nils-herrmann/clean_scirepeval huggingface dataset:
Non-negative graph embedding

We introduce a general formulation, called non-negative graph embedding, for non-negative data decomposition by integrating the characteristics of both intrinsic and penalty graphs . In the past, such a decomposition was obtained mostly in an unsupervised manner, such as Non-negative Matrix Factorization (NMF) and its variants, and hence unnecessary to be powerful at classification. In this work, the non-negative data decomposition is studied in a unified way applicable for both unsupervised and supervised/semi-supervised configurations. The ultimate data decomposition is separated into two parts, which separatively preserve the similarities measured by the intrinsic and penalty graphs, and together minimize the data reconstruction error. An iterative procedure is derived for such a purpose, and the algorithmic non-negativity is guaranteed by the non-negative property of the inverse of any M-matrix. Extensive experiments compared with NMF and conventional solutions for graph embedding demonstrate the algorithmic properties in sparsity, classification power, and robustness to image occlusions.
Bezier Interpolation for 3-D Freehand Ultrasound

Freehand 3-D ultrasound (US) produces 3-D volume data of anatomical objects from a sequence of irregularly located 2-D B-mode US images (B-scans). In 3-D US, the voxel intensities are calculated by interpolating those pixels from raw B-scans. Current interpolation algorithms do not consider sparsity of the raw data and are time consuming in computation. In this paper, we aim to perform the 3-D reconstruction of freehand US with sparse raw data in a more efficient manner. A novel interpolation algorithm takes advantage of Bezier curves. A single sweep of raw B-scans is collected, and the third-order Bezier curves are employed for approximating the voxels located in a control window. In in vitro and in vivo experiments, a fetus phantom and a subject's forearm were scanned using the freehand 3-D US system and reconstructed using the proposed Bezier interpolation algorithm and three popular interpolation algorithms, respectively. The results showed that the proposed algorithm significantly outperformed the other three algorithms when the raw B-scans were relatively sparse and the interpolation error in gray level can be reduced by 0.51-5.07. The speed for 3-D reconstruction can be improved by 90.6-97.2 because a single third-order Bezier curve using four control points (i.e., the pixel points) is able to estimate more than four voxels, whereas the estimation of a voxel value often requires a number of pixels in conventional techniques.
Temporal Multimodal Learning in Audiovisual Speech Recognition

In view of the advantages of deep networks in producing useful representation, the generated features of different modality data (such as image, audio) can be jointly learned using Multimodal Restricted Boltzmann Machines (MRB-M). Recently, audiovisual speech recognition based the M-RBM has attracted much attention, and the MRBM shows its effectiveness in learning the joint representation across audiovisual modalities. However, the built networks have weakness in modeling the multimodal sequence which is the natural property of speech signal. In this paper, we will introduce a novel temporal multimodal deep learning architecture, named as Recurrent Temporal Multimodal RB-M (RTMRBM), that models multimodal sequences by transforming the sequence of connected MRBMs into a probabilistic series model. Compared with existing multimodal networks, it's simple and efficient in learning temporal joint representation. We evaluate our model on audiovisual speech datasets, two public (AVLetters and AVLetters2) and one self-build. The experimental results demonstrate that our approach can obviously improve the accuracy of recognition compared with standard MRBM and the temporal model based on conditional RBM. In addition, RTMRBM still outperforms non-temporal multimodal deep networks in the presence of the weakness of long-term dependencies.
Cascaded Subpatch Networks for Effective CNNs

Conventional convolutional neural networks use either a linear or a nonlinear filter to extract features from an image patch (region) of spatial size <inline-formula> <tex-math notation="LaTeX">$ H\times W $ </tex-math></inline-formula> (typically, <inline-formula> <tex-math notation="LaTeX">$ H $ </tex-math></inline-formula> is small and is equal to <inline-formula> <tex-math notation="LaTeX">$ W$ </tex-math></inline-formula>, e.g., <inline-formula> <tex-math notation="LaTeX">$ H $ </tex-math></inline-formula> is 5 or 7). Generally, the size of the filter is equal to the size <inline-formula> <tex-math notation="LaTeX">$ H\times W $ </tex-math></inline-formula> of the input patch. We argue that the representational ability of equal-size strategy is not strong enough. To overcome the drawback, we propose to use subpatch filter whose spatial size <inline-formula> <tex-math notation="LaTeX">$ h\times w $ </tex-math></inline-formula> is smaller than <inline-formula> <tex-math notation="LaTeX">$ H\times W $ </tex-math></inline-formula>. The proposed subpatch filter consists of two subsequent filters. The first one is a linear filter of spatial size <inline-formula> <tex-math notation="LaTeX">$ h\times w $ </tex-math></inline-formula> and is aimed at extracting features from spatial domain. The second one is of spatial size <inline-formula> <tex-math notation="LaTeX">$ 1\times 1 $ </tex-math></inline-formula> and is used for strengthening the connection between different input feature channels and for reducing the number of parameters. The subpatch filter convolves with the input patch and the resulting network is called a subpatch network. Taking the output of one subpatch network as input, we further repeat constructing subpatch networks until the output contains only one neuron in spatial domain. These subpatch networks form a new network called the cascaded subpatch network (CSNet). The feature layer generated by CSNet is called the <italic>csconv</italic> layer. For the whole input image, we construct a deep neural network by stacking a sequence of <italic>csconv</italic> layers. Experimental results on five benchmark data sets demonstrate the effectiveness and compactness of the proposed CSNet. For example, our CSNet reaches a test error of 5.68% on the CIFAR10 data set without model averaging. To the best of our knowledge, this is the best result ever obtained on the CIFAR10 data set.
Face sketch-photo synthesis based on support vector regression

