Reviewerly/exHarmony huggingface dataset:
Psychological, pedagogical, and digital aspects of distance learning in Kazakhstan in the context of the COVID-19 pandemic

Аbstract. The article provides an assessment by the participants of the educational process (students, teachers, administration) of the transition to distance learning in educational organizations in the context of the COVID-19 pandemic, based on a survey conducted in all regions of Kazakhstan. The results of the study showed that the emergency transition of universities to distance learning in connection with the pandemic declared by WHO had a significant impact on the quality and organization of the pedagogical process. This is largely due to the insufficient level of readiness of the relevant state bodies to ensure the material and technical transition to distance learning linked to the previous low rates of digitalization of infrastructure and the ways of interaction between relevant government agencies and educational institutions of the country. Most of the students studied using a smartphone, which signals the lack of a high-quality and efficient educational online platform. The results of the study revealed the need for diversified psychological, pedagogical, and methodological training of teachers in the framework of cyber pedagogy.
Analysis of E-Learning Acceptance During Distance Learning Using Unified Theory Of Acceptance and Use of Technology (UTAUT)

The new coronavirus disease that was first identified in Wuhan, China, is referred to as the 2019 coronavirus disease pandemic (COVID-19). The impact of this pandemic has affected various fields, one of which is education, and one of the consequences is the implementation of online teaching and learning processes. In this digital era, Telkom University is implementing CeLOE LMS as an E-Learning medium. CeLOE Learning Management System (LMS) is an online learning platform for regular and distance education study programs at Telkom University. For the application of CeLOE LMS, there has been no report that contains user acceptance of the use of CeLOE LMS that tested with an integrated model. Therefore, the solution offered by this research was to conduct an analysis to determine user acceptance of the application of the CeLOE LMS system by using the Unified Theory of Acceptance and Use of Technology (UTAUT) Model at Telkom University to produce report that can be used as a basis for further decision making by Telkom University in managing its services as the UTAUT Model attempts to clarify the intentions of students to use an information system and the usage actions of students. From this research, it was found that the CeLOE LMS has been accepted and used by Telkom University students with all hypotheses accepted, Performance Expectancy as the variable with the greatest influence, and it needs improvements related to technical infrastructure, organizational infrastructure, and socialization of CeLOE LMS due to the influence of Facilitating Conditions variable was the lowest.
Indian Students at a Russian Regional University: Socio-economic Impact of Short-term Educational Programmes

Universities are increasingly seen as drivers of regional economic development. In this research, we undertook an analysis of the short-term academic mobility of university students as a factor in the internationalisation of higher education. The number of foreign students (International Student Ratio) attracted from across the world is an important metric in all global university rankings. As a result, universities aiming to improve their positions in the global educational market are actively implementing programmes to attract foreign students. Short-term educational programmes of academic mobility are characterised by a high level of flexibility in terms of management, thus being capable of meeting a wide range of needs and interests of potential students. In July 2019, we conducted a survey among Indian students (n = 65) participating in short-term international academic mobility programmes at the Ural Federal University (Ekaterinburg, Russia). The main goal of the empirical research was to investigate students’ motivational structure of participation in academic mobility and the role of such programmes in the implementation of students’ life plans. In addition, barriers to the effective implementation of short-term educational programmes at universities were revealed. According to the research results, Russian education is a status characteristic for Indian students, which allows them to gain important socio-cultural and communicative competencies, as well as to build professional trajectories in a foreign country. The respondents expressed willingness to continue their education in Russia as part of Master’s degree educational programmes. Among the main barriers and challenges, the respondents mentioned poor knowledge of the Russian language and financial difficulties.
The Complexity Landscape of Claim-Augmented Argumentation Frameworks

Claim-augmented argumentation frameworks (CAFs) provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective; they extend Dung AFs by associating a claim to each argument representing its conclusion. This additional layer offers various possibilities to generalize abstract argumentation semantics as the re-interpretation of arguments in terms of their claims can be performed at different stages in the evaluation of the framework: One approach is to perform the evaluation entirely at argument-level before interpreting arguments by their claims (inherited semantics); alternatively, one can perform certain steps in the process (e.g., maximization) already in terms of the arguments’ claims (claim-level semantics). The inherent difference of these approaches not only potentially results in different outcomes but, as we will show in this paper, is also mirrored in terms of computational complexity. To this end, we provide a comprehensive complexity analysis of the four main reasoning problems with respect to claim-level variants of preferred, naive, stable, semi-stable and stage semantics and complete the complexity results of inherited semantics by providing corresponding results for semi-stable and stage semantics. Moreover, we show that deciding, whether for a given framework the two approaches of a semantics coincide (concurrence) can be surprisingly hard, ranging up to the third level of the polynomial hierarchy.
ONTOLOGY-BASED DATA MAPPING TO SUPPORT PLANNING IN HISTORICAL URBAN CENTRES

