nils-herrmann/clean_scirepeval huggingface dataset:
Green transport practice in Beijing

To solve the air pollution problem and to achieve an environment-friendly urban transport system, Beijing has taken great efforts to control vehicle emissions, to develop public transportation system with priority, to explore and extend the application of new energy vehicles, etc. The 2008 Olympic Games in Beijing is a milestone and also a demonstration site for all these works, especially for the new energy vehicles. This paper gives an introduction of these practices in Beijing. However, it describes few on bicycles, walking and other measures existing now in Beijing because they are in steadily developing trends, not so sparkling as the public transport and new energy vehicles in the past about 10 to 20 years.
Modeling and simulation of a tri-reflector CATR system at 300GHz

A tri-reflector Compact Antenna Test Range (CATR) system which operates at 300GHz is modeled and verified in this work. An efficient Gaussian Beam (GB) analysis method based on point matching expansion technique is proposed to evaluate the predicted quiet zone performance of the CATR system. Experimental result shows that the amplitude ripple within the quiet zone is less than 1.1dB while the phase vibration is no more than 20 degrees at 300GHz.
Multi-leaf alignment from fluorescence plant images

In this paper, we propose a multi-leaf alignment framework based on Chamfer matching to study the problem of leaf alignment from fluorescence images of plants, which will provide a leaf-level analysis of photosynthetic activities. Different from the naive procedure of aligning leaves iteratively using the Chamfer distance, the new algorithm aims to find the best alignment of multiple leaves simultaneously in an input image. We formulate an optimization problem of an objective function with three terms: the average of chamfer distances of aligned leaves, the number of leaves, and the difference between the synthesized mask by the leaf candidates and the original image mask. Gradient descent is used to minimize our objective function. A quantitative evaluation framework is also formulated to test the performance of our algorithm. Experimental results show that the proposed multi-leaf alignment optimization performs substantially better than the baseline of the Chamfer matching algorithm in terms of both accuracy and efficiency.
Mobile Medical Service System Based on Portable Devices

With the rapid development of mobile network, portable devices have been widely used than before. Mobile computing is flourishing in different areas. In this paper, we present an integrated architecture and its implementation of medical service system. This system aims to provide a system which enables the doctors and the nurses to collaborate in the same working environment. This system has two major components: portable devices and medical service server. The key of medical service system is the continuous exchange and transfer of information between doctors and nurses. Doctors and nurses will provide the basic medical information through the portable devices. The medical service server collects the basic data and processes them. And then the server provides the analyzed medical information to doctors and nurses. And the DICOM images are converted to PNG images for the utilization on portable devices. The proposed system can provide a computer supported cooperative work mode to the users. This system has been demonstrated in the real application environment using portable devices with iOS and Android as the basic platform. The performance evaluation of this system is also experimented, which shows the size of medical images is reduced greatly and the efficiency improvement by this system and.
Implementation of follicle monitoring system based on 3D ultrasound images

The monitoring and analysis of cattle follicle dynamics plays an important role in improving the cattle pregnancy rate, both theoretically and practically. In this paper, we demonstrate a follicle monitoring system based on 3D ultrasound image. This system integrates an image de-noising algorithm, edge detection algorithm and 3D reconstruction algorithm together using the MFC framework and OpenGL technologies. Using this system, we realize the following functions: image de-noising, follicle detection, follicle surface extraction, follicle 3D reconstruction, follicle volume calculation, etc.
Missing Modalities Imputation via Cascaded Residual Autoencoder

Affordable sensors lead to an increasing interest in acquiring and modeling data with multiple modalities. Learning from multiple modalities has shown to significantly improve performance in object recognition. However, in practice it is common that the sensing equipment experiences unforeseeable malfunction or configuration issues, leading to corrupted data with missing modalities. Most existing multi-modal learning algorithms could not handle missing modalities, and would discard either all modalities with missing values or all corrupted data. To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities. Specifically, we propose a novel Cascaded Residual Autoencoder (CRA) to impute missing modalities. By stacking residual autoencoders, CRA grows iteratively to model the residual between the current prediction and original data. Extensive experiments demonstrate the superior performance of CRA on both the data imputation and the object recognition task on imputed data.
Research on transport properties of the metal vapor vacuum arc

