Miracle-dz/UniFAR-FTUs huggingface dataset:
A comparative study of counseling theories & techniques used by practicum students and former graduates

This study investigated the theories and techniques used by the practicum students and former graduates of the master’s level agency counseling program at the University of Nebraska at Omaha (UNO), which is certified and approved by the Council for Accreditation of Counseling and Related Educational Programs (CACREP). Also studied were the theories and techniques learned at UNO and elsewhere. Two-hundred and forty questionnaires were mailed to former graduates of which thirty-seven percent were returned. Twenty-four practicum students were administered the questionnaire in person. Descriptive analysis of the data demonstrated diversity between the theories and techniques used by practicum students, recent graduates (< 5 years out of the program), former graduates (5-9 years out of the program), and former graduates (greater than 9 years out of the program). It can be seen from analysis of the results that counselors become more diversified in their use of theories and techniques as time out of the program increases. The final chapter briefly describes a need for counseling students to know themselves and become more aware of their own beliefs, values, and views and concludes with recommendations for further study. Counseling Techniques & Theories 1
Physiological loading during normal daily life and exercise assessed with electromyography

Tikkanen, Olli Physiological loading during normal daily life and exercise assessed with electromyography. Jyväskylä: University of Jyväskylä, 2014, 104 p. (Studies in Sport, Physical Education, and Health ISSN 0356-1070; 211 ISBN 978-951-39-5812-1 (nid.) ISBN 978-951-39-5813-8 (PDF) Finnish Summary The aims of the present study were to validate shorts implemented with textile electromyography (EMG) electrodes to estimate energy expenditure (EE) and aerobic exercise capacity, to develop analysis methods for long-term EMG measurements, and to quantify EMG activity and inactivity of thigh muscles during normal daily life in adults and elderly. In total, 150 volunteers were measured in the laboratory for EMG, heart rate, accelerometry and ventilatory gases, and during normal daily life EMG was recorded. The results demonstrated that EMG shorts can be used for EE estimations in a wide range of physical activity intensities in a heterogeneous subject group. On the other hand, an increase in EMG slope was detected during running which occurred near the second ventilatory threshold (VT2) indicating that EMG can be used also to estimate aerobic exercise capacity. Furthermore, long-term recordings of EMG provided novel insights into daily inactivity and activity of main locomotor muscles. Daily measurements revealed that thigh muscles are inactive over 65 % of the measurement time and long continuous inactivity periods are common in normal daily life. On average, only a fraction (4%) of muscle’s maximal voluntary strength capacity is used during normal daily life, and the muscles work at high intensities only for very short periods. In daily life older people have less activity bursts with higher intensity and are less time inactive, but have longer continuous inactivity periods than younger adults. Consequently, daily life was shown to be physically more demanding for the elderly due to lower maximum strength levels highlighting the importance of maintaining strength levels with aging. EMG can provide an alternative for accelerometer when greater accuracy is needed and can provide distinct and valuable information of physical inactivity and activity, especially at low intensity levels of normal daily life. The muscle activity patterns reported in this study are significant for understanding intensity, amount and distribution of physical activity which is typical in healthy adults and elderly.
Radiographic and CT Scan Evaluation of Root Resorption of Permanent Incisors Maxillary Impacted Carises

Introduction: Resorptions of permanent incisors are a rare complication of ectopically erupting maxillary canines but when they occur they may lead to extraction, orthodontic treatment or both. The purpose of this study was to assess the incisor root resorption adjacent to impacted maxillary canines using conventional radiographs and CT scan. Materials & Methods: Fourteen patients (13-20 years) with 18 impacted canines were studied. Upper alveolar bone CT scans of the patients and their periapical and panoramic images were analyzed for detecting incisor root resorption caused by impacted canines. Degree of root resorption was assessed on CT scans on the basis of the self-designed classification. Buccolingual, mesiodistal and superioinferior location of resorption of incisors’ roots and their longitudinal extension were assessed using CT scans in regard to the number of CT cuts showing the resorption. Results: All of the teeth adjacent to impacted maxillary canines showed resorption. The most prevalent resorption site (84.6%) was detected in lateral incisors. 61.5% of involved teeth in CT scan did not show any resorption in the conventional radiographs and the others (38.5%) were questionable. In 77% of cases, resorption of palatal and distopalatal surfaces of involved roots were not detectable in conventional radiographs. Assessment of canine eruption angle in panoramic radiography revealed that this angle did not have any significant role in developing root resorption of adjacent teeth. Conclusion: Conventional radiographs are not able to detect root resorption of the incisors adjacent to impacted canines. Therefore, in cases which intensity and extension of root resorption has an effect on treatment plan and prognosis, CT scan is strongly recommended. Key words: Impacted canine, root resorption, CT scan.
Explaining Labor Share Movements: A Regional Analysis

