Miracle-dz/UniFAR-FTUs huggingface dataset:
MOSA A replicated empirical study on automated test case generation

Replication is a fundamental pillar in the construction of scientific knowledge. Test data generation for procedural programs can be tackled using a single-target or a many-objective approach. The proponents of LIPS, a novel single-target test generator, conducted a preliminary empirical study to compare their approach with MOSA, an alternative many-objective test generator. However, their empirical investigation suffers from several external and internal validity threats, does not consider complex programs with many branches and does not include any qualitative analysis to interpret the results. In this paper, we report the results of a replication of the original study designed to address its major limitations and threats to validity. The new findings draw a completely different picture on the pros and cons of single-target vs many-objective approaches to test case generation.
Risk of failure to meet regional and national goals relevant to agricultural development and poverty reduction in South Africa

This study examined whether the Eastern Cape province’s district municipalities are on course to achieve local and regional goals of improving agricultural production and reducing poverty. Results show significant strides towards locally set targets and high uncertainty in meeting the regional targets. Out of seven district municipalities, five municipalities are still about 5% points below the designated 10% of the annual budget to agriculture, and only three district municipalities scored more that the set 6% agricultural growth rate. All the seven district municipalities are not in a position to meet the target of 7% economic growth rate. Econometric simulations using the Hodrick-Prescott filter on data dating from 1995 to 2010 shows that all the seven district municipalities are off-track the set millennium development Goal 1 target with one even retrogressing from that goal. Failure to significantly reduce poverty is largely attributed to the province’s failure to boost agricultural production which is an outcome of low and inefficient public expenditure management, inconsistent and misaligned policies. Regional policies should be built on local policies to bring in the desired impact on local development. This study made a strong case for frequent independent evaluation of set goals.
Bringing Flexibility to the Specification and Coordination of Temporal Dependencies among Multimedia Components

We introduce a methodology for the high-level specification and decentralized coordination of temporal interdependencies among multimedia document objects. Such methodology encompasses a three-step process comprising (i) the design of multimedia presentation scenes by means of a fuzzy descriptive plan; (ii) the parsing of such layout to classify the multimedia entities that compose the scenes and to check the consistency of temporal relationships among them; and (iii) the generation of event-driven time and action managers as distributed mechanisms for the orchestration of the elements presentation. This approach centers around a novel multimedia synchronization model based on fuzzy sets and software components concepts.
A process-aware hot/cold identification scheme for flash memory storage systems

NAND flash memory has attractive features compared to hard disk drives such as small size, no mechanical noise and shock resistance. But it also has some drawbacks such as no support for in-place updates and limited program/erase cycles, which trigger the development of sophisticated buffer management algorithms in order to reduce write and/or erase operations to flash memory. The significant gap in update frequency between hot and cold data motivates us to separate hot and cold data on different flash blocks to avoid unnecessary program/erase cycles. Many buffer management algorithms determine a request to be hot or cold based on its requested data size. However, the data size could become a wrong indicator of update frequency in many applications. In this paper, we propose a new hot/cold identification scheme in order to increase identification accuracy and, thus, to enhance storage performance and durability by reducing program/erase cycles. The proposed technique uses the process identification used in many operating systems as a hot/cold indicator. The experimental results show that the proposed scheme contributes to high performance and durability as compared to previously proposed identification schemes.
Reduction of RF penetration effects in high field imaging

A method of correcting for the RF inhomogeneity in the body by dielectric loading of the coil‐to‐shield space in an RF resonator (coil and shield assembly) is described. The presence of the RF coil and RF shield have significant effects on RF homogeneity. Based on theoretical calculations, a method for adjusting the RF homogeneity by manipulating the axial propagation constant, kz, is proposed. This is accomplished by loading the coil‐to‐shield space with dielectric material of suitable relative permittivity so as to increase kz and decrease the radial propagation constant, kp. In this manner, the radial wavelength (λp, = 2π/kp) can be increased relative to the body dimensions, and the field amplitude variations in the axial plane minimized. Theoretical calculations indicate that a value of between 30 and 40 for the relative permittivity of the dielectric material in the coil‐to‐shield space would reduce the RF field inhomogeneity from ±15% to about ±3% over a central 30‐cm‐diameter region of a homogeneous 40‐cm‐diameter body at both 64 and 170 MHz. The theoretical model was verified in laboratory measurements of the |B1|, field generated in a test coil at 170 MHz which was scaled to correspond to a body at 64 MHz. However, the improved RF field homogeneity would be accompanied by increased RF power requirements and reduced coil sensitivity. © 1992 Academic Press, Inc.
Need to Redefine “ Value ” and Case for a New “ Software Valuation ” Technique : An Analytical Study

