Reviewerly/exHarmony huggingface dataset:
Group Decision Making-Based Fusion for Human Activity Recognition in Body Sensor Networks

Ensemble learning systems (ELS) have been widely utilized for human activity recognition (HAR) with multiple homogeneous or heterogeneous sensors. However, traditional ensemble approaches for HAR cannot always work well due to insufficient accuracy and diversity of base classifiers, the absence of ensemble pruning, as well as the inefficiency of the fusion strategy. To overcome these problems, this paper proposes a novel selective ensemble approach with group decision-making (GDM) for decision-level fusion in HAR. As a result, the fusion process in the ELS is transformed into an abstract process that includes individual experts (base classifiers) making decisions with the GDM fusion strategy. Firstly, a set of diverse local base classifiers are constructed through the corresponding mechanism of the base classifier and the sensor. Secondly, the pruning methods and the number of selected base classifiers for the fusion phase are determined by considering the diversity among base classifiers and the accuracy of candidate classifiers. Two ensemble pruning methods are utilized: mixed diversity measure and complementarity measure. Thirdly, component decision information from the selected base classifiers is combined by using the GDM fusion strategy and the recognition results of the HAR approach can be obtained. Experimental results on two public activity recognition datasets (The OPPORTUNITY dataset; Daily and Sports Activity Dataset (DSAD)) suggest that the proposed GDM-based approach outperforms the well-known fusion techniques and other state-of-the-art approaches in the literature.
Knowledge structures of scientific production on COVID-19 in the sphere of education: the case of publications indexed in the Web of Science during 2020

This study seeks to explore the different knowledge structures in the sphere of educational research into COVID-19 during 2020. Using bibliometric methods, analysis was performed of a sample of 308 scientific articles retrieved from the Web of Science database. Using different data analysis techniques combining co-occurrence analysis, co-citation analysis and factorial analysis, All Keywords and Keywords Plus were used to achieve the main research objective: identification of the main themes and trends of production in the sphere of educational research into COVID-19. The main findings of this study in terms of the conceptual structure show that analysis of the centrality and density of the thematic trends points to a generalised structural change in the entire educational system towards methodological teaching–learning procedures oriented towards distance education. As for the intellectual structure, among the host of authors and sources of information involved only a select few have a greater influence on the scientific community. Finally, in terms of social structure, there is limited collaboration between authors and institutions from different countries. However, this collaboration is more intense within countries themselves and in terms of their own production, with the USA being the country with the strongest links.
Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences

Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imaging (ECGI) to learn efficiently with a relatively small dataset. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables over their respective geometrical domains. We then explicitly model the geometry-dependent physics in between the two domains via a bipartite graph over their graphical embeddings. We applied the resulting network to reconstruct electrical activity on the heart surface from body-surface potentials. In a series of generalization tasks with increasing difficulty, we demonstrated the improved ability of the network to generalize across geometrical changes underlying the data using less than 10% of training data and fewer variations of training geometry in comparison to its Euclidean alternatives. In both simulation and real-data experiments, we further demonstrated its ability to be quickly fine-tuned to new geometry using a modest amount of data.
Reconstruction of the Book Collection of the Ural Owner of Mines of the 18th century A.F. Turchaninov

The article discusses the monograph of E.P. Pirogova, dedicated to the reconstruction of the private library of the Ural owner of mines A.F. Turchaninov (1704—1787). The monograph is based on the unique source — the detailed inventory of property compiled in 1789. The document allowed to create the catalog of the Russian part of the collection and the review of foreign books. The monograph also includes the archaeographic publication of inventory materials related to the books and other printed products. The author of the monograph introduced into scientific circulation the detailed information about one of the largest private libraries in Russia in the second half of the 18th century and probably the most significant in the Urals in that historical period. It allows us to refute the existing view in historiography about the ceremonial nature of A.F. Turchaninovʼs book collection. Now the owner of mines and metallurgical works can be considered as an active and thoughtful collector of books and user of his own library, familiar with the cultural practices of the Era of Enlightenment.
Book Culture in the Charitable Enlightenment Activity of the Merchants Talantsevs

