Reviewerly/exHarmony huggingface dataset:
Towards motivation-driven intelligent interfaces: formal argumentation meets activity theory

Theories about human activity and motivation point out that motives are driving forces behind human activities and development of healthy and unhealthy habits. Activity theory is one of these that has been applied to develop activity-centered user interfaces. Activity theory differentiates between sense-making and stimuli-oriented types of motives that have a strong influence on our daily behavior. Two main challenges are explored in this paper: 1) the personalisation of graphical user interfaces to mediate representations of motivation-based activities to support behaviour change processes; and 2) the proactiveness of such visual representations.As methods, we use activity theory as a framework for defining the motivations' dynamics, and formal argumentation theory as the underlying mechanism for interactive reasoning and decision-making in the process of generating the user interface.Our contributions are two-folded: 1) a dynamic graphical user interface where the background responds to behaviors linked to sense-making motives, and the foreground to stimuli motivation; and 2) a non-monotonic reasoning mechanism endowing the user interface with proactiveness (not only react to the user interactions but trigger and direct attention to potential conflicts), and a motive-based behavior conflict resolution process. Future work includes user studies to explore how triggering of focus may create increased awareness in an individual of conflicting motives in daily activities and how this may support changes of unhealthy habits.
Muon $g-2$ in $U(1)_{\mu-\tau}$ Symmetric Gauged Radiative Neutrino Mass Model

We explore muon anomalous magnetic moment (muon $g-2$) in a scotogenic neutrino model with a gauged lepton numbers symmetry $U(1)_{\mu-\tau}$. In this model, a dominant muon $g-2$ contribution comes from not an additional gauge sector but the Yukawa sector. In our numerical $\Delta \chi^2$ analysis, we show that our model is in favor of normal hierarchy with some features. We also demonstrate two benchmark points, satisfying muon $g-2$ at the best fit value $25.1\times10^{-10}$.
Comparing Deep and Shallow Neural Networks in Forecasting Call Center Arrivals

Abstract Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Deep Learning method known as Echo State Networks, is compared with a completely different shallow Neural Networks strategy, in which the lack of recurrent connections is compensated by a careful input selection. The comparison, carried out on three different real world datasets, reveals similar predictive performance, although the shallow approach seems to be more robust and less demanding in terms of time-to-predict.
PARALLEL TRANSLATION AS A LANGUAGE LEARNING INSTRUMENT

This study seeks to explain the usefulness of parallel translation as a language learning tool and the reasons for its efficacy in the light of the comprehensible input hypothesis and data from polyglottery. In this connection, the discussion of intensive vs. extensive learning is raised and preliminary calculations of the amount of input necessary for language acquisition are offered.A classification of bilingual text types (vertical, horizontal, interlinear and incorporated) is suggested and sample learning materials for several languages (French, Japanese, Middle English, Veps and Newar) are provided. The paper mentions the titles of some particular textbooks based on this method (such as Assimil, Langenscheidt and Gakken publications) and considers the factors affecting its applicability.Parallel translation is shown to be a straightforward way of creating educational materials for smaller and rarer languages as well as for studying recurrent features of different languages and their respective cultures. Learning resources of this kind are also argued to have particular significance in the light of the receptive multilingualism approach that stresses the priority of foreign language comprehension (rather than speech production) for successful and fair international communication.Mention is made of different attitudes towards bilingual texts among polyglots who are traditionally known for using this kind of materials in their study. While some praise parallel translation as a unique method of vocabulary acquisition, others indicate that it diminishes the intellectual work necessary for language learning. Still others point out that the efficiency of this method depends on the structural similarities/differences in a given pair of languages.By way of conclusion, parallel translation is demonstrated to be an efficient way of making extensive language input comprehensible from the earliest stages of the learning process and of customizing it to one’s personal interests. Progress to more advanced levels of language proficiency should probably imply shifting from bilingual texts to adapted literature (graded readers) to authentic foreign speech sources.
Freedom and responsibility as key elements of sustainable development of education