In this era of progress and innovation, every product and service has certain value attached to it. Similar is the case with software products and services. These also assign some value to the stakeholders for whom this product or service has some meaning. Software Engineering (SE) deals with the development of quality software product that fulfills stakeholder’s requirements. There are many valuation techniques which are being used to establish value of software products and services. However, irony of the situation is that current practices in software engineering are value neutral in essence. Value Based Software Engineering (VBSE) takes into consideration the value assigned to the software product and to the stakeholders who have interest in that software. However, VBSE as a theory has not redefined the term “Value” for the discipline of software engineering. In this paper, we present an analysis of current definitions of value as well as valuation techniques and subsequently, we have presented a case for the need to redefine the term value and modification of current valuation techniques for software engineering.
Paternalistic Leadership : A Preliminary Study on Maritime Students

The Manila amendments to the STCW Convention and Code were adopted on 25 June 2010, marking a major revision of the STCW Convention and Code. With the introduction of the 2010 amendments, leadership training becomes a compulsory and essential part of Maritime Education and Training (MET). Appropriate leadership style will not only improve job satisfaction, well being, and motivation of seafarers but also will improve safety onboard of vessels and foster safety culture. In this study, a preliminary study has been performed in order to determine the perceptions of maritime students about paternalistic leadership determinants. A survey tool developed by Aycan (2006) was applied to deck and engine cadets of Dokuz Eylul University, Maritime Faculty. This study will contribute to the literature by analyzing the impact of paternalistic leadership as an effective management tool in maritime domain.
Craving research: future directions.

Many prospective clinical studies have concluded that craving does not reliably predict relapse and that the concept is of little or no clinical utility. Contrary to earlier more simplistic clinical models of addiction, more recent models do not require that craving be present for relapse to occur. New approaches to study human craving may enhance its predictive validity and yield more knowledge of its nature, course, behavioural sequelae and regulatory function in alcohol/drug consumption. These approaches include empirical research that focuses on: (1) the elucidation of the domains of craving (i.e. subjective experience, physiological responses, behavioural sequelae and their inter-relationships); (2) the temporal dynamics of craving (i.e. its course over minutes or days, as well as its natural history over the course of a drinking career); (3) the factors that may mediate/moderate/determine the development and resolution of craving; (4) studies of the predictive validity of craving measures; and (5) the development of valid methods of measuring the different domains of craving. The conclusions are that future craving research should: (1) incorporate more sophisticated general theories of behaviour (conditioning, cognitive social learning, neurobiological, and genetic); (2) apply more sophisticated and standardized measurement methods and experimental paradigms, including studies in which alcohol is made available to human subjects; and (3) effective development of new pharmacological and behavioural therapies for relapse prevention depend on greater understanding of the nature and measurement of craving.
A Bayesian Power Analysis Procedure Considering Uncertainty in Effect Size Estimates from a Meta-analysis

ABSTRACT In conventional frequentist power analysis, one often uses an effect size estimate, treats it as if it were the true value, and ignores uncertainty in the effect size estimate for the analysis. The resulting sample sizes can vary dramatically depending on the chosen effect size value. To resolve the problem, we propose a hybrid Bayesian power analysis procedure that models uncertainty in the effect size estimates from a meta-analysis. We use observed effect sizes and prior distributions to obtain the posterior distribution of the effect size and model parameters. Then, we simulate effect sizes from the obtained posterior distribution. For each simulated effect size, we obtain a power value. With an estimated power distribution for a given sample size, we can estimate the probability of reaching a power level or higher and the expected power. With a range of planned sample sizes, we can generate a power assurance curve. Both the conventional frequentist and our Bayesian procedures were applied to conduct prospective power analyses for two meta-analysis examples (testing standardized mean differences in example 1 and Pearson's correlations in example 2). The advantages of our proposed procedure are demonstrated and discussed.
Learning Robust 3D Face Reconstruction and Discriminative Identity Representation

