Reviewerly/exHarmony huggingface dataset:
A Novel Modified Lévy Flight Distribution Algorithm based on Nelder-Mead Method for Function Optimization

This paper aims to improve one of the recently proposed metaheuristic approaches known as Lévy flight distribution (LFD) algorithm by adopting a well-known simplex search algorithm named Nelder-Mead (NM) method.Three new strategies were utilized to demonstrate the improved capability of the original LFD algorithm.In the first strategy, NM was run twice as much the number of iterations of LFD after the latter completes its task.In the second strategy, NM was applied after each iterations of LFD instead of waiting for the completion of the latter.Lastly, in the third strategy, NM was applied after each iterations of LFD and run for the total number of current iterations of the latter algorithm.Well-known unimodal and multimodal benchmark functions were adopted, and statistical analysis was performed for performance evaluation.Further assessment was carried out through a nonparametric statistical test.The obtained results have shown the proposed versions of LFD algorithm provide significant performance improvement in general.In addition, the efficiency of the third strategy was found to be better for NM modified LFD algorithm which has greater balance between global and local search stages and can be used as an effective tool for function optimization.
A novel e-Government Framework using Blockchain

The e-Government system leverages Information and Communication Technology (ICT) to transform the relationship between government bodies and citizens, businesses and other government ministries, departments, and agencies. The primary aim is to make government services more seamless, efficient and timely for every citizen and organisation. However, e-Government systems are now faced with security threats and cyber attacks, and these challenges have raised concerns about users’ privacy as well as the confidentiality and integrity of user data.
Efficacy of Collaborative Strategic for Reading (CSR) To Improve Students’ Reading Comprehension

The purpose of this research or the study was to improve students' reading comprehension skills. Classroom Action Research (CAR) has been used to adopt this research. The research includes five stages, namely preliminary research, preparation, implementation of plans, observation of results, and reflection of results. The research was conducted in three cycles, each cycle being tested for reading comprehension which aims to evaluate student learning outcomes. The subjects of this study were class VIIIC students of SMP Negeri 11 Malang 44 first semester students (2019/2020). The types of data in this study consisted of field notes, observation sheets, tests, and interviews. This type of data is used as the basis for evaluating student achievement in reading comprehension in each cycle. The result of the study showed the implementation of the Collaborative Strategic Reading (CSR) improved the students’ reading comprehension. As indicated by the increase in the average score of students and the percentage of students who achieve mastery learning to read comprehension descriptive texts in the post test given in three cycles (72 and 77% in the first cycle, 74 and 82% in the second cycle, and 78 and 86% in cycle III). The implementation of CSR in reading comprehension was effective in improving students' reading comprehension skill.
On Finding the K-best Non-projective Dependency Trees

The connection between the maximum spanning tree in a directed graph and the best dependency tree of a sentence has been exploited by the NLP community.However, for many dependency parsing schemes, an important detail of this approach is that the spanning tree must have exactly one edge emanating from the root.While work has been done to efficiently solve this problem for finding the one-best dependency tree, no research has attempted to extend this solution to finding the K-best dependency trees.This is arguably a more important extension as a larger proportion of decoded trees will not be subject to the root constraint of dependency trees.Indeed, we show that the rate of root constraint violations increases by an average of 13 times when decoding with K = 50 as opposed to K = 1.In this paper, we provide a simplification of the K-best spanning tree algorithm of Camerini et al. (1980).Our simplification allows us to obtain a constant time speed-up over the original algorithm.Furthermore, we present a novel extension of the algorithm for decoding the K-best dependency trees of a graph which are subject to a root constraint. 1
Multimedia Learning System In Mathematics As A Student Professional Competence Forming Technique

Regular inventions and development in all areas of information technology provide new ways and opportunities for progress in teaching and education methods.They can improve teachers' skills to present information interactively if compared to conventional methods.This method can help students by providing them information through channels and methods that may be easier for comprehension, processing, and searching/retrieving.The aim of the study is to formulate an applied implementation of student professional competence development in vocational training by means of multimedia technologies a case study of mathematical subjects.The shell software is designed in Borland Delphi 7 development environment.While embodying the training system, theoretical and data-aware and developmental methods are used.The paper reports on an approach to teaching mathematics courses by integrating significant multimedia technologies into the learning process.It is found out that multimedia technology applied in teaching mathematics within the higher education curriculum is a way to enhance the effectiveness of using information technologies in learning activities.The findings show that the appropriate involvement of an animated effect in the resources can assist cognitive processes that focus on selecting information, producing presentation models, and reasoning, thereby contributing to studying capacity.The application of the developed software improves student professional engagement.
Improving Compositional Generalization in Classification Tasks via Structure Annotations

