Reviewerly/exHarmony huggingface dataset:
An Exploratory Study on the Metaverse-Based Learner-Centered English Learning Program: Focusing on Culture and Art Contents

Purpose: The purpose of this study is to develop an English learning program using metaverse that can create a variety of educational environments in a virtual space in the post-COVID era. This study was conducted with culture and art as the content subject, and it was designed so that students could experience various types of metaverse technology and mixed reality to increase their participation in learning and the learning effect. It also introduces effective online learning tools that can be used in metaverse-based English learning programs. Method: Students improve their English skills by examining museums with Google Earth and Art&Culture and exploring exhibits and materials related to Korean cultural assets there, exploring Korean cultural assets and art galleries using VR and AR, and collecting related cultural and artistic materials. . In addition, through the process of creating and presenting materials based on the metaverse, interaction was promoted, so that the understand-ing of each country's culture and arts and English ability could be improved together. Results: The English learning program presented in this study consists of various tasks such as experiencing culture and art contents through AR and VR, collecting cultural and art-related materials, and producing presen-tation materials. Roughly speaking, in the introduction part, learning objectives and key expressions are intro-duced by using the functions of the metaverse platform ZEP and the online learning tool 'edpuzzle', and motiva-tion is induced through videos. In the development stage, AR and VR-based apps such as ZEP, Google Earth, Art & Culture, etc. are used to visit museums in Korea or British and American countries, research related culture and art, and collect data. Then, by specifying the topic for each group, use ZEP, Google Earth, etc. to produce mate-rials that can be presented. After that, practical presentations using ZEP and feedback (instructors and colleagues) are provided. In the final organizing stage, there may be quiz solving using QuizN and sharing opinions about the class through online learning tools such as Jamboard and Padlet. Conclusion: In this program, ZEP, a metaverse platform capable of interactive communication such as various avatars, virtual theme spaces, images, and PDFs, was used to enable task execution and practical interaction. In addition, by using various online learning tools in a new environment and context, students will be able to develop self-directed learning skills, problem-solving skills, and digital literacy skills. Through this metaverse-based Eng-lish learning program, learners will be able to experience various forms of cultural arts and realistic content, and furthermore, they will be able to participate in convergence cultural thinking expansion and future-oriented clas-ses.
Fair Benchmark for Unsupervised Node Representation Learning

Most machine-learning algorithms assume that instances are independent of each other. This does not hold for networked data. Node representation learning (NRL) aims to learn low-dimensional vectors to represent nodes in a network, such that all actionable patterns in topological structures and side information can be preserved. The widespread availability of networked data, e.g., social media, biological networks, and traffic networks, along with plentiful applications, facilitate the development of NRL. However, it has become challenging for researchers and practitioners to track the state-of-the-art NRL algorithms, given that they were evaluated using different experimental settings and datasets. To this end, in this paper, we focus on unsupervised NRL and propose a fair and comprehensive evaluation framework to systematically evaluate state-of-the-art unsupervised NRL algorithms. We comprehensively evaluate each algorithm by applying it to three evaluation tasks, i.e., classification fine tuned via a validation set, link prediction fine-tuned in the first run, and classification fine tuned via link prediction. In each task and each dataset, all NRL algorithms were fine-tuned using a random search within a fixed amount of time. Based on the results for three tasks and eight datasets, we evaluate and rank thirteen unsupervised NRL algorithms.
A Study on the Outcomes by and Constraints of the Change in International Commercial Terms

Recently, international commerce is expanding and logistics costs are rising rapidly. In this regard, the Korea International Trade Association recommends changing the international commercial terms under incoterms as a way to reduce logistics costs for import/export companies. This study tried to examine the constraint factors that appear in the process of changing international commercial terms.&#x0D; In order to achieve the research purpose, this study composed questionnaire items by conducting previous studies, expert interviews, and pilot studies. The questionnaire was distributed to random sample companies based on the list of member companies such as the Korea International Trade Association, the Korea Trade-Investment Promotion Agency, and the Korea Small and Medium Business Association. The survey period was from March 2, 2022 to June 30, 2022. About 1,200 copies of the total questionnaire were distributed and 244 copies were used for empirical analysis, and PLS-SEM was performed to derive the results.&#x0D; The results of this study are as follows. First, It was found that the internal rigidity and lack of expertise in determining international commercial terms in trade companies had a negative impact on the change of international commercial terms. Second, It was found that the change of international commercial terms in import/export companies improves trade performance.
Analysis of Korean-Spanish machine translation performance based on Google Translate and Naver Papago

