Reviewerly/exHarmony huggingface dataset:
Implementation of Interactive Maps for Student Learning Outcomes

Effective learning media can certainly have an impact on the success of learning outcomes.The aim of this paper to find out the implementation of interactive maps for student learning outcomes.The research design is an experiment method with a non-probability sampling type purposive sampling technique.There are two selected groups as sampling, Social Class 1 with 38 students as an experiment and Social Class 2 with 38 students as a control.The results showed the implementation of an interactive map for student learning outcomes, from the testing of hypothesis showed that t count = 9.43 and t table = 1.66, then we accept the alternative hypothesis and rejected the null hypothesis.Thus, the implementation of an interactive map can improve student learning outcomes.
DESIGNING A DATA VISUALISATION AND ANALYSIS TOOL FOR SUPPORTING DECISION-MAKING WITH PUBLIC TRANSPORTATION NETWORK

Abstract Massive data are surrounding us in our daily lives. Urban mobility generates a very high number of complex data reflecting the mobility of people, vehicles and objects. Transport operators are primary users who strive to discover the meaning of phenomena behind traffic data, aiming at regulation and transport planning. This paper tackles the question "How to design a supportive tool for visual exploration of digital mobility data to help a transport analyst in decision making?” The objective is to support an analyst to conduct an ex post analysis of train circulation and passenger flows, notably in disrupted situations. We propose a problem-solution process combined with data visualisation. It relies on the observation of operational agents, creativity sessions and the development of user scenarios. The process is illustrated for a case study on one of the commuter line of the Paris metropolitan area. Results encompass three different layers and multiple interlinked views to explore spatial patterns, spatio-temporal clusters and passenger flows. We join several transport network indicators whether are measured, forecasted, or estimated. A user scenario is developed to investigate disrupted situations in public transport.
Information Entropy Algorithms for Image, Video, and Signal Processing

Information entropy is a basic concept in information theory associated with any random variable [...]
C3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings

We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021.The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the embeddings into a simpler vector used as input to a neural network, the latter for predicting the complexity of words and expressions.Results show that the pre-trained sentence embeddings are not able to capture lexical complexity from the language when applied in cross-domain applications.
Readability and Comprehensibility Analysis of the English Language Comprehension Passages of the West African Senior School Certificate Examination in Ghana

School Certificate Examination in Ghana 1. IntroductionReadability entails linguistic characteristics of texts that are capable of influencing how a reader will successfully read and comprehend written information.There is a difference between readability and legibility.Legibility involves the actual ease through which a text can be read and understood.The readability level of a given text is a pointer towards the textual difficulty level of a text and the level of suitability of the text to the people of a given age or grade level who read it.For a given text, it is always fixed and does not change based on reader characteristics (Zamanian, & Heydari, 2012Wissing, Blignaut, & Van den Berg, 2016; McLaughlin, 1969, p. 640).During the text selection, readability is an important element in text selection because the ultimate goal of reading is understanding the written information.It is also very ideal for instructors to consider other personal and social factors in administering comprehension tests to students to get maximum feedback from the students.In a nutshell, the notion that "readability index analysis alone cannot determine readers" comprehension of a text" is rightly in place (Benjamin, 2012).While Wissing, Blignaut, & Van den Berg, (2016) see no difference in readability and understandability, researchers like Bansiong, & Wan, (2019); Smith and Taffler 1992: 85) bring out a clear difference between the two.According to the latter, there is a similarity between the two but they are intrinsically different in their own way.Accordingly, Smith and Taffler, (1992: 85) points out that it is not right to treat both readability and understandability as synonyms.This is because they have a marked difference that exists between the two concepts.On that note, Wray and Dahlia, (2013) add that readability is a characteristic of a text and understandability indicates the ability of the reader to pick a meaning out of a text.And even though a text has to be readable so as to be understandable, the level of comprehensibility is affected by both syntactical difficulty and the characteristic of a reader such as their background, prior knowledge about the issue, interest and reading ability (Jones, 1997, p. 106).Besuglov, & Crasselt, (2020) argue that there is a considerable relationship that exists between text readability and academic achievement.According to them, readability can be used to foresee the kind of student who is likely to experience difficulty in terms of academics especially in technical subjects such as accounting.The objective of this study is analysis the readability levels of the comprehension passages of the West African Secondary School Certificate Examination (WASSCE) for School Candidates in Ghana between 2014 to 2020 and how the readability levels had affected their comprehensibility in the English language.
SRPOL DIALOGUE SYSTEMS at SemEval-2021 Task 5: Automatic Generation of Training Data for Toxic Spans Detection