Compositional generalization is the ability to generalize systematically to a new data distribution by combining known components.Although humans seem to have a great ability to generalize compositionally, state-of-the-art neural models struggle to do so.In this work, we study compositional generalization in classification tasks and present two main contributions.First, we study ways to convert a natural language sequence-to-sequence dataset to a classification dataset that also requires compositional generalization.Second, we show that providing structural hints (specifically, providing parse trees and entity links as attention masks for a Transformer model) helps compositional generalization.
EVENT-CLASS: Dataset of events in the classroom

This work-in-progress presents a dataset of 360degree videos, called EVENT-CLASS, with associated characteristics in the context of tele-education. The sequences (video and audio) have been captured considering several environments, lighting conditions, acquisition perspectives, and cameras, enriching the dataset. EVENT-CLASS will be helpful for numerous applications related to tele-education, including quality assessment tests, and with the aim of improving the immersive experience of remote users thanks to the detection of relevant events that happen in the class. In this sense, this paper presents preliminary results of using transfer learning for person detection in 360degree scenes, based on Detectron2, and provides insights on the influence of applying it to equirectangular projection and over the viewport. Ongoing works will allow to include more videos and ground-truth annotations to the dataset.
Development of sportsman students' autonomy in the educational environment of electronic information and foreign language teachers’ readiness to this process

The study addresses the major factors preventing foreign language (FL) teachers from effective use of electronic information educational environment (EIEE) at the institutes of higher learning (IHL) in Russia to develop learner autonomy.Upon holding interviews and taking polls of 60 respondents from three universities helped the authors find out that these factors include: sketchy knowledge of information and communication technologies among foreign language teachers; poor standards of material and technical resources capability at universities; inadequate readiness of FL teachers to apply new information and communication technologies; insufficient level of FL teachers motivation to use innovative ITs at work.The contributors have worked out a number of pedagogical conditions that should be provided at the IHL to clear the barriers mentioned above: gradual development of FL teachers readiness to use the EIEE (the IT competence acquisition based on learning management systems (LMS); FL teachers activity stimulation to work in the EIEE through creating modular courses); setting up the EIEE in the IHL, which can translate all e-learning possibilities into action, the LMS, software and documentation among them.The said pedagogical conditions have successfully been tested and implemented at RUDN University.
Multi-Lingual Question Generation with Language Agnostic Language Model

Question generation is the task of generating coherent and relevant question given context paragraph.Recently, with the development of large-scale question answering datasets such as SQuAD, the English question generation has been rapidly developed.However, for other languages such as Chinese, the available training data is limited, which hinders the development of question generation in the corresponding language.To investigate the multi-lingual question generation, in this paper, we develop a language-agnostic language model, which learns the shared representation from several languages in a single architecture.We propose an adversarial training objective to encourage the model to learn both language-specific and language-independent information.We utilize abundant monolingual text to improve the multi-lingual question generation via pre-training.With the languageagnostic language model, we achieve significant improvement in multi-lingual question generation over five languages.In addition, we propose a large-scale Chinese question generation dataset containing more than 220k human-generated questions to benefit the multi-lingual question generation research.
Investigating Text Simplification Evaluation

Modern text simplification (TS) heavily relies on the availability of gold standard data to build machine learning models.However, existing studies show that parallel TS corpora contain inaccurate simplifications and incorrect alignments.Additionally, evaluation is usually performed by using metrics such as BLEU or SARI to compare system output to the gold standard.A major limitation is that these metrics do not match human judgements and the performance on different datasets and linguistic phenomena vary greatly.Furthermore, our research shows that the test and training subsets of parallel datasets differ significantly.In this work, we investigate existing TS corpora, providing new insights that will motivate the improvement of existing state-ofthe-art TS evaluation methods.Our contributions include the analysis of TS corpora based on existing modifications used for simplification and an empirical study on TS models performance by using better-distributed datasets.We demonstrate that by improving the distribution of TS datasets, we can build more robust TS models.
Two Stages Outlier Removal as Pre-processing Digitizer Data on Fine Motor Skills (FMS) Classification Using Covariance Estimator and Isolation Forest

