Reviewerly/exHarmony huggingface dataset:
Cybersickness Testing Of Gender And Experience Factors Using Virtual Reality

One of the technologies that people are starting to get interested in is virtual reality (VR). VR is widely used as a means of entertainment, even more so at this time, the e-sports industry is developing rapidly. However, the use of VR can cause cybersickness, a disease arising from sensory and perceptual mismatches between the visual and vestibular systems. The emergence of cybersickness can be related to gender and experience using VR. There have been studies on cybersickness, but the results obtained had not come to the same conclusion. This research aims to identify the effect of gender and experience using VR, predict the timing of cybersickness by using physiological measurements, and provide recommendations that can minimize cybersickness in activities using VR. The measuring instruments used are the galvanic skin response (GSR) and a simulator sickness questionnaire (SSQ). In this study, the influence test is conducted by using ANOVA and Kruskal-Wallis to determine whether gender and experience using VR affect the potential for cybersickness. Based on the GSR measurement results, it found that gender and experiences of using VR do not affect a person's potential for cybersickness. From the result of SSQ measurement, gender does not affect the cybersickness, but the experience of using VR affected a person's potential for cybersickness. Qualitatively, cybersickness symptoms appear in the 15-20 minutes after the VR game has set in. Therefore, it is recommended to limit the usage of VR to less than 15 to 20 minutes per session.&#x0D; Keywords: cybersickness, galvanic skin response (GSR), simulator sickness questionnaire (SSQ), virtual reality
Using Graphic Organizers to Improve Students’ Reading Comprehension at Grade XI of SMAN 3 Lubuk Basung

The background of this research is the students who their achievement is still under the minimum standard (KKM) of learning success and most of them are unwilling to pay serious attention to the text they are reading.This research aims to solve students' problem in learning process especially reading comprehension using graphic organizer.The method used is classroom action research (CAR).The data were collected trough reading test, observation, field note and interview.The result showed that students' reading comprehension is improved by using graphic organizer technique.The average of their reading test in pre-test is 61, 67, 71.25 is in cycle one and 78.75 is in cycle two.Furthermore, graphic organizer also gives significant effect such as students are more active and confidence in teaching and learning process and they could comprehend text well.
RelWalk - A Latent Variable Model Approach to Knowledge Graph Embedding

Embedding entities and relations of a knowledge graph in a low-dimensional space has shown impressive performance in predicting missing links between entities.Although progresses have been achieved, existing methods are heuristically motivated and theoretical understanding of such embeddings is comparatively underdeveloped.This paper extends the random walk model (Arora et al., 2016a) of word embeddings to Knowledge Graph Embeddings (KGEs) to derive a scoring function that evaluates the strength of a relation R between two entities h (head) and t (tail).Moreover, we show that marginal loss minimisation, a popular objective used in much prior work in KGE, follows naturally from the loglikelihood ratio maximisation under the probabilities estimated from the KGEs according to our theoretical relationship.We propose a learning objective motivated by the theoretical analysis to learn KGEs from a given knowledge graph.Using the derived objective, accurate KGEs are learnt from FB15K237 and WN18RR benchmark datasets, providing empirical evidence in support of the theory.
Elderly Fall Detection using Lightweight Convolution Deep Learning Model

Old people, who are living alone at home face serious problem of Falls while moving from one place to another and sometime life threading also. In order to prevent this situation, several fall monitoring systems based on sensor data were proposed. However, there was an issue of misclassification to identify the fall as daily life activities and also routine activity as fall. Towards this end, a deep learning based model is proposed in this paper by using the data of heart rate, BP and sugar level to identify fall along with other daily life activities like walking, running jogging etc. For accurate identification of fall accidents, a publicly accessible data collection and a lightly weighted CNN model are used. The model reports proposed and 98.21 % precision.
Defeating automotive security systems and exploiting rules of law as the modi operandi of car thieves

Defeating automotive security systems and exploiting rules of law as the modi operandi of car thieves
Feature Selection: An Assessment of Some Evolving Methodologies

Feature selection has predominant importance in various kinds of applications. However, it is still considered as a cumbersome process to identify the vital features among the available set for the problem taken for study. The researchers proposed wide variety of techniques over the period of time which concentrate on its own. Some of the existing familiar methods include Particle Swarm Optimisation (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA). While some of the methods are existing, the emerging methods provide promising results compared with them. This article analyses such methods like LASSO, Boruta, Recursive Feature Elimination (RFE), Regularised Random Forest (RRF) and DALEX. The dataset of variant sizes is considered to assess the importance of feature selection out of the available features. The results are also discussed from the obtained features and the selected features with respect to the method chosen for study.
Prevention Children Sexual Abuse in Preschool with Picture Story Book

