Reviewerly/exHarmony huggingface dataset:
Virtual Reality as a Tool in the Classroom and Integrating Technology into Higher Ed Curriculum: An Abstract

The use of digital technologies has exponentially grown across all academic levels. This has opened more opportunities for educators to embrace these digital technologies and use them in marketing curriculum. In an ability to also enhance students’ learning experience, the use of Virtual Reality (VR) has become a growing digital technology. VR can be defined as “a medium composed of interactive computer simulation that sense the participant’s position and actions and replace or augment the feedback to one or more senses, giving the feeling of being mentally immersed or present in the simulations (a virtual world)” (Sherman and Craig 2002, p. 16). This mixed methods study investigates adoption of VR applications. VR applications are software that enhances an educational experience. In Study 1, two 1-h long focus groups (n = 20) were conducted. Participants had previously experienced Ovation VR, a VR application used to enhance presentation skills. Five major themes emerged: immersion, engagement, enjoyment, satisfaction, and perceived learning effectiveness. These five major themes were integrated with the Technology Acceptance Model (TAM) for Study 2. Students will have a brief presentation introducing themselves and giving interests. Participants will log-in using their account profile, start their presentation recording, complete their presentation, and evaluate their grading. Upon completing these steps, participants will finish and exit the VR lab. They will then participate in an online survey including construct-related items and demographic questions. Data will be analyzed using Structural Equation Modeling (SEM). Study 1 has identified major themes when adopting VR applications which adds to the digital technologies’ literature. It has also further developed the literature on student learning experiences through gamification. Study 2 will then expand TAM literature with an adaptation to the model that integrates the five major themes identified in Study 1. Marketing educators will also have a blueprint to integrating VR applications into their marketing courses. And finally, this study expands the VR literature in marketing education.
Research on the Similarity between Nodes with Hypernymy/Hyponymy Relations based on IC and Taxonomical Structure

The similarity method has an important effect on some tasks of natural language processing, such as information retrieval, automatic translation and named entity recognition. Hypernymy/hyponymy relations are widespread in semantic webs and knowledge graphs, so computing the similarity of hypernymy/hyponymy is a key issue in the text processing field. All measures of both feature-based and IC-based methods have obvious deficiencies. The feature-based method estimated the similarity by the depth of the node, and the IC-based method computed the similarity by the position of the deepest common parent. The deficiency of the feature-based method and IC-based method is that they include one parameter, so the performance is slightly inaccurate and unstable. To address this deficiency, our paper proposed a hybrid method that computes the similarity of hypernymy/hyponymy by a hybrid parameter (dhype(lch)) that implies two parameters: depth of the node and position of the deepest common parent. Compared with several similarity methods, the proposed method achieved better performance in terms of accuracy rate, Pearson correlation coefficient and artificial fitting effect.
Soft Voting-based Ensemble Model for Bengali Sign Gesture Recognition

Human hand gestures are becoming one of the most important, intuitive, and essential means of recognizing sign language. Sign language is used to convey different meanings through visual-manual methods. Hand gestures help the hearing impaired to communicate. Nevertheless, it is very difficult to achieve a high recognition rate of hand gestures due to the environment and physical anatomy of human beings such as light condition, hand size, position, and uncontrolled environment. Moreover, the recognition of appropriate gestures is currently considered a major challenge. In this context, this paper proposes a probabilistic soft voting-based ensemble model to recognize Bengali sign gestures. We have divided this study into pre-processing, data augmentation and ensemble model-based voting process, and classification for gesture recognition. The purpose of pre-processing is to remove noise from input images, resize it, and segment hand gestures. Data augmentation is applied to create a larger database for in-depth model training. Finally, the ensemble model consists of a support vector machine (SVM), random forest (RF), and convolution neural network (CNN) is used to train and classify gestures. Whereas, the ReLu activation function is used in CNN to solve neuron death problems and to accelerate RF classification through principal component analysis (PCA). A Bengali Sign Number Dataset named “BSN-Dataset” is proposed for model performance. The proposed technique enhances sign gesture recognition capabilities by utilizing segmentation, augmentation, and soft-voting classifiers which have obtained an average of 99.50% greater performance than CNN, RF, and SVM individually, as well as significantly more accuracy than existing systems.
A Two-Step Approach for Explainable Relation Extraction

