Miracle-dz/UniFAR-FTUs huggingface dataset:
Uncovering challenges : a case study identifying the barriers of introducing a proposed computer-based substance use intervention program into elementary classrooms

The purpose of this case study was to explore the problems that teachers anticipate facing when facilitating a new online computer-based substance use intervention program to grade six and seven elementary school students. This case study aimed to identify the support resources that teachers predict would be valuable to them in order to facilitate such a program. Four teachers were interviewed. A semi-structured interview protocol was used and then thematic analysis was applied to code the data. The results identified the following themes: technological barriers, autonomy, role apprehension, facilitation resources, and a general anti-drug attitude. Findings of the study include valuing healthy living as a school subject, identification of variation between schools and school districts, a consistent desire to spread an anti-drug message, a request for digital manuals and a support point person, the challenge of relying on a computer-based application, and the preference for teachers to have a reliable source of anti-drug resources while maintaining their ability to tailor the lessons to their teaching style. Future studies may include a more heterogeneous population and investigate schools in urban area. Additionally, further areas for program development can include tailoring online programs to work within the confines of school district technology restrictions, and providing the option for teachers to modify lessons to meet their classroom needs. UNCOVERING CHALLENGES 2
Students and Teachers Attitudes and Satisfaction toward E-Learning : A Case Study in Egypt

: E-learning is an important technological tool that is used by higher educational institutions worldwide to enhance the quality of teaching and learning. However, assessing the attitudes and satisfaction of students and teachers of such a system is becoming increasingly important to its success as it presents a new learning environment for them. The aim of this study, therefore, was to try to determine Egyptian student attitudes and satisfaction and Egyptian teachers’ satisfaction and perception of the usefulness of E-learning in the Faculty of Informatics and Computer Science at the British University in Egypt. Data were gathered from two sources: a student questionnaire and a teacher questionnaire. The findings of this study have shown that a) there were no significant differences between male and female students and the different degree years in their attitudes to and use of E-learning; b) the Preparatory Year appear to be less satisfied with the activities and teacher feedback on E-learning in comparison with the other degree years and c) teachers are satisfied with E-learning and regard it as a useful tool as it complements face-to-face teaching and saves them valuable time. The findings of this study will benefit academics and decision-makers involved in developing and implementing E-learning strategies in higher education institutions in the Middle East and North African regions.
Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874–1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.
Urban Identity as a Complementary Tool to Urban Wellbeing

The society we live in, is a society in transition wherein ideas of growth and wellbeing are equally debated. The call to make cities sustainable and resilient of the Goal number 11 of the Sustainable Development Goals (SDG), was further strengthened by the New Urban Agenda (NUA). The WBGU report on the same highlights the need to understand wellbeing and Eigenart (or identity) and address them at various levels including policies. This PhD research assumes great significance in this context. The first objective of this research is to understand Wellbeing and its attributes in the urban context, especially from the perspective of post-industrial revolution urbanization in the western world. Wellbeing as concept has eluded thinkers and practitioners alike since the time of Aristotle and has been dealt primarily within the realm of philosophy, psychology and even economics but is relatively unexplored from the urban planning perspective. However, the applied modules of wellbeing such as sustainability and quality of life are often discussed. The notion of wellbeing is very exhaustive and to do justification to the resources, this notion was systematically narrowed down by establishing links between its various attributes among which Urban Identity (similar to the Eigenart concept of WBGU) is further researched. This research thoroughly examines the various theoretical aspects of urban identity and highlights the ways to adapt them to the needs of urban planning. This research also discusses four global best practices that relate various theoretical aspects and provide a valuable understanding of how these concepts can be converted into actions and projects. Based on the theoretical framework of this research, the urban identity of three different neighborhoods of the city of Essen is empirically analyzed. This allowed for a comparative understanding of the role the identity of different neighborhoods within the same city plays in their overall wellbeing. These results were finally weighted w.r. t. the overall priorities expressed by the respondents from Germany on the ‘Better Life Index’ created by the OECD. The final part of research highlights avenues through which urban identity can be used as a complementary tool to enhance comprehensive wellbeing. The urban identity of the case study areas thus obtained, supports the hypothesis of this research and indeed provides an alternative understanding of these areas than the prevalent notion about them. This research provides a strong theoretical framework for the wellbeing driven urbanization in the 21st century which with the support by the advancement in technology (such as big data), has the potential to create more participative and flourishing cities for all.
Personality characteristics of mid-career male Anglican clergy

