nils-herrmann/clean_scirepeval huggingface dataset:
An RSU-assisted localization method in non-GPS highway traffic with dead reckoning and V2R communications

In highway environment, many safety-related applications demand localization. Currently, localization is usually achieved by on-vehicle GPS. However, it requires that each car has a GPS device, which increases vehicle's cost and restricts the scope of some applications to some extent. The road side unit (RSU) is a fix infrastructure to collect information, enhance road safety and indicate navigations, and will be furnished on the highway as well as urban environment in the near future. At the same time, the RSU can also be used to provide initial position to the driving-by vehicles because of its fixed position and high capacity of wireless transmission. In addition, the vehicle's velocity follows normal distribution and the trajectory is fixed by one-dimensional and straight-line highway path. Therefore, the dead reckoning can be used to compute the vehicle's current location after obtaining its initial position. In this paper, we propose an RSU-assisted localization method using dead reckoning and vehicle-to-RSU communications. Our method can be used in non-GPS highway traffic environment with high accuracy and feasibility.
The design of the super capacitor group used in heavy gantry portal cranes

In this paper, a system used super capacitor group to recycle the regenerative energy of the heavy gantry portal cranes is briefly introduced, which could save and reuse the energy generated during the download movements of the cargoes and the braking of the trolley, and be more friendly to the environment than the usual portal cranes. What's more the design method of super capacitor group is mainly presented. This method simplifies the calculations, and is easier to define the numbers of the parallel/serial capacitors compared to the usual try and error method.
Commissioning of the Duke Storage Ring

The commissioning of the 1 GeV Duke Storage Ring began in November, 1994 with the demonstration of injection, storage and ramping to 1 GeV at the first attempt. The ring is now operational. The Duke project is unique in that the storage ring and linac were designed, constructed and commissioned by a small new University laboratory, operating on a low budget. The team is comprised of six accelerator physicists and graduate students, eight engineers, and fifteen technicians.
Stress analysis of plain dent on pipeline based on FE calculation

According to the report of the United States transportation department, mechanical damage is one of the most important reasons for the pipeline accident. The most typical form of mechanical damage is indentation. Dent defect is one of the important factors that influence the pipeline stress. For plain typical dent on pipeline, changing various parameters, the finite element models are established under different circumstances by finite element method. A large number of the calculation results are sorted, inducted, drawed. On this basis, the results are analyzed. Non-linear regression analysis was utilized to fit the results by Matlab, some specific expression of the relationships between the dented pipeline maximum stress and the dent depth, diameter and thickness of pipeline, and the dent longitudinal length are obtained after much calculation and analog.
Intra-beam scattering and minimum achievable emittance in the Advanced Light Source

Intra-beam scattering (IBS) potentially limits the minimum emittance of low-energy storage rings, and this effect strongly influences the choice of energy of damping rings for linear colliders. The Advanced Light Source (ALS) is a third-generation synchrotron light source operating with high-intensity, low-emittance beams at energies up to 2 GeV. It can operate with an emittance coupling of below 1%. We present measurements of the beam growth in three dimensions as a function of current, for normalized natural horizontal emittances of approximately 1-10 mm-mrad at energies of 0.7-1.5 GeV, values comparable to the parameters in an NLC damping ring. Using a dedicated diagnostic beamline with an X-ray scintillator imaging system, measurements of the transverse beamsize are made, simultaneously with bunch length measurements using an optical streak camera. The bunch volume growth as a function of bunch current is compared with theoretical estimates, for a parameter space of IBS, where very little experimental data exists so far.
Tracking appearances with occlusions

Occlusion is a difficult problem for appearance-based target tracking, especially when we need to track multiple targets simultaneously and maintain the target identities during tracking. To cope with the occlusion problem explicitly, this paper proposes a dynamic Bayesian network, which accommodates an extra hidden process for occlusion and stipulates the conditions on which the image observation likelihood is calculated. The statistical inference of such a hidden process can reveal the occlusion relations among different targets, which makes the tracker more robust against partial even complete occlusions. In addition, considering the fact that target appearances change with views, another generative model for multiple view representation is proposed by adding a switching variable to select from different view templates. The integration of the occlusion model and multiple view model results in a complex dynamic Bayesian network, where extra hidden processes describe the switch of targets' templates, the targets' dynamics, and the occlusions among different targets. The tracking and inferring algorithms are implemented by the sampling-based sequential Monte Carlo strategies. Our experiments show the effectiveness of the proposed probabilistic models and the algorithms.
Research on Usage Control Model in Work Flow

