nils-herrmann/clean_scirepeval huggingface dataset:
Characterizing of Chinese lexical networks

In this paper, we investigate Chinese lexical based on complex network theory. We study statistical properties of the networks and find that lexical networks show some important characteristics such as small world, the power-law distribution and hierarchical structure. These statistical properties display the global organization of Chinese lexical. The origin and formation of such properties are investigated from a macroscopic Chinese culture and philosophy perspective. It is interesting to find Chinese culture and philosophy may shape the formation and structure of Chinese lexical. The results of the paper can give some insight of Chinese language learning. It strengthens the idea that learning the lexical helps to understand Chinese culture. Furthermore, understanding Chinese culture and philosophy does help to learn Chinese lexical. The hub nodes in the networks show close relationship with Chinese culture and philosophy. Learning the hub characters and lexical which construct should be the first step and most important task for Chinese learning.
3D-visualization of CT images under IDL environment

Conventional CT technology has been widely used in modern medicine to detect cancer and provide help in clinical treatment. However, mass screening based on CT images often leads to large number of images which makes the diagnosis time-consuming and difficult to be used in a clinic. To increase the efficiency of diagnosis and treatment, 3D-visualization of the 2D CT images is helpful to provide the doctor with a realistic 3D environment in which he can concentrate on searching ROI and making decisions. In this paper we describe the process of 3D visualization of 2 dimensional CT images. A series of 2D CT images are stacked to form a 3D volume image, whose rendering is displayed under IDL environment. The relations and matching problem between the visualized 3D image and the original 2D images are analyzed.
A new cooperative spectrum sensing algorithm for cognitive radio networks

Spectrum sensing is a critical phase in building a cognitive radio network. However, when the number of cognitive user is very large, the bandwidth for reporting cognitive users' sensing results will be insufficient. In this paper, we propose a new cooperative spectrum sensing algorithm to alleviate the bandwidth problem of reporting channel. Compared with conventional method, only the cognitive users with reliable information are allowed to report their sensing results. When no user is reliable, only the cognitive user with highest reputation will report its sensing result. Simulation results show that our algorithm achieves better sensing performance and the average number of sensing bits decreases greatly under AWGN channel.
Cooling control of aluminum plate with a Peltier device thermal process by using a robust right coprime factorization approach

In this paper, an aluminum plate with a Peltier devices are used as a model. For the Peltier actuated thermal process is a nonlinear control affine system and the real system is usually contains fault owing to various factors, it is difficult to achieve excellent control consequent. In this paper, robust right coprime factorization is applied to analyse the thermal process. Then the derivation of the controller is also given for achieve robust stability and tracking performance. Finally, simulation and experimental results are presented to support the theoretical results.
l2 Restoration of l∞-Decoded Images Via Soft-Decision Estimation

The l(∞)-constrained image coding is a technique to achieve substantially lower bit rate than strictly (mathematically) lossless image coding, while still imposing a tight error bound at each pixel. However, this technique becomes inferior in the l(2) distortion metric if the bit rate decreases further. In this paper, we propose a new soft decoding approach to reduce the l(2) distortion of l(∞)-decoded images and retain the advantages of both minmax and least-square approximations. The soft decoding is performed in a framework of image restoration that exploits the tight error bounds afforded by the l(∞)-constrained coding and employs a context modeler of quantization errors. Experimental results demonstrate that the l(∞)-constrained hard decoded images can be restored to gain more than 2 dB in peak signal-to-noise ratio PSNR, while still retaining tight error bounds on every single pixel. The new soft decoding technique can even outperform JPEG 2000 (a state-of-the-art encoder-optimized image codec) for bit rates higher than 1 bpp, a critical rate region for applications of near-lossless image compression. All the coding gains are made without increasing the encoder complexity as the heavy computations to gain coding efficiency are delegated to the decoder.
A Dynamic Decision-Maker of Anytime Algorithm for Tasks Decision-Making Problem

In this paper, a mathematical model of dynamic task schedule based on an anytime algorithm is proposed. The factors that affect the task deadline of opponents are studied. We construct the mathematical model according to minimize of surplus threat and maximize of attack value. By using the task deadline and time utility the model can give the decisions timely and coordinately for known and unknown targets. Balancing the task deadline and decision efficiency, the model can schedule the decisions quickly. We applied this model to a real-time dynamic weapon-target assignment (DWTA) problem. The simulation results show that the model based on anytime algorithm is timely and efficiently for the DWTA problems.
A First Look at Android Malware Traffic in First Few Minutes

