Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Automated Program Repair Refine Fault Localization?

Software bugs are prevalent in modern software systems and notoriously hard to debug manually. Therefore, a large body of research efforts have been dedicated to automated software debugging, including both automated fault localization and program repair. However, the existing fault localization techniques are usually ineffective on real-world software systems while even the most advanced program repair techniques can only fix a small ratio of real-world bugs. Although fault localization and program repair are inherently connected, we observe that in the literature their only connection is that program repair techniques usually use off-the-shelf fault localization techniques (e.g., Ochiai) to determine the potential candidate statements/elements for patching. In this work, we explore their connection in the other direction, i.e., can program repair in turn help with fault localization? In this way,we not only open a new dimension for more powerful fault localization, but also extend the application scope of program repair to all possible bugs (not only the bugs that can be directly automatically fixed). We have designed ProFL, a simplistic approach using patch-execution results (from program repair) as the feedback information for fault localization. The experimental results on the widely used Defects4J benchmark show that the basic ProFL can already localize 161 of the 395 studied bugs within Top-1, while state-of-the-art spectrum and mutation based fault localization techniques at most localize 117 within Top-1. We also demonstrate ProFL's effectiveness under different settings. Lastly, we show that ProFL can further boost state-of-the-art fault localization via both unsupervised and supervised learning.
Analyzing the Spotify Top 200 Through a Point Process Lens

Every generation throws a hero up the pop charts. For the current generation, one of the most relevant pop charts is the Spotify Top 200. Spotify is the world's largest music streaming service and the Top 200 is a daily list of the platform's 200 most streamed songs. In this paper, we analyze a data set collected from over 20 months of these rankings. Via exploratory data analysis, we investigate the popularity, rarity, and longevity of songs on the Top 200 and we construct a stochastic process model for the daily streaming counts that draws upon ideas from stochastic intensity point processes and marked point processes. Using the parameters of this model as estimated from the Top 200 data, we apply a clustering algorithm to identify songs with similar features and performance.
Effect of Post Purchase Evaluations on Guest Loyalty in Four and Five Star Hotels in Nairobi and Mombasa, Kenya

Demand for quality hotel service has led to adoption of information and communication technologies. Guests’ post purchase evaluation and loyalty further influence hotel performance, yet information on them is not well documented. The purpose of this study therefore was to establish the effect of post purchase evaluations on guest loyalty in 4& 5-star hotels in Kenya. Concurrent triangulation design was employed. The population comprised of 3644 guests and 68 top managers in 4and 5-star hotels in Nairobi and Mombasa where there ishighest concentration of the hotels as well as hotel bed space of 3644 based on the year 2015 bed occupancy rate calculated at 40% and 31% for Nairobi and Mombasa respectively. Krejcie and Morgan formula was used to determine a sample of 362 guests and top managers. This study employed multi stage random sampling technique. Both questionnaires and interview schedules were used to collect data. Quantitative analysis revealed that post purchase evaluations had a significant effect (R2=0.256. p=0.000) with Purchase importance having significant positive influence (β1= 0.120, p<0.05 while Service performance having insignificant positive influence (β2= 0.076, p>0.05); on guest loyalty. The study concluded that Post purchase evaluations had significant effecton guest loyalty the study recommended that stakeholders in the hotel industry should invest more on guests’ post purchase evaluations since it significantly affects the guest’s loyalty.
Faculty of Business and Law SCHOOL OF ACCOUNTING, ECONOMICS AND FINANCE

Meta-regression analysis (MRA) provides an empirical framework through which to integrate disparate economic research results, filter out likely publication bias, and explain their wide variation using socio-economic and econometric explanatory variables (Stanley and Jarrell, 1989, Stanley, 2001, Doucouliagos, 2005, Stanley, 2005a). In dozens of applications, MRA has found excess variation among reported research findings, some of which is explained by socio-economic variables (e.g., researcher’s gender) and most of which contains publication bias (Card and Krueger, 1995, Stanley, 1998, Stanley and Jarrell, 1998, Ashenfelter et al.,1999, Görg and Strobl, 2001, Stanley, 2001, Doucouliagos and Laroche, 2003, Abreu, de Groot and Florax, 2005, Doucouliagos, 2005, Rose and Stanley, 2005, Stanley, 2005a). Publication bias is itself a socio-economic phenomenon. When researchers’ compensation is based on their publication records, all available research degrees of freedom will be used to increase its probability. MRA can empirically model and test socio-economic theories about economic research. The socio-economics of the academy can explain why excess variation (beyond the classical, random sampling errors that conventional standard errors measure) will likely dominate many areas of empirical economic research, and MRA can explain how. Here, we make two strong claims: socio-economic MRAs, broadly conceived, explain much of the excess variation routinely found in empirical economic research; whereas, any other type of literature review (or summary) is biased. A14, B41, C10. * Department of Economics and Business Accounting, Economics and Finance Faculty of Business and Law Department of Management and International Business Hendrix College Deakin University College of Business 1600 Washington St. 221 Burwood Highway, Burwood, 3193 Western Carolina University Conway, AR 72032 Victoria, Australia Cullowhee, NC 28723 Stanley@Hendrix.edu Chris.doucouliagos@deakin.edu. au Jarrell@email.wcu. edu Meta-Regression Analysis as the Socio-Economics of Economic Research 1
Weightlifting Motion Generation for a Stance Robot with Repeatedly Direct Kinematics