With the Work Flow (WF) applies in the modern system on a large scale, tasks of access control management becomes more and more complex among the modern information system which also advances new requirements. This paper uses the task as core and adopts Usage Control (UCON) model to solve the problems of Access Control in WF, and advances the Usage Control in WF (UCON_WF) Model. So the paper mainly studies the 16 core UCON_WF models and gives logical representations of the 16 model rules.
Research on Pairwise Key Establishment for Cluster Heads in Sensor Networks

In hierarchical sensor networks, pairwise key path construction would make no sense for cluster heads as the security of those higher level nods depends on that of the normal ones along with the key path. Vector-based key pre-distribution in extended modulus is presented, along with corresponding nodepsilas collaborative scheme. Experiments show that the presented scheme does improve the probability of direct key establishment for cluster heads as it has much better performance on nodepsilas degree in key-sharing graph with approximate risk of key space compromise compared with MSKPD.
The Satellite Downlink Replanning Problem: A BP Neural Network and Hybrid Algorithm Approach for IoT Internet Connection

In the era of the Internet of Things, the role of a satellite has become increasingly important. The use of satellite resources can meet the needs of high-speed Internet connection of the Internet of Things. Effective communication between satellites and the ground is crucial. This paper proposes an appropriate combination approach that is based on an improved genetic algorithm (GA) for a satellite downlink replanning problem. The initial population of the GA is optimized using a backpropagation (BP) neural network (NN). First, a variety of scheduling schemes is used to train the BP NN, and characteristics are extracted through self-adaptive learning. The NN model after training provides a good initial solution for the input information of the GA. This hybrid algorithm (HA), which consists of a scheduling GA and a local search algorithm, can quickly complete the replanning of the downlink task sequence. The ability of the HA is enhanced by the BP NN. A series of experiments is used to prove the validity of the HA. As revealed in the results of different scale instances, the proposed algorithm performs better than other scheduling algorithms.
Visual attention region determination using low-level features

Visual attention region determination simulates the behavior of the human visual system and determines visual attention regions in an image. In this study, a visual attention region determination approach using low-level features, including luminance, color, and region information, is proposed. First, the contrast map is attained by computing the contrast between each pixel and its “thresholding” neighboring pixels in eight directions, based on the just noticeable difference (JND) model. Then, the block-based edge map and standard deviation representation are used to generate the region mask. The saliency map is obtained by using the contrast map and the region mask. Finally, a visual attention region determination scheme is presented to determine visual attention region(s) in the image. Based on the experimental results obtained in this study, the proposed approach has better performance than two comparison approaches.
Production of next generation InP-HBT epiwafers by MBE

The current status of InP-based HBT epiwafer production using multi-wafer MBE systems at IQE Inc. is presented. Control and stability of critical material growth parameters demonstrate reproducible quality of standard InP/InGaAs/InAlAs HBT structures grown on 4-inch diameter substrates. Experimental data from HBTs grown on newly developed 6-inch substrates exhibit good material and device parameters. Development of next-generation structures, including GaAsSb-base and metamorphic HBTs, is also discussed.
Sub-300 nm InGaAs/InP Type-I DHBTs with a 150 nm collector, 30 nm base demonstrating 755 GHz fmax and 416 GHz fT

We report InP/InGaAs/InP double heterojunction bipolar transistors (DHBT) fabricated using a simple mesa structure. The devices employ a 30 nm highly doped InGaAs base and a 150 nm InP collector containing an InGaAs/InAlAs superlattice grade. These devices exhibit a maximum f<sub>max</sub> = 755 GHz with a 416 GHz /f<sub>T</sub>. This is the highest f<sub>max</sub> reported for a mesa HBT. Through the use of i-line lithography, the emitter junctions have been scaled from 500-600 nm down to 250-300 nm -all while maintaining similar collector to emitter area ratios. Because of the subsequent reduction to the base spreading resistance underneath the emitter R<sub>b,spread</sub> and increased radial heat flow from the narrower junction, significant increases to f<sub>max</sub> and reductions in device thermal resistance θ<sub>JA</sub> are expected and observed. The HBT current gain β ≈ 24-35, BV<sub>ceo</sub> = 4.60 V, BV<sub>cbo</sub> = 5.34 V, and the devices operate up to 20 mW / μm<sup>2</sup> before self-heating is observed to affect the DC characteristics.
Discovering Thematic Objects in Image Collections and Videos

