Miracle-dz/UniFAR-FTUs huggingface dataset:
Does private money increase party extremism?

ABSTRACT In this paper, I explore whether private money affects party extremism. This is one of the most important questions in the study of party politics, but it has not been systematically studied in the comparative context due to lack of data. I have assembled a novel and most comprehensive cross-national data about private donations that are currently available. Using these data that cover 45 parties in 9 countries from 1996 to 2013, I show that the more private money parties receive, the more likely are they to take extreme positions. I further show that this effect is especially strong in the case of the more ideological, principled issues, and absent in the case of the less ideological, pragmatic ones. These findings contribute to our understanding of party position taking and the role of money in politics.
USER BEHAVIOUR INTENTION USING UTAUT2 MODEL: A SYSTEMATIC LITERATURE REVIEW

The development of information communication technology (ICT) and also derivatives of its technology make it easier for the public to conduct online transactions. This study seeks prior research by using systematic literature to look at customer’ s Behavior Intention by using theories from Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) so as to understand the developments and new discoveries that have emerged in previous studies. A systematic literature review is used in this research methodology so the review can be explained systematically. Based on the findings of this literature review research aspires a method or framework that can be used for researchers as well as practitioners to understand the customer's Behavior Intention by using theories from UTAUT2 so that it can inspire further research.
A Simulation Approach to Predicting College Admissions

Author(s): Lim, Daniel Kibum | Advisor(s): Handcock, Mark S | Abstract: Many educational websites offer tools that predict students' chances of admissions to college using models estimated on crowd-sourced data. Unfortunately, these data are generally not representative of the actual population of college applicants, so admission probability calculators (APCs) which use them offer biased estimates of admission probability, Pr(A). As a solution, I propose a simulation-based framework for estimating Pr(A) that is not modeled on crowd-sourced data. I model the joint distribution of enrollee characteristics at a particular university using a Gaussian copula and official margin data published by the College Board and the university itself. I fit a model for Pr(A) to this data based on several reasonable assumptions about the character of the applicant pool. I show that the estimates of Pr(A) yielded by my framework are excellent predictors of admission outcome for a sample of student data collected from the Internet.
The Insider Perspective: Three Emic Studies About Sensemaking and Organizational Change Over Time

Scholars contend that our understanding of organizational change would benefit from research that more closely represents the lived reality of organizational change. Research on sensemaking is particularly valuable in this vein because of its focus on the individual’s experience and understanding. Existing empirical work on sensemaking about organizational change, however, is largely based on retrospective interview data and/or focuses on a narrow window of time during change implementation and thus does not thoroughly consider how individuals’ make sense of change over time. This thesis seeks to address this limitation through three complementary studies aimed at understanding how individuals make sense of organizational change over time from an emic (or insider) perspective. All three papers are interpretive process-oriented case studies, which use grounded theory data analysis techniques to analyze interview data for the same 26 hospital employees collected at three points in time over a five year period of organizational change. Paper one is about the interpretation stage of sensemaking. This paper identifies four stable sensemaking lenses and develops two new concepts related to how individuals make sense of organizational change over time: (1) sensemaking constancy and (2) sensemaking scope. Paper two considers sensemaking triggers and explores how individuals’ retrospective sensemaking compares to their real-time assessments during change. Findings indicated that in “real-time” the experience of change varied in somewhat stable way between groups but that when “looking back” over the change, change agents tended to view the changes more positively than change recipients. Finally, paper three looks at the enactment stage of the sensemaking process and examines individuals’ strategies for coping with excessive change. Coping (from cognitive appraisal theory) and sensemaking are positioned as overlapping processes that individuals use to make sense of and respond to excessive organizational change. Five patterns, or stories about how individuals cope with and make sense of excessive change over time, emerged. Taken as a group, the three papers contribute to the research and practice of organizational change by offering novel insights and theoretical contributions related to how individuals experience and make sense of change over time.
Unsupervised segmentation of skin lesions

