Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Affecting EFL Teachers' Motivation in Thai University: A Case Study of EFL Teachers at Tertiary Level.

This study aimed to find out factors that motivate and demotivate EFL teachers in Thai university. The participants were 70 Thai teaching staff at Chulalongkorn University Language Institute in Thailand. A self-completed open-ended and close-ended questionnaire was devised. Then, the scores of rate and responses were compared to investigate the factors that affect EFL teachers’ motivation. The result revealed that teachers value interpersonal relationships with students and colleagues along with other intrinsic motivations such as imparting knowledge and providing service to society. The result also showed that teachers are demotivated by extrinsic factors such as heavy workload and low salary which are believed to lead to job dissatisfaction.
Bootstrapping for polling data from the EU referendum vote in the United Kingdom*

Voting polls have been the most prominent and widely used way of forecasting results. The most straightforward way of determining the voter’s intentions would be to ask who or what he or she is going to vote. However, it has been found that even how this questioned is phrased can effect the results. Ambivalent voters as well as vacillating voters who have not fully though their decision can have a significant impact in both the polling results as well as towards a possible divergence from the results during the voting day. For voters in these cases, addition information such as their features can signify the most probable tend for each individual in regards to similar examples that have very strong opinions. By considering these features, what is proposed is a simulation of each person’s experience, when casting the ballot. This paper presents the use of Bootstrapping (or also known as bagging), to normalise the features of the polling population in order to represent the entire population that is eligible to vote. This technique will be applied to data from the 2016 Referendum that held the question of the remain or exit of the United Kingdom from the European Union.
Code Quality Metrics for the Functional Side of the Object-Oriented Language C#

With the evolution of object-oriented languages such as C#, new code constructs that originate from the functional programming paradigm are introduced. We hypothesize that a relationship exists between the usage of these constructs and the error-proneness. Measures deﬁned for this study will focus on functional programming constructs where object-oriented features are used, this often aﬀects the purity of the code. Built on these measures we try to deﬁne a metric that relates the usage of the measured constructs to error-proneness. To validate the metric that would conﬁrm our hypothesis, we implement the methodology presented by Briand et al. [BEEM95] for empirical validation of code metrics. The results of this research granted new insights into the evolution of software systems and the evolution of programming languages regarding the usage of constructs from the functional programming paradigm in object-oriented languages.
Running head: THE DARK TRIAD AND MORAL DISENGAGEMENT The Dark Triad, moral disengagement, and cognitive distortions in adolescents

Youth Care youngsters often have antisocial personality traits, referred to as the Dark Triad traits, including Machiavellianism, Psychopathy and Narcissism. Those traits seem paradoxal; it stands for behaving antisocial but still have a sense of grandiosity. The social cognitive theory offers an explanation via moral disengagement (MD), as one can cognitively make its detrimental conduct accepted or even righteous. The current study focused on the differences in use of MD and the presence of antisocial personality traits between Youth Care participants (n = 35) and healthy controls (n = 305). Additionally, in the subsample of the current study, self-serving cognitive distortions were measured. Results indicated that Youth Care participants score higher on antisocial personality traits and use more MD than healthy controls, except for the narcissism scale. It is concluded that results could have implications for treatment of Youth Care youngsters, but further research is needed.
Registration of Manuscript Images using Rotation Invariant Features

One medieval Slavonic manuscript is recorded, investigated and analyzed by philologists in collaboration with computer scientists. The aim of the project is to develop algorithms that support the philologists by automatically deriving the description and restoration of the scripts. The parchment partially contains two scripts, where the first script was erased. In addition, the script is degraded due to poor storage conditions. In order to enhance the degraded script, the manuscript pages are imaged in seven bands between 330 and 1000 nm. A registration, aligning the resultant images, is necessary so that further image processing algorithms can combine the information gained by the different spectral bands. Therefore, the images are coarsely aligned using rotationally invariant features and an affine transformation. Afterwards, the similarity of the different images is computed by means of the normalized cross correlation. Finally, the images are accurately mapped to each other by the local weighted mean transformation. The algorithms used for the registration and preliminary results are presented in this paper.
ENHANCING THE PERFORMANCE OF SMOTE ALGORITHM BY USING ATTRIBUTE WEIGHTING SCHEME AND NEW SELECTIVE SAMPLING METHOD FOR IMBALANCED DATA SET