The increase of the classification accuracy level has become an important problem in machine learning especially in diverse data-set that contain the outlier data.In the data stream or the data from sensor readings that produce large data, it allows a lot of noise to occur.It makes the performance of the machine learning model is disrupted or even decreased.Therefore, clean data from noise is needed to obtain good accuracy and to improve the performance of the machine learning model.This research proposes a two-stages for detecting and removing outlier data by using the covariance estimator and isolation forest methods as pre-processing in the classification process to determine fine motor skill (FMS).The dataset was generated from the process of recording data directly during cursive writing by using a digitizer.The data included the relative position of the stylus on the digitizer board.x position, y position, z position, and pressure values are then used as features in the classification process.In the process of observation and recording, the generated data was very huge so some of them produce the outlier data.From the experimental results that have been implemented, the level of accuracy in the FMS classification process increases between 0.5-1% by using the Random Forest classifier after the detection and outlier removal by using covariance estimator and isolation forest.The highest accuracy rate achieves 98.05% compared to the accuracy without outlier removal, which is only about 97.3%.
Sentiment Analysis About Indonesian Lawyers Club Television Program Using K-Nearest Neighbor, Naïve Bayes Classifier, And Decision Tree

Indonesia Lawyers Club (ILC) is a talk show on TVOne that discusses topics around public phenomena, legal issues, crime, and other similar topics. In 2018, ILC won the Panasonic Gobel Awards as the best news talk show program. But in 2019, ILC failed to win the award which was won by Mata Najwa which featured a talk show event that appeared on Trans7. As one of the television shows that has won awards, ILC has pros and cons for its shows from the public. This study applies a sentiment analysis approach to examine public opinion on Twitter about Mata Najwa and ILC in 2018 and 2019. This study applies K-Nearest Neighbor, Naïve Bayes Classifier, and Decision Tree classification algorithm to validate the result. The contribution of this study is to show that public opinion on Twitter can be examined to figure out community sentiment on a tv talk show as well as to confirm the Award winner of tv Talkshow.&#x0D; &#x0D; Index Terms—datamining; Decision Tree; K-NN; Naïve Bayes Classifier; sentiment analysis
Prediction of Spam Email using Machine Learning Classification Algorithm

Unsolicited e-mail also known as Spam has become a huge concern for each e-mail user. In recent times, it is very difficult to filter spam emails as these emails are produced or created or written in a very special manner so that anti-spam filters cannot detect such emails. This paper compares and reviews performance metrics of certain categories of supervised machine learning techniques such as SVM (Support Vector Machine), Random Forest, Decision Tree, CNN, (Convolutional Neural Network), KNN(K Nearest Neighbor), MLP(Multi-Layer Perceptron), Adaboost (Adaptive Boosting) Naïve Bayes algorithm to predict or classify into spam emails. The objective of this study is to consider the details or content of the emails, learn a finite dataset available and to develop a classification model that will be able to predict or classify whether an e-mail is spam or not.
Interpretation and machine translation towards google translate as a part of machine translation and teaching translation

Language comprehension is the capacity of someone to properly understand the language to fully communicate the message and details. When dialects are distinct, the problem arises. This condition can lead to misconception as understudies, particularly those whose specialty is not English, cannot gain real importance. Along these lines, interpreting is regarded as one of the suggested arrangements in this area. As the results, the message's basic significance and setting in an unknown dialect can be precisely seen in English. Interpretation is a help to resolve this language boundary for this case. Moreover, finding an individual who is accessible to decipher each and every language is found troublesome. Furthermore, the aftereffect of interpretation is by one way or another influenced and impacted by the interpreter's abilities. In this manner, interpretation application turns into the one to be depended on. A lot of online interpretation applications have been made available for the last few years. The best one is Google Translate which is a multi-lingual online computer interpretation (MT) system. It is said as a multilanguage interpretation programme, as it can decode material from over 90 dialects.
EVOLUTION OF DISTANCE EDUCATION IN THE PANDEMIC

The article analyses the development of distance education in the context of the coronavirus pandemic. The possibility of creating a hybrid education model in the future, combining face-to-face classes with distance learning, is assumed. The factors contributing to the active introduction of distance learning technologies into the education system are distinguished.
A Study on the Existence of Null Labelling for 3-Hypergraphs