During the last decades, the incidence rate of cutaneous malignant melanoma, a type of skin cancer developing from melanocytic skin lesions, has risen to alarmingly high levels. As there is no effective treatment for advanced melanoma, recognizing the lesion at an early stage is crucial for successful treatment. A trained expert dermatologist has an accuracy of around 75 % when diagnosing melanoma, for a general physician the number is much lower. Dermoscopy (dermatoscopy, epiluminescence microscopy (ELM)) has a positive effect the accuracy rate, but only when used by trained personnel. The dermoscope is a device consisting of a magnifying glass and polarized light, making the upper layer of the skin translucent. The need for computer-aided diagnosis of skin lesions is obvious and urgent. With both digital compact cameras and pocket dermoscopes that meet the technical demands for precise capture of colors and patterns in the upper skin layers, the challenge is to develop fast, precise and robust algorithms for the diagnosis of skin lesions. Any unsupervised diagnosis of skin lesions would necessarily start with unsupervised segmentation of lesion and skin. This master’s thesis proposes an algorithm for unsupervised skin-lesion segmentation and the necessary pre-processing. Starting with a digital dermoscopic image of a lesion surrounded by healthy skin, the pre-processing steps are noise filtering, illumination correction and removal of artifacts. A median filter is used for noise removal, because of its edge-preserving capabilities and computer efficiency. When the dermoscope is put in contact with the patient’s skin, the angle between the skin and the magnifying glass impacts on the distribution of the light emitted from the diodes attached to the dermoscope. Scalar multiplication with an illumination correction matrix, individually adapted to each image, facilitates the analysis of the image, especially for skin lesions of light color. Artifacts such as scales printed on the glass of the dermoscope, hairs and felt-pen marks on the patient’s skin are all obstacles for correct segmentation. This thesis proposes a new, robust and computer effective algorithm for hair removal, based on morphological operations of binary images. The segmentation algorithm is based on global thresholding and histogram analysis. Unlike most segmentation algorithms based on histogram analysis, the algorithm proposed in this thesis makes no assumptions on the
Visual Perception For Robotic Spatial Understanding

Humans understand the world through vision without much effort. We perceive the structure, objects, and people in the environment and pay little direct attention to most of it, until it becomes useful. Intelligent systems, especially mobile robots, have no such biologically engineered vision mechanism to take for granted. In contrast, we must devise algorithmic methods of taking raw sensor data and converting it to something useful very quickly. Vision is such a necessary part of building a robot or any intelligent system that is meant to interact with the world that it is somewhat surprising we don't have off-the-shelf libraries for this capability. Why is this? The simple answer is that the problem is extremely difficult. There has been progress, but the current state of the art is impressive and depressing at the same time. We now have neural networks that can recognize many objects in 2D images, in some cases performing better than a human. Some algorithms can also provide bounding boxes or pixel-level masks to localize the object. We have visual odometry and mapping algorithms that can build reasonably detailed maps over long distances with the right hardware and conditions. On the other hand, we have robots with many sensors and no efficient way to compute their relative extrinsic poses for integrating the data in a single frame. The same networks that produce good object segmentations and labels in a controlled benchmark still miss obvious objects in the real world and have no mechanism for learning on the fly while the robot is exploring. Finally, while we can detect pose for very specific objects, we don't yet have a mechanism that detects pose that generalizes well over categories or that can describe new objects efficiently. We contribute algorithms in four of the areas mentioned above. First, we describe a practical and effective system for calibrating many sensors on a robot with up to 3 different modalities. Second, we present our approach to visual odometry and mapping that exploits the unique capabilities of RGB-D sensors to efficiently build detailed representations of an environment. Third, we describe a 3-D over-segmentation technique that utilizes the models and ego-motion output in the previous step to generate temporally consistent segmentations with camera motion. Finally, we develop a synthesized dataset of chair objects with part labels and investigate the influence of parts on RGB-D based object pose recognition using a novel network architecture we call PartNet. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Computer and Information Science First Advisor Kostas Daniilidis This dissertation is available at ScholarlyCommons: https://repository.upenn. edu/edissertations/3242
Understanding the Impact of a Multi-Institutional STEM Reform Network through Key Boundary-Spanning Individuals

ABSTRACT Multi-institutional STEM reform networks have become a popular way to address the challenges facing undergraduate STEM education. Despite an intuitive sense that networks are effective reform mechanisms, few empirical research studies have investigated their impact. This qualitative case study examined the Center for the Integration of Research, Teaching, and Learning (CIRTL), a network of 38 universities that seeks to advance undergraduate STEM education through the preparation of future faculty. Using a case study design involving qualitative network maps and semi-structured interviews, this study investigated the inter- and intra-organizational boundary-spanning behaviors of key institutional representatives who link their campus to the CIRTL Network. Findings indicate that institutional representatives engaged in finding, diffusion, translation, and gaining institutional support behaviors in service to enhancing and expanding their local CIRTL programming. Results and a proposed boundary-spanning framework demonstrate the potential that such behaviors have in helping to advance post-secondary STEM education.
A NEW LOOK AT THE FACTOR STRUCTURE OF THE CENTRALITY OF EVENT SCALE

