Reviewerly/exHarmony huggingface dataset:
University teachers' attitudes towards using Moodle platform in teaching philological academic courses

Moodle educational platform, which is based on socio-constructivist epistemology, is no longer a novelty in university-level teaching. The question is, however, to what extent the potential of the platform for collaborative work and the transfer of the central role from teachers to students has been operationalized in practice. Given that a significant part of the curricula of the philological academic courses involves writing texts, encouraging interaction among students on these tasks, which are usually performed independently, can be quite challenging. In this paper we examine the current practices in using Moodle at philological studies, in order to: identify the types of tasks used for achieving different teaching objectives, (b) analyze the reasons why other types of tasks are neglected and (c) determine the extent to which the platform is used for encouraging active participation and interaction among students. We present the results of the online questionnaire distributed to 44 teachers and associates at six universities from Serbia, Slovenia, and Croatia during the summer semester of 2017/2018. As the obtained data are both quantitative and qualitative, we applied a combined data processing method - descriptive analysis and nonparametric tests: chi-square test, McNemar's test, and Kruskal-Wallis test. According to the results, Moodle is used in philological courses for faster communication, more transparent access to teaching materials, and monitoring students' activities, therefore, more as a support to predominantly transmissive than collaborative teaching. To make Moodle more widely used as a support to teaching based on socio-constructivist epistemology, training in its application should include learning about the benefits of this approach to teaching. The obtained results indicate that there is a need to develop and test new activities which support peer cooperation on textual tasks.
Privacy and Utility Preserving Trajectory Data Publishing for Intelligent Transportation Systems

Nowadays, the extensive collection and storage of massive personal GPS data in intelligent transportation systems every day provide great convenience for trajectory data analysis and mining research, thus bringing valuable information for real-life applications. Yet, protecting personal privacy is also more challenging in the smart environment. When trajectories of individuals are published together with their sensitive attributes such as disease, income etc., one can use partial trajectory knowledge for identity, sensitive locations, and sensitive values of target individuals. We present (α, K)L-privacy model and an anonymization scheme aimed at Identifying and Eliminating Violating privacy Subtrajectories (IEVS), to prevent privacy disclosure while preserving the accuracy and high quality of published trajectories. In particular, IEVS employs three anonymization techniques, i.e., trajectory splitting, location suppression, and sensitive value generalization to eliminate all subtrajectories violating (α, K)L-privacy principle. Experiments show our scheme is effective to improve the data utility of anonymized trajectories when compared with previous work.
The role of quality of work life in Jordanian hotel industry

Success key elements are crucial for the improvement and preservation of competitiveness in the hotel sector.Hotels thus need efficient employees with best possible quality of work life to remain competitive.This paper investigated the impact of organisational structure (OS) and leadership style (LS) on quality of work life (QWL) among five-star hotels employees in Jordan with job satisfaction (JS) as mediator.Questionnaires were distributed among 430 five-star hotel employees to obtain the study data.The levels of relationships among the constructs (OS, LS, JS, and QWL) were analysed using partial least squares structural equation modelling (PL-SEM).The results showed a positive relationship of JS and OS with QWL, while JS showed a partial mediating role on the relationship among LS, OS and QWL.Similar studies should be carried out on other sectors in Jordan and in other settings using different perspectives.
Real-time human detection for electricity conservation using pruned-SSD and arduino

Electricity conservation techniques have gained more importance in recent years. Many smart techniques are invented to save electricity with the help of assisted devices like sensors. Though it saves electricity, it adds an additional sensor cost to the system. This work aims to develop a system that manages the electric power supply, only when it is actually needed i.e., the system enables the power supply when a human is present in the location and disables it otherwise. The system avoids any additional costs by using the closed circuit television, which is installed in most of the places for security reasons. Human detection is done by a Modified-single shot detection with a specific hyperparameter tuning method. Further the model is pruned to reduce the computational cost of the framework which in turn reduces the processing speed of the network drastically. The model yields the output to the Arduino micro-controller to enable the power supply in and around the location only when a human is detected and disables it when the human exits. The model is evaluated on CHOKEPOINT dataset and real-time video surveillance footage. Experimental results have shown an average accuracy of 85.82% with 2.1 seconds of processing time per frame.
Learners’ Perspective of Using 360-degree Video in Reading Classroom