Abstract. Because of the need for new sustainable future alternatives, the re-inhabitation of rural areas, hinterlands, small historical urban centres and villages has become a unique real opportunity. Therefore, it is necessary to define and adopt new sustainable urban planning and building permits to follow this path. These processes involve both various actors and disciplines and a variety of spatial and semantic data. For this reason, the present research aims at providing a methodology to build the necessary spatial documentation of historical centres and villages by adopting an ontology-based workflow. Existing ontologies and conceptualisations have been considered together with classes and rules from city historical core regulations. A case study has been selected considering its available spatial datasets and national data models. The bottom-up approach here adopted aims at validating and enriching a reference ontology previously developed in the domain of historical centre by adding new concepts and relations from selected regulation plans and other existing ontologies and data models. Finally, the obtained ontology is also populated with instances of concepts and relations.
An Activity-Centric Quality Model of Software

In this paper, software activity, software activity instance, and the quality of the activity instance are defined as the ‘activity which is performed on the software product by a person or a group of persons,’ the ‘distinctive and individual performance of software activity,’ and the ‘performer’s evaluation on how good or bad his/her own activity instance is,’ respectively. The representative values of the instance quality population associated with a product and its sub-population are defined as the (software) activity quality and activity quality characteristic of the product, respectively. The activity quality model in this paper classifies activity quality characteristics according to the classification hierarchy of software activity by the goal. In the model, a quality characteristic can have two types of sub-characteristics : Special subcharacteristic and component sub-characteristic, where the former is its super-characteristic too simultaneously and the latter is not its super-characteristic but a part of its super-characteristic. The activity quality model is parsimonious, coherent, and easy to understand and use. The activity quality model can serve as a corner stone on which a software quality body of knowledge, which constituted with a set of models parsimonious, coherent, and easy to understand and use and the theories explaining the cause-andrelationships among the models, can be built. The body of knowledge can be called the (grand) activitycentric quality model of software.
Movie Recommendation System Based on Character Graph Embeddings

This paper presents a novel approach for recommending movies based on weighted Character Graphs. This approach proposes a dedicated crawler that gathers movie screenplays and a methodology of character graphs generation that contains all the necessary information needed for the representation of movie plots. A representative vector is extracted for each graph and used along with user ratings, as an input for a gradient boosting algorithm to predict movie ratings. The proposed method is tested on a publicly available MovieLens dataset and it was experimentally shown that it outperforms the fundamental collaborative filtering recommendation algorithms.
A graph coloring based parallel hill climber for large-scale NK-landscapes

Efficient hill climbers are at the heart of the latest gray-box optimization techniques, where some structural information about the optimization problem is available. Focusing on NK-landscapes as a challenging class of k-bounded pseudo-boolean functions, we propose a quality-enhanced and a time-accelerated hill climber. Our investigations are based on the idea of performing several simultaneous moves at each iteration of the search process. This is enabled using graph coloring to structure the interacting variables of an NK-landscape, and to identify subsets of possibly improving independent moves in the Hamming distance 1 neighborhood. Besides being extremely competitive with respect to the state-of-the-art first- and best- ascent serial variants, our initial design exposes a natural degree of parallelism allowing us to convert our serial algorithm into a parallel hill climber without further design efforts. As such, we also provide a multi-threaded implementation using up to 10 shared-memory CPU-cores. Using a range of large-scale random NK-landscapes with up to 106 variables, we provide evidence on the efficiency and effectiveness of the proposed hill climber and we highlight the strength of our parallel design in attaining substantial acceleration factors for the largest experimented functions.
Further Evidence of Herding Behavior in Cryptocurrency Markets During the COVID-19 Pandemic

