nils-herrmann/clean_scirepeval huggingface dataset:
Learning with multiple Gaussian distance kernels for time series classification

Various distance measures have been proposed for time series classification, and several of them have been used to construct Gaussian distance kernels for support vector machine (SVM) - based classification. Considering that different Gaussian distance kernels may carry complementary information for classification, in this paper, we propose a multiple kernel learning (MKL) method to integrate multiple Gaussian distance kernels to further improve time series classification accuracy. We first adopt the classical Gaussian RBF (GRBF) kernel and the recently developed Gaussian elastic metric distance kernel (i.e. GERP kernel and GTWED kernel), and then use an efficient MKL, SimpleMKL, to learn the kernel classifier. Our experimental results on 12 UCR time series data sets show that the proposed method is superior to SVM with individual Gaussian distance kernel.
Tailoring the luminescence of anodized aluminum oxide with atomic layer deposited ZnO thin films

The combination of zinc oxide (ZnO) thin film and anodized aluminum oxide (AAO) with the assistance of atomic layer deposition (ALD) technique is performed to investigate into the luminescence tailoring effect and mechanism of such a light emitting device, considering the strong luminescence of both ZnO and AAO nanostructures. Nanoscale ZnO thin films were deposited by ALD into the nanopores of AAO template to fabricate large surface-to-volume ratio device with good mechanical and chemical stability. The photoluminescence of large surface-to-volume ratio nanostructured ZnO/AAO system exhibits tunable broad band light emission from ultraviolet to infrared. Given the investigated mechanism of this system, tailoring of luminescence can be achieved and possible applications are proposed according to experimental observations.
FPGA acceleration for feature based processing applications

Feature based vision applications rely on highly efficient extraction and analysis of features from images to reach satisfactory levels of performance and latency. In this paper, we describe the implementation of an algorithm that combines distributed feature detector (D-HCD) with a rotational invariant feature descriptor (R-HOG). Based on an algorithmic comparison with other feature detectors and descriptors, we show that our algorithms have the lowest error rate for 3D aerial scene matching. We present implementation on a low-cost Zynq FPGA that achieves 15x speedup, 5x reduction in latency over a quad core CPU. Our results show the considerable promise of our proposed implementation for fast and efficient robotic and aerial drone / UAV applications.
Regularized Robust Coding for Face Recognition

Recently the sparse representation based classification (SRC) has been proposed for robust face recognition (FR). In SRC, the testing image is coded as a sparse linear combination of the training samples, and the representation fidelity is measured by the l2-norm or l1 -norm of the coding residual. Such a sparse coding model assumes that the coding residual follows Gaussian or Laplacian distribution, which may not be effective enough to describe the coding residual in practical FR systems. Meanwhile, the sparsity constraint on the coding coefficients makes the computational cost of SRC very high. In this paper, we propose a new face coding model, namely regularized robust coding (RRC), which could robustly regress a given signal with regularized regression coefficients. By assuming that the coding residual and the coding coefficient are respectively independent and identically distributed, the RRC seeks for a maximum a posterior solution of the coding problem. An iteratively reweighted regularized robust coding (IR3C) algorithm is proposed to solve the RRC model efficiently. Extensive experiments on representative face databases demonstrate that the RRC is much more effective and efficient than state-of-the-art sparse representation based methods in dealing with face occlusion, corruption, lighting, and expression changes, etc.
Robust filtering by fictitious noises

In this paper, a new approach is presented for robust filtering of a linear discrete-time signal by applying fictitious noise. Modeling errors, in both the numerator and denominator of the transfer functions, are parameterized by using random variables with zero mean and known covariance. The robust performance is obtained by minimizing the mean square estimation error over all of the random parameter and noise. To derive a robust estimator, the uncertainties in the model are incorporated into two mutually uncorrelated fictitious noises with zero means. The covariances of the fictitious noises are computed by using two formulas that are presented in this paper. An illustrative example shows the effectiveness of our approach.
Gabor Surface Feature for face recognition

Gabor filters can extract multi-orientation and multiscale features from face images. Researchers have designed different ways to use the magnitude of the filtered results for face recognition: Gabor Fisher classifier exploited only the magnitude information of Gabor magnitude pictures (GMPs); Local Gabor Binary Pattern uses only the gradient information. In this paper, we regard GMPs as smooth surfaces. By completely describing the shape of GMPs, we get a face representation method called Gabor Surface Feature (GSF). First, we compute the magnitude, 1st and 2nd derivatives of GMPs, then binarize them and transform them into decimal values. Finally we construct joint histograms and use subspace methods for classification. Experiments on FERET, ORL and FRGC 1.0.4 database show the effectiveness of GSF.
Median Filtering-Based Quotient Image Model for Face Recognition with Varying Lighting Conditions