The objective of tolerance analysis is to check the extent and nature of variation of an analyzed dimension or geometric feature of interest for a given GD & T scheme. The parametric approach to tolerance analysis is based on parametric constraint solving. The accuracy of simulation results is dependent on the user-defined modeling scheme. Once an accurate CAD model is developed, it is integrated with tolerance synthesis model. In order to make it cost competent, it is necessary to obtain the cost-tolerance relationships. The neural network recently has been reported to be an effective statistical tool for determining relationship between input factors and output responses. This study deals development of direct constraint model in CAD, which is integrated to an optimal tolerance design problem. A back-propagation (BP) network is applied to fit the cost-tolerance relationship. An optimization method based on Differential Evolution (DE) is then used to locate the combination of controllable factors (tolerances) to optimize the output response (manufacturing cost plus quality loss) using the equations stemming from the trained network. A tolerance synthesis problem for a motor assembly is used to investigate the effectiveness and efficiency of the proposed methodology.
ON VISIBILITY RESTORATION OF DEGRADED IMAGES UNDER INCLEMENT WEATHER CONDITIONS Padmini

Outdoor Images captured under inclement weather conditions are degraded by various atmospheric particles, which vary in size and concentration. To restore the visibility of the degraded images, various state-of-the-art algorithms were developed for the past decade. In this paper, an overview of various research works carried over the past decade related to static weather conditions like Haze, fog and dynamic weather conditions like rain are investigated. Also, the merits and demerits of each algorithm along with the challenges in this area are focused.
LARGE-SCALE WORD REPRESENTATION FEATURES FOR IMPROVED SPOKEN LANGUAGE UNDERSTANDING

Recently there has been great interest in the application of word representation techniques to various natural language processing (NLP) scenarios. Word representation features from techniques such as Brown clustering or spectral clustering are generally computed from large corpora of unlabeled data in a completely unsupervised manner. These features can then be directly included as supplementary features to standard representations used for NLP processing tasks. In this paper, we apply these techniques to the tasks of domain classification and intent detection in a spoken language understanding (SLU) system. In experiments in a personal assistant domain, features derived from both Brown clustering and spectral clustering techniques improved the performance of all models in our experiments and the combination of both techniques yielded additional improvements.
IDENTIFICATION OF BEMISIA TABACI ( GENNADIUS ) INFESTING MULBERRY USING MITOCHONDRIAL COENZYME 1 GENE SEQUENCE

Bemisia tabaci is the vector of Mulberry mosaic disease (MMD) reported from Kerala, India. Bemisia tabaci collected from mulberry plants were maintained on two different host plants, cassava and Nicotiana tabacum for developing pure culture. After completion of 20 generations the mtCO1 gene of the red eye nymphs of B. tabaci collected from this population were analyzed and found to have 11% divergence. The population collected from cassava showed similarity to AsiaII_7 group known from India, China, Indonesia and Taiwan while that from N. tabacum matched with AsiaII_8 group reported from India only. Mulberry is the common host of two species of B. tabaci belonging to two groups. The host preference of these species resulted in the development of only the specific species in each host plant in the pure culture. The presence of B. tabaci belonging to two different groups on mulberry can lead to the formation of new recombinants of virus, as the other host plants of the fly belonging to different families are infected with specific virus and it will be transferred to mulberry while feeding on it.
Source and Accuracy

The estimates in the reports Income and Poverty in the United States: 2016 and Health Insurance Coverage in the United States: 2016 come from the 2017 Annual Social and Economic Supplement (ASEC) of the Current Population Survey (CPS). 1 The U.S. Census Bureau conducts the CPS ASEC over a 3-month period, in February, March, and April, with most data collection occurring in the month of March. The CPS ASEC uses two sets of questions, the basic CPS and a set of supplemental questions. The CPS, sponsored jointly by the Census Bureau and the U.S. Bureau of Labor Statistics, is the country’s primary source of labor force statistics for the entire population. The Census Bureau and the Bureau of Labor Statistics also jointly sponsor the CPS ASEC.
Android malware detection based on Multi-Class Features

