Miracle-dz/UniFAR-FTUs huggingface dataset:
An Overview of Multimodal Biometrics Using the Face and Ear

In the recent years, we have witnessed the rapid development of face recognition, though it is still plagued by variations such as facial expressions, pose, and occlusion. In contrast to the face, the ear has a stable 3D structure and is nearly unaffected by aging and expression changes. Both the face and ear can be captured from a distance and in a nonintrusive manner, which makes them applicable to a wider range of application domains. Together with their physiological structure and location, the ear can readily serve as supplement to the face for biometric recognition. It has been a trend to combine the face and ear to develop nonintrusive multimodal recognition for improved accuracy, robustness, and security. However, when either the face or the ear suffers from data degeneration, if the fusion rule is fixed or with inferior flexibility, a multimodal system may perform worse than the unimodal system using only the modality with better quality sample. The biometric quality-based adaptive fusion is an avenue to address this issue. In this paper, we present an overview of the literature about multimodal biometrics using the face and ear. All the approaches are classified into categories according to their fusion levels. In the end, we pay particular attention to an adaptive multimodal identification system, which adopts a general biometric quality assessment (BQA) method and dynamically integrates the face and ear via sparse representation. Apart from a refinement of the BQA and fusion weights selection, we extend the experiments for a more thorough evaluation by using more datasets and more types of image degeneration.
Implicit Geomodelling of the Merensky and UG2 Reefs of the Bushveld Complex from Open-Source Data: Implications for the Complex’s Structural History

The Bushveld Complex (BC) is the world’s largest source of platinum group metals. Extensive studies on the complex have focused on its geochemistry, magma and platinum group mineral genesis, mineral characterization and intrusion mechanisms. However, relatively little work has been undertaken on the overall 3D geometry of the complex, which detracts from the adequate contextualization of such studies. Furthermore, the absence of a broader 3D model of the complex does not permit the identification of structural trends and mineralization patterns. This contribution details the construction of 3D implicitly-modelled Merensky and UG2 Reefs across the Rustenburg Layered Suite of the BC, using Seequent’s Leapfrog software. Multiple open-source and public-domain data sources and modelling workflows were explored to account for disparities in data resolution, data spacing and clustering, and the resolution of model outputs. Key outcomes are (1) a representative, fully-implicit, dynamic geological model of the Merensky and UG2 Reefs over the main chamber of the BC; (2) identification of modelled features that augment the current understanding of the BC’s kinematic history and cumulative deformation; and (3) identification and analysis of subtle geometrical trends and patterns, such as inter-reef spacing and modelled depths, as well as structural domains that may not have been apparent from numerous, more focused or isolated petrological or geochemical studies. It is anticipated that this baseline 3D model will form the foundation for future, possibly localized, dynamic updates as further information becomes available. The addition of proprietary (viz., non-open-source) data, such as 2D seismic sections and 3D seismic surveys, would enhance the overall resolution and quality of such a model and resulting interpretations.
The Contributions of Crosslinguistic Influence and Individual Differences to Nonnative Speech Perception

Perception of a nonnative language (L2) is known to be affected by crosslinguistic transfer from a listener’s native language (L1), but the relative importance of L1 transfer vis-a-vis individual learner differences remains unclear. This study explored the hypothesis that the nature of L1 transfer changes as learners gain experience with the L2, such that individual differences are more influential at earlier stages of learning and L1 transfer is more influential at later stages of learning. To test this hypothesis, novice L2 learners of Korean from diverse L1 backgrounds were examined in a pretest-posttest design with respect to their perceptual acquisition of novel L2 consonant contrasts (the three-way Korean laryngeal contrast among lenis, fortis, and aspirated plosives) and vowel contrasts (/o/-/ʌ/, /u/-/ɨ/). Whereas pretest performance showed little evidence of L1 effects, posttest performance showed significant L1 transfer. Furthermore, pretest performance did not predict posttest performance. These findings support the view that L1 knowledge influences L2 perception dynamically, according to the amount of L2 knowledge available to learners at that time. That is, both individual differences and L1 knowledge play a role in L2 perception, but to different degrees over the course of L2 development.
The Influence of Relative Advantage Towards e-Procurement Adoption Model in Developing Countries: Tanzania Context

