We investigate enumeration of distinct flat-foldable crease patterns under the following assumptions: positive integer n is given; every pattern is composed of n lines incident to the center of a sheet of paper; every angle between adjacent lines is equal to 2π/n; every line is assigned one of “mountain,” “valley,” and “flat (or consequently unfolded)”; crease patterns are considered to be equivalent if they are equal up to rotation and reflection. In this natural problem, we can use two well-known theorems for flat-foldability: the Kawasaki Theorem and the Maekawa Theorem in computational origami. Unfortunately, however, they are not enough to characterize all flat-foldable crease patterns. Therefore, so far, we have to enumerate and check flat-foldability one by one using computer. In this study, we develop the first algorithm for the above stated problem by combining these results in a nontrivial way and show its analysis of efficiency.

The bug reports expressed in natural language text usually suffer from vast, ambiguous and poorly written, which causes the challenge to the duplicate bug reports detection. Current automatic duplicate bug reports detection techniques have mainly focused on textual information and ignored some useful factors. To improve the detection accuracy, in this paper, we propose a new approach calls LNG (LDA and N-gram) model which takes advantages of the topic model LDA and word-based model N-gram. The LNG considers multiple factors, including textual information, semantic correlation, word order, contextual connections, and categorial information, that potentially affect the detection accuracy. Besides, the N-gram adopted in our LNG model is improved by modifying the similarity algorithm. The experiment is conducted under more than 230,000 real bug reports of the Eclipse project. In the evaluation, we propose a new evaluation metric, namely exact-accuracy (EA) rate, which can be used to enhance the understanding of the performance of duplicates detection. The evaluation results show that all the recall rate, precision rate, and EA rate of the proposed method are higher than treating them separately. Also, the recall rate is improved by 2.96%-10.53% compared to the state-of-art approach DBTM.

In this paper, we propose a method to estimate the most resource-consuming disease from electronic claim data based on Labeled Latent Dirichlet Allocation (Labeled LDA). The proposed method models each electronic claim from its medical procedures as a mixture of resource-consuming diseases. Thus, the most resource-consuming disease can be automatically estimated by applying Labeled LDA to the electronic claim data. Although our method is composed of a simple scheme, this is the first trial for realizing estimation of the most resource-consuming disease.

Excimer laser annealing at 308nm in UV and semiconductor blue laser-diode annealing at 445nm were performed and compared in term of the crystallization depending on electrical properties of Si films. As a result for the thin Si films of 50nm thickness, both lasers are very effective to enlarge the grain size and to activate electrically the dopant atoms in the CVD Si film. Smooth Si surface can be obtained using blue-laser annealing of scanned CW mode. By improving the film quality of amorphous Si deposited by sputtering for subsequent crystallization, both laser annealing techniques are effective for LTPS applications not only on conventional glass but also on flexible sheet. By conducting the latter advanced annealing technique, small grain size as well as large grains can be controlled. As blue laser is effective to crystallize even rather thicker Si films of 1µm, high performance thin-film photo-sensor or photo-voltaic applications are also expected.

In this paper, a new finger vein recognition method based on Gabor wavelet and Local Binary Pattern (GLBP) is proposed. In the new scheme, Gabor wavelet magnitude and Local Binary Pattern operator are combined, so the new feature vector has excellent stability. We introduce Block-based Linear Discriminant Analysis (BLDA) to reduce the dimensionality of the GLBP feature vector and enhance its discriminability at the same time. The results of an experiment show that the proposed approach has excellent performance compared to other competitive approaches in current literatures.

Linear Discriminant Analysis (LDA) is a well-known feature extraction method for supervised subspace learning in statistical pattern recognition. In this paper, a novel method of LDA based on a new L1-norm optimization technique and its variances are proposed. The conventional LDA, which is based on L2-norm, is sensitivity to the presence of outliers, since it used the L2-norm to measure the between-class and within-class distances. In addition, the conventional LDA often suffers from the so-called small sample size (3S) problem since the number of samples is always smaller than the dimension of the feature space in many applications, such as face recognition. Based on L1-norm, the proposed methods have several advantages, first they are robust to outliers because they utilize the L1-norm, which is less sensitive to outliers. Second, they have no 3S problem. Third, they are invariant to rotations as well. The proposed methods are capable of reducing the influence of outliers substantially, resulting in a robust classification. Performance assessment in face application shows that the proposed approaches are more effectiveness to address outliers issue than traditional ones.

