nils-herrmann/clean_scirepeval huggingface dataset:
Learning Contextual Dependency Network Models for Link-Based Classification

Links among objects contain rich semantics that can be very helpful in classifying the objects. However, many irrelevant links can be found in real-world link data such as Web pages. Often, these noisy and irrelevant links do not provide useful and predictive information for categorization. It is thus important to automatically identify which links are most relevant for categorization. In this paper, we present a contextual dependency network (CDN) model for classifying linked objects in the presence of noisy and irrelevant links. The CDN model makes use of a dependency function that characterizes the contextual dependencies among linked objects. In this way, CDNs can differentiate the impacts of the related objects on the classification and consequently reduce the effect of irrelevant links on the classification. We show how to learn the CDN model effectively and how to use the Gibbs inference framework over the learned model for collective classification of multiple linked objects. The experiments show that the CDN model demonstrates relatively high robustness on data sets containing irrelevant links
Accurate localization in combination with wireless sensor networks and laser localization

In order to explore the moon more effectively, it is very important to enhance the ability of navigation and localization of the lunar rover for exploration achievement of scientific goals. In this paper, an Accurate Localization in Combination with Wireless Sensor Networks and Laser localization scheme is proposed. First, the lunar rover position system consisted of the Lander, the lunar rover and a wireless sensor network is introduced. Then, we compare and analyse the advantages and disadvantages of TOA, TDOA, AOA, and RSSI. In the proposed scheme, the wireless sensor networks first localize themselves with the help of the Lander. For increasing the localization accuracy of the sensor nodes, a novel localization algorithm based on APS algorithm is presented. Next, we detail the lunar rover localization especially in that case where the lunar rover is not in the sight of the Lander. In initial experiment, we validate the proposed method using the test platform designed by us.
Fast intra mode decision algorithm based on refinement in HEVC

High Efficiency Video Coding (HEVC) is the next generation video compression standard providing significant coding performance. It adopts 35 intra prediction modes with larger CU size to improve the intra encoding efficiency, so that cause a high computational complexity. In this paper, two fast intra-prediction algorithms are proposed to reduce the number of candidate modes for rate-distortion (RD) optimization. We obtain an optimal adjacent modes (OAM) list consisting of dominant directions through the analysis of costs of several general direction modes. Furthermore, we improve the most probable mode (MPM) algorithm to make full use of the spatial correlation between neighbour prediction blocks instead of simply merging the prediction modes of neighbour prediction blocks into the candidate list. Experimental results show that the proposed algorithms can reduce about 27.3% of the encoding time compared to the HEVC test model 14.0, while the decrease of coding quality is negligible.
Reducing Image Compression Artifacts by Structural Sparse Representation and Quantization Constraint Prior

The block discrete cosine transform (BDCT) has been widely used in current image and video coding standards, owing to its good energy compaction and decorrelation properties. However, because of independent quantization of DCT coefficients in each block, BDCT usually gives rise to visually annoying blocking compression artifacts, especially at low bit rates. In this paper, to reduce blocking artifacts and obtain high-quality images, image deblocking is cast as an optimization problem within maximum a posteriori framework, and a novel algorithm for image deblocking by using structural sparse representation (SSR) prior and quantization constraint (QC) prior is proposed. The SSR prior is utilized to simultaneously enforce the intrinsic local sparsity and the nonlocal self-similarity of natural images, while QC is explicitly incorporated to ensure a more reliable and robust estimation. A new split Bregman iteration-based method with an adaptively adjusted regularization parameter is developed to solve the proposed optimization problem, which makes the entire algorithm more practical. Experiments demonstrate that the proposed image-deblocking algorithm combining SSR and QC outperforms the current state-of-the-art methods in both peak signal-to-noise ratio and visual perception.
Shot change detection on H.264/AVC compressed video

