nils-herrmann/clean_scirepeval huggingface dataset:
Learning and synthesizing MPEG-4 compatible 3-D face animation from video sequence

We present a new system that applies an example-based learning method to learn facial motion patterns from a video sequence of individual facial behavior such as lip motion and facial expressions, and using that to create vivid three-dimensional (3-D) face animation according to the definition of MPEG-4 face animation parameters. The system consists of three key modules, face tracking, pattern learning, and face animation. In face tracking, to reduce the complexity of the tracking process, a novel coarse-to-fine strategy combined with a Kalman filter is proposed for localizing key facial landmarks in each image of the video. The landmarks' sequence is normalized into a visual feature matrix and then fed to the next step of process. In pattern learning, in the pretraining stage, the parameters of the camera that took the video are requested with the training video data so the system can estimate the basic mapping from a normalized two-dimensional (2-D) visual feature matrix to the representation in 3-D MPEG-4 face animation parameter space, in assistance with the computer vision method. In the practice stage, considering that in most cases camera parameters are not provided with video data, the system uses machine learning technology to complement the incomplete 3-D information for the mapping that information is needed in face orientation presentation. The example-based learning in this system integrates several methods including clustering, HMM, and ANN to make a better conversion from a 2-D to 3-D model and better estimation of incomplete 3-D information for good mapping; this will be used to drive face animation thereafter. In face animation, the system can synthesize face animation following any type of face motion in video. Experiments show that our system produces more vivid face motion animation, compared to other early systems.
A Motion-Aligned Auto-Regressive Model for Frame Rate Up Conversion

In this paper, a motion-aligned auto-regressive (MAAR) model is proposed for frame rate up conversion, where each pixel is interpolated as the average of the results generated by one forward MAAR (Fw-MAAR) model and one backward MAAR (Bw-MAAR) model. In the Fw-MAAR model, each pixel in the to-be-interpolated frame is generated as a linear weighted summation of the pixels within a motion-aligned square neighborhood in the previous frame. To derive more accurate interpolation weights, the aligned actual pixels in the following frame are also estimated as a linear weighted summation of the newly interpolated pixels in the to-be-interpolated frame by the same weights. Consequently, the backward-aligned actual pixels in the following frame can be estimated as a weighted summation of the corresponding pixels within an enlarged square neighborhood in the previous frame. The Bw-MAAR is performed likewise except that it is operated in the reverse direction. A damping Newton algorithm is then proposed to compute the adaptive interpolation weights for the Fw-MAAR and Bw-MAAR models. Extensive experiments demonstrate that the proposed MAAR model is able to achieve superior performance than the traditional frame interpolation methods such as MCI, OBMC, and AOBMC, and it is even better than STAR model for the most test sequences with moderate or large motions.
Improving particle filter with support vector regression for efficient visual tracking

Particle filter is a powerful visual tracking tool based on sequential Monte Carlo framework, and it needs large numbers of samples to properly approximate the posterior density of the state evolution. However, its efficiency degenerates if too many samples are applied. In this paper, an improved particle filter is proposed by integrating support vector regression into sequential Monte Carlo framework to enhance the performance of particle filter with small sample set. The proposed particle filter utilizes an SVR based re-weighting scheme to re-approximate the posterior density and avoid sample impoverishment. Firstly, a regression function is obtained by support vector regression method over the weighted sample set. Then, each sample is re-weighted via the regression function. Finally, ameliorative posterior density of the state is re-approximated to maintain the effectiveness and diversity of samples. Experimental results demonstrate that the proposed particle filter improves the efficiency of tracking system effectively and outperforms classical particle filter.
Object tracking using incremental 2D-LDA learning and Bayes inference

The appearances of the tracked object and its surrounding background usually change during tracking. As for tracking methods using subspace analysis, fixed subspace basis tends to cause tracking failure. In this paper, a novel tracking method is proposed by using incremental 2D-LDA learning and Bayes inference. Incremental 2D-LDA formulates object tracking as online classification between foreground and background. It updates the row- or/and column- projected matrix efficiently. Based on the current object location and the prior knowledge, the possible locations of the object (candidates) in the next frame are predicted using simple sampling method. Applying 2D-LDA projection matrix and Bayes inference, candidate that maximizes the posterior probability is selected as the target object. Moreover, informative background samples are selected to update the subspace basis. Experiments are performed on image sequences with the object's appearance variations due to pose, lighting, etc. We also make comparison to incremental 2D-PCA and incremental FDA. The experimental results demonstrate that the proposed method is efficient and outperforms both the compared methods.
Region-based depth-preserving stereoscopic image retargeting

