Reviewerly/exHarmony huggingface dataset:
USING ROLE-PLAYING GAME FOR PROFESSIONAL SKILLS FORMATION OF PROSPECTIVE TEACHERS

The features of using Role-playing games in educating students - prospective teachers are considered. In practical classes on teaching methods of various disciplines, Role-playing games are often used, associated with conducting a fragment of a lesson or a full lesson. Typically, these practical classes are organized on an empirical level. The version of the Role-playing game "Observing Mathematics Lessons", developed on the basis of the theory of contextual learning, is presented. Psychological and pedagogical principles: simulation of specific conditions, game modelling of the content and forms of professional activity, collaborative activities are used in the design of the game. These principles are the basis for creating a game model, which consists of: game goals, a set of roles that indicate the functions of the player, the scenario and the rules of the game. The simulation model contains: didactic goals, the subject of the game and the evaluation system. An important difference developed by the Role-playing game is to take into account the personality types of students - participants in the game. Students with personality types typical for STEM students were selected for the role of "good pupil”. The roles of the "bad pupils" were played by students with personality types typical for students who usually do not have academic success in mathematics. A survey has been conducted, which showed that the proposed Role-playing game enable the simulation of the real situation of an observing mathematics lesson.
Simulating and Analysing delay in Indian Railways

Abstract Delay is one of the serious issue all over the world in railways, however it becomes more important to country like India having the top four biggest railway network all over the world and where the majority of the population depends on railways for making their journey’s in different parts of our country. Categorically most of the train in Indian railway not able to run on its schedule time which in turn affects the passenger to reach their destination timely. So we have make a small step in order to learn and analyze these problems and its factor we have used a real world dataset from internet and perform simulation on the data using regression to predict the total delay take place while planning a particular journey from a provided date by a passenger and our approach accuracy is promising.
An adiabatic method to train binarized artificial neural networks

Abstract An artificial neural network consists of neurons and synapses. Neuron gives output based on its input according to non-linear activation functions such as the Sigmoid, Hyperbolic Tangent (Tanh), or Rectified Linear Unit (ReLU) functions, etc.. Synapses connect the neuron outputs to their inputs with tunable real-valued weights. The most resource-demanding operations in realizing such neural networks are the multiplication and accumulate (MAC) operations that compute the dot product between real-valued outputs from neurons and the synapses weights. The efficiency of neural networks can be drastically enhanced if the neuron outputs and/or the weights can be trained to take binary values $$\pm 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> only, for which the MAC can be replaced by the simple XNOR operations. In this paper, we demonstrate an adiabatic training method that can binarize the fully-connected neural networks and the convolutional neural networks without modifying the network structure and size. This adiabatic training method only requires very minimal changes in training algorithms, and is tested in the following four tasks: the recognition of hand-writing numbers using a usual fully-connected network, the cat-dog recognition and the audio recognition using convolutional neural networks, the image recognition with 10 classes (CIFAR-10) using ResNet-20 and VGG-Small networks. In all tasks, the performance of the binary neural networks trained by the adiabatic method are almost identical to the networks trained using the conventional ReLU or Sigmoid activations with real-valued activations and weights. This adiabatic method can be easily applied to binarize different types of networks, and will increase the computational efficiency considerably and greatly simplify the deployment of neural networks.
Semantic Anomaly Detection in Medical Time Series

The main goal of this project was to define and evaluate a new unsupervised deep learning approach that can differentiate between normal and anomalous intervals of signals like the electrical activity of the heart (ECG). Denoising autoencoders based on recurrent neural networks with gated recurrent units were used for the semantic encoding of such time frames. A subsequent cluster analysis conducted in the code space served as the decision mechanism labelling samples as anomalies or normal intervals, respectively. The cluster ensemble method called cluster-based similarity partitioning proved itself well suited for this task when used in combination with density-based spatial clustering of applications with noise. The best performing system reached an adjusted Rand index of 0.11 on real-world ECG signals labelled by medical experts. This corresponds to a precision and recall regarding the detection task of around 0.72. The new general approach outperformed several state-of-the-art outlier recognition methods and can be applied to all kinds of (medical) time series data. It can serve as a basis for more specific detectors that work in an unsupervised fashion or that are partially guided by medical experts.
SENTI2VEC: AN EFFECTIVE FEATURE EXTRACTION TECHNIQUE FOR SENTIMENT ANALYSIS BASED ON WORD2VEC

