Reviewerly/exHarmony huggingface dataset:
Development of Podcast Spotify-Based Learning Media in Hörverstehen Course Level A2

The European Curriculum Framework or GER (Germanistische Europäische Rahmen) German language proficiency levels are divided into several groups, namely A1- A2 – B1 - B2 – C1 – C2. This requires students to master the four language skills (Speaking, Writing, Reading and Listening). Hörvestehen courses equivalent to level A2 are courses that practice listening skills. In the Hörverstehen Level A2 course, there are still many students who do not pass the listening ability (Hören). This demands that it is necessary to develop Learning Media in the Höverstehen Level A2 course. The Learning Media developed is packaged through learning media based on the Spotify Podcast, Podcast Spotify is a learning media that can be accessed anywhere, anytime and interestingly by integrating aspects of language skills according to the European Curriculum Framework or GER (Germanistische Europäische Rahmen). So, with Podcast Spotify will be able to convey the content of learning Hörverstehen Level A2 to students in the German Language Education Study Program. Therefore, the development of Spotify Podcast-Based Learning Media in the Höverstehen Level A2 course includes 6 themes, namely Leben und Lernen in Europa, Freizeit und Hobbys, Medien im Altag, Vom Land in die Stadt, Kultur erleben and Arbeitswelten. This development aims to develop learning media based on the Spotify Podcast in the Hörverstehen Level A2 Course in the German Language Education Study Program. The target of this Spotify Podcast-based learning media is to improve students' listening skills in learning German Level A2. In developing this Spotify Podcast-based learning media using the ADDIE model development method. In making Audio, the data was taken from the Studio Express A2 book and which was further developed in the form of the Spotify Audio Podcast.
Developing the SibMed information system through automating the educational program preparation processes

The article discusses university information system (IS) development as a digital transformation process. It is noted that automating the development and implementation of educational programs (EP) provides a basis for educational environment digitization. Moreover, it is the main criterion of the extent of the university’s digital transformation.The article analyzes the current state of educational environment digitization at universities and highlights the necessity of using automation tools to form the structure and content of EPs. It is necessary to develop an automated technology for designing EPs at the state level. The technology will enable teachers to automatically comply with the requirements for EP structure and content, to develop EPs to a high methodological standard, and promptly bring them to all educational institutions.The article reveals the specifics of the project to automate the development of higher education EPs drawing on the example of Siberian State Medical University (SibMed). The possibility of solving this problem is based on employing the software product Intellect Info: Educational programs (1С:University PROF extension). The research proposes technological solutions for creating the content of higher education EPs. These solutions take into account professional (occupational) standards, the requirements of the Federal State Educational Standard 3++. The article also outlines methods of the automated formation of competencies achievement indicators, EP description, and the preparation of working programs for disciplines.The research discusses the possibility of developing the IS of any Russian university to solve the problems of forming the content of higher education EP on the basis of 1C:University PROF.
Digital platforms and services to transform the education industry

The article discusses the prospects for Russia’s digital transformation by 2030. The education industry is mandatory for all regions. Thus, variables necessary to calculate its digital transformation indicators have been identified at the federal level. In all constituent entities of the Russian Federation, there is a demand for digital platforms and services that can ensure the fulfillment of federal requirements taking into account regional specifics. To this end, the article provides a detailed analysis of the variables for calculating digital transformation indicators in the education industry. The analysis was performed using 1C software products. In this regard, the model of the integrated automation of a modern educational institution when delivering the modules of the Digital School project on the 1C:Enterprise development platform is of practical interest to the administrations of Russian educational organizations. Using it will make it possible to digitally transform the education industry. The article thoroughly examines the use of mandatory variables for calculating digital transformation performance indicators at the regional level. Additionally, the research focuses on real automation cases that clearly demonstrate how and with what technologies and 1C services specific educational institutions have addressed their needs.
Mathematics e-learning in the conditions of bilinguality

