Miracle-dz/UniFAR-FTUs huggingface dataset:
Test Suite Augmentation via Integrating Black- and White-Box Testing Techniques

Test suite augmentation is an important technique for quality assurance of evolved software. In rapid evolution of software development, engineers usually combine black-box and white-box test suites to facilitate understanding and management. This paper presents a research proposal on test suite augmentation via integrating blackand white-box testing techniques. There are two directions of our test suite augmentation. One direction is from black-box to white-box, augmenting functional test suites to satisfy structure coverage criteria. The other direction is from white-box to black-box, augmenting coverage test suite to satisfy functional requirements. A series of evaluation methods is proposed to verify the effectiveness of our augmentation approaches.
Denoising controlled-source electromagnetic data using least-squares inversion

Strong noise is one of the toughest problems in the controlled-source electromagnetic (CSEM) method, which highly affects the quality of recorded data. The three main types of noise existing in CSEM data are periodic noise, Gaussian white noise, and nonperiodic noise, among which the nonperiodic noise is thought to be the most difficult to remove. We have developed a novel and effective method for removing such nonperiodic noise by formulating an inverse problem that is based on inverse discrete Fourier transform and several time windows in which only Gaussian white noise exists. These critical locations, which we call reconstruction locations, can be found by taking advantage of the continuous wavelet transform (CWT) and the temporal derivative of the scalogram generated by CWT. The coefficients of the nonperiodic noise are first estimated using the new least-squares method, and then they are subtracted from the coefficients of the raw data to produce denoised data. Together with the nonperiodic noise, we also remove Gaussian noise using the proposed method. We validate the methodology using real-world CSEM data.
Does Pay for Performance Reduce Earnings Management in France

The purpose of this paper is to examine the relationship between performance-based CEO compensation and earnings management by French managers. The French context is worthy to study given that firms are mostly held by families. CEO compensation in such firms is used as a tunneling mechanism to expropriate minority shareholders. Using a sample of French listed firms, we study how bonuses and CEO stock-option compensation affect earnings management practices. The findings show that managers are inclined to increase the variable part of their compensation by engaging in earnings management practices. Furthermore, we find that the amount of stock-options granted and exercised is positively associated to upward earnings management. These findings highlight manager’s opportunistic behaviour upon performance-based compensation which rather acts as a tunnelling mechanism than a corporate governance device. Keywords: Stock-option, Bonus, Earnings management
Smart Stamp Duty

Blockchain technology has enjoyed a massive adoption in cryptocurrencies such as Bitcoin. Following the success, many people have started to explore the possibility of implementing blockchain technology in different fields. We propose smart stamp duty, a system which can revolutionize the way stamp duty is managed and paid. The smart stamp duty offers significant improvements on the convenience when paying stamp duty. At the same time, the blockchain technology also provides the auditability of the transaction data. Smart stamp duty enables the expansion of the existing electronic stamp duty application to retail level as well as allows the taxpayers to pay the stamp duty of their electronic documents. Our proposed system also enables the taxpayers to print their electronic documents without losing the paid electronic-based stamps.
LOSSLESS REGIONS OF INTEREST AND ADAPTIVE WAVELET COMPRESSION OF BACKGROUND

A new compression scheme of images with lossless coding for Regions Of Interest (ROI) is developed. It is based on adaptive quantisation scheme in wavelet transform domain. The ROIs are encoded by the lossless JPEG-LS method. The background is encoded by embedded (SPIHT-based) wavelet method. The ROIs can have arbitrary shape. A mask indicating a set of wavelet coefficients corresponding to ROIs is obtained by adequatly defined control low pass filters. Zeroing wavelet coefficients indicating by the mask increases the number of zerotrees in SPIHT coding, and therefore one can expect higher coder’s bitrate. Numerical results to illustrate the performance of the proposed algorithm are provided.
Variance estimation for richness measures

Richness indices are distributional statistics used to measure the incomes, earnings, or wealth of the rich. This paper uses a linearization method to derive the sampling variances for recently introduced distributionally-sensitive richness measures when estimated from survey data. The results are derived for two cases: (1) when the richness line is known, and (2) when it has to be estimated from the sample. The proposed approach enables easy consideration of the effects of a complex sampling design. Monte Carlo results suggest that the proposed approach allows for reliable inference in case of “concave” richness indices, but that it is not satisfactory in case of “convex” richness measures.
Concepts of ability and their effect on approaches to learning and motivational orientation