The use of technology is so diversified and adaptable to various kinds of field including mastering English language skills. In numerous studies, technology has shown that it contributes to improve their language mastery, increase motivation, and promote better comprehension. Since we are moving from IR 3.0 to IR 4.0, it is good to keep up with the current trend in technology and use it for the students’ benefits. One of the technologies is virtual reality which includes the use of 360-degree video. This 360-degree video offers omnidirectional perspective and give almost realistic experience to learners especially when using together with head-mounted device. This type of video exposes students to a new learning experience that is different from watching video on the screen. Therefore, this study aims to uncover learners’ perspectives about the advantages and disadvantages of using 360-degree video in reading classroom.
Random-Sampling Multipath Hypothesis Propagation for Cost Approximation in Long-Horizon Optimal Control

In this paper, we develop a Monte-Carlo based heuristic approach to approximate the objective function in long horizon optimal control problems. In this approach, we evolve the system state over multiple trajectories into the future while sampling the noise disturbances at each time-step, and find the weighted average of the costs along all the trajectories. We call these methods random sampling - multipath hypothesis propagation or RS-MHP. These methods (or variants) exist in the literature; however, the literature lacks convergence results for a generic class of nonlinear systems. This paper fills this knowledge gap to a certain extent. We derive convergence results for the cost approximation error from the MHP methods and discuss their convergence (in probability) as the sample size increases. As a case study, we apply RS-MHP to approximate the cost function in a linear quadratic control problem and demonstrate the benefits of our approach against an existing and closely related approximation approach called nominal belief-state optimization.
THE CORRELATION BETWEEN STUDENTS’ VOCABULARY MASTERY AND SPEAKING ABILITY AT GRADE 8 OF JUNIOR HIGH SCHOOL

This research aimed to find out the correlation between students’ vocabulary mastery and speaking ability at grade 8 of SMP Negeri 26 Padang. This research was quantitative research which used correlational technique design. The sample of the research was 30 students of grade eight selected by clustering sampling. The instruments used in the research were vocabulary test and speaking test. Vocabulary test consisted of 50 questions of multiple choices, and speaking test in the form of role play. The research data were in the form of interpretation of students’ vocabulary and speaking test results, and their correlation. Based on data analysis, it was found that students’ vocabulary mastery and speaking ability were fair. They were proved by the mean score 65 for vocabulary mastery and 68 for speaking ability. Moreover, there was strong correlation between students’ vocabulary mastery and speaking ability. It was shown by the rvalue = 0,703.
REWARDS AND PUNISHMENTS GIVEN BY THE TEACHER IN TEACHING ENGLISH AS FOREIGN LANGUAGE IN INDONESIAN CONTEXT

Classroom management is certainly concerned with behavior to create and maintain an effective learning experience. The effective classroom management includes the give of rewards and punishments. Therefore, giving rewards and punishments is important in order to increase students’ motivation in EFL classroom. The purpose of this study is to analyze the types of reward and punishment as well as looking at the possible reasons that use by the teachers. This study employed qualitative research method. The subjects of this research are 3 teachers from Junior high school in Padang. The data were collected through the observation sheets and stimulated recall interviews (SRI). The findings show that (1) teachers mostly used the type of praise (49%) in giving spoken rewards and also teachers mostly used the type of nonverbal cues (54%) in giving spoken punishments. (2) The reasons that teachers give spoken rewards are to enhance students’ enthusiasm in learning English and to increase students’ motivates in learning process. Meanwhile, the reasons that teachers give spoken punishments are to change students’ behavior and to make students afraid to repeat the same mistakes during learning English. Therefore, it can be concluded that the types of spoken rewards and spoken punishments that mostly used by teachers are the first types are Praise and Use nonverbal cues. As a result, there are several reasons teachers give spoken rewards and spoken punishments to the students.
An improved memetic genetic algorithm based on a complex network as a solution to the traveling salesman problem

A genetic algorithm (GA) is not a good option for finding solutions around in neighborhoods. The current study applies a memetic algorithm (MA) with a proposed local search to the mutation operator of a genetic algorithm in order to solve the traveling salesman problem (TSP). The proposed memetic algorithm uses swap, reversion and insertion operations to make changes in the solution. In the basic GA, unlike in the real world, the relationship between generations has not been considered. This gap is resolved using the proposed complex network to allow selection among possible solutions. The degree measure has been used for analysis the network. Different scenarios have been evaluated to solve seven TSPLib problems. For example, the results indicated that the memetic algorithm with a complex network, the memetic algorithm with the proposed local search and basic GA have 0.31%, 1.15% and 38% errors, respectively, when solving the TSP for 70 cities compared to the best solution in the TSPLib database. These results offered better performance of the memetic algorithm with a complex network compared to the memetic algorithm with the proposed local search and the basic GA. Also, the average run time of the algorithms showed their scalability.
Analysis of the performance of genetic programming on the blood glucose level prediction challenge 2020

