Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Look into Iranian EFL University Students' Critical Thinking and Argumentative Writing

Due to the potent role of critical thinking in learners’ academic success and its connection with factors conducive to learning such as argumentation ability, the present study seeks to primarily probe the correlation between Iranian EFL learners’ critical thinking ability and their argumentative writing achievement, and investigate the predictability of the students’ argumentative writing achievement based on their scores on critical thinking scale. Furthermore, the effect of gender on Iranian EFL learners’ argumentative writing achievement was investigated. In so doing, 'Watson-Glaser Critical Thinking Appraisal' (2002) as well as an argumentative writing assignment was employed, and the participants of the study included 178 EFL learners in three universities in Mashhad, Iran. Structure Equation Modeling (SEM) was utilized to analyze the data. The results substantiated the positive correlation between critical thinking ability and argumentative writing revealing that these two variables significantly and positively related to each other; among the predictors (subscales of the critical
Psychiatric Liaisons in Czechoslovakia, Bulgaria, Turkey, Greece and Yugoslavia

OLLOWING a successful series of joint meetings with various psychiatric F societies in Russia, Hungary and Yugoslavia in the autumn of 1970, the American Association for Social Psychiatry planned a tour of other Eastern European countries* to further the professional exchange of information and the establishment of international friendships. Herewith a brief report of items of cultural, political and scientific interest.
Hierarchical Organization of Molecular Structure Computations

The task of computing molecular structure from combinations of experimental and theoretical constraints is expensive because of the large number of estimated parameters (the 3D coordinates of each atom) and the rugged landscape of many objective functions. For large molecular ensembles with multiple protein and nucleic acid components, the problem of maintaining tractability in structural computations becomes critical. A well-known strategy for solving difficult problems is divide-and-conquer. For molecular computations, there are two ways in which problems can be divided: (1) using the natural hierarchy within biological macromolecules (taking advantage of primary sequence, secondary structural subunits and tertiary structural motifs, when they are known); and (2) using the hierarchy that results from analyzing the distribution of structural constraints (providing information about which substructures are constrained to one another). In this paper, we show that these two hierarchies can be complementary and can provide information for efficient decomposition of structural computations. We demonstrate five methods for building such hierarchies--two automated heuristics that use both natural and empirical hierarchies, one knowledge-based process using both hierarchies, one method based on the natural hierarchy alone, and for completeness one random hierarchy oblivious to auxiliary information--and apply them to a data set for the procaryotic 30S ribosomal subunit using our probabilistic least squares structure estimation algorithm. We show that the three methods that combine natural hierarchies with empirical hierarchies create decompositions which increase the efficiency of computations by as much as 50-fold. There is only half this gain when using the natural decomposition alone, while the random hierarchy suggests that a speedup of about five can be expected just by virtue of having a decomposition. Although the knowledge-based method performs marginally better, the automatic heuristics are easier to use, scale more reliably to larger problems, and can match the performance of knowledge-based methods if provided with basic structural information.
Removal of spurious phase variations in oscillatory signals

The study of oscillatory electroencephalogram (EEG) data has been one of the main sources for the analysis of different brain functions and decoding a variety of cognitive processes. One of the interesting features that has been the focus of study in the last decades is the study of instantaneous phase information in EEG. Phase synchronization techniques are among the main analysis tools that measure the degree of phase re-organization in signals. In phase re-organization approach, the phase alignment in stimulus induced potentials has been shown to be informative for decoding cognitive processes. Despite the developments in this area, the precision of measures may vary based on the level of noise. Phase distortion due to artefacts and a variety of pre-processing steps that are applied on data, can introduce artificial phase alignment. These artificial phase variations (that is referred to spurious phase variations) are not correlated to any stimulus or event. In a new study, it has been showed that a low instantaneous envelope lead to abrupt changes in IP, which could distort the results of phase synchronization. In this work, we propose a method that is able to estimate the true phase based on the measurements and estimate a standard deviation for the computed phase. The method enables to remove the effect of artificial phase changes due to low envelope. In this approach the analytic signal is modeled by a Kalman smoother, and estimates a more reliable version of the IP from the measurements. At last, we evaluate the model on different synthetic data sets.
Behavior Therapy and the Conditioning Model of Neurosis

Abstract The methods of applied science are usually based on the findings of pure science, and the discovery of successful methods of therapy for the neuroses is dependent on the formulation of appropriate theories for these disorders. It is argued that the only viable theory about neurosis at the moment is an adaptation of the Watsonian conditioning model, suitably altered to fit in with more recent discoveries in the fields of the formation and extinction of conditioned responses. Such a model has important relevance to behaviour therapy and the various methods for treating neuroses falling under that heading, and indeed for an explanation of the apparent effectiveness of psychotherapy, psychoanalysis, and whatever events mediate spontaneous remission.
SQl Injection Protector for Authentication in Distributed Applications