The existing face sketch-photo synthesis methods trend to lose some vital details more or less. In this paper, we propose a novel sketch-photo synthesis approach based on support vector regression (SVR) to handle this difficulty. First, we utilize an existing method to acquire the initial estimate of the synthesized image. Then, the final synthesized image is obtained by combining the initial estimate and the SVR based high frequency information together to further enhance the quality of synthesized image. Experimental results on the benchmark database and our new constructed database demonstrate that the proposed method can achieve significant improvement on perceptual quality. Moreover, the synthesized face images can obtain higher recognition rate when used in retrieval system.
Secrecy Outage and Diversity Analysis of Cognitive Radio Systems

In this paper, we investigate the physical-layer security of a multi-user multi-eavesdropper cognitive radio system, which is composed of multiple cognitive users (CUs) transmitting to a common cognitive base station (CBS), {while multiple eavesdroppers may collaborate with each other or perform independently in intercepting the CUs-CBS transmissions, which are called the coordinated and uncoordinated eavesdroppers, respectively}. Considering multiple CUs available, we propose the round-robin scheduling as well as the optimal and suboptimal user scheduling schemes for improving the security of CUs-CBS transmissions against eavesdropping attacks. Specifically, the optimal user scheduling is designed by assuming that the channel state information (CSI) of all links from CUs to CBS, to primary user (PU) and to eavesdroppers are available. By contrast, the suboptimal user scheduling only requires the CSI of CUs-CBS links without the PU's and eavesdroppers' CSI. We derive closed-form expressions of the secrecy outage probability of these three scheduling schemes in the presence of {the coordinated and uncoordinated eavesdroppers}. We also carry out the secrecy diversity analysis and show that the round-robin scheduling achieves the diversity order of only one, whereas the optimal and suboptimal scheduling schemes obtain the full secrecy diversity, {no matter whether the eavesdroppers collaborate or not. In addition, numerical secrecy outage results demonstrate that for both the coordinated and uncoordinated eavesdroppers, the optimal user scheduling achieves the best security performance and the round-robin scheduling performs the worst.} Finally, upon increasing the number of CUs, the secrecy outage probabilities of the optimal and suboptimal user scheduling schemes both improve significantly.
Rank-$k$ 2-D Multinomial Logistic Regression for Matrix Data Classification

The amount of matrix data has increased rapidly nowadays. How to classify matrix data efficiently is an important issue. In this paper, by discovering the shortages of 2-D linear discriminant analysis and 2-D logistic regression, a novel 2-D framework named rank-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> 2-D multinomial logistic regression (2DMLR-RK) is proposed. The 2DMLR-RK is designed for a multiclass matrix classification problem. In the proposed framework, each category is modeled by a left projection matrix and a right projection matrix with rank <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>. The left projection matrices capture the row information of matrix data, and the right projection matrices acquire the column information. We identify the parameter <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> plays the role of balancing the capacity of learning and generalization of the 2DMLR-RK. In addition, we develop an effective framework for solving the proposed nonconvex optimization problem. The convergence, initialization, and computational complexity are discussed. Extensive experiments on various types of data sets are conducted. Comparing with 1-D methods, 2DMLR-RK not only achieves a better classification accuracy, but also costs less computation time. Comparing with other state-of-the-art 2-D methods, the 2DMLR-RK achieves a better performance for matrix data classification.
Single image super resolution with high resolution dictionary

