nils-herrmann/clean_scirepeval huggingface dataset:
Metamorphic testing: A new student engagement approach for a new software testing paradigm

Software testing, as part of the software quality assurance processes in software engineering (SE), is seldom cited as the most engaging part of a software engineer's training. In spite of its importance, it was often a neglected area, and only recently, with the adoption of more agile methodologies, has testing come to the fore. Testers face challenges, including the need to be able to identify when software is not behaving as expected. Metamorphic Testing (MT) first appeared twenty years ago in an effort to help testers facing the oracle problem (not having a mechanism to determine correct software behavior). Although MT is simple in concept, it has demonstrated very impressive fault-finding ability, and has recently been attracting a great deal of interest. Training students and practitioners to effectively apply MT has become an important goal, but to date, no textbook or guidelines have been made readily available. This paper, written by some of the leading voices in the MT community, is a sharing of some of the experiences, reflections and insights gained through teaching MT (and other related subjects). These experiences indicate that not only is MT effective in industry and at fault-finding, it has the potential to really engage students in creative SE classroom activities.
Internal force calculation and stability analysis of the slope reinforced by pre-stressed anchor cables and frame beams

In order to study the force distribution law in the slopes reinforced by pre-stressed anchor cables and frame beams, optimization safety factor calculation method of slopes after reinforcement, the load applied by anchor cables via frame beams is considered as line load on the slope, and we could obtain theoretical solution of additional stress in the slope by means of elastic theory. With parameter of some slope, we get the additional stress distribution by using theoretical solution, and compare it with the result which is obtained by using FEM. With the distribution of the additional stress, we get new safety factor formula of the slopes after reinforcement which takes the function of frame beams on stress dispersion into account. The results manifest: (1) the FEM result shows that the result obtained by theoretical method is reasonable; (2) the stress distribution shows that the relationship between the depth of the potential sliding surface and the interval of frame beams should be taken into consideration when we confirm the interval of the beams;(3) the new formula which takes the function of frame beams on stress dispersion into consideration gets a smaller, but more reasonable safety factor than the traditional one.
Bilateral Electricity Trade Between Smart Grids and Green Datacenters: Pricing Models and Performance Evaluation

Datacenter demand response is a promising approach for mitigating operational instability faced by smart grids. It enables significant potentials in peak load shedding and facilitates the incorporation of distributed generation and intermittent energy sources. This paper considers two key aspects toward real-time electricity pricing for eliciting demand response: 1) two-way electricity flow between smart grids and large datacenters with hybrid green generation capabilities and 2) the geo-distributed nature of large cloud systems, and hence the potential competition among smart grids that serve different datacenters of the cloud. We propose a pricing scheme tailored for geo-distributed green datacenters, from a multi-leader (smart grids) single-follower (cloud) game point of view. At the cloud side, in quest for scalability, robustness, and performance, the energy cost minimization problem is solved in a distributed manner, based on the technique of alternating direction method of multipliers. At the smart grid side, a practical equilibrium of the multi-leader single-follower pricing game is desired. To this end, we employ the technique of equilibrium problem with equilibrium constraints and exact linearization, to accurately transform the multi-leader single-follower pricing game, which is non-convex into a mixed integer linear system that can be readily solved. The effectiveness of the proposed solutions is evaluated based on the real datacenter workload traces and the IEEE 14-bus test systems with real generation and demand data.
Switch Analysis for Running Time Analysis of Evolutionary Algorithms

Evolutionary algorithms (EAs) are a large family of heuristic optimization algorithms. They are problem independent and have been applied in various optimization problems. Thus, general analysis tools are especially appealing for guiding the analysis of EAs in various situations. This paper develops the switch analysis approach for running time analysis of EAs, revealing their average computational complexity. Unlike previous analysis approaches that analyze an algorithm from scratch, the switch analysis makes use of another well-analyzed algorithm and, by contrasting them, can lead to better results. We investigate the power of switch analysis by comparing it with two commonly used analysis approaches, the fitness level method and the drift analysis. We define the reducibility between two analysis approaches for comparing their power. By the reducibility relationship, it is revealed that both the fitness level method and the drift analysis are reducible to the switch analysis, as they are equivalent to specific configurations of the switch analysis. We further show that the switch analysis is not reducible to the fitness level method, and compare it with the drift analysis on a concrete analysis case (the discrete linear problem). The reducibility study might shed some light on the unified view of different running time analysis approaches.
Sliding mode control of uncertain systems with distributed delay parameter-dependent Lyapunov functional approach

