Reviewerly/exHarmony huggingface dataset:
Influence of the Tool Visibility and the Object Highlighting when Interacting with a Virtual Object in a VR Environment

Interaction methods with virtual objects are an important issue for the development of VR. The user experience directly depends on the success of interactions with virtual objects and the environment. Therefore, the factors affecting the effectiveness of human-computer interaction has become a hotspot in this field. The paper describes an experiment focused on the influence of different visibility conditions of the interaction tool and the highlighting of a virtual object of interaction (with indirect interaction). We present the conditions, methodology and results of this study. The influence of different levels of abstraction of objects is considered: from geometric bodies (balls and parallelepipeds) to cartoon fruits, and photo realistic objects after then. For these conditions, a classical two-factor experiment is carried out, in which the first factor is the visibility of the control beam, and the second is the visibility of the highlighting of the captured object. The influence of one of these factors or their combination on the success of the movement of the captured object is expected. The success is determined by the time of movement (the less time, the more successful) and the optimality of the trajectory. The formula is proposed that determines the optimality of the trajectory. It is surprising that the movement of an already captured object is not affected by any of the above factors. Apparently, it is necessary to carefully study the capture process itself, not including the post-capture movement in the calculation.
Election Surveillance

For most of this country’s history we have relied on human eyes and ears to oversee our systems of elections. Modern surveillance tools, from cell phones to video streaming platforms, are now cheap and ubiquitous. Technology holds great promise to increase election transparency. But the 2020 election confirmed what has become quite clear: the use of technology to record election processes does not always serve the goal of reassuring the public of the integrity of elections; it may do the opposite. As legislatures around the country reexamine the rules governing elections, an underexplored question is whether election surveillance should be promoted or prohibited. Important questions like when, if ever, observers and voters should be allowed to record during elections remain unanswered. Who (and what) are the targets of such surveillance and what harms and benefits result? Should states prohibit or encourage election surveillance? How can election surveillance—including election surveillance conducted by election officials—improve public confidence in election outcomes and reduce rather than stoke misinformation? As historic levels of public mistrust of election outcomes threaten American democracy, Election Surveillance takes up these urgent questions.
Bayesian nonparametric analysis of restricted mean survival time

The restricted mean survival time (RMST) evaluates the expectation of survival time truncated by a prespecified time point, because the mean survival time in the presence of censoring is typically not estimable. The frequentist inference procedure for RMST has been widely advocated for comparison of two survival curves, while research from the Bayesian perspective is rather limited. For the RMST of both right- and interval-censored data, we propose Bayesian nonparametric estimation and inference procedures. By assigning a mixture of Dirichlet processes (MDP) prior to the distribution function, we can estimate the posterior distribution of RMST. We also explore another Bayesian nonparametric approach using the Dirichlet process mixture model and make comparisons with the frequentist nonparametric method. Simulation studies demonstrate that the Bayesian nonparametric RMST under diffuse MDP priors leads to robust estimation and under informative priors it can incorporate prior knowledge into the nonparametric estimator. Analysis of real trial examples demonstrates the flexibility and interpretability of the Bayesian nonparametric RMST for both right- and interval-censored data.
Vertical Handover Decision using Machine Learning in Vehicular Platooning

This paper addresses the vertical handover (VHO) decision for vehicle platoons in heterogeneous vehicle networks. In fact, in this context, a vehicular on-board unit may be attached to different radio frequency technologies. More specifically, we consider that a vertical handover should be triggered by the degradation of the quality of service (QoS) level due to disruptive vehicles. In this context, we propose a vertical handover decision based on different machine learning techniques. We elaborate a detailed review of various machine learning techniques and prove that the random forest is the most appropriate technique for our considered scenario.
The Construction of Hybrid Learning Mode Based on Blackboard Network Platform

For cultivating students’ innovative consciousness and practical ability, and improve the quality of experimental teaching, the article explores the experimental teaching and management based on the network platform under the background of the new education reform, first points out the characteristics of the Blackboard network teaching platform, and then explains the Blackboard network experiment. The technical requirements of teaching course design discussed the requirements of Blackboard network experimental teaching content setting again and finally proposed the construction of hybrid learning mode according to Blackboard network platform.
System Design and Development