Knowledge Graphs (KG) offer easy-to-process information. An important issue to build a KG from texts is the Relation Extraction (RE) task that identifies and labels relationships between entity mentions. In this paper, to address the RE problem, we propose to combine a deep learning approach for relation detection, and a symbolic method for relation classification. It allows to have at the same time the performance of deep learning methods and the interpretability of symbolic methods. This method has been evaluated and compared with state-of-the-art methods on TACRED, a relation extraction benchmark, and has shown interesting quantitative and qualitative results.
Rethinking Collaborative Clustering: A Practical and Theoretical Study Within the Realm of Multi-view Clustering

WithMulti-view clustering distributed and multi-view dataMulti-view data being more and more ubiquitous, the last 20 years have seen a surge in the development of new multi-view methods. In unsupervised learning, these are usually classified under the paradigm of multi-view clusteringMulti-view clustering: A broad family of clustering algorithms that tackle data from multiple sources with various goals and constraints. Methods known as collaborative clusteringCollaborative clustering algorithms are also a part of this family. Whereas other multi-view algorithms produce a unique consensus solution based on the properties of the local views, collaborative clusteringCollaborative clustering algorithms aim to adapt the local algorithms so that they can exchange information and improve their local solutions during the multi-view phase, but still produce their own distinct local solutions. In this chapter, we study the connections that collaborative clusteringCollaborative clustering shares with both multi-view clusteringMulti-view clustering and unsupervised ensemble learning. We do so by addressing both practical and theoretical aspects: First we address the formal definition of what is collaborative clusteringCollaborative clustering as well as its practical applications. By doing so, we demonstrate that pretty much everything called collaborative clusteringCollaborative clustering in the literature is either a specific case of multi-view clusteringMulti-view clustering, or misnamed unsupervised ensemble learning. Then, we address the properties of collaborative clusteringCollaborative clustering methods, and in particular we adapt the notion of clustering stabilityStability and propose a bound for collaborative clusteringCollaborative clustering methods. Finally, we discuss how some of the properties of collaborative clusteringCollaborative clustering studied in this chapter can be adapted to broader contexts of multi-view clusteringMulti-view clustering and unsupervised ensemble learning.
The career guidance system as a means to support professional self-determination of adolescents

The article deals with a relevant and important issue of professional selfdetermination of adolescents and describes options of career guidance for school students on the example of physics and mathematics, chemistry and biology lyceums and a secondary school. The conclusion is made about the importance of comprehensive support to students in professional self-determination and the significance of the career guidance system in modern society.
Algorithmic Harms as Corporate Misconduct

Algorithms offer many social benefits, but when they discriminate in lending, manipulate stock markets, or violate expectations of privacy, they can injure us on a massive scale. The problem is that algorithms fit poorly into existing conceptions of liability. Liability requires injurious acts, but what does it mean for an algorithm to act? This Chapter offers a solution. Corporations currently design and run the algorithms that have the most significant social impacts. Corporate law stipulates that corporations act through their employees because corporations have control over and benefit from employee conduct. This Chapter argues that the same control and benefit rationales could extend to corporate algorithms. If the law were to recognize that algorithmic conduct qua- lifies as corporate action, the existing framework of corporate liability would engage when corporate algorithms cause harm.
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data

A major challenge in embedding or visualizing clinical patient data is the heterogeneity of variable types including continuous lab values, categorical diagnostic codes, as well as missing or incomplete data. In particular, in EHR data, some variables are missing not at random (MNAR) but deliberately not collected and thus are a source of information. For example, lab tests may be deemed necessary for some patients on the basis of suspected diagnosis, but not for others. Here we present the MURAL forest – an unsupervised random forest for representing data with disparate variable types (e.g., categorical, continuous, MNAR). MURAL forests consist of a set of decision trees where node-splitting variables are chosen at random, such that the marginal entropy of all other variables is minimized by the split. This allows us to also split on MNAR variables and discrete variables in a way that is consistent with the continuous variables. The end goal is to learn the MURAL embedding of patients using average tree distances between those patients. These distances can be fed to nonlinear dimensionality reduction method like PHATE to derive visualizable embeddings. While such methods are ubiquitous in continuous-valued datasets (like single cell RNA-sequencing) they have not been used extensively in mixed variable data. We showcase the use of our method on one artificial and two clinical datasets. We show that using our approach, we can visualize and classify data more accurately than competing approaches. Finally, we show that MURAL can also be used to compare cohorts of patients via the recently proposed tree-sliced Wasserstein distances.
Investigation of contraction process issue in fuzzy min-max models

