nils-herrmann/clean_scirepeval huggingface dataset:
A Novel Framework for Web Video Thumbnail Generation

When user uploads a video clip to the video sharing websites, a video thumbnail needs to be generated as the cover to represent the video content. In this paper, a novel video thumbnail generation framework is presented. For generating a good thumbnail, three criteria are considered: (1) the thumbnail should be distinct, in order to make user feel more pleasant; (2) the thumbnail should be easily accessed by human vision, especially for the small-scale thumbnails in the web, in order to make user understand the content of the whole video at a glance; (3) the thumbnail should reflect the main content of the video. In this paper, firstly the source video is segmented into shots. Secondly, a high quality key-frame is extracted from each shot based on image quality assessment. Thirdly, visual saliency analysis is adopted to get the accessibility value of each key frame and the visual similarity analysis is employed to obtain the representativeness value of the key-frames. Finally, a linear fusing scheme is explored to fuse these two values and find the best key-frame as the video thumbnail. By using the proposed framework, we attempt to generate accessible thumbnail which may improve the viewing experience of visitors and enhance their desire to watch the video. Experiments are conducted on the videos selected from the websites and the results show that the proposed method is effective.
Learning class-specific pooling shapes for image classification

Spatial pyramid (SP) representation is an extension of bag-of-feature model which embeds spatial layout information of local features by pooling feature codes over pre-defined spatial shapes. However, the uniform style of spatial pooling shapes used in standard SP is an ad-hoc manner without theoretical motivation, thus lacking the generalization power to adapt to different distribution of geometric properties across image classes. In this paper, we propose a data-driven approach to adaptively learn class-specific pooling shapes (CSPS). Specifically, we first establish an over-complete set of spatial shapes providing candidates with more flexible geometric patterns. Then the optimal subset for each class is selected by training a linear classifier with structured sparsity constraint and color distribution cues. To further enhance the robust of our model, the representations over CSPS are compressed according to the shape importance and finally fed to SVM with a multi-shape matching kernel for classification task. Experimental results on three challenging datasets (Caltech-256, Scene-15 and Indoor-67) demonstrate the effectiveness of the proposed method on both object and scene images.
A texture-based tamper detection scheme by fragile watermark

A texture-based tamper detection scheme by a fragile watermarking technique is proposed in this paper. In comparison with other fragile watermarking schemes, the highlight of our scheme is that it is rather sensitive to malicious tamper such as replacing one's face in the image by another's and at the same time it is insensitive to other legal processing such as lossy JPEG compression and brightness/contrast changes. So it is more suitable for tamper detection in practical use.
Modification of the AdaBoost-based Detector for Partially Occluded Faces

While face detection seems a solved problem under general conditions, most state-of-the-art systems degrade rapidly when faces are partially occluded by other objects. This paper presents a solution to detect partially occluded faces by reasonably modifying the AdaBoost-based face detector. Our basic idea is that the weak classifiers in the AdaBoost-based face detector, each corresponding to a Haar-like feature, are inherently a patch-based model. Therefore, one can divide the whole face region into multiple patches, and map those weak classifiers to the patches. The weak classifiers belonging to each patch are re-formed to be a new classifier to determine if it is a valid face patch - without occlusion. Finally, we combine all of the valid face patches by assigning the patches with different weights to make the final decision whether the input subwindow is a face. The experimental results show that the proposed method is promising for the detection of occluded faces
Integrated beam synthesis technique of phased array antenna for variable look-angle SAR

An integrated beam synthesis algorithm is presented to optimize the multiple beams of variable look-angle SAR antenna. With an iterative solution scheme of improved projection matrix algorithm, the amplitude excitations (common to all beams) and the phase of such excitations (corresponding to each radiated beam) are determined simultaneously. Antenna beams are controlled by phase only in SAR system. The sample application of multiple beams SAR antenna design is given to illustrate the effectiveness of the presented algorithm. And the noise-equivalent sigma-zero (NEσ0) of SAR system is improved as well.
A new control scheme in a battery energy storage system for wind turbine generators

This paper introduces a new control strategy for battery energy storage systems used in wind generation. It is specifically targeted to large wind installations, specifically offshore wind applications. This control strategy maximizes the life of the battery and increases the efficiency of the energy storage system while still balancing the power fluctuations of a large wind turbine system. The depth of discharge and remaining battery life are the key parameters considered in this approach. The proposed control scheme is applied to the battery package such that each battery cell is controlled individually. Individual cell depth of discharge is controlled and cell life is calculated periodically so that all cells within the package reach the end of life at the same. Total storage capacity and individual battery cell size are calculated based on the characteristics of the wind turbine. To verify the presented method, a case is studied in PSCAD and corresponding results are provided.
Research of path planning for mobile robot based on improved ant colony optimization algorithm

