Miracle-dz/UniFAR-FTUs huggingface dataset:
Personality Inventory for the DSM-5 (Brief Form) Predictors of Sexual Addiction

Abstract Sexual addiction risk may be elevated by personality maladjustment. The Personality Inventory for the DSM-5 (PID) was identified in the DSM-5 as an exemplar for future personality research. The brief form (DSM-5-BF) canvases the same domains, with concerns raised that it may underestimate levels of maladjustment. PID-5-BF associations were found in this national sample (N = 428) with all five subscales of the Sexual Addiction Screening Test (preoccupation with sex, loss of control, relationship disturbance, affective disturbance, and Internet addiction). PID-5-BF personality maladjustment in this national survey sample appeared to increase the relative risks of elevated sexual addiction symptomatology.
Over 40%: Asian Americans and the Road(s) to Community Colleges

Objectives: What are some of the key reasons Asian American students enroll in community colleges? Method: We utilized qualitative methods, conducting 49 interviews in Southern California with current Asian American community college and transfer students at three institutions. Results: We identified five key reasons why Asian American participants attended community college: cost, strategy, lack of intentionality in college planning, lack of support and accurate information, and needing a second chance. Contributions: Altogether, findings showcase how Asian Americans experience an array of diverse pathways in choosing to access the community college. In particular, findings highlight the major roles of cost and life hardship in Asian American students’ motivations for attending community colleges.
Hashing hardware : identifying hardware during boot-time system verification

Modern systems measure the software loaded at boot-time to ensure the machine starts in a trusted state. Such measurements, however, do not include any information about the underlying hardware of the machine. Recent DRAM-based attacks and the growing complexity of the supply chain attest to the importance of measuring hardware at boot. In this thesis, we propose a technique for designing measurement schemes for hardware components. We then apply this technique to designing and implementing a hardware measurement scheme for DRAM on a real system without hardware modifications. Finally, we evaluate our DRAM hardware measurement scheme and demonstrate that it achieves 89% accuracy in mapping a DRAM measurement to the manufacturing process from which that DRAM was produced. Thesis Supervisor: Stelios Sidiroglou-Douskos Title: Research Scientist, MIT CSAIL Thesis Supervisor: Martin Rinard Title: Professor
Basketball players' versatility: Assessing the diversity of tactical roles

Basketball has seen an increase in the number of players who perform multiple tactical roles. Therefore, the aim of the study was twofold: (i) to define a method to characterize basketball players as versatile or specialists, based on 13 game-related statistics; (ii) to evaluate versatile-specialist tendencies in a professional national league. A predictive model was proposed using the Automated Machine Learning (AutoML) of the H2O framework. The model was tested using data from nine seasons (2008–2017) from the Brazilian national league (NBL), encompassing 1497 players' observations, achieving an accuracy of 70.81%. We classified players as versatile or specialist and observed the following: (i) the number of versatile players has grown over the nine seasons period (from 25.16% to 47.85%), with Small Forward and Power Forward players presenting the fastest growth in versatility; (ii) NBL teams had similar proportions of versatile and specialist players; (iii) for the best players in the NBL (All-Star game players), there was a trend toward a higher number of versatile players (58.33%) compared to specialist ones. In conclusion, the method was effective in indicating the players' degree of versatility and demonstrated a tendency of increasing versatility over the analyzed seasons. In practice, it may support the assessment of player's profile and contribute for coaches' strategic decisions.
Measuring Patient Satisfaction from Attitude toward Complementary and Alternative Medicine (CAM) Moderated by Patient Religiosity

This research aimed to determine whether religiosity was able to be a moderator and strengthen the role of attitudes on CAM (Complementary and Alternative Medicine) as the originator of patient satisfaction in ‘sangkal putung’ treatment. The research applied a quantitative method with accidental sampling technique. The number of research respondents was 90 people aged 18 to 67 years old who visited the ‘sangkal putung’ at least twice and conducted treatment in the last 10 years. The research instrument used was SACAM (Scale for Attitude towards CAM) with the reliability of 0,843, PSQ-18 (Patient Satisfaction Questionnaire-Short Form) with the reliability of 0,859, and the Religiosity Scale was modified and rearranged based on the dimensions of religiosity by Stark & Glock with the reliability of 0,929. The research indicates that religiosity has a significant effect to strengthen the role of attitudes toward CAM as the originator of the treatment satisfaction of ‘sangkal putung’ patients. It means that the higher patient’s religiosity, the higher role of attitude towards CAM as the originator of the treatment satisfaction of ‘sangkal putung’ patients. Around 32,1% of the attitudes role towards cam works as a source of patient satisfaction of ‘sangkal putung’ treatment.
Análise de Métodos e Ferramentas para Reconhecimento de Palavras Relevantes em Microblogs [Analysis of Methods and Tools for Relevant Words Recognition in Microblogs]

