nils-herrmann/clean_scirepeval huggingface dataset:
An outlier detection algorithm based on the degree of sharpness and its applications on traffic big data preprocessing

Outlier detection is one important research area of data mining, which plays key roles in data preprocessing, equipment fault diagnosis, credit fraud detection, traffic incident detection etc. This paper is devoted to a new outlier detection algorithm based on the degree of sharpness. The proposed algorithm takes a new way to solve the outlier detection problem, which employs a measure in image processing, degree of sharpness, to detect the outliers. Compared to the classical outlier detection methods with statistical learning, the proposed algorithm has no iterative processes. It generates a smooth curve to describe the overall distribution of the data firstly, and then computes the sharpness of degree for each data point. Finally, the outliers are recognized as they have larger values of the degree of sharpness. Also, some practical applications on traffic big data are presented to prove the effectiveness of the proposed algorithm.
Effective approach to camera zoom detection based on visual attention

An effective approach based on geometric constraint of the motion vector field is proposed to detect camera zoom operation. The approach exploits the valid motion vectors to estimate the center of focus (COF) for zoom, and then calculates the average distance from the COF to valid motion vectors to identify zoom operation. To reduce the influence of object motion, Visual attention mechanism is employed to select valid motion vectors. Experimental results show that the proposed approach achieves a good performance.
A Model Checking Tool Embedded into Services Composition Environment

This paper proposes a tool for formal verification of Web services composition based on an extended pi-calculus with type system and the conversion between BPEL and pi-calculus. This tool integrates three kinds of formal verification techniques, including open bisimulation, properties checking based on modal mu-calculus and the compatibility checking of composite Web services based on pi-calculus. With the combination checking techniques, the properties that service composition orchestration holds at abstract control flow level are checked, which achieve more completely validity checking about the orchestration. To check automatically, the automatic conversion between BPEL4WS and pi-calculus is introduced to this tool, and also the error slice for the orchestration are automatically marked. Further more, a novel method for discovering and matching web services is proposed, which is under the guidance of checking Web services compatibility
Analysis of PM10 Concentration Time Series and Climiate Factor in Hangzhou 2004

The density of absorbable particulate matter (PM10) is the most important contamination index for air quality monitoring. This article analyzed the temporal variation characteristics of PM10 and its climate influencing factors. We employed wavelet analysis method to extract the overall tendency of annual variation. Likewise, the relationship between PM10 density and meteorological factor was established by correlation analysis. Moreover, a summary of the yearly temporal characteristic of PM10, as well as its daily characteristic in Hangzhou, was provided. A summary of the factors that influence its climate is provided.
Study on the control system of a hardware-in-loop micro-satellite simulator

This paper presents a detailed control system building process for a hardware-in-loop micro-satellite simulator to apply and test system identification or control algorithms. It also designs a new system mechanism to combine the simulator with MATLAB Simulink by DDE, which can enable Simulink to directly control the simulator system and accomplish data sharing and real-time control function. Finally, it designs a controller based on the new control system structure to perform satellite attitude manoeuvre as system verification and gives out the result analysis for the future improvement. The control system building process may be a reference for other simulation environment's control system design.
Distributed model predictive control with actuator saturation for Markovian jump linear system

This paper is concerned with the distributed model predictive control (MPC) problem for a class of discrete-time Markovian jump linear systems (MJLSs) subject to actuator saturation and polytopic uncertainty in system matrices. The global system is decomposed into several subsystems which coordinate with each other. A set of distributed controllers is designed by solving a min-max optimization problem in terms of the solutions of linear matrix inequalities (LMIs). An iterative algorithm is developed to achieve the online computation. Finally, a simulation example is employed to show the effectiveness of the proposed algorithm.
Image classification with CNN-based Fisher vector coding

