradip, K. Nair Shivashankar, B. Indian Institute of Technology Guwahati India

ISBN: (纸本)9783642316852

the Local Binary pattern (LBP) based operators are sensitive to localization errors. To mitigate these errors input images are manually aligned, face is localized using eyes co-ordinates in the image before feature extraction and multi-scale or multi operators are used, which restricts the use of LBP based operators for automatic facial expression recognition. this paper proposes an Extended Asymmetric Region Local Binary pattern (EAR-LBP) operator and automatic face localization heuristics to mitigate the localization errors for automatic facial expression recognition. the proposed operator along with face localization heuristics was evaluated for person-independent facial expression recognition on JAFFE and CK+ databases using a multi-class SVM with Linear and Radial Basis Function (RBF) as kernels. It is observed that face localization and the EAR-LBP method are able to mitigate the localization errors to produce reasonably better performance. Maximum 10-fold cross validation average performance of 58.74% and 60.35% were obtained on JAFFE and in case of CK+ database, maximum performance of 83.09% and 82.21% were obtained using Linear and RBF kernels for SVM multi-class classifier respectively. © 2012 Springer-Verlag.

关键词： Local binary pattern

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Incremental pattern matching for the efficient computation of transitive closure

Incremental pattern matching for the efficient computation o...

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6th international conference on Graph Transformations, ICGT 2012

作者： Bergmann, Gábor Ráth, István Szabó, Tamás Torrini, Paolo Varró, Dániel Budapest University of Technology and Economics Department of Measurement and Information Systems Magyar tudósok krt. 2 1117 Budapest Hungary

ISBN: (纸本)9783642336539

pattern matching plays a central role in graph transformations as a key technology for computing local contexts in which transformation rules are to be applied. Incremental matching techniques offer a performance advantage over the search-based approach, in a number of scenarios including on-the-fly model synchronization, model simulation, view maintenance, well-formedness checking and state space traversal [1,2]. However, the incremental computation of transitive closure in graph pattern matching has started to be investigated only recently [3]. In this paper, we propose multiple algorithms for the efficient computation of generalized transitive closures. As such, our solutions are capable of computing reachability regions defined by simple graph edges as well as complex binary relationships defined by graph patterns, that may be used in a wide spectrum of modeling problems. We also report on experimental evaluation of our prototypical implementation, carried out within the context of a stochastic system simulation case study. © 2012 Springer-Verlag.

关键词： pattern matching

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Applying incremental graph transformation to existing models in relational databases

Applying incremental graph transformation to existing models...

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6th international conference on Graph Transformations, ICGT 2012

作者： Bergmann, Gábor Horváth, Dóra Horváth, Ákos Budapest University of Technology and Economics Department of Measurement and Information Systems Magyar tudósok krt. 2 1117 Budapest Hungary

ISBN: (纸本)9783642336539

As industrial practice demands larger and larger system models, the efficient execution of graph transformation remains an important challenge. Additionally, for real-world applications, compatibility and integration with already well-established technologies is highly desirable. therefore, relational databases have been investigated before as off-the-shelf environments for graph transformation, since they are already widely used for storing, processing and querying large graphs. the graph pattern matching phase of graph transformation typically dominates in cost due to its combinatorial complexity. therefore significant attempts have been made to improve this process;incremental pattern matching is an approach that has been shown to exert favorable performance characteristics in many practical use cases. To this day, however, no solutions are available for applying incremental techniques side by side with already deployed systems built over relational databases. In the current paper, we propose an approach that translates graph patterns and transformation rules into event-driven (trigger-based) SQL programs that seamlessly integrate with existing relational databases to perform incremental pattern matching. Additionally, we provide experimental evaluation of the performance of our approach. © 2012 Springer-Verlag.

关键词： pattern matching

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Polynomial-time algorithms for learning typed pattern languages

Polynomial-time algorithms for learning typed pattern langua...

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6th international conference on Language and Automata theory and Applications, LATA 2012

作者： Geilke, Michael Zilles, Sandra Fachbereich Informatik Technische Universität Kaiserslautern D-67653 Kaiserslautern Germany Department of Computer Science University of Regina Regina SK S4S 0A2 Canada

ISBN: (纸本)9783642283314

this article proposes polynomial-time algorithms for learning typed pattern languages-formal languages that are generated by patterns consisting of terminal symbols and typed variables. A string is generated by a typed pattern by substituting all variables with strings of terminal symbols that belong to the corresponding types. the algorithms presented consitute non-trivial generalizations of Lange and Wiehagen's efficient algorithm for learning patterns in which variables are not typed. this is achieved by defining type witnesses to impose structural conditions on the types used in the patterns. It is shown that Lange and Wiehagen's algorithm implicitly uses a special case of type witnesses. Moreover, the type witnesses for a typed pattern form characteristic sets whose size is linear in the length of the pattern;our algorithm, when processing any set of positive data containing such a characteristic set, will always generate a typed pattern equivalent to the target pattern. thus our algorithms are of relevance to the area of grammatical inference, in which such characteristic sets are typically studied. © 2012 Springer-Verlag.

关键词： Formal languages

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