In today's information age, information sharing and transfer has increased exponentially. The treat of an intruder accessing secret information has been an ever existing concern for the data communication experts. Cryptography and steganography are the most widely used techniques to overcome this threat . Web application has been developed with very rapid progress. Security vulnerabilities due to amendment of intruders and hackers become predominant in the current trends. The work of this paper proposes a technique using hash value of user name and password,to improve the authentication process. We have built an prototype,SQL injection protector for authentication (SQLIPA). In addition to the proposed hash technique we are trying to conceal the logged in information using chaffing and winnowing technique in a stenographical image. These images will be stored as file stream by an encrypted layer in the backend to hide the tuples used for storage in a distributed environment. Validating the XML content with typed dataset will scrutinize the input data further associated with XSD filtration.
Toward a policy-based distributed intruison detection system in cloud computing using data mining approaches

Cloud computing has gain wide-spread popularity in recent years. Despite its numerous advantages, security is still one of the most challenging issues associated with it. An intrusion detection system (IDS), as a common security tool, can be used to increase the level of security in cloud computing. An IDS need to be accurate, efficient, adaptive and extensible. Cloud features, such as being highly distributed, generating large volume of data, variety and dynamism of the provided services on one hand and different security needs of users on the other hand has made traditional IDSs inefficient for this environment. In this paper and by using data mining methods, an efficient policy driven detection strategy for intrusion detection has been proposed for the cloud environment. The proposed approach classifies different security needs, based on CIA triad model, into groups of users with the same security requirements and then selects the appropriate policy. By grouping similar users/security requirements and tuning each IDS accordingly, the proposed approach has been able to improve IDS efficiency. Results of our simulations show that the proposed approach decreases the total detection time by 21% in average while preserving adequate detection coverage. Improving IDS efficiency implies that it also processes a bigger volume of data due to reduction in time, better use of resources and also loads balancing between groups.
A Phenomenological Analysis of Coping Effectiveness in Golf

The purpose of this study was to examine instances when international age-group golfers coped effectively and ineffectively with performance-related stressors during competition. Eighteen male Irish international golfers (M age = 17 years) participated in semistructured interviews pertaining to their coping experiences during golf competition. Data were thematically analyzed using Interpretive Phenomenological Analysis (Smith & Osborn, 2003). Strategies associated with effective coping were rationalizing, reappraising, blocking, positive self-talk, following a routine, breathing exercises, physical relaxation, and seeking on-course social support. Alternatively, different types of coping responses (trying too hard, speeding up, routine changes, negative thoughts, lack of coping) were associated with ineffective coping. Theoretical and applied implications of these findings are discussed.
A Physical Metaphor to Study Semantic Drift

In accessibility tests for digital preservation, over time we experience drifts of localized and labelled content in statistical models of evolving semantics represented as a vector field. This articulates the need to detect, measure, interpret and model outcomes of knowledge dynamics. To this end we employ a high-performance machine learning algorithm for the training of extremely large emergent self-organizing maps for exploratory data analysis. The working hypothesis we present here is that the dynamics of semantic drifts can be modeled on a relaxed version of Newtonian mechanics called social mechanics. By using term distances as a measure of semantic relatedness vs. their PageRank values indicating social importance and applied as variable `term mass', gravitation as a metaphor to express changes in the semantic content of a vector field lends a new perspective for experimentation. From `term gravitation' over time, one can compute its generating potential whose fluctuations manifest modifications in pairwise term similarity vs. social importance, thereby updating Osgood's semantic differential. The dataset examined is the public catalog metadata of Tate Galleries, London.
A research on the design of intelligent oven interface based on the behavior pattern

Through user study, this paper identifies two behavior patterns, namely, the “automatic baking” and “custom baking”. With contextual innovation, it also explores a solution for the design of intelligent oven interface that accords with the user experience, which could provide reference for the designers dedicated to the design of intelligent household electrical appliances.
Semi-Automated 8 Collaborative Online Training Module for Improving Communication Skills

This paper presents a description and evaluation of the ROC Speak system, a platform that allows ubiquitous access to communication skills training. ROC Speak (available at rocspeak.com) enables anyone to go to a website, record a video, and receive feedback on smile intensity, body movement, volume modulation, filler word usage, unique word usage, word cloud of the spoken words, in addition to overall assessment and subjective comments by peers. Peer comments are automatically ranked and sorted for usefulness and sentiment (i.e., positive vs. negative). We evaluated the system with a diverse group of 56 online participants for a 10-day period. Participants submitted responses to career oriented prompts every other day. The participants were randomly split into two groups: 1) treatment - full feedback from the ROC Speak system; 2) control – written feedback from online peers. When judged by peers (p < .001) and independent raters (p < .05), participants from the treatment group demonstrated statistically significant improvement in overall speaking skills rating while the control group did not. Furthermore, in terms of speaking attributes, treatment group showed an improvement in friendliness (p < .001), vocal variety (p < .05) and articulation (p < .01).
Counterfactual Thinking and Ethical Decision Making