This study investigates the explanatory forces behind changes in labor shares over 2000-2014 in Korea. Unlike previous studies focused on cross-national differences in labor shares, our focus is on the changes in labor shares across regions within Korea. Although the labor share of Korea’s national income has been relatively stable over time, the labor shares at the regional level have shown very diverse trends across 16 metropolitan areas and provinces, varying from a 9.7% point increase to a 5.6 % point decrease over the sample period. In this paper, we examine why labor shares vary widely across regions even though they share common economic environments such as trade liberalization, tax system and minimum wage law. By estimating an array of cross-regional models, we find that the concentration of manufacturing industry, the share of university-educated workers, and the average age of firms are important factors affecting labor shares in regional income. Furthermore, we employ the panel VAR model to estimate dynamic responses of the labor shares while allowing for regional heterogeneities. Our results show that shocks to capital-output ratio, total factor productivity and the concentration of both manufacturing and services industries all lead to a decline labor shares over time. We also show that the concentration of manufacturing and service industries are more important in metropolitan cities than in provincial areas. Our results demonstrate that heterogeneities in product and labor markets should be taken into account to understand the changes in labor shares in regional income. Policies aimed at raising labor income would need to take a regional approach.
From point cloud to IndoorGML

While a number of methods have been proposed to build in-door map data, LiDAR is also a promising approach among them.A pilot project was launched in 2018 with an aim to investigate the feasibility of generating indoor maps in a standard format, OGC IndoorGML [4] with public safety features from point cloud data. It may be the eventual goal of the pilot to generate IndoorGML data in fully automated ways but many technical challenges prevent us from achieving this goal in reality. In this paper, we discuss important technical issues that we encountered during the pilot project and present a semi-automatic approaches to generate IndoorGML data from point cloud data collected by LiDAR sensors.
Meaning structure of cognate words in English and Russian: comparing word sense frequency

Polysemy is a key issue in theoretical semantics and lexicography as well as in computational linguistics. When words have several senses, it is important to describe them properly in the dictionary (a lexicographic task) and to be able to distinguish between them in given context (a computational linguistics task, known as WSD). Recently attention has been drawn to the fact that different senses normally have different frequencies in corpora. Elsewhere we reported on our research into that issue and introduced several techniques for determining sense frequency based on dictionary entries matched with data from large corpora. Information about word sense frequency may enrich language learning resources and help lexicographers order senses within a word according to frequency, if needed. When learning a foreign language, a student may encounter a word that exists in his/her native language (as a borrowing or an international word), and is tempted to assume that the foreign word and its equivalent have the same meaning structure. However, sometimes this is not the case, and the most frequent sense of a word in one language may be much less frequent for its cognate. We propose a method for detecting such cases. For that purpose, we selected a set of Russian words included into the Active Dictionary of Russian, which have more than two dictionary senses and have cognates in English. We estimated frequencies for English and Russian senses using SemCor and Russian National Corpus respectively, matched senses in each pair of words and compared their frequencies. In this way, we revealed cases in which the most frequent senses and the whole meaning structures are, cross-linguistically, substantially different and studied them in more detail. As a result, we obtained information that may prove useful for learners of Russian or English as well as for lexicographers and computational linguists dealing with machine translation.
THE EFFECT OF RELIGIOUS CULTURE AND MORAL KNOWLEDGE COURSES BASED ON BRAIN- BASED LEARNING APPROACH ON ACADEMIC SUCCESS AND PERMANENCE

The purpose of this study is to examine the effect of teaching the units of "Belief in Allah" and "Worship in Islam" in Religious Culture and Moral Knowledge course in the 10th grade with lesson plans and activities prepared on the basis of brain-based learning approach on academic achievement and permanence levels of students. Within this framework, the main purpose of this study is to identify to what extent the brain-based learning approach impacts the academic achievement and permanence levels of the students in Religious Culture And Moral Knowledge Course in secondary education. In the research, the design of pretest posttest control group was applied. Pretest and posttest have been used as data collection tools and after about 21 days, the permanence test has been carried out. Results have been analyzed through SPSS programme. ANOVA and ANCOVA variance analysis has been applied for data analysis. According to the findings of the study, teaching based on the brain-based learning approach has been found to have a meaningful effect in favour of the experimental group on permanence and academic achievement.
Reinforcement Learning Applications in Real Time Trading