The popularity of stereo images and various sizes of display screens pose the need of stereo image retargeting techniques which resize stereo image pairs to desired sizes. Many content-aware stereo image retargeting methods adapt the images through non-uniformly resizing regions. However, these methods often make the depth of retargeted version inconsistent with the original one, since they do not explicitly consider different effects of resizing distinct regions on the depths of 3D scenes. In this paper, we analyze the effects of region-wise resizing on the depths of 3D scenes. With such insights, we can properly edit or maintain the depth of a stereo image pair via region-wise resizing. In addition, by taking into account the effects on different regions, we propose a grid-based retargeting model for stereo images, which simultaneously preserve the depths of 3D scenes and the shapes of salient objects. Experimental results demonstrate the superior performance of our method.
Color Image-Guided Boundary-Inconsistent Region Refinement for Stereo Matching

Cost computation, cost aggregation, disparity optimization, and disparity refinement are the four main steps for stereo matching. While the first three steps have been widely investigated, few efforts have been taken on disparity refinement. In this paper, we propose a color image-guided disparity refinement method to further remove the boundary-inconsistent regions on disparity map. First, the origins of boundary-inconsistent regions are analyzed. Then, these regions are detected with the proposed hybrid-superpixel-based strategy. Finally, the detected boundary-inconsistent regions are refined by a modified weighted median filtering method. Experimental results on various stereo matching conditions validate the effectiveness of the proposed method. Furthermore, depth maps obtained by active depth acquisition devices like Kinect can also be well refined with our proposed method.
A low-power 14-bit hybrid incremental sigma-delta/cyclic ADC for X-ray sensor array

This paper presents a column-level 14-bit two-stage analog-to-digital converter (ADC) based on pseudo-differential operational amplifier, which is designed for the readout circuit of X-ray sensor array. This low-power hybrid ADC employs an incremental sigma-delta ADC and a cyclic ADC, achieving a good trade-off between accuracy and conversion speed. The two stages share the same analog circuit to reduce area and power consumption. A test chip is fabricated in 0.18μm CMOS technology. The hybrid ADC in each column is performed in parallel with power consumption of 218.813μW. The simulation result reveals the effective number of bits (ENOB) is 13.775 bits.
A novel rate control scheme for video streaming over wireless networks

TFRC is an equation-based rate control scheme originally developed for media streaming over wired networks. When applied to the wireless networks, it suffers from performance degradation. In this paper, we propose a loss event rate discounting (LERD) scheme to improve the TFRC performance over wireless networks. In our TFRC-LERD scheme, a TFRC connection includes several loss discounting levels. We increase the discounting level of the TFRC connection when the wireless bandwidth is under-utilized, and decrease it when the wireless bandwidth is overestimated. The adjusting of discounting level conforms to the inverse increase additive decrease algorithm. In contrast with most existing solutions, our scheme only employs the general statistical information and does not need to determine the cause of each packet loss, which reduces the computational complexity. In addition, TFRC-LERD is an end-to-end solution and does not assume any additional support from intermediate networks or lower layers. Simulation results demonstrate that our scheme outperforms the original TFRC under a wide range of error rates and the video quality perceived by the receiver is improved considerably.
A Switched-Capacitor Single-ended to Differential stage for A/D conversion of IRFPA ROIC

This paper presents a Switched-Capacitor Single-ended to Differential (SC-STD) stage for A/D conversion of an Infrared Focal Plane Array (IRFPA) Readout Integration Circuit(ROIC). The SC-STD stage converts the output of the Capacitive Trans-Impedance Amplifier (CTIA) into differential signals, which can be used as the input of the subsequent A/D conversion. A novel Switched-Capacitor Common-Mode Feedback (SC-CMFB) technique with only two capacitors and single phase clock is proposed. Performances of the SC-STD stage are verified by a 14-bit column-paralleled Successive-Approximation A/D converter which is fabricated in a 0.35um CMOS-based process technology. The SFDR of the A/D converter is up to 93dB at a sampling clock of 31.25 KHz with an input signal of 1.009 KHz. The THD of the SC-STD circuit is up to -94dB at a 1V Vpp input of 1.009 KHz.
Evaluation of the inertial response of variable-speed wind turbines using advanced simulation

In this paper, we focus on the temporary frequency support effect provided by wind turbine generators (WTGs) through the inertial response. With the implemented inertial control methods, the WTG is capable of increasing its active power output by releasing parts of the stored kinetic energy when the frequency excursion occurs. The active power can be boosted temporarily above the maximum power points, but the rotor speed deceleration follows and an active power output deficiency occurs during the restoration of rotor kinetic energy. We evaluate and compare the inertial response induced by two distinct inertial control methods using advanced simulation. In the first stage, the proposed inertial control methods are analyzed in offline simulation. Using an advanced wind turbine simulation program, FAST with TurbSim, the response of the researched wind turbine is comprehensively evaluated under turbulent wind conditions, and the impact on the turbine mechanical components are assessed. In the second stage, the inertial control is deployed on a real 600kW wind turbine — Controls Advanced Research Turbine, 3-bladed (CART3), which further verifies the inertial control through a hardware-in-the-loop (HIL) simulation. Various inertial control methods can be effectively evaluated based on the proposed two-stage simulation platform, which combines the offline simulation and real-time HIL simulation. The simulation results also provide insights in designing inertial control for WTGs.
Laplace distribution based CTU level rate control for HEVC

