nils-herrmann/clean_scirepeval huggingface dataset:
B-Planner: Planning Bidirectional Night Bus Routes Using Large-Scale Taxi GPS Traces

Taxi GPS traces can inform us the human mobility patterns in modern cities. Instead of leveraging the costly and inaccurate human surveys about people's mobility, we intend to explore the night bus route planning issue by using taxi GPS traces. Specifically, we propose a two-phase approach for bidirectional night bus route planning. In the first phase, we develop a process to cluster “hot” areas with dense passenger pick up/drop off and then propose effective methods to split big hot areas into clusters and identify a location in each cluster as a candidate bus stop. In the second phase, given the bus route origin, destination, candidate bus stops, and bus operation time constraints, we derive several effective rules to build the bus route graph and prune invalid stops and edges iteratively. Based on this graph, we further develop a bidirectional probability-based spreading algorithm to generate candidate bus routes automatically. We finally select the best bidirectional bus route, which expects the maximum number of passengers under the given conditions and constraints. To validate the effectiveness of the proposed approach, extensive empirical studies are performed on a real-world taxi GPS data set, which contains more than 1.57 million night passenger delivery trips, generated by 7600 taxis in a month.
Research on highway alignment decision-making based on complex system risk analysis

Highway project construction is a complex system. The choice of highway route plan is the key job in decision-making and design phase before the construction of highway project. The highway alignment decision-making includes a lot of uncertain risk factors in the complex environment, the uncertain risk factors have characteristic of the dynamic change, the multiple risk factors action, cross-coupling effect , non-structural problem decision-making. For the traditional highway alignment decision-making is not adapt to sytematacially reflecting all kinds of risk factors in the complex system ,as well as the transmission relationship of the effect of the risk factors. In order to solve the Problem, a new method is put forward, which is based on fuzzy influence diagram theory. This paper constructs a fuzzy influence diagram model about the risk decision of high-way alignment plan by the establishment of risk influence diagram and risk space. A case is given to illustrate to application of the model. The model is concise and clear in process of risk decision, and not only it is beneficial to construct information communication channels between risk analysts and decision makers by influence diagram ,but also it reduced the statistical data collection of the probability distribution in probability influence diagram evaluation, that is a rather difficult work in engineering practice. So it expanded the usable range of influence diagram and enhanced its practical applicability.
Detection of micro-motion targets in buildings for through-the-wall radar

In this paper, a micro-motion target detection approach behind an obstacle is presented using a stepped-frequency through-the-wall radar. First, the micro-motion target model is established, and the human movement includes periodical respiration and heart beating, as well as random body movement and sudden body shaking, which is close to the realistic human movement. Then the procedure of micro-motion targets detection is brought about. The micro-motion target location is unvarying during the survey time, so the accumulation of every range cell in slow time dimension is reasonable, which improves the SCNR remarkably. Finally, one dimensional CFAR is performed to detect micro-motion targets. Experimental results demonstrate that the proposed approach can detect micro-motion targets in buildings and distinguish them from stationary targets correctly and effectively.
Positive and negative obstacle sensing with a forward-looking InSAR for UGV

Radar sensors have received more and more interest for Unmanned Ground Vehicle (UGV) to sense positive and negative obstacles in unstructured environments or out fields. We have proposed a forward-looking InSAR can provide more information of harsh off-road environments than existing UGV based radars. The forward-looking InSAR can provide a scattering image, a coherence image and a digital terrain map of the same scene ahead the radar during each scan. Each type of image can highlight some unique features of obstacle. Those images could provide abundant information for autonomous navigation systems, which would consequently increase the survivability of the UGV on the battlefield. We have conducted a field experiment on a ground covered by sparse grasses to support the phenomenological studies of the backscatter from positive and negative obstacles for autonomous UGV navigation.
A Novel Prescreening Method for Land-Mine Detection in UWB SAR Based on Feature Point Matching

Ultrawideband synthetic aperture radar (SAR) (UWB SAR) is a sufficient approach to detecting land mines over large areas from a safe standoff distance. UWB SAR detection involves three steps: imaging, prescreening, and discrimination. Many studies have concentrated on imaging and discrimination, whereas few have highlighted prescreening. We propose a novel prescreening method for extracting the region of interest from an entire SAR image. The proposed method is based on feature point matching in contrast to traditional approaches, which are based on the constant false alarm rate (CFAR) technique. Our method incorporates knowledge on the electromagnetic scattering caused by land mines into the prescreening process and does not rely on clutter environments. Thus, the method more efficiently eliminates nonhomogeneous clutter than do CFAR-based methods and is more advantageous in processing large-area SAR images. The efficiency of the proposed method is validated by real data on an airship-mounted UWB SAR system.
Extended Two-Step Focusing Approach for Squinted Spotlight SAR Imaging