With the Android mobile phone market share rising and the number of its malicious software growing, an effective detection method of malicious software is very necessary. A malicious software detection algorithm considering the Android software multi-class features was proposed in this paper. This method extract the multi-class features sets of Android software and selects the corresponding optimal algorithm as the main algorithm and selects the subprime classification algorithm as the tuning algorithm for different feature sets. In each feature set, the classification results of main algorithm determine the adjustment extent of tuning algorithm. After that, the final result is based on the results of multi-class features sets so as to achieve more accurate recognition rate. The experimental results show that the accuracy of the method is 96%.
Optimal Control of Time-Varying Linear Systems Using Wavelets

In this paper a wavelet-based method is presented to solve the time-varying linear quadratic optimal control problem. The Legendre scaling functions are used as the basis functions. Moreover, this paper present the proof of the result of the multiplication of these functions which is used in approximating the time-varying linear quadratic optimal control problem by a quadratic programming problem. Numerical example is presented to show the effectiveness of the method. Key-Words:Wavelets, Legendre scaling functions, time-varying systems, optimal control.
AUTOMATIC DETECTION OF BACKGROUND DIABETIC RETINOPATHY DISEASE USING HYBRID MULTILEVEL THRESHOLDING AND CONVOLUTIONAL NEURAL NETWORK

Background Diabetic Retinopathy is a medical condition that causes retinal damage due to diabetes mellitus. This paper discusses a novel combination method, namely multilevel thresholding with a convolutional neural network. Multilevel thresholding resulting in disease characteristics by performing morphological vessel segmentation accurately. The segmentation process was carried out automatically by performing resizing on the grayscale image then histogram equalization. After that, the histogram equalization result was performed a Gaussian filter and Sobel edge detection to detect blood vessels. Lastly, the segmentation process was carried out using Otsu's multilevel thresholding, and then the classification phase using convolutional neural network was performed. The proposed method tested by using Stare datasets. In order to measure the performance of this technique, specifications, sensitivity, and accuracy, used. The results showed 100% specifications, 60% sensitivity and 92% accuracy using 2 level thresholding and at 3 levels of thresholding results 100% in Specifications, Sensitivity and Accuracy.
THERMAL LOAD AND SIGNAL LEVEL OF THE ESS WIRE SCANNER

In the ESS linac [1] and in the transfer line to the target, a number of wire scanners will be installed. Due to the high power of the beam, the measurement can not be done during the production mode (2.86 ms, 62.5 mA, 14 Hz), the beam power has to reduced in order to preserve the wire integrity. Two modes will be dedicated to commissioning and specific beam studies where the insertion of interceptive devices are allowed, a slow tuning mode (i.e. 100 μs, up to 62.5 mA, 1 Hz) and a fast tuning mode (i.e. 10 μs, up to 62.5 mA, 14 Hz). The ESS linac consists in two section, one equipped with normal conduction cavities and the second with superconduncting cavities, the current layout of the ESS linac is shown in Fig. 1.
XQueC : Embedding Compression Into XML Databases

XML compression is becoming attractive these last years, as it reduces the disadvantage of “verbose” data representation in XML. While in many applications, such as data exchange and data archiving, compressing and decompressing one document at a time is sufficient, in applications where queries must be run over the compressed documents, decompression is an extra effort for the query processor, and its performance penalty may overcome data compression benefits. Smoothly balancing the interests of compression and query processing received significant attention in the domain of relational databases. However, these results do not directly translate to XML data. In this paper, we address the problem of seamlessly embedding compression in XML databases, without degrading query processing performance. This opens up several new interesting questions. The choice of compression granularity and compression algorithms has to be revisited, since the setting is not the same as for relational databases. Executing queries as much as possible in the compressed domain has to be rethought in the framework of XML query processing, due to the richer structure of XML data. Indeed, a proper storage design plays a crucial role here. Our XQueC system (standing for XQuery Processor and Compressor) covers a wide set of XQuery queries in the compressed domain, and relies on a workload-based cost model to perform the choices of the compression granules and of their corresponding compression algorithms. As a consequence, XQueC provides efficient query processing on compressed XML data. An extensive experimental assessment is also presented, showing the effectiveness of the cost model, the compression ratios and the query execution times.
Comparison between Teacher-centered and Student-centered Instructional Strategies Table 1 Pros and cons of teacher-centered instructional strategy Teacher-centered Instructional Strategy : Pros Teacher-centered Instructional Strategy

