Reviewerly/exHarmony huggingface dataset:
Pedagogics of formation of future teachers ’ social outlook in the conditions of informatization of education

In the article the organizational and pedagogical conditions of readiness of future teachers for the project activity in conditions of digitalization of education are analyzed. The structure of readiness is defined, which includes the following components: motivational-value, content procedural, organizational-managerial, reflex-appraisal. The motivation-value component is aimed at stimulating the motivational attitude of the students to the project activity, understanding its significance for future pedagogical activity. The content-procedural component is aimed at the phased development of the project activities, including the entire life cycle of the project: research, planning, development, deployment and quality assessment. The organizational and management component is aimed at the organization of interaction between teachers, students, administration of the university, developing strategic goals, predicting the result of project activities, performing diagnostics and control in project management. The impact-assessment component is aimed at the formation of a reflective readiness for the project activity. Materials and methods: When writing the article, the following methods were used: theoretical and methodological analysis and synthesis of existing special domestic and foreign scientific and methodological literature, a conceptual analysis of scientific articles and publications. An interdisciplinary scientific and theoretical analysis of the problem was conducted on the subject of the research, as a result of which the conclusion was made that the organizational and pedagogical conditions for the readiness of future teachers for the project activity are to be developed. Results: The set of organizational and pedagogical conditions necessary and sufficient for the successful formation of the project activity of future teachers in conditions of digitalization of education is justified. The program of experimental and experimental research of the readiness of future teachers for the project activity on motivational-value, substantive-procedural, organizational and managerial, reflective-evaluative components is implemented, ensuring objectivity, complexity, consistency of monitoring and evaluation of the results of project activities. Discussion and Conclusions: The willingness and ability of future teachers to carry out project activities in the digital environment will effectively organize the educational process in the digital age
Weighted PCA-LDA Based Color Quantization Method Suppressing Saturation Decrease

In this letter, we propose a new color quantization method suppressing saturation decrease. In the proposed method, saturation-based weight and intensity-based weight are used so that vivid colors are selected as the representative colors preferentially. Experiments show that the proposed method tends to select vivid colors even if they occupy only a small area in the image.
Image Forgery over Social Media Platforms - A Deep Learning Approach for its Detection and Localization

Social media platforms play a significant role in spreading news in the current digital era. However, they have also been spreading fake images. Forged images posted on social media platform such as Twitter create misrepresentation and generate harmful user emotions. Thus, detecting fake images over social media platforms has become a critical need of time. Deep learning convolutional networks can learn the intrinsic feature set of images and can detect forged images. This paper proposes a convolutional neural network to spot fake images shared over social media platforms. High pass filters from image processing are used in the first layer for weight initialization. This helps the neural network converge faster and achieve better accuracy. Interpretability is a common concern in deep learning models. The proposed framework employs Gradient-weighted Class Activation Mapping to generate heatmaps and localize the image's manipulated area. The model is verified against the publicly available CASIA dataset. An accuracy of 92.3% is achieved, which is better than the other previous models. From the social media perspective, the model is verified against the latest Twitter dataset. The experiment proves that convolutional neural networks perform well in detecting forged images over social media platforms, and interpretability can be achieved.
A SYSTEMATIC REVIEW OF SPARKOL VIDEOSCRIBE MEDIA IN TEACHING WRITING SKILL

The research aimed to investigate the contribution of using sparkol videoscribe media in teaching writing skill. This research reviewed the previous studies on sparkol videoscribe in teaching writing skill which were published within the past ten years. This research used systematic review as the method. The population of this research was eighteen studies, then it screened used flow charts based on inclusion and exclusion criteria. There were five studies as the sample. This research used indirect observation as a technique and document analysis as the tools. To analyze the data, this research used thematic analysis as the technique analysis of data. Based on the result of the review, the result indicated that (1) The use of sparkol videoscribe media helped the teacher in teaching writing skill. (2) The successful of using sparkol videoscribe media in teaching writing skill based on students’ achievement increased. (3) Sparkol videoscribe media encouraged students’ interest, motivation, activeness, and able to engage students’ attention. From the results above, the sparkol videoscribe media gave the contribution in teaching writing skill.Keywords: Systematic review, sparkol videoscribe, writing skill.
Formation of environmental competence of future specialists in pedagogical universities

