Reviewerly/exHarmony huggingface dataset:
MULTILINGUAL SENTIMENT NORMALIZATION FOR SCANDINAVIAN LANGUAGES

In this paper, we address the challenge of multilingual sentiment analysis using a traditional lexicon and rule-based sentiment instrument that is tailored to capture sentiment patterns in a particular language. Focusing on a case study of three closely related Scandinavian languages (Danish, Norwegian, and Swedish) and using three tailored versions of VADER, we measure the relative degree of variation in valence using the OPUS corpus. We found that scores for Swedish are systematically skewed lower than Danish for translational pairs, and that scores for Norwegian are skewed higher for both other languages. We use a neural network to optimize the fit between Norwegian and Swedish respectively and Danish as the reference (target) language.
Understanding the Concept of Different Types of Web Crawling and Its Implementation

Web crawling is the method in which the topics and information is browsed in the world wide web and then it is stored in big storing device from where it can be accessed by the user as per his need. This paper will explain the use of web crawling in digital world and how does it make difference for the search engine. There are a variety of web crawling available which is explained in brief in this paper. Web crawler has many advantages over other traditional methods of searching information online. Many tools are made available which supports web crawling and makes the process easy.
Factors Facilitating Teacher’s Professional Competence Development

The objective of the article is to further study the concept “teacher’s professional competence” as well the ways of its formation. The research was conducted among the students as prospective teachers, and teaching staff of Yuriy Fedkovych Chernivtsi National University. The analysis of the research helped prove the essential role of some factors in the professional development of future teachers. In particular, such factors as knowledge of the teaching subject, knowledge of effective teaching methods and skills to implement them, the importance of teaching practice, the personality factor, teacher’s self-education were singled out. Therefore, the paper aims to outline a few ideas on how to ensure the implementation of the above-mentioned considerations. We also dwell upon the impact of the human behavior on the process of foreign language learning and teaching. That is why knowledge about affective factors is consequential in the academic process and using some interactive methods can also help lower the affective filter. Types of motivation and ways to enhance it are described in the paper too.&#x0D;
Bridge Over Troubled Water: A Teacher Education Program’s Emergent Methods for Constructing an Online Community of Practice During a Global Pandemic

With the sudden shift to emergency remote instruction during the COVID-19 global pandemic in the spring of 2020, teacher education programs were confronted with the dual responsibility of teaching pre-service teachers how to teach and how to teach in synchronous P-12 classroom settings utilizing educational technologies. Building a community of practice (CoP) and attending to P-12 students' socio-emotional health and learning were the backdrop to a Masters of Arts and Teaching (MAT) program as it prepared its teacher candidates for full-year teaching residencies. In this retrospective, researchers reflect on practices for building community in an online setting and strategies for supporting teacher candidates' and P-12 students' socio-emotional learning.
Pairwise-measurement-induced synthesis of quantum coherence

Quantum coherent superpositions of states with different energies, i.e., states with coherence with respect to energy basis, are an important resource for modern quantum technologies. States with small coherence can be obtained either autonomously, due to the effect of a weak coherent drive, or, potentially, due to the presence of an environment. In this paper, we propose a measurement-based protocol for quantum coherence synthesis from individual systems (with low initial coherence) into a global (and higher) coherence of the joint system. As an input, it uses $N$ noninteracting copies of two-level systems (TLS), with low initial energy and coherence. These can be supplied by, e.g., a weak external drive or can result from an interaction with a bath. This protocol conditionally synthesizes an output state with higher energy and coherence than the initial state had, representing a universal process the rules of which have not been well studied, yet. In addition to energy and coherence, we study the quantity called mutual coherence, showing increase after the protocol application, as well. This approach is based on application of sequential pairwise projective measurements on TLS pairs (conditionally removing their ground states), that are diagonal in the TLS energy basis. The functionality of the coherence synthesis is robust with respect to dephasing effects of the TLS environment on the system. Our approach may show its benefits in quantum sensing, quantum batteries charging, or other applications where synthesis of a larger coherent system from smaller (weaker) resources is useful.
Identification of Online Statistical Translation and Text Issues in Communication Technologies

