Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing AutoML in Educational Data Mining for Prediction Tasks

Educational Data Mining (EDM) has emerged over the last two decades, concerning with the development and implementation of data mining methods in order to facilitate the analysis of vast amounts of data originating from a wide variety of educational contexts. Predicting students’ progression and learning outcomes, such as dropout, performance and course grades, is regarded among the most important tasks of the EDM field. Therefore, applying appropriate machine learning algorithms for building accurate predictive models is of outmost importance for both educators and data scientists. Considering the high-dimensional input space and the complexity of machine learning algorithms, the process of building accurate and robust learning models requires advanced data science skills, while is time-consuming and error-prone in most cases. In addition, choosing the proper method for a given problem formulation and configuring the optimal parameters’ values for a specific model is a demanding task, whilst it is often very difficult to understand and explain the produced results. In this context, the main purpose of the present study is to examine the potential use of advanced machine learning strategies on educational settings from the perspective of hyperparameter optimization. More specifically, we investigate the effectiveness of automated Machine Learning (autoML) for the task of predicting students’ learning outcomes based on their participation in online learning platforms. At the same time, we limit the search space to tree-based and rule-based models in order to achieving transparent and interpretable results. To this end, a plethora of experiments were carried out, revealing that autoML tools achieve consistently superior results. Hopefully our work will help nonexpert users (e.g., educators and instructors) in the field of EDM to conduct experiments with appropriate automated parameter configurations, thus achieving highly accurate and comprehensible results.
Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as “geolocation”) in the form of a tweet (also referred to as a “check-in”), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user’s activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user’s daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time.
Studying the Effect of Montelukast in the Treatment of Dysmenorrhea: A single-Blind, Placebo-controlled Trial

Background: In the field of gynecology, studies have found high levels of leukotriene in the endometrium and uterine smooth muscles of patients with dysmenorrheal and menstrual pain. Aim: to evaluate the effectiveness of montelukast in relieving the symptoms of dysmenorrhea. Patients and methods: A randomized, single-blind, prospective, placebo-controlled study was designed. Seventy patients suffering from dysmenorrhea complete this study, divided randomly into two groups: montelukast group (34 patients) and placebo group (36 patients). Values before and after treatment were compared over two menstrual cycles, using visual analog scale (VAS) score and NSAID usage per menstrual cycle. Results: Post-treatment VAS score and NSAID usage decreased significantly in both montelukast and placebo group, compared to their pre-treatment values (P 0.05). The ratio of the highly effective cases, for both VAS score and NSAID usage, was significantly higher in the montelukast group than placebo group (P<0.05). Means of VAS score and NSAID usage didn't change in all of endometriosis patients after administration of montelukast or placebo therapy. Conclusions: For some women, montelukast may be effective in relieving symptoms of primary dysmenorrhea. It's safe and has no serious adverse effects. Thus, it can be considered as a good option for treatment of primary dysmenorrhea before starting with hormonal therapy.
Encouraging disposal of unused opioid analgesics in Appalachia

Abstract The Appalachian region has been disproportionately impacted by the opioid epidemic. This study, informed by the Health Belief Model (HBM), explored Appalachian community members’ perspectives on prescription opioid misuse and community-based programs to dispose of unused opioid analgesics. In 2018, we conducted ten focus groups (n = 94 participants) in 5 Appalachian counties. Thirteen themes across 5 of the HBM constructs emerged from our analysis. Participants perceived that their communities are susceptible to the harms associated with opioid misuse, these harms are serious, suggesting they could be motivated to change disposal behaviors. Many participants recognized the benefit of disposing unused prescription opioids including protecting household members from misusing and protecting the home from robbery. Nevertheless, participants identified barriers to proper disposal, including keeping the medications ‘just in case’ for future ailments and the location of drop boxes near law enforcement (due to deep-seated mistrust of law enforcement agencies). Self-efficacy was difficult to assess as many participants were completely unaware of the presence of dropboxes in the community and also expressed concerns about the inconvenience of proper disposal using dropboxes These findings have implications for developing community-based campaign messages promoting proper disposal of unused opioids.
A new approach to the study of similarity structurein the area of green growth in OECD countries