This study investigates the herding behavior in the Singaporean cryptocurrency markets during the full crisis period, differentiates its presence before (pre-event) and after (post-event) the World Health Organization officially declared COVID-19 as a global pandemic on March 11, 2020. Discriminates its occurrence between up & down market conditions and further distinguishes the phenomenon between periods of low & high volatilities. Our results show that herding effect is present in but is contingent in a specific combination of market state and market condition, only during down markets in periods of high volatility (down market - high volatility) and up markets in periods of low volatility (up market - low volatility). Furthermore, results indicate an asymmetric herding effect and is robust in a market capitalization-weighted model.
The Application of Radon Measurement In the Active Fault Detection of Xiadian Fault

The Xiadian active fault is one of the most important seismological faults in the vicinity of Beijing-Tianjin-Hebei region. This paper mainly focuses on the geochemical characteristics of the fault to unveil and evaluate its activity. Via the application of soil gaseous radon measurement, a detailed field study at Qixinzhuang segment of Xiadian fault is proposed. Based on all the radon impulse value we acquired of 3 profiles in the study area. We plotted the radon impulse trend chart of 3 profiles, analyzed anomaly distribution of soil gaseous radon and discussed the scale and activity of the Xiadian fault. The radon multiple-peaks anomaly of Qixinzhaung segemnt demonstrates the hidden faults underground exist.
Noise Resilient Local Gradient Orientation for Content-Based Image Retrieval

In a world of multimedia information, where users seek accurate results against search query and demand relevant multimedia content retrieval, developing an accurate content-based image retrieval (CBIR) system is difficult due to the presence of noise in the image. The performance of the CBIR system is impaired by this noise. To estimate the distance between the query and database images, CBIR systems use image feature representation. The noise or artifacts present within the visual data might confuse the CBIR when retrieving relevant results. Therefore, we propose Noise Resilient Local Gradient Orientation (NRLGO) feature representation that overcomes the noise factor within the visual information and strengthens the CBIR to retrieve accurate and relevant results. The proposed NRLGO consists of three steps: estimation and removal of noise to protect the local visual structure; extraction of color, texture, and local contrast features; and, at the end, generation of microstructure for visual representation. The Manhattan distance between the query image and the database image is used to measure their similarity. The proposed technique was tested using the Corel dataset, which contains 10000 images from 100 different categories. The outcomes of the experiment signify that the proposed NRLGO has higher retrieval performance in comparison with state-of-the-art techniques.
Manifold multi-view learning for cartoon alignment

Cartoon alignment is a key to retrieve cartoon characters and synthesise new cartoon clips. To successfully achieve the tasks, it is necessary to extract visual features that comprehensively denote cartoon characters and to align the feature points accurately between cartoon characters. In this paper, Speed Up Robust Feature (SURF) and Shape Context (SC) are introduced to characterise the cartoon character from multi-view. To increase accuracy rate of cartoon character alignment, semi-supervised alignment and Procrustes alignment require predetermining the correspondence. To overcome the flaw, we propose a Manifold Multi-View Learning (MML) to align cartoon characters. MML learns a projection that maps data instance (from cartoon characters with different dimensionality) to a lower-dimensional space, which simultaneously matches the local geometry and preserves the neighbourhood relationship within each cartoon character. The matching relationship can be obtained from local geometry structure. Experimental results show the good performance.
Novelti 2.0: Enhanced Assistive Robot Navigation with Low Throughput Interfaces

People with severe motor impairment oftentimes rely on unconventional assistive interfaces to complete daily activities. In this work, we developed a holistic shared control framework to help them achieve fast and accurate indoor navigation using low throughput human machine interfaces. Based off our previously developed shared position control method, we added orientation control, action control, and a state machine architecture to create the new framework Novelti 2.0 . The framework provides generic support for various LTIs. We validated the framework using Emotiv EPOC headset and a single-switch interface. A comprehensive comparative study involving 10 human subjects navigating a wheelchair in a real indoor environment demonstrated that our framework could achieve high navigation accuracy about 0.05 m for position and 5° for orientation. Compared to steering control, our method required half the navigation time and half the number of commands in routes longer than 20 m. The NASA TLX survey results also indicated lower subjective workload with our method. Experimental data is provided for download and future evaluation.
One Head, Multiple Hats – Lessons Learned from Online Teaching

