Reviewerly/exHarmony huggingface dataset:
SOME EVALUATIONS ON THE CURRENT SITUATION OF FACTORS AFFECTING STUDENTS’ INTEREST IN LEARNING ENGLISH AT UNIVERSITY IN THE NORTH OF VIETNAM

In this paper we consider some factors affecting the students' interest in learning English at university in the North of Vietnam and assess the current situation of the factors affecting the students' interest in learning English. Thereby proposing appropriate solutions to increase the interest in learning English for students. And so that the students get better English learning results, use English language skills more fluently, have English ability close to requirements and increasing demand in the labor market
Emotional responses to motion sickness in autonomous driving

In future autonomous cars, users, free from the primary task of driving, will have time and space to engage in other activities while traveling, such as reading a book, working on a laptop or watching a movie. Although the option for these activities are one of the great advantages of autonomous cars, this will also likely increase motion sickness (MS) inside the car. MS affects numerous individuals, and it occurs when the information received through the eyes differs from what is perceived by the body and the inner ear. Plus, MS can have an impact on the emotional component of the individuals experiencing it, making the experience of traveling in autonomous cars uncomfortable and difficult. Emotional design studies focus on the emotional response of individuals to a product or service. These studies typically employ self-report scales as assessment tools, such as SAM (Self-Assessment Manikin) and PrEmo (Product Emotion Measurement instrument). We present the first study measuring emotional responses to MS using both SAM and PrEmo scales. In our study, we induced MS by asking participants to watch a highly dynamic video of a first-person car trip. We also asked subjects to answer to SAM and PrEmo before and after the visualization of the video. Our results showed a change in the answers in time, that is, before vs. after the experience of MS. MS significantly altered individuals' emotional responses, worsening their condition. These results support the need for studies that reduce MS to improve the experience and well-being of individuals in autonomous cars.
Education in context and the variation of moods throughout Covid-19: An emotional design perspective, using the BRUMS methodology

The year 2020 was marked by the Covid-19 pandemic, where in addition to the whole situation of the danger of the virus, it had several emotional and social impacts. As a result of this scenario, in some places – such as Brazil – higher education classes have migrated to remote spaces. It is worth to notice that the educational sector is an important sector for the market and for society, and one of its metrics for strategic decisions is the understanding of student satisfaction. In turn, emotional design aims to understand the human being from an affective perspective, beyond functional issues. In view of this, this study aims to analyze the fluctuations of students' moods in the educational context, throughout the 2020 pandemic year, in the face of various milestones of the academic cycle, through the self-report tool BRUMS (Brunel Mood Scale) from the perspective of the emotional design. In total, there were 6 data collection moments, in two educational cycles, between April and December 2020, accounting for a total of 144 responses. As a result, it was possible to perceive the fluctuation of students' moods throughout the educational cycle in the pandemic period, evidencing the tension in the test periods, the fatigue during the lockdown, the vigor in the beginning and end of the semester. Anger was the least activated mood throughout the cycle, followed by depression. It was possible to perceive that BRUMS is a tool that helps in understanding mood, throughout the service cycle, enabling decision-making and planning of more user-centered services, in addition to a greater concern in emotional aspects to, in this case, promote a better academic experience in the educational context.
Score-Based Hypothesis Testing for Unnormalized Models

Unnormalized statistical models play an important role in machine learning, statistics, and signal processing. In this paper, we derive a new hypothesis testing procedure for unnormalized models. Our approach is motivated by the success of score matching techniques that avoid the intensive computational costs of normalization constants in many high-dimensional settings. Our proposed test statistic is the difference between Hyv&#x00E4;rinen scores corresponding to the null and alternative hypotheses. Under some reasonable conditions, we prove that the asymptotic distribution of this statistic is Chi-squared. We outline a bootstrap approach to learn the test critical values, particularly when the distribution under the null hypothesis cannot be expressed in a closed form, and provide consistency guarantees. Finally, we conduct extensive numerical experiments and demonstrate that our proposed approach outperforms goodness-of-fit benchmarks in various settings.
A planning structure of water conservancy management based on Artificial Intelligence

The implementation of the project can effectively solve the contradiction of water shortage in urban economic development, change the single water supply pattern of taking water from Shawan channel in the lower reaches of Beijiang River in Nansha District of Guangzhou and Dongjiang River in Shenzhen and Dongguan, improve the water supply safety and emergency standby guarantee capacity, appropriately improve the ecological environment flow in the lower reaches of Dongjiang River in dry season, and contribute to the maintenance of Nansha District of Guangzhou Shenzhen and Dongguan play an important role in water supply security and sustainable economic and social development.
Laplacian Pyramid-like Autoencoder

