nils-herrmann/clean_scirepeval huggingface dataset:
On the Fault-Detection Capabilities of Adaptive Random Test Case Prioritization: Case Studies with Large Test Suites

An adaptive random (AR) testing strategy has recently been developed and examined by a growing body of research. More recently, this strategy has been applied to prioritizing regression test cases based on code coverage using the concepts of Jaccard Distance (JD) and Coverage Manhattan Distance (CMD). Code coverage, however, does not consider frequency, furthermore, comparison between JD and CMD has not yet been made. This research fills the gap by first investigating the fault-detection capabilities of using frequency information for AR test case prioritization, and then comparing JD and CMD. Experimental results show that "coverage" was more useful than "frequency" although the latter can sometimes complement the former, and that CMD was superior to JD. It is also found that, for certain faults, the conventional "additional" algorithm (widely accepted as one of the best algorithms for test case prioritization) could perform much worse than random testing on large test suites.
iTSum: one agent-based system for automated text summarizing

With the explosive increase of information especially the news within the cyber world, it is necessary to find a way to illuminate the workload for a person to manually access the meta-data day by day. Based on a modified KPC algorithm, one improved engine is designed and the prototype system iTSum was developed by the project group within Nanyang Technological University of Singapore, which allows the users to specify the website URLs he wishes to visit and generates the summarized Web page for him automatically. The performance evaluation of iTSum and KPC shows the improvement is outstanding. iTSum is an agent-based, personalized and domain-specific text summarizing and publishing system, tests show it can perform better than KPC itself and most other current methodologies.
Ca2+ Sparks Detection and Classification using Gaussian-Mexican Hat Wavelet

It is evidenced that Ca2+ plays a critical role in cell activities. Understanding and analysis of a Ca2+ sparks event can provide critical information in cell communications. It is known that a Ca2+ spark spatial spread in a local area can be modeled by a Gaussian distribution. There are some available methods in Ca2+ spark detection, but little research has been done to quantitatively analyze and classify the Ca2+ sparks into single or multiple release events. In this paper, a new method based on Gaussian-Mexican Hat Wavelet is proposed to automatically classify Ca2+ sparks into single release events or multiple releases events. Then Ca2+ sparks from single release events are further categorized into different energy levels. The experiment results using mouse skeletal muscle fiber cells show this method is promising.
Resource allocation for heterogeneous multiuser OFDM-based cognitive radio networks with imperfect spectrum sensing

In this paper we study the resource allocation in OFDM-based cognitive radio (CR) networks, under the consideration of many practical limitations such as imperfect spectrum sensing, limited transmission power, different traffic demands of secondary users, etc. We formulated this general problem as a mixed integer programming task. Considering that this optimization task is computationally intractable, we propose to address it in two steps. For the first step, we perform subchannel allocation to satisfy heterogeneous users' rate requirement roughly and remove the integer constraints of the optimization problem. For the second step, we perform power allocation among the subchannels. By exploiting the problem structure to speedup the Newton step, we propose a Barrier-based method which is able to achieve the optimal power distribution with a complexity of O(N), where N is the number of active OFDM subchannels, significantly better than the complexity of O(N3) of standard techniques. Moreover, we proposed a method which is able to approximate the optimal solution with a constant complexity. Numerical results validate that our proposal exploits the overall capacity of CR systems well subjected to different traffic demands of users.
Relative radiometric normalization of SAR images based on bi-direction linear regression model

The Relative radiometric normalization (RRN) of multi-temporal synthetic aperture radar (SAR) images is very important for change detection. Many RRN techniques have been developed up to now. Among them, the No-Change set regression normalization algorithm (NC algorithm) is proved to have the best performance in optical image processing field. However, as our paper shows, the presence of speckle noise in SAR image will make the estimations of RRN parameters biased in NC algorithm. In order to deal with this problem, we propose a RRN method suitable for SAR images. In the approach, the biases of RRN parameters estimations are removed by a bi-direction linear regression model. And a recursive weighted least square method is included to improve robustness. The effectiveness of the proposed approach is confirmed with experimental results compared to NC algorithm.
Transforming traditional iris recognition systems to work on non-ideal situations