An extended two-step focusing approach (ETSFA) for processing the squinted spotlight synthetic aperture radar (SAR) data is proposed in this paper. The effect of the squint angle on the azimuth coarse focusing is analyzed and discussed. Based on the analysis results, a nonlinear shift preprocessing method is introduced, which can completely remove the squint angle impacts on the azimuth coarse focusing. Furthermore, based on the squinted spotlight SAR imaging model and the preprocessed echo data, derivations of the azimuth coarse focusing with the deramping-based technique and precise focusing with the modified Stolt-based technique are carried out in detail. Moreover, to produce an acceptable image by the proposed ETSFA for high-resolution (<; 3 m) squinted spotlight SAR with large scene, a subscene processing method is introduced. The experimental results on simulated data prove the validity of the whole analysis and the proposed methods.
Belief Rule Base Structure and Parameter Joint Optimization Under Disjunctive Assumption for Nonlinear Complex System Modeling

Nonlinear complex system modeling has drawn attention from diverse fields and many approaches have been developed. Among those approaches, the advantages of the belief rule base (BRB) expert system have been shown for managing multiple types of information under uncertainty and modeling the nonlinearity present in many theoretical and practical complex systems. However, two challenges still need to be addressed. First, BRB needs to be downsized to conserve modeling and computational effort. For this challenge, a new disjunctive assumption is applied, which can significantly downsize BRB while maintaining its completeness. Second, the structure and parameters of BRB need to be jointly optimized. For this challenge, a new Akaike information criterion (AIC)-based optimization objective is derived to represent both modeling accuracy and modeling complexity. Moreover, a joint bi-level optimization model with an AIC-based objective is constructed for the BRB structure and parameters, and a bi-level optimization algorithm is proposed. Three evolutionary algorithms, namely, the genetic algorithm, particle swarm optimization algorithm, and differential evolutionary algorithm, are tested in a comparative fashion to determine the best fit for the optimization engine. The results of two practical case studies show that the joint optimization approach can identify an optimal configuration for both its structure and parameters, which is referred to as the best decision structure in this paper.
Applying effect of three forecasting methods for the occurrence trend of Alternaria alternata (Fries) Keissler

Rainfall (mm) of the second month after transplanting, average temperature (°C) at the same period and rainfall days of the first month after transplanting, Rainfall (mm) of June, the daily average temperature of June and rainfall days of June were selected using anomalous sign method from 39 factors from 9 continuous years data at Ningyuan county. Using data of spore trap for 11 continuous years at Chenzhou county. The occurrence trend of Alternaria alternata (Fries) Keissler was studied with the grade-statistics method. and correlation coefficient. Results showed that the back forecasting was in line with the actual value. The accurate percent of forecasting for Alternaria alternata (Fries) Keissle at Yongzhou for 10 years was 90%, the accurate percent of forecasting for Alternaria alternata (Fries) Keissle at different counties for 2009 was 75% with the forecasting model during the early growing stage of tabacco, the accurate percent of forecasting for Alternaria alternata (Fries) Keissle at Yongzhou for 10 years was 80% with the forecasting model during the late growing stage of tabacco; the accurate percent of forecasting for Alternaria alternata (Fries) Keissle at Yongzhou for 10 years was 90% with the both forecasting models and relied on the contributing percent; at different counties was 83.3% for 2009; the number of trapped spore at peak was used as x, the end disease index was used as y, the forecasting model is y=−0.5951+0.04x (r=0.8985∗∗ n=11), the span from spore peak to end were 2 d at least, 22 d at most, the average was 8.3 d, so the model can be used as the forecasting of the disease for short period. Both models of the early and the late growing stage can be used to forecast occurrence trend of Alternaria alternata (Fries) Keissler for short-to medium-term.
A New BRB Model for Cloud Security-State Prediction Based on the Large-Scale Monitoring Data

Considering the reliability of the cloud computing system, this paper aims to predict the security state with multiple large-scale attributes in cloud computing system. A double-layer method for predicting the security state of cloud computing system based on the belief rule-base model is proposed, where the evidential reasoning (ER) algorithm is employed to fuse the multiple system indicators of actual cloud system and make a reasonable assessment to describe the cloud security state. This method can utilize quantitative and qualitative information simultaneously. By using the ER algorithm to integrate multiple indicators whose attributes contain much uncertain information, the security state of the cloud computing system can be predicted accurately. Moreover, due to the initial parameters of the proposed models are given by experts that might cause imprecise results, the constraint CMA-ES algorithm is employed as the optimization tool to obtain the optimal parameters. A practical study about the cloud security-state prediction is verified to indicate the potential applications about the proposed prediction model in a cloud computing platform.
Stress fields in the infinite electrostrictive plate with an elliptic inhomogeneity