Metal vapor arc is inevitably occurred during the interruption of the vacuum circuit breaker (VCB). In this paper, a micro arc model of the metal vapor for the VCB is proposed based on the Eindhoven model, and the transport property coefficients and the electrical conductivity distribution are calculated. And the influences of temperature, pressure and particle collision on the electrical conductivity have been obtained. Based on the thermodynamic equilibrium model and the Boltzmann method, the variations of arc conductivity with the particle density, temperature and vacuum degree has been found, and the quantitative descriptions of the transport properties have been obtained.
Research on rotary-gas high voltage SF6 circuit breaker with novel blade nozzle Part I: Design and research on novel blade nozzle

In order to improve the dielectric recovery characteristic in high voltage circuit breaker (HVCB), the method of rotating gas flow to extinguish the arc is presented in the studying of a 126kV single-break SF6 CB. And a novel arc quenching chamber structure with blade nozzle is presented and three dimension (3-D) mathematical model of arc quenching chamber in SF6 CB is proposed. The Finite Volume Method (FVM) and total variation diminishing (TVD) schemes are used to numerically calculate the gas flow model. The characteristics of cold gas flow in arc quenching chamber during the interruption for different structures are simulated. The distributions and variations of gas flow field quantities in the arc quenching chamber such as velocity, pressure and density have been obtained and compared. The feasibility and validity of the proposed blade novel nozzle structure has been proved.
LPSM: Fitting shape model by linear programming

We propose a shape model fitting algorithm that uses linear programming optimization. Most shape model fitting approaches (such as ASM, AAM) are based on gradient-descent-like local search optimization and usually suffer from local minima. In contrast, linear programming (LP) techniques achieve globally optimal solution for linear problems. In , a linear programming scheme based on successive convexification was proposed for matching static object shape in images among cluttered background and achieved very good performance. In this paper, we rigorously derive the linear formulation of the shape model fitting problem in the LP scheme and propose an LP shape model fitting algorithm (LPSM). In the experiments, we compared the performance of our LPSM with the LP graph matching algorithm(LPGM), ASM, and a CONDENSATION based ASM algorithm on a test set of PUT database. The experiments show that LPSM can achieve higher shape fitting accuracy. We also evaluated its performance on the fitting of some real world face images collected from internet. The results show that LPSM can handle various appearance outliers and can avoid local minima problem very well, as the fitting is carried out by LP optimization with l1 norm robust cost function.
A high performance multicasting mechanism in Mobile Grid

The grid would see the countless mobile devices becoming the most important users. However, the bottleneck is still at the last mile from the backbone to these mobile terminals where wireless environment places limitations on the rate and amount of communication. Currently, point-to-multipoint (PTM) communication is an effective solution to this limitation. This paper developed a new PTM mechanism to support efficient access to the multicasting information on the mobile grid. Furthermore, we studied the parallel channels content organization and concurrent content retrieval with or without indexing and caching. The presented algorithms with higher performance could deliver information to more users so as to achieve shorter response time and less network latency, both for the content organization side and for the content retrieval side. The performance was validated in many experiments.
Automated Online Exam Proctoring

Massive open online courses and other forms of remote education continue to increase in popularity and reach. The ability to efficiently proctor remote online examinations is an important limiting factor to the scalability of this next stage in education. Presently, human proctoring is the most common approach of evaluation, by either requiring the test taker to visit an examination center, or by monitoring them visually and acoustically during exams via a webcam. However, such methods are labor intensive and costly. In this paper, we present a multimedia analytics system that performs automatic online exam proctoring. The system hardware includes one webcam, one wearcam, and a microphone for the purpose of monitoring the visual and acoustic environment of the testing location. The system includes six basic components that continuously estimate the key behavior cues: user verification, text detection, voice detection, active window detection, gaze estimation, and phone detection. By combining the continuous estimation components, and applying a temporal sliding window, we design higher level features to classify whether the test taker is cheating at any moment during the exam. To evaluate our proposed system, we collect multimedia (audio and visual) data from $\text{24}$ subjects performing various types of cheating while taking online exams. Extensive experimental results demonstrate the accuracy, robustness, and efficiency of our online exam proctoring system.
Attribute preserved face de-identification