A 3-uniform hypergraph H consists of a set V of vertices, and $$E\subseteq {V\atopwithdelims ()3}$$ triples. Let a null labelling be an assignment of $$\pm 1$$ to the triples such that each vertex has signed degree equal to zero. Assumed as necessary condition the degree of every vertex of H to be even, the Null Labelling Problem consists in determining whether H has a null labelling. Although the problem is NP-complete, the subclasses where the problem turns out to be polynomially solvable are of interest. In this study we define the notion of 2-intersection graph related to a 3-uniform hypergraph, and we prove that the existence of a Hamiltonian cycle there, is sufficient to obtain a null labelling in the related hypergraph. The proof we propose provides an efficient way of computing the null labelling.
Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems

In this paper we explore the improvement of intent recognition in conversational systems by the use of meta-knowledge embedded in intent identifiers.Developers often include such knowledge, structure as taxonomies, in the documentation of chatbots.By using neurosymbolic algorithms to incorporate those taxonomies into embeddings of the output space, we were able to improve accuracy in intent recognition.In datasets with intents and example utterances from 200 professional chatbots, we saw decreases in the equal error rate (EER) in more than 40% of the chatbots in comparison to the baseline of the same algorithm without the meta-knowledge.The metaknowledge proved also to be effective in detecting out-of-scope utterances, improving the false acceptance rate (FAR) in two thirds of the chatbots, with decreases of 0.05 or more in FAR in almost 40% of the chatbots.When considering only the well-developed workspaces with a high level use of taxonomies, FAR decreased more than 0.05 in 77% of them, and more than 0.1 in 39% of the chatbots.
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals

Most previous studies integrate cognitive language processing signals (e.g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly concatenating word embeddings with cognitive features, ignoring the gap between the two modalities (i.e., textual vs. cognitive) and noise in cognitive features.In this paper, we propose a CogAlign approach to these issues, which learns to align textual neural representations to cognitive features.In Co-gAlign, we use a shared encoder equipped with a modality discriminator to alternatively encode textual and cognitive inputs to capture their differences and commonalities.Additionally, a text-aware attention mechanism is proposed to detect task-related information and to avoid using noise in cognitive features.Experimental results on three NLP tasks, namely named entity recognition, sentiment analysis and relation extraction, show that CogAlign achieves significant improvements with multiple cognitive features over state-of-the-art models on public datasets.Moreover, our model is able to transfer cognitive information to other datasets that do not have any cognitive processing signals.The source code for CogAlign is available at https://github.
The Development of Cryptocurrencies as a Payment Method in South Africa

The use of currency as a medium of exchange for goods and services is essential in our daily lives. The concept of currency evolved from bartering to the use of coins and notes and now to the current digital age as the continuation of society's advancement has led to a new series of technological innovations with regard to payment methods around the world. Methods of payment are built on various platforms such as mobile phones, the internet, and digital storage cards. These payment systems have fostered the development and growth of fintech companies such as Paypal, Apple Pay, Samsung Pay, Alipay and others. The latest development is the use of blockchain to facilitate payments, more specifically the use of cryptocurrencies to facilitate transactions. The most popular cryptocurrency is bitcoin. Payments form part of commercial contracts which parties have a legal obligation to perform. South Africa has a legislative framework for payment systems wherein users have rights and obligations. This article will discuss the development of currency in South Africa and determine whether the current legislative framework for payment systems is applicable to cryptocurrencies such as bitcoin.&#x0D; &#x0D;
Comprehensive Study: How the Context Information of Different Granularity Affects Dialogue State Tracking?

Dialogue state tracking (DST) plays a key role in task-oriented dialogue systems to monitor the user's goal.In general, there are two strategies to track a dialogue state: predicting it from scratch and updating it from previous state.The scratch-based strategy obtains each slot value by inquiring all the dialogue history, and the previous-based strategy relies on the current turn dialogue to update the previous dialogue state.However, it is hard for the scratch-based strategy to correctly track shortdependency dialogue state because of noise; meanwhile, the previous-based strategy is not very useful for long-dependency dialogue state tracking.Obviously, it plays different roles for the context information of different granularity to track different kinds of dialogue states.Thus, in this paper, we will study and discuss how the context information of different granularity affects dialogue state tracking.First, we explore how greatly different granularities affect dialogue state tracking.Then, we further discuss how to combine multiple granularities for dialogue state tracking.Finally, we apply the findings about context granularity to fewshot learning scenario.Besides, we have publicly released all codes.