Extracting accurate information from the huge volumes of data, much of them unstructured, generated in social media is currently a big challenge. However, it has several relevant applications, some of them latent yet. One of the first and most decisive steps in this information extraction process is the recognition of relevant words in texts. This article presents a comparative study of methods and tools for recognizing relevant words on microblog posts. Among several analyzed tools, five have been selected for experments with 100,000 tweets. These experiments showed high variability of the results generated by different tools, suggesting a need for improvements
Testing the hybrid battery approach to evaluating sports-related concussion in the National Hockey League: A factor analytic study

Abstract Objective: There has been an increased interest in the use of “Hybrid” neuropsychological test batteries to evaluate neurocognitive functioning prior to and following sports-related concussion. Typically, these test batteries include a combination of traditional “paper and pencil” (P&P) and computerized measures. The National Hockey League (NHL) employs a Hybrid approach consisting of ImPACT and a brief battery of P&P measures. The purpose of this paper was to examine the NHL’s Hybrid model by means of factor analytic techniques in order to determine the extent to which the measures included in the battery are independent of each other or share common variance. Method: Principal components analyses with promax rotations were conducted on the P&P measures alone and when combined with ImPACT. A total of 360 recently concussed NHL players underwent testing using the combined hybrid battery. Results: A principal components analysis with promax rotation of the combined hybrid battery yielded 5 factors (Verbal Learning/Memory, Visual Learning/Memory, Processing Speed/Executive Functioning, Cued/Recognition Memory and Reaction Time/Speed). The factors appear to be relatively independent of each other with the P&P measures comprising the Verbal Learning/Memory, Visual Learning/Memory, and Processing Speed/Executive Functioning and ImPACT loading on the Cued/Recognition Memory and Reaction Time/Speed factors. Conclusions: These data provide support for the Hybrid model with evidence that the P&P measures and ImPACT do not measure the same domains of cognitive functioning. The clinical implications of the findings are discussed in detail.
From software threads to parallel hardware with LegUp high-level synthesis

From Software Threads to Parallel Hardware with LegUp High-Level Synthesis Jongsok Choi Doctor of Philosophy Graduate Department of Department of Electrical and Computer Engineering University of Toronto 2016 High-level synthesis (HLS) can automatically synthesize software to hardware. With the design specification in software, HLS can reduce the lengthy design cycles of hardware, and make the performance and energy-efficiency benefits of hardware accessible to those without hardware skills. Since the introduction of the first C-based HLS tools more than a decade ago [49], however, the adaption of the technology has been slow by both software and hardware engineers. We attribute this to two key factors: 1) For hardware engineers, there is still a gap between HLS-generated hardware and human-designed hardware, partly due to the inability of HLS tools to fully exploit hardware parallelism, and 2) for software engineers, HLS remains to be a difficult endeavour, as many parts of the design, such as system integration, largely remain a manual process. This dissertation provides an HLS framework, LegUp, which seeks to address both issues. LegUp can compile an entire software program to hardware to produce a hardware-only system, or it can also automatically partition the program to generate a processor-accelerator hybrid system, wherein the compute-intensive program segments are accelerated by hardware, with the remaining segments executed in software on a processor. In both cases, a complete system is generated, including necessary memories and interconnect. To allow one to easily exploit hardware parallelism, we provide HLS support for synthesizing parallel software to parallel hardware. In particular, we support automatically compiling a multi-threaded program with Pthreads and OpenMP to parallel hardware accelerators that operate concurrently within a hardware-only or a processor-accelerator hybrid system. In the context of parallel hardware, we investigate architectural and memory optimizations that help to improve circuit performance and area, and discuss a method of using the producer-consumer pattern in software to infer a streaming circuit in hardware. With these techniques, we show that LegUp can produce high-performance hardware that can be competitive to circuits that are generated by commercial HLS tools, and demonstrate that LegUp-generated circuits can also outperform software executing on x86 processors.
Trans Students Who Leave College: An Exploratory Study of Their Experiences of Gender Minority Stress