The paper proposes an improved ant colony optimization algorithm. This method first designs two fuzzy controllers to optimize three parameters α, β, ρ. Then it establishes a dynamic searching window for ants and chaos information are added when near-neighbour city table is constituted in order to increase research speed in initial stages of algorithm. In addition, the concept of active degree of city node is presented as future information to supervise ants to construct solution and update pheromone. Finally a new evaluation criterion is produced to distinguish where paths are excellent or not. So the strategy not only conquers the weakness of easily running into local optimization while making route optimization, but also enhances efficient convergence of ant colony optimization algorithm. Results of large numbers of computer simulations demonstrate that this novel algorithm can plan optimal path rapidly in intricate three dimension (3-D) environment.
An efficient VLSI implementation of MC interpolation for MPEG-4

Quarter sample mode interpolation is one of critical paths of the MPEG-4 decoder because it has a finite impulse response (FIR) digital filter which is a computationally expensive process. Normal FIR architectures are not suitable for this application due to the set of short input data streams. After reforming a referenced pure systolic FIR, the paper gets an efficient architecture of quarter sample mode interpolation and the architecture is suitable for VLSI. Experimental result shows that the proposed architecture can satisfy MPEG-4 decoder applications.
Enhanced line-based intra prediction with fixed interpolation filtering

In this paper, a novel intra prediction algorithm, named enhanced line-based intra prediction (ELIP), is proposed to improve the traditional intra prediction methods for image/intra-frame coding. Different from the existing schemes where the interpolation filtering only depends on the intra prediction mode, in the proposed ELIP, the linear filtering coefficients are further refined by imposing both mode and position dependencies. For each intra prediction mode and each position within one block, a set of linear filtering coefficients are obtained from off-line training using the least square method. The proposed algorithm has been implemented in the latest ITU-T VCEG KTA software. Experimental results demonstrate that, compared with the intra coding scheme of KTA with new intra coding tool enabled, up to 0.44dB additional coding gain is achieved by the proposed method, while keeping applicable computational complexity for practical video codecs.
Stereoscopic learning for disparity estimation

In this paper, we propose a learning based approach to estimating pixel disparities from the motion information extracted out of input monoscopic video sequences. We represent each video frame with superpixels, and extract the motion features from the superpixels and the frame boundary. These motion features account for the motion pattern of the superpixel as well as camera motion. In the learning phase, given a pair of stereoscopic video sequences, we employ a state-of-the-art stereo matching method to compute the disparity map of each frame as ground truth. Then a multi-label SVM is trained from the estimated disparities and the corresponding motion features. In the testing phase, we use the learned SVM to predict the disparity for each superpixel in a monoscopic video sequence. Experiment results show that the proposed method achieves low error rate in disparity estimation.
Face identification from a single example image based on Face-Specific Subspace (FSS)

In this paper, a novel face representation, named Face-Specific Subspaces (FSS), is proposed first. Then the corresponding face identification strategy is provided. This method is motivated, but essentially different, from Eigenface. In the proposed method, each face holds one private face subspace, while in Eigenface all faces share one common face subspace. To enable the proposed approach to tackle single example problem, a technique to derive multi-samples from single example is further developed. Extensive experiments on Yale face database Bern face database, and our 350 subjects database show that our method makes impressive performance improvement when compared with conventional Eigenface and template matching, which intensively indicates the robustness of our approach. against appearance variances due to expression, illumination and pose.
A novel MASH 2-1 sigma-delta A/D converter with high-to-low conversion for closed-loop MEMS accelerometer interface

In this paper, an A/D converter offering the high-to-low level shifter function applied to the high range digital closed-loop MEMS accelerometer interface is presented. In the interface circuit, the analog front-end circuit works in the high-voltage domain and the digital back-end circuit works in the low-voltage domain, however, the cascade MASH 2-1 sigma-delta ADC just can achieve high-voltage signal converting to low-voltage signal without introducing additional high-to-low transition module. The level shifter is mainly realized by the first integrator by means of using switched-capacitor charge transfer principle. The cascade MASH 2-1 sigma-delta ADC performs a maximum signal-to-noise-plus-distortion ratio (S/(N+D)) rms/rms of 113.6 dB with 200 Hz bandwidth and sampling clock of 100-kHz. The interface was designed in TSMC 0.35 um CMOS process and was sent to the foundry to prototype.
Face Recognition under Varying Lighting Based on the Probabilistic Model of Gabor Phase