Now-a-days Artificial Intelligence growing fast towards new communication technologies and it provide easiness to the users as per there demand. Mobile/cell phone is one of the most reliable examples of latest technologies. It is easy to communicate with peoples of different countries and also in different languages. For the communications different technologies were used to solve this major issue around the globe. Translation is one of them to translate the one language to another language. The research methodology is based on the five phases. In this research study, analysis of the text by process on online translator, bilingual corpus (English and Sindhi) were used. After the translation from English to Sindh and Translation from Sindhi to English many issues were faced at the various communication technologies. When input data of large sentence to the translator it takes more time for the translation, but mostly this issue came in translation of text from English to Sindhi. Moreover, the results are based on translated text of English Languages and total three hundred fifty two words (352) were used.
Capturing changing user goals in information seeking process using information behavioral grammar model

Background.Problem-solving support services in Japan's public libraries offer users' problem-solving support by providing information in business, health and medical, legal, and administrative support.Though such services are quite welldeveloped in Japan's public libraries, there is little research on the information needs of local citizens who make use of these services.Objective.This study investigated the information seeking processes of two groups of library users-aspiring entrepreneurs and cancer patients-who used public libraries to attain their problem-solving goals.This study used an information behavioral grammar model as the theoretical framework.Method.Existing interview data of aspiring entrepreneurs and cancer patients were analyzed using the model, applying a constant-comparative method adopted from the grounded theory approach in order to elicit how problem-solving goal (distal goal) and information needs (proximal sub-goals) are modified in information seeking process.Results.The information behavioral grammar model was found to be useful for characterizing and explaining the change in the distal goal and the information needs for problem solving, together with the emotions of the actor that result in the change.Entrepreneurs and cancer patients use various external services before and after using the public library's problem-solving support service.Practical implications.It is desirable for public libraries to collaborate with external specialized organizations and professionals in offering user-training on library use, Web search, and specialized data base retrieval.
Usability and user satisfaction rate evaluation on e-learning application from student's perspective using Nielsen usability method

Covid-19 is a new and contagious disease that has spread throughout the world community. This disease has spread to Indonesia since early March 2020. One way to prevent the spread of this virus is by implementing activities from home. Budi Luhur University is one of the educational institutions that has implemented work and learning from home during the Covid-19 pandemic. In the teaching and learning process, the E-Learning application is the main tool used both from the faculty and student sides. The total and urgent implementation of E-Learning application which began in March 2020 caused several problems that reduced the comfort of students in carrying out learning process. The purpose of this study is to measure the level of student satisfaction and the level of use of the E-Learning application and its features. This study used a survey sampling method by distributing questionnaires and getting results from 115 respondents. The method used in this research is the Nielsen Usability method. Measurements were made using five criteria: Learnability, Memorability, Efficiency, Errors, and Satisfaction. Result show, although the average of each dimension is in the satisfied interpretation, there are significant differences in the level of satisfaction in each dimension. The dimension with the lowest average rating is the Errors dimension. This shows that respondents still find some errors when using the E-Learning application so that respondents do not show significant satisfaction in terms of the Errors dimension. In other side, result shows that the respondent thinks face-to-face meetings are still needed.&#x0D;
Website Design and Trust Elements: A/B Testing on a Start-up's Website

Start-ups are young companies that are hardly known, especially during their early stages, by the relevant stakeholders. A start-up's website is, therefore, often the first point of contact for potential customers, investors, or partners. Such a website usually explains the new product or service and presents the founding team with its competencies. The user's perception of the website and its design can be crucial in determining whether the user is interested in getting in touch with the start-up or even considering the purchase of the respective product or service. User’s trust in the website and its operator is essential for this. The so-called trust elements, such as logos, testimonials, or seals, are intended to create trust on websites. So far, the influence of these elements on user behaviour has hardly been empirically proven in a real-life context. Therefore, we have applied the method of A/B testing to the website of a fictive start-up. Trust elements were placed on one variant of the website (A), whereas on the other variant, there were none (B). The experiment shows that the duration of the user sessions does not differ between the two variants. However, more requests were made on the website variant with trust elements.&#x0D; This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Applicability Of Blockchain Technology In The Global Logistics Systems

