Miracle-dz/UniFAR-FTUs huggingface dataset:
Test-driven fault navigation for debugging reproducible failures

Debugging failing test cases, particularly the search for failure causes, is often a laborious and timeconsuming activity. With the help of spectrum-based fault localization developers are able to reduce the potentially large search space by detecting anomalies in tested program entities. However, such anomalies do not necessarily indicate defects and so developers still have to analyze numerous candidates one by one until they find the failure cause. This procedure is inefficient since it does not take into account how suspicious entities relate to each other, whether another developer is better qualified for debugging this failure, or how erroneous behavior comes to be. We present test-driven fault navigation as an interconnected debugging guide that integrates spectrumbased anomalies and failure causes. By analyzing failure-reproducing test cases, we reveal suspicious system parts, developers most qualified for addressing localized faults, and erroneous behavior in the execution history. The Paths tool suite realizes our approach: PathMap supports a breadth first search for narrowing down failure causes and recommends developers for help; PathFinder is a lightweight back-in-time debugger that classifies failing test behavior for easily following infection chains back to defects. The evaluation of our approach illustrates the improvements for debugging test cases, the high accuracy of recommended developers, and the fast response times of our corresponding tool suite.
Identification of Those at Greatest Risk of Harm Among Cognitively Impaired People Who Live Alone

Our purpose was to determine whether we could predict, from among seniors with cognitive impairment who lived alone, those at highest risk of experiencing harm or needing emergency services. Specifically, our focus was on the problems that occurred as a result of negligence due to cognitive impairment. We assessed 139 people, 65 years of age and older, who lived alone in a large urban centre and who showed cognitive impairment on a screening test. On average, participants were 83-year-olds, had 11 years of education, and a Mini-Mental State Score of 23. A total of 39% met diagnostic criteria for dementia. All underwent assessment of their neuropsychological abilities, behavioural characteristics and social supports. A study informant was identified for each participant who, together with the family physician, provided detailed information regarding the participants’ use of emergency medical, fire or police services, their experience of harm or loss of property over the last 12 months. We determined that negligence due to cognitive impairment caused harm or the need for emergency services based on informants’ and primary care physicians’ reports and by three independent raters. Logistic regression analyses indicated that the best predictors of harm or use of emergency services were male sex, self-care deficits, and poor performance on the Trail Making Test, Part B. The sensitivity of this three-variable regression model was 81% and the specificity was 66%. The likelihood ratio of both the positive and negative tests for harm represented a small but meaningful change in pretest to posttest probability of harm. These results indicate that we can estimate an individual’s probability of harm based on performance on these three variables. The next step is to confirm the findings with prospectively collected data.
The Financialization of Accumulation

In 1997, in his last published article, Paul Sweezy referred to "the financialization of the capital accumulation process" as one of the three main economic tendencies at the turn of the century (the other two were the growth of monopoly power and stagnation). Those familiar with economic theory will realize that the phrase was meant to be paradoxical. All traditions of economics, to varying degrees, have sought to separate out analytically the role of finance from the "real economy. " Accumulation is conceived as real capital formation, which increases overall economic output, as opposed to the appreciation of financial assets, which increases wealth claims but not output. In highlighting the financialization of accumulation, Sweezy was therefore pointing to what can be regarded as "the enigma of capital" in our timeThis article can also be found at the Monthly Review website, where most recent articles are published in full.Click here to purchase a PDF version of this article at the Monthly Review website.
The 'Simple View' of Reading: Some evidence and possible implications

This article explores components of reading accuracy and comprehension in 14 teenagers. The study contrasted the explanatory power of decoding and listening comprehension subskills (the 'simple view of reading'; Gough & Tunmer, 1990), and 'verbal cognitive ability' (a more traditional psychometric model). Research also investigated teenagers' literacy self-percepts. Listening comprehension was the best predictor of reading comprehension, although reading accuracy was an additional predictor. Decoding skills best predicted reading accuracy. Reading self-percepts correlated with decoding but not comprehension skills. Possible uses of 'the simple view of reading' for conceptualisations of literacy problems, interventions and effective professional roles are considered.
Inhomogeneity correction of magnetic resonance images by minimization of intensity overlapping

This work presents a new algorithm (NIC; nonuniform intensity correction) for the correction of intensity inhomogeneities in magnetic resonance images. The algorithm has been validated by means of realistic phantom images and a set of 24 real images. Evaluation using previously proposed phantom images for inhomogeneity correction algorithms allowed us to obtain results fully comparable to the previous literature on the topic. This new algorithm was also compared, using a real image dataset, to other widely used methods which are freely available in the Internet (N3, SPM'99 and SPM2). Standard quality criteria have been used for determining the goodness of the different methods. The new algorithm showed better results removing the intensity inhomogeneities and did not produce degradation when used on images free from this artifact.
Discovery of Candidate Serum Proteomic and Metabolomic Biomarkers in Ankylosing Spondylitis*