SMOTE is one of the well-known algorithms for balancing train data by adding synthetic data on minor class data. One of the stages in SMOTE is finding the nearest neighbors (kNN) as the basis for creating synthetic data using Euclidean distance. In cases where a small number of attributes having high correlation value than others, finding kNN using Euclidean without considering this correlation may not find representative neighbors. This paper introduces AWH-SMOTE (Attribute Weighted and kNN Hub on SMOTE), which enhances SMOTE in improving neighbors and noise identification using attribute weighting and also improving selective sampling method using occurrence data in the kNN hub. Wojna and Information Gain methods are used for attribute weighting. A small number of occurrences in the kNN hub results in more synthetic data generated so that minority data in dangerous region are more represented. Nine public datasets from Keel repository are used to evaluate AWH-SMOTE. Evaluation shows AWH-SMOTE has better performance on minority precision and minority f-measure for both pruned and unpruned condition than other oversampling algorithms. Information Gain as attribute weighting method in AWH-SMOTE achieves best performance in unpruned condition when compared to other weighting methods for minority recall, minority precision and minority f-measure.
Use of GIS for the analysis of ecological variables distribution at Bacia de Campos (RJ)

The phytoplankton biomass distribution and related physical and chemical variables were studied at Bacia de Campos (RJ), during winter (June and July/ 1992) through Geographic Information Systems (GIS). Thematic maps of chlorophyll-a, water masses, nitrate and cluster analysis, were matched spatially for the depths of 0, 20, 50 and 100 m. The spatial distribution of the groups, individualized by cluster analysis, was strongly associated to chlorophyll-a distribution, more evidently than those of water masses or nutrients.
Trip volume seasonal variations at regional level – case study of Małopolska GSM OD matrices

In this paper we analyze the big-data set of GSM origin-destination matrices collected between 360 districts (Powiat) in Poland to indirectly observe the trip volume fluctuations: weekly and seasonal. We utilized the dataset obtained from BTS position for all clients registered to a single provider. The data was collected over several days, which allows for a valuable temporal analysis of trip volumes. We analyze internal and external (inbound and outbound) trips in Małopolska region, intra-zonal trips (within district), inter-zonal (between districts of Małopolska). We discuss the general variability of observed trip volumes. We present the weekly fluctuations (working day, Friday, Saturday, Sunday) and the seasonal ones (winter, holiday, etc.). We verify the results obtained from GSM data with the traffic counts and their seasonal variations provided by national road administration (GDDKiA). Main contribution of the paper is presenting the observed fluctuations from the GSM data and comparing them with the classically collected data, as we demonstrate the results are comparable.
Glucose metabolic disorder in primary hyperparathyroidism: a systematic review and meta-analysis

Background: Primary hyperparathyroidism (PHPT) is associated with increased morbidity and mortality of cardiovascular disease (CVD). Studies have demonstrated that patients with PHPT have more metabolic disorders than age-matched healthy controls, which mainly include obesity, insulin resistance, dyslipidemia, hyperglycemia and hypertension. In this meta-analysis, metabolic disorders of PHPT patients were systematically reviewed. Methods: Databases were searched for studies published between 2005 and 2017 that reported body mass index (BMI), insulin resistance and blood glucose in PHPT patients and healthy controls. Results: A total of nine eligible studies were identified. Compared to healthy controls, PHPT patients had higher levels of plasma insulin (SMD = 0.37, CI: 0.23~0.52, P = 0.014) and insulin resistance index (HOMA-IR) (SMD = 0.65, CI: 0.27~1.02, P = 0.009). However, BMI was not statistically different between PHPT patients and healthy controls (SMD = 0.09, CI: -0.14~0.31, P = 0.32). Although fasting blood glucose (FBG) levels of PHPT patients were similar to healthy controls (SMD = 0.06, CI: -0.36~0.47, P = 0.21), subgroup analysis showed that hypercalcemic primary hyperparathyroidism (HPHPT) patients had higher FBG levels than healthy controls (SMD = 0.43, CI: 0.18~0.68, P = 0.025). Conclusions: Compared with healthy controls, PHPT patients had heavier insulin resistance, but only hypercalcemia PHPT patients had higher FBG levels. There was no significant difference in BMI between PHPT patients and healthy controls.
THE UNIVERSITY OF CHICAGO FAST AND RELIABLE MISSING DATA CONTINGENCY ANALYSIS WITH PREDICATE-CONSTRAINTS A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE PHYSICAL SCIENCE IN CANDIDACY FOR THE DEGREE OF MASTER

