Reviewerly/exHarmony huggingface dataset:
Comparing Mediated and Unmediated Agent-Based Negotiation in Wi-Fi Channel Assignment

Channel allocation in dense Wi-Fi networks is a complex problem due to its nonlinear and exponentially sized solution space. Negotiating over this domain is a challenge, since it is difficult to estimate opponent’s utility. Based on our previous work in mediated techniques, we propose the first two fully-distributed multi-agent negotiations for Wi-Fi channel assignment. Both of them use a simulated annealing sampling process and a noisy model graph estimation. One is designed for Alternating Offers protocols, while the other uses the novel Multiple Offers Protocol for Multilateral Negotiations with Partial Consensus (MOPaC), with experimental promising features for our particular domain. Our experiments compare both proposals against their mediated counterparts, showing similar results on social welfare, Nash product and fairness, but improving privacy and communication overhead.
STEAM project "The secret of the Aeolian Harp. In the footsteps of the Bernardazzi brothers"

The article discusses aspects of organizing extracurricular activities in informatics in secondary school using the STEAM approach. This approach is implemented through various forms of education and promising areas of digital technologies. One of the forms of education is the STEAM project — a system of interconnected blocks built on the basis of the principle of interdisciplinarity and aimed at solving the identifed problem. The article provides an example of the implemented interdisciplinary project aimed at developing in schoolchildren the ability to work with information, the ability to solve creative problems, and the formation and development of students' information culture. STEAM project "The secret of the Aeolian Harp. In the footsteps of the Bernardazzi brothers" is dedicated to the Year of Cultural Heritage of the Peoples of Russia, which was announced in Russia in 2022. During the STEAM project, schoolchildren solve problematic tasks and problematic situations with interdisciplinary content, practice-oriented tasks, and research tasks.
A Transitivity Analysis of the Character's Actions in Joseph Jacobs's Fairy Tales

This study aims at examining the transitivity processes occurred in the characters’ action in Joseph Jacobs’s Fairy Tales. Language style for children literature has its distinction from other fictions. Such lexical and the dictions selection become its focus primarily at the main figure’s action. The purpose of this study is not only to provide worthwhile information particularly at the language style of literature for children in case of Joseph Jacobs’s fairy tales but also to remedy the neglected focus on the earlier studies which analyzes the mentioned object as well. This study used the methodology of descriptive qualitative research. To analyze the research objective, this study used Halliday’s transitivity theory which examines the processes of a sentence. Based on the data analysis, the results found 56 data in total contained with several processes as follows; 30 material processes, 5 mental processes, 5 behavioural processes, 4 verbalization, 3 relational processes, and an existential processes.
Transitivity Analysis of Welcome to Sydney: A Systemic Functional Linguistics Approach on Tourism Text

The goal of this study is to identify the different forms of transitivity processes in the tourism text "Welcome to Sydney," as well as the most common types of the process employed in the text. This study is carried out by utilizing a quantitative descriptive method and a content analysis approach. The data was collected from a tourism text with the title "Welcome to Sydney," which is located on the internet. Halliday and Matthiessen developed the theory that was applied in this study. Based on the analysis, the "Welcome to Sydney" text contains 0 (0%) clauses that indicated material process, 2 (11,76%) clauses that indicated mental process, 3 (17,77%) clauses that indicated verbal process, 8 (47,56%) clauses that indicated relational process, 0 (0%) clauses that indicated behavioral process, and 4 (23,53%) clauses that indicated existential process. As a result, it assumed that the dominating types of processes discovered in this text were relational processes. According to this study, the "Welcome to Sydney" text tries to utilize the relational process to describe and inform readers about Sydney.
Cyberbullying Analysis on Instagram Using K-Means Clustering