This paper presents a system used for SemEval-2021 Task 5: Toxic Spans Detection.Our system is an ensemble of BERT-based models for binary word classification, trained on a dataset extended by toxic comments modified and generated by two language models.For the toxic word classification, the prediction threshold value was optimized separately for every comment, in order to maximize the expected F1 value.
Exploring Instructional Design in Arabic Education

This review examines instructional design principles from Behaviorist, Cognitivist, Constructivist, and Contemporary learning inspired models within the landscape of Arabic language education research. Specific characteristics from instructional design models suchs as ADDIE, Morrison, Ross, and Kemp, ASSURE, and Rapid Prototyping are explored within the context of the Arabic classroom that uses technology integration. It will be shown that there is a need for more studies to emphasize the importance of design within course development to benefit both instructors and learners of Arabic. This synthesis of current research will be helpful to Arabic educators and researchers in determining best collaborative and comprehensive strategies for designing effective Arabic language learning environments to propel the field into the future.&amp;nbsp;
Overcoming Poor Word Embeddings with Word Definitions

Modern natural language understanding models depend on pretrained subword embeddings, but applications may need to reason about words that were never or rarely seen during pretraining.We show that examples that depend critically on a rarer word are more challenging for natural language inference models.Then we explore how a model could learn to use definitions, provided in natural text, to overcome this handicap.Our model's understanding of a definition is usually weaker than a well-modeled word embedding, but it recovers most of the performance gap from using a completely untrained word.
Image Denoising Using Nonlocal Means with Shape-Adaptive Patches and New Weights

Digital images captured from CMOS/CCD image sensors are prone to noise due to inherent electronic fluctuations and low photon count. To efficiently reduce the noise in the image, a novel image denoising strategy is proposed, which exploits both nonlocal self-similarity and local shape adaptation. With wavelet thresholding, the residual image in method noise, derived from the initial estimate using nonlocal means (NLM), is exploited further. By incorporating the role of both the initial estimate and the residual image, spatially adaptive patch shapes are defined, and new weights are calculated, which thus results in better denoising performance for NLM. Experimental results demonstrate that our proposed method significantly outperforms original NLM and achieves competitive denoising performance compared with state-of-the-art denoising methods.
Research of Interactive and Traditional Tasks Aimed at Studying Information Technologies among Students

The results of a study conducted within the framework of an interactive approach to the task formation, which is an actual branch of computer methods in education, are presented. The author notes that the branch is currently not used for teaching information technologies practice in higher educational institutions due to the labor cost of forming, implementing and taking into account the impact of interactive tasks on the success of the educational process participants. The purpose of the study is to substantiate the introduction of relevant information technologies into the educational process, as well as approaches to the verification and formation of knowledge. It tested the main hypothesis: tasks aimed at interactive activity better help students in mastering the material, based on their own perception, than static tasks based on traditional approaches in the educational process with information technologies application. An intraindividual scheme of the experiment is used on the basis of traditional and interactive tasks, different types of tasks are presented to the same group of students. The sample for the participants of the experiment is recruited among the most capable students of the Information Technologies Faculty, bachelors, 2-4 year of experience according to the criterion of academic performance in the framework of specialized disciplines (28 people participated). The variables are empirically selected characteristics that reflect the useful qualities of the task in the context of the educational process. Methods of descriptive statistics are used to test hypotheses. It is noted that the results of the study make it possible to determine and formulate conditions that allow maximum use of the advantages of each of the studied types of tasks. The research confirms the demand for the development of teaching tools for students using interactive tasks, indicating the boundary conditions under which, according to the author, an interactive approach to the construction and implementation of tasks can demonstrate its effectiveness.
Anonymous and Distributed Authentication for Peer-to-Peer Networks

Well-known authentication mechanisms such as Public-key Infrastructure (PKI) and Identity-based Public-key Certificates (ID-PKC) are not suitable for integration in a Peer-to-Peer (P2P) network environment, the reason being either the lack of or the difficulty in maintaining a centralized authority to manage the certificates.Authentication becomes even harder in anonymous environments.In this study, we present three authentication protocols such that the users can authenticate themselves in an anonymous P2P network, without revealing their identities.The first protocol uses existing ring signature schemes to obtain anonymous authentication, the second is an anonymous authentication protocol utilizing secret sharing schemes, and lastly a zero-knowledge-based anonymous authentication protocol.We provide security justifications for the three aforementioned protocols in terms of anonymity, completeness, soundness, resilience to impersonation attacks, and resilience to replay attacks.We also provide examples of conceptual topologies and how the peers would behave and rearrange in case of failure.
Comparative analysis of support vector machine and k-nearest neighbors with a pyramidal histogram of the gradient for sign language detection