Today, data analysts largely rely on intuition to determine whether missing or withheld rows of a dataset significantly affect their analyses. We propose a framework that can produce automatic contingency analysis, i.e., the range of values an aggregate SQL query could take, under formal constraints describing the variation and frequency of missing data tuples. We describe how to process SUM, COUNT, AVG, MIN, and MAX queries in these conditions resulting in hard error bounds with testable constraints. We propose an optimization algorithm based on an integer program that reconciles a set of such constraints, even if they are overlapping, conflicting, or unsatisfiable, into such bounds. We also present a novel formulation of the Fractional Edge Cover problem to account for cases where constraints span multiple tables. Our experiments on 4 datasets against several statistical imputation and inference baselines show that statistical techniques can have a deceptively high error rate that is often unpredictable. In contrast, our framework offers hard bounds that are guaranteed to hold if the constraints are not violated. In spite of these hard bounds, we show competitive accuracy to statistical baselines.
Experimental investigation of beams under coupled bending and torsion

The design of doubly-curved systems called for innovative modelling techniques to evaluate the final geometry of the structure. They can simulate the behaviour of beams under large displacements. Even though it is never analysed, the deformation path is can be critical in such complex configurations. This paper analyses the potential of digital technologies to investigate the behaviour of beams under bendingtorsion coupling in large deformation. In a first part, we will present the setup for an experiment, based on the use of robotic arms. The versatility of robotic arms allows to access complex configurations or loading paths, while controlling the test either in displacements or in forces. In a second part, an enhanced photogrammetry procedure is introduced. The aim is to retrieve the geometry of the beam even in the case of complex 3D shapes. The acquisition yields a point cloud representation of the beam. Finally, we propose a workflow to make the interaction with robotic arm, simple and efficient. The novel experimental setup aims at making possible the study of beams with complex form, under ever more complex force.
Vildagliptin reduces lipid levels possibly by mechanisms associated with HMGCR and CYP7A1

Objective: The present study aims to investigate the mechanism by which dipeptidyl peptidase 4 (DPP4) inhibitors influence lipid profile. Methods: C57BL/6 mice were fed a high fat diet (HFD), and HepG2 cells were treated with vildaglipitin, a DDP-4 inhibitor. Liver cholesterol/triglyceride content assay was performed to determine the content of liver cholesterol and triglyceride. Western blot was used to determine the expression of protein. To show the structure of the liver, liver samples were stained with H&E, oil red O and Filipin. Results: Vildagliptin failed to modify normal plasma glucose levels. Meanwhile, vildagliptin improved lipid profiles, reducing both lowdensity lipoprotein (LDL) in blood and total cholesterol (TC) in the liver. Vildagliptin increased the expression of both 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and cholesterol 7 alpha-hydroxylase (CYP7A1) in the liver from mice fed a HFD. In vitro, vildagliptin increased the expression of HMGCR and CYP7A1 in HepG2 cells. By contrast, there were no clear changes in the levels of TC, free cholesterol, and TG after treatment with Vildagliptin for 24 h. Conclusion: The study demonstrates that vildagliptin improves lipid profile possibly by mechanisms that are associated with functional expression of HMGCR and CYP7A1.
COMBINE YIELD MONITOR ACCURACY