In this paper we present results for the Blood Glucose Level Prediction Challenge for the Ohio2020 dataset. We have used four variants of genetic programming to build white-box models for predicting 30 minutes and 60 minutes ahead. The results are compared to classical methods including multi-variate linear regression, random forests, as well as two types of ARIMA models. Notably, we have included future values of bolus and basal into some of the models because we assume that these values can be controlled. Additionally, we have used a convolution filter to smooth the information in the bolus volume feature. We find that overall tree-based GP performs well and better than multi-variate linear regression and random forest, while ARIMA models performed worst on the here analyzed data.
Handling User-Oriented Cyber-Attacks: STRIM, a User-Based Security Training Model

Privacy has become an increasingly rare commodity these days as personal information can never again be private once it enters a social network. That latter became an incubator environment and a carrier for cyber-attacks, either by providing the necessary information about victims or facilitating the task of reaching them. Social media create relationships and trust between individuals, without any authority checking and validating their identity. This paper analyses the different attack vectors and techniques used against end-users to target their organizations. It shows how the available disclosed information can be transformed into a useful image about the organization and the role of the victim inside it. These leaks not only expose users to the risk of cyberattacks, but they also give attackers the opportunity to create personalized cyber-attacks that are difficult to avoid. This paper highlights these user-oriented attacks. It first demonstrates the impact of the disclosed information on the process of the attack formulation in addition to group influence on an individual’s vulnerability. Next, the various psychological manipulation factors and cognitive bias behind the user’s failure to detecting these attacks demonstrated. This research introduces a theoretical user-based security training model called STRIM, which addresses the above security concerns. It aims to educate and train users to detect, avoid, and report cyberattacks in which they are the primary target. The proposed model is a solution to help organizations establish security-conscious behaviors among their employees.
Concept of Outlier Study: The Management of Outlier Handling with Significance in Inclusive Education Setting

Collection of data and to check its suitability is the first step in any statistical data analysis. In such analyses, the presence of outliers appears as an unavoidable important problem. Outliers are unexpected random values in dataset, and they can alter the statistical conclusion and also affect their assumptions. Thus, in order to manage the data properly, outliers must be defined and treated. So all statisticians have to confront the analysis and forced to take a decision. There is only being one of the two extreme choices left for the researcher or statistician during the analysis of outliers. First, either to reject the outlier with the risk of loss of genuine information and the second one is to include them with the risk of error in drawing conclusion. The study therefore summarize the various potential causes of extreme scores in a data set (e.g., data recording or entry errors, sampling errors, and legitimate sampling), how to detect them, and whether they should be removed or not. Another objective of this study was to explore how significantly a small proportion of outliers can affect even simple analyses. The study was explored with citing suitable examples including outlier value and also excluding the outlier data. The examples show a strong beneficial effect of repetition of the study based on extreme of scores. One way ANOVA test was performed and the significance of extreme outlier was described.
ICT Integration to Support Online Learning During The Covid-19 Outbreaks

ICT is considered as one of the main elements in transforming the country to the future development.Moreover, ICT is becoming more increasingly important as Coronavirus (CoV) disease or Covid-19 is triggered by the end of 2019.This paper aims to analyze the integration of ICT to support online learning during the covid-19 outbreaks.The methods being used for this research is questionnaire survey, that was response randomly to the total of 118 total of respondents.However, just 114 of the respondents are qualified to be analyzed.The questionnaire was distributed through the google form application.The results indicate that ICT integration has a great effectiveness for both teachers and the students during the Covid-19 outbreaks, with some improvements needed.
Entangled subspaces and generic local state discrimination with pre-shared entanglement

Walgate and Scott have determined the maximum number of generic pure quantum states that can be unambiguously discriminated by an LOCC measurement [Journal of Physics A: Mathematical and Theoretical, 41:375305, 08 2008]. In this work, we determine this number in a more general setting in which the local parties have access to pre-shared entanglement in the form of a resource state. We find that, for an arbitrary pure resource state, this number is equal to the Krull dimension of (the closure of) the set of pure states obtainable from the resource state by SLOCC. Surprisingly, a generic resource state maximizes this number.Local state discrimination is closely related to the topic of entangled subspaces, which we study in its own right. We introduce <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>r</mml:mi></mml:math>-entangled subspaces, which naturally generalize previously studied spaces to higher multipartite entanglement. We use algebraic-geometric methods to determine the maximum dimension of an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>r</mml:mi></mml:math>-entangled subspace, and present novel explicit constructions of such spaces. We obtain similar results for symmetric and antisymmetric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>r</mml:mi></mml:math>-entangled subspaces, which correspond to entangled subspaces of bosonic and fermionic systems, respectively.
Single-mode nonclassicality criteria via Holstein–Primakoff transformation