3D face reconstruction from a single 2D image is a very important topic in computer vision. However, the current reconstruction methods are usually non-sensitive to face identities and over-sensitive to facial poses, which may result in similar 3D geometries for faces of different identities, or obtain different shapes for the same identity with different poses. When such methods are applied practically, their 3D estimates are either changeable for different photos of the same subject or over-regularized and generic to distinguish face identities. In this paper, we propose a robust solution to solve this problem by carefully designing a novel “Siamese” Convolutional Neural Network (SCNN). Specifically, regarding the 3D Morphable face Model (3DMM) parameters of the same individual as the same class, we employ the contrastive loss to enlarge the interclass distance and meanwhile reduce the intra-class distance for the output 3DMM parameters. We also propose an identity loss to preserve the identity information for the same individual in the feature space. Training with these two losses, our SCNN could learn representations that are more discriminative for face identity and generalizable for pose variants. Experiments on the challenging database 300W-LP and AFLW2000-3D have shown the effectiveness of our method by comparing with state of the arts.
Pre-processing Protocol for Nonlinear Regression of Uneven Spaced-Data

Regression of experimental or simulated data has important implications in sensitivity studies, uncertainty analysis, and prediction accuracy. The fitness of a model is highly dependent on the number of data points and the locations of the chosen points on the curve. The objective of the research is to find the best scheme for a nonlinear regression model using a fraction of total data points without losing any features or trends in the data. Six different schemes are developed by setting criteria such as equal spacing along axes, equal distance between two consecutive points, constraint in the angle of curvature, etc. A workflow is provided to summarize the entire protocol of data preprocessing, training and testing nonlinear regression models with various schemes using a simulated temperature profile from an enhanced geothermal system. It is shown that only 5% of data points are sufficient to represent the entire curve using a regression model with a proper scheme.
Spectra of Random Regular Hypergraphs

In this paper, we study the spectra of regular hypergraphs following the definitions from Feng and Li (1996). Our main result is an analog of Alon's conjecture for the spectral gap of the random regular hypergraphs. We then relate the second eigenvalues to both its expansion property and the mixing rate of the non-backtracking random walk on regular hypergraphs. We also prove the spectral gap for the non-backtracking operator of a random regular hypergraph introduced in Angelini et al. (2015). Finally, we obtain the convergence of the empirical spectral distribution (ESD) for random regular hypergraphs in different regimes. Under certain conditions, we can show a local law for the ESD.
All at Once: Transient Pulsations, Spin-down, and a Glitch from the Pulsating Ultraluminous X-Ray Source M82 X-2

The first pulsating ultraluminous X-ray source (PULX) to be identified is M82 X-2. After the discovery in 2014, NuSTAR observed the M82 field 15 times throughout 2015 and 2016. In this paper, we report the results of pulsation searches in all of these data sets and find only one new detection. This new detection allows us to refine the orbital period of the source and measure an average spin-down rate between 2014 and 2016 of ∼−6 × 10−11 Hz s−1, which is in contrast to the strong spin-up seen during the 2014 observations, representing the first detection of spin-down in a PULX system. Thanks to the improved orbital solution allowed by this new detection, we are also able to detect pulsations in additional segments of the original 2014 data set. We find a glitch superimposed on the very strong and variable spin-up already reported—the first positive glitch identified in a PULX system. We discuss the new findings in the context of current leading models for PULXs.
The Strength of Rural Nursing: Implications for Undergraduate Nursing Education

Nursing in rural communities offers opportunities for independent nursing practice and community participation. However, recruitment of nurses to rural settings can be difficult. In response to this challenge and the rising demand within nursing education for community clinical placements, intensive, short-term, rural community clinical placements are being developed by urban universities. As yet, little research has examined the use of these placements for undergraduate nursing education. The purpose of this qualitative research study was to examine the experiences of students, registered nurse mentors, and clinical instructors in rural health clinical placements, as part of a larger study examining alternative clinical placements. Through use of the interpretive descriptive method, the perspectives of participants were elicited from focus groups and interviews. The paradox of nursing student placements in rural health is that limitations of the rural site became the impetus for nursing student learning and partnership. An implication is that service learning partnerships be pursued for mutual benefit of students, communities, and rural nurses.
Adaptive Sensor Placement for Continuous Spaces

We consider the problem of adaptively placing sensors along an interval to detect stochasticallygenerated events. We present a new formulation of the problem as a continuum-armed bandit problem with feedback in the form of partial observations of realisations of an inhomogeneous Poisson process. We design a solution method by combining Thompson sampling with nonparametric inference via increasingly granular Bayesian histograms and derive an O˜(T2/3) bound on the Bayesian regret in T rounds. This is coupled with the design of an efficent optimisation approach to select actions in polynomial time. In simulations we demonstrate our approach to have substantially lower and less variable regret than competitor algorithms.
Mapping the d1 and d2 dwarfing genes and the purple foliage color locus P in pearl millet.