Non-ideal iris images can significantly affect the accuracy of iris recognition systems for two reasons: 1) they cannot be properly preprocessed by the system; and/or 2) they have poor image quality. However, many traditional iris recognition systems have been deployed in law enforcement, military, or many other important locations. It will be expensive to replace all these systems. It will be desirable if the traditional systems can be transformed to perform in non-ideal situations without an expensive update. In this paper, we propose a method that can help traditional iris recognition systems to work on the non-ideal situation using a video image approach. The proposed method will quickly identify and eliminate the bad quality images from iris videos for further processing. The segmentation accuracy is critical in recognition and would be challenging for traditional systems. The segmentation evaluation is designed to evaluate if the segmentation is valid. The information distance based quality measure is used to evaluate if the image has enough quality for recognition. The segmentation evaluation score and quality score are combined to predict the recognition performance. The research results show that the proposed methods can work effectively and objectively. The combination of segmentation and quality scores is highly correlated with the recognition accuracy and can be used to improve the performance of iris recognition systems in a non-ideal situation. The deployment of such a system would not cost much since the core parts of the traditional systems are not changed and we only need to add software modules. It will be very practical to transform the traditional system using the proposed method.
Fuzzy logic based flight control system for hypersonic transporter

A fuzzy logic based flight control system for a hypersonic transporter is proposed, in order to provide the longitudinal stability in the hypersonic region and to improve the response of the vehicle as well as to make the response exactly follow the commands. The construction of the fuzzy logic controller is set up, in which angle of attack and pitch rate are chosen as input linguistic variables, and the deflection of the elevator is the control variable. Fourteen fuzzy inference rules, which are designed to model a human operator's behaviour, are employed, and the fuzzy algorithm of maxmin composition is used as an inference mechanism. Performance evaluation of the fuzzy logic controller for the hypersonic transporter is carried out at four flight points, which are chosen from the whole flight envelope. The calculation results show that the fuzzy logic controller has a good ability to stabilize the vehicle in the hypersonic region, and is fairly robust across the flight envelope.
Human micro-Doppler signature extraction in the foliage-penetration environment

It is a challenge to detect and identify the targets in the foliage-penetration environment. Radar is superior to other sensors in this poor visibility condition. The micro-Doppler signature is one of the most prominent information for target classification and identification in radar applications. In this paper, a complete procedure is proposed for human micro-Doppler signature extraction in the foliage-penetration environment. It consists of six steps, including clutter suppression, signal coherent accumulation, target tracking, trajectory interpolation, micro-Doppler calculation and signature extraction. The experimental results verified the effectiveness of the proposed procedure.
Dynamic scheduling of operating reserves in co-optimized electricity markets with wind power

We propose a probabilistic methodology to estimate a demand curve for operating reserves, where the curve represents the amount that a system operator is willing to pay for these services. The demand curve is quantified by the cost of unserved energy and the expected loss of load, accounting for uncertainty from generator contingencies, load forecasting errors, and wind power forecasting errors. The methodology addresses two key challenges in electricity market design: integrating wind power more efficiently and improving scarcity pricing. In a case study, we apply the proposed operating reserve strategies in a two-settlement electricity market with centralized unit commitment and economic dispatch and co-optimization of energy and reserves. We compare the proposed probabilistic approach to traditional operating reserve rules. We use the Illinois power system to illustrate the efficiency of the proposed reserve market modeling approach when it is combined with probabilistic wind power forecasting.
Study on Percolation Law in Low Permeability Reservoir of Deformable Media

According to the basic idea of fluid-solid coupling theory, combining percolation mechanics with rock-soil mechanics and considering threshold pressure gradient and fluid-solid coupling character of low permeability reservoirs, a numerical model is established, which can describe the fluid-solid coupling character in saturated deformable low permeability reservoirs and can be solved by coupling numerical method combining finite difference with finite element. The percolation law for deformable medium in low permeability reservoir is studied. The typical examples indicate that the dynamic variation of petrophysics parameters, stress-strain and their effects on producing performance can be predicted by this method. Through comparing the result of the numerical simulation with the one which doesn't consider the fluid-solid coupling effect, it is seen that the fluid-solid coupling effect of low permeability reservoirs can not be neglected.
Semi-Proving: An Integrated Method for Program Proving, Testing, and Debugging

We present an integrated method for program proving, testing, and debugging. Using the concept of metamorphic relations, we select necessary properties for target programs. For programs where global symbolic evaluation can be conducted and the constraint expressions involved can be solved, we can either prove that these necessary conditions for program correctness are satisfied or identify all inputs that violate the conditions. For other programs, our method can be converted into a symbolic-testing approach. Our method extrapolates from the correctness of a program for tested inputs to the correctness of the program for related untested inputs. The method supports automatic debugging through the identification of constraint expressions that reveal failures.
Hybrid Modeling and Simulation of Shifting Process Involving Multi-group Clutches