Fisher vector coding methods have been demonstrated to be effective for image classification. With the help of convolutional neural networks (CNN), several Fisher vector coding methods have shown state-of-the-art performance by adopting the activations of a single fully-connected layer as region features. These methods generally exploit a diagonal Gaussian mixture model (GMM) to describe the generative process of region features. However, it is difficult to model the complex distribution of high-dimensional feature space with a limited number of Gaussians obtained by unsupervised learning. Simply increasing the number of Gaussians turns out to be inefficient and computationally impractical. To address this issue, we re-interpret a pre-trained CNN as the probabilistic discriminative model, and present a CNN based Fisher vector coding method, termed CNN-FVC. Specifically, activations of the intermediate fully-connected and output soft-max layers are exploited to derive the posteriors, mean and covariance parameters for Fisher vector coding implicitly. To further improve the efficiency, we convert the pre-trained CNN to a fully convolutional one to extract the region features. Extensive experiments have been conducted on two standard scene benchmarks (i.e. SUN397 and MIT67) to evaluate the effectiveness of the proposed method. Classification accuracies of 60.7% and 82.1% are achieved on the SUN397 and MIT67 benchmarks respectively, outperforming previous state-of-the-art approaches. Furthermore, the method is complementary to GMM-FVC methods, allowing a simple fusion scheme to further improve performance to 61.1% and 83.1% respectively.
A new method of iteratively modifying phase pattern for liquid crystal phased array

In this paper several factors affecting the beam steering performance of laser phased array radar are analyzed completely, also simple models for some typical factors are developed. Then, a new method based on iteratively modifying the output phase pattern of liquid crystal phase shifters is proposed. Using this method, the influence of factors can be compensated to some extent; the steering angle accuracy and efficiency with laser phased array radar can be improved. Through the simulation, the error of steering angle can be reduced three orders of magnitude, and the efficiency can be increased almost 30% after 10 iterations.
Three-Dimensional Multiscale Texture Synthesis

Traditional methods for texture synthesis can only capture a limited band of spatial scales. If we wish to synthesize a texture at a variety of zoom levels, an impractical high input resolution must be required. In this paper, we present a new method for synthesizing real-world 3D surface textures with different spatial scales. We only require as few as three low-resolution input images with near Lambertian reflectance. Then we can generate two surface gradient maps and an albedo map by Photometric Stereo to show 3D surface texture characteristics and accordingly to extend the 2D multiscale texture synthesis algorithm to the three-dimensional domain. Finally, we produce synthesized multiscale surface texture with varied illumination directions.
10b 25 MS/s pipelined SAR ADC with dual- phase zero-crossing detector

A successive approximation register (SAR)-assisted two-stage pipeline ADC is presented, achieving low power consumption, high resolution and high speed. A dual-phase zero-crossing detector is used to replace the power-hungry OTA-based MDAC. Since the SAR architecture and zero-crossing circuit are proved to be voltage scalable, this ADC will be benefited from the technology scaling. Simulation results show that the ADC consumes 1.66mW at 25MS/s with 1.5V supply voltage. And it achieves a FoM of 91.7 fJ/conv.-step at the ENOB of 9.50b. The SNDR and SFDR are 58.94dB and 70.62dB, respectively.
Depending on Marketing to an Ecological Vision: A Case Study of Overseas Chinese Town's Developing Management in Shenzhen, China

Marketing explores how to use "market" to attain win-win results of the two sides in exchange activities. Human beings neglect that environment promotes human society development. That caused conflicts between urban development and environment protection. The experiences of Overseas Chinese Town, China, proved that employing the concept of environment marketing in developing management can smooth the relationship between urban development and environment protection. The techniques of developing management based on environment marketing includes: Creating environment-product; establishing brand; satisfying users' demands; and employing the catalyst effects.
DOA estimation of the moving targets in shallow water based on vector optimization beamforming and normal-mode theory

DOA estimation is one of the most important tasks for sonar system. However, the performance of conventional high-resolution DOA estimation methods is influenced because of the neglect of some important factors, such as effect of underwater sound channel, the array position errors and so on. In order to improve the robustness of the high-resolution DOA estimation methods, the vector optimization robust beamforming method is realized by adding a strong joint constraint to the weight vector norm and the steering vector errors. In addition, simulating the matched-field processing method, the DOA estimation of underwater moving targets can be computed by combining adaptive robust beamforming and normal-mode theory. It is shown that the adaptive beamforming which is presented in this paper has higher peak to maximum sidelobe ratio (PSR) and narrower ~3dB beamwidth.
Peak Tree: A New Tool for Multiscale Hierarchical Representation and Peak Detection of Mass Spectrometry Data

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.
Disparity-compensated inter-layer motion prediction using standardized HEVC extensions