Environmental education and upbringing is one of the important tasks of training young professionals. Today, every young specialist becomes a competent, competent person in their profession, and environmental competence is formed in the University and in matters of postgraduate education. In this context, it is stated that students of the specialty chemistry and biology, along with the main basic disciplines, increase the teaching of specialized disciplines. As a result of testing the expertise: the value of environmental competence is determined, the need for the formation of which was noted in the conditions that provide high-quality training of specialists of a pedagogical University to practice by forming its environmental consciousness.It was found that students in conducting experimental research on environmental problems, in combination with theory, can cause environmental and pedagogical competence.
Restoring the Sister: Reconstructing a Lexicon from Sister Languages using Neural Machine Translation

The historical comparative method has a long history in historical linguists.It describes a process by which historical linguists aim to reverse-engineer the historical developments of language families in order to reconstruct proto-forms and familial relations between languages.In recent years, there have been multiple attempts to replicate this process through machine learning, especially in the realm of cognate detection (List et al., 2016;Ciobanu and Dinu, 2014;Rama et al., 2018).So far, most of these experiments aimed at actual reconstruction have attempted the prediction of a proto-form from the forms of the daughter languages (Ciobanu and Dinu, 2018;Meloni et al., 2019).Here, we propose a reimplementation that uses modern related languages, or sisters, instead, to reconstruct the vocabulary of a target language.In particular, we show that we can reconstruct vocabulary of a target language by using a fairly small data set of parallel cognates from different sister languages, using a neural machine translation (NMT) architecture with a standard encoderdecoder setup.This effort is directly in furtherance of the goal to use machine learning tools to help under-served language communities in their efforts at reclaiming, preserving, or reconstructing their own languages.
Explainable Multi-hop Verbal Reasoning Through Internal Monologue

Many state-of-the-art (SOTA) language models have achieved high accuracy on several multi-hop reasoning problems.However, these approaches tend to not be interpretable because they do not make the intermediate reasoning steps explicit.Moreover, models trained on simpler tasks tend to fail when directly tested on more complex problems.We propose the Explainable multi-hop Verbal Reasoner (EVR) to solve these limitations by (a) decomposing multi-hop reasoning problems into several simple ones, and (b) using natural language to guide the intermediate reasoning hops.We implement EVR by extending the classic reasoning paradigm General Problem Solver (GPS) with a SOTA generative language model to generate subgoals and perform inference in natural language at each reasoning step.Evaluation of EVR on Clark et al. (2020)'s synthetic question answering (QA) dataset shows that EVR achieves SOTA performance while being able to generate all reasoning steps in natural language.Furthermore, EVR generalizes better than other strong methods when trained on simpler tasks or less training data (up to 35.7% and 7.7% absolute improvement respectively). 1
AN ANALYSIS OF MOTHER TONGUE INTERFERENCE IN ENGLISH PRONUNCIATION

This research aimed to investigate the interference of the Malay language in Pronouncing English voiceless stops by EFL students in Pontianak. The design of this research was a descriptive study. The total participants were 15 EFL students whose mother tongue is Malay. This research used a pronunciation test to collect pronunciation data through an audio recorder as the main tool. Pronunciation test sheet consisted 21 English words contains English voiceless stops phoneme /p/, /t/, and /k/. Based on the research findings, after conducting a pronunciation test the data showed that from 315 English words that had been tested there were 129 words in total found mispronounced by the students. Mostly, mispronunciation happened when pronouncing phoneme /p/ that was 56 times from 105 chances, followed by phoneme /t/ that was 42 times from 105 chances and phoneme /k/ which 31 errors from 105 chances. Based on the research findings, mother tongue indicated interfere students` pronunciation in pronouncing English words at Pontianak. Keywords: Mother tongue, Interference, English pronunciation.
On the Embeddings of Variables in Recurrent Neural Networks for Source Code

Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality.An example of this specificity is that the semantics of a variable is defined not only by its name but also by the contexts in which the variable occurs.In this work, we develop dynamic embeddings, a recurrent mechanism that adjusts the learned semantics of the variable when it obtains more information about the variable's role in the program.We show that using the proposed dynamic embeddings significantly improves the performance of the recurrent neural network, in code completion and bug fixing tasks.
Improved Local Search with Momentum for Bayesian Networks Structure Learning

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.
Hubungan Antara Dukungan Sosial Orang Tua dengan Motivasi Belajar Siswa SDK St. Ursula Ende

