nils-herrmann/clean_scirepeval huggingface dataset:
An empirical study of facial components classification by integrating dimensionality reduction and clustering

In this paper we present an empirical study of facial components classification by integrating dimensionality reduction and unsupervised clustering. The proposed framework contains two iterative steps: 1) Fixing cluster labels, the facial samples are projected onto lower dimensional subspace through dimensionality reduction method; 2) Fixing the subspace, the clustering algorithm is performed to generate cluster labels. Through iterative steps, clusters are discovered in the lower dimensional subspaces to avoid the curse of dimensionality, while the subspaces are adaptively re-adjusted for global optimality. In order to achieve an effective and robust system, we compare the PCA with the LLE for dimensionality reduction, and compare K-means with LDA-guided K-means(LDA-Km) for unsupervised clustering. The quantitative experimental results prove the LLE and LDA-Km are superior for facial data on public Lotus Hill Institute(LHI) dataset. We also apply the presented study to improve the portrait sketching results.
Noise rejection of nonlinear systems with measurement noises

This paper investigates control design focusing on noise rejection of disturbed nonlinear systems. The disturbance rejection scheme is proposed with a noise estimator, constructed upon a concern-most disturbances can be approximately described by a differential equation. A controller is further proposed to stabilize the considered system and simultaneously fulfill the disturbance compensation by using the disturbance estimator. Superiority of our strategy is addressed by means of a case study.
Deep Human Parsing with Active Template Regression

In this work, the human parsing task, namely decomposing a human image into semantic fashion/body regions, is formulated as an active template regression (ATR) problem, where the normalized mask of each fashion/body item is expressed as the linear combination of the learned mask templates, and then morphed to a more precise mask with the active shape parameters, including position, scale and visibility of each semantic region. The mask template coefficients and the active shape parameters together can generate the human parsing results, and are thus called the structure outputs for human parsing. The deep Convolutional Neural Network (CNN) is utilized to build the end-to-end relation between the input human image and the structure outputs for human parsing. More specifically, the structure outputs are predicted by two separate networks. The first CNN network is with max-pooling, and designed to predict the template coefficients for each label mask, while the second CNN network is without max-pooling to preserve sensitivity to label mask position and accurately predict the active shape parameters. For a new image, the structure outputs of the two networks are fused to generate the probability of each label for each pixel, and super-pixel smoothing is finally used to refine the human parsing result. Comprehensive evaluations on a large dataset well demonstrate the significant superiority of the ATR framework over other state-of-the-arts for human parsing. In particular, the F1-score reaches 64.38 percent by our ATR framework, significantly higher than 44.76 percent based on the state-of-the-art algorithm .
Intelligent Agents for Pragmatic Web Services

In semiotics, pragmatics is about purpose, context, communication and negotiation. The concept of pragmatics could be introduced into the web service technology to become the pragmatic web service. Intelligent agents play a vital role in this web service architecture especially for web service integration to provide better solution based on service consumers' overall requirement. In this paper, intelligent agents and are described to manage users' intention, service context, information communication and negotiation between service consumers and service providers. In addition, the agents need to be able to analyze user's requirements, construct web service abstract workflow and rank the semantic equivalent web services from the service registry. Section2 describes the research background of pragmatic web services. Section3 is about the basic structure of pragmatic web service agents and section4 concerns the agent-based web service integration.
A Novel Adaptive Quasi-Constant On-Time Current-Mode Buck Converter

There have been a variety of constant on-time (COT) controllers for dc power converter applications reported in recent years <xref ref-type="bibr" rid="ref1 ref2 ref3 ref4">[1]</xref>–<xref ref-type="bibr" rid="ref5">[5] </xref>. In this paper, a novel adaptive quasi-constant on-time current-mode control scheme is proposed and implemented in buck converters. While preserving the advantages of the conventional COT current-mode converters, this scheme allows fast transient response to step-load that is a critical requirement of a computer load with smart management. A small-signal model of the proposed circuit is also developed. Experimental results are also provided. This scheme is well suited for the next-generation dc converters for power management central-processor-unit devices.
Design of a 3-D modified proportional navigation guidance law

This paper intends to develop a terminal guidance law against very high speed targets. Traditionally, proportional navigation guidance (PNG) is mostly effective for interceptors and targets with ideal dynamics and constant speeds during the collision course, however, which is non-robust for two objects with non-constant speeds and non-ideal dynamics. To overcome the weakness, the interceptor considered is guided by a 3-dimensional (3-D) modified proportional navigation guidance (MPNG) law which is developed to account for the interceptor's axial acceleration. The guidance law is applicable to interceptors with thrust vector control to provide lateral accelerations. Engagement performance of the proposed design is verified by numerical simulation.
Isolation of Defects in Radiographic Weld Images with Wavelet Denoising Using Log-Gabor Filter