Social Media, in addition to having a positive impact on society, also has a negative effect. Based on statistics, 95 percent of internet users in Indonesia use the internet to access social networks. Especially for young people, Instagram is more widely used than other social media such as Twitter and Facebook. In terms of cyberbullying cases, cases often occur through social media, Twitter, and Instagram. Several methods are commonly used to analyze cyberbullying cases, such as SVM (Support Vector Machine), NBC (Naïve Bayes Classifier), C45, and K-Nearest Neighbors. Application of a number of these methods is generally implemented on Twitter social media. Meanwhile, young users currently use Instagram more social media than Twitter. For this reason, the research focuses on analyzing cyberbullying on Instagram by applying the K-Mean Clustering algorithm. This algorithm is used to classify cyberbullying actions contained in comments. The dataset used in this study was taken from 2019 to 2021 with 650 records; there were 1827 words and already had labels. This study has successfully classified the tested data with a threshold value of 0.5. The results for grouping words containing bullying on Instagram resulted in the highest accuracy, which is 67.38%, a precision value of 76.70%, and a recall value of 67.48%. These results indicate that the k-means algorithm can make a grouping of comments into two clusters: bullying and non-bullying.
Mathematical Description of the Network Field Model

In this addendum, which was the basis for an article published at the network conference 2021, we discuss a mathematical description of a network field. We describe the exchange of capital between objects in a team which we call a network. We make the assumption that exchanging capital between the actors in the field is the same as exchanging kinetic and potential energy. In our model, we use three types of capital: financial, human, and social to represent the qualifications of an object. By analogy, a non-relativistic gravitational field can be described by a time dependent Kinetic Energy part minus a position-dependent Potential Energy part. Here we describe a non-relativistic network field as Lagrangian with a time-dependent Financial Capital part minus a relative position-dependent Potential energy part. The description of the network field and especially the potential energy for a certain area in the field is comparable to the description of a Graph Neural Network for a set of nodes, a concept from deep learning theory. We use the Graph Neural Network to analyze the effects of exchanging potential energy in a network. We also use it to calculate the optimum distribution of qualifications of the actors in a team.
Sign2Sign: A Novel Approach Towards Real-Time ASL to ISL Translation

Sign language is the main form of communication for people with speech and hearing imparity. There are over 300 types of sign languages across the world, with American Sign Language (ASL) and Indian Sign Language (ISL) most popularly used in the United States of America and India, respectively. When a person familiar with ASL has to communicate with a person familiar with ISL, there is bound to be a communication gap and calls for the need of skilled translators. We aim to automate this process by proposing a novel solution that can translate real-time ASL input into ISL. The proposed methodology uses a CNN trained to recognize 36 different classes of ASL signs. The recognized signs are mapped to the respective ISL signs and joined together to form a video. The CNN model for recognizing ASL achieved a testing accuracy of 96.43%.
The Qualitative Case Study on the Empowerment of College Professors Experiencing Distance Learning : Focused on F&amp;B Subjects

The purpose of this study is to identify the changed empowerment experience and meaning of distance learning through the qualitative case study on professors conducting food and beverage practical classes, and to find ways to use teaching empowerment in future remote learning. The results of this study are as follows. First, it is said that there were many difficulties in preparing classes and knowing distance learning programs to adapt to teaching styles that most professors did not experience. Second, professors continuously try to encourage participation and communication to induce class concentration and students' independent learning during distance learning classes. The results of this study will improve the teaching empowerment of professors who conduct remote learning in food and beverage practical subjects in the future, to overcome the limitations of classes as much as possible and improving the learning effect of students.
Study on Consumers Perceptions on “Protein Supplements” Using Big Data Analysis

This study aimed to examine consumers perceptions on protein supplements which have newly attracted attention by using a big data analysis. To this end it collected data about the protein supplements form a variety of portal sites and drew a total of 50 key words from the data collected by using a morphological analysis. It categorized the key words into the final four constituent nodes by using the CONOCR analysis a group analysis. On the basis of such findings this study provided the right method of taking protein supplements as a final suggestion based on consumers perceptions on them.
Analysis of Factor in User Intention to Use the Covid-19 Tracking Application

