Miracle-dz/UniFAR-FTUs huggingface dataset:
A strengths-based approach in goal pursuit among young professionals : a mixed method design

Introduction: A strengths based approach has been suggested to be of potential benefit for goal pursuit with respect to health goals. Yet, a clear theory on the working mechanism of personal strengths use in goal pursuit is lacking. The study at hand therefore aimed at examining young professionals’ strengths use in order to work on an individually chosen and personally relevant health goal, thereby exploring the context of relevant concepts such as motivation, well-being and need satisfaction as well as potential determinants of strengths use during study participation. Method: The study employed a mixed method design, including qualitative interviews, surveys and an ecological momentary assessment. In total, 9 participants compromising 2 men and 7 women with a mean age of 23.4 (SD=1.51) completed the four week study. Participants were asked to apply self-chosen personal strengths during health goal pursuit. During participation, surveys assessed participants’ motivation, psychological need satisfaction and overall well-being. Besides, interviews aimed at assessing use of strengths and potential working mechanism while an ecological monetary assessment via the mobile application PACO continuously measured the context of strengths use during goal pursuit. Results: Even though applying one’s strengths seemed to happen rather unconsciously, qualitative analyses suggest strengths use to fulfil various purposes which, explored in the light of a conceptual model, seems to resemble self-regulation theory. Furthermore, strengths use was shown to possibly support goal pursuit by means of increasing self-efficacy and potentially result in some degree of self-actualization. Analyses of survey data suggest a significant increase in relatedness among participants, which is in accordance with social environment repeatedly appearing as a crucial factor in both goal pursuit and strengths use. Linear mixed model analyses suggest use of strengths to be rather unstable over time, yet reinforce health goal pursuit among the sample. Discussion: The rich amount of data as well as mixed methods design allow for triangulation of findings, thereby offering insights in strengths use among a highly educated sample. Yet, the suggested working mechanism of strengths use needs verification in future research. Besides, external validity of the findings with respect to less motivated and educated samples as well as non-self-concordant health goals is questionable. Further research with respect to diverse populations as well as non-self-concordant health goals is therefore suggested.
Student practices in copyright culture: accessing learning resources

ABSTRACT Using Schatzki’s practices framework as a lens, this paper reports on the practices of university students accessing learning resources at a research-intensive university in South Africa. Using a mixed-methods approach, 1001 survey responses and 6 focus groups were analysed to explore how students in three professional disciplines access learning resources, with the focus on digitally mediated piracy practices. The findings suggest a blurring between the legal and the illegal and indicate the normalcy of piracy practices, with nuanced distinctions and understandings manifest.
Analysis of Individual Loan Defaults Using Logit under Supervised Machine Learning Approach

Financial institutions have a large amount of data on their borrowers, which can be used to predict the probability of borrowers defaulting their loan or not. Some of the models that have been used to predict individual loan defaults include linear discriminant analysis models and extreme value theory models. These models are parametric in nature since they assume that the response being investigated takes a particular functional form. However, there is a possibility that the functional form used to estimate the response is very different from the actual functional form of the response. The purpose of this research was to analyze individual loan defaults in Kenya using the logistic regression model. The data used in this study was obtained from equity bank of Kenya for the period between 2006 to 2016. A random sample of 1000 loan applicants whose loans had been approved by equity bank of Kenya during this period was obtained. Data obtained was on the credit history, purpose of the loan, loan amount, nature of the saving account, employment status, sex of the applicant, age of the applicant, security used when acquiring the loan and the area of residence of the applicant (rural or urban). This study employed a quantitative research design, it deals with individual loans defaults as group characteristics of a borrower. The data was pre-processed by seeding using R- Software and then split into training dataset and test data set. The train data was used to train the logistic regression model by employing Supervised machine learning approach. The R-statistical software was used for the analysis of the data. The test data set was used to do cross-validation of the developed logistic model which later was used for analysis prediction of individual loan defaults. This study focused on the analysis of individual loan defaults in Kenya using the logistic regression model in Machine learning. The logistic regression model predicted 303 defaults from train data set, 122 non-defaults and misclassified loans were 56 and 69. The model had an accuracy of 0.7727 with the train data and 0.7333 with the test data. The logistic regression model showed a precision of 0.8440 and 0.8244 with the train and test data respectively. The performance of the model with both the train and test data was illustrated using a plot of train errors and test errors against sample size on the same axes. The plot showed that the performance of the model increases with an increase in sample size. The study recommended the use of logistic regression in conjunction with supervised machine learning approach in loan default prediction in financial institutions and also more research should be carried out on ensemble methods of loan defaults prediction in order to increase the prediction accuracy.
Measurement and Evaluation in Education by Active Learning for Students in Mathematics Major Usage of Activities Base