This study aims to determine the relationship between parental social support and learning motivation of elementary school students. This research was correlational research with a quantitative approach. The population in this study were students of SDK St. Ursula, amounting to 435 people and the sample were 80 students in 5 and 6 grades with a purposive sampling technique. The data collection method used a questionnaire with the measuring instrument that consisted of the scale of parents social support and the scale of student learning motivation. Data analysis was carried out by using product moment correlation which was calculated using SPSS version 26. The results showed the r value of 0.624, which means that there is a significant positive relationship between parents social support and student learning motivation at SDK St. Ursula where the higher the parents social support, the higher the learning motivation of elementary school students, and conversely the lower the parents social support, the lower the learning motivation of elementary school students.
Reduce Artificial Intelligence Planning Effort by using Map-Reduce Paradigm

While several approaches have been developed to enhance the efficiency of hierarchical Artificial Intelligence planning (AI-planning), complex problems in AI-planning are challenging to overcome. To find a solution plan, the hierarchical planner produces a huge search space that may be infinite. A planner whose small search space is likely to be more efficient than a planner produces a large search space. In this paper, we will present a new approach to integrating hierarchical AI-planning with the map-reduce paradigm. In the mapping part, we will apply the proposed clustering technique to divide the hierarchical planning problem into smaller problems, so-called sub-problems. A pre-processing technique is conducted for each sub-problem to reduce a declarative hierarchical planning domain model and then find an individual solution for each so-called sub-problem sub-plan. In the reduction part, the conflict between sub-plans is resolved to provide a general solution plan to the given hierarchical AI-planning problem. Pre-processing phase helps the planner cut off the hierarchical planning search space for each sub-problem by removing the compulsory literal elements that help the hierarchical planner seek a solution. The proposed approach has been fully implemented successfully, and some experimental results findings will be provided as proof of our approach’s substantial improvement inefficiency.
LangResearchLab_NC at CMCL2021 Shared Task: Predicting Gaze Behaviour Using Linguistic Features and Tree Regressors

Analysis of gaze data behaviour has gained momentum in recent years for different NLP applications.The present paper aims at modelling gaze data behaviour of tokens in the context of a sentence.We have experimented with various Machine Learning Regression Algorithms on a feature space comprising the linguistic features of the target tokens for prediction of five Eye-Tracking features.CatBoost Regressor performed the best and achieved fourth position in terms of MAE based accuracy measurement for the ZuCo Dataset.
Supporting Spanish Writers using Automated Feedback

We present a tool that provides automated feedback to students studying Spanish writing.The feedback is given for four categories: topic development, coherence, writing conventions, and essay organization.The tool is made freely available via a Google Docs add-on.A small user study with post-secondary level students in Mexico shows that students found the tool generally helpful and that most of them plan to continue using it as they work to improve their writing skills.In an analysis of 6 months of user data, we see that a small number of users continue to engage with the app, even outside of planned user studies.
Mineralogical and geochemical studies on soils and Nile bottom sediments of Luxor–Aswan area, South Egypt

Abstract Background The geochemical studies on the rare earth elements to demonstrate sources of the toxic metals for soils and Nile sediments for Aswan area are seldom. Results Minerallogically, the studied soil agriculture samples consist mainly of quartz (42%), clay minerals (33%), plagioclase (20%) and magnesin-calcite (5%). Clay minerals are composed of montmorillonite as bentonite. The studied Nile sediments consist mainly of quartz (75%), clay minerals (15%), plagioclase (5%) and calcite (5%). Clay minerals are composed of calcian-montmorillonite (10%) and Kaolinite (5%). Geochemically, the chemical analytical techniques of the environment-sensitive elements, including Pb, Cd, As, Ni, Co, Cu, Cr, Zn, U and Th, have been performed for agricultural soils and sediments of Aswan–Luxor District to assess the geochemical characteristics of these elements and their impact on soil environmental and plant, as well as their provenance. Conclusions Average content of Cd was about 1.5-fold for the studied Nile sediments and slightly higher soil agriculture than (MPL). Pb and Cr average contents twofold of (MPL) for Nile sediments and Cr average of soil is represented 2.5-fold of (MPL). The average content of As, Cu, Co, Ni and U in both Nile sediments and cultivated soil in the studied area is lower than the maximum permissible limit (MPL). Average content of Pb, Zn and Cr was about 2, 2- and 2.5-fold, respectively, according to USPHS for the Nile sediments and threefold of Cr for the agriculture soils. The agricultural soil of Luxor–Aswan district is characteristic highly enrichment of As, Co, Zn, Cu, Ni, Mn, U and Th than those of the Nile sediments. In addition, the Nile sediments have high concentration average of Cr, Zn, Mn and Pb and low contamination of the other elements. The sources of the toxic metals in the studied area may probably be natural or anthropogenic. The anthropogenic source is resulting from paper, Ferrosilicon factories and Phosphate mines at Edfu, as well as Sand quarry, Shale mine and the Nitrogen Fertilizer factory at Aswan. In addition, natural sources such as waste of the drains floods.
TCN Units, Solution in Recognition of Human Activities