Abstract:This article reports findings from an exploratory study of 14 trans college students who had taken leave, dropped out, and/or transferred institutions. Participants shared complex reasons for leaving college, including institutional and interpersonal experiences of gender-related stress, namely unwelcoming or insensitive campus and classroom climates, as well as a lack of trans-affirming supports and services, which made gender transitions difficult and stressful. Lack of family support and financial and academic concerns, were often intertwined with gender minority stress and contributed to some participants' leaving.
Elementary 1:1 iPad Implementation: Successes and Struggles During the First Year

This mixed-method case study followed K-5 elementary teachers through their first year of 1:1 iPad adoption in one elementary school. All teachers in the school were surveyed in September and December 2015 and May 2016. Focus groups consisting of two teachers from each grade level were held during these same months. Teacher perceptions were gathered in regard to iPads in curriculum, management, differentiation, collaboration, assessment, and inquiry learning. Struggles during the first year included lack of professional development, rushed decisions made by administration, technical problems, and poor planning. Success was found in differentiation and improvement throughout the year.
Innovative Research of Full Informational Teaching Mode Under Mobile Internet Cloud Teaching Platform

Under the background of information society ,the mobile Internet, cloud computing, big data, artificial intelligence and internet of things becoming more important, in response to the urgent need of the society for innovative talents, the traditional teaching mode is "imparted by knowledge, Teachers as the center "has not adapted to the practical teaching problems, through the research and practice application of the mobile Internet cloud teaching platform-Mosoteach of various functional sections, put forward a comprehensive framework of teachers and students based on mobile Internet cloud teaching platform Interactive teaching mode, this model is based on action-oriented task-driven innovation-based O2O (online to offline ) teaching mode, the purpose is to promote the cultivation of self- learning ability, selflearning ability is to cultivate innovative talents a factor. At the same time for the implementation of a comprehensive information technology teaching reference.
RECONSTRUCTION OF MaxEnt IMAGES FROM PET CAMERA

We study here one of the imaging techniques, used in nuclear medicine, called a Positron Emission Tomographic (PET) imaging that provides information about many biological processes that are essential to the functioning of the organ that is being visualized. Our emphasis is given to applications of the maximum entropy image reconstruction method called “Cambridge MaxEnt Package” (CMEP) for recovering images of the human brain from data obtained by PET camera. Computer simulations demonstrate its usefulness.
Scripts for Computer-Supported Collaborative Learning

Using computers as tools to mediate collaborative learning has had a profound impact on how we construct and communicate knowledge today. Computer interfaces have not been genuinely designed, however, to support collaborative knowledge construction. This study shows how computer interfaces can be designed based on the instructional script-approach to facilitate productive interactions of learners and improve learning outcomes of collaborative knowledge construction in e-learning scenarios. A social script aims to support specific interactions of learning partners. An epistemic script aims to facilitate specific task-related activities. In a 2×2-design the factors “social cooperation script” (with vs. without) and “epistemic cooperation script” (with vs. without) are independently varied. 96 students of Pedagogy are randomly assigned to the four experimental conditions in learning groups of three. The epistemic script increases the amount of specific epistemic activities, and facilitates the joint application of focused knowledge. Simultaneously, the epistemic script reduces the frequency of important social modes of co-construction, such as elicitation. With respect to learning outcomes, it can be further shown that the epistemic script impedes individual knowledge acquisition. Epistemic scripts may therefore be particularly appropriate for the facilitation of joint knowledge application, where individual knowledge acquisition may be less important than effective processing of a given problem in groups. In contrast, however, social cooperation scripts may be more appropriate for individual knowledge acquisition than group problem solving. Social scripts facilitate individual knowledge acquisition as an outcome of collaborative knowledge construction. The results suggest that cooperation scripts for individual knowledge acquisition should consider social components.
Research on forest insurance policy simulation in China