The stress fields in the infinite electrostrictive plate with an elliptic inhomogeneity are discussed in this paper. Firstly, the general solutions of functions for electric and stress fields are derived with the help of analytical complex method. Secondly, explicit results by the complex series expanding, are given for the cases of electrostrictive material with an elliptic inhomogeneity. Finally, numerical calculations are made to discuss the effects of applied electric loading on the certain problem. It is found that, although the electric fields in the inhomogeneity are uniform, the stresses in the inhomogeneity are not uniform in general case, which is different from the solution of Eshelby theory for elastic materials.
DNN-based unit selection using frame-sized speech segments

This paper presents a deep neural network (DNN)-based unit selection method for waveform concatenation speech synthesis using frame-sized speech segments. In this method, three DNNs are adopted to calculate target costs and concatenation costs respectively for selecting frame-sized candidate units. The first DNN is built in the same way as the DNN-based statistical parametric speech synthesis, which predicts target acoustic features given linguistic context inputs. The distance between the acoustic features of a candidate unit and the predicted ones for a target unit is calculated as the target cost. The other two DNNs are constructed to predict the acoustic features at current frame using its context features and the acoustic features of previous frames. At synthesis time, these two DNNs are employed to calculate the concatenation cost for each candidate unit given its preceding units. Experimental results show that our proposed method can achieve better naturalness than the hidden Markov model (HMM)-based frame selection method and the HMM-based parametric synthesis method.
Improved background subtraction based on word consensus models

The motion detection approach plays a crucial role in the intelligent video surveillance technology. A universal background subtraction algorithm called PAWCS (Pixel-based Adaptive Word Consensus Segmenter), based on word consensus models, is proven that it performs better in video motion detection recently. In this paper, we present an algorithm to improve the robustness of PAWCS. Specifically, the background models' update can be inhibited when the pixels locate in the edge of foreground objects. Then, the bi-updating approach is used in the models updating strategy, and the persistence of the word will be updated according to their matching accuracy. Finally, the experiments' results demonstrate the effectiveness of our method.
Quantitative Analysis of Nonlinear Embedding

A lot of nonlinear embedding techniques have been developed to recover the intrinsic low-dimensional manifolds embedded in the high-dimensional space. However, the quantitative evaluation criteria are less studied in literature. The embedding quality is usually evaluated by visualization which is subjective and qualitative. The few existing evaluation methods to estimate the embedding quality, neighboring preservation rate for example, are not widely applicable. In this paper, we propose several novel criteria for quantitative evaluation, by considering the global smoothness and co-directional consistence of the nonlinear embedding algorithms. The proposed criteria are geometrically intuitive, simple, and easy to implement with a low computational cost. Experiments show that our criteria capture some new geometrical properties of the nonlinear embedding algorithms, and can be used as a guidance to deal with the embedding of the out-of-samples.
2D Feature Selection by Sparse Matrix Regression

For many image processing and computer vision problems, data points are in matrix form. Traditional methods often convert a matrix into a vector and then use vector-based approaches. They will ignore the location of matrix elements and the converted vector often has high dimensionality. How to select features for 2D matrix data directly is still an uninvestigated important issue. In this paper, we propose an algorithm named sparse matrix regression (SMR) for direct feature selection on matrix data. It employs the matrix regression model to accept matrix as input and bridges each matrix to its label. Based on the intrinsic property of regression coefficients, we design some sparse constraints on the coefficients to perform feature selection. An effective optimization method with provable convergence behavior is also proposed. We reveal that the number of regression vectors can be regarded as a tradeoff parameter to balance the capacity of learning and generalization in essence. To examine the effectiveness of SMR, we have compared it with several vector-based approaches on some benchmark data sets. Furthermore, we have also evaluated SMR in the application of scene classification. They all validate the effectiveness of our method.
Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights

Class-imbalance is very common in real data mining tasks. Previous studies focused on binary-class imbalance problem, whereas multi-class imbalance problem is more challenging. Error correcting output codes (ECOC) technique can be applied to class-imbalance problem, however, the standard ECOC aims at maximizing accuracy, ignoring the fact that, when class-imbalance is really a problem, the minority classes are more important than the majority classes. To enable ECOC to tackle multi-class imbalance, it is desired to have an appropriate code matrix, an effective learning strategy and a decoding strategy emphasizing the minority classes. In this paper, based on the aforementioned consideration, we propose the imECOC method which works on dichotomies to handle both the between-class imbalance and within-class imbalance. As the dichotomy classifiers contribute differently to the final prediction, imECOC assigns weights to dichotomies and uses weighted distance for decoding, where the optimal dichotomy weights are obtained by minimizing a weighted loss in favor of the minority classes. Experimental results on fourteen data sets show that, imECOC performs significantly better than many state-of-the-art multi-class imbalance learning methods, no matter whether multi-class F1, G-mean or AUC are used as evaluation measures.
Evaluation of the Channel State Information Effect to MIMO-OFDM System Performance