The Yield Monitor Test Facility (YMTF) was used to evaluate the GreenStar mass flow sensor in a 9600 clean grain elevator under dynamically varying inflow rates. It was found that the GreenStar yield sensor predicted accumulated mass with less than 4% error (under dynamically varying step and ramp flow rates) within normal operating conditions of 4.2 to 16.9 kg/s (10 to 40 bu/min) and predicts flow at upper limits of 20 kg/s with a 7.3% error. The yield sensor followed ASAE Draft Standard X578 variable step flow rates, while operating under 20 kg/s, but exhibited significant errors for flows above 20 kg/s. The maximum error in accumulated mass for the variable step profile was less than 2%, while the approximate average instantaneous flow error at steady state was approximately 4.4% with a 3.4% standard deviation. It was found that transient flow had maximum accumulated flow errors of approximately 5%, while oscillating flow had errors around 2.5%. These errors were attributed to the fact that the transient flow maintains flow at the extremity of the GreenStar operation range (based on 12.7 kg/s calibration) for longer periods. Additionally, 2−s time averaging of flow readings smoothed noise in the original signal with minimal loss of signal definition.
Early attrition among suicidal clients

ABSTRACT The study aimed to identify the level of suicidal ideation in early attrition clients and their reasons for the early termination of their therapy. The cross-sectional design involved early attrition clients (CA) who withdrew from therapy before their second session (n = 61), and continuing clients who (CC) progressed beyond their second session (n = 73). All completed the Suicidal Behaviours Questionnaire-Revised scale, and the CA group also completed the Reasons for Terminating Therapy Scale. Clients were significantly more likely to disengage if they were self-referred, had no health-care support or had lower suicidal ideation. The study identifies the significant role of suicidal ideation and referral source in therapy retention and the implications this has for therapists and counsellors.
The efficacy of methylene blue for back pain: a meta-analysis of randomized controlled trials

Introduction: The efficacy of methylene blue for back pain remains controversial. We conduct a systematic review and meta-analysis to explore the impact of methylene blue versus placebo on back pain. Methods: We searched PubMed, EMbase, Web of science, EBSCO, and Cochrane library databases through June 2018 for randomized controlled trials (RCTs) assessing the effect of methylene blue versus placebo on the alleviation of back pain. This meta-analysis is performed using the random-effect model. Results: Three RCTs involving 229 patients are included in the meta-analysis. Overall, compared with control group for back pain, methylene blue can remarkably decrease pain scores at 24-48 h (Std. MD = -1.69; 95% CI = -2.07 to -1.32; P < 0.00001), 2-3 month (Std. MD = -1.09; 95% CI = -1.62 to -0.56; P < 0.0001), and ODI at 2-3 month (Std. MD = -0.85; 95% CI = -1.18 to -0.52; P < 0.00001), as well as moderate-severe functional disability after 3-6 months (RR = 0.46; 95% CI = 0.24 to 0.87; P = 0.02), but has no significant impact on pain scores (Std. MD = -1.60; 95% CI = -3.50 to 0.30; P = 0.10) and ODI (Std. MD = -1.95; 95% CI = -4.34 to 0.44; P = 0.11) at 6 months. Conclusions: Methylene blue can substantially decrease pain scores and ODI within 3 months, as well as moderate-severe functional disability after 3-6 months in patients with back pain.
Disaggregating economic inequality from space