The article reveals the life and activities of the Talantsevs merchants family in the pre-revolutionary Chuvashia. At the end of the 19th — beginning of the 20th century, along with entrepreneurship and commercial activities, the Talantsev brothers were also engaged in charity. Unlike many other merchants, who proved themselves primarily as benefactors in the development of temple construction and public charity facilities of religious type, members of the Talantsev family distinguished themselves in the enlightenment direction — in the construction of schools, hospitals, libraries, cultural and educational institutions in multinational Chuvashia, in the organization of agriculture and household life, in preservation and further development of the ethnoculture of Russian and Chuvash peoples. The Talantsevs were particularly active in promoting secular culture and books among the population. That was clearly expressed in their participation in the development of public, factory and private family libraries. The partially preserved library of the Talantsev brothers, located since 1954 in the structure of the National Library of the Chuvash Republic, is a rare example of the noble service of representatives of the merchants of Chuvashia to book culture, enlightenment and preservation of the ethnoculture of the peoples of the Middle Volga region. The historical experience and traditions of merchant families in organizing charity in the field of culture, interrupted in October 1917 and partially revived in the practice of modern entrepreneurs, can now be used in the educational process in all types of educational institutions, taking into account modern realities.
Performance Analysis of Deep Learning based Human Activity Recognition Methods

Human Activity Recognition (HAR) is one of the most important branches of human-centered research activities. Along with the development of artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In recent years, there is a growing interest in Human Activity Recognition systems applied in healthcare, security surveillance, and human motion-based activities. A HAR system is essentially made of a wearable device equipped with a set of sensors (like accelerometers, gyroscopes, magnetometers, heart-rate sensors, etc.). Different methods are being applied for improving the accuracy and performance of the HAR system. In this paper, we implement Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) in combination with Long Short-term Memory (LSTM) methods with different layers and compare their outputs towards the accuracy in the HAR system. We compare the accuracy of different HAR methods and observed that the performance of our proposed model of CNN 2 layers with LSTM 1 layer is the best.
Single-magnon excited states of a Heisenberg spin chain using a quantum computer

Excited states of spin-chains play an important role in condensed matter physics. We present a method of calculating the single magnon excited states of the Heisenberg spin-chain that can be efficiently implemented on a quantum processor for small spin chains. Our method involves finding the stationary points of the energy vs wavenumber curve. We implement our method for 4-site and 8-site Heisenberg Hamiltonians using numerical techniques as well as using an IBM quantum processor. Finally, we give an insight into the circuit complexity and scaling of our proposed method.
Social And Emotional Influence Of Hate-Related Speeches In Social Media On Intra-Muslims Conflict In Northern Nigeria: Implications For Conflict Resolution

The study investigated the social and emotional influence of hate-related speeches, expressed on social media platforms on the intra-Muslims conflict in Northern Nigeria. The respondents of the study are northern Nigerians who are using Facebook and Twitter social media platforms. Out of 1435 social media users tracked for the study, only 336 respondents agreed to participate in the study. Therefore, they were selected using the accidental and purposeful sampling method. The respondents were reached through internet-based social media platforms. A 19 items questionnaire was used for both quantitative and qualitative data collection, through the online tracking. The study found some of the social and emotional influence of hate-related speeches on social media to include; the creation of disunity among Muslims, providing fertile ground for arguments, poor interpersonal relationships, frustration, provocation, and feeling of enmity among others. The study, therefore, suggested some recommendations as resolutions to the problems identified, which include; the establishment of a law that sanctions expressions that are related to intra-Muslims conflicts on social media, as well as advocacy by religious leaders on the use of relative words in social media posts and comments.
The dialogue facilitator bot: Reflections on design and evaluation

With the growing popularity of chatbots, there is also a need for targeted evaluation criteria and metrics to assess their efficacy. Multiparty chatbots are designed to communicate with multiple users in a group, functioning as tools that support group interaction. Facilitator bots are a specific type of multiparty bots that assume the role of dialogue facilitator, to motivate and guide meaningful conversation in creative, professional, education or other settings. In this work we present the findings from a literature review of existing approaches to the evaluation of multiparty bots, informed also by the wider domain of chatbot design and evaluation. We examine and reflect upon the function and importance of different aspects of evaluation in relation to the specific chatbot objectives. These aspects include general ones, like user experience and chatbot personality, as well as more specific ones for multiparty bots, such as the function of the bot as dialogue facilitator and the quality of the dialogue between the chatbot and the users themselves.
Data-Driven Probabilistic Model of Magneto-Rheological Damper for Intelligent Vehicles using Gaussian Processes