Abstract The coronavirus (COVID-19) pandemic has caused an unprecedented crisis in all areas of human activity. In the field of education, this new and unexpected situation has led to an extraordinary shift of paradigm, as a considerable amount of institutions and faculty have been forced to pivot from purely face-to-face lessons to wholly online contexts, despite minimal preparation time and training. This paper aims to analyse the implications of this turn, as well as some of the key lessons that can be garnered from it. The gauge of this article will be on lessons learned, from the perspective of the changes that occurred in role of the teacher in the online context and the ramifications these have on teachers’ role and responsibilities.
Wavelets, probability, and statistics: Some bridges

The rôle of some wavelet methods in probability and statistics is illustrated via a sample of three problems: We show how properties of processes can be read from properties of their wavelet transform. We discuss how the missing data problem can be approached via frames of complex exponentials. We explain how wavelets can be used to span classes of admissible estimators in nonparametric function estimation. It is also the purpose of this paper to show that bridges can be crossed in the other directions: Random products of matrices determine the smoothness of compactly supported wavelets. Nonstationary prediction theory gives new results on frames in Hilbert space.
The Main Factors of Formation and Development of Professional Competence of Teachers of Preschool Educational Organizations

The article is devoted to the development of professional competence of teachers of preschool educational organizations. The author has shown the main factors of development of professional competence of teachers in preschool educational organizations.
A Method for Deriving Information from Running R Code

It is often useful to tap information from a running R script.Obvious use cases include monitoring the consumption of resources (time, memory) and logging.Perhaps less obvious cases include tracking changes in R objects or collecting the output of unit tests.In this paper, we demonstrate an approach that abstracts the collection and processing of such secondary information from the running R script.Our approach is based on a combination of three elements.The first element is to build a customized way to evaluate code.The second is labeled local masking and it involves temporarily masking a user-facing function so an alternative version of it is called.The third element we label local side effect.This refers to the fact that the masking function exports information to the secondary information flow without altering a global state.The result is a method for building systems in pure R that lets users create and control secondary flows of information with minimal impact on their workflow and no global side effects.
Designing Android-Based Augmented Reality as an ICT Learning Media

Augmented Reality can be defined as a real environment in which virtual objects are added with the integration of computer technology. This technology can present interesting interactions for the user, because with this technology the user can feel virtual objects as if they actually exist in a real environment. This study will include AR technology to create learning media, so that the use of practicum time is maximized and allows students to understand and recognize computer networks. Research and application development using the Waterfall model provides a sequential or sequential software lifeflow approach starting from analysis, design, coding and testing. Testing the application using Black Box Testing and Augmented Reality Application Questionnaire for Network Devices went well. Minimum specifications of Augmented Reality Applications Network Devices with Operating System (OS) Android v6.0 Marshmallow, 2.1 GHz Deca-core Processor, 12 MP Camera, 2 GB Ram and 32 GB Memory with application specifications can run well.
Research Plan to Analyze the Role of Compliance in Influencing Cybersecurity in Organizations

Today, in order to be successful, every organization needs to be cyber secure. Cybersecurity is the practice of protecting the confidentiality, integrity, and availability of critical organizational assets. However, this practice can be complex and time-consuming, and typically is not the primary strategic goal for the majority of organizations. Conversely, compliance is one of the most powerful motivating forces behind most business investments due to the financial or reputational impact it may have. Compliance is generally defined as the act of conforming to rules or policies. In most industries, ensuring the application of these rules or policies often means meeting a third partyâ€™s regulatory requirements, such as a government or a cybersecurity framework. Nevertheless, meeting compliance regulations doesn't necessarily enable an organization to cover all cybersecurity needs. This means that achieving the highest level in compliance doesn't often mean that is also possible to achieve an equally high level in cybersecurity.
Task-agnostic Continual Learning with Hybrid Probabilistic Models

Learning new tasks continuously without forgetting on a constantly changingdata distribution is essential for real-world problems but extremelychallenging for modern deep learning. In this work we propose HCL, a Hybridgenerative-discriminative approach to Continual Learning for classification. Wemodel the distribution of each task and each class with a normalizing flow. Theflow is used to learn the data distribution, perform classification, identifytask changes, and avoid forgetting, all leveraging the invertibility and exactlikelihood which are uniquely enabled by the normalizing flow model. We use thegenerative capabilities of the flow to avoid catastrophic forgetting throughgenerative replay and a novel functional regularization technique. For taskidentification, we use state-of-the-art anomaly detection techniques based onmeasuring the typicality of the model's statistics. We demonstrate the strongperformance of HCL on a range of continual learning benchmarks such assplit-MNIST, split-CIFAR, and SVHN-MNIST.