This paper present a novel method for robust illumination-tolerant face recognition based on the Gabor phase and a probabilistic similarity measure. Invited by the work in Eigenphases of J. Lou, et al. (2002) by using the phase spectrum of face images, we use the phase information of the multi-resolution and multi-orientation Gabor filters. We show that the Gabor phase has more discriminative information and it is tolerate to illumination variations. Then we use a probabilistic similarity measure based on a Bayesian (MAP) analysis of the difference between the Gabor phases of two face images. We train the model using some images in the illumination subset of CMU-PIE database and test on the other images of CMU-PIE database and the Yale B database and get comparative results
Low-delay window-based rate control scheme for video quality optimization in video encoder

The consistent video quality and encoding latency due to buffering are two important aspects in designing rate control scheme for the application of real-time video coding system. To well balance these two contrary objectives, we firstly analyze the constraint of buffer latency and the definition of a “consistent” video quality. Then a window-based rate control scheme is proposed with one window for controlling the rate and latency, while the other window for optimizing video quality. By applying low complexity frame level ratedistortion model in the testing sequences, our proposed method shows excellent performance in balancing the encoder buffer latency and optimized video quality. Besides, this one-pass rate control scheme is highly practical for the real-time video coding application.
Evaluation of the UHF method based on the investigation of a partial discharge case in post insulators

The ultra-high frequency (UHF) method is treated as an effective means for partial discharge (PD) detection in field monitoring in smart substations due to its outstanding noise rejection capacity and satisfying sensitivity to typical insulation defects. However, abundant evidence is still lacked to prove its universality. In this paper, the PD test of an actually manufactured post insulator with potential defects is carried out using several kinds of PD detection methods. Nevertheless, the UHF signal is not able to be detected with the coupler, while PD current pulses with large magnitude are already observed within the coupling device connected to the test object. The balanced measuring system is used to verify the origin of the detected pulses, and the limitation of the UHF method on detecting PD of this type is proved. Attempt are made to locate the defect on the test object by measuring the distribution of the temperature on the surface of the insulator. All the results and data analysis show the specific characteristics of the defect compared with those well investigated before, and demonstrates its value to be further researched in the future.
Interference suppression in communication systems with controlled crosstalk

In this paper, we consider the problem of fitting two users into a channel normally occupied by one user. The users share the same physical channel, but crosstalk is controlled. A linear receiver, which maximizes the signal-to-noise ratio (SNR) is employed, and several well known time-limited symbol waveforms are tested. Performance analysis shows that two users can be easily accommodated in a single-user channel with slight degradation of bit error rate (BER), and furthermore, near single-user performance is achievable by properly selecting the symbol waveform. A simple LMS-type algorithm is also discussed for adaptive implementation.
Study of the switching arc characteristics of a 500 kV HVDC self-excited oscillatory metallic return transfer breaker

Studies of circuit breakers are of interest to high voltage direct current (HVDC) power system. The method for investigation of the switching arc characteristics of a 500 kV metallic return transfer breaker (MRTB) is presented in this paper, by coupling magneto-hydro-dynamics (MHD) arc model with the resonant circuit equations. 2-D MHD arc model taking account of important physical processes of mass flow, Ohmic heating, radiation energy transfer, momentum exchange, Lorentz force and current flow is developed, and then the influence of stray resistor is analyzed under the interrupting current of DC 4 kA. Calculation results of the distribution of temperature and gas pressure in the nozzle region are presented, where the shock wave and negative voltage-current characteristic of arc are discussed. The calculated arc current and voltage are in agreement with the experimental results to some extent, which could verify the validity of the calculation method partially.
A novel FGS base-layer encoding model and weight-based rate adaptation for constant-quality streaming

We examine the coding problem of base layer (BL) in fine granularity scalable videos and rate adaptation of enhancement layer (EL) during streaming in order to support constant quality streaming. The problem arises from the facts that different frames of BL often exhibit significant quality variation in the usual FGS BL encoding methods, and consequently the actual EL rate-distortion (R-D) curves of different frames present significant difference. Under this condition it would be computationally exhaustive to scale the EL to flatten out the fluctuating BL quality and to attain constant quality. We propose, in this paper, an accurate constant quality encoding method of BL, under which we investigate the similarity of actual EL R-D curves, and then a simple but effective weight-based EL rate allocation algorithm is introduced. Experiments show, compared with default methods, our fast and simple methods not only provide constant-quality streaming, but also decrease the average MSE of videos.
An interactive method for curve extraction