Radiography is one of the oldest and most widely used NDT methods for the detection of volumetric defects in welds and castings. Radiographic films are chosen to record the images, due to higher resolution and their flexible usability for any kind of job. When digitized any radiographic image is characterized by three parameters, viz., optical density, contrast, resolution. Of these parameters, resolution of the film depends on the film quality and geometrical unsharpness. The objective of presented work is to compare the radiographic film image acquired at three different resolutions and denoise those images using wavelet technique and to find out the best filter that furnishes the maximum details of the acquired radiographic image. The denoising operation is performed using non orthogonal log-gabor wavelet function. This paves way to decide the best filter to retrieve the information recorded in the radiographic weld images which in turn helps to locate the defects and its dimensions in the corresponding weld piece. Radiographic image offline scattered porosity defect that occurs in weld is taken for this work.
Viewpoint-Independent Object Detection Based on Two-Dimensional Contours and Three-Dimensional Sizes

We propose a viewpoint-independent object-detection algorithm that detects objects in videos based on their 2-D and 3-D information. Object-specific quasi-3-D templates are proposed and applied to match objects' 2-D contours and to calculate their 3-D sizes. A quasi-3-D template is the contour and the 3-D bounding cube of an object viewed from a certain panning and tilting angle. Pedestrian templates amounting to 2660 and 1995 vehicle templates encompassing 19 tilting and 35 panning angles are used in this study. To detect objects, we first match the 2-D contours of object candidates with known objects' contours, and some object templates with large 2-D contour-matching scores are identified. In this step, we exploit some prior knowledge on the viewpoint on which the object is viewed to speed up the template matching, and the viewpoint likelihood for each contour-matched template is also assigned. Then, we calculate the 3-D widths, heights, and lengths of the contour-matched candidates, as well as the corresponding 3-D-size-matching scores. The overall matching score is obtained by combining the aforementioned likelihood and scores. The major contributions of this paper are to explore the joint use of 2-D and 3-D features in object detection. It shows that, by considering 2-D contours and 3-D sizes, one can achieve promising object detection rates. The proposed algorithms were evaluated on both pedestrian and vehicle sequences. It yielded significantly better detection results than the best results reported in PETS 2009, showing that our algorithm outperformed the state-of-the-art pedestrian-detection algorithms.
Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection

This paper presents a novel compositional contour-based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations. Our approach contains two key steps: 1) contour feature generation and 2) generative model pursuit. For each category, we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and describe each segment using three different types of distance metrics. These metrics are diverse and complementary with each other to capture various shape deformations. We regard the parameterized contour segment plus an additive residual ε as a basic subspace, namely, ε-ball, in the sense that it represents local shape variance under the certain distance metric. Using these ε-balls as features, we then propose a generative learning algorithm to pursue the compositional shape model, which greedily selects the most representative features under the information projection principle. In experiments, we evaluate our model on several public challenging data sets, and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations, articulations, and background clutter, hence boosting system performance.
The Development of Wireless Sensor Network for ECG Monitoring

The main problem we want to solve contains two subjects: The first one is the patient's pressure due to wired physiological signal estimation. With wireless sensor network technique, patients only need to carry a few small nodes, and then the physiological signal can be transmitted in the air. The other subject of the vital problem is that some protocols, like Bluetooth, provide a peer to peer wireless communication technique, but such peer to peer network may need a complex algorithm to find the best data transmission path. In this study, we use the hierarchy routing as network topology that three-layer architecture contains PAN coordinator, router and device. The study focuses on implementation of a prototype electrocardiography (ECG) system which replaces wired connections between sensor points and a central node with wireless links. Successful implementation of the final system would be of benefit to all involved in the use of ECG as access to and movement of the patient would not be impeded by the physical constraints imposed by the cables. Most aspects of the design would also be portable to other sensor applications, making the work relevant to a vast range of systems where movement of sensors is desirable and constrained by hard-wired links
Layered graph matching by composite cluster sampling with collaborative and competitive interactions