The paper reveals the future teachers’ attitude towards freedom and responsibility in the context of sustainable development of education. Sustainable development seems to be impossible without the new generation of free, dignified, respectful, and responsible individuals. Freedom and responsibility are outlined from a pedagogical point of view. The concepts under study are considered to be of great significance for child’s development. The information obtained during the survey and questionnaire conducted with first-year students of the pedagogical university indicates that future teachers do not yet have sufficient experience to correlate adequately the possibility of revealing school students’ freedom and the need to be in charge of the behavior, actions and thoughts of their own. University students believe that school students generally take responsibility for completing individual assignments rather than common ones. Future teachers do not think that school students’ attitude towards education is responsible and trustworthy, would-be teachers doubt that school students are able to manifest freedom and responsibility to full extent. The empirical data drive to the conclusion that it is necessary to change future teachers’ attitude towards freedom and responsibility in their pedagogical activities. Future teachers are advised to change their attitude to freedom and responsibility in their activities.
DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy ensures a robust prediction despite the risk of generalization failure of some individual networks. Second, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). Finally, to learn a more generalizable representation of MS lesions, we propose a hierarchical specialization learning (HSL). HSL is performed by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice.
TPACK of Prospective Mathematics Teachers at an Early Stage of Training

The objective of this work is to present the results of an investigation on the technological, pedagogical and content knowledge evidenced by mathematics teachers in the second-level of initial training (preservice) at the Universidad Nacional, from the TPACK model perspective, on the subject of functions. The research has a qualitative approach with an interpretive hermeneutical stance. A sample of 27 teachers in training who were enrolled in courses related to the three base domains of the TPACK model (pedagogical, technological and content of quadratic function) during the first semester (I cycle) of 2020 was used. A theoretical inquiry was carried out that allowed the creation of an instrument that made possible the description of the participants&amp;rsquo; knowledge based on this model focused on the topic of quadratic function. The results show that participants possess instrumental dominance over the basic forms of knowledge underlying the model. It is concluded that, although participants have already experimented in courses related to these forms of knowledge, there is insufficient evidence to ascertain that their current knowledge would allow them to integrate technologies as a didactic resource within the teaching of the subject of quadratic function.
A Context-aware Image Recognition System with Self-localization in Augmented Reality

The diffusion of augmented-reality (AR) frameworks has facilitated the implementation of support systems for several real-world tasks. This paper introduces a system that supports Mahjong scoring for beginners. Mahjong is a globally popular strategic board game. Playing Mahjong improves cognitive functions and promotes social interactions. However, it is complex for beginners to accumulate a score according to the combinations of Mahjong tiles. We aim to develop an offline system to tally the score by visually recognizing the Mahjong tiles, which have classes and attributes based on their positional context. This system, therefore, requires a context-aware image recognition. The system recognizes their contextual attributes via self-localization and detects each tile using OpenCV and a convolutional neural network to classify them. The accuracy of detecting tiles and recognizing attributes was good enough to provide an acceptable support system. Our experimental results demonstrate that the system is accurate enough to detect tiles and to recognize attributes. We concluded that the system provides adequate support for the novices.
Influence of foreign direct investment and exchange rate on fisheries in Nigeria

Many studies have explored the impact of macroeconomic factors on the growth and output of the agricultural sector in Nigeriawith focus on the aggregate output. Some studies narrowed down focus on the crop subsector while neglecting the fisheriessubsector which is an important source of cheap protein for our increasing population, a source of employment for the unemployed and also key to achieving the first three sustainable development goals 2030. In this study, we investigated the influence of FDI on agriculture and exchange rates on the output of the fisheries subsector using time series data that spans from1980-2018. A Vector Autoregressive Model was also used alongside a growth model. The findings indicate positive growthin the fisheries subsector. FDI to agriculture and exchange rate movements were both found to affect the fisheries subsectorpositively in the long run, whereas only FDI to agriculture was found to exert a positive influence in the short run. Policies toattract FDI to the sector are thus advocated for, while macroeconomic policies to stabilise the Nigerian currency (naira) againstthe US dollar are also advised.
Abstract clones for abstract syntax

We give a formal treatment of simple type theories, such as the simply-typed $\lambda$-calculus, using the framework of abstract clones. Abstract clones traditionally describe first-order structures, but by equipping them with additional algebraic structure, one can further axiomatize second-order, variable-binding operators. This provides a syntax-independent representation of simple type theories. We describe multisorted second-order presentations, such as the presentation of the simply-typed $\lambda$-calculus, and their clone-theoretic algebras; free algebras on clones abstractly describe the syntax of simple type theories quotiented by equations such as $\beta$- and $\eta$-equality. We give a construction of free algebras and derive a corresponding induction principle, which facilitates syntax-independent proofs of properties such as adequacy and normalization for simple type theories. Working only with clones avoids some of the complexities inherent in presheaf-based frameworks for abstract syntax.
Discoverability of information on clinical trial data-sharing platforms