The scalable and multiview extensions of the High Efficiency Video Coding share the same high-level syntax coding structure. For the scalable extension, the motion field of the inter-layer reference picture is modified through Motion Field Mapping before used for motion vector prediction. However, the motion field of the inter-layer reference picture is used without modification in the multiview extension. In this paper, a disparity-compensated inter-layer motion prediction is proposed for multiview video coding to achieve disparity compensation in inter-layer motion prediction using the scaled reference layer offset. The experimental results show that comparing with the multiview extension anchor, the proposed method achieves an average of 1.1% bitrate reduction.
An image inpainting approach based on the Poisson equation

A novel algorithm is proposed for inpainting digital images based on the Poisson equation. In this method, the image to be repaired is decomposed into the structure image and the texture image, which are processed respectively according to their image characteristics. The texture image is repaired by the exemplar-based method; for the structure image, the Laplacian operator is employed to enhance the structure information, and the Laplacian image is inpainted by the exemplar-based algorithm, followed by a reconstruction based on the Poisson equation. The inpainting result is obtained by adding back these two sub-images. Experiments show the proposed algorithm provides a better visual effect, especially for large area inpainting
An affine motion compensation framework for high efficiency video coding

As the translational motion model used in recent video coding standards cannot represent the complex motion such as rotation and zooming well, a simple local affine motion compensation framework supporting multiple reference frames is proposed in this paper to characterize the complex motion. Besides, since the commonly used fast motion estimation for affine motion model is still quite complex, a fast gradient based affine motion estimation algorithm is proposed to decrease the encoder complexity. The proposed algorithm is implemented into the test model of the newest video coding standard High Efficiency Video Coding (HEVC). Experimental results show that the bit rate reduction for sequences with complex motion can be up to 16.8%.
Compressive tracking with adaptive color feature selection and foreground modeling

Part-based trackers have achieved promising performance in many tracking tasks. However, most part-based trackers use the same feature representation for all parts and simply combine them together to form an integral representation for the tracking target. It may not guarantee that all parts of the tracking target can well distinguish the foreground from the background. Better performance is expected by exploring different feature representations on different parts of the tracking target. In this paper, following the framework of the classic Compressive Tracker (CT), we model each part of the target adaptively by using a multi-dimensional color representation. By using color name, we select the color feature presentation that best distinguishes the foreground from background. In order to better handle deformation and illumination change, we use multi-Gaussian to model different appearance changes of the tracking target. Both qualitative and quantitative evaluations demonstrate that the proposed method makes a consistent performance improvement compared with the conventional Compressive Tracker on tracking benchmark dataset. Besides, it also outperforms many state-of-the-art trackers while running at averagely 20 frames per second (FPS).
Asymmetric spatial scalability in stereoscopic video coding

Under many viewing circumstances, asymmetric stereoscopic video coding provides similar perceived quality with significant decrease in bitrate, computational complexity, and memory usage compared to conventional stereoscopic video coding. However, in other circumstances, users may prefer conventional symmetric stereoscopic video to asymmetric stereoscopic video. This paper presents a stereoscopic video coding scheme including asymmetric spatial scalability to provide the bitstream adaptation capability to meet heterogeneous network conditions, receiver devices, and user preferences. The spatial scalability property is enabled for the non-base view, and hence the coded bitstream can be decoded as conventional or mixed-resolution stereoscopic video. Simulation results show that the proposed method outperforms the tested other coding schemes providing the same scalability property by at least 8.2% in terms of average bitrate saving.
Joint multiview video plus depth coding

Multiview video plus depth (MVD), where per-pixel depth map sequences are associated with multiview texture video, is a promising approach for three-dimensional (3D) video solutions requiring view synthesis. The Multiview Video Coding (MVC) standard has been typically used to compress the MVD representation, resulting into the multiview texture video and the multiview depth video being in two separate MVC bitstreams. Such a coding scheme does not, however, utilize the similarities of the motion information of the multiview texture video and the multiview depth video. The joint multiview video plus depth coding (JMVDC) scheme presented in this paper uses inter-view prediction similarly to MVC as well as the inter-layer motion prediction tool of the Scalable Video Coding (SVC) standard to exploit the correlation of the motion in the texture and depth video sequences. The simulation results show that the JMVDC method achieves about 10 to 20% saving in depth bitrate compared with conventional MVC-based coding of MVD representations.
Joint texture and depth map video coding based on the scalable extension of H.264/AVC