Background: Children sexual abuse (CSA) is a form of concern for parents and society, given the phenomenon that occurs is like an iceberg with an increasing prevalence every year. Parents play an important role in CSA prevention education, but the majority are confused about how to explain it. The picture book is expected to be a medium for educating parents to their children regarding the prevention of CSA in preschool children. Purpose: to produce a picture book as an effort to prevent KSA. Methods: This study used a research and development design. The development of this pictorial story book uses information from parents and teachers for initial data collection, while for product validation a team of experts consisting of material experts, media experts and linguists are used for product validation. Meanwhile, for the public test was 34 parents. The instruments used were interview guides and questionnaires. The data were analyzed qualitatively and quantitatively. Result: There is creation of ABIJAGARI a story book in the prevention of child sexual abuse that consist of 17 pages. Conclusion The picture story book entitled ABIJAGARI can be used as an alternative media in education on prevention of CSA
User-Controlled Content Translation in Social Media

As it has become increasingly common for social network users to write and view post in languages other than English, most social networks now provide machine translations to allow posts to be read by an audience beyond native speakers. However, authors typically cannot view the translations of their posts and have little control over these translations. To address this issue, I am developing a prototype that will provide authors with transparency of and more personalized control over the translation of their posts.
Classification of Fall Detection System for Elderly: Systematic Review

Elderly are the world’s largest growing population, categorized over the age of 60 to 65 years. They are the ones who prone to fall due to their old age and low self-efficacy, thus making them vulnerable to different accidents. Even doing daily activities can also expose the elderly to a fall incident. As a result, it has gained the attention of many researchers in conducting studies related to the elderly daily health care, especially in relation to the fall detection system. This paper aims to provide a systematic review on the classification of fall detection systems for the elderly. This systematic review is designed based on the existing and extensive literature review on fall detection systems guided by the prisma statement (preferred reporting items for systematic reviews and meta-analyses) review method. Based on this systematic review, four overarching themes that provide in-depth information on fall detection to detect fall events have been identified; classification of fall detection, basis development, type of sensor and detection technique. In a nutshell, the fall detection approach has successfully provided an alternative health care services for elderly who choose to live independently. Therefore, it is important to continue to develop a fall detection system that integrates with technology in order to provide a safe living environment for elderly, and for children, it can offer as an alternative for monitoring systems.
The Effect of Magic Book to Increase Interest in Learning Mathematics of Primary School Students

Elementary School is a place to understand all learning for the next level, especially mathematics. Mathematics is also considered difficult by elementary school students because of its abstract and complex nature, especially with the large number of mathematical formulas, and its lack of interest in learning. So that student interest in learning is reduced. One interesting way to learn mathematics is by using learning media. This research introduces MagicBook contains a collection of formulas and mathematical games. This Magicbook is arranged in such a way with an attractive layout and color. Sources of data obtained from questionnaires and direct research on elementary school students in one of the lessons in Yogyakarta. The data of this study were analyzed descriptively quantitative and qualitative. The results showed that the magic book learning media can increase elementary school students' mathematics learning interest.the effect.
Optimal Use Of ANN In The Integration Between Digital Image Processing And Encryption Technique

Recently An ANN is the very much powerful and strong technology for solving various real world-real time issues in area of image processing such as medical, research center etc. This paper represents the Artificial Neural network in combination with the image processing. The main aim of this paper is to study the ANN in integration with digital image processing and encryption technologies. So in this sense we discussed the basics of image processing, digital image processing, artificial neural network and encryption. With the help of proposed block diagram we discussed ANN with and without encryption. When an ANN is used with digital image processing several algorithms are available. Here we discussed the few algorithms in shortly with their iteration equation used at time of learning the ANN.
Framework for the Comparison and Selection of Schemes for Multi-Factor Authentication