Science and technology can be collaborated to create an application that can help to track the contacts of COVID-19 patients. Smartphone-based contact tracing applications have been adopted by more than 50 countries. One of which is in Indonesia. In March 2020, Indonesia launched a mobile application to track the contact of COVID-19 patients namely PeduliLindungi. During its usage, users find some issues about PeduliLindungi, such as potential data leaks, data misuse, and data inaccuracies. This research is aimed to develop a conceptual model to analyze factors that affect user intentions in using the PeduliLindungi application. The proposed conceptual model is the integration of EUCS, DeLone and McLean that is equipped by the system security variables. There were 288 respondents. The data is processed using SmartPLS 3.0. According to the results of the analysis, the proposed conceptual model has 83.1 percent for its accuracy. User satisfaction and system security give a positive and significant impact on user intentions. The variables of content, accuracy, format, ease of use, and timeliness give a positive and significant impact on user satisfaction. On the other hand, the system security has no positive and significant impact on user satisfaction. Meanwhile, user satisfaction and system security itself affects the user's intentions in using the PeduliLindungi application.
A Study on the Experiences of Byeongjahoran Composed of Jo Moon-soo"s Poems

전직 관료였던 조문수는 병자호란 발발 직후 인조를 호종하여 남한산성에 들어갔다. 시임 대신도 임금을 버리고 부모를 모시고 피난을 가던 상황이었다. 45일에 걸친 농성전에서 독전어사의 임무를 수행한 조문수는 자신의 전란 체험을 11수의 칠언절구로 남겼다. 병자호란을 남한산성 위주로 잘 정리한 이 시편들에는 충신으로서의 자기 현시와 복잡한 정치 현실에 놓인 곤혹스러운 처지가 잘 드러나 있다. 매일 시시콜콜한 사정을 드러내는 일기나 기사(記事) 같은 산문보다는 축약, 상징, 비유 등이 가능한 짧은 시편이 적당한 표현 매체였다. 강화도 함락에 따른 가족 이산, 패전의 책임, 이국 땅에서의 인질 생활 등 병자호란에서 배태된 다양한 상처와 어려움에서 배제된 관료 시인 조문수는 한유와 두보를 적절하게 인용하고, 중국의 역사를 원용함으로써 자신의 곤란한 상황을 잘 정리했다. 시로 이루어진 조문수의 병자호란 체험담에서 겉으로 드러난 절절한 정서와 별개로 주욕신사(主辱臣死)의 상황에서 그가 원만하게 처신하고 있음을 엿볼 수 있었다. Jo Moon-soo, a former official, entered Namhansanseong Fortress while protecting the king immediately after the outbreak of the Byeongja Horan. Incumbent officials were also evacuating with their parents instead of wages. Jo Moon-soo, who served as a general"s assistant in the battle that took place for 45 days, summarized his war experiences into 11 short poems. He showed his own loyalty and clarified his plight in the complex political realities. Because of that purpose, rather than diary or story-type prose that requires detailed situations, short poems that can be abbreviated, symbolized, and metaphors were a suitable medium of expression. After the fall of Ganghwa Island, Jo Moon-soo was able to avoid the damage of his family being separated, the responsibility of losing the battle, and the life of hostage in a distant foreign country. However, he couldn"t show his spontaneity. So, he appropriately cited Han Yu and Du Fu, and made use of historical events in China to create a track record that was not criticized by anyone.
The GRaffa project: final considerations after one year of activities

GRaffa is a project designed and carried out by the Associazione Giovanile Le Scie Fisiche, funded by the Lazio Region, with the aim of sharing the excitement of science and increasing appreciation for it, mainly through daily experiences of physical phenomena.It was conceived by young physics and philosophy researchers (mainly postdocs and PhD students) to spread physics and scientific culture by using a simple and fun language dedicated to the general public, especially school students.The main goal of the activities realized within the project is to allow people to understand that physics is not just the study of distant galaxies, particle accelerators or complex mathematical formulas.When we use the computer or decide what is the right oven temperature for baking our cupcakes, we are already doing science!Here we detail the results achieved with the GRaffa project over the last year, as well as a report of a survey carried out with students and teachers involved in the project's activities.
Research on Cloud Computing Task Scheduling Based on PSOMC