The discovery of an active feature extraction technique has been the focus of many researchers to improve the performance of classification methods, such as for sentiment analysis. Many of them have shown interest in using word embeddings especially Word2Vec as the features for text classification tasks. Its ability to model high-quality distributional semantics among words has contributed to its success in many of the functions. Despite the success, Word2Vec features are high dimensional that lead to an increase in the complexity of the classifier. In this paper, an effective method for feature extraction based on Word2Vec is proposed for sentiment analysis. The process discovers polarity clusters of the terms in the vocabulary through Word2Vec and opinion lexical dictionary. The features vector for each text is constructed from the polarity clusters, which lead to a lower-dimensional vector to represent the text. This paper also investigates the effect of two opinion lexical dictionaries on the performance of sentiment analysis, and one of the dictionaries are created based on SentiWordNet. The effectiveness of the proposed method is evaluated on the IMDB with two classifiers, namely the Logistic Regression and the Support Vector Machine. The result is promising, showing that the proposed method can be more effective than the baseline approaches.
Intercept/resend and translucent attacks on the quantum key distribution protocol based on the pre- and post-selection effect

We investigate the security against the intercept/resend and translucent attacks on the quantum key distribution protocol based on the pre- and post-selection effect. In 2001, Bub proposed the quantum cryptography scheme, which was an application of the so-called mean king’s problem. We evaluate a probability that legitimate users cannot detect eavesdropper’s malicious acts for Bub’s protocol. We also estimate a probability that the eavesdropper guesses right at the random secret key one of the legitimate users tries to share with the other one. From rigorous mathematical and numerical analyses, we conclude that Bub’s protocol is weaker than the Bennett–Brassard protocol of 1984 (BB84) against both the intercept/resend and translucent attacks. Because Bub’s protocol uses a two-way quantum channel, the analyses of its security are tough to accomplish. We refer to their technical points accurately in the current paper. For example, we impose some constraints upon the eavesdropper’s strategies in order to let their degrees of freedom be small.
THE OPEN SCIENCE PLATFORM AND APPLICATION OF ITS COMPONENTS IN THE EDUCATIONAL PROCESS

The article considers the concept of open science and open data in the context of educational applications. As the Roadmap for Ukraine's integration into the European Research Area was approved in 2018, some steps have already been taken to use the ideas of open science.&nbsp; In some educational establishments, the learning content includes issues and topics related to open data or open science. The current state of the art and the experience of using open science systems in educational process in Ukraine is undertaken. Such experience will contribute to the further creation and development of a national scientific infrastructure, for the promotion and providing free access to any state-funded research. However, the analysis showed that currently there is lack of methods (or methodological systems) of using open science services to train educators and teachers in particular for work in scientific lyceums. A survey of educators "Using open science services to pose and resolve the research problem" was conducted, which showed that most respondents are primarily interested in the materials of their colleagues from professional journals mainly not widely accessed. Only a quarter of respondents know what open science is. It has been found that open access services (including scientific materials) are tools for using the ideas of open science. As an example, the open access service arXiv is considered: the basics of working with this service and the features of its use are described. Services for collaborative work on educational projects can also be considered as open science services, as most of them have the tools for further, public disclosure of the results. An example is the review of Discord Messenger, which recently claims to be used in cloud-based systems, contains tools that are cloud-based and easily integrated with other services (or systems) through open source. In addition, there are tools available to create open communities (servers) for further connection of any user. The European Open Science C;oud (EOSC) deserves special attention, as it is a rather powerful platform for the&nbsp; use both by&nbsp; scientists and educators. The advisable ways of application of its components in the educational process are outlined.
Modelling Case-Based Reasoning in Situation-Aware Disaster Management