The fuzzy min-max (FMM) network is one of the most powerful neural networks. It combines a neural network and fuzzy sets into a unified framework to address pattern classification problems. The FMM consists of three main learning processes, namely, hyperbox contraction, hyperbox expansion and hyperbox overlap tests. Despite its various learning processes, the contraction process is considered as one of the major challenges in the FMM that affects the classification process. Thus, this study aims to investigate the FMM contraction process precisely to highlight its usage consequences during the learning process. Such investigation can assist practitioners and researchers in obtaining a better understanding about the consequences of using the contraction process on the network performance. Findings of this study indicate that the contraction process used in FMM can affect network performance in terms of misclassification and incapability in handling the membership ambiguity of the overlapping regions.
Differing benefits of artificial intelligence-based computer-aided diagnosis for breast US according to workflow and experience level

This study evaluated how artificial intelligence-based computer-assisted diagnosis (AICAD) for breast ultrasonography (US) influences diagnostic performance and agreement between radiologists with varying experience levels in different workflows.Images of 492 breast lesions (200 malignant and 292 benign masses) in 472 women taken from April 2017 to June 2018 were included. Six radiologists (three inexperienced [<1 year of experience] and three experienced [10-15 years of experience]) individually reviewed US images with and without the aid of AI-CAD, first sequentially and then simultaneously. Diagnostic performance and interobserver agreement were calculated and compared between radiologists and AI-CAD.After implementing AI-CAD, the specificity, positive predictive value (PPV), and accuracy significantly improved, regardless of experience and workflow (all P<0.001, respectively). The overall area under the receiver operating characteristic curve significantly increased in simultaneous reading, but only for inexperienced radiologists. The agreement for Breast Imaging Reporting and Database System (BI-RADS) descriptors generally increased when AI-CAD was used (κ=0.29-0.63 to 0.35-0.73). Inexperienced radiologists tended to concede to AI-CAD results more easily than experienced radiologists, especially in simultaneous reading (P<0.001). The conversion rates for final assessment changes from BI-RADS 2 or 3 to BI-RADS higher than 4a or vice versa were also significantly higher in simultaneous reading than sequential reading (overall, 15.8% and 6.2%, respectively; P<0.001) for both inexperienced and experienced radiologists.Using AI-CAD to interpret breast US improved the specificity, PPV, and accuracy of radiologists regardless of experience level. AI-CAD may work better in simultaneous reading to improve diagnostic performance and agreement between radiologists, especially for inexperienced radiologists.
Multi-modal Ensembles of Regressor Chains for Multi-output Prediction

Multi-target regression is a predictive task involving multiple numerical outputs per instance. In the domain of multi-label classification there exist a large number of techniques that successfully model outputs together. Classifier Chains is one such example that is naturally extendable to the multi-target regression task (as Regressor Chains). However, although this method is straightforward to adapt to the regression setting, large improvements over independent models (as seen already in the multi-label classification context over the recent decade) have not as of yet been obtained from Regressor Chains. One of the reasons for this is the adoption of squared-error-based loss metrics which do not require consideration of joint-target modeling. In this paper, we consider cases where the predictive distribution can be multi-modal. Such a scenario, which easily manifests in real-world tasks involving uncertainty, motivates a different loss metric and, thereby, a different approach. We thus present a new method for multi-target regression: Multi-Modal Ensemble of Regressor Chains (mmERC), which performs competitively on datasets exhibiting a multi-modal distribution, both against independent regressors and state-of-the-art ensembles of regressor chains. We argue that such distributions are not sufficiently considered in the regression and particularly multi-target regression literature.
NLP techniques, tools, and algorithms for data science

Natural language processing (NLP) is the subdomain of artificial intelligence that explores different methods of using computers to understand, analyze, and process the huge amount of human language or text. Technically, NLP focuses on knowledge acquisition and manipulation of the natural language in order to develop tools and techniques to help the computer understand and accomplish the desired task. The process of NLP is done in two phases: data preprocessing and development of an algorithm. This chapter summarizes these techniques. The tools used for NLP are also included in the discussion. NLP is used in text classification, extraction, summarization, and many other areas, which are also discussed in the chapter.
Snap Zones

