Miracle-dz/UniFAR-FTUs huggingface dataset:
Religiosity, altruism, and altruistic hypocrisy : Evidence from protestant adolescents

What influence does religion have on altruistic belief and prosocial behavior? This is a question of substantial ethical and religious relevance that currently has no con-sensual answer. We attempt to address this question by applying measurements of personal religious orientations, doctrinal orthodoxy, and faith maturity to the data from an extensive survey of adolescents in an evangelical Protestant church. Results suggest that horizontal or "love-of-neighbor" faith is a powerful predictor of altruism. Intrinsic and orthodox religion are aligned with positive views toward helping others but inversely related to actual altruistic behavior. This link between altruistic hypocrisy and intrinsic-orthodox religion is striking; therefore, potential explanations on the source of this relationship are explored.
Enhancing the play and commenting abilities of toddlers with autism spectrum disorders through caregiver-implemented teaching of play

Children are being diagnosed with autism spectrum disorders (ASD) more frequently and at younger ages, with a growing number of toddlers receiving ASD diagnoses and accessing early intervention (EI) services. Young children with ASD, including toddlers, show marked delays in play and benefit from interventions that move them forward in their play skill development. Evidence-based approaches, including incidental teaching, have been used to teach play skills to preschool-aged children with ASD, and, in some instaces, have yielded simultaneous improvements in their commenting skills, although language goals were not targeted directly in the teaching. Despite these positive empirical outcomes, gaps currently exist in the play intervention literature. Although ASD prevalence and EI eligibility rates are rising, few studies have examined the impact of direct teaching in play on the play and commenting abilities of toddlers with ASD; the majority of studies have involved preschool-aged children who received play instruction in school settings or in home settings from research assistants, but not from their primary caregivers. For most play intervention studies with preschool-aged children, the skills that were targeted for intervention were vague, derived arbitrarily, and disconnected from children’s specific learning needs. A few studies used incidental teaching approaches to teach developmentally specific play activities, yet not to toddlers with ASD. This study was designed to investigate how caregiver-delivered instruction in play impacted the play and commenting abilities of toddlers with ASD. Five male toddlers (23-32 months old) with ASD were recruited from a local EI agency and received instruction based on least-to-most prompting that targeted play skills that were at the emergent level, evidenced by results from the Developmental Play Assessment (DPA) and baseline session data. Caregiver-Implemented Play 10 The children’s response to intervention varied. Although limited conclusions can be made about differences in response to intervention because of methodological study constraints, results provide preliminary evidence that the children showed play and commenting skills that were not present during baseline. The results of this study contribute to the literature base by describing the rationale, methods, and outcomes of a play intervention with empirical and theoretical relevance that was linked to individualized assessment targets based on each child’s developmental level. Directions for future research include refining intervention procedures and conducting studies with more robust research designs and statistical procedures, both of which are important for informing the practice of EI service providers and school psychologists who work with early childhood populations. Caregiver-Implemented Play 11
Clone2clone (c2): Enable Peer-to-peer Networking Smartpones on the Cloud Clone2clone (c2c): Enable Peer-to-peer Networking of Smartphones on the Cloud

The smartphone and mobile apps markets continue to have an extraordinary growth. However, short-life battery problems make it hard for users to fully exploit their mobiles. Recent research considers offloading of mobile apps to software clones hosted on cloud platforms to dramatically improve efficiency and to extend battery life. In this work we study the performance of device-clones hosted in various virtualization environments in both private (local servers) and public (Amazon EC2) clouds. We build the first Amazon Customized Image (AMI) for Android-OS and show how it boosts up performance of Android images on the Amazon cloud service. We then design, build, and implement Clone2Clone (C2C), a distributed P2P platform for cloud clones of smartphones that associates a software clone to every smartphone. C2C enables services such as content sharing, search, distributed execution and many more. Upon C2C we build CloneDoc, a secure real-time collaboration system for smartphone users, and CloneBox, a file-sharing and storing system with DropBox-like features. We measure the performance of CloneDoc and CloneBox by means of experiments done on a testbed of 16 Android smartphones and exploiting clones hosted on both private and public cloud services.
Teaching Basic Probability and Statistics Using Computer Graphics