This paper proposes a coding tree unit (CTU) level rate control for HEVC based on the Laplace distribution modeling of the transformed residuals. Firstly, we give a study on the relationship model among the optimal quantization step, the Laplace parameter and the Lagrange multiplier. Based on the relationship model, the quantization parameter for each CTU can be dynamically adjusted according to the distribution of the transformed residual. Secondly, a CTU level rate control scheme is proposed to achieve accurate rate control as well as high coding performance. Experimental results show that the proposed rate control scheme achieves better coding performance than the state-of-the-art rate control schemes for HEVC in terms of both objective and subjective quality.
Mode mapping method for H.264/AVC spatial downscaling transcoding

Transcoding is a very effective method for universal media adaptation. Spatial resolution downscaling is one of the most popular transcoding applications. Although a number of transcoding methods have been proposed in the past years, they are not very suitable for the most up-to-date H.264/AVC standard due to its many new features. In this paper, a coding mode mapping method is proposed for H.264/AVC spatial downscaling transcoding. Different from the traditional schemes, the proposed method is focused on the mode decision part, which plays a key role in H.264/AVC video encoding. The mapping scheme is exploited based on the statistics and analysis that reveals the inherent relationship between the frames having the same content but with different spatial resolutions.
Secure watermark verification scheme

Copyright protection of digital contents, as one of the most important application of digital watermarks, works by watermark verification. In traditional watermark authentication schemes, a prover exposed a watermark to be present in a digital data to the possible dishonest verifier. However, a potential attacker is able to spoil or remove the watermark entirely once classified information like the watermark or the embedding key is known. Some of previous schemes proposed as solutions have not really achieved desirable results. Their lack of security and validity is the most serious problem. In this paper, we propose a secure watermark verification scheme based on zero knowledge protocols and public-key encryption scheme in order to solve this problem. There is no secret information that can be used for removing watermark disclosed during the verification process. It has considerable advantages over previously proposed schemes in terms of security and validity
Status quo of research on the application of dynamometer card in oil production process control

The applications of dynamometer card in the process of oil production are widely, some of which are evaluation well yield on well top, failure diagnosis of downhole, pressure calculation, judgment of heavy oil well, etc., but only the two major applications in theory, calculation and evaluation of oil yield on well top, failure diagnosis under the well are introduced in the paper, in which some representative theory research achievements and engineering projects are presented, and the prospect of dynamometer card application in the process of oil production is discussed, too.
A Flexible Stem-Based Local Search Algorithm for Predicting RNA Secondary Structures Including Pseudoknots

We demonstrate a new algorithm named FlexStem to predict RNA secondary structures with pseudoknots. Our approach is based on the free energy minimization criterion, and utilizes a sophisticated energy model that is more accurate and supports more types of pseudoknots. By using a "maximal stem" and "stem merging" strategy, the search space for RNA structure prediction is significantly reduced. We have also developed a greedy search algorithm with perturbation on stems. The FlexStem algorithm is applied to a large number of sequences taken from Sprinzl, Pseudobase and other reliable resource. We find that FlexStem outperforms the well-known optimal and heuristic algorithms such as Mfold, PKNOTS, HotKnots and ILM in overall sensitivity and has comparable results to those algorithms in overall specificity. Performance evaluation demonstrate that FlexStem requires significantly less time than the optimal algorithm PKNOTS. In addition, our algorithm has better prediction results on pseudoknot-free sequences compared to competing algorithms.
Enhancing Training Set for Face Detection

We present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. The motivation is from support vector machines (SVM) that, for classification problems, examples lying close to class boundary usually have more influence and thus are more informative than those far from the boundary. We utilize a nonlinear technique - reduced set (RS) method and a new image distance metric to generate new examples, and then add them to the original collected database to enhance it. Extensive experiments show that the proposed approach has an encouraging performance
Structure preserving single image super-resolution

