nils-herrmann/clean_scirepeval huggingface dataset:
Online multi-object tracking based on hierarchical association and sparse representation

In recent years, sparse representation has been applied to multi-object tracking and shows promising performance. But existing methods often lead to considerable computation. In this paper, we propose a two-level hierarchical association approach to improve the accuracy and efficiency of online multi-object tracker based on sparse representation. We employ a time-saving affinity measure and a discriminative sparse representation classifier to handle objects with disparate and similar appearances, respectively. We also propose a novel strategy for track termination to protect the reliable tracks containing more detections and restrain the unreliable tracks at the same time. Experimental results demonstrate that the proposed method outperforms state-of-the-art online methods.
Robust face recognition based on iterative sparse coding and pixel selection

Face recognition based on sparse representation has attracted broad interest in recent years. In many existing sparse coding works, the distribution of error term (coding residual) is modeled with a Laplacian or Gaussian function, which leads to an l1-norm or l2-norm minimization problem. However, it is hard to fit the error term satisfactorily in practice, especially when occlusion or corruption exists. In order to improve the robustness of sparse coding algorithms to outliers, we propose an efficient pixel-selection strategy in this paper, which can pick out unspoiled pixels from a seriously damaged face image. However, it is very challenging to determine the positions of the outliers directly. We propose an iterative coding approach to improve the selection. Extensive experiments demonstrate the robustness and effectiveness of the proposed method in the presence of occlusion and corruption.
Link Between and Comparison and Combination of Zhang Neural Network and Quasi-Newton BFGS Method for Time-Varying Quadratic Minimization

Since 2001, a novel type of recurrent neural network called Zhang neural network (ZNN) has been proposed, investigated, and exploited for solving online time-varying problems in a variety of scientific and engineering fields. In this paper, three discrete-time ZNN models are first proposed to solve the problem of time-varying quadratic minimization (TVQM). Such discrete-time ZNN models exploit methodologically the time derivatives of time-varying coefficients and the inverse of the time-varying coefficient matrix. To eliminate explicit matrix-inversion operation, the quasi-Newton BFGS method is introduced, which approximates effectively the inverse of the Hessian matrix; thus, three discrete-time ZNN models combined with the quasi-Newton BFGS method (named ZNN-BFGS) are proposed and investigated for TVQM. In addition, according to the criterion of whether the time-derivative information of time-varying coefficients is explicitly known/used or not, these proposed discrete-time models are classified into three categories: 1) models with time-derivative information known (i.e., ZNN-K and ZNN-BFGS-K models), 2) models with time-derivative information unknown (i.e., ZNN-U and ZNN-BFGS-U models), and 3) simplified models without using time-derivative information (i.e., ZNN-S and ZNN-BFGS-S models). The well-known gradient-based neural network is also developed to handle TVQM for comparison with the proposed ZNN and ZNN-BFGS models. Illustrative examples are provided and analyzed to substantiate the efficacy of these proposed models for TVQM.
Matching Objects between Multiple Cameras Based on Wavelet Salient Features

This paper proposes a novel and effective method to match objects between multiple cameras, which is based on wavelet salient features consisting of color information and salient values at salient points. Learning templates are set up and updated based on salient features in one camera, and used to match objects in the other camera without any geometric constraints. Experiments show the effectiveness of the proposed method in matching different views of the same objects, even when occlusion occurs.
Texture-based palmprint retrieval using a layered search scheme for personal identification

This paper presents a new approach to palmprint retrieval for personal identification. Three key issues in image retrieval are considered: feature extraction, similarity measurement and fast search for the best match of the queried image in an image database. We propose a texture-based approach for palmprint feature representation. The concept of texture energy is introduced to define both global and local features of a palmprint, which are characterized with high convergence of inner-palm similarities and good dispersion of inter-palm discrimination. The searching is carried out in a layered fashion: the global features are first used to guide the fast selection of a small set of similar candidates from the database and then the local features are applied to determine the final output from the selected set of similar candidates. The experimental results illustrate the effectiveness of the proposed approach.
Thickness Dependence of Low-Frequency Noise in MoS2 Field-Effect Transistors With Enhanced Back-Gate Control