Situation-aware disaster management (SADIM) is a continuous decision-making and action taking process. This process requires prior knowledge of the ongoing environment and context. SADIM comprises two major processes: situation awareness (SA) - a cognitive process that assesses current situations and anticipate future situations in the environment; and, disaster management (DM) which is a decision-making process preventing, preparing, responding, and recovering for and from a disaster. One of the decision-making technologies used in current SADIM is case-based reasoning (CBR) CBR is used for the disaster management element only. Situation awareness process in current SADIM is carried out using domain rules, statistical reasoning and other methods. This paper therefore presents a method of using CBR to carry out both situation assessment and disaster management decision-making processes in SADIM, building on previous work focusing in SA alone. Using CBR for both processes provides the capability of using past experiences to understand the state of the environment and also solve specific disaster problems. The paper evaluates the method through implementation in disaster prevention in the petroleum drilling domain for early kick detection to prevent a blowout disaster. The results show a clear improvement in similarity assessment and problem solving prediction to prevent blowout.
A World Full of Stereotypes? Further Investigation on Origin and Gender Bias in Multi-Lingual Word Embeddings

Publicly available off-the-shelf word embeddings that are often used in productive applications for natural language processing have been proven to be biased. We have previously shown that this bias can come in different forms, depending on the language and the cultural context. In this work, we extend our previous work and further investigate how bias varies in different languages. We examine Italian and Swedish word embeddings for gender and origin bias, and demonstrate how an origin bias concerning local migration groups in Switzerland is included in German word embeddings. We propose BiasWords, a method to automatically detect new forms of bias. Finally, we discuss how cultural and language aspects are relevant to the impact of bias on the application and to potential mitigation measures.
Financial Integration, Competition and Bank Risk-Taking Behavior: Evidence from Africa’s Sub-Regional Markets

Financial integration is generally associated with the development of synergies through cross-border banking and investment activities and increased competitiveness among banks. This paper examines the effects of shifts in financial freedom and competition on bank risk-taking behavior using data from 405 banks in 47 African countries across five regional economic communities from 2007-2014. The core findings suggest that financial integration directly increases bank risk-taking behavior in Africa through rising competition. The results also support an inverted U-shaped relationship between competition and bank risk-taking behavior. However, disparities in the results across sub-regional markets suggest that financial integration policies must be tailored to suit the market characteristics of each regional bloc. Overall, the study identifies deficiencies in competitiveness as a fundamental variable that hinders banks’ ability to benefit from the opportunity of stability offered by financial integration in emerging economies.
The Comparison of Chunking Methods to Enhance the Cognitive Capacity of Short-term Memory to Retain Textual Information among High School Students

Our short-term memory has a limited capacity of taking in information and retaining it the memory storage. However, this can be enhanced by various memory techniques especially dividing the information into smaller chunks. To investigate this memory enhancement strategy, this study compared the effectiveness of three chunking methods, namely One-Chunk, Two-Chunk, and Three-Chunk to enhance the capacity to retain information in the forms of letters and numbers in the short-term storage. Participants in this study were 50 high school students who took part in an experimental design of online short-term memory assessment. The results revealed that the ability to remember 10 distinct alphabets and 10 distinct digits statistically varied, depending on how the information was chunked. To be more precise, the student participants could memorize the information when divided into 2 or 3 chunks more effectively than one full set of the information (1 chunk) as the mean scores gained in the two chunking methods were statistically greater than that of the One-Chunk delivered information. However, the findings only relied on single types of information so that further research could be done to explore this with more complicated information. Educational implications can be drawn from this present study that in order to assist students to memorize and retain learning materials more effectively, it is essential to help classify them into 2-3 groups of information. This could be done through the use of tree thinking, binary thinking, and computational thinking.
Keyword Extraction Using Unsupervised Learning on the Document’s Adjacency Matrix

This work revisits the information given by the graph-of-words and its typical utilization through graph-based ranking approaches in the context of keyword extraction.Recent, well-known graph-based approaches typically employ the knowledge from word vector representations during the ranking process via popular centrality measures (e.g., PageRank) without giving the primary role to vectors' distribution.We consider the adjacency matrix that corresponds to the graph-of-words of a target text document as the vector representation of its vocabulary.We propose the distributionbased modeling of this adjacency matrix using unsupervised (learning) algorithms.The efficacy of the distribution-based modeling approaches compared to state-of-the-art graphbased methods is confirmed by an extensive experimental study according to the F 1 score.Our code is available on GitHub 1 .
Grappling with the learning modules: Experience of Public Elementary Pupils Attending English Written Modular Classes