This paper studies a framework for matching an unknown number of corresponding structures in two images (shapes), motivated by detecting objects in cluttered background and learning parts from articulated motion. Due to the large distortion between shapes and ambiguity caused by symmetric or cluttered structures, many inference algorithms often get stuck in local minimums and converge slowly. We propose a composite cluster sampling algorithm with a “candidacy graph” representation, where each vertex (candidate) is a possible match for a pair of source and target primitives (local structure or small curves), and the layered matching is then formulated as a multiple coloring problem. Each two vertices can be linked by either a competitive edge or a collaborative edge. These edges indicate the connected vertices should/shouldn't be assigned the same color. With this representation, the stochastic sampling contains two steps: (i) Sampling the competitive and collaborative edges to form a composite cluster, in which a few mutual-conflicting connected components are in different colors; (ii) Sampling the new colors to this cluster remaining consistency with Markov Chain Monte Carlo (MCMC) mechanism. The algorithm is applied to many applications on many public datasets and outperform the state of the art approaches.
Time-delay compensation using nonlinear extended network disturbance

This paper investigates control design for time-delay compensation for the time-varying delayed systems, especially, the network transmission dynamics. We propose an idea of virtual disturbance, named nonlinear extended network disturbance (NEND), such that the delayed behavior of the considered model is treated as the effect of injecting NEND into the system. The larger the magnitude of NEND means that the system exhibits the greater time delay. In other words, performing NEND compensation achieves time-delay compensation. This NEND compensation is achieved by introducing the control scheme with an observer for NEND estimation. The proposed design has been verified in the wired network.
Depth-Based Texture Coding in AVC-Compatible 3D Video Coding

The target of 3D video coding is to compress Multiview Video plus Depth (MVD) format data, which consist of a texture image and its corresponding depth map. In the MVD format, the depth map plays an important role for successful services in 3D video applications, because it enables the user to experience 3D by generating arbitrary intermediate views. The depth map has a strong correlation with its associated texture data, so it can be utilized to improve texture coding efficiency. This paper introduces a novel and efficient depth-based texture coding scheme. It includes depth-based motion vector prediction, block-based view synthesis prediction, and adaptive luminance compensation, which were adopted in an AVC-compatible 3D video coding standard. Simulation results demonstrate that the proposed scheme reduces the total coding bitrates of texture and depth by 19.06% for the coded PSNR and 17.01% for the synthesized PSNR in a P-I-P view prediction structure, respectively.
IPv6-based dynamic coordinated call admission control mechanism over integrated wireless networks

Many wireless access systems have been developed recently to support users mobility and ubiquitous communication. Nevertheless, these systems always work independently and cannot simultaneously serve users properly. In this paper, we aim to integrate IPv6-based wireless access systems and propose a coordinated call admission control mechanism to utilize the total bandwidth of these systems to minimize the call blocking probabilities, especially the handoff call dropping probabilities. First, we propose an integrated hierarchical wireless architecture over IPv6-based networks to combine the wireless access systems including cellular systems (second-generation, General Packet Radio Service, or third-generation), IEEE 802.11 a/b/g WLAN, and Bluetooth. In the proposed architecture, mobile user can request a call with quality-of-service (QoS) requirements by any wireless network interfaces that can be accessed. When the proposed coordinated call admission control (CCAC) mechanism receives a request, it takes the QoS requirements of the incoming call and the available and reserved bandwidth of this wireless system into consideration to accept or reject this request. Besides, the mechanism can coordinate with other wireless systems dynamically to adjust the bandwidth reserved for handoff calls at each wireless system in this architecture so as to reduce the call blocking probabilities. Once the call is admitted, the mobile user is able to access heterogeneous wireless access networks via multiple interfaces simultaneously. Finally, we evaluate this system to show that the CCAC on the proposed architecture outperforms other mechanisms proposed before.
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures With Edge-Preserving Coherence

Driven by recent vision and graphics applications such as image segmentation and object recognition, computing pixel-accurate saliency values to uniformly highlight foreground objects becomes increasingly important. In this paper, we propose a unified framework called pixelwise image saliency aggregating (PISA) various bottom-up cues and priors. It generates spatially coherent yet detail-preserving, pixel-accurate, and fine-grained saliency, and overcomes the limitations of previous methods, which use homogeneous superpixel based and color only treatment. PISA aggregates multiple saliency cues in a global context, such as complementary color and structure contrast measures, with their spatial priors in the image domain. The saliency confidence is further jointly modeled with a neighborhood consistence constraint into an energy minimization formulation, in which each pixel will be evaluated with multiple hypothetical saliency levels. Instead of using global discrete optimization methods, we employ the cost-volume filtering technique to solve our formulation, assigning the saliency levels smoothly while preserving the edge-aware structure details. In addition, a faster version of PISA is developed using a gradient-driven image subsampling strategy to greatly improve the runtime efficiency while keeping comparable detection accuracy. Extensive experiments on a number of public data sets suggest that PISA convincingly outperforms other state-of-the-art approaches. In addition, with this work, we also create a new data set containing 800 commodity images for evaluating saliency detection.
Pragmatic Agent Based QoS Aware Web Services Composition