Image super resolution (SR) is a technique to estimate or synthesize a high resolution (HR) image from one or several low resolution (LR) images. This paper proposes a novel framework for single image super resolution based on sparse representation with high resolution dictionary. Unlike the previous methods, the training set is constructed from the HR images instead of HR-LR image pairs. Due to this property, there is no need to retrain a new dictionary when the zooming factor changed. Given a testing LR image, the patch-based representation coefficients and the desired image are estimated alternately through the use of dynamic group sparsity, the fidelity term and the non-local means regularization. Experimental results demonstrate the effectiveness of the proposed algorithm.
Re-Weighted Discriminatively Embedded $K$ -Means for Multi-View Clustering

Recent years, more and more multi-view data are widely used in many real-world applications. This kind of data (such as image data) is high dimensional and obtained from different feature extractors, which represents distinct perspectives of the data. How to cluster such data efficiently is a challenge. In this paper, we propose a novel multi-view clustering framework, called re-weighted discriminatively embedded  $K$ -means, for this task. The proposed method is a multi-view least-absolute residual model, which induces robustness to efficiently mitigates the influence of outliers and realizes dimension reduction during multi-view clustering. Specifically, the proposed model is an unsupervised optimization scheme, which utilizes iterative re-weighted least squares to solve least-absolute residual and adaptively controls the distribution of multiple weights in a re-weighted manner only based on its own low-dimensional subspaces and a common clustering indicator matrix. Furthermore, theoretical analysis (including optimality and convergence analysis) and the optimization algorithm are also presented. Compared with several state-of-the-art multi-view clustering methods, the proposed method substantially improves the accuracy of the clustering results on widely used benchmark data sets, which demonstrates the superiority of the proposed work.
Mining Spatial-Temporal Patterns and Structural Sparsity for Human Motion Data Denoising

Motion capture is an important technique with a wide range of applications in areas such as computer vision, computer animation, film production, and medical rehabilitation. Even with the professional motion capture systems, the acquired raw data mostly contain inevitable noises and outliers. To denoise the data, numerous methods have been developed, while this problem still remains a challenge due to the high complexity of human motion and the diversity of real-life situations. In this paper, we propose a data-driven-based robust human motion denoising approach by mining the spatial-temporal patterns and the structural sparsity embedded in motion data. We first replace the regularly used entire pose model with a much fine-grained partlet model as feature representation to exploit the abundant local body part posture and movement similarities. Then, a robust dictionary learning algorithm is proposed to learn multiple compact and representative motion dictionaries from the training data in parallel. Finally, we reformulate the human motion denoising problem as a robust structured sparse coding problem in which both the noise distribution information and the temporal smoothness property of human motion have been jointly taken into account. Compared with several state-of-the-art motion denoising methods on both the synthetic and real noisy motion data, our method consistently yields better performance than its counterparts. The outputs of our approach are much more stable than that of the others. In addition, it is much easier to setup the training dataset of our method than that of the other data-driven-based methods.
Motion Blur Detection With an Indicator Function for Surveillance Machines

Motion is an important clue for industrial inspection, video surveillance, and service machines to localize and recognize products and objects. Because blur co-occurs with motion, it is desirable for developing efficient and robust motion blur detection algorithm. However, existing algorithms are inefficient for detecting spatially varying motion blur. To deal with the problem, this paper presents a theorem, according to which, motion blur can be efficiently detected and segmented. According to the theorem, the proposed algorithm requires a simple filtering operation and variance computation. Classification as either blurred or unblurred pixel can be done by substituting the variance into the proposed simple formula and checking the sign of the resulting value. Moreover, a geometric interpretation and two extensions of the algorithm are given. Importantly, based on the geometric interpretation of the indicator function, we develop a one-class classifier, which is more effective than the indicator function and has comparable computational cost of the indicator function. Experimental results on detecting motion-blurred cars, motorcycles, bicycles, bags, and persons demonstrate that the proposed algorithm is very efficient without loss of effectiveness.
Single-Image Super-Resolution via Sparse Coding Regression

In this paper, it has been shown that the sparse coding algorithm for single-image super-resolution is equivalent to a linear regression algorithm in the sparse coding space. Following the idea, the sparse coding algorithm are generalized by a novel $L_{2}$-Boosting-based single-resolution super-resolution algorithm which focuses on the relationship between sparse codings corresponding to the low- and high-resolution image patches. The experimental results demonstrate the effectiveness of the proposed algorithm by comparing with other state-of-the-art algorithms.
A Robust Gradient-Based Algorithm to Correct Bias Fields of Brain MR Images