In this chapter, we’ll take a look at Snap Zones provided by the VRTK. A Snap Zone is a spatial zone that an Interactable object can be dropped into. You can create a lot of different behaviors by snapping objects together. Snapping objects together involves inserting one object into another. Here, we will be inserting an Interactable into a Snap Zone. Some real-life examples would be snapping a wheel onto a car, inserting a magazine into a weapon, inserting a USB stick into a USB port, inserting inventory into slots, and inserting weapons into weapon Holsters.
Adopting a Proxy Database to Prevent Direct Access to Distributed Transaction Databases Ensuring Information Security

In this scientific and technical practical-oriented research paper, it is proposed a concept of Proxy Database to prevent direct access to distributed transaction databases and also ensuring the data and information security. The same approach has already been implemented in our one live project. A Proxy Database is created using a set of proxy tables and views from all relational distributed databases running for all different domains for any organization/institution. Creating a proxy table, this is an alternative way of looking at the data in one or more tables and this is also an alternate way of looking at the data through one or more views from relational distributed databases. Proxy table provides indirect access to the data in the distributed table(s) of the distributed databases. Location transparency of the local data at the local distributed databases will definitely be maintained by creating a proxy table. A proxy table will be mapped with the local distributed table and tables in the form of view. A schema for all proxy tables created, that resides in the Proxy Database, but the data resides in each local distributed databases. It is proposed here a practical framework to construct a Proxy Database with a set of proxy tables created from all local relational distributed databases which are being used for all different domains. Two or more proxy tables may further be merged to get results from two or more local distributed tables located in the respective distributed databases. Illustrated some methods of creating a Proxy Database with some examples. Illustrated about the query submitted on the proxy table without join, union or intersect and this is straightway go to the respective local distributed table(s) or tables in the form of view and sending back results to the concerned client user. Illustrated about the query submitted using join, union or intersect on two or more proxy tables, query decomposition with a set of sub queries going to the respective local distributed tables or tables in the form of view, execution of each individual sub-query at each local distributed database server and finally about the query composition of all sub-queries and sending back results to the concerned client user. Discussed well about the network security proposing a viable solution by implementing a software firewall. Discussed about the local distributed databases structure modifications and propagation to the Proxy Database so that all the local distributed databases and the Proxy Database remain consistent.
Digitalization: educational context

The article is devoted to identifying approaches to the phenomenon of digitalization in the educational context.&#x0D; The differences in the understanding of the concepts of "informatization" and "digitalization" are considered and the problems arising in the digital transformations of education are emphasized.&#x0D; The approaches of domestic scientists to digitalization of education are given and it is revealed, the main problem is seen in acquisition of digital skills by all participants of educational process, the main problem directions of researches are chosen. Autors of publications form models of competencies and function of a teacher in a digital educational environment.&#x0D; The article emphasizes the unrepresentation of works in which the authors would identify possible negative socio-cultural and moral problems of digitalization and suggest ways to prevent and overcome them.&#x0D; Adequate to modern problems of digitalization of education is the direction of research proposed by Ukrainian teachers called "digital humanistic pedagogy".&#x0D; Attention is paid to the new term "digital didactics" in the terminology of pedagogical research, the approaches to its understanding are singled out.&#x0D; Risks of humanitarian character which arise in the conditions of digitalization of education are revealed, attention is paid to cases of sharply negative attitude of teachers to distance learning.&#x0D; Emphasis is placed on the existence of "stress of social change", which is caused by the latest technological advances and the introduction of innovations that change the format of education, its didactic and technological components.&#x0D; It is concluded that the processes of digitalization of education require close attention to its socio-psychological component, information and psychological security of all participants in the educational process and further development of theory and practice of "digital humanistic pedagogy" — in its broadest sense.
Mobile Assisted Vocabulary Learning with Chinese EFL Learners: Effectiveness and Evaluation

This study aims to examine the effectiveness of vocabulary gains for EFL learners with intermediate English proficiency on one reading application "Fluent Reading" in a Chinese university. This reading treatment lasted for 30 days, and the 4 participants were asked to use this reading app every day. The data were collected through the vocabulary pre-test, and post-test to measure the learners’ vocabulary gains in the 100 randomly selected target words. The results revealed that relative large vocabulary gains may occur through mobile assisted language learning.