This paper deals with the problem of shot-change detection on H.264/AVC compressed video. As H.264/AVC employs several new coding tools, the statistic information of the macroblock is different from the former MPEG video. How to detect shot-changes efficiently on H.264/AVC compressed video is a challenging work. In this paper, we propose a novel scheme, a macroblock type analysis plus the intra-mode statistical constraint rule, to detect shot-changes for P-frame and B-frame coded video of H.264/AVC. For detecting shot-changes in I-frame coded video, we propose an intra mode histogram and use the weighted city block distance to measure the similarities among I-frames. Experimental results show that the proposed algorithms achieve satisfactory performance and very fast detection.
Feature extraction and pattern recognition of multi-source PD signals

Partial discharge signals from industrial models of stator winding bars were used to compose multisource PD patterns, which were represented by 3-D charts. The tabulated data were calculated and served as characteristic features. Pattern recognition was performed using a back-propagation (BP) network. Successful classification of multi-source PD signals showed that tabulated data were sensitive and stable to reflect different PD patterns. The suitability and robustness of the BP network for classification was further verified by analyzing the untrained patterns' recognition.
Sigma Set Based Implicit Online Learning for Object Tracking

This letter presents a novel object tracking approach within the Bayesian inference framework through implicit online learning. In our approach, the target is represented by multiple patches, each of which is encoded by a powerful and efficient region descriptor called Sigma set. To model each target patch, we propose to utilize the online one-class support vector machine algorithm, named Implicit online Learning with Kernels Model (ILKM). ILKM is simple, efficient, and capable of learning a robust online target predictor in the presence of appearance changes. Responses of ILKMs related to multiple target patches are fused by an arbitrator with an inference of possible partial occlusions, to make the decision and trigger the model update. Experimental results demonstrate that the proposed tracking approach is effective and efficient in ever-changing and cluttered scenes.
Numerical Simulation of Deep-Hole Controlled Cutting Blasting and Its Practice

Blasting action plays a very important role in slope stability and the effect of rock fragmentation in stonework cutting excavation. Based on engineering practices, this paper applies dynamic finite element method and conducts a numerical simulation of the cutting excavation process. It have conducted researches on the stress field inside rocks in millisecond delay blasting, Von-Mises and other domino effects, as well as the propagation rules for particle vibration velocity. Results show that based on the numerical simulation of the engineering practice, this paper largely represents the real physical mechanical process of the rock fragmentation and can hence serve as a guide for both design and conduction of cutting blasting.
Social welfare based nodal pricing with FACTS cost models in hybrid electricity markets

In this paper, nodal prices for real and reactive power have been obtained formulating social welfare based approach. The reactive power cost of generators has been incorporated in the model as reactive power support has been identified as an important ancillary service in the electricity market and it can not be neglected. The generator's capability curve has also been incorporated in the model. The impact of FACTS controllers has also been considered in the model formulation taking their cost function into account. The constraints for incorporation of bilateral transactions along with the pool transactions have been incorporated in the hybrid electricity market model. Mixed integer non-linear programming approach has been formulated for hybrid electricity market model. The optimal location of FACTS devices have been obtained using integer variable in the mathematical model. The proposed approach has been tested on IEEE 24-bus Reliability Test System (RTS).
Effect of pulse propagation characteristics in power transformer on partial discharge recognition

An experimental method is utilized to investigate the propagation characteristic of discharge pulse and its influence on the recognition of partial discharge in this paper. Firstly, the discharge model tests of common defect in oil-paper insulation are finished. A series of tests are conducted to study the propagation characteristic of discharge pulse along the winding. Thus the identification of discharge waveform could be implemented. The restoring method of the original wave and the extracting method of wave characteristic are also put forward. Finally the backpropagation (BP) neural network is utilized to recognize the wave characteristic of discharge signal. The validity of recognition system is illustrated.
Macroblock level rate control for low delay H.264/AVC based video communication

In this paper, we propose a macro-block (MB) level rate control algorithm for low delay H.264/AVC video communication based on the ρ domain rate model. In the proposed algorithm, an exponential model is used to characterize the relation between ρ and the quantization step (Qstep) at the MB level, with which the quantization parameter (QP) for a MB can be obtained. Furthermore, a switched QP calculation scheme is introduced to obtain the QP for each MB to avoid large deviation of the actual frame size from the target bit budget. Compared with the original ρ domain rate control, the proposed method can achieve better video quality and improved bit-rate accuracy. Meanwhile, the computational complexity is also significantly reduced.
An analysis on plug-in electric vehicle's operating cost considering cost of battery capacity degradation