The aim of the study is an assessment of current level of development in the green growth area of the OECD countries. For this purpose, the seven main indicators characterized the green growth in 2004 and 2015 were utilized. To identify the relations between them, the multi-dimensional correspondence analysis with the complex matrix of markers was used. The main advantage of this method is possibility to implement division into groups of OECD countries taking into account the level of each of analysed indicators, not only a mean level of development in this area. In the paper, the OECD countries were separated into four groups with different level of development in the green growth area. The results of the study confirmed visible changes in the area of the green growth in 2004 and 2015 for the majority of countries. There are also visible changes between these years.
Cardiovascular Risk Prediction in Rheumatic Patients by Artificial Intelligence Paradigms

According to World Health Organization, cardiovascular diseases are the first cause of death globally. People that present cardiovascular diseases or are at high cardiovascular risk (because of the presence of one or more risk factors such as hypertension, diabetes, hyperlipidemia or already established diseases) need early detection and preventive treatment. In this context, patients affected by inflammatory arthritis present an increased cardiovascular risk. In this field, cardiovascular diseases diagnosis is very tricky even for experts, due to the presence of many concurrent risk factors, some of which uncertain or unknown. Performances of traditional cardiovascular risk algorithms (such as Framingham, CUORE and SCORE) have already been assessed on patients with inflammatory arthritis, but the results show that they tend to underestimate the risk. For this reason, recently, the European League Against Rheumatism recommended to adapt the traditional algorithms with a multiplicative factor of 1.5 in patients with inflammatory arthritis. This work aims at exploring the use of the machine learning techniques to predict cardiovascular risk on patients affected by rheumatic diseases. Machine learning is a subfield of artificial intelligence that introduced a novel paradigm in programming methods. It can be defined as the ability of computers to learn how to solve a given problem without being explicitly programmed for this. In this work several supervised machine learning algorithms were employed to evaluate cardiovascular risk on rheumatic patients. Results of this explorative study open interesting perspectives for future developments of risk predictors.
Convolutional Model with Classification through Izhikevich Neuron

This paper presents the fusion of two paradigms of neural networks: the Convolutional Neural Networks from deep learning and the third generation Izhikevich neuron. This fusion has the purpose to replace the multilayer perceptron layers, that usually represent a computational cost and a large training time, for a paradigm created to classify with a single neuron. The experimentation is carried out in the classiﬁcation domain, predicting the directions of rotation in a simulator of self-driven vehicles. The experiments show similar results to the multilayer perceptron model in the evaluation metrics but the training time is reduced.
On bandwidth maximization of fixed-geometry arrays through convex programming

ABSTRACT We propose a new approach to solve the problem of optimal power synthesis of array antennas, so that maximum possible bandwidth can be granted to fixed sidelobe-level performances. The proposed approach can be applied to any kind of fixed-geometry array that radiates pencil beams and to linear equispaced arrays that generate shaped patterns. The designing problem is cast as a sequence of Convex Programming optimizations. Numerous numerical experiments, including full-wave synthesis of realistic antennas, were carried out and their results discussed here to assess the array antennas’ capability of achieving ultra-wideband performances.
Construction and Application of Height Control Model of Urban Building based on GIS: Taking Guanyun County as an Example

It has become an urgent demand of urban residents to shape the appropriate urban space style and strengthen the sharing of landscape resources through the height control of urban buildings. Starting from the visual corridor of urban landscape, taking Guanyun County, Jiangsu Province as an example, a scientific and efficient height control model and planning technical framework for urban buildings were constructed based on GIS. Based on the demonstration of the surrounding area of Dayi Mountain in Guanyun County, scientific viewpoint selection and multi-dimensional line-of-sight control analysis were carried out to construct the three-level urban building height control model: reference model of urban height control, modified model of urban height control, status quo correction model of urban height control. Finally, the height control zones of urban buildings were divided and planning guidelines were proposed. It can provide reference for the research on height control of urban buildings and related planning and construction.
Mineral chemistry, geochemistry, Raman spectroscopy and geotectonic significance of Neoproterozoic ophiolitic peridotites and pyroxenites from Kab Amiri district, central Eastern Desert, Egypt