Low-frequency noise has become one of the major limitations on further exploration for the ultimate performance of nanoelectronic devices based on novel two-dimensional (2D) materials. Here, the low-frequency 1/<inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math> </inline-formula> noise has been qualitatively investigated on MoS<sub>2</sub> field-effect transistors (FETs) with different layer numbers. 1/<inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math> </inline-formula> noise in multi-layer (2-layer and thicker) MoS<sub>2</sub> devices follows the Hooge mobility fluctuation model. But for monolayer MoS<sub>2</sub> FET, 1/<inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math> </inline-formula> noise is dominated by the carrier number fluctuation model in a weak accumulation regime, while the mobility fluctuation model is the dominant source in a strong accumulation regime. Such 1/<inline-formula> <tex-math notation="LaTeX">${f}$ </tex-math> </inline-formula> noise characteristics are explained with the depth of intrinsic defect trap energy levels in MoS<sub>2</sub> thin films through first-principle calculation and the enhanced gate control by using engineered back-gate stack.
Comparison of Three Different Types of Wrist Pulse Signals by Their Physical Meanings and Diagnosis Performance

Increasing interest has been focused on computational pulse diagnosis where sensors are developed to acquire pulse signals, and machine learning techniques are exploited to analyze health conditions based on the acquired pulse signals. By far, a number of sensors have been employed for pulse signal acquisition, which can be grouped into three major categories, i.e., pressure, photoelectric, and ultrasonic sensors. To guide the sensor selection for computational pulse diagnosis, in this paper, we analyze the physical meanings and sensitivities of signals acquired by these three types of sensors. The dependence and complementarity of the different sensors are discussed from both the perspective of cardiovascular fluid dynamics and comparative experiments by evaluating disease classification performance. Experimental results indicate that each sensor is more appropriate for the diagnosis of some specific disease that the changes of physiological factors can be effectively reflected by the sensor, e.g., ultrasonic sensor for diabetes and pressure sensor for arteriosclerosis, and improved diagnosis performance can be obtained by combining three types of signals.
Motion blur removal for humanoid robots

Removing motion blur caused by camera shake is a tough problem which received much attention in past decades. While, blur removal for the images captured by the camera on humanoid robot is more difficult because of the heavy shaking and unpredictable movement at each pace. To account for this challenging blur problem, we propose a hybrid image deblurring algorithm in this paper. Specifically, the images blurred by robot movement are classified as less blurred and severely blurred by using Just Noticeable Blur Metric (JNBM) as a quantitative criterion. For less blurred images, we propose a maximum a posteriori (MAP) framework by taking advantage of the previous sharp image as reference. For severely blurred images, since most details are lost and hard to recover by deconvolution, we refer to the previous neighboring less blurred images, and directly warp the better deblurred one by SIFT matching as the deblurred result. Experimental results demonstrate the proposed algorithm is superior over the existing methods both qualitatively and quantitatively.
Three dimensional palmprint recognition

Palmprint has been widely studied as its high accuracy and low cost. Most of the previous studies are based on two dimensional (2D) image of the palmprint. However, 2D image can be easily forged, which will threaten the security of palmprint authentication system. Furthermore, 2D image can be easily affected by noise, such as scrabbling and dirty in the palm. To overcome these shortcomings, we develop a three dimensional (3D) palmprint identification system. The structured-light imaging technology is adopted to collect the 3D palmprint data, from which the stable Mean Curvature Image (MCI) is extracted. Then the Competitive Coding (CompCode) technique is used to code the 3D palmprint pattern according the MCI. By using score level fusion of MCI and its CompCode, promising recognition performance is achieved on our established 3D palmprint database.
A Tongue-Print Image Database for Recognition

The tongue is a unique organ in that it can be stuck out of mouth for inspection, in this act offering a proof of life, and yet it is otherwise well protected in the mouth and is difficult to forge. The tongue also presents both geometric shape information and physiological texture information which are potentially useful in identity verification applications. Furthermore, the act of physically reaching or thrusting out is a convincing proof for the liveness. Despite these obvious advantages, little work has hitherto been done on tongue features. A primary factor for preventing such research is the lack of an available 3D tongue image database. In this paper, we present a newly developed 3D tongue image database, which includes both tongue geometric shape and surface textures of 134 subjects. This is the first attempt at making a 3D tongue image database available for the research community, with the ultimate goal of fostering the research on tongue biometrics. The new database can be a valuable resource for algorithm assessment, comparison and evaluation.
Low pressure growth of diamond films under fluorine addition

Phase diagrams in the C-H-F system for low pressure diamond growth have been calculated in the usual conditions: 0.1-6.67 kPa, 900-1100 K, and the agreements with experiments are good. A series of ternary phase diagrams imply that the suitable composition for low pressure diamond growth in C-H-F system is confined in the small CH/sub 4/-H-HF triangle. There is no diamond growth region while F/(F+H)>0.5, only a HF-CF/sub 4/ line appears. The diamond growth region changes greatly with substrate temperature and fluorine concentration.
Heel Crack and Lead-Free Soldering Issues Affecting Power Electronics Packages