These The goals are: to develop and discover efficiency of textbook on the subject of Measurement and Evaluation in Education by active learning for students in mathematics major usage of activities base to meet with criteria at 80/80 and to study satisfaction level of students by using the sufficiency economy philosophy textbook for sustainable learning . The target group herein was 40 students who studied in Academic year of 2014 and interested in online registration. Target group was determined by using purposive sampling. Tools used in this research were 20 items of post-test contained in textbook, student’s satisfaction evaluation form towards textbook usage. Data analysis was conducted to find efficiency of textbook as defined by criteria at 81.50 / 82.00 and student’s satisfaction level towards textbook usage of 40 students. The obtained mean was 4.52 and standard deviation was 0.61. The results showed that the efficiency of textbook on the subject of Measurement and Evaluation in Education was at 81.50 / 82.00 that was higher than defined criteria at 80/80. In addition, Overall satisfaction of target group was in the highest level with the mean of 4.52 and S.D. at 0.61.
Study the Effect of Glucanoacetobacter diazotrophicus and PSB Formulations on Quality Parameter on suru Sugarcane

Sugarcane (Saccharum officinarum L.) is one of the most important food and cash crop of the tropics and subtropics which was cultivated in about 121 countries encompassing approximately half of the world. Sugarcane (Saccharum spp. hybrid) is a tall, perennial grass (family Poaceae, subfamily Panicoide), and is cultivated in tropical and warm-temperate regions between 35°N and 35°S and from sea level to altitudes of 1,000 m in a wide variety of soil types (Reis et al., 2007). Most of the commercial sugarcane varieties are hybrids with Saccharum officinarum. The optimal temperature for sugarcane cultivation is between 20 and 35°C and the minimum rainfall requirement is 1,200mm per year (Ando, 2010). The stalks (stems) of sugarcane are harvested at 9 to 18 months after planting the mother stem cutting (setts). It over 5.05 million hectare area in the country with a production of 348.19 million tonnes, out of which more than 66% is concentrated in the sub-tropical states. The production and productivity of sugarcane is severely affected by the various diseases. They are playing an important role in Indian economy and a key role to the socio-economic prosperity in the International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 05 (2019) Journal homepage: http://www.ijcmas. com
Time perception and autistic spectrum condition: A systematic review

Problems with timing and time perception have been suggested as key characteristics of autism spectrum condition (ASC). Studies and personal accounts from clinicians, parents, caregivers, and self‐reports from autistic people themselves often refer to problems with time. Although a number of empirical studies have examined aspects relating to time in autistic individuals, there remains no clear consensus on whether or how timing mechanisms may be affected in autism. A key reason for this lack of clarity is the wide range of timing processes that exist and subsequently the wide range of methodologies, research paradigms, and samples that time‐based studies have used with autism populations. In order to summarize and organize the available literature on this issue, a systematic review was conducted. Five electronic databases were consulted. From an initial 597 records (after duplicates were removed), 45 papers were selected and reviewed. The studies are reviewed within different sections based on the different types of timing ability that have been explored in the neurotypical (NT) population: time sensitivity, interval timing, and higher‐order time perception. Within each section cognitive models, methodologies, possible clinical implications, and research results are discussed. The results show different consistency across studies between the three types of timing ability. The highest consistency of results showing atypical time perception abilities is found in high‐level time perception studies. It remains unclear if autism is characterized by a fundamental time perception impairment. Suggestions for future research are discussed. Autism Res 2019, 12: 1440–1462. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.
REGIONAL DEVELOPMENT AND ECONOMIC GROWTH APPROACH IN EUROPE AND GCC COUNTRIES