In the physical sciences it has been long known that the most effective way to teach students is by having them "discover" the basic principles themselves. Unfortunately, probability and statistics have traditionally not been taught this way. With the advent of the persona1 computer, all that has changed. Microcomputers can do more than take the drudgery out of calculations. Their unique graphics and interactive capabilities can be used as well to teach basic concepts and ideas. Many of the basic ideas in probability and statistics seem exceedingly difficult for most students to grasp. It is extremely important to give students a sound intuitive feeling for basic concepts such as randomness and the normal curve before teaching them formal statistical theory. The micro makes it now possible to add a laboratory component to the teaching of probability and statistics. Key concepts such as the law of large numbers, random walks, and the central limit theorem can be made to come alive before one's eyes through computer graphics. Direct experience and actual-experimentation is the best way the student can obtain a feeling for these basic concepts. It cannot be emphasised enough how important it is that students obtain at an early stage a feeling for probabilistic reasoning. People are not born with a natural feeling for probabilistic thinking, but it is of utmost importance in everyday life. Managers in industry realise more and more that without probabilistic thinking many of their problems cannot be adequately solved. The micro is the ideal tool for students to develop a sound probabilistic intuition. Its most powerful feature computer graphics should be exploited. Most people find it much easier to grasp concepts in visual terms and through direct experiences than in terms of formulae. Of course, many authors have advocated the use of computer graphics to support the teaching of basic concepts in probability and statistics, but only a few have actually done the painful work of implementing their ideas in user-friendly software for the beginning student. The educational software
University of Pennsylvania Working Papers in Linguistics Signaling and Simulations in Sociolinguistics Signaling and Simulations in Sociolinguistics Signaling and Simulations in Sociolinguistics

Along with game theory, the emerging science of networks has given us a framework for analyzing social systems plausible to both intuition and implementation. As an interaction structure in computer simulation models, social networks provide a way to envision phenomena like information spread, dialect formation, and language change in a more robust way. In this sense a multitude of sociolinguistic issues are potential 'objects of study' for a) being delineated with methods from game theory and/or network theory and b) being analyzed by simulations of multi-agent interactions, with the goal of exploring the interplay between social factors and linguistic usage. In this sense we i) consider network structure as an important social variable; ii) depict the usage of computer simulations as an appropriate, valid, and powerful technique to analyze sociolinguistic issues; and iii) put a premium on game theory as a method for adequately modeling communicative behavior, with the conclusion that network theory & game theory in simulation models represents a powerful combination for the analysis of sociolinguistic phenomena. This makes it a crucial supplement towards enhancing current sociolinguistic experimentation and theories.
Correlating Product and Process Measures as a Model for Systems Engineering Measurement

Current measurement industry emphasis on process measures and compliance ignores elements of the history of measurement. The failure to establish product measures and provably correlate them with process measures leads to open‐loop management systems that can only measure process compliance, with unknown effect on product quality. The new INCOSE Measurement Primer attempts to begin addressing this important distinction. In this paper a set of standard categories of measures are applied to various Systems Engineering work products identified as “output” in the INCOSE SE Handbook. Process and product measures for each process output are proposed, along with correlations between them, and associations to the INCOSE SE Leading Indicators where applicable.
Effectiveness of Group Therapy with a Positive-Oriented Approach on Rumination, Illness Perception and Depression in Patients with Multiple Sclerosis (MS)

This study was carried out in order to investigate the effectiveness of group therapy with a positive-oriented approach on the illness perception, rumination and depression in patients with multiple sclerosis. This study was conducted as a quasi-experimental research with pretest-posttest research design with control group. The number of 28 patients as the member of the MS Society in Tonekabon city who had high rumination, illness perception and depression based on questionnaires of rumination scale by Nolen-Hoeksema and Morrow (1991), Broadbent’s illness perception scale (2006) and Beck’s depression scale were selected in simple random way and were assigned into two experimental and control groups randomly (11 women and 17 men). Experimental group received eight 90-minute sessions (once a week) group therapy with a positive-oriented approach and the control group had no training in this respect. After running group therapy with a positive-oriented approach, the rumination, depression and illness perception of both groups were measured again. Multivariate analysis of covariance (MANCOVA) with the Bonferroni correction at P <0.0001 showed that the group therapy had an effective role in reducing rumination (F = 7.51), the illness perception (F = 7.43) and depression (F = 25.70). The findings of this study demonstrated convergence with similar studies that group therapy with a positive-oriented approach has had an effective role in reducing rumination, illness perception and depression in patients with MS.
Transtracheal jet ventilation in a porcine model