Depth-Image-Based Rendering (DIBR) is widely used for view synthesis in 3D video applications. Compared with traditional 2D video applications, both the texture video and its associated depth map are required for transmission in a communication system that supports DIBR. To efficiently utilize limited bandwidth, coding algorithms, e.g. the Advanced Video Coding (H.264/AVC) standard, can be adopted to compress the depth map using the 4:0:0 chroma sampling format. However, when the correlation between texture video and depth map is exploited, the compression efficiency may be improved compared with encoding them independently using H.264/AVC. A new encoder algorithm which employs Scalable Video Coding (SVC), the scalable extension of H.264/AVC, to compress the texture video and its associated depth map is proposed in this paper. Experimental results show that the proposed algorithm can provide up to 0.97 dB gain for the coded depth maps, compared with the simulcast scheme, wherein texture video and depth map are coded independently by H.264/AVC.
Coding techniques in Multiview Video Coding and Joint Multiview Video Model

Since early 2006,  Team has been devoting on the development of Multiview Video Coding (MVC) standard as an extension of H.264/AVC. This MVC standard has been finalized in 2008. During the standardization of MVC, there was also a project namely Joint Multiview Video Model (JMVM), which focused on the advanced coding tools that are potentially useful. Those coding tools adopted into JMVM, including illumination compensation and motion skip, have not been added into MVC specification. In this paper, coding techniques in MVC as well as the tools in JMVM are described and discussed, focusing on the coding efficiency.
Depth-level-adaptive view synthesis for 3D video

In the multiview video plus depth (MVD) representation for 3D video, a depth map sequence is coded for each view. In the decoding end, a view synthesis algorithm is used to generate virtual views from depth map sequences. Many of the known view synthesis algorithms introduce rendering artifacts especially at object boundaries. In this paper, a depth-level-adaptive view synthesis algorithm is presented to reduce the amount of artifacts and to improve the quality of the synthesized images. The proposed algorithm introduces awareness of the depth level so that no pixel value in the synthesized image is derived from pixels of more than one depth level. Improvements on objective quality of the synthesized views were achieved in five out of eight test cases, while the subjective quality of the proposed method was similar to or better than that of the view synthesis method used by Moving Picture Experts Group (MPEG).
Effective Active Skeleton Representation for Low Latency Human Action Recognition

With the development of depth sensors, low latency 3D human action recognition has become increasingly important in various interaction systems, where response with minimal latency is a critical process. High latency not only significantly degrades the interaction experience of users, but also makes certain interaction systems, e.g., gesture control or electronic gaming, unattractive. In this paper, we propose a novel active skeleton representation towards low latency human action recognition . First, we encode each limb of the human skeleton into a state through a Markov random field. The active skeleton is then represented by aggregating the encoded features of individual limbs. Finally, we propose a multi-channel multiple instance learning with maximum-pattern-margin to further boost the performance of the existing model. Our method is robust in calculating features related to joint positions, and effective in handling the unsegmented sequences. Experiments on the MSR Action3D, the MSR DailyActivity3D, and the Huawei/3DLife-2013 dataset demonstrate the effectiveness of the model with the proposed novel representation, and its superiority over the state-of-the-art low latency recognition approaches.
Volume Graph Model for 3D Facial Surface Extraction

3D facial extraction from volume data is very helpful in virtual plastic surgery. Although the traditional Marching Cubes algorithm (MC) can be used for this purpose, it could not separate the facial surface from other tissue surfaces. This weakness greatly limited the accuracy of the facial model and its application in plastic surgery. In this paper a volume graph model is proposed, in which the facial surface extraction is formulated as a min-cut problem and can be solved by existing graph cut algorithm. Based on this model, irrelevant tissue surfaces are effectively excluded and a more accurate 3D virtual face can be built for plastic surgery.
Chinese sign language recognition with adaptive HMM

Sign Language Recognition (SLR) aims at translating the sign language into text or speech, so as to realize the communication between deaf-mute people and ordinary people. This paper proposes a framework based on the Hidden Markov Models (HMMs) benefited from the utilization of the trajectories and hand-shape features of the original sign videos, respectively. First, we propose a new trajectory feature (enhanced shape context), which can capture the spatio-temporal information well. Second, we fetch the hand regions by Kinect mapping functions and describe each frame by HOG (pre-processed by PCA). Moreover, in order to optimize predictions, rather than fixing the number of hidden states for each sign model, we independently determine it through the variation of the hand shapes. As for recognition, we propose a combination method to fuse the probabilities of trajectory and hand shape. At last, we evaluate our approach with our self-building Kinect-based dataset and the experiments demonstrate the effectiveness of our approach.