The robust stability and robust sliding mode control problems are studied for a class of linear distributed delay systems with polytopic-type uncertainties in this paper by applying the parameter-dependent Lyapunov functional approach combining with a new method of introducing some relaxation matrices and tuning parameters, which can be chosen properly to lead to a less conservative result. First, a sufficient condition is proposed for robust stability of the autonomic system. Then the sufficient conditions of the robust stabilization controller and the existence condition of sliding mode are also developed. The results are all given in terms of linear matrix inequalities (LMIs), which can be solved via efficient interior-point algorithms. A numerical example is presented to illustrate the feasibility and advantages of the proposed design scheme
Design of the pesticide residues detection instrument based on RBFNN

A new pesticide residues detection instrument based on enzyme inhibition method is developed. The amount of pesticide residues is determined by measuring the light absorbency of the sample liquid after inhibition reaction. The influence of temperature on the activity of enzyme and the inhibition rate of enzyme is analyzed. A new method of temperature calibration based on RBFNN (Radial Basis Function Neural Network) is applied in the system, so that the instrument can accomplish precise measurements without the thermostatic device and overcome the shortcomings of these kinds of instruments. Experimental results show that the instrument of using RBFNN compensation have detection accuracy, high sensitivity, small size and low power consumption characteristics.
Using Coverage Information to Guide Test Case Selection in Adaptive Random Testing

Random Testing (RT) is a fundamental software testing technique. Adaptive Random Testing (ART) improves the fault-detection capability of RT by employing the location information of previously executed test cases. Compared with RT, test cases generated in ART are more evenly spread across the input domain. ART has conventionally been applied to programs that have only numerical input types, because the distance between numerical inputs is readily measurable. The vast majority of computer programs, however, involve non-numerical inputs. To apply ART to these programs requires the development of effective new distance measures. Different from those measures that focus on the concrete values of program inputs, in this paper we propose a method to measure the distance using coverage information. The proposed method enables ART to be applied to all kinds of programs regardless of their input types. Empirical studies are further conducted for the branch coverage Manhattan distance measure using the replace and space programs. Experimental results show that, compared with RT, the proposed method significantly reduces the number of test cases required to detect the first failure. This method can be directly applied to prioritize regression test cases, and can also be incorporated into code-based and model-based test case generation tools.
Intelligent Control Rule for Tractor with Continuously Variable Transmission

The problem considered in this paper is the design of the control rule for a tractor equipped with continuously variable transmission. In a traditional control system the optimum fuel economy or dynamic performance control rules only reflect the tractor working state, the driver's intention is partially ignored in control system, therefore, their applications are limited in current tractors. To solve this problem, in this paper, an intelligent control rule based on fuzzy inference of driver's intention was proposed, whose aim is to improve tractor dynamic performance under transient operating conditions and fuel economy under steady operating conditions. Using a fuzzy inference engine, the tractor dynamic factor which was introduced to indicate the driver's demand for tractor dynamic performance based on the change and the change rate of accelerator pedal was obtained. According to the tractor dynamic factor the intelligent transient dynamic control rule can be worked out by interpolating between the fuel economy and dynamic performance control rules. After the tractor acceleration process finishes, the transition control rule was adopted to achieve the smooth transition from intelligent transient dynamic rule to steady fuel economy rule. At last, the simulation results showed that the intelligent control rule enable tractors to reach the optimum comprehensive performance. The works above provided a new design method for developing an intelligent tractor equipped with continuously variable transmission
Observer-Based Sliding Mode Control for a Class of Nonlinear Systems with Distributed Delays

This paper is concerned with the sliding mode observer design and observer-based controller design for a class of uncertain nonlinear distributed delay systems. According to the sliding mode control theory, first, a sufficient condition is proposed for robust asymptotic stability of the sliding mode dynamics. A sliding mode observer is designed, and at the same time, a sufficient condition is given for the existence of such an observer in terms of LMI. Based on estimated system state, a controller is synthesized by using the sliding mode control theory combining with the reaching law technology. This proposed control scheme guarantees the reachability of the sliding surfaces defined in both the state estimation space and the state estimation error space. Numerical example is given to illustrate the proposed design scheme
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion

The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works usually utilize metric distances which are ofen epistemologically different from the perceptual distance of human beings. In this paper a novel non-metric partial similarity measure is introduced, which is born to automatically capture the prominent partial similarity between two images while ignoring the confusing unimportant dissimilarity. This measure is potentially useful in face recognition since it can help identify the inherent intra-personal similarity and thus reducing the influence caused by large variations such as expression and occlusions. Moreover; to make this method practical, this paper proposes an automatic and class-dependent similarity threshold setting mechanism based on the maximal margin criterion, and uses a Self- Organization Map-based embedding technique to alleviate the computational problem. Experimental results show the feasibility and effectiveness of the proposed method.
SS-based sketch recognition for graphics of traffic accident

To improve the efficiency of drawing the graphic symbols of the scene of traffic accident, a SS-based sketch recognition technology is introduced into the identification of traffic sketches. While four basic cartography factors, including straight line, polyline, circle and arc, are put forward to partition the intricate sketch into the combination of multi-strokes, according to the basic characteristic of the graphics. Online sketch recognition can reveal latent primitive shapes and show the regularized shape on screen immediately through feature recognition, shape fitting and regularization. After the primitive stroke are grouped according to the spatial relationships, the user's intention is caught and suggested through computing their similarity with the predefined templates both in partial structures as well as in overall configuration. Practical application proves the proposed method both effective and efficient.
Fusion detection of ship targets in low resolution multi-spectral images

Aiming at ship detection in multi-spectral images at low resolution, this paper proposes a new method for ship detection based on fusion detection which combines spectral feature with thermal feature. Firstly, it selects infrared band instead of visible band image to detect cloud according to size feature. With cloud available, it does segmentation work in thermal infrared image for the removal of cloud pixels. Then, the result is mapped to the IR images and cloud masking is completed. Next, the fusion of two kinds of images using wavelet transform is adopted. At last, the fused image is used to detect ships and morphological operations are used to discriminate ships. The experiment result on multi-spectral data of Landsat 8 shows that the proposed method which is robust against clutter can detect ships effectively.
A novel health estimation model for CNC machine tool servo system based on belief-rule-base

Health condition estimation is an important means to guarantee the normal work and the optimal maintenance for CNC machine tool servo system. The health condition is denoted by features. In order to estimate the health condition for servo system, belief-rule-base (BRB) model is employed as estimation method in this paper. This method can composite multiple knowledge including some uncertain information to improve the accuracy of health condition estimation. Because of the multiplicity of the feature parameters, infinite irrelevance method is used to extract main characteristics. As the initial knowledge by experts is not accurate enough, CMA-ES is used to optimize the parameters in BRB. The case study show that the model can estimate the health of servo system accurately.
System performance simulation of an UWB FLGPSAR based on Matlab/Simulink

The simulation system of an ultra-wideband forward looking ground penetrating synthetic aperture radar (FLGPSAR) is analyzed and designed according to the characteristics of FLGPSAR. The ideal of modularization is used in this simulation system, so it is flexible and effective to realize the simulation system. After applying the simulation system in the Matlab/Simulink (MS), the methods of choosing system parameters including pulse width and IF bandwidth are obtained. At the same time, the effect of the movement and incline of the platform, the harmonics levels, and the environment noise and so on is analyzed. And the radar system architecture is finally determined by system analyzing and simulation. By using this system architecture and the chosen parameters the radar system performance is optimized. This simulation system would be a foundation for effectively developing the FLGPSAR system. (4 pages)
The real-time monitoring system for inspecting car based on RFID, GPS and GIS

The author designs and implements a real-time monitoring system for inspecting car with MapX based on the technologies of RFID, GPS and GIS. In the thesis, the architecture and function frame are introduced, then the key technologies of implementation are discussed in detail. The practice proves that the system is stable and efficient. The system could not only offer real-time and precise position of inspecting car but also monitor the information of RFID cards of surrounding vehicles for traffic department, which supports scientific, effective and prompt decision for related managers. The system has good prospects on traffic control, environment monitoring, city plan and geological exploration.
A Synergetic Control Scheme for HVDC Transmission System