In this paper, we develop the Laplacian pyramid-like autoencoder (LPAE) by adding the Laplacian pyramid (LP) concept widely used to analyze images in Signal Processing. LPAE decomposes an image into the approximation image and the detail image in the encoder part and then tries to reconstruct the original image in the decoder part using the two components. We use LPAE for experiments on classifications and super-resolution areas. Using the detail image and the smaller-sized approximation image as inputs of a classification network, our LPAE makes the model lighter. Moreover, we show that the performance of the connected classification networks has remained substantially high. In a super-resolution area, we show that the decoder part gets a high-quality reconstruction image by setting to resemble the structure of LP. Consequently, LPAE improves the original results by combining the decoder part of the autoencoder and the super-resolution network.
COBRA-GCN: Contrastive Learning to Optimize Binary Representation Analysis with Graph Convolutional Networks

The ability to quickly identify whether two binaries are similar is critical for many security applications, with use cases ranging from triaging millions of novel malware samples, to identifying whether a binary contains a known exploitable bug. There have been many program analysis approaches to solving this problem, however, most machine learning approaches in the last 5 years have focused on function similarity, and there have been no techniques released that are able to perform robust many to many comparisons of full programs. In this paper, we present the first machine learning approach capable of learning a robust representation of programs based on their similarity, using a combination of supervised natural language processing and graph learning. We name our prototype COBRA: Contrastive Learning to Optimize Binary Representation Analysis. We evaluate our model on several different metrics for program similarity, such as compiler optimizations, code obfuscations, and different pieces of semantically similar source code. Our approach outperforms current techniques for full binary diffing, achieving an F1 score and AUC .6 and .12, respectively, higher than BinDiff while also having the ability to perform many-to-many comparisons.
Interval-Valued Fuzzy Trees and Cycles

Interval-valued fuzzy tree (IVFT) and interval-valued fuzzy cycle (IVFC) are defined in this chapter. We characterize interval-valued fuzzy trees. We also prove that if G is an IVFG whose underlying crisp graph is not a tree then G is an IVFT if and only if G contains only $$ \alpha $$ strong arcs and weak arcs. It is shown that an IVFG G whose underlying crisp graph is a cycle is an IVFC if and only if G has at least two $$ \beta $$ strong arcs.
Who is the Potential Microchip Implant User?

The passive radio frequency identification microchip implants are known to be a subject of intense debate and argument about their advantages and disadvantages. While one group sees the opportunities of microchip implants in healthcare and the positive impact on life, the other group emphasizes a variety of issues related to privacy, impact on the human body, and others. Given recent changes in lifestyle and other areas, it is reasonable to assume that microchip implants are an inevitable part of our future. The purpose of this study was to determine the characteristics and habits of potential future users. Using a sample of survey participants from four European countries, we conducted a cross-sectional study. The regression model was used to test our research question, which aimed to identify predictors of willingness to use microchip implants. The results show that the potential microchip implant user is male, has multiple social media profiles, and has a history of losing wallets, keys, or ID cards. He uses online shopping and group purchases and has a number of loyalty cards. According to the sample results, age is not a significant predictor of microchip implant use.
Developing a Meta-Suggestion Engine for Search Queries

Typically, search engines provide query suggestions to assist users in the search process. Query suggestions are very important for improving user&#x2019;s search experience. However, most query suggestions are based on the user&#x2019;s search logs, and they can be influenced by infrequently searched queries. Depending on the user&#x2019;s query, query suggestions can be ineffective in global search engines but effective in a domestic search engine. Conversely, it can be effective in global engines and weak in domestic engines. In addition, log-based query suggestions require many search logs, which makes them difficult to construct outside of a large search engine. Some search engines do not provide query suggestions, making searches difficult for users. These query suggestion vulnerabilities degrade the user&#x2019;s search experience. In this study, we develop a meta-suggestion, a new query suggestion scheme. Similar to meta-searches, meta-suggestions retrieve candidate queries of suggestions from other search engines. Meta-suggestions generate suggestions by reranking the aggregated candidate queries. We develop a meta-suggestion engine (MSE) browser extension that generates meta-suggestions. It can provide query suggestions for any webpage and does not require a search log. Comparing our meta-suggestions to major search engines such as Google, showed a 17% performance improvement on normalized discounted cumulative gain (NDCG) and a 31% improvement on precision. If more detailed factors, such as user preferences are discovered through continued research, it is expected that user searches will greatly improve. An enhanced user search experience is possible if factors, such as user preference, are examined in future work.
Synchronous Refinement for Neural Machine Translation