We study different representations of a many-particle system. We reveal the connections among the nonclassicalities of many-particle, single-mode and two-mode systems in the infinite particle number limit. First, we demonstrate that (separable) atomic coherent states, of a many-particle system, mimic the coherent states of single-mode light as the number of particles goes to infinity. This enables the utilization of a many-particle entanglement (MPE) criterion as a single-mode nonclassicality (SMNc) condition via Holstein–Primakoff transformation in this limit. As an example, we show that spin-squeezing criterion, for MPE, can be transformed into quadrature-squeezing, a nonclassicality condition for a single-mode system. Second, we demonstrate a similar connection between the two-mode separable states and separable atomic coherent states of a many-particle system for the infinite particle number limit. The second connection makes us be able to obtain an SMNc condition from a two-mode entanglement (TME) criterion, using the many-particle system as a bridge. As an example, we obtain Mandel's Q-parameter from the TME criterion of Hillery and Zubairy.
Research on Optimization of Improved Gray Wolf Optimization-Extreme Learning Machine Algorithm in Vehicle Route Planning

With the rapid development of intelligent transportation, intelligent algorithms and path planning have become effective methods to relieve traffic pressure. Intelligent algorithm can realize the priority selection mode in realizing traffic optimization efficiency. However, there is local optimization in intelligence and it is difficult to realize global optimization. In this paper, the antilearning model is used to solve the problem that the gray wolf algorithm falls into local optimization. The positions of different wolves are updated. When falling into local optimization, the current position is optimized to realize global optimization. Extreme Learning Machine (ELM) algorithm model is introduced to accelerate Improved Gray Wolf Optimization (IGWO) optimization and improve convergence speed. Finally, the experiment proves that IGWO-ELM algorithm is compared in path planning, and the algorithm has an ideal effect and high efficiency.
Digitalization in modern education (case study of Herzen State Pedagogical University of Russia)

This article focuses on the question of introducing digital technology into modern education. The use of digital technology is becoming obligatory and has a major impact on the life in the modern society, as it is involved in all the areas of human activity. Technological skills contribute to the development of professional competencies and better professional training for students, since information culture and computer literacy are an integral part of the educational process. Digital technologies today contribute to the provision of education with the methodology and practice for the development and optimal use of modern information technologies aimed at attaining professional and cultural education goals. Distance learning at the Herzen State Pedagogical University of Russia is organized on the Moodle platform. The purpose of our study was to find out what elements of the Moodle system teachers use to engage in distance learning. To do so, an online survey was conducted on the Moodle distance learning platform. 1795 bachelor and specialist students in years 1 through 4 participated in the online survey. The introduction of digital technology into education will force us to review curricula and integrate conceptual and relevant innovations. New curricula should not only include mandatory information acquisition by students, but also focus on allowing them to achieve other goals, namely creativity, imagination and teamwork, regardless of the location of team members. Finally, it should be noted that global modern education has faced major shifts caused by the further integration of new digital technologies into educational activities and the active search for effective implementation models that would harness new digital tools. Distance learning—as a new type of educational process—contributes to the systematization of knowledge which allows interactive presentation of lecture and practical material to students. In our opinion, however, it is far from an ideal form of education. Distance learning will never replace the communication between the teacher and the student, but can only serve as an additional element.
Robust RGB-D tracking via compact CNN features

Feature representation is at the core of visual tracking. This paper presents a robust tracking method in RGB-D videos. Firstly, the RGB and depth images are separately encoded using a hierarchical convolutional neural network (CNN) features. Secondly, in order to reduce computation cost, we exploit random projection to compress the CNN features. The high dimensional CNN features are randomly projected into a low dimensional feature space. The correlation filter tracking framework is then independently carried out in RGB and depth images. And backward tracking scheme is adopted to evaluate the tracking results in these two images. The final position is determined according to the tracked location in the two image channels. In addition, model updating is implemented adaptively. Our tracker is evaluated on two RGB-D benchmark datasets and achieves comparable results to the other state-of-the-art RGB-D tracking methods.