Heel crack is one of the most complicated reliability problems of wire bonding in the power electronic package. In this work, we investigate the effects of solder IR reflow to the heel crack both in experimental and FEA simulation study for the 2-5 mil (diameter) aluminum wire. The results show that the plastic strain at the heel region induced by the wire bonding process, influence of molding process and the coefficient of thermal expansion (CTE) mismatches between different components in package are the main causes of heel crack happened in IR reflow. With respect to the trend of lead free, the simulation is also processed under the three temperature hierarchies with different peak reflow temperature (220 D, 240 D, 260 D) and wetting time. From von mises stress and related plastic strain distributions, it can been seen that, during the reflow, the heel region of the wire is endured larger stress and plastic strain than other areas, and with the peak reflow temperature and wetting time increasing, the plastic strain also increases about 20%, which is very critical for material fatigue
Use case modeling for real-time application

The use case driven approach has been widely used in object-oriented development. This paper presents the initial achievement in applying use case models for a real-time oriented system-the Digital Loop Carrier (DLC) system. In this study, we have developed a complete use case model of more than 20 use cases including hundreds of scenarios. During the course, we have encountered several practical issues, and developed initial solution strategies through several iterations of vigorous debates over the use case model development. We have also introduced a few new concepts to ease use case modeling. It is hoped that these solution strategies and new concepts will facilitate the use case model and will reduce the modeling time by minimizing practical confusions.
Computational Intelligence-Based Biometric Technologies

Computational intelligence (CI) technologies are robust, can be successfully applied to complex problems, are efficiently adaptive, and usually have a parallel computational architecture. For those reasons they have been proved to be effective and efficient in bio-metric feature extraction and biometric matching tasks, sometimes used in combination with traditional methods. In this article, we briefly survey two kinds of major applications of CI in biometric technologies, CI-based feature extraction and CI-based biometric matching. Varieties of evolutionary computation and neural networks techniques have been successfully applied to biometric data representation and dimensionality reduction. CI-based methods, including neural network and fuzzy technologies, have also been extensively investigated for biometric matching. CI-based biometric technologies are powerful when used in the representation and recognition of incomplete biometric data, discriminative feature extraction, biometric matching, and online template updating, and promise to have an important role in the future development of biometric technologies
Efficient joint 2D and 3D palmprint matching with alignment refinement

Palmprint verification is a relatively new but promising personal authentication technique for its high accuracy and fast matching speed. Two dimensional (2D) palmprint recognition has been well studied in the past decade, and recently three dimensional (3D) palmprint recognition techniques were also proposed. The 2D and 3D palmprint data can be captured simultaneously and they provide different and complementary information. 3D palmprint contains the depth information of the palm surface, while 2D palmprint contains plenty of textures. How to efficiently extract and fuse the 2D and 3D palmprint features to improve the recognition performance is a critical issue for practical palmprint systems. In this paper, an efficient joint 2D and 3D palmprint matching scheme is proposed. The principal line features and palm shape features are extracted and used to accurately align the palmprint, and a couple of matching rules are defined to efficiently use the 2D and 3D features for recognition. The experiments on a 2D+3D palmprint database which contains 8000 samples show that the proposed scheme can greatly improve the performance of palmprint verification.
Image denoising and zooming under the linear minimum mean square-error estimation framework

Most of the existing image interpolation schemes assume that the image to be interpolated is noise free. This assumption is invalid in practice because noise will be inevitably introduced in the image acquisition process. Usually, the image is denoised first and is then interpolated. The denoising process, however, may destroy the image edge structures and introduce artefacts. Meanwhile, edge preservation is a critical issue in both image denoising and interpolation. To address these problems, in this study the authors propose a directional denoising scheme, which naturally endows a subsequent directional interpolator. The problems of denoising and interpolation are modelled as to estimate the noiseless and missing samples under the same framework of optimal estimation. The local statistics is adaptively calculated to guide the estimation process. For each noisy sample, the authors compute multiple estimates of it along different directions and then fuse those directional estimates for a more accurate output. The estimation parameters calculated in the denoising processing can be readily used to interpolate the missing samples. Compared with the conventional schemes that perform denoising and interpolation in tandem, the proposed noisy image interpolation method can reduce many noise-caused interpolation artefacts and preserve well the image edge structures.
A novel agent bidding based optimization approach in manufacturing planning and scheduling