The small gold mining Kab Amiri district ultramafic ophiolitic section (central Eastern Desert) comprises highly serpentinized peridotites (serpentinites) and few pyroxenites. It lies within a region of Neoproterozoic ophiolitic rocks and associated derivatives. The chromian spinel textural and chemical compositional features suggest transitional greenschist-amphibolite up to lower amphibolite facies metamorphism and indicates that they represent highly-depleted mantle residues. The presence of the Si-Ob-Si antisymmetric ν1(A1), symmetric ν2(A1) bands and the hydroxyl group (OH) modes are conclusive for the discrimination of antigorite. Furthermore, the speculation of serpentines as antigorite can also be indicated from the hydroxyl group modes. The Kab Amiri ophiolite suite (central Eastern Desert) represents fragments of oceanic lithosphere that are developed in forearc setting in a supra-subduction zone (SSZ) environment. Thus the low TiO2 contents, the enrichment in LILE and LREE and the Nb depletion reflect the remelting of a highly depleted mantle source and suggest their formation in a SSZ environment (i.e forearc).
A 3-D printed square loop frequency selective surface for harmonic radar applications

Abstract This paper presents a 3-D printed frequency selective surface (FSS) for harmonic radar applications. The proposed FSS design consists of a square loop printed on the cost-effective acrylonitrile butadiene styrene (ABS) plastic material to pass the signals at 5 GHz resonant frequency band. The proposed FSS is tested for various oblique angles up to 45° and even for different polarization modes. A prototype of the proposed design FSS is fabricated. The results demonstrate a good agreement of simulated results with experimental ones. The 3-D printing technique provides a simple and low-cost method to fabricate FSS and make it suitable for future FSS mass production.
Do Fixed-Income ETFs Overreact?

Este artículo investiga la predictibilidad de los precios a corto plazo de los ETF de renta fija de EE. UU. en respuesta a choques extremos de precios. A través de una evaluación de 582 movimientos de precios extremos de los ETF en el período 2007-2014, comparamos los retornos durante el período normal (desde la apertura hasta el final de la sesión) y los retornos fuera de ese horario para un grupo de 87 ETFs. Encontramos un fuerte contraste entre lo que ocurre en estos dos períodos: en promedio, solo los retornos extremos que ocurren fuera del horario normal representan una reacción exagerada, lo que lleva a una reversión significativa en el siguiente período. Nuestros resultados sugieren que los mercados tienden a ser significativamente más ineficientes durante las horas extras. Estos resultados tienen implicaciones importantes tanto para los reguladores como para los profesionales del mercado.
WEB MINING USING K-MEANS CLUSTERING AND LATEST SUBSTRING ASSOCIATION RULE FOR E-COMMERCE

User latency plays a significant role in e-commerce. This latency can be minimized by a priori predicting and fetching probable web pages for web users to run the e-commerce activities. Those prediction techniques are normally supported by clustering, classification and some association rules based on the data set of web logs of navigations, searching and attached web links with the e-commerce web pages. This paper proposes an integrated web page prediction technique by analyzing web users’ previous navigational behavior. K-means clustering and latest substring association rule are considered for developing the proposed method of e-commerce web page prediction. The proposed method is evaluated by analyzing the precisions values of the output clusters using the proposed prediction technique.
The anatomy of fear: interplay between metaphor, metonymy and image schema in the conceptualization of fear/strach in English and Polish

The article makes a contribution to a large body of research on emotion metaphors within the framework of cognitive linguistics. it is part of a bigger study conducted by the author, which has resulted in producing a consistent conceptual model underlying our understanding of this emotion. The present study focuses on the interaction between the imaginative structures of metaphor, metonymy and image schema within the conceptual model. it also examines the role of embodiment in forming the concepts, identifies and specifies the source domains whose elements are projected onto the domain of FEaR and seeks to demonstrate cross-linguistic differences which show in some mappings in English and Polish. as stated in the title, the emotion under study is referred to as fear in English and strach in Polish. One could argue that as for the semantic content of the words ‘strach’, it may not be identical with that of the English word ‘fear’, therefore they should not be taken as equivalent to each other. However, there is enough overlap to regard them as equivalent for the purpose of this work, because the main question raised here does not concern the detailed study of particular word meanings, but rather the prototypical semantic matter of the emotion that underlies the specificity of fear/strach, panic/panika or anxiety/lęk.
Validation of the apperception test God representations, an implicit measure to assess God representations. Part 3: associations between implicit and explicit measures of God representations and self-reported level of personality functioning