With the advent of mobile era, mobile terminals are going through a trend of surpassing PC to become the most popular computing device. Meanwhile, the hackers and viruswriters are paying close attention to the mobile terminals, especially the Android platform. The growing of malwares on the Android system has drawn attentions from both the academia and security industry. Recently, mobile network traffic analysis has been used to identify the malware. But due to the lack of a large-scale malware repository and a systematic analysis of network traffic features, the existing research mostly remain in theory. In this paper, we design an Android malware traffic behavior monitoring scheme to capture traffic data generated by malware samples in a real Internet environment. We capture the network traffic from 5560 malware samples in the first 5 minutes, and analyze the major compositions of the traffic data. We discover that HTTP and DNS traffic are accounted for more than 99% on the application layer traffic. We then present an analysis of related network features: DNS query, HTTP packet length, ratio of downlink to uplink traffic amount, HTTP request and Ad traffic feature. Our statistical results illustrate that: (1) more than 70% malwares generate malicious traffic in the first 5 minutes, (2) DNS query and HTTP request can be used to identify the malware, and the detection rate reaches 69.55% and 40.89% respectively, (3) Ad traffic can greatly affect the malware detection. We believe our research provides an in-depth analysis into mobile malwares' network behaviors.
Design and experimental verification of high power density interior permanent magnet motors for underwater propulsions

This paper presents the design details for IPMs developed for underwater propulsion with a peak power to weight ratio of 4.09kW/kg. The technical aspects of the increase in power density and the reduction of losses are discussed. Load characteristics and motor design requirements are analyzed. Optimization procedure is presented and comparisons are made between different designs in order to examine the effect of critical design parameters and materials. The electromagnetic, thermal and stress fields are computed with the finite-element method (FEM). Special attentions are paid to the electromagnetic and thermal designs in order to extend the duration of operation at short-time duty. Practical IPMs are designed and manufactured. Finite element analysis (FEA) and experimental results show that the designs fulfill the applications effectively.
A Column-Generation Based Approach for Integrating Surgeon and Surgery Scheduling

This paper addresses the operating room management problem for private hospitals aimed at the high-end customer market. A patient preference-driven policy that incorporates surgeon scheduling into the surgery scheduling process is proposed to satisfy patients’ personalised preferences for surgeons and surgery dates. A stochastic programming model is formulated with the objective of minimizing operating rooms’ staffing costs. A column generation-based heuristic algorithm is developed to solve the integrated surgeon and surgery scheduling issue. The performance of the algorithm is tested on different scale instances. Numerical results indicate that the patient-preference driven approach can identify an effective solution to operating room scheduling that satisfies patients’ preferences with only slightly increased staffing costs. Additionally, the column generation-based heuristic algorithm can obtain solutions within a 1.6% gap of the lower bound obtained by the linear relaxed problem.
Area spectral efficiency of underlay cognitive radio transmission over rayleigh fading channels

Cognitive radio is a promising technology to improve the spectrum utilization by allowing the unlicensed (secondary) user share a frequency bandwidth with the licensed (primary) owner under the condition that no harmful interference is imposed on the licensee. In this paper, we analyze the spatial spectrum utilization efficiency of underlay cognitive radio transmission. In particular, we apply the performance metric, generalized area spectral efficiency (GASE), to the performance evaluation and design of underlay cognitive radio transmission. Closed-form expressions are given on the ergodic capacity of the primary and secondary user in underlay cognitive radio transmission over Rayleigh fading channels, which are then utilized to evaluate GASE metric. Mathematical analysis and numerical examples show the asymptotic GASE performance of the underlay cognitive radio transmission with respect to the interference constraint on the primary receiver. We show that GASE performance metric provides a new perspective to the design of underlay cognitive radio transmission.
A System and Vision Localization Method for the Opening of Railway Oil Tank Wagon Based on Shape Matching

The opening localization for railway oil tank wagon using machine vision technology is an effective way to load petroleum automatically. In this paper, we introduce a localization system for the opening of railway oil tank wagon, which consists of industrial camera, lens, LED lighter, industrial PC, and IO module. In addition, we also propose a shape matching localization method, which can overcome the adverse effects produced by illumination changes, various types of railway oil tank wagon, and various poses of camera. The shape template is extracted from a standard opening image using edge detection and shape fitting methods, which can help avoid the randomness of template selection. An optimized similarity measure is exploited to avoid the edge extraction, so that the threshold setting of edge extraction can be avoided. In addition, we also exploit scalable template to find various types opening and exploit image pyramid during shape matching procedure to gain a speedup. The indoor and outdoor experiments show that the localization system and the proposed method fully meet the requirements of practical industrial applications.
Aerosol Size Distribution and Its Implication for Radiation Dose Estimation in a Copper Mine