There are a wide variety of experiences, psychological attributes, beliefs, prior knowledge and skills that students enter a learning situation with, that have been acknowledged (Biggs, 1999). They also enter with a range of expectations about their learning (Prosser and Trigwell, 1999). F or example, the value that students' place on the learning experience will have an impact on their motivational orientation during their learning (James, 1892). Their self-efficacy will also affect their learning behaviours, with low self-esteem and motivation leading to lack of effort and persistence (Bandura, 1986). The concept of ability (whether it is fixed or changeable), is a belief that has been shown to impact on a learner's behaviour through persistence, effort, and motivation (Dweck, 1999), but no work had been conducted about the effect concepts of ability have on students' approaches to learning. This study explores the relationships between HE students' concepts of ability and motivational orientation, examining whether these factors are related to the approach students take to their learning. It then goes on to explore whether concepts of ability can be influenced by the approach taken to teaching. The implications for educators are discussed in the light of the findings.
Antifungal activity of different size controlled stable silver nanoparticles biosynthesized by the endophytic fungus Aspergillus terreus

Silver nanoparticles (AgNPs) synthesized by using the aqueous extract of the endophytic fungus Aspergillus terreus F37 (KX024595) as reducing agent is reported here. The reaction medium employed in the synthesis process was optimized under a narrow range of pH and temperature to attain better yield, controlled size, and more stable of AgNPs. Further, the microbially synthesized AgNPs were studied through UV-vis spectroscopy, transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) spectroscopy analyses. The obtained results indicated the formation of high crystalline spherical AgNPs with an average diameter of 45.2±0.5 nm at room temperature (22 oC). Quantitative analyses indicated that reduction of the Ag+ precursor was promoted at elevated pH due to increased activity of biomolecules in the fungal extract. As a result, the size of the AgNPs decreased with increased pH of the reactions. The optimum conditions for maximum production of small control sized AgNPs (12± 0.5 nm) were pH (10) and temperature (100 oC). The outcomes of the antifungal activity of different controlled sized AgNPs showed their efficiently to inhibit the mycelial growth of the pathogenic fungus Alternaria solani, the causal agent of tomato early blight disease and reduced their viability in a pH and temperature dependent manner. These findings revealed that the fine tuning of the reaction synthesis parameters, will increase the chance to obtain desired well shaped and small sized AgNPs with potent antifungal activities, may have important applications as new bio-fungicides in controlling various plant diseases caused by fungi.
Infrared fixed points in the minimal momentum subtraction scheme

We analyze the behavior of several renormalization group functions at infrared fixed points for $SU(N)$ gauge theories with fermions in the fundamental and two-indexed representations. This includes the beta function of the gauge coupling, the anomalous dimension of the gauge parameter and the anomalous dimension of the mass. The scheme in which the analysis is performed is the minimal momentum subtraction scheme through third loop order. Due to the fact that scheme dependence is inevitable once the perturbation theory is truncated we compare to previous identical studies done in the minimal subtraction scheme and the modified regularization invariant scheme. We find only mild to moderate scheme dependence.
Performance and Volatility of Oil and Gas Stocks: A Comparative Study on Selected O&G Companies

This paper examines the effect of oil prices movements on the stock price of Oil and Gas companies in three different markets (US, India ad UK). Using daily data; the dynamic interaction between oil prices and stock prices is investigated in the presence of economic variables like interest rates and industrial productions. The results suggest that there exists significant short run and long run relationship between oil price and the oil stocks including the effect of the other variables such as interest rate and the stock index. The oil price volatility transmission has a persistent effect on the volatility of the stocks of the oil companies in all the countries that were studied.
A New Linear Classifier Based on Combining Supervised and Unsupervised Techniques

The aim of the research reported in the paper is to obtain an alternative approach in using Support Vector Machine (SVM) in case of non- linearly separable data based on using the k-means algorithm instead of the standard kernel based approach. The SVM is a relatively new concept in machine learning and it was introduced by Vapnik in 1995. In designing a classifier, two main problems have to be solved, on one hand the option concerning a suitable structure and on the other hand the selection of an algorithm for parameter estimation. The algorithm for parameter estimation performs the optimization of a conven- able selected cost function with respect to the empirical risk which is directly related to the representativeness of the available learning sequence. The choice of the structure is made such that to maximize the generalization capacity, that is to assure good performance in classifying new data coming from the same classes. In solving these problems one has to establish a balance between the accuracy in encoding the learning sequence and the generalization capacities because usually the over-fitting prevents the minimization of the empirical risk.
SCALER MODE OF THE AUGER OBSERVATORY AND SUNSPOTS

Recent data from the Auger Observatory on low-energy secondary cosmic ray particles are analyzed to study temporal correlations together with data on the daily sunspot numbers and neutron monitor data. Standard spectral analysis demonstrates that the available data show 1/fβ fluctuations with β ≈ 1 in the low-frequency range. All data behave like Brownian fluctuations in the high-frequency range. The existence of long-range correlations in the data was confirmed by detrended fluctuation analysis. The real data confirmed the correlation between the scaling exponent of the detrended analysis and the exponent of the spectral analysis.
A Simulation Study of Effects of Collinearity on Forecasting of Bivariate Time Series Data