In this paper, we propose a novel gait recognition framework - Spherical Space Model with Human Point Clouds (SSM-HPC) to recognize front view of human gait. A new gait representation - Marching in Place (MIP) gait is also introduced which preserves the spatiotemporal characteristics of individual gait manner. In comparison with the previous studies on gait recognition which usually use human silhouette images from image sequences, this research applies three dimensional (3D) point clouds data of human body obtained from stereo camera. The proposed framework exhibits gait recognition rates superior to those of other gait recognition methods.

In Digital Library (DL) applications, digital book clustering is an important and urgent research task. However, it is difficult to conduct effectively because of the great length of digital books. To do the correct clustering for digital books, a novel method based on probabilistic topic model is proposed. Firstly, we build a topic model named LDAC. The main goal of LDAC topic modeling is to effectively extract topics from digital books. Subsequently, Gibbs sampling is applied for parameter inference. Once the model parameters are learned, each book is assigned to the cluster which maximizes the posterior probability. Experimental results demonstrate that our approach based on LDAC is able to achieve significant improvement as compared to the related methods.

Opto-Thermal analysis of Semiconductor Blue-Multi-Laser-Diode Annealing (BLDA) for amorphous Si (a-Si) film is conducted by varying the irradiation power, the scanning velocity and the beam shape of blue-laser of 445 nm. Thermal profiles, maximum temperature of the a-Si film and the melting duration are evaluated. By comparing the simulated results with the experimental results, the excellent controllability of BLDA for arbitrary grain size can be explained consistently by the relation between irradiation time and melting duration. The results are useful to estimate poly-crystallized phase such as micro-polycrystalline Si, polycrystalline Si and anisotropic lateral growth of single-crystal-like Si.

The optimization of polarimetric contrast enhancement (OPCE) is a widely used method for maximizing the received power ratio of a desired target versus an undesired target (clutter). In this letter, a new model of the OPCE is proposed based on the Fisher criterion. By introducing the well known two-class problem of linear discriminant analysis (LDA), the proposed model is to enlarge the normalized distance of mean value between the target and the clutter. In addition, a cross-iterative numerical method is proposed for solving the optimization with a quadratic constraint. Experimental results with the polarimetric SAR (POLSAR) data demonstrate the effectiveness of the proposed method.

We present a novel model named Integrated Latent Topic Model (ILTM), to learn and recognize natural scene category. Unlike previous work, which considered the discrepancy and common property separately among all categories, Our approach combines universal topics from all categories with specific topics from each category. As a result, the model is implemented to produce a few but specific topics and more generic topics among categories, and each category is represented in a different topics simplex, which correlates well with human scene understanding. We investigate the classification performance with variable scene category tasks. The experiments have shown our model outperforms latent-space methods with less training data.

In this paper, we propose a novel image projection technique for face recognition applications based on Fisher Linear Discriminant Analysis (LDA). The projection is performed through a couple subspace analysis for overcoming the "small sample size" problem. Also, weighted pairwise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than that produced by the conventional LDA. The proposed technique has been successfully tested on three face databases. Experimental results indicate that the proposed algorithm outperforms the conventional algorithms.

We present a novel model, named Category Constraint-Latent Dirichlet Allocation (CC-LDA), to learn and recognize natural scene category. Previous work had to resort to additional classifier after obtaining image topic representation. Our model puts the category information in topic inference, so every category is represented in a different topics simplex and topic size, which is consistent with human cognitive habit. The significant feature in our model is that it can do discrimination without combined additional classifier, during the same time of getting topic representation. We investigate the classification performance with variable scene category tasks. The experiments have demonstrated that our learning model can get better performance with less training data.