This article performs performance evaluation and error analysis of the Korean-Spanish translation service engine. Unlike previous studies, the research is conducted by diversifying the types of texts to be studied and dividing them into texts with and without official translations. The translation services for study are Google Translate and Naver Papago. The translation engine performance evaluation uses BLEU score, which is one of the types of automatic evaluation, and as a result, both translation services showed relatively low evaluation scores. Also, in the case of error analysis, it is divided into four types: words, grammar, sentences, and typos. As a result of the error evaluation, word-related errors were seen the most, and the word substitution phenomenon was particularly prominent. In the case of word errors, it is characteristic that grammatical errors have a great influence on the meaning transfer of the translation, whereas there are often cases where it does not have a great influence on the meaning transfer of the translation. This study is significant in that it suggested the future direction of the development of Korean-Spanish machine translation through the evaluation of the Korean-Spanish translation service.
The Idioms and Culture-Specific Items Translation Strategy for a Classic Novel

A translator must have a thorough grasp of a language and its culture in order to determine the most appropriate interpretation while being true to the culture's intent. They must understand the translation technique and category to maintain consistency while translating cultural texts such as idioms and culture-specific items. This study explored translation procedures for idioms and culture-specific items, as well as their categories, in light of these considerations. The researcher did a descriptive qualitative study utilizing two instruments from Lucy M. Montgomery's original novel "Anne's House of Dreams," which was first published in 1917 and had 275 pages. Meanwhile, Maria M. Lubis released a 275-page Indonesian translation in 1998. According to the findings, there were 146 data points for idioms and 26 data points for culture-specific things. The most common translation approach utilized was Translation by Paraphrase, and the least common was Translation by Omission, according to the initial statistics on idioms. Meanwhile, the second set of data on Culture-specific Items revealed that Transfer was the most regularly utilized translation approach, while Deletion was the least. Finally, the most often used category for classification was Ecology. Overall, we believe that this study will aid readers in comprehending translation.
Playing with the Strings: Designing Puppitor as an Acting Interface for Digital Games

Interactive drama has focused on how to allow the player agency in influencing their experience through plot action. Interactive drama's preoccupation with changing plot structure bears little resemblance to theater's emphasis on character expression and dramatic play. Dramatic play allows the player to embody the character through actions, focusing on how characters express themselves and react rather than on how or even if that impacts the overarching sequence of events. In this paper, we present Puppitor, a system for character expression of emotion for interactive storytelling built using acting practice and fighting games as the foundation for its core design and describe its usage in conjunction with Ren'Py to facilitate a novel interactive narrative experience as a case study.
Global institutional transformations and modern educational and scientific strategies for the paradigm of sustainable development of society

The article considers the processes of democratization of social institutions of education and science on the basis of the world-system global-stadial concept developed by the author and presented in his earlier works as an institutionalized process of functioning of deliberative socioeconomic development in the institutional model of globalization. The author considers “The 2030 Agenda for Sustainable Development” (UN) and the Global Action Program “Education for Sustainable Development” (UNESCO) as key documents shaping the tasks of further development of these social institutions. It highlights the key educational concepts for the transformation of the global world and shows how they correspond to the goals and objectives of these key international documents; substantiates specific directions for key areas of the educational goal of global sustainable development (for all levels of education), which should be included in the education and training system, formulates the tasks of further transformation of educational institutions; states that the principles of education defined in these global programs are most successfully implemented today within the framework of the “Education for Sustainable Development” model, which allows the formation of such key competencies as a sustainable lifestyle, knowledge of human rights and mechanisms for their observance, gender equality, promoting a culture of peace and non-violence and recognizing cultural diversity.
Hybrid convolution network for medical images processing and breast cancer detection