In this paper, we present a novel structure preserving method for single image super-resolution to well construct edge structures and small detail structures. In our approach, the sharp edges are recovered via a novel edge preserving interpolation technique based on a well estimated gradient field and the edge preserving method, which incorporate the local and non-local structure information. The gradient of interpolated high-resolution(HR) image is then regarded as an edge preserving constraint to reconstruct the detail structures. Experimental results demonstrate that the new approach can reconstruct faithfully the HR images with sharp edges and texture structures, and annoying artifacts (blurring, jaggies, ringing, etc.) are greatly suppressed. It outperforms the state-of-the-art approaches, based on subjective and objective evaluations.
A fast super-resolution method based on sparsity properties

Super-resolution enhancement is a kind of promising approach to enhance the spatial resolution of images. To super-resolve a satisfying result, regularization term design and blur kernel estimation are two important aspects which need to be carefully considered. In this paper, we propose a robust regularized super-resolution reconstruction approach based on two sparsity properties to deal with these two aspects. Firstly, we design a sparse reweighted TV L1 prior to restrict the first derivative of the upsampled image. Then, noticing that only deblurring sparse high gradient areas can sharpen the super-resolution result, we design an over-deblurring control method to decrease the artifacts caused by inaccurate blur kernel estimation. We also design a fast optimization algorithm to solve our model. The experimental results show that the proposed approach achieves a remarkable performance both in visual quality and run time.
An Optimal Non-Uniform Scalar Quantizer for Distributed Video Coding

In this paper, we propose a novel algorithm to design an optimal non-uniform scalar quantizer for distributed video coding, which aims at achieving a coding rate close to joint conditional entropy of the quantized video frames given the side information. Wyner-Ziv theory on source coding is employed as the basic coding principle and the asymmetric scenario is considered. In this algorithm, a probability distribution model, which considers the influence of the joint distribution of input source and side information to the coding performance, is established and used as the optimality condition firstly. Then, a modified Lloyd Max algorithm is used to design the scalar quantizer to give an optimal quantization for input source before coding. Experimental results show that compared to uniform scalar quantization, proposed algorithm can improve coding performance largely, especially at low bit rate
Size effect of partial discharge in solid void defects

The void defect in solid insulation can be considered as one of the important defects which will induce partial discharges (PD) and insulation degradation. In this paper, the relations between the cylindrical void size and PD characteristics are presented and summarized particularly. The PD inceptive electrical field (PDIE), the maximal PD magnitude and the impulse waveforms are especially studied with different void defects' depths and diameters. These PD characteristics, especially for PD impulse waveforms, changed dramatically with the increase of void size, which imply the various ionization states in PD process. The results show that large size voids always induce low PDIE and large maximal PD magnitude, which confirm the certain relations between insulation degradation degree and PD characteristics. Some of the results show that the PD with more residual ions may cause much serious degradation of solid insulations. The floating electrode defect test has been designed as a proving trial lastly.
A small-area low-mismatch multi-channel constant current LED driver

This paper proposes a new structure of LED(Light-emitting diode) driver for obtaining a low mismatch output current between different channels and even reduces the chip area. It's fabricated with TSMC 0.35µm DDD process. The chip contains 16 channels and the maximum/minimum output current is 3mA/45mA, respectively. The value of each channel's output current is the same and controlled by a programmable 6-bits digital input signals. The circuit uses constant gate voltage of the power MOS working in the linear region whose (Vgs — Vth) is 10 to 50 times of Vds. The advantage is no DAC(Digital-to-Analog Converter) and no complex gate voltage generating circuit. Simple gate voltage generating circuit can also adapt to a wide range of external resistance changes. Because of the lower mismatch caused by threshold voltage mismatch, it can achieve a highly matched output current. The chip has only ±1.1% mismatch between different channels. The area of each channel's power MOS is only 200µm× 100µm. The area of analog part including current bias, bandgap reference, current mirror, and other control circuits is only 400µm×200µm.
Algorithms of integrated student modeling in online virtual educational community

The paper focuses on how to construct student models in the online virtual educational community. First, we analyze social interaction between students in the community, and present some algorithms to model students' (personal and shared) needs, preferences or knowledge structures in interaction activities. Then, the paper proposes an integrated student model combined with student modeling in information services and task processes, and emphasizes that it builds up a foundation to personalize information services and customize online educational programs.
An hour ahead wind speed prediction by Kalman filter

This paper proposes a trustworthy and practical approach to predict wind speed, which could be used as input to predict wind power generation. In the prediction model, we applied the variable speed wind as input of this model. Then the Gaussian noise is added to the input due to existing of noise in measuring of wind speed. Kalman filtering is used as the prediction method in this study. The parameters of shaping filter are calculated using power spectral analysis and Gauss-Markov theory. Results under different time horizon of prediction are compared with actual data and the comparison shows that the results obtained using Kalman filter is reliable. The proposed method works well for an hour ahead prediction and for short-time prediction works even better. The application of Kalman filter on wind speed prediction is implemented in MATLAB software and results are provided in this paper.