Given a collection of images or a short video sequence, we define a thematic object as the key object that frequently appears and is the representative of the visual contents. Successful discovery of the thematic object is helpful for object search and tagging, video summarization and understanding, etc. However, this task is challenging because 1) there lacks a priori knowledge of the thematic objects, such as their shapes, scales, locations, and times of re-occurrences, and 2) the thematic object of interest can be under severe variations in appearances due to viewpoint and lighting condition changes, scale variations, etc. Instead of using a top-down generative model to discover thematic visual patterns, we propose a novel bottom-up approach to gradually prune uncommon local visual primitives and recover the thematic objects. A multilayer candidate pruning procedure is designed to accelerate the image data mining process. Our solution can efficiently locate thematic objects of various sizes and can tolerate large appearance variations of the same thematic object. Experiments on challenging image and video data sets and comparisons with existing methods validate the effectiveness of our method.
60nm collector InGaAs/InP Type-I DHBTs demonstrating 660 GHz fT, BVCEO = 2.5V, and BVCBO = 2.7V

We report InP/InGaAs/InP double heterojunction bipolar transistors (DHBT) fabricated using a conventional mesa structure. The devices employ a 14 nm highly doped InGaAs base and a 60 nm InP collector containing an InGaAs/InAlAs superlattice grade. Devices employing a 400 nm emitter exhibit a maximum fT = 660 GHz with a 218 GHz f max - this is a record fT for a DHBT. The devices have been scaled vertically for reduced base and collector electron transit times, and the base-collector mesa has been further scaled to minimize the capacitance Ccb associated with the base contact area. The peak current gain beta ap 95, BVCEO = 2.5 V, BV CBO = 2.7 V, and the devices operate in excess of 30 mW/mum 2
Robust Dictionary Learning by Error Source Decomposition

Sparsity models have recently shown great promise in many vision tasks. Using a learned dictionary in sparsity models can in general outperform predefined bases in clean data. In practice, both training and testing data may be corrupted and contain noises and outliers. Although recent studies attempted to cope with corrupted data and achieved encouraging results in testing phase, how to handle corruption in training phase still remains a very difficult problem. In contrast to most existing methods that learn the dictionary from clean data, this paper is targeted at handling corruptions and outliers in training data for dictionary learning. We propose a general method to decompose the reconstructive residual into two components: a non-sparse component for small universal noises and a sparse component for large outliers, respectively. In addition, further analysis reveals the connection between our approach and the ``partial'' dictionary learning approach, updating only part of the prototypes (or informative code words) with remaining (or noisy code words) fixed. Experiments on synthetic data as well as real applications have shown satisfactory performance of this new robust dictionary learning approach.
Modulation Classification of MPSK Signals Based on Relevance Vector Machines

In this paper, a new classification method based on relevance vector machine (RVM) is used in the MPSK signals classification. Compared with the support vector machine (SVM), RVM is sparse model in the Bayesian framework, not only the solution is highly sparse, but also it does not need to adjust model parameter and its kernel functions don't need to satisfy Mercer's condition. The fourth order cumulants of the received signals are used as the classification vector firstly, and then multi-class classifier of RVM is designed. We first introduce the sparse Bayesian classification model, then transform the RVM learning to the maximization of marginal likelihood, and select the fast sequential sparse Bayesian learning algorithm. Through the experiment compared with SVM classifier proves the advantage of RVM.
The MPSK signals modulation classification based on Kernel methods

In this paper, a new classification method based on Kernel Fisher Discriminant Analysis(KFDA) is brought forward in the MPSK signals modulation classification. The fourth order cumulants of the received signals are used as the classification vector firstly, then the kernel thought is used to map the feature vector impliedly to the high dimensional feature space and linear fisher discriminant analysis is applied to signal classification. The two classifiers based on kernel function - Support Vector Machine and Kernel Fisher Discriminant Analysis are introduced in detail. In order to build effective and robust SVM and KFDA classifiers and compared with each other, the radial basis kernel function is selected, one against one or one against rest of multi-class classifier is designed, and method of parameter selection using cross-validating grid is adopted. Through the experiments it can be concluded that compared with SVM classifier, KFDA can get almost the same classification accuracy and requires less time.
Learning Reconstruction-Based Remote Gaze Estimation