Authentication is the process of verifying a user’s identity for them to access a system’s resources. An authentication factor is a piece of information used for this authentication. Three well-known groups of authentication factors exist: knowledge-based (what you know), possession-based (what you have) and inherence-based (what you are). Authentication schemes belonging to distinct authentication factors can be combined in a multi-factor manner to increase security. Although multiple multi-factor proposals are seen in literature, the absence of a method that allows a proper comparison and selection of these authentication methods, based on an application’s security requirements, can be observed. Existing frameworks for the analysis of authentication methods have been identified through the realization of a systematic literature review, but most of these focus on specific contexts and do not provide a generic enough solution. Due to the above, this research focuses on the creation of a recommendation framework that guides in the comparison and selection of single and multi-factor authentication schemes, considering both the application’s requirements and its context. This has been attained not only through the knowledge found in literature, but the experience from industry experts has been compiled as well through the collaboration with a multinational software development company. Consequently, the knowledge found in literature has been obtained from a systematic literature review, whereas the experience from industry experts was obtained through a survey and interviews. The framework proposal has been generated based on the above and has been validated through an expert panel and a case study methodology in collaboration with the partnered software development company. A tool prototype has been constructed as well. The result is a recommendation framework for the comparison and selection of authentication methods that can support this decision process in multiple contexts.
AUGMENTED REALITY AND VIRTUAL REALITY IN RETAIL - A BIBLIOMETRIC ANALYSIS

Purpose – “Augmented and Virtual Reality” is a trend in retail that is lately catching a lot of attraction. These interactive technologies enhance the retail process by offering a better shopping experience and affects consumer's preference towards the retailers. Retailers need to align their business model to counteract the changing traffic patterns. This study will help in inferring trends, themes researched, identifying the prolific authors, researchers, and present the big picture.&#x0D; Methodology - To understand the current research state, Biblioshiny and VOS viewer has been employed to conduct a quantitative study on research papers done in the field of AR/VR in retail during the period 1993-2020 from the “Web of Science core database”. &#x0D; &#x0D; Findings - (1) Increase in publication of papers in the past 27 years, divided into 2 parts: a low exploration period and developmental period. (2) The research covers 38 countries. With respect to the research volume, the top five countries being – (i) “China”, (ii) “the United States”, (iii) “the United Kingdom”, (iv) “Germany”, and (v) “South Korea”. There has been a lot of collaboration between these countries, with maximum collaboration between the USA and China. (3) Virtual Reality, Retailing, Interactivity are amongst the trending keywords in this field in recent years. (4) The main research themes in area of AR/VR in retail mainly focuses on the research directions like – the adoption of the technology, impact on consumer behavior, and performance and user's information control.&#x0D; &#x0D; An overview of present state of academic studies was generated by employing a knowledge-based map which recognises researcher collaboration networks. This study is riveting as it has rarely been examined in this in a new manner.
Game theory framework for agent decision-making in communication constrained environments

We have designed a game theory based framework in order to compute effective agent asset laydown and courses of action (COAs) for adversarial scenarios. Our technical approach is based on Stackelberg security game theory, which is a specialization of game theory for adversarial situations and deterrence. Security games approaches provide a scalable optimization framework to determine geospatial COAs for agents. Specifically it can exploit intelligence about adversaries by constraining courses of action search and eliminating dominated COAs. Several issues arise in providing a game theory for a communication-constrained environment. Current minimax payoff models for sensing an adversary/obstacles consider the probability for the defender to sense an adversary. For limited acoustic sensing, this term now has path dependence such as building interiors and areas with transmission issues. Next, an extension to account for loss or degradation of defender assets is required. A candidate solution being considered is to have an agent update/choose degraded contingency strategies at each communication. We are also evaluating providing refined strategies as a function of time if communications are out and how to account for effect of uncertainty in our knowledge of agent member loss for updated strategies. We are employing simulators at NRL DC to model multiagent trajectories and allowing the testing of the game theory approach based on environmental conditions.
Sparsity Increases Uncertainty Estimation in Deep Ensemble

Deep neural networks have achieved almost human-level results in various tasks and have become popular in the broad artificial intelligence domains. Uncertainty estimation is an on-demand task caused by the black-box point estimation behavior of deep learning. The deep ensemble provides increased accuracy and estimated uncertainty; however, linearly increasing the size makes the deep ensemble unfeasible for memory-intensive tasks. To address this problem, we used model pruning and quantization with a deep ensemble and analyzed the effect in the context of uncertainty metrics. We empirically showed that the ensemble members’ disagreement increases with pruning, making models sparser by zeroing irrelevant parameters. Increased disagreement im-plies increased uncertainty, which helps in making more robust predictions. Accordingly, an energy-efficient compressed deep ensemble is appropriate for memory-intensive and uncertainty-aware tasks.
ESTIMASI KURVA REGRESI SPLINE PADA DATA LONGITUDINAL DENGAN METODE KUADRAT TERKECIL