The research paper starts from the reality that each country faces challenges in the balanced economic development of its territory. Inequalities in the levels of development of the regions of a country have arisen either as a result of changing economic conditions, the development of economic areas, and the entry of others into decline, either as a result of the concentration of economic growth in certain regions or economic sectors or by the existence of regions sparsely populated or with resources largely derived from primary sectors that have not been able to maintain economic development or social infrastructure at a satisfactory level. The issue of creating a regional development policy started in European structures and spread around the world. The research has shown that economic development is being approached differently in Europe and the Middle East. The author concluded that the difference in the solution of the regional differences is related to the state economic sovereignty and to the state's involvement in the economy, as well as to the facilitation of the interregional financing or to the provisions of the economic development agreements concluded.
Automatic Arabic Part-of-Speech Tagging: Deep Learning Neural LSTM Versus Word2Vec

Part-of-speech (POS) tagging is the process of selecting an appropriate POS tag for each word in a natural language sentence. POS tagging is a vital part of most natural language processing (NLP) applications. In comparison to other languages, there is a dearth of studies on NLP applications for the Arabic language. Recently, neural networks (NNs) and deep learning technologies have shown excellent results for some English and Latin NLP applications. However, for Arabic, the practice is still in its infancy, and more work is needed to determine whether neural technologies will lead to convincing results for NLP applications. In this paper, a long short-term memory (LSTM) model has been used to investigate the effectiveness of NNs in Arabic NLP. The model has been specifically applied to identify the POS tags for Arabic words and morphemes taken from the Quranic Arabic Corpus (QAC) data set. QAC is a well-known gold standard dataset prepared by researchers from Leeds University. It is interesting to note that LSTM tagger achieved 99.72% accuracy for tagging morphemes and 99.18% for tagging words, while the Word2Vec tagger achieved 99.55% for tagging morphemes and 97.33% for tagging words.
Designing Robust Monetary Policy Using Prediction Pools

How should a forward-looking policy maker conduct monetary policy when she has a finite set of models at her disposal, none of which are believed to be the true data generating process? In our approach, the policy makerfirst assigns weights to models based on relative forecasting performance rather than in-sample fit, consistent with her forward-looking objective. These weights are then used to solve a policy design prob-lem that selects the optimized Taylor-type interest-rate rule that is robust to model uncertainty across a set of well-established DSGE models with and without financial frictions. We find that the choice of weights has a significant impact on the robust optimized rule which is more inertial and aggressive than either the non-robust single model counterparts or the optimal robust rule based on backward-looking weights asin the common alternative Bayesian Model Averaging. Importantly, we show that a price-level rule has excellent welfare and robustness properties, and therefore should be viewed as a key instrument for policy makers facing uncertainty over the nature offinancial frictions.
Meeting the Challenges of Financial Difficulties: How Metro-Atlanta School Districts Act.

School districts of the United States have to address their financial problems when economy is in difficulty. This study examines the financial practices of the school districts in metro-Atlanta area to understand how they operate their systems to meet with the challenges. Personal interviews were held with financial officers of six participating school districts. A researcher-developed questionnaire was used to solicit data in four areas of school finance: budgeting, cash management, auditing and financial forecast. Findings of the study indicate that school districts monitor their current budget carefully by working closely with state and local tax commissioners. Districts strictly control their expenditures and trim their current budget with priorities. Additionally, they work with site administrators to ensure their full compliance of the financial procedures.
Learning about Language Learning on a MOOC: How Massive, Open, Online and "Course"?.

This paper reports on an exploratory research project on the evaluation of the engagement with a Massive Open Online Course (MOOC) carried out by six members of staff and two ‘expert students’ involved in the MA in English Language Teaching (ELT) in the Department of English and Languages at Coventry University (CU), United Kingdom, between November and December 2014. Its main aim was to investigate how both expert and trainee English teachers and teacher trainers would find the experience of engaging with a FutureLearn MOOC on this subject created by the University of Southampton in collaboration with the British Council – Understanding Language: Learning and Teaching. The participants involved agreed to record their thoughts while they were taking part in the MOOC and then met once per week for four weeks at the end of each MOOC unit to carry out a collaborative staff/student reflective evaluation of their experience. The paper will discuss the way in which the participants engaged with this project, which became a blended learning community of professional development practice linked to the global community of practice on the MOOC, and present their perspectives on the pros and cons of integrating a MOOC as an Open Educational Resource (OER) into an existing curriculum.
Three Essays on the Term Structure of Eurocurrency Rates.