In this paper, we recognize the need of de-identifying a face image while preserving a large set of facial attributes, which has not been explicitly studied before. We verify the underling assumption that different visual features are used for identification and attribute classification. As a result, the proposed approach jointly models face de-identification and attribute preservation in a unified optimization framework. Specifically, a face image is represented by the shape and appearance parameters of AAM. Motivated by k-Same, we select k images that share the most similar attributes with those of a test image. Instead of using the average of k images, adopted by k-Same methods, we formulate an objective function and use gradient descent to learn the optimal weights for fusing k images. Experimental results show that our proposed approach performs substantially better than the baseline method with a lower face recognition rate, while preserving more facial attributes.
Gas discharge simulation of vacuum circuit breaker based on fractal theory

Gas discharge model of vacuum circuit breaker based on fractal theory is established. For reflecting the influence of the variation of electric field on the discharge development, the gas discharge process was simulated and analyzed. Moreover, the combination effect of the electric field and the random consideration on the discharge was investigated. The whole spatio-temporal evolvement behavior of the gas discharge path was computed using finite element method (FEM) and figured out. From the simulation results, it can be found that the micro-discharge-channels have the tendency of being in the whole discharge space. The discharge development routine is mainly determined by the electric field and the material diversity of the dielectric medium.
Human behavior understanding for video surveillance: Recent advance

With the wide applications of video cameras in surveillance, video analysis technologies have attracted the attention from the researchers in computer vision field. In video analysis, human behavior recognition and understanding is an important research direction. By recognition and understanding the human behaviors, we can predict and recognize the happening of crimes and help to the police or other agencies to react immediately. In the past, large amount of intensive papers have been published on human behavior understanding in videos. Generally speaking, the procedure of human behavior understanding can be divided into the following stages: human segmentation and tracking, and human behavior recognition. In this paper, we provide a comprehensive survey of the recent development of all these stages. We will also discuss the difficulties in behavior understanding and identify possible future directions.
A Modular Gaussian Beam Analysis Method Based on 3-D Diffraction Technique

A modular Gaussian Beam (GB) analysis method is proposed in this letter. The analysis method consists of frame-based Gabor transformation, GB reflection, and a novel 3-D GB diffraction technique that was developed to handle the scattering from the reflector edge. This 3-D GB diffraction method gracefully extends Zogbi's solution into practical scenarios via coordinate transformation. The proposed method can analyze not only normal quadric surfaces but also shaped reflectors. Also, modular treatment has been introduced to make this method capable of analyzing multielement systems. Simulation results of an electrically large offset reflector and a tri-reflector compact antenna testing range (CATR) system show good agreement with physic optics (PO) method. The measured result of tri-reflector CATR system demonstrates the modularization and efficiency of the proposed method.
Study on the Tunneling Mode in a Sub-Wavelength Open-Cavity Resonator Consisting of Single Negative Materials

We have investigated the process which establishes the tunneling mode in a conjugately matched pair consisting of ε-negative (ENG) and μ-negative (MNG) materials in theory and experiment. The one-dimensional conjugately matched pairs is realized by two L-C loaded transmission lines and the dynamical behavior of the transmitted signal along the pair is measured and recorded on an oscilloscope. It is found that the field does not decay to zero in a finite length single negative material and the average power flow is a constant since the phase difference between electric field and magnetic field varies with the position. While for a conjugately matched ENG/MNG pair, the incident field attenuates in ENG material in the first instance and then the fields are rapidly enhanced at the ENG-MNG interface and reach the steady state due to the resonating effect of the structure. It is revealed that there is a combination of travelling wave and reactive standing wave inside the bilayer with the ratio between them varying with the position. It is also proved that the bilayer can also be treated as an open resonator, in which the charging up characteristic time is in proportion to the Q factor of the structure.
Small target detection in sea clutter based on linear prediction

To detect small target in sea clutter, a method based on local backscattering amplitude prediction error is proposed in the paper. This approach builds on linear prediction: calculating the mean of the fourth power of prediction error of backscattering amplitude. By analyzing the IPIX radar datasets, it is found that the prediction error's mean and variance of the range bin containing a small target is much smaller than that of other range bins containing only sea clutter. The range bin, in which a small target is contained, gets smaller prediction error than those bins in which only pure sea clutter is occupied. Upon this point, we detect the small target in sea clutter with small amount of priori knowledge. The experimental results show that the proposed method can effectively detect small target in sea clutter with better performance.
Automatic facial landmark labeling with minimal supervision