HVDC power transmission system is a typical time varying system with many nonlinear relationships among different variables, it is difficult for the conventional PI controller to work effectively for large changes in the operation. In this paper, the synergetic control theory is discussed, and a nonlinear controller based on such theory is proposed for the HVDC power transmission system, in which a constant DC current controller for the rectifier and a constant extinction angle controller for the inverter are designed respectively. Details of design for such control scheme are also given in the paper. In order to verify the effectiveness of the proposed control, the performance the proposed control scheme in the CIGRE benchmark DC transmission system is simulated with PSCAD/EMTDC. The simulation results show that the proposed control works effectively in the start-up process as well as in the conditions when the operation mode shifts for the HVDC system.
Mechanism Analysis of Smart Film for Crack Monitoring of Concrete Bridges

In the field of structural health monitoring of concrete bridges, one of the most important problems is the crack identification. In previous studies, sensitive skin for bionic crack monitoring of concrete bridges had been used to monitor the crack initiation and development in a concrete structure, and it was visualized by computer. But there are several problems that prevented this method from being widely applied. In this paper, smart film is proposed for crack monitoring of large scale concrete structures. To monitor the crack of concrete bridge by smart film, the mechanical and electrical mechanism of smart film were analyzed separately based on fracture mechanics and a LED (light emitting diode) circuit designed. The results showed that smart film can monitor the crack initiation and development in concrete bridges effectively.
Semi-naive Exploitation of One-Dependence Estimators

It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies, one-dependence estimators (ODEs) have been shown to be able to approximate the ground-truth attribute dependencies whilst keeping the effectiveness of probability estimation, and therefore leading to excellent performance. In previous studies, however, ODEs were exploited in simple ways, such as by averaging, for classification. In this paper, we propose a semi-naive exploitation of ODEs that fits a function of ODEs to pursue higher-order attribute dependencies. Extensive experiments show that the proposed SNODE approach can achieve better performance than many state-of-the-art Bayesian classifiers.
An Adaptive LDPC-coded MIMO-OFDM System with Partial Channel State Information

An adaptive transmission scheme of MIMO-OFDM system based on MQAM and LDPC code is proposed. An transmit optimization algorithm to obtain an bit and power allocation is used which selects the MCS requiring only the mean and variance assuming the received LLR distribution as Gaussian. Simulation results show the performance of the proposed scheme is up to about 3dB better than other transmission schemes at the expense of a few bits in the feedback information.
Estimation of wall parameters for cognitive imaging in through-the-wall radar

The surrounding environment has serious influence on through-the-wall imaging (TWI). The image of behind-the-wall targets will defocus and misplace when neglecting the effect of walls. Cognitive imaging is a recently proposed concept, which is integrated with environment sensing capability and thus suitable for TWI. In this paper, a novel method is proposed to estimate the wall parameters, width and permittivity, to build the environmental model for cognitive imaging of behind-the-wall targets. Firstly, a forward model of electromagnetic wave propagation in a homogeneous wall is developed. Based on the forward model, the estimation of wall parameters is modeled as a least square problem and can be solved efficiently. The simulation data of the finite-difference time-domain method are used to validate our proposed method.
Single-Phase Uninterruptible Power Supply Based on Z-Source Inverter

This paper presents a new topology of uninterruptible power supply (UPS) by using a Z-source inverter, where a symmetrical LC network is employed to couple the main power circuit of an inverter to a battery bank. With this new topology, the proposed UPS can maintain the desired ac output voltage at the significant voltage drop of the battery bank with high efficiency, low harmonics, fast response, and good steady-state performance in comparison with traditional UPSs. The simulation and experimental results of a 3-kW UPS with the new topology confirm its validity.
Man-made targets pose estimation using time-frequency distribution in UWB SAR

A man-made target pose estimation method using time-frequency distribution is proposed in this paper. The timefrequency distribution is performed to the azimuth signals after range migration correction, which can not only accomplish the azimuth compress but also extract the aspect angle dependence information of the target to decide the orientation of the target relative to the radar line-of-sight (LOS). Simulation results validate the efficiency of the proposed man-made targets pose estimation method.