In this paper we present our investigation into improving the performance of our computer-assisted language learning (CALL) system through exploiting the acoustic model and features within the speech recognition framework. First, to alleviate channel distortion, speaker-dependent cepstrum mean normalization (CMN) is adopted and the average correlation coefficient (average CC) between machine and expert scores is improved from 78.00% to 84.14%. Second, heteroscedastic linear discriminant analysis (HLDA) is adopted to enhance the discriminability of the acoustic model, which successfully increases the average CC from 84.14% to 84.62%. Additionally, HLDA causes the scoring accuracy to be more stable at various pronunciation proficiency levels, and thus leads to an increase in the speaker correct-rank rate from 85.59% to 90.99%. Finally, we use maximum a posteriori (MAP) estimation to tune the acoustic model to fit strongly accented test speech. As a result, the average CC is improved from 84.62% to 86.57%. These three novel techniques improve the accuracy of evaluating pronunciation quality.

An age-group classification method based on a fusion of different classifiers with different two-dimensional feature extraction algorithms is proposed. Theoretically, an integration of multiple classifiers can provide better performance compared to a single classifier. In this paper, we extract effective features from one sample image using different dimensional reduction methods, construct multiple classifiers in each subspace, and combine them to reduce age-group classification errors. As for the dimensional reduction methods, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) are used. These algorithms are antisymmetric in the treatment of the rows and the columns of the images. We prepared the row-based and column-based algorithms to make two different classifiers with different error tendencies. By combining these classifiers with different errors, the performance can be improved. Experimental results show that our fusion-based age-group classification method achieves better performance than existing two-dimensional algorithms alone.

In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA) method, based on Two-Dimensional Principle Component Analysis (2DPCA) concept. In particular, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the Small Sample Size (SSS) problem, thus overcoming the traditional LDA. We combine 2DPCA and our proposed 2DLDA on the Two-Dimensional Linear Discriminant Analysis of principle component vectors framework. Our framework consists of two steps: first we project an input face image into the family of projected vectors via 2DPCA-based technique, second we project from these space into the classification space via 2DLDA-based technique. This does not only allows further reducing of the dimension of feature matrix but also improving the classification accuracy. Experimental results on ORL and Yale face database showed an improvement of 2DPCA-based technique over the conventional PCA technique.

This paper presents a personal identification method based on BMME and LDA for images acquired at anterior and posterior occlusion expression of teeth. The method consists of teeth region extraction, BMME, and pattern recognition for the images acquired at the anterior and posterior occlusion state of teeth. Two occlusions can provide consistent teeth appearance in images and BMME can reduce matching error in pattern recognition. Using teeth images can be beneficial in recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, the algorithm was successful in teeth recognition for personal identification for 20 people, which encouraged our method to be able to contribute to multi-modal authentication systems.

In this paper, we present the biometric authentication system based on the fusion of two user-friendly biometric modalities: Iris and Face. Using one biometric feature can lead to good results, but there is no reliable way to verify the classification. To achieve robust identification and verification we are combining two different biometric features. We specifically apply 2-D discrete wavelet transform to extract the feature sets of low dimensionality from the iris and face. And then to obtain Reduced Joint Feature Vector (RJFV) from these feature sets, Direct Linear Discriminant Analysis (DLDA) is used in our multimodal system. This system can operate in two modes: to identify a particular person or to verify a person's claimed identity. Our results for both cases show that the proposed method leads to a reliable person authentication system.

Much of the data we deal with every day is organized hierarchically: file systems, library classification schemes and yellow page categories are salient examples. Business data too, benefits from a hierarchical organization, and indeed the hierarchical data model was quite prevalent thirty years ago. Due to the recently increased importance of X. 500/LDAP directories, which are hierarchical, and the prevalence of aggregation hierarchies in datacubes, there is now renewed interest in the hierarchical organization of data. In this paper, we develop a framework for a modern hierarchical data model, substantially improved from the original version by taking advantage of the lessons learned in the relational database context. We argue that this new hierarchical data model has many benefits with respect to the ubiquitous flat relational data model.