Vector Autoregression (VAR) and Bayesian VAR (BVAR) were first introduced in the early eighties of the last century and have since proven to be practical and effective economic forecasting methodologies. This paper tends to study the forecasting performances of the unrestricted VAR model and four versions of the Sims-Zha BVAR models in the presence of collinearity using Monte Carlo simulation technique. We considered ten (10) collinearity levels (0.8, -0.8, 0.85, -0.85, 0.9, -0.9, 0.95, -0.95, 0.99 and -0.99), the results from our simulation study revealed that the forecasting performances of the models vary as the collinearity levels varied. Furthermore, the values of the criteria increases as the time series length and the collinearity levels increased. We therefore recommend that if VAR modelers know that collinearity is acting upon the model, one can choose the forecasting model that is preferred for the criteria and the time series length selected.
Web page multi-label classification for filtering content from the web

In this paper, we describe a simple approach to filter unwanted web pages, according to their content. The result of this work is a demo of an application that is usable in realtime filtering and in non-real-time indexing of any given web pages. We describe a proposed technique step by step, while discussing possible alternative ways for each part. In the end we discuss the overall quality and proposed next steps that could lead to a fully usable business application.
Gap detection thresholds in young musicians with normal hearing and with veryhigh frequency sloping hearing loss in 1/3-octave white noise centered at4kHz

Gap detection thresholds in 1/3-octave white noise centred at 4kHz were determined in young musicians with normal hearing preserved and with very high frequency sloping hearing loss. The thresholds were determined in the control group of 20 with normal hearing, i.e. flat audiogram up to 16kHz, and in the experimental group of 15 with high frequency sloping hearing loss exceeding 30dB at 12kHz or at 16kHz, the so called peculiar hearing loss. A group of 7 older subjects exhibiting also peculiar hearing loss was included for comparison. The results show gap detection thresholds in the normal hearing subjects ranging from 1ms to 6ms, which are in good agreement with the earlier data from the present authors. However, few examples of higher gap detection thresholds (11, 26ms) in normal hearing were also observed. For the experimental group with high frequency sloping hearing loss at 12kHz or at 16kHz, the gap detection thresholds found were on the average significantly higher ranging from 6ms to 26ms. In the group of older subjects gap detection thresholds were on the average still significantly higher.
Geoscientific Model Development Aerosol microphysics modules in the framework of the ECHAM 5 climate model – intercomparison under stratospheric conditions

In this manuscript, we present an intercomparison of three different aerosol microphysics modules that are implemented in the climate model ECHAM5. The comparison was done between the modal aerosol microphysics module M7, which is currently the default aerosol microphysical core in ECHAM5, and two sectional aerosol microphysics modules SALSA, and SAM2. The detailed aerosol microphysical model MAIA was used as a reference to evaluate the results of the aerosol microphysics modules with respect to sulphate aerosol. The ability of the modules to describe the development of the aerosol size distribution was tested in a zero dimensional framework. We evaluated the strengths and weaknesses of different approaches under different types of stratospheric conditions. Also, we present an improved method for the time integration in M7 and study how the setup of the modal aerosol modules affects the evolution of the aerosol size distribution. Intercomparison simulations were carried out with varying SO2 concentrations from background conditions to extreme values arising from stratospheric injections by large volcanic eruptions. Under background conditions, all microphysics modules were in good agreement describing the shape of the aerosol size distribution, but the scatter between the model results increased with increasing SO 2 concentrations. In particular in the volcanic case the setups of the aerosol modules have to be adapted in order to dependably capture the evoluCorrespondence to: H. Kokkola (harri.kokkola@fmi.fi) tion of the aerosol size distribution, and to perform in global model simulations. In summary, this intercomparison serves as a review of the different aerosol microphysics modules which are currently available for the climate model ECHAM5.
Towards Learning of a Joint Geometry-Structure Manifold for Shape Exploration

We present a first attempt at producing a continuous generative model of 3D objects from a joint representation that incorporates the discrete structural variability as well as the continuous geometric variability that are often present in collections of man-made shapes. Starting from a set of compatibly segmented shapes, our main contribution consists in demonstrating the construction of the joint representation. Then, by using Gaussian Process learning to produce a predictive manifold from the joint representation, we investigate its capabilities and limitations for reproducing and synthesizing new shapes.
Learning Force Fields from Stochastic Trajectories