Abstract Activities and human faces recognition infrastructure are used everywhere behavior analysis is required. Since a video approach is built over an already existing infrastructure comprised of CCTV-Closed Circuit Television and a central computer, it can be used in any space where actions’ monitoring is necessary. Main objective of this paper consists of building a reliable and lightweight human faces and actions’ recognition classifier, able to classify a large number of actions, lightweight enough that it can work in real time. It processes at least 30 frames per second, using of-the-shelf computer hardware, connected to a normal CCTV infrastructure. The temporal convolutional network - TCN represents a viable solution for a proposed problem. It classifies a large number of actions - 60, using only RGB (red-green-blue) images of fairly low resolution, in real time. Deciding which class of action belongs to should not be connected to environment, background, person, view angle, or other specific identifiers. This selection should be associated only with the person executing it and the spatial-temporal context of the person. As technology and processing power improve, the problem slightly shifts. When more processing power to a system is added, in this model is possible either to increase the number of frames per second or the number of cameras in the infrastructure, or to increase the quality of the images, resulting most likely higher accuracy of the predictions. This model can be extended to a larger number of classes, with a minimal impact on performance. The proposed model has a tested accuracy of 82% which can be attributed to the recurrent property of the network. The model performs close to the most performing existing solutions. The present TCN + 3D Convolution Model is built with the smaller TCN units. Its architecture uses an alternation of a Simple Unit and a Complex Unit, in order to maximize the diversity of features the model learns. This paper illustrates a deep learning classifier based on TCNs for human actions recognition. Is relatively lightweight compared to other methods, and performs very well, competing with the best architectures. Ideally, it is able to classify an action irrespective of the person executing it or the environment where it was executed. This is achieved as much as possible through a diverse dataset on which the model is trained and tested, namely NTU RGB+D. After a simple and a complex unit, an Average Pool 3D layer reduces at least one dimension to half.
WikiBERT models: deep transfer learning for many languages

Deep neural language models such as BERT have enabled substantial recent advances in many natural language processing tasks. Due to the effort and computational cost involved in their pre-training, language-specific models are typically introduced only for a small number of high-resource languages such as English. While multilingual models covering large numbers of languages are available, recent work suggests monolingual training can produce better models, and our understanding of the tradeoffs between mono- and multilingual training is incomplete. In this paper, we introduce a simple, fully automated pipeline for creating language-specific BERT models from Wikipedia data and introduce 42 new such models, most for languages up to now lacking dedicated deep neural language models. We assess the merits of these models using the state-of-the-art UDify parser on Universal Dependencies data, contrasting performance with results using the multilingual BERT model. We find that UDify using WikiBERT models outperforms the parser using mBERT on average, with the language-specific models showing substantially improved performance for some languages, yet limited improvement or a decrease in performance for others. We also present preliminary results as first steps toward an understanding of the conditions under which language-specific models are most beneficial. All of the methods and models introduced in this work are available under open licenses from this https URL.
Privacy Preserving Authenticated Key Agreement based on Bilinear Pairing for uHealthcare

With the growth of wireless communication technologies and sensor technologies, ubiquitous Healthcare (uHealthcare) based on Internet of Things (IoT) is becoming a big research focus from various researchers.However, security and privacy issues are top most important focuses to be solved for the success of uHealthcare services.This paper shows that Mahmood et al.'s authentication and prescription safety protocol is prone to denial of service attack and stolen-verifier attack.Furthermore, we propose a privacy preserving authenticated key agreement protocol for IoT based uHealthcare, which is based on hash function, symmetric key cryptosystem and bilinear pairing.The proposed protocol efficiently solves the security and privacy problems in Mahmood et al.'s protocol and also provides computational efficiency compared to the related protocols.