How to better reduce the task scheduling time and consumption cost in cloud computing has always been a hot topic of current research. In this paper, we propose a cloud computing task scheduling strategy based on the fusion of Particle Swarm Optimization and Membrane Computing. Firstly, a task scheduling model with time function and cost function as the target is proposed, secondly, on the basis of particle swarm algorithm, chaos operation is used in population initialization to improve the diversity of rich understanding, adaptive weight factor based on sinusoidal function is used to avoid the algorithm falling into local optimum, Membrane Computing is used in individual screening to improve the quality of individual solutions, and finally, in The performance of the PSOMC algorithm is illustrated by comparing six benchmark test functions in simulation experiments, and it is also verified that the completion time and consumption cost are significantly better than those of the ACO, PSO and MC algorithms for different number of tasks.
Sequence Encoder-based Spatiotemporal Knowledge Graph Completion

Knowledge graph (KG) completion aims to infer new facts from incomplete knowledge graphs. Most existing solutions focus on learning from time-aware fact triples and ignore the spatial information. In reality, knowledge graphs can evolve with time as well as the changing locations, such as the flight domain. Therefore, integrating spatiotemporal information into knowledge graph representation is important for the knowledge graph completion. To address this problem, this paper proposes two Spatio Temporal-aware knowledge graph completion models based on the Sequence Encoder, namely STSE and S-TSE, which incorporate the spatial and temporal information into relations. Specifically, the model consists of two steps: spatiotemporal-aware relation encoding and final scoring function evaluation. The first stage composes the spatiotemporal information into different tokens. Then two methods are proposed to obtain the embedding of spatiotemporal-aware relation by utilizing the Recursive Neural Network. The second stage proposes different scoring functions for two models. Empirically evaluation of the proposed models is conducted on spatiotemporal-aware KG completion task on two public datasets. Experimental results demonstrate the effectiveness of the proposal for spatiotemporal knowledge graph completion.
Robust Motion Pose Capture Method of Aerobics Based on Visual Simulation Imaging

During the exercise of bodybuilders, the accuracy of capturing is reduced due to the complexity of human joint motion and the fast speed of movement. In order to effectively capture the movement posture of Aerobics Athletes in real time, a method of Aerobics posture acquisition based on visual simulation imaging is proposed. Through the visual simulation imaging method, collect data of Aerobics Athletes' postures, and extract the characteristic data by denoise and windowing processing, establish the aerobics body position structure recognition model, and achieve the acquisition of Aerobics body position. The experimental results show that the motion posture prediction accuracy of the proposed method is high. And it can minimize the interference caused by the magnetic field and capture the posture of the athletes in real time. It shows that this method has certain application value.
Predicting Likeliehood of Patients Survival After an Accident Using Statistical Regression

The purpose of this study is to develop a model employing regression techniques and deep learning to predict the likelihood of survival for patients hospitalized with illnesses typically observed in intensive care units (ICVs). Survival models are a sort of statistical technique that is commonly employed in clinical research. The objective of these models is to identify potential risk variables and forecast the risk of various clinical outcomes, such as the overall survival of patients with a variety of diseases The results show that our model can outperform the CPH prediction model when it comes to predicting the prognosis of CCV patients.
Auxiliary Detection Method of Illegal Actions in Football Teaching Based on Binocular Vision