This chapter presents the Socrates DigitalTM system's design and development process. It describes the four phases of design and development: understand, explore, materialize, and realize. The completion of these four phases results in a Socrates DigitalTM system that leverages artificial intelligence services. The artificial intelligence services include a natural language processor provided by several artificial intelligence service providers, including Apple, Microsoft, Google, IBM, and Amazon.
INTERPERSONAL SPACE AS CONTEXT FOR 21ST CENTURY ADULT LEARNING

All analyses (quantitative, qualitative) of issues related to the discipline's functioning indicate that in Central and Eastern Europe, the research, publishing and environmental activity of adult-education researchers remains low compared to such centres as Sweden and Portugal.The present publication, therefore, may serve as an important element, with its inclusion of discourses and research results from Polish, Czech, Slovak, Ukrainian and Russian universities, in the awareness of a wider audience, along with conveying messages about research topics, developing andragogical theories and scientific activities of local andragogical communities in this region of Europe.Our publication is also an attempt to establish a critical dialogue with the publications mentioned above -while they are from the West, we will show perspectives from our part of Europe, perhaps still little known and underrecognised among other research circles.This publication's aim is to reflect theoretically on contemporary forms of lifelong educational activity among adults, groups and communities, and to present research results concerning the areas, models, goals and functions of adult education, teacher and educator competences, and adult-learning processes in Central and Eastern European countries.In the pages that follow, the reader will find the results of research related to contemporary adult-learning spaces, presented from different methodological perspectives and introducing different points of view.The editors' aim has been to create a space for discussion, an opportunity to present the latest research findings, thoughts and ways of interpreting educational reality.We seek to explore conditions shaping contemporary adult-learning spaces, including a comprehensive comparative perspective which holds great cognitive value, further enriched by the publication's international form.Readers are introduced to trends in developing andragogical theory and practice, and to informal learning areas as well.Attention is paid to (auto)reflection, whether shaped through knowledge or by individual experience and even common knowledge.The turn towards positively evaluating the idea of localism and social commitment has brought significant changes in many areas of contemporary adult education.Problems that adult educators face are also raised here, both in terms of their key competences and challenges they must contend with today.Institutional education is a space that researchers have abandoned in recent decades, yet it merits indepth reflection, exploration and reflection.Activities carried out by associations and foundations, the dissemination of knowledge, activities of open-educational institutions, the sector of non-public educational services: these important topics can all inspire contemporary researchers and practitioners in the adult-education field.We invite the reader to treat this publication as an inspiring, comprehensive,
Multimodal Meme Sentiment Analysis with Image Inpainting

In the contemporary world, social media has proved to be a crucial platform in shaping and influencing the public view. With the rise in popularity of platforms such as Instagram, Facebook, Twitter, etc. we have seen an equal rise in the prevalence of internet memes. Memes are used by people to share their experiences and convey their opinions. To get a broader understanding of memes, this paper presents a Multimodal model for Sentiment Analysis of Memes with an accuracy of 78.16% which beats the existing methods with the same objectives.
Diversity and Importance of Diversity and Inclusion in the Education System

Diversity and inclusion is one of the most crucial parts of our education system. It adds value and purpose to teaching. Dictating the emphasis paid on equality and liberation, the message that gets conveyed is love, respect and equity that by all means will be practiced and preached. Out of many known benefits, one of the major advantages is the confidence that gets spurred up in the eyes of our nation builders when they know that they are accepted and not discriminated. The article talks about the importance and relevance of diversity and inclusion. Furthermore, it also speaks on how to effectively practice and incorporate some of the widely known and acceptable strategies that’d ensure that it is not just the concept that is told but it could be practiced too. The main emphasis placed in the entire article is to convey the message to everyone within the education system to now gear up and change some of the traditional ideas and teaching methods. While doing so, and learning the newest ways, as mentioned, would give a new direction and meaning to what already existed but was not stressed on so much.
An Approach to Balance the Load in Cloud Environment

In today’s era, cloud computing is the most interesting technology in the field of computer science. While there are numerous concerns in this field like power management, security & the most complex concern is balancing the load. Numerous algorithms are being developed for load balancing in cloud computing. Load Balancing is mainly the process for the workload distribution across multiple servers. Attaining a high user gratification and resource utilization has ever been a remarkable topic for the researchers. In this research paper, we suggested a new approach which describes how to balance the workload using ant colony optimization.
An In-Depth Analysis of Automatic Speech Recognition System