The world is going through a period of the rapid development of electronic learning systems. In addition, the creation of a digital educational environment and the formation of scientific ideas about using such systems in the educational process are currently taking place. This is of particular importance when teaching small-numbered indigenous peoples living in bilingual and multilingual regions. The article aims to develop an approach to organizing mathematics e-learning for students in a bilingual environment using mechanisms for adapting educational content to specific linguistic features. The study used scientific literature analysis and empirical methods. We suggest that adaptive e-learning courses be a means of e-learning in mathematics. They enable students to pursue an individual educational route and ensure content adaptation according to students’ language and the national peculiarities of their perception of learning materials. The result of the present study is an individual educational route created in an adaptive e-learning course in mathematics in a bilingual environment. In the future, the results of the research can become an element in the methodological system of personalized adaptive learning aimed at the preparation of a university student in a bilingual environment.
User-centred Interface Design for a Robot Teleoperation System for General Medical Examination

Thailand, as well as many other countries worldwide, is facing a shortage of medical staff.We propose a solution to improve medical services in rural areas: a robot teleoperation system that allow patients to consult with doctors from public hospitals, and for doctors to examine and make decisions about their required care.To develop such a system, we are following a user-centred design (UCD) process.Two research questions have been addressed: how should an effective robot teleoperation system be designed to support medical diagnosis and patient communication?And does the use of a social robot improve the social interaction and examination quality in a remote consultation?This paper discusses the overall UCD process, starting from requirement analysis with Thai end-users (finished), system design and development on Pepper robot (in-progress), and user-centred evaluation (future plan).
Evolving complex terrain navigation: Emergent contour following from a low-resolution sensor

This paper investigates evolutionary approaches to enable robotic agents to learn strategies for energy-efficient navigation through complex terrain, consisting of water and different heights.Agents, equipped with a low-resolution depth sensor, must learn how to navigate between a randomly chosen start/end position in a procedurally generated world, along a path which minimises energy usage.The solution that consistently emerged, was an agent that followed the contours of the map, resulting in near-optimal performance in little evolutionary time.Further, initial experiments with a real robot and Kinect sensor showed that the simulated model successfully predicted the correct movement that would be needed to follow contours.This demonstrated both that the evolved strategies are robust to noise and capable of crossing the reality gap.We suggest that this robustness is due to the use of a low-resolution sensor.
Universal Distributional Decision-based Black-box Adversarial Attack with Reinforcement Learning

The vulnerability of the high-performance machine learning models implies a security risk in applications with real-world consequences. Research on adversarial attacks is beneficial in guiding the development of machine learning models on the one hand and finding targeted defenses on the other. However, most of the adversarial attacks today leverage the gradient or logit information from the models to generate adversarial perturbation. Works in the more realistic domain: decision-based attacks, which generate adversarial perturbation solely based on observing the output label of the targeted model, are still relatively rare and mostly use gradient-estimation strategies. In this work, we propose a pixel-wise decision-based attack algorithm that finds a distribution of adversarial perturbation through a reinforcement learning algorithm. We call this method Decision-based Black-box Attack with Reinforcement learning (DBAR). Experiments show that the proposed approach outperforms state-of-the-art decision-based attacks with a higher attack success rate and greater transferability.
Automatic Engagement Detection for Social Situation Assessment

In previous work, we collected simple interaction data through an online game.This collection method meant there was limited signal collection.From this, we created a metric to measure visual social engagement.In this work, we wish to collect an in-person dataset where we can capture a greater number of social signals.With this, we look to enrich our metric to investigate whether we can create more accurate profiles of engagement.Also this dataset looks to capture dynamic group interactions, both human-human and human-robot, with participants being asked to complete a series of tasks both individually and alone.From this data, we will create a model of social engagement to automatically detect engagement levels, in order to improve a robots social awareness.
Modernizing the structure of educators’ ICT competencies to train future teachers to use e-learning methods and networking