Ankylosing Spondylitis (AS) is a common inflammatory rheumatic disease with a predilection for the axial skeleton, affecting 0.2% of the population. Current diagnostic criteria rely on a composite of clinical and radiological changes, with a mean time to diagnosis of 5 to 10 years. In this study we employed nano liquid-chromatography mass spectrometry analysis to detect and quantify proteins and small compounds including endogenous peptides and metabolites in serum from 18 AS patients and nine healthy individuals. We identified a total of 316 proteins in serum, of which 22 showed significant up- or down-regulation (p < 0.05) in AS patients. Receiver operating characteristic analysis of combined levels of serum amyloid P component and inter-α-trypsin inhibitor heavy chain 1 revealed high diagnostic value for Ankylosing Spondylitis (area under the curve = 0.98). We also depleted individual sera of proteins to analyze endogenous peptides and metabolic compounds. We detected more than 7000 molecular features in patients and healthy individuals. Quantitative MS analysis revealed compound profiles that correlate with the clinical assessment of disease activity. One molecular feature identified as a Vitamin D3 metabolite—(23S,25R)-25-hydroxyvitamin D3 26,23-peroxylactone—was down-regulated in AS. The ratio of this vitamin D metabolite versus vitamin D binding protein serum levels was also altered in AS as compared with controls. These changes may contribute to pathological skeletal changes in AS. Our study is the first example of an integration of proteomic and metabolomic techniques to find new biomarker candidates for the diagnosis of Ankylosing Spondylitis.
Prognostic Role of Serum Antibody Immunity to p53 Oncogenic Protein in Ovarian Cancer: A Systematic Review and a Meta-Analysis

Objective Serum p53 autoantibodies (p53-AAbs) are the product of an endogenous immune response against p53 overexpression driven by the ovarian tumour. The p53-AAbs are detectable only in a subset of patients. To date, the evidence of an association between the presence of p53-AAbs and ovarian cancer outcomes has been poorly investigated. Methods A systematic literature search was performed to identify eligible studies investigating the association of serum p53-AAbs and overall survival (OS) and disease free survival (DFS). Associations between presence of serum p53-AAbs and baseline tumour characteristics were also evaluated. Pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CI) were computed to estimate the prognostic impact of serum p53-AAbs. Heterogeneity between studies was assessed. Results A total of 583 patients (7 studies) for OS and 356 patients (4 studies) for DFS were included in the meta-analysis. Presence of p53-AAbs was not associated to OS (pooled uni- multivariate HR = 1.09; 95% CI: 0.55–2.16), and a large heterogeneity was found. When only multivariate HRs were pooled together (4 studies), presence of p53-AAbs was significantly associated to a better OS (pooled HR = 0.57; 95% CI: 0.40–0.81), and no significant heterogeneity was observed. A reduced DFS was associated to p53-AAbs (pooled uni- multivariate HR = 1.37; 95% CI: 0.83–2.25), though not significantly and with a moderate heterogeneity. Conclusions The prognostic significance of serum p53-AAbs in ovarian cancer was diverging according to uni or multivariate models used. Since the results of this work were based on only few investigations, large prospective studies are needed to better define the role of antibody immunity against p53.
The Operator and Early Promoter Region of the Shiga Toxin Type 2-Encoding Bacteriophage 933W and Control of Toxin Expression

ABSTRACT The genes encoding Shiga toxin (Stx), the major virulence factor of Shiga toxin-producing Escherichia coli, are carried in the genomes of bacteriophages that belong to the lambdoid family of phages. Previous studies demonstrated that induction of prophages encoding stx significantly enhances the production and/or release of Stx from the bacterium. Therefore, factors that regulate the switch between lysogeny and lytic growth, e.g., repressor, operator sites, and associated phage promoters, play important roles in regulating the production and/or release of Stx. We report the results of genetic and biochemical studies characterizing these elements of the Stx-encoding bacteriophage 933W. Like λ, 933W has three operator repeats in the right operator region (OR), but unlike λ and all other studied lambdoid phages, which have three operator repeats in the left operator region (OL), 933W only has two operator repeats in OL. As was observed with λ, the 933W OR and OL regions regulate transcription from the early PR and PL promoters, respectively. A lysogen carrying a 933W derivative encoding a noncleavable repressor fails to produce Stx, unlike a lysogen carrying a 933W derivative encoding a cleavable repressor. This finding provides direct evidence that measurable expression of the stx genes encoded by a 933W prophage requires induction of that prophage with the concomitant initiation of phage gene expression.
PLAStiCC: Predictive Look-Ahead Scheduling for Continuous Dataflows on Clouds