Reducing inequality is number 10 of the United Nations sustainable development goals. Inequality in society affect economic growth, development and poverty reduction, and through these indirectly affect environmental degradation, food security and other major problems facing society today. Analyzing the temporal and spatial dynamics of economic inequality requires comparable data on finer scales. In most places, such detailed data is not available due to the high cost of production and uncertainty in the data and methods used to produce the inequality indices. In order to fill the data gap, this thesis attempts to develop an inequality index from nigh time light (NTL) satellite images. Previous studies suggest the use of NTL images as a proxy for missing data on SE-variables such as GDP and population. The advantages of these satellite-based proxies for measures human society suggests that similar methodology can be used to develop a proxy for inequality and thereby solve the data gap in this area. The results presented in this paper confirms the use of NTL to model wealth, however modelling economic inequality is more complicated, and introduces problems based on the modifiable area unit problem and specific problems related to the definition of the Gini coefficient, a measure of inequality. These problems are reflected by the weak and in some cases non-significant associations found between the modelled and the actual Gini coefficients. The results of this thesis show that both the correlations between actual and modelled Gini coefficients, the distribution of error as well as the mean absolute error varies depending on spatial scale of the analysis. Although spatial scale is suggested as a controlling factor, some patterns between the scales are still unexplained. Further investigation into other explanatory variables as well as avoiding errors due to discrepancies between different datasets might clear up the weak trends suggested in this thesis.
VEGETATION ANALYSIS IN DESERT ENVIRONMENT USING SAR IMAGERY

Orbital synthetic aperture radar (SAR) C-band data acquired by ERS-1/2 in vv-polarization and Radarsat in hh-polarization during the period from 1996 to 1999 were used to evaluate their combined information potential for classification of land cover in the arid environment of Kuwait. Individual SAR scenes were orthorectified using a digital elevation model (DEM) of Kuwait, radiometrically adjusted for incidence angle effects, and mosaics were generated for the whole country. Thirteen classes of the joint ERS-1/2 and Radarsat images were identified based on Bhattacharya distance and geospatial pattern. The high degree of correlation between the C-band radar backscatter observed by ERS and Radarsat made unambiguous classification of surface material difficult when using C-band data alone. However, the physical data collected at the ground verification sites were used to assign nominal categories to the radar clusters that resulted from unsupervised classification. This categorization or relabeling of the clusters then provided the basis both for the generation of thematic maps and for accuracy assessment. Backscatter is shown to be related to the percent cover by annual vegetation for ERS, while its influence appears to be somewhat less for Radarsat. Backscatter was observed to be positively related to the percent cover by perennial vegetation. Radar backscatter is more highly correlated with total vegetation volume for ERS than for Radarsat, and is probably a consequence of both the lower angle of incidence for ERS and its vv-polarization. In addition to vegetation cover, the C-band radar data were found to be related to surface roughness, percentage of coarse material in the surface layer, and moisture conditions.
DEEP at HASOC2019: A Machine Learning Framework for Hate Speech and Offensive Language Detection

In this paper, we describe the system submitted by our team for Hate Speech and Oﬀensive Content Identiﬁcation in Indo-European Languages (HASOC) shared task held at FIRE 2019. Hate speech and oﬀensive language detection have become an important task due to the overwhelming usage of social media platforms in our daily life. This task has been applied for three languages namely, English, Germany and Hindi. The proposed model uses classical machine learning approaches to create classiﬁers that are used to classify the given post according to diﬀerent subtasks.
Large Scale Music Recommendation

Recommending tracks in the music domain is typically a demanding task, mainly due to the size of the catalog (e.g., last.fm claims a 12-million track catalog) and of the events per user (an active user can listen dozens of songs per day). Adding further dimensions, such as contextual information, usually makes the problem di cult to treat. In this work we propose an innovative approach namely the implicit playlist recommender (IPR) and compare it with two context-aware, popularity-based solutions used as baselines. All algorithms are implemented using the Apache Spark programming model to face with the large-scale problem. The IPR model is trained using the user implicit listening sessions and proves to outperform the two baseline solutions when the user session is longer than 5 tracks.
Triangulation as a method of the research in jurisprudence

The purpose of the article is proposing new possibilities in using the triangulation in jurisprudence. This paper consists of two main substantial parts ended in summarizing conclusion. The first component discusses the paradigm of the social science in the context of using the research methods. After that, the essence and meaning of the triangulation were explained. It was the foundation to suggest triangulation as a research method in jurisprudence.