Hypoxemia is a frequent event during difficult airway management. We propose to use transtracheal jet ventilation (TTJV) early during the management of complex difficult airway scenarios. The objective of this porcine study is to highlight the benefit of oxygenation via prophylactic TTJV. Eighteen pigs (Sus scrofa) were divided into two equal groups. In Group A, pigs were anesthetized and no lung ventilation was conducted following administration of succinylcholine and prior to tracheal intubation. Group B, after induction of anesthesia, received transtracheal ventilation using 100% oxygen. In both groups intubation was performed after waiting 90 seconds. All intubations were achieved in less than 30 seconds. Post-intubation arterial blood gases demonstrated significant hypoxemia in Group A (PaO2 22.6 + 5.8 mm Hg), while in Group B oxygenation substantially improved (PaO2 470.3 + 17.0 mm Hg). The arterial CO2 retention was associated with mild respiratory acidosis (pH 7.26 ± 0.05) in Group A only. These findings prove that prophylactic TTJV can improve oxygenation and allow extra time for definitive management of difficult airway.
Gender Differences in Mental Health: the Mediating Role of Perceived Personal Discrimination

Why is women's mental health inferior to that of men? This study hypothesized that women's mental health is not as good as men's because women perceive more personal discrimination. It was confirmed that women obtained higher scores than did men on a subjective scale of psychological distress. Additionally, women perceived greater personal and group discrimination than did men. Perceived personal discrimination proved to be the more robust predictor of psychological distress. This was evident when results of a mediation analysis revealed that gender differences in subjective distress were mediated by the measure of perceived personal discrimination , but not by the measure of group discrimination. Is the mental health of women inferior to that of men? Several studies have provided strong evidence for a gender gap in mental health (e. biological, behavioral, and social factors. In the present study, we examine the role of perceived discrimination, a social psychological factor that we hypothesize is at least partly responsible for the observed gender gap in psychological distress. It is well documented that women and men hold different social positions in the social hierarchy, with men having easier access to positions of higher status (Astbury, 1999; Sidanius & Pratto, 1999). Furthermore, it is not difficult to observe that even when their status is equal, men and women are compensated differentially. For example, in France, the National Institute of Economic Statistics (INSEE, 2000) reported that, given equal status, the level of women's compensation is significantly inferior to that of men. Women are also much more significantly affected by unemployment (INSEE, 2000). Thus, there is objective evidence that even when women have the same credentials as do men, women do less well. It is interesting to note that in the
MASSIV: Mass Assemby Survey with SINFONI in VVDS. I. Survey description and global properties of the 0.9 < z < 1.8 galaxy sample

Understanding how galaxies evolve and assemble their mass across cosmic time is still a fundamental unsolved issue. To get insight into the various processes of galaxy mass assembly, the Mass Assembly Survey with SINFONI in VVDS (MASSIV) aims at probing the kinematical and chemical properties of a significant and representative sample of high-redshift (0.9 5 Msun/yr. Together with the size of the sample, the spatially-resolved SINFONI data therefore enables us to discuss global, volume averaged, galaxy kinematic and chemical properties all accross the mass and SFR range of the survey to derive robust conclusions on galaxy mass assembly over cosmological timescales.
A Distributed Deep Representation Learning Model for Big Image Data Classification

This paper describes an effective and efficient image classification framework nominated distributed deep representation learning model (DDRL). The aim is to strike the balance between the computational intensive deep learning approaches (tuned parameters) which are intended for distributed computing, and the approaches that focused on the designed parameters but often limited by sequential computing and cannot scale up. In the evaluation of our approach, it is shown that DDRL is able to achieve state-of-art classification accuracy efficiently on both medium and large datasets. The result implies that our approach is more efficient than the conventional deep learning approaches, and can be applied to big data that is too complex for parameter designing focused approaches. More specifically, DDRL contains two main components, i.e., feature extraction and selection. A hierarchical distributed deep representation learning algorithm is designed to extract image statistics and a nonlinear mapping algorithm is used to map the inherent statistics into abstract features. Both algorithms are carefully designed to avoid millions of parameters tuning. This leads to a more compact solution for image classification of big data. We note that the proposed approach is designed to be friendly with parallel computing. It is generic and easy to be deployed to different distributed computing resources. In the experiments, the largescale image datasets are classified with a DDRM implementation on Hadoop MapReduce, which shows high scalability and resilience.
The efficacy of 320-detector row computed tomography for the assessment of preoperative pulmonary vasculature of candidates for pulmonary segmentectomy.