Using the framework of quotient image model, this paper presented a median filtering-based method (MFQI) for the illumination normalization of facial images. The proposed method first uses adaptive median filtering to derive an estimation of the received light, and then uses the quotient image model for local illumination normalization. Compared with the other quotient image methods, MFQI does not require any assumptions on lighting conditions and shadows, and is easy to be implemented. Experimental results indicate that the proposed method is effective in face recognition with varying lighting conditions, and can achieve high recognition accuracy using the Yale face database B.
A Framework of Joint Graph Embedding and Sparse Regression for Dimensionality Reduction

Over the past few decades, a large number of algorithms have been developed for dimensionality reduction. Despite the different motivations of these algorithms, they can be interpreted by a common framework known as graph embedding. In order to explore the significant features of data, some sparse regression algorithms have been proposed based on graph embedding. However, the problem is that these algorithms include two separate steps: (1) embedding learning and (2) sparse regression. Thus their performance is largely determined by the effectiveness of the constructed graph. In this paper, we present a framework by combining the objective functions of graph embedding and sparse regression so that embedding learning and sparse regression can be jointly implemented and optimized, instead of simply using the graph spectral for sparse regression. By the proposed framework, supervised, semisupervised, and unsupervised learning algorithms could be unified. Furthermore, we analyze two situations of the optimization problem for the proposed framework. By adopting an ℓ2,1-norm regularization for the proposed framework, it can perform feature selection and subspace learning simultaneously. Experiments on seven standard databases demonstrate that joint graph embedding and sparse regression method can significantly improve the recognition performance and consistently outperform the sparse regression method.
Always Stay Flexible! WfMS-independent Business Process Controlling in SOA

Nowadays, businesses need to change their operational processes in a ?exible and agile manner to keep the competitive edge. The fruitful combination of SOA (beyond'only' Web Services / BPEL based SOA implementations)and WfMS can be used to run business processes through the 'orchestration' of services with business value. However, work?ow applications (WfApp) often use proprietary interfaces from WfMS resulting in a tight coupling between both. If the WfMS has to change that easily becomes a critical cost factor. Beside service orchestration a continuous process optimizationis crucial. This requires business process controlling (BPC)that provides a substantial contribution to the competitiveness of enterprises. We thus address two problems: Firstly, we present an integration architecture that decouples a WfApp from the underlying WfMS. Secondly, BPC components are integrated into the architecture, which are based on a data warehouse and a service ('BPC logger') to record business objects. A prototypic implementation using a standardized insurance process validates the results.
Rotation invariant texture classification using adaptive LBP with directional statistical features

Local Binary Pattern (LBP) has been widely used in texture classification because of its simplicity and computational efficiency. Traditional LBP codes the sign of the local difference and uses the histogram of the binary code to model the given image. However, the directional statistical information is ignored in LBP. In this paper, some directional statistical features, specifically the mean and standard deviation of the local absolute difference are extracted and used to improve the LBP classification efficiency. In addition, the least square estimation is used to adaptively minimize the local difference for more stable directional statistical features, and we call this scheme the adaptive LBP (ALBP). By coupling the directional statistical features with ALBP, a new rotation invariant texture classification method is presented. Experiments on a large texture database show that the proposed texture feature extraction and classification scheme could significantly improve the classification accuracy of LBP.
Monitor blood glucose levels via breath analysis system and Sparse Representation approach

It has been reported that the abnormal concentration of acetone in exhaled air is an indicator of diabetes and the concentration rises progressively with the blood glucose level of patients. Therefore, the acetone in human breath can be used to monitor the development of diabetes. In this paper, we introduce a breath analysis system to measure acetone in human breath, and therefore to evaluate the blood glucose levels of diabetics. The system structure, breath collection method, and signal preprocessing method are introduced. To enhance the system performance, we use a novel classification approach, i.e., Sparse Representation based Classification (SRC), to classify diabetics' breath samples into different blood glucose levels. Experimental results show that coupling with SRC, the system is able to classify these levels with satisfactory accuracy.
A novel MOSFET structure with asymmetric Schottky and P-N junction source/drain on bulk silicon substrate