The aim of this study was to fill the knowledge gap through assessing the influence of relative advantage towards e-procurement adoption in developing countries, Tanzania in particular. The study adopted positivism philosophy and cross-sectional survey research design. The study also used stratified sampling technique. Sample size was 157. Questionnaires and documentary review were used for data collection. The collected data were analysed by using Partial Least Squares Structural Equation Modelling with the help of SmartPLS 3 software. Findings reveal that in the presence of attitude, relative advantage has direct and indirect influences towards e-procurement adoption. Grounding on the theoretical and empirical gaps, the hypothesized relationships of the determinants of new technology (relative advantage and attitude) have therefore filled these gaps which leads to the theoretical, empirical and practical contributions.The study recommends attention to be paid for relative advantage towards e-procurement adoption in Tanzania and in all developing countries which intend to adopt new technologies in public sector.
BIDIRECTIONAL GATED RECURRENT UNIT FOR SHALLOW PARSING

- Shallow parsing is one of the natural language processing tasks, where various syntactic phrases such as noun ph r ases, verb phrases, and others, are identified. Here shallow parsing is considered as a sequence tagging task, and this paper presents the application of deep learning approach using recurrent neural networks (RNN) for shallow parsing. Specifically, gated recurrent unit (GRU), and bidirectional gated recurrent unit (BiGRU) are applied in parsing Khasi, an Austro-Asiatic language. These variants of the RNN address the vanishing gradient problem and the dependency of the chunk tags on both preceding and subsequent information from the sequential input data. The results have shown an improved performance compared to an existing shallow parser for Khasi.
Salt dome related soil salinity in southern Iran: Prediction and mapping with averaging machine learning models

In order to manage soil salinity effectively, it is necessary to understand the origin and the spatial distribution of salinity. There are about 120 salt dome outcrops in southern Iran and little is known about their contribution as the potential sources of salts and the spatial pattern of salts around them. Six machine learning algorithms were applied to model topsoil electrical conductivity (EC) and sodium adsorption ratio (SAR) in the Darab Plain (surrounded by six salt domes), Fars Province. Decision trees (DT), k‐nearest neighbours (kNN), support vector machines (SVM), Cubist, random forests (RF) and extreme gradient boosting (XGBoost) were used as primary models and the Granger–Ramanathan (GR) method was used to combine the predictions of these models. The results showed that remotely sensed data contributed more to predict EC and SAR than terrain‐based data. In terms of root mean square errors (RMSE), Cubist followed by the RF model, tended to give the best estimates of EC, whereas for SAR, RF performed best and was followed closely by SVM and Cubist. Compared to the primary models, the GR method on average resulted in a decrease of 6.1% and 3.9% in RMSE and an increase of 10% and 10.9% in R2 for EC and SAR, respectively. The spatial pattern of SAR and EC suggested that the contribution of salt domes in soil salinization varied significantly according to their hydraulic behaviour in relation to adjacent aquifers and their activity. In general, the model averaging approach showed the potential to improve the estimates of EC and SAR.
Method for increasing the dynamic range of digital images based on modular arithmetic

The article proposes a new method of synthesis of digital images from several digital images to increase the dynamic range of intensity. The method is based on modular arithmetic. The increased range is divided into several residual classes. The full value of the intensity is calculated as the solution of the system of comparisons of residues for given modules. An analysis is made of the stability of the proposed method to the nonlinearity of the transfer characteristic and digital image recording devices under various types of interference. To this end, the maximum range for changing the brightness of a digital image is limited. By selecting the magnitude of the modules and the maximum brightness value, the distance between the diagonals in the decision table is selected so that this distance is greater than the level of distortion and (or) interference. Experimental validation of the method has been performed.
Fine Tuned VGG19 Convolutional Neural Network Architecture for Diabetic Retinopathy Diagnosis

- Diabetic Retinopathy (DR) is an eye-related complication experienced by individuals with long-standing diabetes. Usually diagnosed by the healthcare professional by retinal fundus examination during Medical check-ups or mass screenings. Early detection of diabetic retinopathy will avoid vision loss and other issues. The objective of this work is to diagnose the Diabetic Retinopathy from retinal fundus images using deep learning (DL) techniques for better detection accuracy.The proposed fine-tunedVGG19 CNN architecture is performing well with the Kaggle data set and effectively dealing with this multi-class classification problem. The proposed model uses pre-trained weights from image net data which lessens the training time and improves the performance in detecting DR from retinal fundus images in terms of sensitivity, specificity, and accuracy. Deep transfer learning with fine-tuning method implementation was carried out to get the highest test accuracy of 73.60%.
Role of Economic Policy Uncertainty in the Connectedness of Cross-Country Stock Market Volatilities