The 21st century has brought about Blockchain technology, which became a vital innovation in supply chain management and logistics.Blockchain technology is a digital ledger of replicated and disseminated transactions across all the nodes on the Blockchain network.Global logistics is the flow of data and resources between the point of origin and consumption.The arrival of the fourth industrial revolution enhanced the incorporation of technology into all logistics processes and led to the advent of Smart Logistics.The application of Blockchain technology in global logistics effectively advances and allows decentralization and immutable data storage.Blockchain technology has the capability to diminish errors, damage to goods, order delays, and multiple data entries.Essentially, Blockchain technology streamlines global logistics by making it more integrated, efficient, and transparent.The approach of this study can be described as a theory-building from multiple case studies.The study furnishes insight into Blockchain technology, its application and importance in global logistics and smart logistics, and possible opportunities and challenges to be encountered.The goal of this study is to emphasize how Blockchain technology can be advantageous to global logistics systems.
ClothNet: A Neural Network Based Recommender System

The traditional collaborative filtering recommendation systems have many deficiencies, which make them incompetent in the domain of clothing recommendation; we proposed a new ClothNet model based on CNN, RNN, collaborative filtering and the characteristics of the fashion industry. The accuracy and generalization performance of this model are improved compared with traditional systems. The visual information integrated into the ClothNet model enables the recommendation system to alleviate the cold start problem, and new clothes can be added to the recommendation list faster through the visual information. The addition of temporal information enables ClothNet sharply capturing the impact of seasonal and time changes on user preferences. However, because RNN and CNN have the disadvantage of requiring a large amount of data, combining RNN and CNN will make the model more difficult to converge, so we have adopted the LearningToRank training mode and obtained good results.
Why Learning and Machine Learning Are Different

Machine learning intends to be a biology-mimicking learning method, implemented by means of technical computing.Their technology and methods, however, differ very much; mainly because technological computing is based on the time-unaware classic computing paradigm.Based on the time-aware computing paradigm, the paper discovers the mechanism of biological information storing and learning; furthermore, it explains, why biological and technological information handling and learning are entirely different.The consequences of the huge difference in transmission speed in those computing systems may remain hidden in "toy"-level technological systems but comes to the light in systems having large size and/or mimicking neuronal operations.The biology-mimicking technological operations are in resemblance to the biological operations only when using time-unaware computing paradigm.The difference leads also to the need of introducing "training" mode (with desperately low efficiency) in technological learning, while biological systems have the ability of life-long learning.It is at least misleading to use technological learning methods to complement biological learning studies.The examples show evidence for the effect of transmission time in published experiments.
DFFCN: Dual Flow Fusion Convolutional Network for Micro Expression Recognition

Recently, micro-expression recognition (MER) has attracted much attention due to its wide application in various fields such as crime trials and psychotherapy. However, the short duration and subtle movement of facial muscles make it difficult to extract micro-expression features. In this article, we propose a Dual Flow Fusion Convolutional Network (DFFCN) that combines the learning flow and optical flow to capture spatiotemporal features. Specifically, we adopt a trainable Learning Flow Module to extract the frame-level motion characteristics, fused with the mask generated from hand-crafted optical flow, and finally predict the micro-expression. Additionally, to overcome the shortcomings of limited and imbalanced training samples, we propose a data augmentation strategy based on Generative Adversarial Network (GAN). Comprehensive experiments are conducted on three public micro-expression datasets: CASME II, SAMM and SMIC with Leave-One-Subject-Out (LOSO) cross-validation. The results demonstrated that our method achieves competitive performance when compared with the existing approaches, with the best UF1 (0.8452) and UAR (0.8465).
Email Forensic from Phishing Attack using Network Forensics Development Life Cycle Method

Phishing is a technique used by attackers to steal email users' social media accounts by tricking the target into visiting a fake website that has a login form.Due to lack of awareness and insight in internet network education, users are very vulnerable to fall into the trap of attackers.A phishing email attack is a data manipulation activity that is visible in email headers.The purpose of this study is to assist email users in assessing email messages so as not to become victims of phishing attacks.The purpose of this study is to conduct forensics on phishing email attacks.NFDLC method is used to examine cybercrime digital forensic processes and create digital evidence.Wireshark and NetworkMiner programs are used to deduce the IP and IP address of the perpetrator.This research produces an analysis of phishing email attacks against fake login forms.The evidence obtained in the form of a wireshark data packet capture file that managed to capture the victim's IP address, the attacker's IP address, the sender's email, and the phishing website.The experimental results, it is proven that the Network Forensic Development Life Cycle method can analyze phishing email attacks detected on network traffic activity, with the results of data packet analysis carried out header comparison analysis.
Live It - Recommendation System based on Emotion Detection