An asymmetric Schottky and P-N junction source/drain MOSFET contains a conventional P-N junction and a hybrid junction for the source and drain or vice versa. Owing to the asymmetric source/drain structure, this device could be used in two different situations: Schottky-junction source MOSFETs and Schottky-junction drain MOSFETs. Performances of MOSFETs featuring a gate length of 100 nm with Schottky-junction source, Schottky-junction drain, Schottky-junction source/drain and conventional P-N junction have been obtained via numerical simulation by using Silvaco software package. The Schottky-junction source MOSFET exhibits significantly lower GIDL leakage current compared with the pure Schottky-barrier MOSFETs and slightly lower drive current compared with conventional P-N junction MOSFETs when the Schottky barrier height is set at 0.13 eV. For the Schottky-junction drain MOSFET, its drive current is found to be similar to that of conventional P-N junction MOSFETs and less sensitive to variations of Schottky barrier height compared with pure Schottky-barrier MOSFET.
A reorganized innovation approach to linear estimation

This note will address a linear minimum variance estimation of discrete-time systems with instantaneous and delayed measurements. Although the problem may be approached via system augmentation and standard Kalman filtering, the computation of filter may be expensive when the dimension of the system is high and the measurement lag is significant. In this note, a new tool, termed as reorganized innovation sequence, is presented for deriving the optimal filter. The optimal filter is given by two Riccati difference equations (RDEs) with the same dimension as that of the original system. The approach is shown to induce saving of computational cost as compared to the system augmentation approach, especially when the delay is large. Further, it can be extended to solving the more complicated H/sub /spl infin// fixed-lag smoothing in Krein space.
Distal-Interphalangeal-Crease-Based User Authentication System

Touchless-based fingerprint recognition technology is thought to be an alternative to touch-based systems to solve problems of hygienic, latent fingerprints, and maintenance. However, there are few studies about touchless fingerprint recognition systems due to the lack of a large database and the intrinsic drawback of low ridge-valley contrast of touchless fingerprint images. This paper proposes an end-to-end solution for user authentication systems based on touchless fingerprint images in which a multiview strategy is adopted to collect images and the robust fingerprint feature of touchless image is extracted for matching with high recognition accuracy. More specifically, a touchless multiview fingerprint capture device is designed to generate three views of raw images followed by preprocessing steps including region of interest (ROI) extraction and image correction. The distal interphalangeal crease (DIP)-based feature is then extracted and matched to recognize the human's identity in which part selection is introduced to improve matching efficiency. Experiments are conducted on two sessions of touchless multiview fingerprint image database with 541 fingers acquired about two weeks apart. An EER of ~ 1.7% can be achieved by using the proposed DIP-based feature, which is much better than touchless fingerprint recognition by using scale invariant feature transformation (SIFT) and minutiae features. The given fusion results show that it is effective to combine the DIP-based feature, minutiae, and SIFT feature for touchless fingerprint recognition systems. The EER is as low as ~ 0.5%.
"Non-locality" Preserving Projection and Its Application to Palmprint Recognition

This paper develops a "non-locality" preserving projection (NLPP) technique for feature extraction. In contrast to the existing locality preserving projection (LPP), a technique based on the characterization of the local scatter, NLPP is a method based on the characterization of the non-local scatter. Intuitively, NLPP should be more effective than LPP when the non-local information plan a dominant role in discrimination. NLPP is tested using the PolyU palmprint database and the experimental results show that NLPP outperforms PCA, LDA and LPP
Palmprint Texture Analysis Using Derivative of Gaussian Filters

This paper presents a novel approach of palmprint texture analysis based on the derivative of Gaussian filter. In this approach, the palmprint image is respectively preprocessed along horizontal and vertical direction using derivative of Gaussian (DoG) filters. And then the palmprint is encoded according to the sign of the value of each pixel of the filtered images. This code is called DoGCode of the palmprint. The size of DoGCode is 256 bytes. The similarity of two DoGCode is measured using their Hamming distance. This approach is tested on the PolyU Palmprint Database, which containing 7605 samples from 392 palms, and the EER is 0.19%, which is comparable with the existing palmprint recognition methods
Controllable Filament With Electric Field Engineering for Resistive Switching Uniformity

Variations in SET and RESET voltages during resistive switching cause problems for transition-metal-oxide-based resistive memory applications. The random circuit breaker network model has illustrated that the random formation and rupture of conducting filaments result in this instability. A solution consisting of a carefully designed electric field redistribution is proposed to minimize these variations. This switching behavior occurs in an ultrathin Al2O3 film very near to the anode rather than occurring in a random distribution over a large region. The SET voltage distribution is narrowed by a factor of seven, and consequently, the RESET voltage distribution is confined near a fixed value.
Macroelectronics: Perspectives on Technology and Applications