The communication method using sign language is very efficient considering that the speed of information delivery is closer to verbal communication (speaking) compared to writing or typing. Because of this, sign language is often used by people who are deaf, speech impaired, and normal people to communicate. To make sign language translation easier, a system is needed to translate symbols formed from hand movements (in the form of images) into text or sound. This study aims to compare performance such as accuracy and computation time of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) with Pyramidal Histogram of Gradient (PHOG) for feature extraction, to know which one is better at recognizing sign language. Yield, both combined methods PHOG-SVM and PHOG-KNN can recognize images from hand movements that form certain symbols. The system built using the SVM classification produces the highest accuracy of 82% at PHOG level 3, while the system built with the KNN classification produces the highest accuracy of 78% at PHOG level 2. The total computation time of the fastest training and testing by the SVM model is 236.53 seconds at PHOG level 3, while the KNN model is 78.27 seconds at PHOG level 3. In terms of accuracy, PHOG-SVM is better, but in terms of computation time, PHOG-KNN takes the place.
Dampak Pandemi Covid-19 Terhadap Proses Pembelajaran Online di SDN Cabean 3 Demak

This study aims to: 1) identify the implementation of online learning at SDN Cabean 3 Demak during the Covid-19 pandemic, 2) identify the impact that occurs in online learning on teachers, students, and parents at SDN Cabean 3 during the Covid-pandemic era.19, and 3) identify the reasons for this impact that could occur in online learning for teachers, students and parents at SDN Cabean 3 during the Covid-19 pandemic.This research is a qualitative research with a case study method approach.Respondents in this study were teachers, students, and parents of grade 1 to grade V SDN Cabean 3. Collecting data using open questionnaires, interviews, and documents.Data analysis in this study used a qualitative descriptive analysis with the stages of data reduction, data display, and conclusion drawing / verification.The results showed that, 1) the implementation of online learning at SDN
UTNLP at SemEval-2021 Task 5: A Comparative Analysis of Toxic Span Detection using Attention-based, Named Entity Recognition, and Ensemble Models

Detecting which parts of a sentence contribute to that sentence's toxicity-rather than providing a sentence-level verdict of hatefulnesswould increase the interpretability of models and allow human moderators to better understand the outputs of the system.This paper presents our team's, UTNLP, methodology and results in the SemEval-2021 shared task 5 on toxic spans detection.We test multiple models and contextual embeddings and report the best setting out of all.The experiments start with keyword-based models and are followed by attention-based, named entitybased, transformers-based, and ensemble models.Our best approach, an ensemble model, achieves an F1 of 0.684 in the competition's evaluation phase.
Information interventions as a new dimension of Ukraine's cyber-vulnerability

In the article, the author analyzes information interventions as threats to the cybernetic security of Ukraine. The relevance of this study is due to the fact that large number of socially dangerous acts aimed at harming state interests can now be used both in the information space and in purely cyberspace. Since such actions are performed using computer systems and performed in cyberspace, we propose to define this type of intervention as “cybernetic intervention”, describing it as a separate group of socially dangerous acts aimed at damaging the information infrastructure of States, vital areas of society's existence. The main aim of this study is to analyze information intervention as a threat to cyber security of Ukraine. The interpretation of terms that make up the conceptual and categorical apparatus of the subject of research is carried out. A narrow definition of the concept of “information intervention” is proposed as the violent intervention of one or more subjects of information relations in the activities of another or others, and a broad definition – a certain set of aggressive actions that are aimed at influencing public opinion and decision-making within one or another country and achieving clearly defined results.
Evaluating a Joint Training Approach for Learning Cross-lingual Embeddings with Sub-word Information without Parallel Corpora on Lower-resource Languages

Cross-lingual word embeddings provide a way for information to be transferred between languages.In this paper we evaluate an extension of a joint training approach to learning cross-lingual embeddings that incorporates sub-word information during training.This method could be particularly well-suited to lower-resource and morphologically-rich languages because it can be trained on modest size monolingual corpora, and is able to represent out-of-vocabulary words (OOVs).We consider bilingual lexicon induction, including an evaluation focused on OOVs.We find that this method achieves improvements over previous approaches, particularly for OOVs.
YoungSheldon at SemEval-2021 Task 5: Fine-tuning Pre-trained Language Models for Toxic Spans Detection using Token classification Objective