Landmark labeling of training images is essential for many learning tasks in computer vision, such as object detection, tracking, and alignment. Image labeling is typically conducted manually, which is both labor-intensive and error-prone. To improve this process, this paper proposes a new approach to estimate a set of landmarks for a large image ensemble with only a small number of manually labeled images from the ensemble. Our approach, named semi-supervised least-squares congealing, aims to minimize an objective function defined on both labeled and unlabeled images. A shape model is learnt on-line to constrain the landmark configuration. We also employ a partitioning strategy to allow coarse-to-fine landmark estimation. Extensive experiments on facial images show that our approach can reliably and accurately label landmarks for a large image ensemble starting from a small number of manually labeled images, under various challenging scenarios.
Design and numerical simulation of a novel permanent magnetic 2-phase linear stepper motor

A novel model of permanent magnetic double-phase linear stepper motor has been presented and the numerical computation model of magnetic field for the linear stepper motor has been established. Moreover, the dynamic variations of magnetic field varying with the current cycle during the whole moving process have been numerically simulated using the finite element method. Furthermore, influences of different considerations, such as, the effect of the variation of air-gap, permanent magnetic materials and maximum current density, magnetic field and electromagnetic force on the mover have been computed, compared and analyzed
Sports Videos in the Wild (SVW): A video dataset for sports analysis

Considering the enormous creation rate of usergenerated videos on websites like YouTube, there is an immediate need for automatic categorization, recognition and analysis of videos. To develop algorithms for analyzing user-generated videos, unconstrained and representative datasets are of great significance. For this purpose, we collected a dataset of Sports Videos in the Wild (SVW), consisting of videos captured by users of the leading sports training mobile app (Coach's Eye) while practicing a sport or watching a game. The dataset contains 4100 videos selected by reviewing ~85,000 videos and consists of 30 sports categories and 44 actions. Videos of sports practice, which frequently happens outside the typical sports field, have huge intra-class variations due to background clutter, unrepresentative environment, existence of different training equipment and most importantly, imperfect actions. On the other hand, using smartphones for video capturing by ordinary people, in comparison to videos captured by professional crew for broadcasting, leads to challenges due to camera vibration and motion, occlusion, view point variation, and poor illumination. Given various manual labels, this dataset can be used for a wide range of computer vision applications, such as action recognition, action detection, genre categorization, and spatio-temporal alignment. On the sport genre categorization problem, we design the evaluation protocol and evaluate three different methods to provide baselines for future works.
Vessel segmentation in retinal images with a multiple kernel learning based method

Blood vessel segmentation is an important problem for quantitative structure analysis of retinal images, and many diseases are related to the structure changes. Manual segmentation is time consuming and computer aided segmentation is required to deal with large amount images. This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. Multiple kernel learning (MKL) is introduced to deal with the problem, utilizing features from Hessian matrix based vesselness measure, response of multiscale Gabor filter, and multiple scale line strength features. The method is evaluated on the publicly available DRIVE and STARE databases. The performance of the MKL method is evaluated and experimental results show the high accuracy of the proposed method.
Enhanced Kepler framework for self-archiving

In this paper, we introduce the enhanced Kepler framework, an evolution of the original Kepler framework that implements an OAI-PMH-compliant data provider for the individual. The enhanced Kepler framework is influenced by Fasttrack, a peer-to-peer network. In particular, we introduce the concept of buddy nodes and SuperNodes to improve scalability. We also propose enhancements of OAI-PMH, the harvesting protocol used in the existing framework, to support the "push" and a hybrid "push/pull" model for metadata harvesting. The enhanced Kepler framework provides the following benefits: improved availability of content published by individuals, rapid discovery of content as soon as it is published, and works with firewall and network address translation proxy.
A data mining based algorithm for traffic network flow forecasting

Recent development in ITS (intelligent transportation systems) methods and technologies has moved traffic operating systems from passive to proactive control and management, where real-time and accurate traffic flow information is critical to actual implementation. So far many algorithms have been proposed for traffic network flow forecasting, but problems in accuracy and timeliness still remain to be the major obstacle for their successful applications. For example, presumed human travel habit and vehicle turning probabilities at intersections have greatly limited the use of dynamic assignment algorithm. In order to improve the forecasting and real-time responsiveness, a new algorithm based on data mining which can do association rules mining and association analysis is proposed here for predicting traffic network flow. Simulation results using Corsim 5.0 have demonstrated effectiveness of the new algorithm in both accuracy and timeliness.