When monitoring the dynamics of stochastic systems, such as interacting particles agitated by thermal noise, disentangling deterministic forces from Brownian motion is challenging. Indeed, we show that there is an information-theoretic bound, the capacity of the system when viewed as a communication channel, that limits the rate at which information about the force field can be extracted from a Brownian trajectory. This capacity provides an upper bound to the system's entropy production rate, and quantifies the rate at which the trajectory becomes distinguishable from pure Brownian motion. We propose a practical and principled method, Stochastic Force Inference, that uses this information to approximate force fields and spatially variable diffusion coefficients. It is data efficient, including in high dimensions, robust to experimental noise, and provides a self-consistent estimate of the inference error. In addition to forces, this technique readily permits the evaluation of out-of-equilibrium currents and the corresponding entropy production with a limited amount of data.
Disambiguate: An open-source application for disambiguating two species in next generation sequencing data from grafted samples

Grafting of cell lines and primary tumours is a crucial step in the drug development process between cell line studies and clinical trials. Disambiguate is a program for computationally separating the sequencing reads of two species derived from grafted samples. Disambiguate operates on DNA or RNA-seq alignments to the two species and separates the components at very high sensitivity and specificity as illustrated in artificially mixed human-mouse samples. This allows for maximum recovery of data from target tumours for more accurate variant calling and gene expression quantification. Given that no general use open source algorithm accessible to the bioinformatics community exists for the purposes of separating the two species data, the proposed Disambiguate tool presents a novel approach and improvement to performing sequence analysis of grafted samples. Both Python and C++ implementations are available and they are integrated into several open and closed source pipelines. Disambiguate is open source and is freely available at https://github.com/AstraZeneca-NGS/disambiguate.
Kalman Filters for Reference Current Generation in Shunt Active Power Filter (APF)

Shunt active power filter (APF) method have been used by many researchers as a solu - tion in reducing the harmonics creating by the non-liner loads. Therefore, this research is targeted to design and implement a three-phase shunt APF employing Kalman filter esti - mator. Conventionally, low-pass filter (LPF) is used to filter out the unwanted DC compo nent of the non-linear load to produce the sinusoidal waveform called the reference current. However, when applying LPF it contributes with the phase shift and high transient at the supply current. Therefore, to reduce these problems, the digital Kalman filter estimator is used to replace the LPF for generating the reference current. Details on the investigation between conventional and proposed methods under simulation based on Matlab Simulink platform and experimental that are made for two types of load, namely, three-phase recti fier with RC-load and three-phase induction motor, are presented. The performance criteria of the shunt APF are determined by the supply current waveform, total harmonic distortion (THD), harmonic spectrum and power quality measurements, which were also obtained by simulation and experimental. In conclusion, by employing Kalman filter estimator for generating the reference current, it reduces the time delay and high transient current at the power supply and, thus, improved the overall THD from 0.1 to 0.42% compared to the LPF.
PLANAR MULTIBAND BANDPASS FILTER WITH MULTIMODE STEPPED-IMPEDANCE RESONATORS

Planar multiband bandpass fllters are implemented based on the versatile multimode stepped-impedance resonators (SIRs). The resonant spectrum of a SIR can be calculated as functions of the length ratios for various impedance ratios of the high- and low-impedance sections. Thus, by properly selecting the geometric parameters and designing the input/output coupling structure, the SIRs are feasible to realize multiband multimode fllters. Using a single SIR, a dual- mode dual-band, a dual-mode triple-band or a hybrid dual-/triple- mode dual-band bandpass fllter can be realized. Emphasis is also placed on designing specifled ratios of center frequencies and fractional bandwidths of the passbands. To extend the design ∞exibility, extra shunt open stubs are used to adjust the ratio of the passband frequencies. In addition, sharpness of the transition bands is improved by designing the input/output stages. Simulation results are validated by the measured responses of experimental circuits.
Managing E-Governance: A Framework for Analysis and Planning

This chapter presents and discusses the application of an analytical framework for managing digital innovation initiatives in the public sector. The chapter positions e-Government and digital innovation initiatives in the public sector within the broader framework of e-Governance (i.e. governance aided by ICT). It first introduces the conceptual framework on which the discussion is based, and then proposes a higher order conceptualization of the relationship between e-Governance and its underpinning value drivers of performance, openness and inclusion. In particular, the analysis focuses on the role that these value drivers plays in the enactment of digital governance initiatives such as the ones related to open government and smart cities. To this end, the chapter discusses its application to initiatives carried out in Barcelona and Tallinn. A typology of e-Governance ‘attitudes’ is then identified to provide evidence of further specific interventions required for an appropriate management of similar initiatives in other countries. The chapter concludes by highlighting the policy implications for administrative reform and offering practical recommendations for implementing digital governance initiatives. DOI: 10.4018/978-1-4666-4245-4.ch010