Modular Distance Learning is one of the educational trends brought about by the pandemic. The recent use of this model of learning delivery has put teaching and learning into an adjustment period. This particular challenge has been the urgent imperative to have modular classes. It implies designing and organizing for better learning. English is the primary medium of instruction in education, and most of the modules written in the given language posing additional challenges and demands to school learners. Hence, this paper endeavors to investigate the lived experiences of the pupils in answering English-written modules through a qualitative design with a phenomenological approach. The participants of the study were eight Grade 6 pupils determined using the purposive sampling technique with some inclusion criteria. Data were gathered through an in-depth interview and were analyzed using a recursive textual analysis aided by Lichtman’s 3c's: coding, categorizing, and conceptualizing. The study revealed that quality education in the locality during this time of pandemic is greatly affected by the delivery modality, the curriculum content and standards, educational equipment and resources, the competence of parents to give assistance, and the readiness of the learners to read and learn independently. &#x0D;
Students' perception toward gamification applied in English language classroom

This study aims to find out the students' perception toward gamification applied on English language Classroom. Gamification emerged as an innovation in classroom teaching. The basic concept is to incorporate game elements into teaching, taking advantage of students' interest in games that can be used for the purposes of the teaching process. This study is conducted since there is still limited research that is conducted in Indonesia which analyses the students' perception toward the gamification utilization. This research used qualitative methods as a way to present research results from data collected. Data from this study were collected using two methods, namely online questionnaires, and interviews. This research shared questionnaires to 30 students. The questionnaire was a Likert-style close-ended question. From the research conducted, it was found that the majority of students' perceptions of gamification used in English classes was positive. The students believed that gamification was effective and fun to bring to classroom learning. This teaching method allows students to engage more in-class activities, reduces boredom because it creates a learning atmosphere, and increases learning motivation without disturbing the understanding of the material obtained by students.
A Survey on Hybrid Recommendation Engine for Businesses and Users

Various techniques have been used over the years to implement recommendation systems.In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system.To build a recommender system, we require abundant amount of data which comprises of a spectrum of reviews and sentiments from all user domains.Websites like Yelp and TripAdvisor, allow users to post reviews for various businesses, products and services.In this work we have two objectives 1) Recommend restaurants to user based on user reviews in Yelp dataset and 2) Suggest improvements to business based on user reviews.In the first scenario, we will use the comments and ratings available in the Yelp dataset to generate restaurant recommendations and personalize them with user profile data.In the second scenario, we intend to suggest improvements to businesses based on various user reviews and provide them with a ranked list of predefined parameters to help them understand where they stand with respect to their competitors and where they should improve to do better.For both scenarios, we will perform two major steps to achieve our objective 1) Sentiment Analysis and 2) Content Based Recommendation.The first step gives us the -sentiment, quality, subject of discussion relevant to product and in the second step we use the outcomes of first step for personalizing and ranking our results.We came across Gensim and Latent Dirichlet Allocation which seemed to be interesting and was tailored to our requirements.In the yelp dataset, user comments are a mixture of various topics which are extracted by the algorithm (LDA) to provide accurate recommendation for all the users.A prototype of this method provided us with 93% accuracy.
Learning the Precise Feature for Cluster Assignment

Clustering is one of the fundamental tasks in computer vision and pattern recognition. Recently, deep clustering methods (algorithms based on deep learning) have attracted wide attention with their impressive performance. Most of these algorithms combine deep unsupervised representation learning and standard clustering together. However, the separation of representation learning and clustering will lead to suboptimal solutions because the two-stage strategy prevents representation learning from adapting to subsequent tasks (e.g., clustering according to specific cues). To overcome this issue, efforts have been made in the dynamic adaption of representation and cluster assignment, whereas current state-of-the-art methods suffer from heuristically constructed objectives with representation and cluster assignment alternatively optimized. To further standardize the clustering problem, we audaciously formulate the objective of clustering as finding a precise feature as the cue for cluster assignment. Based on this, we propose a general-purpose deep clustering framework which radically integrates representation learning and clustering into a single pipeline for the first time. The proposed framework exploits the powerful ability of recently developed generative models for learning intrinsic features, and imposes an entropy minimization on the distribution of the cluster assignment by a dedicated variational algorithm. Experimental results show that the performance of the proposed method is superior, or at least comparable to, the state-of-the-art methods on the handwritten digit recognition, fashion recognition, face recognition and object recognition benchmark datasets.