Machine translation typically adopts an encoder-to-decoder framework, in which the decoder generates the target sentence word-by-word in an auto-regressive manner.However, the auto-regressive decoder faces a deep-rooted one-pass issue whereby each generated word is considered as one element of the final output regardless of whether it is correct or not.These generated wrong words further constitute the target historical context to affect the generation of subsequent target words.This paper proposes a novel synchronous refinement method to revise potential errors in the generated words by considering part of the target future context.Particularly, the proposed approach allows the auto-regressive decoder to refine the previously generated target words and generate the next target word synchronously.The experimental results on three widely-used machine translation tasks demonstrated the effectiveness of the proposed approach.
Contrast in Cohesion Between Human and Machine Translations

While many consider that machine translation is helpful and handy, a large body of research has revealed that machine translation remains marred by inaccuracy issues.This is a concern, especially when it comes to cohesion as it determines the coherence of the text.However, this area seems to have not caught the attention of translation researchers and remains under researched.This study attempts to investigate the contrast in cohesion of the cohesive devices attempted by machine translation and human translation.Using qualitative method, the findings suggest that while the two analyzed texts contain similar number of cohesive devices, the machine translations of the cohesive devices incur significant accuracy issues which affect the overall coherence of the target text.
Integration of a child with special needs into the educational process as a social and pedagogical problem

The article is devoted to the analysis of factors influencing the integration of children with special needs into the educational process and the relevance of creating a favorable environment in educational organizations. The article considers the legal basis for the organization of the necessary conditions for the integration of a child with special needs, the issue of adaptation, the characteristic features of the impact on the educational process, the features and problems of educational institutions, and the readiness of teachers to implement inclusive education.
Conclusion

Throughout this book, we discussed how Orleans originated to meet the need of cloud development to take advantage of the decentralized structure. In addition, Orleans was created to make decentralized development easier for developers to learn by handling a magnitude of the work with the Orleans framework.
Design and Development of Pipeline of Preprocessing Tools for Kazakh Language Texts

Nowadays, the Kazakh language belongs to the category of less-resourced languages, as there is a small number of resources developed and accessible to a wide range of users, such as text corpora, electronic dictionaries, morphological analyzers, thesauri, which allow to analyze text documents. The aim of this work is the design and development of pipeline of preprocessing tools for media-corpus of the Kazakh language. Media-corpus is hosted by al-Farabi Kazakh National University and serves linguists as an empirical basis for research in the contemporary written Kazakh language. The development of pipeline of preprocessing tools for media-corpus, the lexical and grammatical features of the Kazakh language were analyzed, on the basis of which the composition of the fundamental rules for changing the words (inflection) of the Kazakh language was determined. In the process of research, the tools for generation and lemmatization of the word forms of the Kazakh language were created. The proposed tools can be applied at the stage of morphological analysis in the systems of automatic analysis of the texts, in the creation of thesauruses and ontologies. For the case of the presence of homonymy, the template method was used, which allow to reduce the level of homonymy.
Utilities and Development Dynamics in Early-Stage Government Securities Markets

Government securities market (GSM) development in lower-income economies (LIEs) needs a local roadmap. This research presents models explaining GSM development in LIEs for the road-mapping. The GSM development processes include positive externalities and government intervention, utilities’ role in inducing the investor to trade, the utility-driven cyclicity of a market structure, the growth resistance from a market structure’s carrying capacity, utilities’ embeddedness in a market structure, and the phase-end inertia period. Thus, the models evolve around dynamic relationships between utilities and prices. They help policymakers make a restart for their once-failed market development or a start for their fresh one.
THEORETICAL FUNDAMENTALS OF FUTURE UKRAINIAN AND LITERATURE TEACHER TRAINING FOR ETHNOCULTURAL ACTIVITY