Scalable stream processing and continuous dataflow systems are gaining traction with the rise of big data due to the need for processing high velocity data in near real time. Unlike batch processing systems such as MapReduce and workflows, static scheduling strategies fall short for continuous data flows due to the variations in the input data rates and the need for sustained throughput. The elastic resource provisioning of cloud infrastructure is valuable to meet the changing resource needs of such continuous applications. However, multi-tenant cloud resources introduce yet another dimension of performance variability that impacts the application's throughput. In this paper we propose Plastic, an adaptive scheduling algorithm that balances resource cost and application throughput using a prediction-based look-ahead approach. It not only addresses variations in the input data rates but also the underlying cloud infrastructure. In addition, we also propose several simpler static scheduling heuristics that operate in the absence of accurate performance prediction model. These static and adaptive heuristics are evaluated through extensive simulations using performance traces obtained from Amazon AWS IaaS public cloud. Our results show an improvement of up to 20% in the overall profit as compared to the reactive adaptation algorithm.
Relating Cohesion to Stress, Strain, Disintegration, and Performance: An Organizing Framework

The purpose of this article is to propose new directions for group cohesion research, specifically for military psychology, and more broadly, for social psychology. In addition to group members' provision of mutual support, military studies of cohesion often include descriptions of stressful events, individual experiences of stress or strain, group members' departure from the group, and individual and group performance of assigned tasks or mission. Yet, conspicuously absent is an organizing framework that ties together these variables, indicates their interrelations, and specifies hypotheses to be tested. By drawing on early sociological definitions of cohesion and by linking evidence regarding the parallel relation of social support to both stress and individual functioning, we propose a conceptual framework incorporating stress, strain, group disintegration, and performance as important and relevant concepts in studying cohesion. The framework offers specific directions for theory, analysis, and practic.
A Novel Neutrosophic Weighted Extreme Learning Machine for Imbalanced Data Set

Extreme learning machine (ELM) is known as a kind of single-hidden layer feedforward network (SLFN), and has obtained considerable attention within the machine learning community and achieved various real-world applications. It has advantages such as good generalization performance, fast learning speed, and low computational cost. However, the ELM might have problems in the classification of imbalanced data sets. In this paper, we present a novel weighted ELM scheme based on neutrosophic set theory, denoted as neutrosophic weighted extreme learning machine (NWELM), in which neutrosophic c-means (NCM) clustering algorithm is used for the approximation of the output weights of the ELM. We also investigate and compare NWELM with several weighted algorithms. The proposed method demonstrates advantages to compare with the previous studies on benchmarks.
Comparison of the Vertec and Just Jump Systems for Measuring Height of Vertical Jump by Young Children

To assess differences between the Vertec and the Just Jump Systems in measuring height of vertical jump, 248 boys and 232 girls between 7 and 11 years were required to perform four maximal countermovement vertical jumps. A maximum of the four trials recorded was the dependent variable. Each vertical jump was simultaneously evaluated by each measuring device and a paired t test indicated the systems were significantly different; however, the experimentwise difference of . 93 in. has no practical value. The Just Jump System seems a viable alternative. Advantages and potential pitfalls regarding the use of each system are discussed.
SAWATZKY AND ELLS ON HYDROGEN IN ZIRCONIUM 33 side corrosion

Selected examples of the Canadian contribution to the development of understanding of the behavior of hydrogen in zirconium alloys are reviewed. To solve the technical problems faced by the nuclear industry, it was necessary to have values for the solubility and diffusivity of hydrogen in the alloys, the diffusivity needed for the three gradients of concentration, temperature, and stress. With these quantities known, sensible models for the accumulation of hydrogen at points in the reactor components and the morphology of the resultant hydride phase were developed.
THE EFFICACY OF AN EMOTIONAL ROLE-PLAY INTERVENTION VERSUS A PROBLEM-SOLVING APPROACH ON STRESS LEVELS AND AFFECT EXPERIENCED BY COLLEGE FRESHMAN

Many people experience negative feelings associated with life transition but choose not to seek therapy to alleviate stress-related symptoms. The purpose of this study was to examine if role-play techniques, shown to be effective for clinical issues, could also reduce college students’ stress and negative affect associated with the adjustment to their first year in college. The study also investigated if differences in emotional processing style moderated the effectiveness of the role-play intervention. Participants were randomly assigned to a role-play intervention, problem-solving intervention, or control group condition. Perceived stress, positive affect, and negative affect were measured before and after the intervention and two weeks following the intervention. The results indicated the role-play intervention was not effective in reducing stress or negative affect. The results also signified that individual differences in emotional processing ability may be a key ingredient in developing intervention strategies that are successful.
Flow Timestamps