Purpose Forest insurance is a popular way to reduce the loss of forest disasters, so it is necessary to actively involve stakeholders. In the multi-agent simulation model, the government, insurance companies and forest farmers participate as three main stakeholders. The purpose of this paper is to mainly simulate the behavior of forest farmers under different environmental variables in order to find the important factors affecting the coverage of forest insurance, so as to improve the ability of forest farmers to resist risks in the face of disasters. Design/methodology/approach In the simulation process, the decision-making rule of a forest farmer’s purchasing behavior is a binary selection chain, which is created at random. Forest farmer agents who adapt to the environment will remain; on the contrary, those will be eliminated. The eliminated agents will renew their behavior selection chains through learning others’ successful behavior based on genetic algorithm. The multi-agent mode is set up on the Eclipse platform by using Java language. Findings The adjustment simulation experiments of insurance premium, insurance subsidy and forest area were carried out. According to the result, conclusions and suggestions are as follows: at present, government subsidies are necessary for the implementation of forest insurance; in the future, with the expansion of the insured forest area and the upgrading and large-scale operation of forest farms, forest farmers will be more willing to join forest insurance program, and, then, the implementation of forest insurance no longer requires government subsidies for forest insurance premiums. Originality/value This paper explores the impact of three important factors on the implementation of forest insurance.
A Framework of Applying Process Mining for Fraud Scheme Detection

Process mining has been used for auditing and internal control risk assessment as an audit analytical tool. This paper aims at providing a framework on how process mining can be applied to identify fraud schemes and assessing the riskiness of business processes. Specifically, the proposed framework captures how the patterns in process mining can be used to detect potentially fraudulent transactions. This paper contributes to the existing literature by associating notable variants/activities with potential fraud schemes and then assigning risk levels, which could be used as an automatic tool to test the fraud risk of every transaction. In addition, process mining enables auditors with the full population of event logs that can be used as non-financial information in fraud scheme detection.
Emotional Intelligence among Radiation Therapists

University of Wollongong Copyright Warning You may print or download ONE copy of this document for the purpose of your own research or study. The University does not authorise you to copy, communicate or otherwise make available electronically to any other person any copyright material contained on this site. You are reminded of the following: This work is copyright. Apart from any use permitted under the Copyright Act 1968, no part of this work may be reproduced by any process, nor may any other exclusive right be exercised, without the permission of the author. Copyright owners are entitled to take legal action against persons who infringe their copyright. A reproduction of material that is protected by copyright may be a copyright infringement. A court may impose penalties and award damages in relation to offences and infringements relating to copyright material. Higher penalties may apply, and higher damages may be awarded, for offences and infringements involving the conversion of material into digital or electronic form. Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.
Beyond Pre/Post Measurement: Discourse Analysis as an Alternative Methodology in Reporting on an Intervention Programme

Pre/post comparison has become the most prominent method in reporting on a psychological intervention programme, to the extent that it is almost impossible to report on such programmes without a pre/post evaluation framework. This article introduces discourse analysis methodology as an alternative in reporting on an intervention programme, highlights some of its benefits, and provides an empirical example of its application. Discourse analysis methodology is applied to analyze a diversity workshop, conducted by the author, in a youth refuge in Surabaya (N = 15). Three key discourses were identified, namely, a discourse of (different) abilities, a discourse of peaceful resolution, and a discourse of crime as a natural consequence of inequality; which are discussed in relation to the methodological benefits of the application of discourse analysis, in an intervention programme.
Native American Resiliency Resources and Educational Goal Achievement

The purpose of this qualitative narrative study was to understand Native American life experiences which foster resilience and how resiliency resources contribute to their educational goal achievement. In a context of historical trauma, poverty, and oppression it is theorized that achieving one’s educational goals requires a measure of resilience and a number of protective and promotive factors. These factors are what this strengths-based study sought to identify. The theory guiding this study was resiliency theory and the research approach was strengths-based. Proposed data collection was a series of semi-structured interviews of 6 Native American individuals age 18 or older who lived on a reservation for at least 70% of their K-12 education and have achieved or are in the process of completing a college degree. Data analysis strategies included the use of Atlas.ti software as well as detailed transcript analysis, manual coding and theme development.
Classifier Evaluation for Software Defect Prediction

Feature selection is an essential step in the process of software defect prediction due to the negative effect of irrelevant features on classification algorithms. Hence selecting the most relevant and representative features is critical to the success of software defect detection. Another problem in software defect prediction is the availability of a large number of classification models. This paper applies feature selection and classifier evaluation in the context of software defect prediction. An empirical study is presented to validate the proposed scheme using 9 classifiers over 4 public domain software defect data sets. The results indicate that the proposed scheme can improve the performance of classifiers using the most representative features and recommend classifiers that are accurate and reliable in software defect prediction.