Topical issues of future Ukrainian and literature teachers’ preparations for the ethnocultural activity in general secondary educational institutions, in particular, the introduction of active, personal, cultural approaches in the process of professional training are considered in the article.&#x0D; The implementation of the active approach in the process of training future teachers of the Ukrainian language and literature ensures the development of future teachers’ personal traits and qualities. It influences the formation of their attitude, interests, values, professional positions, etc. The peculiarity of the active approach in the process of preparing future Ukrainian and literature teachers for ethnocultural activity in general secondary educational institutions is primarily in the mastering of the integrative system of knowledge of Ukrainian history, culturology, ethnography, Ukrainian studies, general pedagogy (ethno-pedagogy), general and social psychology, the Ukrainian language and literature and methods of their teaching.&#x0D; The competent approach is aimed at developing the creative potential of the individual both a teacher and a graduate, and also contributes to the formation of the necessary skills, abilities, experience, and behavioral patterns of personality in the learning process. A teacher acts as an organizer of an educational activity and a student is an active independent and creative personality.&#x0D; The organization of the educational process in the context of a personal approach in higher educational institutions involves focusing on the student’s personality, goals, motives, preferences, as well as creating the necessary conditions and taking into consideration the individual experience. In addition, such activity provides the mutual development of a student’s and a teacher’s personalities in the process of their joint activity to promote mutual respect, mutual understanding, increasing the authority of a teacher.&#x0D; The application of the cultural approach contributes to the formation of the basic Ukrainian and literature teacher’s culture. Such a combination of the scientific approaches ensures the organization of the optimal educational process aimed at mastering the content of pedagogical culture, as well as the formation of the teacher’s ethnocultural outlook.
Realization Of Assertive Speech Acts Performed By The National University Debating Championship Grand Final 2020

This study was focused on evaluating the realization of the assertive speech acts performed by the government and opposition teams in the NUDC Grand Final 2020. The analysis included the analysis of assertive speech acts, the comparison between the assertive speech act performed by both teams, and the pedagogical implication toward English education. The results of the study showed that twelve types of assertive speech acts were found in their utterances during their speech, such as asserting, arguing, informing, claiming, predicting, suggesting, stating, criticizing, rebutting, reminding, complaining, and reporting assertive speech act in which stating assertive speech act had achieved significant used during the government teams’ speeches. Meanwhile, arguing and criticizing assertive speech act almost reached the highest number of altogether just slightly below stating assertive speech act which confirmed that aside from the declarative statements employed within the arguments, the opposition team was revealed to show their strong disagreement toward the government team’s arguments. Moreover, the realization of the assertive speech act helps English education students to build their communicative skills by considering the purpose and structure of the assertive speech act. For further study, it is expected that future research could examine the combination of assertive speech act and the other speech acts in the debate since the speakers do not merely use assertive speech acts. Since this study also focused on the pedagogical implication for English education, it would be better if future research could scrutinize the implementation of speech acts during the ESL or EFL classroom.
Practical application of innovative technologies in foreign language classes for engineering students

The purpose of the study is to consider the theoretical aspects of innovative education, as well as to analyze the practical application of innovative technologies in foreign language classes for students of engineering specialties. The article notes that innovative educational technologies are based on three interrelated components: competence-based approach; modern teaching methods; modern learning infrastructure. Scientific novelty lies in an attempt to analyze the innovative practice of teachers and determine the main advantages of innovative technologies in teaching a foreign language to students of technical universities. As a result, such advantages of these technologies were identified as the development of all types of speech activity of students, an individual approach to learning, an increase in motivation for learning a language, the ability to express oneself creatively, the formation of a personality capable of solving professionally oriented tasks.
Developing RGB Image Steganography using Travel Salesman Problem Modelling

Data security is a problem that the public often faces in the era of globalization. The use of the internet in sending and storing identity makes data security more vulnerable. One of the protection techniques, steganography or data hiding by embedding data in media, is developed. This study hides data on RGB color image media employing Travel Salesman Problem (TSP) modeling. In this study, the data are hidden in the three RGB layers to increase accuracy. The experiment uses color images measuring <tex>$512\times 512$</tex> and <tex>$256\times 256$</tex> from the SIPI dataset. The research uses PSNR and pixel similarity to calculate the accuracy of the system. As a result, the system gives the best average PSNR value of 61.56 dB for images with <tex>$512\times 512$</tex> size and an average of 55.47 dB for <tex>$256\times 256$</tex> images. The average pixel similarity value of the system is 96.87&#x0025; for <tex>$512\times 512$</tex> images and 87.48&#x0025; for <tex>$256\times 256$</tex> images.