Football teaching videos have high dynamic, which leads to poor detection performance of football teaching violations. In order to solve this problem, an assistant detection method of soccer teaching violations based on binocular vision is proposed. Using binocular vision technology to collect football teaching images and extract their image features. Through the collected image texture features to achieve image matching, determine the key frame image, and realize the auxiliary detection of football teaching violations. The experimental results show that this method can obtain accurate football teaching target positioning results, realize the accurate detection of football teaching violations, and provide reference value for the future football teaching.
Sentiment analysis for emotional navigation in written communication

A subfield of artificial intelligence is that of natural language processing and sentiment analysis. The interest in sentiment analysis has increased with the development of the internet and web 2.0. With sentiment analysis it is possible to analyse the sentiment or emotions of written communication through dictionary-based sentiment analysis or machine learning algorithms. However, sentiment analysis also holds the potential of supporting people with disadvantages in interpreting the nuances in written communication. One such group is autistic people. The aim of the study is to examine autistic peoples’ perceptions of important design factors and functionality for an application with sentiment analysis to support emotional navigation in written communication.&#x0D; This study has been conducted with the first steps of design science to outline the requirements of a potential application that can support autistic people to navigate the emotions in written communication with sentiment analysis. The problem to be addressed was identified through related research and one of authors’ own experience of navigating written communications with autism. The requirements for the application were the main focus for this study and has been the primarily concern for data collection. Data have been collected through semi-structured interviews with autistic people and analysed with thematic analysis.&#x0D; Results of the study provide several important recommendations for the design of applications with sentiment analysis to support autistic people navigate the emotions in written communication. The study further provides an understanding of autistic peoples’ needs when navigating written communication. These findings can be used by researchers and developers to design support-applications with autistic peoples’ needs in the centre. An interesting next step of research would be to develop a prototype with the findings of this study addressed in the application’s functionality and design, which could then be evaluated on a larger scale.
Evaluation of a SPOC for online teaching: the case of the #Dienlínea course of the International University of Andalusia

This paper presents the results of an innovative teaching training experience developed by a Spanish public university, the International University of Andalusia (UNIA), under the title “Training for innovative teaching of online programs” and in the format of a SPOC course. Focusing on the expectations and previous training needs, on the one hand, and on the results in terms of participants’ perception, on the other, we analyze the results of two editions, developed between 2020 and 2021, in which more than 302 professionals linked to education and training have participated as students.
Recommendation System to Propose Final Project Supervisors using Cosine Similarity Matrix

The selection of a supervisor is an important thing and one of the determinants of whether or not a student's final project research is successful. At the location of this research, students select a supervisor by considering his academic records, and recommendations from classmates or seniors. Words of mouth dominate their motivation, and many students do not have a basis for their choice. Selection of the fit supervisor has a significant impact on students' progression. Students will be more enthusiastic about doing the final project and may get facilitation in their research because the topics of students' projects match supervisors' interests and ongoing works. This study aims to make a recommendation system that suggests a supervisor for a student. The student fills in the title, abstract, and keywords of his proposal. The application proposes a prospective supervisor by calculating the similarity of the data with titles, abstracts, and keywords of published articles found in Google Scholar. This recommendation system uses the content-based filtering method to produce a list of recommendations. The cosine similarity algorithm calculates how similar the topic proposed by students is to the lecturer's interests. In building a website-based recommendation system, the author uses two Django web frameworks as the backend and ReactJs as the frontend. The system is successful in suggesting final project supervisors that have matched interest and expertise with students' proposals.
Directing Tangible Controllers with Computer Vision and Beholder

We present Beholder, a computer vision (CV) toolkit for building tangible controllers for interactive computer systems. Beholder facilitates designers to build physical inputs that are instrumented with CV markers. By observing the properties of these markers, a CV system can detect physical interactions that occur. Beholder provides a software editor that enables designers to map CV marker behavior to keyboard events; thus connecting the CV-driven tangible controllers to any software that responds to keyboard input. We propose three design scenarios for Beholder—controllers to support everyday work, alternative controllers for games, and transforming physical therapy equipment into controllers to monitor patient progress.