We consider the problem of a packet multiplexor that receives packets from multiple input flows, then forwards them to a single output channel. The multiplexor ensures a predefined forwarding rate for each flow. This is achieved as follows. The multiplexor assigns a timestamp to each received packet. When the channel becomes idle, the multiplexor forwards the packet with the smallest timestamp. In this paper, we show that the multiplexor efficiency can be improved without sacrificing packet delay by maintaining one timestamp per flow rather than one timestamp per packet. We show that this approach improves the computational efficiency of two known multiplexing methods, namely, virtual clock and self-clock.
CONTROL AND AUTOMATION SYSTEMS FOR ELECTRICITY DISTRIBUTION NETWORKS OF THE FUTURE – AN UPDATE ON THE ACTIVITIES OF JOINT WORKING GROUP CIGRE C6.25/ B5 /CIRED

This paper presents an overview of the activities of the CIGRE/CIRED C6.25/B5 Joint Working Group (JWG), focusing on the control and automation systems for the future electric networks. This JWG is mainly focus to evaluate the level of automation and control necessary to better manage distribution networks with large penetrations of Distributed Energy Resources (DERs), as seen from the both the TSO and DSO perspectives, and to provide services and information about the two level of management (i.e at TSO and DSO level). The C6.25/B5 JWG aims at addressing the following issues: (i) Survey on the state of the art on planning for active distribution systems; (ii) Requirements of planning methodologies; (iii) Identification of short, medium and long term models for active distribution system planning; (iv) reliability models of active distribution systems; and (v) algorithms for active distribution system expansion and upgrade planning, including demand-side integration and storage.
Defining the Relation Between Linguistics and Neuroscience

The popularity of the study of language and the brain is evident from the large number of studies published in the last 15 or so years that have used PET, fMRI, EEG, MEG, TMS, or NIRS to investigate aspects of brain and language, in linguistic domains ranging from phonetics to discourse processing. The amount of resources devoted to such studies suggests that they are motivated by a viable and successful research program, and implies that substantive progress is being made. At the very least, the amount and vigor of such research implies that something significant is being learned. In this article, we present a critique of the dominant research program, and provide a cautionary perspective that challenges the belief that explanatorily significant progress is already being made. Our critique focuses on the question of whether current brain/language research provides an example of interdisciplinary cross-fertilization, or an example of cross-sterilization. In developing our critique, which is in part motivated by the necessity to examine the presuppositions of our own work (e.g. Embick, Marantz, Miyashita, O'Neil, Sakai, 2000; Embick, Hackl, Schaeffer, Kelepir, Marantz, 2001; Poeppel, 1996; Poeppel et al. 2004), we identify fundamental problems that must be addressed if progress is to be made in this area of inquiry. We conclude with the outline of a research program that constitutes an attempt to overcome these problems, at the core of which lies the notion of computation.
Using Simulations to Model Shared Mental Models

Abstract : Good team members seem to have the ability to simulate what others on the team will do in different situations. Team researchers have long studied what makes an effective team. Their methodology has been to examine how high and low performing teams accomplish team-related tasks. They have suggested that a good team-member has three knowledge components (Cannon-Bowers, Salas, & Converse, 1993): (1) Knowledge of own capabilities [meta-knowledge], (2) Knowledge of the task, and (3) Knowledge about the capabilities of their teammates. Most researchers have suggested that these three components are deeply inter-related; without any one of these, a person is not a good team member. However, without a computational theory, these claims can be difficult to examine empirically. The focus of this paper is the third component, the cognitive modeling done of a teammate's cognitive processes. This shared understanding of teammates frequently called a shared mental model (Mathieu, Heffner, Goodwin, Salas, & Cannon-Bowers, 2000). We start with the premise that humans use themselves as an initial model of their teammate, and then refine it as the team (and individuals within the team) gains experience. Our primary research goal is to create a computational theory of teamwork by modeling the individuals within the team so that we can eventually build plausible robots for teamwork and human-robot interaction.
Distributed Monitoring with Collaborative Prediction

Isolating users from the inevitable faults in large distributed systems is critical to Quality of Experience. We formulate the problem of probe selection for fault prediction based on end-to-end probing as a Collaborative Prediction (CP) problem. On an extensive experimental dataset from the EGI grid, the combination of the Maximum Margin Matrix Factorization approach to CP and Active Learning shows excellent performance, reducing the number of probes typically by 80% to 90%.
Hard-sphere statistics along the metastable amorphous branch

Using simulation techniques that we discussed in a previous work, we create random dense hard-sphere systems above the freezing point ~i.e., along the metastable branch! and measure key quantities that characterize the structure for such high densities. These include certain statistics of the void space, nearest-neighbor statistics, and the mean coordination number. Our numerical results turn out to be generally in very good agreement with recent theoretical predictions. @S1063-651X~98! 07707-1#