In this paper, we focus on Magneto-rheological damper (MRD) which is essential to intelligent vehicle suspension systems. We attempt to build a data-driven model of MRD with complex dynamics in a probabilistic view in order to address the issues of sample efficiency and the robustness against environmental disturbances in the current non-parametric approaches. Compared with the conventional approaches like neural networks that are sensitive to the noisy data and require a massively training samples, we employ Gaussian processes to model the target system in a full Bayesian way while considering both mean and variance of the prediction. Evaluated by a MRD simulation platform in different working conditions and a noisy external environment, the model learned via GP clearly demonstrated not only good performances in prediction accuracy and uncertainties representation, but also a good balance between the model quality and computational complexity under different sparse scales. These results indicate the great potential of GP as an emerging direction of modeling MRD in intelligent vehicles.
Road Network Generation with City Block Attributes Using Link Attribute Aggregation

We propose a method of generating road networks with city block attributes using an encoder-decoder network and link attribute aggregation. The encoder encodes the structure of surrounding local road patterns, which enables incremental road network generation conditioned on existing road network patterns. Our contributions are threefold. First, we extend the encoder-decoder network to enable it to generate both road network graphs and the attribute of city blocks enclosed by roads. The generated links hold the attribute of adjacent city blocks as link attributes, and the aggregation of the surrounding link attributes determines the attribute of the city block surrounded by the links. Second, we propose new similarity measures and extensively evaluate the range of the similarity between actual and generated road networks. Third, we evaluate the effect of the method to integrate features of multiple paths in road networks on the similarity. We demonstrate that the proposed method can generate diverse road networks including the reproduction of actual road networks with high precision.
Generalized Urban Traffic Surveillance (GUTS): World-Coordinate Tracking for Traffic Safety Applications

We present a new world-coordinate tracking algorithm for road users seen from static surveillance cameras, denoted GUTS. It is based upon the previously published UTS method but simplifies and replaces parts allowing association logic to work in world coordinates, by using a novel convolutional neural network denoted SAMHNet to convert every detection into world coordinates. Experimental evaluation on synthetic data shows a MOTA increase of 41 % or 153% depending on distance metric, compared to UTS. Furthermore, the system is verified to work on a real-world recording. We further introduce a synthetic dataset denoted UTOCS which is the first of its kind to be standardized and made publicly available, allowing fair comparison between methods.
Influence of Relief Feature Selection on Random Forest and Support Vector Machine Classification Algorithm

The classification technique is one of the popular techniques used in helping humans decide the target class of a data based on machine learning principles. Unfortunately the construction of a classification model has no limits and will always evolve over time. There is no surefire way to make a perfect classification model, but there are ways that at least make the classification model better. This study applies the feature selection method to produce a more optimal classification model accuracy value. Of the many feature selection algorithms, this research chooses Relief which is combined with a classification algorithm, namely Random Forest and Support Vector Machine. This research also applies the Grid Search Optimization method in selecting the most influential features. In addition, it is also used to select the best hyperparameters to build the classification model. For splitting the data set, the K Fold Cross Validation technique is used in order to get the most optimal proportion of data splitting. Compared to the accuracy values before and after feature selection, both classification algorithms after feature selection significantly outperform the classification model before feature selection. It was also found that the model’s capabilities in the real world, through validation with new data, performed quite well.
Performance Comparison of Hybrid CNN-XGBoost and CNN-LightGBM Methods in Pneumonia Detection

Pneumonia is an acute respiratory infection that affects the lungs. Pneumonia causes inflammation of the air sacs (alveoli) of the lungs, so the individual who has pneumonia experiences shortness of breath, coughing up phlegm, fever, and chills. Pneumonia can be detected through chest X-ray examination. A radiologist generally performs the acquisition of chest X-ray images. However, the availability of radiologists is limitation. Several machine learning applications have been used to classify chest X-rays automatically. The classification method used in this research is a hybrid Convolutional Neural Network (CNN)-Extreme Gradient Boosting (XGBoost) and Convolutional Neural Network (CNN)-Light Gradient Boosting (LightGBM). CNN is used as a primary classification method before proceeding to the main classification. The main classification methods for each classification method are XGBoost and LightGBM. Before classification, the chest X-ray image is processed through image preprocessing. Image preprocessing applied in this research includes grayscaling, image resizing, image stretching, and thresholding. The results of testing for the CNN-XGBoost classification obtained an accuracy value of 97.60%. While the results of testing for the CNN-LightGBM classification produced an accuracy value of 97.45%.
Performance Comparison of Technological Solutions for Spark Applications in AWS