In this paper, the breast cancer detection problem using convolutional neural networks (CNN) is considered. The review of known works in this field is presented and analysed. Most of them rely only on feature extraction after the convolutions and use the precision of classification of malignant tumors as the main criterion. However, because of the huge number of parameters in the models, the time of computation is very large. A new structure of CNN is developed — a hybrid convolutional network consisting of convolutional encoder for features extraction and reduction of the complexity of the model and CNN for classification of tumors. As a result, it prevented overfitting the model and reduced training time. Further, while evaluating the performance of the convolutional model, it was suggested to consider recall and precision criteria instead of only accuracy like other works. The investigations of the suggested hybrid CNN were performed and compared with known results. After experiments, it was established the proposed hybrid convolutional network has shown high performance with sensitivity, precision, and accuracy of 93,50%, 91,60%, and 93%, respectively, and requires much less training time in the problem of breast cancer detection as compared with known works.
Reference architecture design for computer-based speech therapy systems

With the current international shortage of speech-language pathologists (SLPs), there is a demand for online tools to support SLPs with their daily tasks. For this purpose, several online speech therapy systems (OSTSs) have been proposed and discussed in the literature. However, developing these OSTSs is not trivial since it involves the consideration of various functional and quality concerns. Hence, for communicating the design decisions and guiding the development and analysis of these systems, a proper architecture design is important. Unfortunately, the architecture design of OSTSs has not been explicitly addressed in the literature. To this end, we present a reference architecture for OSTSs which has been designed following well-established architecture design methods. The reference architecture captures the reusable design elements of OSTSs and can be used to derive various different application architectures. A case study approach is used to illustrate and validate the use of the presented reference architecture.
Research on the filming techniques and promotion strategies of Youtube's popular horror short movies

In terms of film genres, horror is a very specific one that requires some very specific lighting techniques. This paper analyzed some determining elements in some successful horror short videos that helped to win ideal number of views, and proposed a promotion strategy that could be used to boost the wide spread of YouTube horror short films. The research can contribute to discussions on how to integrate shooting techniques and desired promotion strategies in terms of online short horror videos.
Development of Desktop Application with Three Dimensional (3D) Pageflip Professional Based on Network Design and Construction Class XI TKJ

This research and development aims to produce products in the form of E-Modules or electronic modules based on three dimensions (3d) Pageflip Professional in NETWORK DESIGN AND BUILDING Subjects (RBJ) Class XI Computer and Network Engineering (TKJ) at SMK Negeri 2 Padang. After the validity test has been carried out by the experts, the overall assessment of the validator test on the E-Modul or electronic applicationbased on three dimensions (3d) Pageflip Professional in the NETWORK DESIGN AND BUILDING (RBJ) Subject is 89.94%, so that the level of validity can be interpreted as valid. . The results of the overall practicality test assessment of the E-applicationbased on 3d Pageflip Professional in the NETWORK DESIGN AND BUILDING (RBJ) Eye is 89.50%, so that the practicality level can be interpreted as very practical to use. The results of the overall effectiveness test assessment of the effectiveness of the 3d Pageflip Professional-based E-Modul in NETWORK DESIGN AND BUILDING (RBJ) subjects are 90.63%, so that the level of effectiveness can be interpreted as very effective to use.
Game System Models: Toward Semantic Foundations for Technical Game Analysis, Generation, and Design

Game system models introduce abstractions over games in order to support their analysis, generation, and design. While excellent, models to date leave tacit what they abstract over, why they are ontologically adequate, and how they would be realized in the engine underlying the game. In this paper we model these abstraction gaps via the first-order modal mu-calculus. We use it to reify the link between engines to our game interaction model, a player-computer interaction framework grounded in the Game Ontology Project. Through formal derivation and justification, we contend our work is a useful code studies perspective that affords better understanding the semantics underlying game system models in general.
Adventures of AI Directors Early in the Development of Nightingale

Players can sometimes engage with parts of a video game that they do not enjoy if the game does not try to adapt the experience to the player’s preference. AI directors have been used in the past to tailor player experience to different people. In industry, AI directors are relatively uncommon and are typically domain-specific and rules-based. In this paper, we present a reinforcement learning-based AI director developed for the industry game Nightingale with the help of Inflexion Games. We ran an experiment to evaluate the effectiveness of the AI director in creating a desired player experience, but found inconclusive evidence. In line with this year’s theme, we present our negative results and their implications for future AI directors, along with general discussion from working closely with an industry partner.
Security of the traditional quantum key distribution protocols with finite-key lengths