Recently broad steps have been made in the field of automatic speech recognition by applying the hidden Markov model, and different end-to-end deep learning models. The automatic speech recognition system is a conversion of human speech into the text or control signal by the means of smart methods and performs an essential part in multiple biometric authentication systems and voice-controlled automation systems. In this study, we review the benefits and limitations of the hidden Markov model-based design and end-to-end approaches, which are connectionist temporal classification-based, recurrent neural network-transducer, and attention-based. Their respective advantages and disadvantages and the possible future improvement of the end-to-end approaches are finally pointed out. We reported every factor that potentially influences the accuracy of automatic voice recognition. As a result, we believe this paper will serve as a suitable starting point for academics interested in automatic speech recognition.
The conceptual system of Russian didactics in the limelight of subject orientation of modern education

This article examines the conceptual system of didactics in the conditions of integration of the modern scientific knowledge. An attempt is made to determine the method for overcoming the current deficit of specific didactic concepts, which reflect the essence and peculiarities of the subject of didactics in the conditions of dynamically changing characteristic of the object of study &amp;ndash; the learning process that acquires fundamentally new value and performance goals. The relevance of this topic lies in the fact that the use of solely psychological methods for description and explanation of the new phenomena of learning process, substantiated by the subject context of its consideration, contradicts the subject of didactics, which should focus not on the psychological mechanisms that characterize subjectivity, but on answering the question of what needs to be done for the students to take subjective position in the course of their educational activity. The scientific novelty consists in substantiation of the variant of broadening the conceptual system of modern didactics through introduction of the new didactic concept that aligns with the specifics of its subject &amp;ndash; student's subjective learning experience. The introduction of this concept reflects the trend that is important for the current stage of the development of didactics &amp;ndash; shift to a fundamentally new level that corresponds with post-nonclassical type of scientific rationality, when unlike previous approaches that focused on the educational activity of the pedagogue, who organizes and maintains learning activity of the student, central figure in didactic problematic is the student and their educational activity,
Application of Educational Informatization in College Teaching

It is an important task of current educational reform to use modern information technology to promote educational reform and development in the field of education. Educational informatization is not a simple addition of modern information technology and education, but a deep integration of the two. The core content of educational informatization is teaching informatization. It is of great practical significance to study the application of educational informatization in college teaching. This paper analyzes and discusses the present situation and concrete application of educational informatization in the teaching practice of colleges and universities, so as to help improve the degree of informatization and improve the quality and effect of teaching in colleges and universities.
Severity Classification of Mental Health Related Tweets

The use of social media has drastically gone up over the last decade. With this comes more opportunity and also more problems. There is a rise in the number of mental health-related issues, and it is to some extent possible to detect such cases via user posts and tweets (in our case). Previous research has focused on classifying mental health diseases such as depression, bipolar disorder, schizophrenia, etc., from already filtered data. However, not much has been done to filter out tweets that might be sarcastic, which are generally misclassified, or tweets that might not be intended in a harmful way and, in general, classify tweets based on their severity with regards to mental health. This paper uses multiple models to classify tweets based on their severity and classify them into three classes that help determine whether they help people with mental conditions or sarcasm. We use famous neural network architectures such as Bidirectional LSTMs, GRUs, and a custom HYBRID model to carry out the classification. The models could detect sarcasm in tweets and identify tweets that were helpful despite having words like “depression” and “anxiety.” We obtained F1 scores of 74% on completely unseen data, which is a good starting point considering the limited available data. This paper should serve as a utility for future research in this area and act as a primary data collection and segregation filter.
Visual Servo Control of a Legged Mobile Robot Based on Trifocal Tensor*

This paper investigates a control scheme for the visual servoing of a legged mobile robot equipped with a fixed monocular camera. The kinematics of the legged mobile robot and trifocal tensor-based visual servoing are employed to allow the robot to achieve the desired location. By investigating the trifocal tensor between the initial, current and desired location, the approach has no need for transcendental knowledge of the three-dimensional geometry of the observation target. The feature points are directly extracted from the images to evaluate the trifocal tensor. Furthermore, a controller is designed for visual servoing task, and a Lyapunov-based approach is used to analyze the stability of the control system. With the aid of MATLAB software, a simulation is implemented to verify the effectiveness of the proposed method.
A Toolkit for Modeling and Simulation of Dynamic Virtual Machine Consolidation Approach