The research investigated teacher training in the area of using ICT tools in professional activity as well as educational standards of higher education in pedagogical specialties. The analysis has demonstrated that those academic disciplines of the standard that reflect the problems of employing ICT tools in education do not fully address the issues of creating electronic learning resources (ELR). Moreover, the same observation concerns developing distance learning systems. Such systems automate the processes of collecting, processing, producing information, information interaction between participants in online learning, planning learning sessions in class and out-of-class time as well as managing students’ group, individual educational, and research activity in the digital environment. Basically, the competencies formed are oriented towards the classical learning model of education. Thus, they do not describe the above- mentioned specific features of the teacher’s professional activity in the digital environment in a clear and general way. The educational specialty-specific standards, standard syllabuses of disciplines, and curricula to prepare graduates developed on a competence-based basis constitute the regulatory and methodological framework for modernizing the educational process at pedagogical universities. The structure of teachers’ ICT competencies in the field of e-learning methods and networking is based on the requirements of the professional qualification standard of a teacher, the 3+ educational standards in the system of higher education of the Republic of Belarus as well as the National and European Qualifications Frameworks for Higher Education. The analysis of the requirements of the standard revealed a range of labor actions and functions associated with the need to form and develop certain media and ICT competencies of a teacher in the process of continuous pedagogical education. The article presents the general structure of teachers’ ICT competencies in the field of e-learning and networking methods. It is recommended to detail these competencies through developing relevant descriptors. The given structure of ICT competencies is also intended for designing electronic course descriptors in the Moodle distance learning system to assess the quality of teacher education in university electronic information and educational environment.
An intelligent model to assess the level of the computational algorithmic component of students’ computational thinking

The importance of developing students’ computational thinking (CT) determines the need to devise an objective and valid method for assessing its level. In this regard, it is relevant to build a universal model for diagnosing computational thinking on the basis of mathematical methods. The article aims to develop a model for diagnosing students’ computational algorithmic thinking. This model makes it possible to automate assessment procedures and contributes to developing techniques for enhancing this cognitive ability of learners. The diagnostic evaluation of students’ computational algorithmic component of CT includes an assessment of their subject knowledge, cognitive abilities, and cognitive activity. In this regard, we identified three diagnostic criteria. The content criterion (CC) is determined by the knowledge acquired through subject-specific learning. The operational criterion (OC) is a set of identified intellectual strategies necessary to solve problems using computers. The cognitive activity criterion (CAC) is a cognitive-psychological response to the cognitive process. It is manifested in the intensification of learning activity, a strong focus on a proactive attitude, independence, and a creative approach to learning. It is necessary to establish indicators to provide three assessment levels for each criterion, namely low, medium, and high. The integrated assessment of the level of the computational algorithmic component of a student’s CT is determined by the total of these three criteria values. For the adopted diagnostic model, we have developed a method based on cluster analysis to determine the CT level. The object of recognition is a learner with an information vector (CC, OC, CAC). The weights of the vector elements are determined by experts. The array of students with their information vectors is divided into three classes: L1 (low level), L2 (medium level), L3 (high level). The diagnostics of the computational algorithmic component level of a particular student’s CT consists in determining their belonging to the corresponding group. The method devised to assess the level of the computational algorithmic component of students’ CT was implemented as a software product. The proposed original model for diagnosing students’ CT provides an opportunity to automate assessment procedures. It also facilitates the integrated assessments of the diagnostic characteristics of the learning process. The materials of this work are of interest to teachers and employees of the educational departments of universities, and also have theoretical significance for the development of the theory and practice of diagnosing educational results.
SIGNIFIKANSI INFORMASI ISU KEBOCORAN DATA PRIVASI TOKOPEDIA TERHADAP PERILAKU FANDOM

This research is motivated by the case of user privacy data leaks experienced by Tokopedia in 2020. This study aims to see the effect of information about the leakage of user data experienced by Tokopedia on the behavior of the audience studied using attribution theory. The method used in this research is quantitative explanatory to explain the causal relationship between variables. Information data leakage is measured by its quality value and consists of four characteristics, namely accurate, timeless, relevant, and complete, while audience behavior is measured by attribution theory based on internal factors and external factors, namely paying attention to the principle of consensus, the principle of consistency and the principle of distinctiveness. The results of this study explain that Ha is accepted and H0 is rejected so that the quality of information about Tokopedia user data leakage affects people's behavior. Information about the leakage of Tokopedia user data received by the public is of good quality, and internal factors influence the audience's behavior. The conclusion of this study explains that there is an influence between the leakage of Tokopedia user data and the behavior of the audience with a value of 0.341 at a low level of relationship.
Using Audiovisual Media to Improve Student Writing Skills at SMP Negeri 4 Ambon