The characteristics of automatic transmission shifting process involving multi-group clutches were studied and it was pointed out that the shifting process is a hybrid system including a continuous system and a discrete system; then a hybrid modeling method, based on the continuous system under bond graph theory and on the discrete system under Finite State Machine, was proposed. In the paper, a model of shifting process of hydro-mechanical transmission which was equipped in the Dongfanghong 1302R rubber tracked tractor was established on the platforms of Matlab/Simulink and Matlab/Stateflow. The simulation showed that this hybrid system was rather effective in simulating the dynamic behavior of clutches in shifting process, and its influence on the shift quality was analyzed through simulation, which served as an effective tool for the design of clutch control strategy in the shifting process.
Reseach of security risk identification based on rough set theory for highway subgrade stability in the slope of reservoir

There are many security risk factors which affect highway subgrade stability in the slope of reservoir. These security risk actors not only are uncertainty and information imperfect, but also have very complex risk characteristic, which rock-soil of primary landform of highway subgrade along the reservoir line is polyphase difference construction and interrelationship of risk factors. In this paper, aiming at these characteristics of security risk of highway subgrade stability in the slope of reservoir, based on rough set theory, a security risk identification model has been established to control the security risk. Application of the model has been provided through the case of security risk identification of highway subgrade stability in the slope of reservoir, in Bainan section Yunyang county, YuBa road. The results of the application demonstrate that small amount of information is required only, analysis and calculation have higher efficiency, and the accuracy can meet the needs of risk analysis through the security risk identification model based on rough set theory. The case confirms that this model can be provided to decision support for the late disaster management of Three Gorges Reservoir.
Data-fusion method for flaws detection based on reliability evaluation

In order to solve the problem of low accuracy of flaws detection in non-destructive testing, a data-fusion method based on RBF neural network and evidential reasoning (ER) rule is studied. Firstly, a data-fusion model is proposed which contains two parts. Secondly, the first part RBF neural network is used to fuse the feature layer, and its output is normalized as the basic probability assignment (BPA) of each evidence for the next part. Then, according to the traditional D-S evidence theory, the accuracy of the flaws detection is not high enough and the evidence conflict is obvious. So it's necessary to introduce the evidence reliability factor to evaluate the reliability of the evidence, so that all kinds of evidence pass evaluation and then make fusion. Finally, the defect data of a composite material is obtained by means of ultrasonic detection, and the method proposed in this paper is verified. The results show that the method can accurately identify the flaw types, compared with other methods to improve the accuracy of detection.
A fuzzy multi-objective decision-making method for agent-based negotiation

In the e-commerce world, buyers are faced with several sellers who provide similar services. Buyers should make a decision from the solutions given by several sellers, select the best seller with the best solution, and accept his solution or negotiate further. For an agent-based negotiation support system, it is very important that the buyer agent can use this method to select the most appropriate seller agent. To select the right seller agent, the process is as follows: after the buyer agent submits the buyer's requirement to the broker agent, the broker collects all the seller agents which probably meet the buyer's requirement by bidding or searching, and formalizes their solutions. Then, based on the concepts of fuzzy set theory and TOPSIS, a fuzzy multiobjective decision-making approach is presented, in which quantitative analysis and qualitative judgment are integrated. Finally, a numerical example is given.
Focusing one-stationary bistatic circular SAR data using fast backprojection algorithm

This study presents a fast backprojection algorithm (FBPA) for one-stationary bistatic circular synthetic aperture radar (OS-BCSAR) imaging. This method reconstructs an illuminated SAR scene in a ground plane instead of a slant-range plane. First, the imaging geometry of the OS-BCSAR is given. Then, based on the subaperture imaging model, the sampling requirements of the subimage polar grid are derived from the bandwidth angle. Finally, the implementation of the proposed FBPA is discussed. Simulation results are shown to demonstrate its validity.
Diffusion and interpolation method for Gradient Vector Flow