This study was intended to (1) provide clinical trial data-sharing platform designers with insight into users' experiences when attempting to evaluate and access datasets, (2) spark conversations about improving the transparency and discoverability of clinical trial data, and (3) provide a partial view of the current information-sharing landscape for clinical trials.We evaluated preview information provided for 10 datasets in each of 7 clinical trial data-sharing platforms between February and April 2019. Specifically, we evaluated the platforms in terms of the extent to which we found (1) preview information about the dataset, (2) trial information on ClinicalTrials.gov and other external websites, and (3) evidence of the existence of trial protocols and data dictionaries.All seven platforms provided data previews. Three platforms provided information on data file format (e.g., CSV, SAS file). Three allowed batch downloads of datasets (i.e., downloading multiple datasets with a single request), whereas four required separate requests for each dataset. All but one platform linked to ClinicalTrials.gov records, but only one platform had ClinicalTrails.gov records that linked back to the platform. Three platforms consistently linked to external websites and primary publications. Four platforms provided evidence of the presence of a protocol, and six platforms provided evidence of the presence of data dictionaries.More work is needed to improve the discoverability, transparency, and utility of information on clinical trial data-sharing platforms. Increasing the amount of dataset preview information available to users could considerably improve the discoverability and utility of clinical trial data.
INVOLVING STUDENTS IN ONLINE LEARNING

Educational process in the period of pandemic has demonstrated not only professionalism of the teaching staff at MGIMO-University but also its flexibility, mobilization and assistance of colleagues in any emergency situation. Switching teaching/learning process into the online or distance format required a lot of efforts on the part of both professors and students to make it successful, and a lot of changes were brought about to this end. Still, students’ creativity caused by their involvement into the learning process is what the study at the university is about. That is why creativity should be at the core of educational process whether in-person, remote or any other format, and the following steps can be seen helpful:1) Adaptation of the programs to the online learning process with respect to the specifics of the Internet communication on the whole: as learning online these days is fraught with danger of poor or even the loss of connection caused by the network congestion, it’s better to transfer all audio and video assignments to homework, which could resolve listening problems and free time for creative assignments. 2) Creativity: when students are challenged by the assignments addressing their life at the university or at home, their interests or problems, they get willingly involved into different activities: from the story making/telling or participation in round-table talks to discussions and decision-making. Students enjoy this creative ‘flight’ and are ready to share it with their group mates. The online platform is the efficient and appropriate platform for spontaneous self-reflection with all the students participating in it. 3) Delegation of class management to the students can challenge the latter and give them an opportunity to switch the roles with the professor. Students enthusiastically perform the role of a tutor, which, however, enables a professor as well to learn more about his/her students and find an individual approach and methods working for them better. 4) Spontaneous interaction with a professor at the beginning or in any other part of a class enhances motivation and makes interaction real and purposeful, allows seeing students’ progress. It always enlivens the atmosphere, establishes contact between a professor and his/her students and brings interaction to the point, when students can freely speak their mind. Solve-problem questions and discussions elicit the greatest response.5) Adaptation of assignments is another good stimulus for students to study thoughtfully, when working on text assignments at home. Modification and deviation from the student-book assignments prepared at home and checked in class helps to see students’ progress in mastering the program at home and in class and their flexibility when knocked off the beaten track, which in the long run improve students’ academic performance. Although singled out as separate, these steps are often intertwined in classwork; they are aimed at a student’s personality and direct interaction, which yield only positive results.
Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Membangun Pengetahuan Diagnosa Penyakit Diabetes