This paper analyzes the battery capacity degradation under different state of charge(SOC) ranges, which indicates that battery capacity degradation can be notably reduced by adjusting the way of charging electric vehicles(EVs). A customary way of charging is defined according to practical driving data. Economic analysis and numerical example, including ideal way of charging(IWC), the worst way of charging (WWC) and customary way of charging(CWC), are made to evaluate the cost of battery capacity degradation. Results show that the cost of battery degradation is a nonnegligible and elastic part of the total cost for a single time charging.
A wavelet-based method to extract frequency feature for power system fault/event analysis

Frequency is a vital factor for power system operation and protection. This paper is on extraction of frequency feature for fault/event analysis based on wavelet transform (WT) and fractal geometry (FG). The frequency signal is decomposed by WT-based multiresolution analysis (MRA) and a family of wavelet coefficients is obtained. A maxima line is constructed by connecting the maximum point in the wavelet coefficients across all the decomposition levels. A differential box counting (DBC) method based on FG is applied to compute the fractal dimension of the maxima line. It is realized that the feature of a frequency signal can be characterized by a fraction number-fractal dimension. A simulation is carried out in PSS/E to generate different faults in the power system. The proposed algorithm is implemented to extract the features of the faults. The results verify the effectiveness of the proposed method.
Local Linear Regression (LLR) for Pose Invariant Face Recognition

The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is well known as one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given non-frontal view to obtain a virtual gallery/probe face. By formulating this kind of solutions as a prediction problem, this paper proposes a simple but efficient novel local linear regression (LLR) method, which can generate the virtual frontal view from a given non-frontal face image. The proposed LLR inspires from the observation that the corresponding local facial regions of the frontal and non-frontal view pair satisfy linear assumption much better than the whole face region. This can be explained easily by the fact that a 3D face shape is composed of many local planar surfaces, which satisfy naturally linear model under imaging projection. In LLR, we simply partition the whole non-frontal face image into multiple local patches and apply linear regression to each patch for the prediction of its virtual frontal patch. Comparing with other methods, the experimental results on CMU PIE database show distinct advantage of the proposed method
Spectrum sensing for OFDM systems employing pilot tones

OFDM spectrum sensing algorithms are presented in this paper. The proposed spectrum sensing algorithms are based on Time-Domain Symbol Cross-Correlation (TDSC-MRC and TDSC-NP methods) and can be applied to all existing wireless OFDM systems. The statistical behaviors of the TDSCbased spectrum sensors are explicitly analyzed. The sensing performances of the TDSC-based spectrum sensors in terms of misdetection probability are derived in detail. In addition, the spectrum sensing method employing the Cyclic Prefix of the OFDM modulated signals (CP method) is described for comparison purposes. The DVB-T Standard  is adopted as an application example to illustrate the proposed spectrum sensing algorithms. Simulation results show that the TDSC-MRC method outperforms the CP method for all values of CP ratio considered. The TDSC methods have the advantage that the detection performances are the same for different CP ratios, while the detection performance of the CP method degrades dramatically when the CP ratio becomes small. From simulation results, for a sensing time of 50 ms, the TDSC-MRC method can achieve misdetection probabilities of 0.1 and 0.01 with respect to a false alarm probability of 0.01 for SNR values of -20.5 dB and -19.5 dB, respectively.
Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition

A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate
Design sparse features for age estimation using hierarchical face model

A key point in automatic age estimation is to design feature set essential to age perception. To achieve this goal, this paper builds up a hierarchical graphical face model for faces appearing at low, middle and high resolution respectively. Along the hierarchy, a face image is decomposed into detailed parts from coarse to fine. Then four types of features are extracted from this graph representation guided by the priors of aging process embedded in the graphical model: topology, geometry, photometry and configuration. On age estimation, this paper follows the popular regression formulation for mapping feature vectors to its age label. The effectiveness of the presented feature set is justified by testing results on two datasets using different kinds of regression methods. The experimental results in this paper show that designing feature set for age estimation under the guidance of hierarchical face model is a promising method and a flexible framework as well.
Rate control for JVT video coding scheme with HRD considerations