This paper presents the development of a web application that integrates a system of movie recommendations using the collaborative filtering algorithm with a component of realtime recording and detection of emotions.So far, there were no implementations that combine recommendations and emotions, so this application proposes these two to work together to make our lives better regarding the moviewatching experience.To recognize emotions, we created a component that examines facial expressions, which offers, as a result, one of the emotion types: happy, sad, neutral, anger, surprise.This component was later integrated into a movie recommendation application, which analyzes the user's emotions in real-time while watching the presentation video.In the second part of the paper, we presented how we performed usability tests in order to improve the quality of the application.The results were promising, with a high degree of accuracy and usefulness coming from end-users, showing the future potential of this application, for instance adding new functionalities or recommendation algorithms.
A STUDY OF GENERALIZED LINEAR MIXED MODEL FOR COUNT DATA USING HIERARCHICAL BAYES METHOD

Poisson Log-Normal Model is one of the hierarchical mixed models that can be used for count data. Several estimation methods can be used to estimate the model parameters. The first objective of this study was to examine the performance of the parameter estimator and model built using the Hierarchical Bayes method via Markov Chain Monte Carlo (MCMC) with simulation. The second objective was applied the Poisson Log-Normal model to the West Java illiteracy Cases data which is sourced from the Susenas data on March 2019. In 2019, the incidence of illiteracy is a very rare occurrence in West Java Province. So that, it is suitable as an application case in this study. The simulation results showed that the Hierarchical Bayes parameter estimator through MCMC has the smallest Root Mean Squared Error of Prediction (RMSEP) value and the absolute bias is relatively mostly similar when compared to the Maximum Likelihood (ML) and Penalized Quasi-Likelihood (PQL) methods. Meanwhile, the empirical results showed that the fixed variable is the number of respondents who have a maximum education of elementary school have the greatest risk of illiteracy. Also, the diversity of census blocks significantly affects illiteracy cases in West Java 2019.
Generating Realistic Natural Language Counterfactuals

Counterfactuals are a valuable means for understanding decisions made by ML systems.However, the counterfactuals generated by the methods currently available for natural language text are either unrealistic or introduce imperceptible changes.We propose Counter-factualGAN: a method that combines a conditional GAN and the embeddings of a pretrained BERT encoder to model-agnostically generate realistic natural language text counterfactuals for explaining regression and classification tasks.Experimental results show that our method produces perceptibly distinguishable counterfactuals, while outperforming four baseline methods on fidelity and human judgments of naturalness, across multiple datasets and multiple predictive models.
An Experiment on Mobile Learning to Leverage EFL Learners’ Engagement, Emotional Intelligence, and Learning Motivation

Research has acknowledged Mobile-assisted language learning (MALL) as one efficacious method for leveraging language proficiency across skills and components, leaving the investigation on language learners' motivation and emotional intelligence of peripheral focus, albeit being essential drives for academic success.This study seeks to fill the gaping void by investigating the impact of team-based mobile learning (TBML) as an instructional framework to leverage EFL learners' online learning engagement (OLE), trait emotional intelligence (TEI), and EFL learning motivation.This instructional framework involves a mobile instant messenger (MIM), Telegram, to support collaborative learning both within and beyond class hours.Employing a quasi-experimental approach, the study involved 475 undergraduate students at an Indonesian state university across majors, including nursing, management, science education, and non-formal education, with ages ranging from 18 to 22 years.The findings corroborate significant increment in the abovementioned dimensions resulting from TBML, as opposed to the unworthy increase associated with computer-based LMS.Moderate positive correlations also exist among three pertinent variables.Implications suggest how TBML accords English learners with the authentic learning community armed with myriads discourse resources to empower their multidimensional engagement in meaningful literacy practices.
Suggested Strategies for Writing Narrative Essay

Learning strategies are many, varied, and very important, especially when it comes to expressing and narrating events. Learning to write a narrative essay in the second language is essential, and writing articles by following special strategies develops the second language for the learner and opens new horizons for him in the field of language learning. Using methods and strategies while teaching pupils makes students more intelligent and aware and not make mistakes that they may make while writing articles or narrating on any topic. The study aims at helping students to start writing and ultimately to produce more writing and Encourage fluency in generating ideas for writing on any topic. Moreover, this work will help students begin organizing ideas. The research also includes the study of writing the narrative essay, how to write it, its patterns, and knowledge. The study ends with a conclusion that summarizes the current study.