ABSTRACT Research with self-report measures of God representations suggests an association with personality pathology. However, according to object-relations theory, God representations are predominantly implicit. This observational study aimed at validating the implicit Apperception Test God Representations (ATGR). In a group of 74 patients with personality pathology and a group of 71 non-patients, correlations of measures of self-reported personality functioning with the implicit ATGR were compared with correlations with the explicit Questionnaire God Representations (QGR). Only in the clinical group, results corroborated the validity of three ATGR main scales by showing significant correlations with mostly nearly medium effect sizes.
Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

Natural habitats are being impacted by human pressures at an alarming rate. Monitoring these ecosystem-level changes often requires labour-intensive surveys that are unable to detect rapid or unanticipated environmental changes. Here we developed a generalisable, data-driven solution to this challenge using eco-acoustic data. We exploited a convolutional neural network to embed ecosystem soundscapes from a wide variety of biomes into a common acoustic space. In both supervised and unsupervised modes, this allowed us to accurately quantify variation in habitat quality across space and in biodiversity through time. On the scale of seconds, we learned a typical soundscape model that allowed automatic identification of anomalous sounds in playback experiments, paving the way for real-time detection of irregular environmental behaviour including illegal activity. Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts.
Tensor Methods for Minimizing Convex Functions with Hölder Continuous Higher-Order Derivatives

In this paper we study p-order methods for unconstrained minimization of convex functions that are p-times differentiable (p ≥ 2) with n-Holder continuous p th derivatives. We propose tensor schemes with and without acceleration. For the schemes without acceleration, we establish iteration complexity bounds of O(e-1/(p+n-1))$ for reducing the functional residual below a given e I\in (0,1). Assuming that n is known, we obtain an improved complexity bound of O(e-1/(p+n))$ for the corresponding accelerated scheme. For the case in which n is unknown, we present a universal accelerated tensor scheme with iteration complexity of O(e-p/[(p+1)(p+n-1)]). A lower complexity bound of O(e-2/[3(p+n)-2]) is also obtained for this problem class.
Method for Reduction the Errors of Likelihood in the Authentication by the Iris

— The article describes the influence of lighting on the accuracy of users authentication in access control systems on the iris. The low contrast of the iris image is the reason that increases the number of errors because of different lighting at the stages of registration and authentication of the user. Depending on the wavelength of light in which the iris is registered, various details appear on it, and their severity depends on the type of eye. Most light eyes give the clearest picture in visible light. On the contrary, the structures of dark eyes clearly appear in the infrared range. It is recommended to use an iris biometric authentication system with the functionality of visible and infrared scanning ranges. Then the system should evaluate the quality of the images obtained in the visible and infrared scanning ranges and select the best quality. This lead to the addition of a new step in the recognition algorithm, which increases the running time of the algorithm as a whole. To compensate the time required for that, it is recommended to use an optimal set of modules of residual classes system, which will improve the performance and technical characteristics of digital filters.
The Use of Evolutionary Algorithms for Optimization in the Modern Entrepreneurial Economy: Interdisciplinary Perspective

Objective: The objective of the article is to present the concept of evolutionary algorithms and indicates the possibility of their implementation for the needs of the economy, especially the entrepreneurial economy. Research Design & Methods: This conceptual article relies on literature review and desk research. The article elaborates on available literature via a systematic literature review methodology. Findings: The article elaborates on the idea of action and typology of evolutionary algorithms as the broadly applied search and optimisation technique based on Darwin’s theory of evolution and modern natural genetics. The article focuses on the examples of evolutionary algorithms application in economics and management. Implications & Recommendations: The current state of applications of evolutionary algorithms for the needs of the economy and business confirms that we still await an implementation breakthrough. The growing interest in evolutionary algorithms in connection with the dynamic development of information technologies may lead to the use of evolutionary algorithms in hybrid systems, which in turn will contribute to significant progress in optimization theory. Contribution & Value Added: The article structures scientific knowledge on the application of evolutionary algorithms in business and economy. The promotion of the application of evolutionary algorithms in economics, finance, and management is mainly limited to journals in operational research, decision-making process, or financial engineering, whereas this article includes entrepreneurship. Article type: conceptual article