The JVT video coding scheme (MPEG-4 AVC/H.264) is a promising technique due to its high coding efficiency. Hypothetical reference decoder (HRD) is a very important part in JVT video coding, which represents a set of normative requirements on bitstream for the purpose of avoiding buffer overflow and underflow. The problem of HRD requirements can be solved by rate control. This paper proposes an effective rate control scheme for JVT video coding with HRD considerations. First, bit allocation with HRD constraints is presented, and second, based on a simple rate distortion model, a single pass rate control is implemented on both frame level and macroblock level. Experimental results show that the proposed rate control algorithm can achieve the target bit rate with very little bit rate or image quality fluctuation, and meanwhile it can well meet the HRD requirements. Furthermore, the proposed algorithm is so simple that it only introduces little computation complexity. Therefore, it can be used in real time video coding.
A new approach of recovering 3-D shape from structure-lighting

This paper presents a new approach to recover 3-D shape from only one simple structured-lighting image without knowing any information about the surface of the object and the structured-lighting system. Analysis of a structured-lighting image can estimate the calibration parameters of the system including the focal length of the camera and the position of the grating. Without any corresponding, the approach, has been applied to recover rather complicated 3-D shape in a varied environment.
New control of two three-phase induction machines using an integrated inverter for applications in hybrid electric vehicles

An electric drive system consisting of two three — phase induction motors independently controlled by an integrated dual inverter is modeled and simulated in Matlab/Simulink. The introduced topology can be used in an electrical vehicle where the main machine is used as a traction drive and the auxiliary machine can be used for air conditioning purposes. While the traction motor is driven by three legs of the used inverter, the auxiliary motor requires only two legs in the introduced topology. This is achieved by connecting a phase of the auxiliary motor to the neutral point of the traction motor. Closed loop control is carried out under different conditions including trapezoidal speed profile tracking and steady — state performance with load variation.
Development and characterization of a through-multilayer TSV integrated SRAM module

In this study, a stacked SRAM module with a built-in decoder was proposed with a through-multilayer TSV integration process. The through-multilayer TSVs provided data passages for all common signals, including the address bus, data bus, power, read and write control, which were redistributed at each individual chip, while the chip select signals were connected separately to the built-in decoder. Regarding this process, a novel double-layer spin coating technology was employed to prevent photoresist residue left inside TSVs, and the RDLs in this process could be fabricated using lift-off process prior to via filling. As a result, the front side CMP process was not necessary. A 10-layer through-multilayer TSV integration sample was successfully fabricated with this process. Preliminary testing results suggested that this process was promising for integration of memory chips with similar layout.
A novel approach to automatically extracting basic units from Chinese sign language

In sign language recognition, using subwords instead of whole signs as basic units scales well with increasing vocabulary size. However, there are no subwords defined in the signs' lexical forms. How to automatically extract subwords is a challenging issue. In this paper, a novel approach is proposed to automatically extract these subwords from Chinese sign language (CSL). Signs can be broken down into several segments using hidden Markov models in which each state represents one segment. Temporal clustering algorithm is presented to extract subwords from these segments. The 238 subwords are automatically extracted from 5113 signs, and they can be used as the basic units for large vocabulary CSL recognition with good performance.
Background aided surveillance-oriented distributed video coding

Distributed video coding (DVC) was proposed to meet the low complexity encoding requirement, and it was verified to work more efficiently than H.264/AVC intra coding on video sequences with low motion. This makes DVC suitable for surveillance application. This paper presents a background aided surveillance-oriented distributed video coding system. A high quality background frame is encoded for each group of pictures (GOP), which can provides high quality SI for the background parts of the Wyner-Ziv (WZ) frames. Consequently, bit rate for the WZ frames can be reduced. Experimental results demonstrate that the proposed system can decrease the bit rate by up to 67.4% when compared with traditional DVC codec.
Hierarchical ensemble of global and local classifiers for face recognition.

In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.