We introduce a curve process framework to solve the challenging problem of curve extraction from “non-traceable” curve groups. We propose a comprehensive curve model, which consists of the geometric, photometric and topological sub-models. Two typical categories of the non-traceable curve groups are considered. First, for the interlaced curves with complex structures, we show how to use the proposed curve model especially the topological sub-model to extract curves from the group. Second, for the non-interlaced but over-dense or faint curves we leverage the curve group pattern priors in addition, and extract the whole pattern in a global optimization. Applications and experiments demonstrate the competence of our models and methods.
A Robust Split-and-Merge Text Segmentation Approach for Images

In this paper we describe a robust approach to segment text from color images. The proposed approach mainly includes four steps. Firstly, a preprocessing step is utilized to enhance text blocks in images; Secondly, these image blocks are split into connected components and most of them are eliminated by a component filtering procedure; Thirdly, the left connected components are merged into several text layers, and a set of appropriate constraints are applied to find the real text layer; finally, the text layer is refined through a post-processing step to generate a binary output. Our experimental results show that the proposed approach has a good performance in character recognition rate and processing speed. Moreover, it is robust to text color, font size, as well as different styles of characters in different languages
Computing importance of 2D contour parts by reconstructability

Part-based representation has become an essential representation for objects. Different parts of a shape have various importance to shape perception. Concerned by the contributions of local parts to the perception of global shapes, we introduce an importance measure of shape parts from the viewpoint of their “reconstructability”. Given one shape part as a local observation, we estimate the global object shapes through a proposed novel shape reconstruction method. The reconstruction quality is determined by part geometry variation and part uniqueness. Then a conditional entropy formulation is introduced to measure the part importance considering the reconstruction quality, part uniqueness and variation. Extensive experiments demonstrate the benefit of the proposed part importance to object detection applications. Moreover, psychological experiments are conducted to further support the consistency of our importance measure with human perception towards shape parts.
Speech Emotion Recognition Using Deep Convolutional Neural Network and Discriminant Temporal Pyramid Matching

Speech emotion recognition is challenging because of the affective gap between the subjective emotions and low-level features. Integrating multilevel feature learning and model training, deep convolutional neural networks (DCNN) has exhibited remarkable success in bridging the semantic gap in visual tasks like image classification, object detection. This paper explores how to utilize a DCNN to bridge the affective gap in speech signals. To this end, we first extract three channels of log Mel-spectrograms (static, delta, and delta delta) similar to the red, green, blue (RGB) image representation as the DCNN input. Then, the AlexNet DCNN model pretrained on the large ImageNet dataset is employed to learn high-level feature representations on each segment divided from an utterance. The learned segment-level features are aggregated by a discriminant temporal pyramid matching (DTPM) strategy. DTPM combines temporal pyramid matching and optimal Lp-norm pooling to form a global utterance-level feature representation, followed by the linear support vector machines for emotion classification. Experimental results on four public datasets, that is, EMO-DB, RML, eNTERFACE05, and BAUM-1s, show the promising performance of our DCNN model and the DTPM strategy. Another interesting finding is that the DCNN model pretrained for image applications performs reasonably good in affective speech feature extraction. Further fine tuning on the target emotional speech datasets substantially promotes recognition performance.
Manifold–Manifold Distance and its Application to Face Recognition With Image Sets

In this paper, we address the problem of classifying image sets for face recognition, where each set contains images belonging to the same subject and typically covering large variations. By modeling each image set as a manifold, we formulate the problem as the computation of the distance between two manifolds, called manifold-manifold distance (MMD). Since an image set can come in three pattern levels, point, subspace, and manifold, we systematically study the distance among the three levels and formulate them in a general multilevel MMD framework. Specifically, we express a manifold by a collection of local linear models, each depicted by a subspace. MMD is then converted to integrate the distances between pairs of subspaces from one of the involved manifolds. We theoretically and experimentally study several configurations of the ingredients of MMD. The proposed method is applied to the task of face recognition with image sets, where identification is achieved by seeking the minimum MMD from the probe to the gallery of image sets. Our experiments demonstrate that, as a general set similarity measure, MMD consistently outperforms other competing nondiscriminative methods and is also promisingly comparable to the state-of-the-art discriminative methods.