Due to the heavy computational cost and being vulnerable to noise pollution, traditional Gradient Vector Flow(GVF), which is a most popular method in image segmentation, often presents poor performance. A novel generating method of GVF field is presented in this letter. Only a part of image pixels will still use the standard GVF vector diffusion method. More pixels will use the proposed interpolation method. Experimental results show that the proposed method can reduce the computational cost significantly and improve robustness to noise pollution.
A novel preprocessing method for vehicular-mounted forward-looking UWB SAR system

The forward-looking ground penetrating radar (FLGPR) has been widely considered as an applicable landmines detection technology, but the self-signature and the strong ground clutter will severely degrade the SAR image. We present a novel method for interference and ground clutter suppression in the image domain based on the distance-band partition and iterative operation techniques, which can realize the parallel processing and adaptive update of the parameters on line. By using the property of FLGPR systems that the same spatial region is observed several times at different standoff distances, we exploit a range multi-look processing based on the iterative technique to obtain the high quality image. The proposed method in this paper is adaptive and efficient enough to work in a real-time operational scenario, so it can effectively meet the needs of the preprocessing in the vehicular-mounted forward-looking (VMFL) detection system. The effectiveness of our approaches is demonstrated with an experimental data set.
Spectral Clustering on Multiple Manifolds

Spectral clustering (SC) is a large family of grouping methods that partition data using eigenvectors of an affinity matrix derived from the data. Though SC methods have been successfully applied to a large number of challenging clustering scenarios, it is noteworthy that they will fail when there are significant intersections among different clusters. In this paper, based on the analysis that SC methods are able to work well when the affinity values of the points belonging to different clusters are relatively low, we propose a new method, called spectral multi-manifold clustering (SMMC), which is able to handle intersections. In our model, the data are assumed to lie on or close to multiple smooth low-dimensional manifolds, where some data manifolds are separated but some are intersecting. Then, local geometric information of the sampled data is incorporated to construct a suitable affinity matrix. Finally, spectral method is applied to this affinity matrix to group the data. Extensive experiments on synthetic as well as real datasets demonstrate the promising performance of SMMC.
Exact BER Analysis of Differential Chaos Shift Keying Communication System in Fading Channels

An exact method is employed to analyze the bit error rate (BER) performances of differential chaos shift keying (DCSK) communication system over fading channels. The exact BER performances of DCSK in Nakagami-m, Rayleigh and Rician fading channels are derived, respectively. The Gaussian approximation (GA) method is compared with the exact method. The numerical and simulation results for the two methods are presented and compared in the fading channels, and these results support our theoretical analysis.
Numerical simulation of DC positive corona discharge under atmospheric environment

This paper presents an improved self-consistent, multi-component and two-dimensional plasma hybrid model for the simulation of DC positive corona discharge under atmospheric environment. The model is based on the plasma hydrodynamics and the chemical modles, it includes 12 species and 27 reactions. Besides, the photoionization effect is also considered in the proposed model. Based on this model, the volume of dynamics charged species generation, the discharge current waveform, electron temperature, and electric field variations are investigated in details. The results indicate that the model provides valuable insights to the physics of air corona discharge.
A novel bag generator for image database retrieval with multi-instance learning techniques

In multi-instance learning, the training examples are bags composed of instances without labels and the task is to predict the labels of unseen bags through analyzing the training bags with known labels. In content-based image retrieval (CBIR), the query is ambiguous because it is hard to ask the user precisely specify what he or she wants. Such kind of ambiguity can be gracefully dealt with by multi-instance learning techniques, and previous research shows that bag generators can significantly influence the performance of a CBIR system. In this paper, a novel bag generator named ImaBag is presented, where the pixels of each image are first clustered based on their color and spatial features and then the clustered blocks are merged and converted into a specific number of instances. Experiments show that ImaBag achieves comparable results to some existing bag generators but is more efficient in retrieving images from databases.
Metamorphic Testing: Beyond Testing Numerical Computations

The concept of metamorphic testing (MT) is often illustrated using numerical computation programs that implement mathematical functions. This is because mathematical functions have well-known properties that can be readily understood by the audience. It is to be noted, however, that the application domain of MT is far larger than numerical computations. Hence, this talk will cover some of our research on MT beyond the testing of numerical programs. We first look at the potential of MT for cybersecurity and show how MT was used to detect bugs in real-life obfuscators. Obfuscators are a type of software that can be as important as compilers, but the testing of obfuscators was almost never studied in the past. We then look at the application of MT to search related services such as Google Maps and search engines, and show how the concept of MT has evolved from a verification technique to a more unified framework that covers verification, validation and other types of quality assessment.