For accurate estimation of radiation dose from radon exposure in a copper mine, the size distributions of aerosols in the different areas of the mine lane were analyzed. The size distribution of accumulated aerosols was analyzed according to the method of screen diffusion battery, the size distribution for the coarse mode of aerosols was estimated based on the size-selective measurements of mass concentrations measured by personal aerosol monitors. The results showed that the size distribution of aerosol significantly varied from different working areas and human activities. The coarse mode of aerosols widely distributed in the mine, while the nucleation mode of aerosols was negligible. Theoretical calculation shows that the dose conversion factor significantly depends on the size distribution of aerosols. Therefore, it is important to measure the size distribution of radon progeny for accurate estimation of radiation dose from radon exposure in mines.
A novel surface self-adapting small mobile robot for free-form surface polishing

The finish machining of large free-form is very difficult because of its very complicated surface and dimension. To improve the machining efficiency and quality of surface, a novel small mobile robot is designed to meet the demand for finish machining of large free-form surface. At first, the structure of the designed mobile robot is introduced and its kinematics analysis is deduced, the transformation matrices is also established. According to the above analysis, simulation work is performed by given an arc as the polishing path to verify the analysis results. Finally, it is proved that the proposed novel configuration of small mobile robot is feasible to polish a large free-form surface workpiece.
A Background Reconstruction Algorithm Based on Two-Threshold Sequential Clustering

A new background subtraction algorithm based on two thresholds sequential clustering is proposed in this paper. First, pixel intensity in period of time is classified based on two thresholds sequential clustering. Second, merging procedure is run to classified classes. Finally, the backgrounds of scene are selected, so the background model can represent the scene well. The simulation results show that the proposed algorithm is robust to the thresholds, those near classes are avoided at all, and the effect of input order of data has been reduced greatly. And the background model can represent the scene well.
An edge-guided image interpolation algorithm via directional filtering and data fusion

Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts
A formalized architecture-centric evolution process for component-based software system

System quality is key part of software system in industry. It not only directly affects the customers/users' satisfaction, but also influences the entire lifecycle of system products from requirement to maintenance. Many quality assurance development methodologies and standards are proposed. However, software evolution as an another important part in software system lifecycle is less studied from the viewpoint of software quality assurance. Architectures, as the most basic and important factor in modern software engineering, are key to guarantee software system quality by replacing codes. Thus, in this paper, we propose a controlled evolution process based on ADLs and formalized by SPEM standard.
Compressive sensing for very high frequency radar with application to low-angle target tracking under multipath interference

For low-angle targets, the performance of altitude measurement is affected by multipath phenomenon. Generally, the response at the receiver is contributed by the echoes of target and its image. Such sparsity of signals in the elevation direction provides a foundation for parameter estimation based on compressive sensing with parameterized dictionary. As the dictionary atoms are multi-parameter, it may increase the dimension of the parameterized dictionary. This may increase the cost of computation and storage violently. Aiming at this problem, a novel compressive sensing based algorithm, combined with alternative optimization and dictionary refinement, is proposed in this paper. The algorithm is based on the criterion of maximizing the correlation coefficient of target signal component and the atoms of a parameterized dictionary, where the dictionary grids can be iteratively refined to estimate the parameters more precisely. Numerical results based on simulated data and real data show the efficiency of the proposed algorithm.
Design of improved Luby transform codes with decreasing ripple size and feedback

In this study, the design of improved Luby transform codes with decreasing ripple size (LTC-DRS) with feedback is presented. Under the proposed design, a new degree distribution algorithm named generalised degree distribution algorithm (GDDA) is proposed, which can achieve arbitrary ripple size revolution accurately. On the basis of GDDA, an accurate ripple size revolution based on binomial fitting is proposed, which can keep the ripple size to a suitable value throughout the decoding process. Furthermore, the authors introduce the feedback and propose a shifted ripple size revolution to diversify the degree values. The improved LTC-DRS with feedback is evaluated and compared with the existing schemes. The simulation results demonstrate that it outperforms other existing schemes in terms of average overhead, average degree of encoded symbols, memory usage and energy consumption.
A novel nonsinusoidal orthogonal band division modulation method