Forty male Anglican clergy completed the Eysenck Personality Questionnaire during mid-career development consultations. The findings that the clergy display elevated neuroticism scores and psychoticism scores no lower than men in general contradict predictions emerging from recent studies concerned with the relationship between personality and religion and suggest the need for further research.
HIV/AIDS Knowledge of Undergraduate Students at a Historically Black College and University

Objective: This study among 400 undergraduate students enrolled at Jackson State University (JSU) study aimed to assess knowledge about HIV and AIDS among African-American undergraduate students attending a historically black college and university. A cross-sectional survey was conducted. Data were collected using a validated, self-administered, and standardized questionnaire on knowledge regarding risks for HIV and AIDS. Three hundred and eighty-six students (96.5%) had good knowledge about HIV and AIDS, although some participants had misconceptions about the modes of HIV infection transmission. There were no significant gender differences for HIV and AIDS knowledge among the participants (χ2 = 3.05; P = 0.08). In general we concluded that JSU undergraduate students had adequate knowledge about HIV transmission modes and AIDS, although some participants had misconceptions about the routes of HIV infection transmission. Hence, this study calls for strengthening HIV and AIDS awareness education among undergraduate students.
Reduced Lung Function among Workers in Primary Coffee Processing Factories in Ethiopia: A Cross Sectional Study

Dust exposure is one of the major risk factors for respiratory health in many workplaces, including coffee factories. The aim of this study was to assess the prevalence of respiratory symptoms and lung function reduction among workers in Ethiopian primary coffee processing factories, compared to a control group of workers. A total of 115 coffee workers and 110 water bottling workers were involved in this study, from 12 coffee and 3 water bottling factories in Ethiopia, respectively. The chronic respiratory symptoms were assessed using a structured interview, using a standardized questionnaire adopted from the American Thoracic Society (ATS). The lung function tests were performed according to the ATS recommendation for spirometry. The coffee workers had a significantly higher prevalence of coughing, coughing with sputum, breathlessness, work-related shortness of breath, and wheezing compared with the controls. The prevalence ratio of work-related shortness of breath (PR = 3.7, 95% CI: 1.6–8.7) and wheezing (PR = 3.3, 95% CI: 1.3–8.4) was significantly higher for the coffee workers compared to the controls. The coffee workers in the age groups 28–39 years and ≥40 years, had a significantly lower forced vital capacity and forced expiratory volume in 1 s compared to the controls in the similar age groups. The findings indicated the need for longitudinal studies on the possible effect of coffee dust on respiratory health of coffee production workers.
Removing objects from videos with a few strokes

We present a system for the removal of objects from videos. As an input, the system only needs a user to draw a few strokes in at least one frame, roughly delimiting the objects to be removed. These rough masks are then automatically refined and propagated through the video. The corresponding regions are resynthesized using video inpainting techniques. Our system is able to deal with multiple, possibly crossing objects, with complex motions and with dynamic textures. This results in a computational tool that can alleviate tedious manual operations for editing high-quality videos.
Dunhuang mural restoration using deep learning

As time goes by, the art pieces inside Dunhuang Grottoes have suffered from tremendous damage such as mural deterioration, and they are usually difficult to be repaired. Although we can achieve digital preservation by modeling the caves and preserving mural as textures in virtual environment, we still cannot have a glimpse of how the grottoes look like without damage. In this work, we propose a systematic restoration framework, which is based on Generative Adversarial Network (GAN) technique, for these high-resolution but deteriorated mural textures. The main idea is to make the machine learn the transformation between deteriorated mural textures and restored mural textures. However, the resolution of training texture images (i.e. 8192×8192) is too high to be applied with GAN technology directly due to GPU RAM limitation. Instead, our method restores a set of high-resolution yet color-inconsistent textures patch-by-patch and a set of low-resolution but color-consistent full textures, and then combines them to get the final high-resolution and color-consistent result.
Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems

Goal-oriented dialogue systems typically rely on components specifically developed for a single task or domain. This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained. It is also harder to extend such dialogue systems to different and multiple domains. The dialogue state tracker in conventional dialogue systems is one such component - it is usually designed to fit a well-defined application domain. For example, it is common for a state variable to be a categorical distribution over a manually-predefined set of entities (Henderson et al., 2013), resulting in an inflexible and hard-to-extend dialogue system. In this paper, we propose a new approach for dialogue state tracking that can generalize well over multiple domains without incorporating any domain-specific knowledge. Under this framework, discrete dialogue state variables are learned independently and the information of a predefined set of possible values for dialogue state variables is not required. Furthermore, it enables adding arbitrary dialogue context as features and allows for multiple values to be associated with a single state variable. These characteristics make it much easier to expand the dialogue state space. We evaluate our framework using the widely used dialogue state tracking challenge data set (DSTC2) and show that our framework yields competitive results with other state-of-the-art results despite incorporating little domain knowledge. We also show that this framework can benefit from widely available external resources such as pre-trained word embeddings.
The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

One of the main challenges of deep learning tools is their inability to capture model uncertainty. While Bayesian deep learning can be used to tackle the problem, Bayesian neural networks often require more time and computational power to train than deterministic networks. Our work explores whether fully Bayesian networks are needed to successfully capture model uncertainty. We vary the number and position of Bayesian layers in a network and compare their performance on active learning with the MNIST dataset. We found that we can fully capture the model uncertainty by using only a few Bayesian layers near the output of the network, combining the advantages of deterministic and Bayesian networks.
Document Structure Measure for Hypernym discovery

Hypernym discovery is the problem of finding terms that have is-a relationship with a given term. We introduce a new context type, and a relatedness measure to differentiate hypernyms from other types of semantic relationships. Our Document Structure measure is based on hierarchical position of terms in a document, and their presence or otherwise in definition text. This measure quantifies the document structure using multiple attributes, and classes of weighted distance functions.
Sub-Convulsing Dose Administration of Pilocarpine Reduces Glycemia, Increases Anxiety-Like Behavior and Decelerates Cortical Spreading Depression in Rats Suckled on Various Litter Sizes

Epilepsy and malnutrition constitute two worldwide health problems affecting behavior and brain function. The cholinergic agonist pilocarpine (300–380 mg/kg; single administration) reproduces the human type of temporal lobe epilepsy in rats. Pilocarpine-induced epilepsy in rodents has been associated with glycemia, learning and memory and anxiety disturbances. Cortical spreading depression (CSD) is a neural response that has been linked to brain excitability disorders and its diseases, and has been shown to be antagonized by acute pilocarpine. This study aimed to further investigate the effect of chronic pilocarpine at a sub-convulsing dose on weight gain, blood glucose levels, anxiety-like behavior and CSD. In addition, we tested whether unfavorable lactation-induced malnutrition could modulate the pilocarpine effects. Wistar rats were suckled under normal size and large size litters (litters with 9 and 15 pups; groups L9 and L15, respectively). From postnatal days (PND) 35–55, these young animals received a daily intraperitoneal injection of pilocarpine (45 mg/kg/day), or vehicle (saline), or no treatment (naïve). On PND58, the animals were behaviorally tested in an open field apparatus. This was immediately followed by 6 h fasting and blood glucose measurement. At PND60–65, CSD was recorded, and its parameters (velocity of propagation, amplitude, and duration) were calculated. Compared to the control groups, pilocarpine-treated animals presented with reduced weight gain and lower glycemia, increased anxiety-like behavior and decelerated CSD propagation. CSD velocity was higher (p < 0.001) in the L15 groups in comparison to the corresponding groups in the L9 condition. The results demonstrate an influence of chronic (21-day) administration of a sub-convulsing, very low dose (45 mg/kg) of pilocarpine on CSD propagation, anxiety-like behavior, glycemia and body weight. Furthermore, data reinforce the hypothesis of a relationship between CSD and brain excitability. The lactation condition seems to differentially modulate these effects.
An Introduction to Deep Reinforcement Learning