Abstract This study focuses on applying reinforcement learning techniques in real time trading. We first briefly introduce the concept of reinforcement learning, definition of a reward function, and review previous studies as foundations on why reinforcement learning can work, specifically in the setting of financial trading. We demonstrate that it is possible to apply reinforcement learning and output valid and simple profitable trading strategy in a daily setting (one trade a day), and show an example of intraday trading with reinforcement learning. We use a modified Q-learning algorithm in this scenario to optimize trading result. We also interpret the output policy of reinforcement learning, and illustrate that reinforcement learning output is not completely void of economic sense.
Omnibus Model Checks of Linear Assumptions through Distance Covariance

An enormous amount of research performed on checking the adequacy of linearity are available in the statistical literature, but there exists very limited amount of work from the viewpoint of a measure of association. Inspired by the well-known distance covariance (Székely, Rizzo and Bakirov, 2007, dCov), we propose two omnibus tests for the goodness-of-fit of linearity. Methodologically, our tests do not involve any tuning parameters and are conveniently implemented. Theoretically, the technical details are of independent interest mainly due to the fact that the kernel induced by the dCov is not smooth. Convergence of our tests under null, fixed and local alternative hypotheses is investigated, and a bootstrap scheme is devised to approximate their null distributions and its consistency is justified. Numerical studies are employed to demonstrate the effectiveness of our proposals in comparison with some existing counterparts.
Mean-offset classifier based Wi-Fi indoor positioning system

A mean-offset classification technique was identified. It was found that the mean-offset classifier provides stability under dynamic indoor conditions and provides consistent re-sults when training and test data combinations are swept from 10 – 95%. In this paper the mean-offset classifier is compared to the K-Nearest Neighbors (KNN) and Naïve Bayesian (NB) classifiers, with a view of developing an adaptable and computationally efficient indoor localization model using machine learning principles. It was seen that the mean-offset classifier improved results considerably and achieved an accuracy of 0.85 m and 1.15 m under line-of-sight (LOS) and non-line-of-sight (NLOS) conditions in residential areas.
Inferring Roll Call Estimates of Ideology from Campaign Contributions Using Supervised Machine Learning

Abstract. This paper develops a generalized supervised learning methodology for inferring roll call scores for incumbent and non-incumbent candidates from campaign contribution data. Rather than use unsupervised methods to recover the latent dimension that best explains patterns in giving, donation patterns are instead mapped onto a target measure of legislative voting behavior. Supervised learning methods applied to contribution data are shown to significantly outperform alternative measures of ideology in predicting legislative voting behavior. Fundraising prior to entering office provides a highly informative signal about future voting behavior. Impressively, contribution-based forecasts based on fundraising as a non-incumbent predict future voting behavior with the same accuracy as that achieved by in-sample forecasts based on votes casts during a legislator’s first two years in Congress. The combined results demonstrate campaign contributions are powerful predictors of roll-call voting behavior and resolve an ongoing debate as to whether contribution records can be used to make accurate withinparty comparisons.
Nonlinear Wave Propagation and Breaking in the Coastal Area

Boussinesq model gives horizontal and vertical components of velocity that for any arbitrary depth cab be obtained using potential flow theory as Droplet jet and bubbles created in wave breaking events are responsible for a variety of oceanic and atmospheric processes, including the exchange of heat, momentum, and moisture between the air and the ocean. Thus, different features of breaking waves in shallow waters such as undular non-breaking bores, purely breaking bores, together with swash zone motions, as the main sources of sediment mobilization near the coast, should be accurately modelled for the study of the surface zone processes. Meanwhile, due to the complex competitions among nonlinearities, dispersive effects, and energy dissipation in the surface zones, the numerical modeling to predict and capture these interactions is a challenging task. In this work, we investigate numerical modeling of near-shore undular bore dynamics using a Boussinesq model accompanied with surface and bottom frictions using a Galerkin technique [4]. Numerical results are used for constructing kinematic and dynamic breaking criteria. Furthermore, results are compared with some published laboratory data which give a good agreement for confirming the skills of the applied numerical technique.
Artificial Intelligence in Mobile Applications of Dermatology: A Systematic Mapping Study

Skin diseases are considered the fourth most common cause of human disease and are more common than it is thought. With the advancement of technology, new concepts have been incorporated into the field of mobile health and, specifically, into dermatology. On the other hand, some factors make access to medical care difficult which is why the use of mobile applications for medical care and diagnosis is very common today, but are these applications suitable? In this study, a systematic literature mapping is carried out of mobile applications that use techniques of artificial intelligence for the diagnosis of skin diseases. Its focus is mainly on the level of sensitivity, specificity and overall accuracy of said diagnoses in comparison to the accuracy of a dermatologist. Several applications of care and diagnosis of skin diseases were found. However, only 9 studies describe the techniques that use these applications for the classification of the disease; several of them with high levels of precision, and mainly those that utilize artificial neural networks and vector-support machine algorithms. Despite the research conducted in this field, these techniques are still in development and are available only for a number of limited diseases. Thus, we believe it is necessary to direct the research towards the field of dermatology and contribute to the minimization of the gap between doctor and patient.
Changing the balance of power: the case of highperformance work systems in Brazil