The d(1) and d(2) dwarfing genes and the P purple foliage color gene were placed on the restriction fragment length polymorphism (RFLP)-based molecular marker linkage map of pearl millet [Pennisetum glaucum (L.) R. Br. ] using a mapping population based on a cross of inbred lines IP 18293 (D(1)/D(1), d(2)/d(2), P/P) and Tift 238D1 (d(1)/d(1) D(2)/D(2) p/p). A skeleton genetic linkage map of 562 cM (Haldane function) was constructed using 33 RFLP markers and these three morphological markers. The D(1)/d(1) plant height locus mapped to pearl millet linkage group 1, while the D(2)/d(2) plant height locus and the P/p foliage color locus mapped to pearl millet linkage group 4. Loose genetic linkage was observed between the D(2)/d(2) and P/p loci, with 42% repulsion-phase recombination corresponding to 92 cM (Haldane). This loose linkage of morphological marker loci detected on pearl millet LG4 can likely find use in applied pearl millet breeding programs, as host plant resistances to both downy mildew and rust have previously been identified in this genomic region. Such exploitation of these morphological markers in an applied disease resistance breeding program would require development of appropriate genetic stocks, but the relatively loose genetic linkage between d(2) and P suggests that this should not be difficult.
The Search direction Correction makes first-order methods faster

The so-called fast inertial relaxation engine is a first-order method for unconstrained smooth optimization problems. It updates the search direction by a linear combination of the past search direction, the current gradient and the normalized gradient direction. We explore more general combination rules and call this generalized technique as the search direction correction (SDC). SDC is extended to composite and stochastic optimization problems as well. Deriving from a second-order ODE, we propose a fast inertial search direction correction (FISC) algorithm as an example of methods with SDC. We prove the $\mathcal{O}(k^{-2})$ convergence rate of FISC for convex optimization problems. Numerical results on sparse optimization, logistic regression as well as deep learning demonstrate that our proposed methods are quite competitive to other state-of-the-art first-order algorithms.
FROM REVOLVING DOORS TO REGULATORY CAPTURE? EVIDENCE FROM PATENT EXAMINERS∗

Many regulatory agency employees are hired by the firms they regulate, creating a “revolving door” between government and the private sector. We study these transitions using detailed data from the US Patent and Trademark Office. We find that patent examiners grant significantly more patents to the firms that later hire them and that much of this leniency extends to prospective employers. These effects are strongest in years when firms are actively hiring, and these relationships hold for the intensive margin of intellectual property protection. Ultimately, this leads the agency to issue lower quality patents, which we measure in citations. Together with other supporting evidence, we argue these results are suggestive of regulatory capture. JEL: K23, D72, O34.
An interdisciplinary survey of network similarity methods

Comparative graph and network analysis play an important role in both systems biology and pattern recognition, but existing surveys on the topic have historically ignored or underserved one or the other of these fields. We present an integrative introduction to the key objectives and methods of graph and network comparison in each field, with the intent of remaining accessible to relative novices in order to mitigate the barrier to interdisciplinary idea crossover. To guide our investigation, and to quantitatively justify our assertions about what the key objectives and methods of each field are, we have constructed a citation network containing 5,793 vertices from the full reference lists of over two hundred relevant papers, which we collected by searching Google Scholar for ten different network comparison-related search terms. We investigate its basic statistics and community structure, and frame our presentation around the papers found to have high importance according to five different standard centrality measures.
Contextualized Spatial–Temporal Network for Taxi Origin-Destination Demand Prediction

Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of the destination passengers. We believe it is suboptimal to preallocate the taxi into each region-based solely on the taxi origin demand. In this paper, we present a challenging and worth-exploring task, called taxi origin-destination demand prediction, which aims at predicting the taxi demand between all-region pairs in a future time interval. Its main challenges come from how to effectively capture the diverse contextual information to learn the demand patterns. We address this problem with a novel contextualized spatial–temporal network (CSTN), which consists of three components for the modeling of local spatial context (LSC), temporal evolution context (TEC), and global correlation context (GCC), respectively. First, an LSC module utilizes two convolution neural networks to learn the local spatial dependencies of taxi, demand respectively, from the origin view and the destination view. Second, a TEC module incorporates the local spatial features of taxi demand and the meteorological information to a Convolutional Long Short-term Memory Network (ConvLSTM) for the analysis of taxi demand evolution. Finally, a GCC module is applied to model the correlation between all regions by computing a global correlation feature as a weighted sum of all regional features, with the weights being calculated as the similarity between the corresponding region pairs. The extensive experiments and evaluations on a large-scale dataset well demonstrate the superiority of our CSTN over other compared methods for the taxi origin-destination demand prediction.