Quantum key distribution (QKD) in principle can provide unconditional secure communication between distant parts. However, when finite-key length is taken into account, the security can only be ensured within certain security level. In this paper, we adopt the Chernoff bound analysis method to deal with finite-key-size effects, carrying out corresponding investigations on the relationship between the key generation rate and security parameters for different protocols, including BB84, measurement-device-independent and twin-field QKD protocols. Simulation results show that there exists a fundamental limit between the key rate and the security parameters. Therefore, this study can provide valuable references for practical application of QKD, getting a nice balance between the key generation rate and the security level.
The Influence of the Parent Family on the Formation of Labor Values of Students

Labor values play a significant role in people’s lives, influencing the perception and evaluation ideas about various aspects of labor activity and shaping labor behavior. In the context of social transformations, the issue of the role of social institutions in the formation of the value consciousness of the younger generation, including in the field of work, is of particular relevance. The influence of the parent family is one of the most important factors influencing this process. The article presents the results of the author’s sociological study of the role of the family in the formation of labor values among students. The results of the survey indicated similarities and differences in the value orientations of students and their parents in the field of work. An increase in the degree of family influence on the choice of profession by young people has been established. For both students and their parents, the content aspect turned out to be a priority in the work. For the older generation, the social component of work is more important in comparison with children. For young people, the opportunities for personal self-realization and the material side of work are more significant than for their parents.
Coordinated and specific restricted Boltzmann machine for cross-modal retrieval

With the rapid growth of multimodal web data, the task of cross-modal retrieval, i.e., using a text query to search for images or vice versa, has attracted a lot of attention from researchers. Existing approaches usually learn a common representation space where different modalities can be directly compared. However, little work has been done to verify that the learned common representation space contains only common part shared between different modalities. In this paper, we present a coordinated and specific restricted Boltzmann machine (a.k.a. CSRBM) that can distinguish the common part from modality-specific part of different modalities. The proposed CSRBM consists of two RBMs, each with two hidden layers. The common hidden layer learns the common patterns shared within different modalities. And the modality-specific hidden layer learns the modality-specific patterns owned by individual modalities. To verify the split effectiveness of our proposed model, we construct a multimodal dataset based on the popular MNIST dataset. Moreover, we evaluate our model on three publicly real-world datasets with the task of cross-modal retrieval. The extensive experiments demonstrate the effectiveness of our CSRBM.
Multi-granularity information enhancement text sentiment classification model integrating attention mechanism

Aiming at the sparse features of Chinese short texts, a multi-granularity information-enhanced text emotion classification model is proposed, which combines convolutional neural network and attention mechanism. First, fine-tune BERT model for specific tasks to obtain different granularity information contained in different layers, and express the text at sentence level as character-level vector. Then, using the feature of CNN with local semantic feature extraction, the multi-granularity local semantics of each layer of BERT is extracted with different scale convolutional kernels to obtain the multi-granularity local semantic representation under different layers. Considering that CLS at different levels of BERT contains global information at different levels of the text, it integrates and interacts with multi-granularity local semantic representation through attention mechanism to obtain multi-granularity deep semantic information. Finally, the final global semantic representation of BERT is fused with it to obtain the final text representation, which is input to the classifier for classification. The experimental comparison between this model and several models shows that the indexes of this model on two real data sets are obviously improved compared with the comparison model, which proves the effectiveness of this model.
A survey of argumentative essay element and relation extraction in essay knowledge graph construction

Knowledge extraction is a key task in the construction of essay knowledge graphs. This paper summarizes and analyzes the progress of research related to element and relation extraction, using discourse structure as the knowledge level. We find that existing methods treat element and relation extraction as two independent tasks, both relying on contextual information, without considering the semantic interaction between the two tasks. Thus, we propose the joint extraction of elements and relations as a novel task, migrate the joint method of entity-relation extraction task to this task, and focus on the research advances of the joint method to summarize.
Exploring neural architecture search for text classification

Recent years have witnessed the impressive progress in applying well-designed neural networks to text classification tasks, due to their capabilities of extracting semantic information from text. However, designing such a neural network often requires a lot of expertise and repeated testing. In this paper, we aim to overcome this problem by exploring automatic neural architecture search (NAS) for text classification. We propose a genetic algorithm based one-shot neural architecture framework to automatically generate proper neural architectures for specific tasks or datasets without further re-training. The experimental results show that, for text classification, our searched neural networks achieve competitive accuracy compared with hand-crafted networks and other NAS algorithms. Our models also show promising results when transfer to some large datasets.