Objective : Centrality of Event theory suggests that traumatic events are overly integrated into the memory network as such events become central to the identity, and serve as a reference and turning point. Berntsen and Rubin (2006, 2007) developed Centrality of Event Scale in order to assess these features, and gathered evidence mostly from Western cultures with contradictory findings. However, centrality of event theory is closely related how the negative event integrated into the self-concept, and therefore scientific support across different cultures is required as well as across qualitatively distinct negative events. The current study investigated the psychometric properties of the scale for Turkish culture with a confirmatory factor analytic approach to compare validity of various factor structures, on distinct negative event histories to cross-validate the factor structure across distinct samples. Method : A sample of 340 undergraduate students completed Turkish versions of Centrality of Event Scale, Posttraumatic Stress Checklist-Civilian version, and Beck Depression Inventory. Results : Confirmatory factory analyses and measurement invariance tests revealed that the short version of CES with a single factor solution is a valid measure sample from Turkish culture and samples with distinct negative event histories. Conclusion : The results revealed that the short form of the scale to be a reliable and valid instrument for Turkish culture.
A Dual Process Theory Explanation for Door-in-the-Face Effectiveness

Abstract Two field experiments demonstrate that additive effects of heuristic and systematic processing can explain door-in-the-face technique (DITF) compliance. In Study 1, additive effects of heuristic and systematic processing resulted in an increase in charitable donations. Study 2 replicated the additive effects mean differences observed in the first. Attitude toward a charitable issue was also more strongly correlated with donations in a strong arguments than a no arguments DITF condition, compatible with an additive effects explanation. Both studies indicate that rational processing of argument information has a significant and independent effect on donations in addition to traditional heuristic processing typically inferred in explaining DITF compliance.
Real-Time Behaviour Planning and Highway Situation Analysis Concept with Scenario Classification and Risk Estimation for Autonomous Vehicles

The development of autonomous vehicles is one of the most active research areas in the automotive industry. The objective of this study is to present a concept for analysing a vehicle’s current situation and a decision-making algorithm which determines an optimal and safe series of manoeuvres to be executed. Our work focuses on a machine learning-based approach by using neural networks for risk estimation, comparing different classification algorithms for traffic density estimation and using probabilistic and decision networks for behaviour planning. A situation analysis is carried out by a traffic density classifier module and a risk estimation algorithm, which predicts risks in a discrete manoeuvre space. For real-time operation, we applied a neural network approach, which approximates the results of the algorithm we used as a ground truth, and a labelling solution for the network’s training data. For the classification of the current traffic density, we used a support vector machine. The situation analysis provides input for the decision making. For this task, we applied probabilistic networks.
Energy Consumption Estimation for Routing EVs based on Driver Behavior

There has been a significant increase in the sales of electric vehicles (EVs) in the United States and abroad in the last few years. Nevertheless, the overall adoption of these vehicles is hindered by range limits of EVs in conjunction with long charging times. In this context, it is essential to determine current energy demands and to predict future demand. This paper presents approaches for predicting energy consumption of EVs and discusses their eligibility for this purpose. Four modeling approaches (i.e., dynamic systems, neural networks, statistical models, physics-based models) have primarily been used in recent literature. In order to predict an EV’s energy demand, several modeling techniques are combined to give an accurate prediction of the future energy consumption. For the battery EVs a combination of physics-based modeling and statistical modeling have shown to be an effective and efficient choice.
Comparative characteristics of anthropometric indicators, level of physical and technical readiness of young players of 12 and 15 years of different playing fields

Purpose: To develop model characteristics of physical and technical fitness of players of 12 and 15 years of different playing roles.Material and methods. The study was attended by football players of 12 and 15 years of sport school "Areal" Kharkiv, in which the developed technologies were applied in the educational process. In total, 23 forvards, 28 midfielders, 30 defenders and 15 goalkeepers were invited for the survey. Indicators of the running time of segments of 15 m from the course, 30 m from the course and 60 m from the course were determined; shuttle running time 5 to 20 m. Determined the length and weight of the body; Heart rate at rest and heart rate after a shuttle run. From the technical readiness indicators were determined: the time of holding the soccer ball on the foot; juggling, that is, the number of shots of the ball with the foot without losing the ball; time to run a soccer ball on a mission; goal kicks for accuracy; strikes the ball at flight range.Results. Young players of 12 and 15 years of different game specializations differ in terms of physical and technical fitness. The largest number of significant differences were found in the technical readiness of football players for 15 years. Field players at speed capabilities are significantly superior to goalkeepers. Goalkeepers, on the contrary, have lower running speeds. The level of speed endurance is relatively high for defenders and midfielders, as opposed to goalkeepers. The obtained data allowed us to build models of physical development, physical and technical readiness of players of 12 and 15 years of different playing fields, on the basis of which training programs for representatives of different playing roles can be developed.Conclusions. The data obtained indicate the need for a differentiated approach in the training process of young football players, taking into account their playing role. The differentiated approach is more relevant at the age of 15 compared to the age of 12.
Pengaruh Gender, Tekanan Ketaatan, Kompleksitas Tugas, Pengetahuan dan Pengalaman terhadap Audit Judgment