This paper is based on a longitudinal case study of a Brazilian multinational company which has changed its way of organizing work processes by implementing a highperformance work system (HPWS). The article argues that, as the firm attempts to improve its internal processes, it requires access to internal and external resources, as well as the expertise that may increase and support this change. However, when these resources are not easily found locally, the firm needs to pool and recombine different sources of expertise to succeed in its efforts. At this stage, the balance of power between different organizational actors starts to shift towards greater mutual dependence, thus reducing power imbalances.
Deep Convolutional Neural Networks for Semantic Segmentation of Multispectral Sentinel-2 Satellite Imagery: An Open Data Approach to Large-Scale Land Use and Land Cover Classification

: Large-scale land use and land cover (LULC) maps with up to global coverage still incorporate large amounts of manual work and are thus only available after production times of several years. In the domain of computer-vision, deep convolutional neural networks (CNNs) dominate the state-of-the-art in image recognition, a task closely related to LULC classification. By adopting a state-of-the-art CNN architecture for the classification of multispectral Sentinel-2 image patches, the close relation of both tasks is exploited. Since no suitable dataset for training is available as of now, a quasi-automatic method for deriving labels from existing sources of volunteered geographic information, in particular the OpenStreetMap, is presented. The results indicate that CNNs for image recognition are well-suited for LULC classification with only minor adaptions and are furthermore robust to label noise, originating from the error-prone data acquisition method.
Investigating the infamous #Pizzagate conspiracy theory

In the 2016 US Presidential elections, we saw the rise of the so-called “fake news” phenomenon –a term used to describe lies, formatted as news articles, aiming to confuse and/or anger the public. One of the better-known instances of “fake news” is the infamous “Pizzagate” conspiracy theory that received significant attention in the media. In this paper Metaxas P, Finn Investigating the infamous #Pizzagate conspiracy theory. Technology Science. 2019121802. December 18, 2019. https://techscience.org/a/2019121802 2 we use the TwitterTrails tool to investigate it. TwitterTrails (twittertrails.com) is an interactive, Web-based investigative tool that allows its users to examine on Twitter the origin and propagation characteristics of a rumor and its refutation. We designed TwitterTrails as a tool for journalists (amateur and professional) investigating recent and breaking stories. However, with little training, it can be used by anyone who wants to find answers to some basic facts about the spreading of rumors online. Results Summary: Searching the Web, one finds dozens of news articles and analyses related to the debunked Pizzagate conspiracy theory. There are several facts that previous articles missed, however, and our TwitterTrails investigation reveals some of them. In particular, it points to an earlier starting date on Twitter than the one previously reported. It visualizes the identities and the close relationship between the main actors of the online echo chamber promoting the Pizzagate conspiracy theory, and it reveals how a bilingual troll misled a Turkish journalist who played a significant role in internationalizing the conspiracy theory.
Mediating influence on customer loyalty

In this study, the focus is on customer loyalty, and the antecedents of perceived value, and the marketing mix that creates the value. The mediating role of consumer perceived value in the marketing strategy-customer loyalty relationship is examined. A theoretical framework is established that is supported by empirical evidence. The findings indicate an inconsistent measure for perceived value that does not fully explain its mediating role.
Analysis of the Effect of Query Shapes on Performance over LDF Interfaces

Abstract. The adoption of Semantic Web technologies, and in particular the Open Data initiative, has contributed to the steady growth of the number of datasets and triples accessible on the Web. Most commonly, queries over RDF data are evaluated over SPARQL endpoints. Recently, however, alternatives such as TPF have been proposed with the goal of shifting query processing load from the server running the SPARQL endpoint towards the client that issued the query. Although these interfaces have been evaluated against standard benchmarks and testbeds that showed their benefits over previous work in general, an evaluation of the effects of the query shapes on query performance of the different available interfaces has never been done. In this paper, we present the results of our in-depth evaluation of query shapes impact on the performance of existing LDF interfaces. Using representative and diverse query loads that are designed to include relevant query shapes and are based on the query log of a public SPARQL endpoint, we stress test the different interfaces and identify their strengths and weaknesses.