The researches about conditions of the resource allocation algorithm in wireless communication have been done a lot recently. An evaluation model for the adaptive algorithm of MIMO-OFDM system is built in this paper. After formulating system efficiency under static and adaptive conditions, the effect of imperfect channel state information (CSI) to MIMO-OFDM system is analyzed. Analytical and numerical results show effect to the frequency efficiency when the CSI is imperfect, and simulations show that MIMO-OFDM can resist the channel delay. At last we propose the compensatory policy for channel delay and channel estimation error.
Maximum-delay-spread aided channel estimator for OFDM systems

An improved channel estimator with maximum-delay-spread measurement for OFDM systems is proposed by exploiting the inherent correlation between the cyclic prefix and port of the received OFDM signal. This scheme make use of real time measured size of inter-symbol interference (ISI) free window to estimate the maximum delay spread of channel, then a transform domain channel estimator is adopted to further reduce the noise variance. Due to the ISI-free window can be estimated before the application of FFT to the received signal, it is a parallel processing, and very suitable for the practical systems. Simulation result demonstrated that the proposed method can track the variation of delay spread of wireless channel, and improve the MSE of channel estimation significantly.
Study on the interval of pre-stressed anchor cables and cantilever length of frame beams in reinforcing slopes

In the design of the slope reinforced by pre-stressed cables and frame beams, the interval of cables and cantilever length of frame beams are two significant parameters. In this paper, we take the characteristic length of frame beams into consideration to reveal the reaction force distribution laws of the beams using the solution of Winkler foundation beam when the cable interval and the cantilever length vary. Reasonable range of cable interval and cantilever length is obtained according to the distribution laws of reaction force. The results show: (1)the interval of pre-stressed cable should be not more than 3 times of the characteristic length of frame beam; (2)the cantilever of frame beams should be 0.4–0.45 times of the interval of cables; (3)when the cable interval is less than the characteristic length, and the cantilever length is 0.45 times of the cable interval, the reaction force of the frame beam could be seen as uniform distribution.
3D hybrid TOA-AOA source localization using an active and a passive station

Hybrid source localization methods have proved to be able to improve the accuracy of position estimation. But several existing hybrid algorithms deal with two-dimensional (2D) scenarios only or require numerical search. This paper introduces a three-dimensional (3D) passive localization method using the active time of arrival (TOA) measurement and the one TOA and two angle of arrival (AOA) pairs observed at two stations. A closed-form solution is derived for the 3D source location with hybrid measurements from two stations. The experimental results demonstrate that the proposed method outperforms the state-of-art ones in terms of root mean-square error (RMSE), especially in large measurement error situation.
Design of Arch Bridge with Variable Steel-Concrete Composite Section Based on Vertical Erection-Rotation Method

This paper presented an innovative use of a variable steel-concrete composite section for the main arch ribs to take combined advantages of steel and concrete strength. And vertical erection method that can significantly simplify and accelerate arch bridge construction with better structure integrity and higher stability during construction is developed. Since steel construction is superior to RC due to its advantages of high strength to weight ratio, offsite prefabrication and quick on-the-spot assembly, the use of steel box as the stiffening core of arch rib section becomes an ideal choice for the initial arch framing using the developed erection method. The prefabricated steel box can be erected vertically on temporarily restrained horizontal hinge device at the spring line with minimal hoisting equipments. The completed two half-leaf can then rotate down on its own weight to meet at the mid-span for closure in a few hours. The completed steel arch frame can serve as formwork and supporting system to provide a sufficient and safe platform for the placement of concrete that forms a variable composite rib section, and for erection of spandrel columns and deck system. This construction technique and structure optimization can significantly simplify and speed up arch bridge construction with increased work zone safety and better constructability, thus provides a viable solution for arch bridge construction with difficult site condition. Pilot project, a 75-meter highway bridge, was presented to demonstrate the basic concept, design features, general construction technology.
Spatial resolution analysis of low frequency UWB SAR

In this study, the more accurate spatial resolution for the low frequency (LF) UWB SAR is analyzed considering the wavenumber coupling. First, the SAR imaging geometry is provided, and then the spatial wavenumber of the LF UWB SAR echo data is analyzed. Second, the spatial resolution of the LF UWB SAR is derived based on the spatial wavenumber domain support. Besides, the squint angle is also considered in the spatial resolution derivation. Finally, simulation results are given to prove the validity.