The implied volatility index is a forward-looking indicator of fear among stock market participants. We examine the extent to which the connectedness of fear among global stock markets is driven by the cross-country connectedness of economic policy uncertainty (EPU). We use data on stock market fear and EPU indices for 13 countries, which spans from January 2011 to December 2018. To measure the connectedness among stock market fear and EPU of our sample countries, we employ two connectedness models. A cross-sectional regression model is further employed to ascertain the extent to which EPU connectedness between two countries explains the connectedness of fear between their stock markets, while controlling for bilateral linkage and country-specific factors. We find that EPU connectedness between any two partner countries significantly drives the connectedness of fear between their stock markets. The driving potential not only holds for short- and long-term connectedness, but also after controlling for bilateral linkages (bilateral trade, geographical distance, common language) and country-specific (trade and financial openness of the transmitter country) factors indicating robustness in our results.
Chaotic Convection in a Ferrofluid with Internal Heat Generation

The nonlinear stability analysis of a ferrofluid layer system is formulated mathematically. This system considered the upper and lower free isothermal boundary with the system heated from below. A mathematical formulation is produced to study the behaviour of the chaotic convection in a ferrofluid layer system using Galerkin truncated expansion. The Boussinesq approximation is opted with the existence of internal heating and the magnetic number. It is found that the transition to chaos in this present study is identical to the Lorenz attractor and thus validate the method and analysis of this study. The impact of elevating the internal heat generation is found to hasten the instability of the system and as for the magnetic number, at M1 = 2.5 the homoclinic bifurcation occurs and thus accelerates the convection process.
Turbulence-permitting air pollution simulation for the 
Stuttgart metropolitan area

Air pollution is one of the major challenges in urban areas. It can have a major impact on human health 10 and society and is currently a subject of several litigations at European courts. Information on the level of air 11 pollution is based on near surface measurements, which are often irregularly distributed along the main traffic 12 roads and provide almost no information about the residential areas and office districts in the cities. To further 13 enhance the process understanding and give scientific support to decision makers, we developed a prototype for 14 an air quality forecasting system (AQFS) within the EU demonstration project “Open Forecast”. 15 For AQFS, the Weather Research and Forecasting model together with its coupled chemistry component (WRF16 Chem) is applied for the Stuttgart metropolitan area in Germany. Three model domains from 1.25 km down to a 17 turbulence permitting resolution of 50 m were used and a single layer urban canopy model was active in all 18 domains. As demonstration case study the 21 January 2019 was selected which was a heavy polluted day with 19 observed PM10 concentrations exceeding 50 μg m. 20 Our results show that the model is capable to reasonably simulate the diurnal cycle of surface fluxes and 2-m 21 temperatures as well as evolution of the stable and shallow boundary layer typically occurring in wintertime in 22 Stuttgart. The simulated fields of particulates with a diameter of less than 10 μm (PM10) and Nitrogen dioxide 23 (NO2) allow a clear statement about the most heavily polluted areas apart from the irregularly distributed 24 measurement sites. Together with information about the vertical distribution of PM10 and NO2 from the model, 25 AQFS will serve as a valuable tool for air quality forecast and has the potential of being applied to other cities 26 around the world. 27
Reciprocity theorem in optics and its application to X-ray diffraction topographs

The reciprocity theorem is applied to X-ray diffraction topographs. The intensity distribution of a traverse-type topograph is obtained by knowing the integrated intensity of the section-type topograph which would be produced with the same crystal by X-rays emitted from a source located virtually at the point concerned. This relation holds in general irrespective of the shape, absorption and distortion of the crystal and the polarization of X-rays.
Essential oil composition of Hypericum perfoliatum L. growing in different locations in Greece

The essential oils of the aerial parts of Hypericum perfoliatum L. , collected in two different locations in Greece, were obtained by hydrodistillation and analysed by GC– MS. Fifty constituents were identified. The monoterpene hydrocarbons had the highest contribution. The major constituents in sample A were α-pinene (48.6%) n-nonane (8.5%) and δ-cadinene (4.6%), while those of sample B, were α-pinene (34.2%) β-pinene (9.2%) and δ-cadinene (8.1%). Copyright © 2001 John Wiley & Sons, Ltd.
Children’s practices with electronic sensors to understand and mitigate sound pollution in primary school