The present study extends understanding of the self-regulatory aspects of ethical decision making by integrating and exploring relationships among counterfactual thinking, attribution, anticipatory emotions, and ethical decision-making constructs and processes. Specifically, we examine the effects of a manipulation designed to stimulate a counterfactual mind-set, which is found to significantly affect negative anticipatory emotions and intention associated with unethical conduct. Furthermore, we explore the basis of these effects by delineating and supporting responsibility attributions working through perceived negative consequences to influence both negative anticipatory emotions and intention to perform the unethical behavior. This research addresses both academic and managerial imperatives in the marketing education literature as to how to effectively influence ethical decisions through an understanding of how individuals combine theoretically significant cognitions.
Exercise increases cutaneous nerve density in diabetic patients without neuropathy

Early diabetic neuropathy is characterized by loss of unmyelinated axons, resulting in pain, numbness, and progressive decline in intraepidermal nerve fiber density. Patients with type 2 diabetes, without neuropathy, were assigned to quarterly lifestyle counseling (N = 40) or structured, supervised weekly exercise (N = 60) for 1 year. Distal leg IENFD significantly increased in the exercise cohort and remained unchanged in the counseling cohort (1.5 ± 3.6 vs. −0.1 ± 3.2 fibers/mm, P = 0.03). These results suggest preclinical injury to unmyelinated axons is potentially reversible, and that IENFD may be a responsive biomarker useful in future neuropathy prevention clinical trials.
Effects of Exercise Intensity on Flow Mediated Dilation in Healthy Humans

Abstract Previous studies have demonstrated conflicting results on the effects of acute exercise on FMD. The aim of the study was to examine brachial artery FMD before and after 3 bouts of acute exercise performed at different intensities. 10 healthy males (mean±SD age: 22±1 years) completed 30 min of cycling at 50, 70 and 85% maximal heart rate (HRmax). Brachial artery FMD and the shear rate area-under-the-curve (cuff deflation to peak dilation; SRAUC) were assessed pre- and immediately post-exercise using high-resolution echo-Doppler. A generalized estimating equation (GEE) analysis was used to estimate the effect magnitudes of exercise intensity and time (pre/post) on FMD, whilst controlling for the influence of baseline diameter and SRAUC. Both baseline diameter and SRAUC were elevated by exercise. With covariate-control of these variables, the decrease in brachial artery FMD was negligible after exercise at 50% HRmax (6.3±2.6 vs. 5.9±2.5%; 95%CI for difference: − 0.59–1.34%) with larger decreases in FMD after exercise at 70% (6.1±1.8 vs. 4.7±1.9%; 95%CI for difference: 0.08–2.58%) and at 85% HRmax (6.6±1.6 vs. 3.6±2.2%; 95%CI: 0.41–5.42%). In conclusion, even after accounting for exercise-mediated changes in shear and baseline diameter, our data indicate that a negative relationship exists between exercise intensity and FMD.
Software Configuration Management in Global Software Development: A Systematic Map

Many companies use Global Software Development (GSD) to access skilled people, reduce costs and utilize around the clock development. GSD has numerous social and technical difficulties, but most literature only examines social difficulties. Few studies concern technical difficulties or address Software Configuration Management (SCM) issues. SCM is widely used, and supports the infrastructure and practices that enable change management and version control. SCM has potential to support more effective GSD, but is more difficult in GSD, because coordination and synchronization are more complex. This paper presents our findings of a systematic mapping study of SCM in GSD. Systematic mapping is a methodology to discover and categorize research on a topic, and can be used to identify common themes and areas requiring further study. We find most research on SCM in GSD has used case studies, and there has been little empirical validation. The lack of coordination and group awareness causes difficulties for SCM in GSD, but no SCM process has been proposed to address this. More research is required on Software Configuration Control for GSD.
University-Affiliated Lab Schools: A Collaborative Partnership Between the University of Pittsburgh's Falk School and the State University of Malang Lab Schools