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.
AdaFrame: Adaptive Frame Selection for Fast Video Recognition

We present AdaFrame, a framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame contains a Long Short-Term Memory network augmented with a global memory that provides context information for searching which frames to use over time. Trained with policy gradient methods, AdaFrame generates a prediction, determines which frame to observe next, and computes the utility, i.e., expected future rewards, of seeing more frames at each time step. At testing time, AdaFrame exploits predicted utilities to achieve adaptive lookahead inference such that the overall computational costs are reduced without incurring a decrease in accuracy. Extensive experiments are conducted on two large-scale video benchmarks, FCVID and ActivityNet. AdaFrame matches the performance of using all frames with only 8.21 and 8.65 frames on FCVID and ActivityNet, respectively. We further qualitatively demonstrate learned frame usage can indicate the difficulty of making classification decisions; easier samples need fewer frames while harder ones require more, both at instance-level within the same class and at class-level among different categories.
Functional Prediction of Chronic Kidney Disease Susceptibility Gene PRKAG2 by Comprehensively Bioinformatics Analysis

The genetic predisposition to chronic kidney disease (CKD) has been widely evaluated especially using the genome-wide association studies, which highlighted some novel genetic susceptibility variants in many genes, and estimated glomerular filtration rate to diagnose and stage CKD. Of these variants, rs7805747 in PRKAG2 was identified to be significantly associated with both serum creatinine and CKD with genome wide significance level. Until now, the potential mechanism by which rs7805747 affects CKD risk is still unclear. Here, we performed a functional analysis of rs7805747 variant using multiple bioinformatics software and databases. Using RegulomeDB and HaploReg (version 4.1), rs7805747 was predicated to locate in enhancer histone marks (Liver, Duodenum Mucosa, Fetal Intestine Large, Fetal Intestine Small, and Right Ventricle tissues). Using GWAS analysis in PhenoScanner, we showed that rs7805747 is not only associated with CKD, but also is significantly associated with other diseases or phenotypes. Using metabolite analysis in PhenoScanner, rs7805747 is identified to be significantly associated with not only the serum creatinine, but also with other 16 metabolites. Using eQTL analysis in PhenoScanner, rs7805747 is identified to be significantly associated with gene expression in multiple human tissues and multiple genes including PRKAG2. The gene expression analysis of PRKAG2 using 53 tissues from GTEx RNA-Seq of 8555 samples (570 donors) in GTEx showed that PRKAG2 had the highest median expression in Heart-Atrial Appendage. Using the gene expression profiles in human CKD, we further identified different expression of PRKAG2 gene in CKD cases compared with control samples. In summary, our findings provide new insight into the underlying susceptibility of PRKAG2 gene to CKD.
Motivational Regulations Across the Stages of Change for Exercise in the General Population of Monterrey (Mexico)

Few studies have examined exercise adherence in the Mexican population using self-determination theory proposals and the stages of change model. The objectives of this study were:(a) to translate and adapt the Behavioral Regulation in Exercise Questionnaire-3 (BREQ-3) to Mexican Spanish and examine its internal consistency and factorial structure (six dimensions); and (b) to analyse variations in behavioral regulations using the stages of change model. This study included 530 participants between 11 and 76 years old who lived in the metropolitan area of the city of Monterrey, Nuevo Leon, Mexico. The Mexican version of the BREQ-3 presented an acceptable six-factor model that agrees with the theory and has good internal consistency. Results showed that the less self-determined regulations (i.e., external and amotivation) predominated in the first stages of change (i.e., pre-contemplation and contemplation) and decreased in the last stages (i.e., action and maintenance); by contrast, the more self-determined regulations (i.e., intrinsic, integrated, and identified) predominated in the last stages (i.e., action and maintenance) and were lower in the first stages (i.e., pre-contemplation and contemplation). Linking these two theoretical constructs contributes to understanding physical exercise adherence.