It is a challenging problem to accurately estimate gazes from low-resolution eye images that do not provide fine and detailed features for eyes. Existing methods attempt to establish the mapping between the visual appearance space to the gaze space. Different from the direct regression approach, the reconstruction-based approach represents appearance and gaze via local linear reconstruction in their own spaces. A common treatment is to use the same local reconstruction in the two spaces, i.e., the reconstruction weights in the appearance space are transferred to the gaze space for gaze reconstruction. However, this questionable treatment is taken for granted but has never been justified, leading to significant errors in gaze estimation. This paper is focused on the study of this fundamental issue. It shows that the distance metric in the appearance space needs to be adjusted, before the same reconstruction can be used. A novel method is proposed to learn the metric, such that the affinity structure of the appearance space under this new metric is as close as possible to the affinity structure of the gaze space under the normal Euclidean metric. Furthermore, the local affinity structure invariance is utilized to further regularize the solution to the reconstruction weights, so as to obtain a more robust and accurate solution. Effectiveness of the proposed method is validated and demonstrated through extensive experiments on different subjects.
A Study of Web 2.0 Website Usage Behavior Using TAM 2

Web 2.0 is a new Internet platform that allows users to build personalized content through participation and sharing. The technologies used and business opportunities brought by this new platform have impacted many conventional Websites. This paper aims to explore the relationships among constructs in Technology Acceptance Model 2 (TAM 2) and survey the general userspsila acceptance of Web 2.0 Websites. The proposed hypotheses are further tested by Structural Equation Modeling (SEM). Based on the obtained results, userspsila acceptance of Web 2.0 Websites and factors affecting their acceptance are discussed to provide a substantial reference for Web 2.0 Website operators.
A novel bi-directional diffusion image interpolation model with two layer constraint

In this paper a novel bi-directional diffusion model with two layer constraint is proposed. The new model can smooth the artifacts and jaggies in the interpolated image and make image clearly by enhancing the edge. It considers both along and orthogonal to edge diffusion, and the orthogonal to edge backward diffusion enhances the edge in image. There is severe block effect in the interpolated image, if the orthogonal to edge diffusion is directly adopted in the interpolated image, jaggies will be strengthened. With the two layer constraint, the gradient magnitude of original un-interpolated image is mapped to the interpolated image as a sign matrix. The selection of diffusion direction is controlled by the mapped sign matrix; only the edge pixel in original image is enhanced when interpolating. Using the novel model proposed in this paper, processed image is clearer than that processed by the along edge diffusion model; with the two layer constraint, jaggies are diminished and the image is smoothed
Human Tracking Using Convolutional Neural Networks

In this paper, we treat tracking as a learning problem of estimating the location and the scale of an object given its previous location, scale, as well as current and previous image frames. Given a set of examples, we train convolutional neural networks (CNNs) to perform the above estimation task. Different from other learning methods, the CNNs learn both spatial and temporal features jointly from image pairs of two adjacent frames. We introduce multiple path ways in CNN to better fuse local and global information. A creative shift-variant CNN architecture is designed so as to alleviate the drift problem when the distracting objects are similar to the target in cluttered environment. Furthermore, we employ CNNs to estimate the scale through the accurate localization of some key points. These techniques are object-independent so that the proposed method can be applied to track other types of object. The capability of the tracker of handling complex situations is demonstrated in many testing sequences.
Capturing natural hand articulation

Vision-based motion capturing of hand articulation is a challenging task, since the hand presents a motion of high degrees of freedom. Model-based approaches could be taken to approach this problem by searching in a high dimensional hand state space, and matching projections of a hand model and image observations. However, it is highly inefficient due to the curse of dimensionality. Fortunately, natural hand articulation is highly constrained, which largely reduces the dimensionality of hand state space. This paper presents a model-based method to capture hand articulation by learning hand natural constraints. Our study shows that natural hand articulation lies in a lower dimensional configurations space characterized by a union of liner manifolds spanned by a set of basis configurations. By integrating hand motion constraints, an efficient articulated motion-capturing algorithm is proposed based on sequential Monte Carlo techniques. Our experiments show that this algorithm is robust and accurate for tracking natural hand movements. This algorithm is easy to extend to other articulated motion capturing tasks.
Fractal study on infrared thermal-imaging

Infrared thermal imaging technology is one new method of the modern non-destructive testing. Expanding the qualitative analysis to quantitative characterization is hot and difficult of the infrared thermal imaging technology, that is to say, expanding assessing whether damage and judging injury location to structural integrity evaluation. The key lies in the extraction and description of the infrared thermal imaging characteristics parameters. This paper did fractal study on the experimental samples of infrared thermal imaging, including infrared thermal imaging graphics processing, fractal dimension calculation, realization of calculation program and gray, network and scale and so on, which is the foundation for further study on concrete structure damage and fracture or fracture criterion and the experimental calibration.