Conducting realistic cloud experiments to evaluate new management strategies is very expensive and risky. In recent years, the research community proposes various simulation software toolkits and frameworks to provide a platform and the necessary building blocks for modeling an optimized virtual machine consolidation in cloud data centers. The models proposed by these frameworks are usually not exhaustive and address a specific management problem or task. The proposed simulation toolkit addresses the dynamic virtual machine consolidation problem, provides various types of simulation, ensures a wide range of logging and debugging information such as performance indicators and charts, and allows to determine optimal model parameters for various modes of operation minimizing the number of active physical machines and decreasing the number of SLA violations.
Graph Classification with Minimum DFS Code: Improving Graph Neural Network Expressivity

Graph neural networks (GNNs) generally follow a recursive neighbors aggregation scheme. Recent GNNs are not powerful than the 1-Weisfeiler Lehman test, which is a necessary but insufficient condition for graph isomorphism, hence limiting their abilities to utilize graph structures properly. Moreover, deep GNNs with many convolutional layers suffer from over-smoothing, thus cannot capture long-range dependencies. As a result, downstream applications, such as graph classification, are impacted. To this end, we design GNNs on top of the minimum DFS code, which is a canonical form of a graph, and being injective it captures the graph structure precisely along with node and edge labels. Due to the sequential structure of the minimum DFS code, we employ state-of-the-art RNNs (LSTM, BiLSTM, GRU) and Transformer-based sequence classification techniques. While one can compute the minimum DFS code efficiently in practice, LSTM, BiLSTM, GRU, and Transformers capture long-term dependencies in arbitrary length sequences. We also consider a novel variant of the minimum DFS code, which is not injective, but it reduces the complexity of the feature space, increases generalizability, and also improves the classification performance over many real-world graph datasets. Our thorough empirical comparisons with six real-world network datasets demonstrate the accuracy and efficiency of our methods. We have open-sourced our solution framework [17] in which one can plug in different graph datasets and get their classification results. This will benefit researchers and practitioners, biologists, social scientists, and data scientists, among others.
Clustering and anomaly detection of crowd hotspot regions

Real-time detection of crowd hotspots in a metro station can accurately induce evacuation of high-density passenger flow. Anomaly detection can effectively avoid the occurrence of safety accidents. This chapter studies clustering and anomaly detection of crowd hotspot regions based on crowd Global Positioning System (GPS) trajectory data. Firstly, the GPS data is described and preprocessed, and then the clustering effects of K-means and density-based spatial clustering of applications with noise (DBSCAN) methods are compared. Finally, the Markov chain is used to realize the anomaly detection of individual behaviors and mark the area where abnormal distribution of the crowd might occur in smart metro stations.
Multi-preference Social Recommendation of Users Based on Graph Neural Network

In recent years, social-based recommendation uses social information to alleviate the problems of cold start and data sparseness in traditional recommendation, which is widely used in e-commerce and movie recommendation. However, most of the existing researches make recommendations based on users' short-term preferences or historical records, and fail to fully dig out the user's preference characteristics. This paper proposes a user long-term and short-term preference model based on graph neural network, which uses the nodes of the user's social graph and the user's product graph to aggregate neighbor information, and iteratively updates the feature representation of the target node. The gated recurrent units is used to extract the long-term and short-term preferences of users, and the attention mechanism is used for weight distribution. In addition, we use specific vectors to represent the long-term characteristics of slow changes in users. By conducting experiments on two commonly used data sets, it can be shown that our proposed model is better than the compared baseline method.
Can Association Rules be Used to Improve Additive Bayesian Network Models?

The study of antimicrobial resistance patterns is a challenging task due to the complexity of the mechanisms involved and the potentially large number of relations that must be inferred. Additive Bayesian networks have proved to be a useful tool in this respect, but when data is scarce, modeling results can be improved by integrating complementary information in the learning process. We propose an experimental study to investigate the benefits of including information extracted by association rule mining as prior knowledge into the inference of additive Bayesian networks. We start with an existing additive Bayesian network, generate synthetic datasets from the joint probability distribution that it represents, and attempt to retrieve the original network structure from these datasets by means of model averaging based on a structural Markov Chain Monte Carlo approach, using uninformative priors as well as priors based on the conviction of corresponding association rules. We show that the conviction-guided approach performs consistently better in identifying the true dependency patterns on the simulated datasets.