OBJECTIVES The purpose of this study was to compare the efficacy of 320-detector row computed tomography (CT) with that of 64-detector row CT for three-dimensional assessment of pulmonary vasculature of candidates for pulmonary segmentectomy. METHODS We included 32 patients who underwent both 320- and 64-detector CT before pulmonary segmentectomy, which was performed by cutting the pulmonary artery and bronchi of the affected segment followed by dissection of the intersegmental plane along the intersegmental vein. Before the operation, three-dimensional pulmonary vasculature images were obtained for each patient, and the arteries and intersegmental veins of the affected segments were identified. Two thoracic surgeons independently assessed the vessels with visual scoring systems, and kappa analysis was used to determine interobserver agreement. The Wilcoxon signed-rank test was used to compare the visual scores for the assessment of the visualization capabilities of the two methods. In addition, the final determination of pulmonary vasculature at a given site was made by consensus from thoracic surgeons during operation, and receiver operating characteristic analysis was performed to compare their efficacy of pulmonary vasculature assessment. Sensitivity, specificity and accuracy of either method were also compared by means of McNemar's test. RESULTS Of the 32 cases, there were no operative complications, but 1 patient died of postoperative idiopathic interstitial pneumonia. Visualization scores for the pulmonary vessels were significantly higher for 320- than those for 64-detector CT (P < 0.0001 for the affected arteries and P < 0.0001 for the intersegmental veins). As for pulmonary vasculature assessment, the areas under the curve showed no statistically significant differences in between the two methods, while the specificity and accuracy of intersegemental vein assessment were significantly better for 320- than those for 64-detector row CT (P < 0.05). Interobserver agreement for the assessment yielded by either method was almost perfect for all cases. CONCLUSIONS Three hundred and twenty-detector row CT is more useful than conventional 64-detector row CT for preoperative three-dimensional assessment of pulmonary vasculature, especially when we identify the intersegmental veins, in candidates for pulmonary segmentectomy.
Application domain and programming language readability yardsticks

Software maintainability is one of the most factors that used to ensure software quality. It targets the structure of the software rather than its functionality. Hence, code readability is highlighted whenever maintainability is discussed. There is a huge difference between an organized code and a messy code, or between easy to read code and a difficult to read code. This difference can be very subjective but many efforts have been put together in order to formalize it. Therefore, the result was a set of readability factors that have a direct or indirect impact on software readability. These factors measure to which extent readers can understand the text of the software code. Many metrics were developed to generalize the readability score across a set of pieces of code. Unfortunately, not all proposed metrics take into account the possibility of having variable readability factors. This paper tries to investigate to which extent the readability factors considering different application domains. Since readability is very subjective, it will be interesting to see whether it is useful to have specialized readability models that measure this attribute. This also implies looking into the impact of these readability models on measuring software quality.
Modulation formats recognition technique using artificial neural networks for radio over fiber systems

In this paper, we present and investigate a novel method for modulation formats recognition for radio-over-fiber (RoF) systems. The technique uses the artificial neural network (ANN) in conjunction with the features of asynchronous amplitude histograms of the detected signals at high bit rates using direct detection. The use of ANN method for this purpose mainly driven by its ability to learn complex classification problems and the use of sequential training algorithm to increase the estimation accuracy of all modulation formats. The efficiency of this technique is demonstrated under different transmission impairments such as chromatic dispersion (CD) in the range of 34 to 170 ps/nm, differential group delay (DGD) in the range of 0 - 20 ps and the optical signal to-noise ratio (OSNR) in the range of 4 - 30 dB. The results of numerical simulation for various modulation formats demonstrate successful recognition from a known bit rates with a higher estimation accuracy, which exceeds 99.9%.
ON AN IMPLEMENTATION OF SELF-REGULAR PROXIMITY BASED FEASIBLE IPMS

Self-regular based interior point methods present a unified novel approach for solving linear optimization and conic optimization problems. So far it was not known if the new Self-Regular IPMs can lead to similar advances in computational practice as shown in the theoretical analysis. In this paper, we present our experiences in developing the software package McIPM that uses the dynamic version of SR IPMs based on the homogeneous self-dual embedding model. After a brief review of the underlying algorithm, various issues with respect to implementation are addressed. Numerical stability and sparsity of the normal equation system are explored as well. Extensive testing shows that the McIPM package is competitive with other leading academic packages and that the Self-Regular proximity based approach allows to improve the performance of interior point method software when solving difficult linear optimization problems, and offers avenues for further improvements.
A Review of Resilient Approaches to Peer-to-Peer Overlay Multicast for Media Streaming

The success of peer-to-peer overlay multicast for media streaming depends on their scalability. However, as multicast functionality is pushed to autonomous, unpredictable end systems, significant performance loss can be resulted from their higher degree of churn when compared to routers. Data distribution path over an overlay consisting of unreliable peers is inherently subject to disturbances. Resilience is thus inevitably a key requirement to peer-to-peer streaming architectures. Currently, a number of techniques have been proposed to improve overlays resilience by exploiting path diversity to minimize nodes dependencies and data redundancy. In this article, we present a survey of the media distribution resilient approaches proposed in literature. We classify them based on overlay topology design and error-resilient control approaches, illustrating with some representative protocols. And to achieve better performance in P2P video streaming systems, multiple-tree or mesh-based overlay topologies combining with error-resilient transport is essential.