Cloud computing is providing a pay-as-you-go in-frastructure for the deployment of complex applications, with auto-scaling support and the ability to manage and process huge amount of data. However, due to the underlying complexity of the cloud infrastructure, it is not trivial to evaluate the setup providing the best performance of such scenario. To this aim the present paper proposes a thorough performance evaluation of a real application in a Cloud platform, measuring the impact of several design choices and technological solution. The experimental results, based on a real application and on realistic data can provide a significant insight that can integrate the traditional approach of cloud performance evaluation based on synthetic benchmarks.
Modelling Assessment Rubrics through Bayesian Networks: a Pragmatic Approach

Automatic assessment of learner competencies is a fundamental task in intelligent tutoring systems. An assessment rubric typically and effectively describes relevant competencies and competence levels. This paper presents an approach to deriving a learner model directly from an assessment rubric defining some (partial) ordering of competence levels. The model is based on Bayesian networks and exploits logical gates with uncertainty (often referred to as noisy gates) to reduce the number of parameters of the model, so to simplify their elicitation by experts and allow real-time inference in intelligent tutoring systems. We illustrate how the approach can be applied to automatize the human assessment of an activity developed for testing computational thinking skills. The simple elicitation of the model starting from the assessment rubric opens up the possibility of quickly automating the assessment of several tasks, making them more easily exploitable in the context of adaptive assessment tools and intelligent tntorino systems.
Web Application for Monitoring Large-scale Virtualized Computing Resources

The Flask framework is used for implementing a simple web app that allows any user to visualize and monitor computing resources that it has access to. The resources are virtualized, deployed, and managed through the OpenStack infrastructure. Within the web app, the user can see the entire set of virtual machines as a topological view or check system information as graphical representations (i.e., pie charts, histograms, and bar charts with machine status, network information, system metrics, and so on). These features can help any scientist dealing with complex simulations that require computational power, by granting a quick way of checking potential issues on the machines executing calculations.
The effectiveness of sustainable tourism education for tourism-specialized high school students

Continuing efforts are being made to contribute to sustainable development in the tourism industry, and numerous researchers have emphasized the significance of education. However, the number of studies on sustainable tourism education was extremely limited, and the primary research subjects were limited to university tourism majors. This study examined the dimensions of perception, knowledge, attitude, and behavioral intention for sustainable tourism among tourism-specialized high school students, who can be considered the future leaders of the tourism industry, in order to overcome this limitation. In addition, three types of educational materials, including handouts, card news, and videos, were provided to students using a scenario-based design, and the response results of each group were compared to determine the efficacy of each type of educational material provision. Consequently, education improved the overall level of perception, knowledge, attitude, and behavioral intentions; however, there was no significant relationship between the effectiveness of each group's education and any of the variables measured. This study targeted tourism-specialized high school students, which had never been done before in the context of sustainable tourism education, and the results of this study have both theoretical and practical implications for sustainable tourism education.
Simulation in the aerospace industry: review of technologies, methods and prospects of their use in future industries

The paper presents a review of modern technologies used today to various degrees in simulating the production activity. The emphasis is primarily made on production in aviation, space and rocket industries. The work objective is to develop approaches to introduction of technologies in practice today without waiting for the final formation of their implementation environment, which in most cases requires serious preparation, fundamental changes and long-term implementation. Without them, the use is becoming impossible in principle or inefficient. Based on these positions, the methodological foundation of technologies was studied, they were systemized, their potential in solving the current production problems was evaluated, and the prospects for their use in future were considered. Main sources of technology containment were shown. Attention was paid to the issues of standardization in the digital transformation. Possibilities and limitations of using the considered technologies in the education were studied in order to train the highly sought specialists familiar with technologies and possessing skills of their practical implementation. In this context, these technologies are conditionally divided into those that could be studied in higher educational aerospace institutions and those that reasonably could be studied within the framework of additional professional educational programs.