Audit judgment is a policy or consideration of the auditor in determining the result of its audit opinion that refers to the formation of an idea, opinion or estimate of an object or event. Objectives of this study is to examine how gender, comflexity of tasks, knowlwdge and experience influence ti audit judgment. Respondent of this study is auditor who works in the office of public accountants in east java. , both the level of auditor junior and senior auditor. The sampling technique in this study simple random sampling. Data were obtained through questionaires sent direcly and through the mail survey (postal). Of the 118 questionaires sent, only 89 can be analyzed. Data analysis technique using multiple regression . Based on the result of statistical test, indicates that gender, did not significant affecet to the auditor judgment. Pressure of compliance, complexity of task, knowledge and experience affrect of the audit judgement.
Using Survey Questions to Measure Preferences: Lessons from an Experimental Validation in Kenya

Can a short survey instrument reliably measure a range of fundamental economic preferences across diverse settings? We focus on survey questions that systematically predict behavior in incentivized experimental tasks among German university students (Becker et al. 2016) and were implemented among representative samples across the globe (Falk et al. 2018). This paper presents results of an experimental validation conducted among low-income individuals in Nairobi, Kenya. We find that quantitative survey measures -- hypothetical versions of experimental tasks -- of time preference, attitude to risk and altruism are good predictors of choices in incentivized experiments, suggesting these measures are broadly experimentally valid. At the same time, we find that qualitative questions -- self-assessments -- do not correlate with the experimental measures of preferences in the Kenyan sample. Thus, caution is needed before treating self-assessments as proxies of preferences in new contexts.
Recognizing Emotional Expression in Game Streams

Gameplay is often an emotionally charged activity, in particular when streaming in front of a live audience. From a games user research perspective, it would be beneficial to automatically detect and recognize players' and streamers' emotional expression, as this data can be used for identifying gameplay highlights, computing emotion metrics or to select parts of the videos for further analysis, e.g., through assisted recall. We contribute the first automatic game stream emotion annotation system that combines neural network analysis of facial expressions, video transcript sentiment, voice emotion, and low-level audio features (pitch, loudness). Using human-annotated emotional expression data as the ground truth, we reach accuracies of up to 70.7%, on par with the inter-rater agreement of the human annotators. In detecting the 5 most intense events of each video, we reach a higher accuracy of 80.4%. Our system is particularly accurate in detecting clearly positive emotions like amusement and excitement, but more limited with subtle emotions like puzzlement.
Socio-Demographic, Biopsychosocial and Lifestyle Behaviors Related with Eating Disorders of Children and Adolescents of Costa Rica

The objective of the study was to evaluate the association of sociodemographic, biopsychosocial and lifestyle behaviors (diet, sedentarism and physical activity) with eating disorders (ED) in a national sample of Costa Rican children and adolescents. This was a cross-sectional and descriptive study. A total of 2667 students of elementary, middle and high school from sixty-four educative centers (40 schools and 24 high schools) of Costa Rica participated in the study. The predictive factors associated with ED are: body image dissatisfaction (β = 0.367; p β = 0.131; p β = 0.109; p 0.001), intentionally hurt or harm itself (β = 0.074; p β = 0.053; p β = 0.052; p β = 0.051; p β = -0.071; p β = -0.068; p = 0.010) and the age (β = -0.064; p = 0.001) are not related with the development of ED. Psychosocial factors (body image dissatisfaction and intentionally self-injure) had the greatest influence (near 27%) of the global variance of the regression model that explained the association with ED. Programs should be promoted to monitor children and adolescents with excess weight, as well as, the coexistence of erroneous weight and body image perceptions in these population. This could protect against the development of ED in children and adolescents. Evidence-Based Medicine: Level V of evidence (descriptive study).