We developed a novel algorithm to estimate bias fields from brain magnetic resonance (MR) images using a gradient-based method. The bias field is modeled as a multiplicative and slowly varying surface. We fit the bias field by a low-order polynomial. The polynomial's parameters are directly obtained by minimizing the sum of square errors between the gradients of MR images (both in the x-direction and y-direction) and the partial derivatives of the desired polynomial in the log domain. Compared to the existing retrospective algorithms, our algorithm combines the estimation of the gradient of the bias field and the reintegration of the obtained gradient polynomial together so that it is more robust against noise and can achieve better performance, which are demonstrated through experiments with both real and simulated brain MR images.
2.5-D Extended Field-of-View Ultrasound

Recently, the growing emphasis on medical ultrasound (US) has led to a rapid development of US extended field-of-view (EFOV) techniques. US EFOV techniques can be classified into three categories: 2-D US EFOV, 3-D US, and 3-D US EFOV. In this paper, we propose a novel EFOV method called 2.5-D US EFOV that combines both the advantages of the 2-D US EFOV and the 3-D US by generating a panorama on a curved image plane guided by a curved scanning trajectory of the US probe. In 2.5-D US EFOV, the real-time position and orientation of the US image plane can be recorded via an electromagnetic spatial sensor attached to the probe. The scanning direction is not necessarily straight and can be curved according to the regions of interest (ROI). To form the curved panorama, an image cutting method is proposed. Finally, the curved panorama is rendered in a 3-D space using a surface rendering based on a texture mapping technique. This allows 3-D measurements of lines and angles. Phantom experiments demonstrated that 2.5-D US EFOV images could show anatomical structures of ROI accurately and rapidly. The overall average errors for the distance and angle measurements are −0.097 ± 0.128 cm (−1% ± 1.2%) and 1.50° ± 1.60° (1.9% ± 2%), respectively. A typical extended US image can be reconstructed from 321 B-scans images within 3 s. The satisfying quantitative result on the spinal tissues of a scoliosis subject demonstrates that our system has potential applications in the assessment of musculoskeletal issues.
Object Discovery via Cohesion Measurement

Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a cohesion measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new CM, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM-based method has achieved promising performance for these two tasks.
Lazy Random Walks for Superpixel Segmentation

We present a novel image superpixel segmentation approach using the proposed lazy random walk (LRW) algorithm in this paper. Our method begins with initializing the seed positions and runs the LRW algorithm on the input image to obtain the probabilities of each pixel. Then, the boundaries of initial superpixels are obtained according to the probabilities and the commute time. The initial superpixels are iteratively optimized by the new energy function, which is defined on the commute time and the texture measurement. Our LRW algorithm with self-loops has the merits of segmenting the weak boundaries and complicated texture regions very well by the new global probability maps and the commute time strategy. The performance of superpixel is improved by relocating the center positions of superpixels and dividing the large superpixels into small ones with the proposed optimization algorithm. The experimental results have demonstrated that our method achieves better performance than previous superpixel approaches.
Contrast Enhancement-Based Forensics in Digital Images

As a retouching manipulation, contrast enhancement is typically used to adjust the global brightness and contrast of digital images. Malicious users may also perform contrast enhancement locally for creating a realistic composite image. As such it is significant to detect contrast enhancement blindly for verifying the originality and authenticity of the digital images. In this paper, we propose two novel algorithms to detect the contrast enhancement involved manipulations in digital images. First, we focus on the detection of global contrast enhancement applied to the previously JPEG-compressed images, which are widespread in real applications. The histogram peak/gap artifacts incurred by the JPEG compression and pixel value mappings are analyzed theoretically, and distinguished by identifying the zero-height gap fingerprints. Second, we propose to identify the composite image created by enforcing contrast adjustment on either one or both source regions. The positions of detected blockwise peak/gap bins are clustered for recognizing the contrast enhancement mappings applied to different source regions. The consistency between regional artifacts is checked for discovering the image forgeries and locating the composition boundary. Extensive experiments have verified the effectiveness and efficacy of the proposed techniques.
Gait Components and Their Application to Gender Recognition

Human gait is a promising biometrics resource. In this paper, the information about gait is obtained from the motions of the different parts of the silhouette. The human silhouette is segmented into seven components, namely head, arm, trunk, thigh, front-leg, back-leg, and feet. The leg silhouettes for the front-leg and the back-leg are considered separately because, during walking, the left leg and the right leg are in front or at the back by turns. Each of the seven components and a number of combinations of the components are then studied with regard to two useful applications: human identification (ID) recognition and gender recognition. More than 500 different experiments on human ID and gender recognition are carried out under a wide range of circumstances. The effectiveness of the seven human gait components for ID and gender recognition is analyzed.