Web services are software platform-independent applications that display their description and functionalities, which make it flexible to integrate different services by using standard technologies. Though the interoperability between diverse applications via using Web service is mentioned in different solutions, there are still a lot of challenges in this area, one of which is how to satisfy overall QoS constraints when composite Web service. Semantic Web service provides a feasible semantic annotation of QoS attributes. However, how to capture this feature and enable dynamic decision is critical in achieving efficient QoS aware Web service composition. In this paper, based on semantic Web extension, pragmatic Web, we propose pragmatic agent based QoS aware Web service composition architecture. The detail structure and design process is elaborated and it is argued that this approach provide a new perspective on Web service composition.
A power-of-two FFT algorithm and structure for DRM receiver

Digital Radio Mondiale (DRM) is the most important technology to replace analog AM broadcasting into digital one. The core of DRM is based on coded orthogonal frequency division multiplexing (COFDM), so it has high spectrum efficiency including the other advantages. Modern OFDM is based on discrete Fourier transform (DFT) or inverse discrete Fourier transform (IDFT). However, due to the particularity of DRM, the numbers of sub-carriers in four DRM robust modes are not same. And these numbers are nonpower- of-two. Typically, the fast Fourier transform (FFT) is based on a power-of-two algorithm. We do have some algorithms to compute the non-power-of-two FFT such as the prime-factor algorithm (PFA). However, these algorithms are complex and inconvenient to implement. Therefore, we propose a new algorithm and structure to use a power-of-two FFT algorithm in DRM receiver. This algorithm has low complexity, more general and flexible characteristics. In the end the paper gives out the bit error rate (BER) simulation results, these results indicate that the proposed algorithm's performance is same as the traditional one.
Motion vector coding techniques for HEVC

High Efficiency Video Coding (HEVC) is a new international video coding standard that has been developed by the Joint Collaborative Team on Video Coding (JCT-VC). In this paper, an overview of the motion vector coding techniques for HEVC is presented. Our three proposed coding tools for the motion vector predictor (MVP) in the Inter, Skip and Merge modes of HEVC are also presented, which includes a new location of the temporal MVP, a priority-based derivation method of spatial MVPs, and a derivation method of temporal MVPs. A combination of these three tools can achieve on average 1.3%, 1.8%, 1.2% and 2.2% bit rate reductions for high efficiency random access, low complexity random access, high efficiency low delay, and low complexity low delay, respectively.
The Concept and Technical Analysis on Cryogenic VSC-HVDC System

Cryogenic operation of power converter will result in improved control speed and power handling capability, so a concept and layout of cryogenic VSC-HVDC is proposed to link AC networks. The bipolar coaxial HTS DC cable is described in details. The PWM control strategy based on the change of DC voltages is analyzed. In the system, SMES provides necessary power channel to protect the cable under the faults. HTS self-limiting-current smoothing reactor not only can be regarded as a regulator to reduce the fault current / voltage, but also may provide a damping for the oscillations. The transient stability assessment indicates high rotor angle stability. Some problems of the cable are emphasized, which are related to the stability of the cable, cryogenic dielectric and the losses of the system, etc.
Laplacian Regularized Least Squares Regression and its Dynamic Parameter Optimization for Near Infrared Spectroscopy Modeling

Partial least square (PLS) is the most commonly used algorithm for near infrared (NIR) modeling. NIR modeling features that it's cheap, easy and fast to measure the NIR spectroscopy while expensive, difficult and time-consuming to measure the reference value for this spectroscopy. PLS often faces the challenge of that limited samples are available in training set to build a predicative model. To tackle this problem, a novel NIR modeling method - Laplacian regularized least squares regression (LapRLSR) and its dynamically adaptive parameters optimization method was presented. Based on the semi-supervised learning framework, LapRLSR can take the advantage of many unlabeled spectra to promote the prediction performance of the model though there are only few labeled samples. The proposed LapRLSR modeling algorithm was applied to the online monitoring of the concentration of salvia acid B in the column separation procedure of TCM manufacturing, and the results demonstrated that its prediction capability outperformed PLS and regularized least square regression method.