This dissertation investigates the term structure relationship in financial markets by using Eurocurrency rates of several countries. The first essay tests the restrictions of the expectations hypothesis of term structure and the Fisher hypothesis in an interrelated fashion to characterize the changes in the slope and the level of Eurocurrency yield curves of numerous countries. It is argued that the Fisher relation explains the level of the yield curve and the expectations hypothesis determines the long run relationship between yields at different maturities at that level. Special emphasis is given to the information content of the yield curve for future inflation. A factor decomposition technique is used to extract the common trend driving the yield curve and it is found that this common trend contains information about the long-run behavior of the inflation rate for each country in the sample. This finding is consistent with the argument that rational agents in the market incorporate the predictable portion of the expected inflation into interest rates while setting prices. The second essay focuses on convergence of international money market term structures. According to the covered interest parity theorem, nominal yield curves should converge in the long ran. The common factor driving each term structure is extracted to test for long run linkages and transitory components are used to test for short run linkages. The findings indicate that an international convergence has not occurred although the term structures of countries that are a member of the exchange rate mechanism of the European Union have a
What are the effects of technology shocks on international labor markets

How do international labor markets respond to a technology shock and what is the main transmission channel across countries with different labor market institutions? To answer these questions, I identify technology shocks using the approach of Gali (1999) and decompose the responses of total hours worked into movements along the extensive and the intensive margins. Overall, my analysis shows that technology shocks have a negative effect on total hours. This effect is stronger in countries with flexible labor markets, where the adjustment takes place along both margins. In contrast, the responses of total hours are smaller in countries with strict labor market legislation, where labor adjustment takes place along the intensive margin. These differences can be linked to the strictness of institutions that target quantity and price adjustments in the labor market.
Deep Architectures for Human Activity Recognition using Sensors

Human activity recognition (HAR) is a renowned research field in recent years due to its applications such as physical fitness monitoring, assisted living, elderly–care, biometric authentication and many more. The ubiquitous nature of sensors makes them a good choice to use for activity recognition. The latest smart gadgets are equipped with most of the wearable sensors i.e. accelerometer, gyroscope, GPS, compass, camera, microphone etc. These sensors measure various aspects of an object, and are easy to use with less cost. The use of sensors in the field of HAR opens new avenues for machine learning (ML) researchers to accurately recognize human activities. Deep learning (DL) is becoming popular among HAR researchers due to its outstanding performance over conventional ML techniques. In this paper, we have reviewed recent research studies on deep models for sensor–based human activity recognition. The aim of this article is to identify recent trends and challenges in HAR.
Community-Based Settlements Regularization: Lessons for Scaling up from Makongo Juu Informal Settlement, Dar es Salaam, Tanzania

Given the need to scale the regularization of the many informal areas growing rapidly in African cities, this paper presents the experience, from efforts to regularize a mixed household income and mixed land use unplanned area called Makongo Juu in Dar es Salaam City, Tanzania. Realising the need to plan this area, the government prepared a number of land use plans which were however, not accepted by the landowners, asking permission to prepare their own. The first community-based plan of the mid-1990s took long to be approved and was overtaken by events. In 2015 the government tasked land owners to come up with an acceptable land use plan, which landowners, working within a community organization, did, and which was approved in 2017. The strengths of this approach included an acceptance by most of the landowners, who contributed in both cash in kind to the preparation of a land use plan, which, after approval, enabled the surveying of the area and of issuing titles. Weaknesses included failure to consider public goods such as the environment and public spaces. Planned neighbourhood roads, which were in any case, not standard, remained in private hands, uncleared and unsecured. It is concluded that government regulation is necessary, even in the case of a community-based regularization scheme, with enforceable provisions made, for regularization and post-planning transactions.
Machine-Learning-Based Controller Design for Discrete-Valued Input Systems

SUMMARY Switching and ON/OFF controls are effective control techniques for control systems equipped with low-resolution actuators. Such control mechanisms can be modeled as control systems that restrict the control input to discrete values. In this paper, a controller design method based on a machine-learning technique is discussed. The relationship between the current situation (previous input sequence and previous output sequence), applied input, and output evolution is learned by applying certain machine-learning methods. Specifically, machine-learning methods such as the approximate nearest neighbor (ANN) method and support vector machine (SVM) are used in this study. The trained classifier will be a controller that connects the current situation and a suitable control input that can drive the current output to the desired one. The effectiveness of the proposed method is verified for discrete input systems via simulations and experiments.