ABSTRACT Background To contribute to smart, inclusive, sustainable, and multisensory practices in science education, this paper presents research that studied the use of electronic sensors by children to address a sound pollution problem in their school. A didactic sequence was specifically designed and implemented to that purpose. Participants The participants of this study were a project Teacher/Researcher, and the children of eight 4th grade classes (four experimental groups and four control groups), in four primary schools. Purpose The research aim was to study whether and how, throughout the didactic sequence, children developed practices that are needed to understand and mitigate sound pollution in school, including acquiring, analysing, interpreting, and sharing environmental data, as well as interpreting, and applying information, making suggestions to solve the problem. The importance of the use of the electronic sensors was also assessed. Design and methods To answer to the research questions, during the didactic sequence, data were collected through the data collection forms, the collaborative platform, the pre and post-tests, and the children’s suggestions to solve the sound pollution problem. Statistical methods were used to process pre- and post-tests results, and a content analysis allowed the treatment of the children’s suggestions. Results The vast majority of children properly collected, registered, classified, and shared the acquired data. The statistical analysis showed that there was an improvement from the pre to the post-tests results, with the experimental group having more significant improvements. The content analysis of the children’s suggestions revealed that all the children made productive suggestions, but the experimental group made more specific and diversified suggestions. Conclusion Those results suggest that the experimental didactic sequence allowed and scaffolded children in using ICT tools to develop science education hands-on experimental practices, while understanding, analysing and contributing to solve a sound pollution problem in their schools.
Bonding Agents in Dentistry and Recent Advances

Introduction: The main aim of restorative dentistry is to restore the tooth tissue in its proper form, function, aesthetics as well as maintaining physiologic integrity in harmony with surrounding soft and hard tissues. Since many of the restorative materials do not adhere to dental tissues like enamel and dentin due to different physical and chemical properties, which lead to microleakage at tooth restoration interface, thus bonding agents play an essential role in restorative dentistry. Bonding agents have evolved through different generations in the last few decades, along with some recent advances that are changing the entire restorative procedure. The bonding agents evolved in terms of chemistry, several bottles, mechanism of action, the technique of application, and as well as its clinical implication. The most widespread is self -etching primer, which has been used for more than a decade, whereas all-in-one adhesives came into consideration for the past two years. With the development of this system, it has significantly simplified resin bonding since a separate etching step is no longer requires, thereby saving time. Aim of the Study: The purpose of the review is to understand the different generation of bonding agents and newer modification for better adhesive properties. Methodology: The review is a comprehensive research of PUBMED since the year 1952 to 2019. Conclusion: Dentin bonding is one challenging concept in clinical practice due to an increase in demand for aesthetic restorative treatments. It lead to recent advances in dentistry and more development of adhesive material used in adhesive systems and composites, as well as the techniques used in restoring the teeth to give it a natural tooth appearance. With the advent of acid etching into clinical practice, many bonding agents are developed until this date to improve the quality of adhesives and composite restorations. The manufactures are progressing more in the development of a new adhesives system to simplify the process and improve results in terms of stability and bond strength, further leading to a more durable resin bond.
An Innovative Approach to Generational Motivation

Purpose: The study aims to determine whether the achievement of the cardinal objective of employment is motivated by more sophisticated preferences for the broad aspect of working conditions articulated by labor market adepts. The study aims to indicate what factors influence the achievement of a given general objective. The publication also assesses whether a given factor stimulates a person to formulate a goal or a de-stimulant. Approach/Methodology/Design: The subject of the analysis results from a survey carried out on a group of 400 people aspiring to, or partially present on the labor market. Respondents have the status of a student or will obtain higher education in a short time. The survey was carried out on representatives of various faculties and universities in Poland. The survey included 73 Likert scale questions concerning such aspects: expected features of the future direct superior, selected elements of the respondent's self-assessment, and key motivators to take up employment. Findings: As the analysis of the collected statistical material shows, three dimensions of the broad phenomenon form an orthogonal system: uncorrelated. They bring some unique knowledge about the analyzed group of respondents. The structure of 73 detailed questions creates an overly complicated picture of the issue. Practical Implications: The analysis of survey results indicated no measurable relationship between the expectations profile of a young person and the fact that he or she wants to earn satisfactorily. This objective applies to all respondents equally, regardless of their expectations or preferences. However, it is different in the case of promotion and professional development. Originality/Value: Research originality focuses on narrowing research to a specific group of workers with a broad view of the problem. The study of one of the most important management functions, which is motivation from various aspects and the pursuit of cause and effect relationships, will always be important for further developing the management discipline.