Writing includes the ability to express students' opinions or be taught clearly and effectively in written form. Using audiovisual media can improve students writing skills. The purpose of this study is to find out the process and results of improving writing skills by using audiovisual media. The method used Class Action Research (CAR) with a qualitative approach. In collecting the data using observation and documentation. To analyze the success rate or percentage of student success after the teaching and learning process each cycle is done by providing the same evaluation in the form of written test questions at the end of each cycle. After the data is collected, data analysis is calculated using simple statistics that are to assess formative tests and learning completion. The result of the reduction of the data is presented in the form of a data display and then last concluded. The research procedures are planning, implementation, observation, and reflection. The results have shown the average student learning outcome was 72.33% and the number of students who had completed there was 25 children was 83.33%, and the unfinished 5 children (16.67%). This research was said to be successful if the average test score of students writing above KKM scores, which is 70, and students who score above KKM is at least 80%. At the end of Cycle 2 obtained data: the average student learning outcome was 72.33% and the number of students who had completed there was 25 children was 83.33%, and the unfinished 5 children (16.67%). So, based on data on cycle 2 of Class Action Research is said to have been successful. The findings showed that using audiovisual media could better give a positive effect on the motivation and attention of less active students. The result of this study is that audiovisual media in the form of animated videos can improve students' writing skills in English. Learning by using audiovisual media in the form of animated videos will awaken the spirit of student learning amid the saturation of online learning.
#UKRAS22: The 5th UK Robotics and Autonomous Systems Conference

As chairs of the UKRAS 2022 conference, we are happy to welcome you in person after a break from in-person events. The theme of this year’s conference is “Robotics for Unconstrained Environments”, reflecting much of the robotics research that happens at Aberystwyth University. Unconstrained environments include any indoor and outdoor environment that has not been modified specifically for the robot to perform its task. The premise is that the environment must be representative of the task rather than being artificially simplified.
User-Specific Bicluster-based Collaborative Filtering: Handling Preference Locality, Sparsity and Subjectivity

Collaborative Filtering (CF), the most common approach to build Recommender Systems, became pervasive in our daily lives as consumers of products and services. However, challenges limit the effectiveness of Collaborative Filtering approaches when dealing with recommendation data, mainly due to the diversity and locality of user preferences, structural sparsity of user-item ratings, subjectivity of rating scales, and increasingly high item dimensionality and user bases. To answer some of these challenges, some authors proposed successful approaches combining CF with Biclustering techniques. This work assesses the effectiveness of Biclustering approaches for CF, comparing the impact of algorithmic choices, and identifies principles for superior Biclustering-based CF. As a result, we propose USBFC, a Biclustering-based CF approach that creates user-specific models from strongly coherent and statistically significant rating patterns, corresponding to subspaces of shared preferences across users. Evaluation on real-world data reveals that USBCF achieves competitive predictive accuracy against state-of-the-art CF methods. Moreover, USBFC successfully suppresses the main shortcomings of the previously proposed state-of-the-art biclustering-based CF by increasing coverage, and coclustering-based CF by strengthening subspace homogeneity.
Generative Long-form Question Answering: Relevance, Faithfulness and Succinctness

In this thesis, we investigated the relevance, faithfulness, and succinctness aspects of Long Form Question Answering (LFQA). LFQA aims to generate an in-depth, paragraph-length answer for a given question, to help bridge the gap between real scenarios and the existing open-domain QA models which can only extract short-span answers. LFQA is quite challenging and under-explored. Few works have been done to build an effective LFQA system. It is even more challenging to generate a good-quality long-form answer relevant to the query and faithful to facts, since a considerable amount of redundant, complementary, or contradictory information will be contained in the retrieved documents. Moreover, no prior work has been investigated to generate succinct answers. We are among the first to research the LFQA task. We pioneered the research direction to improve the answer quality in terms of 1) query-relevance, 2) answer faithfulness, and 3) answer succinctness.
The State and Conditions of School Libraries in Vhembe District, Limpopo Province, South Africa

School libraries or media centres are an essential element of quality in education and learning for all the South African learners. The objective of this paper is to assess the state of library materials and resources obtained in school libraries. It is therefore important that schools should have a library or a media centre for the provision of materials for the purpose of conducive learning and reading for learners. This study investigated the state and conditions of school libraries in rural areas of Vhembe District in Limpopo Province, South Africa. The method used in this study is a qualitative research approach through face-to-face interviews with foundation phase literacy teachers and teacher librarians in eight schools, which were purposely selected for being known to have a school library or media centre buildings in their premises. The conclusion from the findings is that school libraries and media centres in these rural schools do not provide a suitable and conducive learning environment to learners.