Flexible, large area electronics - macroelectronics - using amorphous silicon, low-temperature polysilicon, or various organic and inorganic nanocrystalline semiconductor materials is beginning to show great promise. While much of the activity in macroelectronics has been display-centric, a number of applications where macroelectronics is needed to enable solutions that are otherwise not feasible are beginning to attract technical and/or commercial interest. In this paper, we discuss the application drivers and the technology needs and device performance requirements to enable high performance applications to include RF systems.
Interscale image denoising with wavelet context modeling

The paper presents a wavelet-based linear minimum mean square-error estimation (LMMSE) scheme to exploit the strong wavelet interscale dependencies for image denoising. Using overcomplete wavelet expansion (OWE), we group the wavelet coefficients with the same spatial orientation at adjacent scales as a vector. The LMMSE algorithm is then applied to the vector variable. This scheme exploits the correlation information of wavelet scales to improve noise removal. To calculate the statistics of wavelet coefficients more adaptively, we classify them into different clusters by the context modeling technique, which yields a good local discrimination between edge structures and backgrounds. Experiments show that the proposed scheme outperforms some existing denoising methods. A biorthogonal wavelet, which well characterizes the interscale dependencies, is found very suitable for the scheme.
Sensor Evaluation in a Breath Analysis System

Breath analysis systems contain arrays of correlated chemical sensors. For such systems, sensor selection is needed. From the process of sensor selection, some insight behind the performance of different sensor arrays can be gotten. Thus, we can know more about the sensors, which could help us with the selection work in turn. In this paper, a breath analysis system for diabetes diagnosis with 16 sensors is described. Based on this system, several methods are proposed to evaluate the importance, unique discriminant information and redundancy of each sensor. They are based on the results of exhaustive sensor selection. These methods are made convenient to observe and draw intuitive conclusions. They are applied to the breath analysis system and some useful discoveries about the sensors in the system are made accordingly.
Joint Learning of Single-Image and Cross-Image Representations for Person Re-identification

Person re-identification has been usually solved as either the matching of single-image representation (SIR) or the classification of cross-image representation (CIR). In this work, we exploit the connection between these two categories of methods, and propose a joint learning frame-work to unify SIR and CIR using convolutional neural network (CNN). Specifically, our deep architecture contains one shared sub-network together with two sub-networks that extract the SIRs of given images and the CIRs of given image pairs, respectively. The SIR sub-network is required to be computed once for each image (in both the probe and gallery sets), and the depth of the CIR sub-network is required to be minimal to reduce computational burden. Therefore, the two types of representation can be jointly optimized for pursuing better matching accuracy with moderate computational cost. Furthermore, the representations learned with pairwise comparison and triplet comparison objectives can be combined to improve matching performance. Experiments on the CUHK03, CUHK01 and VIPeR datasets show that the proposed method can achieve favorable accuracy while compared with state-of-the-arts.
F-SVM: Combination of Feature Transformation and SVM Learning via Convex Relaxation

The generalization error bound of the support vector machine (SVM) depends on the ratio of the radius and margin. However, conventional SVM only considers the maximization of the margin but ignores the minimization of the radius, which restricts its performance when applied to joint learning of feature transformation and the SVM classifier. Although several approaches have been proposed to integrate the radius and margin information, most of them either require the form of the transformation matrix to be diagonal, or are nonconvex and computationally expensive. In this paper, we suggest a novel approximation for the radius of the minimum enclosing ball in feature space, and then propose a convex radius-margin-based SVM model for joint learning of feature transformation and the SVM classifier, i.e., F-SVM. A generalized block coordinate descent method is adopted to solve the F-SVM model, where the feature transformation is updated via the gradient descent and the classifier is updated by employing the existing SVM solver. By incorporating with kernel principal component analysis, F-SVM is further extended for joint learning of nonlinear transformation and the classifier. F-SVM can also be incorporated with deep convolutional networks to improve image classification performance. Experiments on the UCI, LFW, MNIST, CIFAR-10, CIFAR-100, and Caltech101 data sets demonstrate the effectiveness of F-SVM.
A performance evaluation of filter design and coding schemes for palmprint recognition

Palmprint recognition, as one of the most promising biometrics, has received considerable recent biometric research interest. Among various palmprint recognition techniques, coding based methods have been very successful since of its simplicity, high precision, small size of feature and rapidness for both feature extraction and matching. Several filters, such as Gabor and Gaussian, and coding schemes, such as competitive and ordinal measure, have been proposed for palmprint verification and identification. In this paper, we evaluate three filters, Gabor, Gaussian, and the second derivative of Gaussian filter, and two coding schemes, competitive code and ordinal measure on PolyU palmprint database. Results of verification experiment show that Gabor filter and competitive coding scheme is superior to other methods.