This article focuses on contemporary discussions about university-affiliated lab schools and their growing international networks. Topics include university-affiliated lab schools, the State University of Malang-University of Pittsburgh partnership created by a consortium of rectors in Indonesia, and the growing importance of international professional development networks for university-affiliated lab schools. The instructional leadership side of the partnership has shared ways: (a) to design a better aligned curriculum; (b) to make learning activities more active and student-oriented; (c) to trigger creativity, critical thinking, and independence; (d) to practice ongoing assessment; and (e) to enrich the cultural repertoire of students. The institutional management side has focused greater attention on: professionalism in management; use of technology; human resources issues of recruitment, induction, and continuing professional development; and more innovative, balanced, transparent and sustainable funding sources. The following recommendations are made. First, fostering international partnerships is a good way for already strong schools to make continuous improvements in both instruction and institutional leadership. Second, partnership sustainability is paramount, especially during transitions in senior university management. Careful and thoughtful construction of the universities’ core memorandum of understanding is time well spent because it becomes embedded in institutional policy.
Disk scheduling for mixed-media workloads in a multimedia server

Most multimedia applications require storage and retrieval of large amounts of continuous and discrete data at very high rates. Disk drives should be servicing such mixed workloads achieving low response times for discrete requests, while guaranteeing the uninterrupted delivery of continuous data. Disk scheduling algorithms for mixed workloads, although they play a central role in this task, have been overlooked by related multimedia research efforts, which so far have mostly concentrated on the scheduling of continuous requests only. The focus of this paper is on efficient disk I/O scheduling algorithms for mixed workloads in a multimedia storage server. We propose novel algorithms, a taxonomy of relevant algorithms, and study their performance through experimentation. Our results show that our proposed algorithms offer drastic improvements in discrete request average response times, low response-time variability, while serving continuous requests without interruptions.
Use of new treatment modalities for non-small cell lung cancer care in the Medicare population.

BACKGROUND Many older patients with early stage non-small cell lung cancer (NSCLC) do not receive curative therapy. New surgical techniques and radiation therapy modalities, such as video-assisted thoracoscopic surgery (VATS), potentially allow more patients to receive treatment. The adoption of these techniques and their impact on access to cancer care among Medicare beneficiaries with stage I NSCLC are unknown. METHODS We used the Surveillance, Epidemiology and End Results-Medicare database to identify patients with stage I NSCLC diagnosed between 1998 and 2007. We assessed temporal trends and created hierarchical generalized linear models of the relationship between patient, clinical, and regional factors and type of treatment. RESULTS The sample comprised 13,458 patients with a mean age of 75.7 years. The proportion of patients not receiving any local treatment increased from 14.6% in 1998 to 18.3% in 2007. The overall use of surgical resection declined from 75.2% to 67.3% ( P , .001), although the proportion of patients undergoing VATS increased from 11.3% to 32.0%. Similarly, although the use of new radiation modalities increased from 0% to 5.2%, the overall use of radiation remained stable. The oldest patients were less likely to receive surgical vs no treatment (OR, 0.12; 95% CI, 0.09-0.16) and more likely to receive radiation vs surgery (OR, 13.61; 95% CI, 9.75-19.0). CONCLUSION From 1998 to 2007, the overall proportion of older patients with stage I NSCLC receiving curative local therapy decreased, despite the dissemination of newer, less-invasive forms of surgery and radiation.
Automatic selection of ROIs using a model-based clustering approach

This paper presents a new method for automatic selection of Regions of Interest (ROIs) of functional brain images based on a Gaussian Mixture Model (GMM). This method allows avoiding the so-called small sample size problem in the construction of a CAD system that performs the automatic diagnosis of Alzheimers disease (AD). First we generate an image that holds the differences between normal and AD subjects and then, we model the ROIs from this image by using GMM and the Expectation Maximization algorithm. These regions are used to select a reduced set of features from the activation map of each patient and allow us to train statistical classifiers such as Support Vector Machines (SVMs). We have tested this approach on a SPECT images database and the accuracy rate achieved by the CAD system was 94.5%. This value significantly improves the results obtained by previously developed approaches.
Procrastination for leakage-aware rate-monotonic scheduling on a dynamic voltage scaling processor

As the dynamic voltage scaling (DVS) technique provides system engineers the flexibility to trade-off the performance and the energy consumption, DVS has been adopted in many computing systems. However, the longer a job executes, the more energy in the leakage current the device/processor consumes for the job. To reduce the energy consumption resulting from the leakage current, a system might enter the dormant mode. This paper targets energy-efficient rate-monotonic scheduling for periodic real-time tasks on a uniprocessor DVS system with non-negligible leakage power consumption. An on-line simulated scheduling strategy and a virtually blocking time strategy are developed for procrastination scheduling to reduce energy consumption. The proposed algorithms derive a feasible schedule for real-time tasks with worst-case guarantees for any input instance. Experimental results show that our proposed algorithms could derive energy-efficient solutions.