In this paper, we describe our system used for SemEval 2021 Task 5: Toxic Spans Detection.Our proposed system approaches the problem as a token classification task.We trained our model to find toxic words and concatenate their spans to predict the toxic spans within a sentence.We fine-tuned Pre-trained Language Models (PLMs) for identifying the toxic words.For fine-tuning, we stacked the classification layer on top of the PLM features of each word to classify if it is toxic or not.PLMs are pre-trained using different objectives and their performance may differ on downstream tasks.We, therefore, compare the performance of BERT, ELECTRA, RoBERTa, XLM-RoBERTa, T5, XLNet, and MPNet for identifying toxic spans within a sentence.Our best performing system used RoBERTa.It performed well, achieving an F1 score of 0.6841 and secured a rank of 16 on the official leaderboard.
Teach the Rules, Provide the Facts: Targeted Relational-knowledge Enhancement for Textual Inference

We present InferBert, a method to enhance transformer-based inference models with relevant relational knowledge.Our approach facilitates learning generic inference patterns requiring relational knowledge (e.g.inferences related to hypernymy) during training, while injecting on-demand the relevant relational facts (e.g.pangolin is an animal) at test time.We apply InferBERT to the NLI task over a diverse set of inference types (hypernymy, location, color, and country of origin), for which we collected challenge datasets.In this setting, InferBert succeeds to learn general inference patterns, from a relatively small number of training instances, while not hurting performance on the original NLI data and substantially outperforming prior knowledge enhancement models on the challenge data.It further applies its inferences successfully at test time to previously unobserved entities.InferBert is computationally more efficient than most prior methods, in terms of number of parameters, memory consumption and training time.
Development Of Concept Model For Evaluation, Enhancement And Promotion Of University Teachers

Mongolian universities are at the edge of huge paradigm shiftthat leads significant changes in academic work.Therefore, new conceptual model accounting complex and changingnature of academic work is need to be developed.The current study is purposed to develop concept model for evaluation, enhancement, promotion of university teachers, and for accelerating the leadership.The concept model was developed based on the idea derived from "Think Thank" group of Mongolian University of Science and Technology.A number of studies and international sources related teacher development framework, teacher's standard and teacher leadership were investigated over past 5 years.Developed concept model starts with definiting university-department-professorship relationship and further extended identifying university professor's five core roles.Professor's workload capacity was expressed by function with 5x5 matrices defined by professor's responsible activities as well as their coefficient of activity types, which can be used as key performance index.The model offers opportunities to develop quantitative assessment tool for professor's performance, to enhance human resource management system, and to update existing compensation or leadership promotionsystem.The developed concept model can be used as a benchmark for further development of professional development framework, university teacher standard, as well as national system of HE capacity building.
BOUN at SemEval-2021 Task 9: Text Augmentation Techniques for Fact Verification in Tabular Data

In this paper, we present our text augmentation based approach for the Table Statement Support Subtask (Phase A) of SemEval-2021 Task 9. We experiment with different text augmentation techniques such as back translation and synonym swapping using Word2Vec and WordNet.We show that text augmentation techniques lead to 2.5% improvement in F1 on the test set.Further, we investigate the impact of domain adaptation and joint learning on fact verification in tabular data by utilizing the SemTabFacts and TabFact datasets.We observe that joint learning improves the F1 scores on the SemTabFacts and TabFact test sets by 3.31% and 0.77%, respectively.
Humanoid Robot Path Planning Using Rapidly Explored Random Tree and Motion Primitives

Path planning is an essential part of the control system of any mobile robot. In this article the path planner for a humanoid robot is presented. The short description of an universal control framework and the Motion Generation System is also presented. Described path planner utilizes a limited number of motions called the Motion Primitives. They are generated by Motion Generation System. Four different algorithms, namely the: Informed RRT, Informed RRT with random bias, and RRT with A* likeheuristics were tested. For the last one the version with biased random function was also considered. All mentioned algorithms were evaluated considering three dif ferent scenarios. Obtained results are described and discussed.
DytectiveU application: contribution to the literacy in children with dyslexia

&#x0D; &#x0D; &#x0D; The result of research on DytectiveU application and its contribution to the reading-writing process in children with dyslexia is offered. An explanatory-descriptive research was developed, with the use of quantitative and qualitative methods to explore the reading-writing process mediated by technologies of the education. It was found that the DytectiveU application offers multiple actions to learn to read and write, during the learning of children with dyslexia. It concludes that the DytectiveU application reveals potentialities to develop the literacy process in children with dyslexia, as well as the reasons that lead to its use in the teaching-learning process of Basic Education.&#x0D; &#x0D; &#x0D;