This paper attempts to establish a theory which ascertains the effectiveness of integrating both teacher-centered and student-centered instructional strategies especially in teaching physics. Integration in learning is a more intimate procedure in which ideas and individuals get together via suitable instructional strategies towards making the learning process effective rather than simply making them interactive. The proposed theory is being further analyzed and verified for support from major learning theories, both psychological and educational. Major types, levels, process/logic and execution of integration are also discussed in detail. In the meantime, a comprehensive comparison is made between advantages and disadvantages of both these strategies when implemented alone during physics instruction. © 2015 Guru Journal of Behavioral and Social Sciences
Classifying Tweets Based on Climate Change Stance Natural Language Processing

Climate change is real yet it has become a polarizing topic. We, along with many others, believe that the language used to describe the issue is to blame. We believe climate change needs a new lexicon, one that replaces phrases that unintentionally trigger resistance or confusion with phrases that elicit support and unity. In order to create this lexicon, we need the ability to first identify and classify text by whether or not it indicates belief in human-caused climate change.
Tilburg University In which European countries is homeownership more financially advantageous ?

Previous research consistently shows that homeowners accumulate more wealth compared with tenants. In this paper, we describe the size of this ‘tenure wealth gap’ for 10 European countries. Furthermore, we explain why the size of the tenure wealth gap differs between countries by including cross-level interactions between institutional variables and housing tenure in a series of country-fixed effects regression models. Cross-country differences arise as the costs of owning versus renting, as well as the profitability of homeownership versus other investments, differ along the lines of welfare policies and housing regime arrangements. We attempt to control for selection bias related to tenure status by using propensity score matching techniques, using data from the Household Finance and Consumption Survey (HFCS). Our findings suggest that the tenure wealth gap is largest in familialistic welfare states, in which marginalised tenants are unable to save, whereas homeownership is a family resource that provides an in-kind retirement income (‘passive’ asset-based welfare). We find smaller tenure wealth gaps in countries with a financialised promotion of homeownership, where housing wealth functions as a privatised welfare arrangement (‘active’ asset-based welfare). The smallest tenure wealth gaps occur in countries with more affordable rental housing, allowing tenants to accumulate savings.
Medical Images Classification and Diagnostics Using Fuzzy Neural Networks

The problem of medical images of cervix epithelium classification for express diagnostics is considered…The following states of cervix epithelium are to be recognized and classified: normal state - columnar epithelium; squamous epithelium (normal state); metaplasia-benign changes of cervix uterus epithelium; CIN1-displasia of light degree, CIN 2-displasia of middle degree, CIN 3-displasia of high degree- intra-epithelium cancer: For its solution the application of fuzzy neural network (FNN NEFClass M) is suggested. The application of FNN is grounded by its following properties: it may work with fuzzy and qualitative information; it has accelerated convergence as compared with crisp classification methods; it enables to attain better classification accuracy than conventional classifiers. The structure of FNN NEFClass and its model description are presented. Training algorithm stochastic gradient descent for membership functions of fuzzy sets is considered and implemented. Data set of medical images of cervix epithelium which was obtained by special device colposcope is described and some images are presented. The experimental investigations of FNN NEFClass application for medical images recognition on real data are carried out, the results are presented. The comparison with NN Back Propagation, RBF NN and cascade RBF NN was made and estimation of efficiency of the suggested approach was performed. The problem of reduction of features number in classification tasks using principal component method (PCM) method is considered and implemented.
Upgradable Software Product Customization by Code Query

The professional press on enterprise systems warns its readers: “Costly. ”, “A return on investment killer. ”, “Be prepared! ”. What can create some much affliction and turmoil in an otherwise very successful industry? Come and meet the dreadful upgrade problem. Enterprise systems are prime examples of a subset of software systems that we call software products: software that needs special support for customization. Through customization, external companies can modify part of the original product to better fit the needs of a niche market. Upon the release of a new version of the original software product, external companies must port their customizations to the latest version of the base software product, a process called an upgrade. Companies typically consider upgrades as mandatory, and hence must bear their high cost on a regular basis. The objectives of customizability and upgradability are conflicting – this constitutes the upgrade problem. We study the upgrade problem in the field of enterprise systems from a technical point of view, and consider the large spectrum of existing software engineering techniques for customization. We ground our work in an empirical study, that shows that customizations cannot be anticipated accurately. This result puts an important constraint on the solution and calls for an approach that complements the traditional customization techniques. We present the novel concept of code query by example, an approach that (a) requires little anticipation, (b) is simple and (c) may be adopted incrementally. Last but not least, this solution makes a subset of customizations amenable to upgrades. The implementation of our prototype is based on bytecode matching and bytecode instrumentation, in a managed . Net environment. We study the advantages, the disadvantages, and the limitations of our approach, both of the concept of code query by example, and of the implementation strategy. Finally, we show how our proposal can be used in other contexts where code query is needed, for example rule-based lightweight static analysis.