In this paper, a novel nonsinusoidal orthogonal band division modulation method novel nonsinusoidal orthogonal band division modulation method (NOBDM) is proposed. The idea of frequency division multiplexing is used in the nonsinusoidal orthogonal modulation in time domain (NOM). The frequency band of the system is divided into a lot of shoulder-to-shoulder subbands and the NOM are used in each subband. Due to the best energy concentration character of the Prolate Spheroidal Wave Function (PSWF), the modulated signals of different bands are not interfered each other. The novel method not only has the advantages for compensation and bit loading but also reduces the complexity of orthogonal pulse sets design and implementation. The results of the Monte Carlo simulation show that the proposed method has approximate the same BER performance as the old ways. The scheme is suitable for the wire or wireless broadband communication system.
BoMW: Bag of Manifold Words for One-Shot Learning Gesture Recognition From Kinect

In this paper, we study one-shot learning gesture recognition on RGB-D data recorded from Microsoft’s Kinect. To this end, we propose a novel bag of manifold words (BoMW)-based feature representation on symmetric positive definite (SPD) manifolds. In particular, we use covariance matrices to extract local features from RGB-D data due to its compact representation ability as well as the convenience of fusing both RGB and depth information. Since covariance matrices are SPD matrices and the space spanned by them is the SPD manifold, traditional learning methods in the Euclidean space, such as sparse coding, cannot be directly applied to them. To overcome this problem, we propose a unified framework to transfer the sparse coding on SPD manifolds to the one on the Euclidean space, which enables any existing learning method to be used. After building BoMW representation on a video from each gesture class, a nearest neighbor classifier is adopted to perform the one-shot learning gesture recognition. Experimental results on the ChaLearn gesture data set demonstrate the outstanding performance of the proposed one-shot learning gesture recognition method compared against the state-of-the-art methods. The effectiveness of the proposed feature extraction method is also validated on a new RGB-D action recognition data set.
Cooperative Spectrum Sensing with Double Threshold Detection Based on Reputation in Cognitive Radio

Cooperative spectrum sensing can mitigate the effects of shadowing and fading. However, when the number of cognitive users is very large, the bandwidth for reporting their sensing results will be insufficient. In order to eliminate the fail sensing problem for a cognitive radio system with double threshold detector, a new cooperative spectrum sensing algorithm is presented based on reputation in this paper. In particular, the closed forms for the normalized average number of sensing bits, the probabilities of the detection and the false-alarm are derived. Simulation results show that the average number of sensing bits decreases greatly without failing sensing, and the sensing performance is improved comparing with the conventional double threshold detection and the conventional single threshold detection.
Anisotropic-Scale-Space-Based Salient-Region Detection for SAR Images

Currently, salient-region detection, which can be used to provide important man-made target information for synthetic aperture radar (SAR) image analysis and interpretation, is a valuable tool in SAR image processing. However, because SAR images are blurred by a large amount of multiplicative speckle noise, it is difficult for existing methods to produce satisfying results with simple intensity variation. Based on the anisotropic scale space, which can describe edge variations, this letter presents a new salient-region detection method for intensity SAR images. In this method, by using the constant false alarm rate operator of the statistical distribution of the intensity, the edge intensity of each scale is measured with a corresponding-scale window to create the anisotropic scale space. Then, the dual Kullback-Leibler divergence between adjacent scales is computed to obtain the saliency metric of the pixel at a salient scale. To select salient regions, a simple iterative algorithm is presented. Experiments demonstrate the descriptive capacity of our anisotropic scale space and the performance of our method for intensity SAR images.
Centra catadioptric camera calibration with single image

Central catadioptric camera is a class of omnidirectional sensor having single effective viewpoint with reflective surfaces and lenses. This work studies the geometry of central catadioptric projection of a set of lines and adopts it into calibration. In this paper, we propose a practical calibration method for central catadioptric cameras by manually labeling the corners on planar grids. This method analytically parameterizes the intersection of a set of lines, regardless whether they are parallel or not. Algebraic distance is introduced in order to robustify this method since the conic which is the projection of line, is difficult to be obtained precisely. Moreover, it is proved that central catadioptric is able to calibrate by at least one image with two lines. The performance of this method is validated by both simulation data and real image data.