Diabetes is caused by a deficiency of the hormone insulin, which is secreted by the pancreas to lower blood sugar levels. The factors that trigger the occurrence of diabetes are derived from various factors such as a combination of genetic and environmental factors. The phenomenon of the emergence of various beverage brand outlets can be one of the triggers for blood sugar levels in humans. Normal blood sugar levels in the body range from 70-130 mg/dL before eating, less than 180 mg/dL two hours after eating, less than 100 mg/dL after not eating or surviving for eight hours, and 100-140 mg/dL at bedtime. This research aims to determine which algorithm is suitable for building knowledge about diabetes using the Naïve Bayes and K-Nearest Neighbor (KNN) algorithm. The accuracy results from Naïve Bayes are 85.60% and K- Nearest Neighbor of 91.61%. The results showed that K-Nearest Neighbor proved to have the best accuracy.
An Improved BAT Algorithm for Solving Job Scheduling Problems in Hotels and Restaurants

One popular example of metaheuristic algorithms from the swarm intelligence family is the Bat algorithm (BA). The algorithm was first presented in 2010 by Yang and quickly demonstrated its efficiency in comparison with other common algorithms. The BA is based on echolocation in bats. The BA uses automatic zooming to strike a balance between exploration and exploitation by imitating the deviations of the bat’s pulse emission rate and loudness as it searches for prey. The BA maintains solution diversity using the frequency-tuning technique. In this way, the BA can quickly and efficiently switch from exploration to exploitation. Therefore, it becomes an efficient optimizer for any application when a quick solution is needed. In this paper, an improvement on the original BA has been made to speed up convergence and make the method more practical for large applications. To conduct a comprehensive comparative analysis between the original BA, the modified BA proposed in this paper, and other state-of-the-art bio-inspired metaheuristics, the performance of both approaches is evaluated on a standard set of 23 (unimodal, multimodal, and fixed-dimension multimodal) benchmark functions. Afterwards, the modified BA was applied to solve a real-world job scheduling problem in hotels and restaurants. Based on the achieved performance metrics, the proposed MBA establishes better global search ability and convergence than the original BA and other approaches
Advanced Particle Swarm Algorithm For Bicycle Robot Balancing Optimization Control Under Constant Speed

The bicycle robot is one of the Single Track Two-wheel robot that its balance control is so important as the forward velocity control. The existing researches on balance control focus on the single-objective control, in reality multiple objectives are required to be considered, that they are not proper for the complicate autonomous control. The multi-objective optimization issue for the bicycle robot with constant forward velocity is studied in this paper. The State-space model is verified based on the nonlinear Euler-Lagrange model. A new fitness function with multiple objectives is proposed for the Advanced Particle Swarm Optimization to tune the gains in the feedback controller. The conventional Lyapunov method is involved in the APSO process. The simulation example prove the validity of the given method in this paper.
INTERMEDIATION OF EYE-TRACKING TECHNOLOGY FOR STUDENTS IN AN INTRODUCTORY WEB DESIGN COURSE TO ESTABLISH THE BENEFITS OF INCORPORATING ACTIVE LEARNING EXPERIENCES FOR ASSESSING USERS’ EXPERIENCES

Information about the paper titled "INTERMEDIATION OF EYE-TRACKING TECHNOLOGY FOR STUDENTS IN AN INTRODUCTORY WEB DESIGN COURSE TO ESTABLISH THE BENEFITS OF INCORPORATING ACTIVE LEARNING EXPERIENCES FOR ASSESSING USERS’ EXPERIENCES" at IATED Digital Library
Optimization of Support Vector Machine Method Using Feature Selection to Improve Classification Results

The performance of the organizations or companiesare based on the qualities possessed by their employee. Both of good or bad employee performance will have an impact on productivity and the impact of profits obtained by the company. Support Vector Machine (SVM) is a machine learning method based on statistical learning theory and can solve high non-linearity, regression, etc. In machine learning, the optimization model is a part for improving the accuracy of the model for data learning. Several techniques are used, one of which is feature selection, namely reducing data dimensions so that it can reduce computation in data modeling. This study aims to apply the method of machine learning to the employee data of the Bank Rakyat Indonesia (BRI) company. The method used is SVM method by increasing the accuracy of learning data by using a feature selection technique using a wrapper algorithm. From the results of the classification test, the average accuracy obtained is 72 percent with a precision value of 71 and the recall value is rounded off to 72 percent, with a combination of SVM and cross-validation. Data obtained from Kaggle data, which consists of training data and testing data. each consisting of 30 columns and 22005 rows in the training data and testing data consisting of 29 col-umns and 6000 rows. The results of this study get a classification score of 82 percent. The precision value obtained is rounded off to 82 percent, a recall of 86 percent and an f1-score of 81 percent.