The problem of process planning and scheduling in manufacturing systems can be simply concluded as an optimization problem in terms of minimizing processing cost and time with the constraints of different manufacturing resources capabilities and capacities and fixed operating sequence of job features. Some previous literatures involve centralized optimization approaches which are able to find a close to optimal solution, but the computational complexity is extremely high and the model is not flexible. Some others adopted dispatching rules, which can achieve good solutions, but not optimal ones. This paper presents a novel agent bidding based mechanism to solve the problem. A weight function is introduced to facilitate the search for optimal allocation, where the weight factors control the preference of production cost and time. Meanwhile, in each bidding round, there is an exploration process which gives small possibility to the agents with non-optimal bids to be selected as the winner. After iterations for adjusting parameters and exploration, the near optimal solution with minimized cost and satisfied lead time can be found.
Plasma enhanced atomic layer deposition of molybdenum oxide from Mo(CO)6 and O2 plasma for 2D electronic device application

This work is focused on synthesis of molybdenum oxide (MoO3) by Plasma Enhanced Atomic layer deposition (PEALD) using molybdenum hexacarbonyl (Mo(CO)6) is selected as precursor for Mo and O2 is adopted as precursor for plasma. Ex-situ growth characterizations were carried out by X-ray reflectivity (XRR), scanning electron microscope (SEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). XRR result indicates the linear growth of the thin films. While amorphous as-deposited film was observed by XRD. XPS and SEM was applied to further analyze the quality of the film. The growth of MoO3 can be layer controllable and have good film continuity. There merits are helpful in further synthesis of MoS2 by sulfurization.
Fusion of Multiple Features for Palmprint Authentication

A palmprint image contains abundant features, such as the ridges, lines and textures, etc. Different features reflect the different characteristic of the palmprint. Fusion of multiple palmprint features may enhance the performance of a palmprint authentication system. In this paper, we investigate the fusion of two novel palmprint representations: orientationCode and diffCode. OrientationCode are defined using four directional templates and diffCode is computed using differential operation. Then these two codes are fused to measure the similarity of palmprints. The experimental results show that the proposed approach can get a very high accuracy
An automatic method for detecting objects of interest in videos using surprise theory

Automatically detecting objects of interest in videos is a challenging issue since there is no prior knowledge about which objects should be detected and what these objects look like. The objects of interest can be defined as salient ones and the saliency can be measured by surprise theory. Therefore, this paper proposes a new method for automatic object detection. It involves two modules: surprise estimation and object localization. The surprise estimation module first uses the surprise theory to obtain a saliency map which indicates the novelty of each pixel compared with its previous states. The object localization module then determines where the salient objects locate based on the branch-and-bound search algorithm. Experimental results have shown that the objects of interest in videos can be successfully localized by using the proposed automatic detection method.
Effects of Carbon on Schottky Barrier Heights of NiSi Modified by Dopant Segregation

The presence of carbon at the interface between NiSi and Si has been found to participate in the process of modification of effective Schottky barrier heights using the dopant segregation (DS) method. Carbon alone results in an increased phibn from 0.7 to above 0.9 eV. Boron diffusion in NiSi is inhibited by carbon, and no B-DS at the NiSi/Si interface occurs below 600degC. Above this temperature, B-DS at this interface is evident thus keeping phibn high. The presence of interfacial carbon leads to an increased interfacial As concentration resulting in beneficial effects in tuning phibp above 1.0 eV by As-DS.
A single algorithm combining exposure and focus fusion

In scenes of significantly varying lighting conditions, under and over exposed regions can suffer from a loss of information. Similarly, the presence of spatial depth within a scene can cause some image regions to be out of focus. Several methods of addressing these issues exist, including tone mapping for true high dynamic range representation and exposure fusion for combining varied-exposure low dynamic range images, as solutions to the former, and image fusion and segmentation etc. to address the latter. This paper proposes an overhauled method of exposure fusion that solves the exposure and focus problems simultaneously, achieving a well-exposed, all-in-focus result. Smart, scene-based data acquisition techniques for reducing both required input data and computational resources are discussed. A platform for a realtime system implementation is also presented.
Gaussian ERP Kernel Classifier for Pulse Waveforms Classification

While advances in sensor and signal processing techniques have provided effective tools for quantitative research on traditional Chinese pulse diagnosis (TCPD), the automatic classification of pulse waveforms is remained a difficult problem. To address this issue, this paper proposed a novel edit distance with real penalty (ERP)-based k-nearest neighbors (KNN) classifier by referring to recent progresses in time series matching and KNN classifier. Taking advantage of the metric property of ERP, we first develop a Gaussian ERP kernel, and then embed it into kernel difference-weighted KNN classifier. The proposed Gaussian ERP kernel classifier is evaluated on a dataset which includes 2470 pulse waveforms. Experimental results show that the proposed classifier is much more accurate than several other pulse waveform classification approaches.