This article examines the estimation of spline regression, especially its use in longitudinal data. Longitudinal data is data obtained based on observations made as many as n objects that are independent with each object being repeatedly observed in different time periods and between observations in the same object are dependent besides longitudinal data is data that can distinguish the diversity of responses caused by The regression curve of the spline was estimated using the least squares. It can be seen that the estimation of the spline regression curve for longitudinal data is a class of linear estimation in response observations and is highly dependent on the knots point k1,k2,...,Kn
Toward Next-Generation Robust Cryptosystems

A variety of technologies including Internet-of-Things (IoT), IoT-edge/fog computing, and embedded systems are gaining more importance in designing smart applications (e.g., smart healthcare) with minimal human interventions. All the devices and datacenters in these applications are connected to the Internet. Considering the Internet properties, cybersecurity attackers get an easy chance to become part of the system and participate in the data communication process. This article presents thoughts on paradigm-shift next generation cryptosystems to overcome the vulnerabilities of the omnipresent conventional cryptosystems.
Developing Students' 'Creative Thinking' Skills In The Course Of ‘Law Of Conservation'

This article discusses the opportunities for 10th and 11th graduators in developing “imagination” and “creative thinking” skills. In the teaching of the natural sciences, especially physics, students are required to "indirectly imagine" physical processes. This, in turn, helps students develop 'imagination' and 'creative thinking' skills. The biggest problem in explaining physical processes is the processes that take place in the micro and macrocosm, and the difficulties in visualizing the quantities that characterize them. This problem can be solved with the help of "Mind Map", which serves to visualize the data. For the first time, the article presents the "Laws of Conservation" and the "Law of Conservation and Circulation of Energy" mind maps. They not only help students understand the topics, but also help them develop their “imagination” and “creative thinking” skills.
The Cybersecurity of The Future Nevadans

With the increased level of digitization, the role of cybersecurity has been at the forefront of security discussion. However, the literature displays a lack of proper knowledge and preparation in everyday individuals. This study examined the potential need for and implementation of cybersecurity education in CCSD. The results indicated interest within students and an advocation by cybersecurity experts. This study concludes and advises for an awareness training campaign, especially a real-life treat simulation.
GPRStudio: an extensible cross-platform GPR signal processing tool

In this study, we present a new, user friendly, easily extensible and platform independent (cross-platform) GPR signal processing tool named GPRStudio. The tool contains numerous pre-processing and post processing techniques such as background subtraction, various filtering, adjustable gain function, visualization of data, and automatic detection of buried objects. The tool has been developed in Python which is a very popular programming language with its rich and versatile free library alternatives. It has been aimed that the tool can be used on Windows, Linux and MacOS. For this reason, Qt5 is used in graphical user interface design. As an innovative approach to existing GPR signal processing tools, the GPRStudio allows users to write and import their own signal processing algorithms coded in Python. Thus, users can easily observe the effects and results of their own algorithms on GPR data. The project-based structure of the GPRStudio allows the user to work on different collections of GPR data without mixing things up and keep detailed log of every processing step. Users can also import multiple raw GPR data to a project. A raw GPR data can be used in multiple series of processing steps. The tool supports common GPR data types, such as GSSI’s “.dzt”, Sensors and Software’s “.dt1”, MALA’s ”.rd3" and “.rd7” and ASCII “.txt”. Processed data can be exported as “.csv”, “.txt” or “.jpg”.
The Gutenberg Dialogue Dataset

Large datasets are essential for neural modeling of many NLP tasks.Current publicly available open-domain dialogue datasets offer a trade-off between quality (e.g., DailyDialog (Li et al., 2017b)) and size (e.g., Opensubtitles (Tiedemann, 2012)).We narrow this gap by building a high-quality dataset of 14.8M utterances in English, and smaller datasets in German, Dutch, Spanish, Portuguese, Italian, and Hungarian.We extract and process dialogues from public-domain books made available by Project Gutenberg 1 .We describe our dialogue extraction pipeline, analyze the effects of the various heuristics used, and present an error analysis of extracted dialogues.Finally, we conduct experiments showing that better response quality can be achieved in zero-shot and finetuning settings by training on our data than on the larger but much noisier Opensubtitles dataset.Our open-source pipeline 2 can be extended to further languages with little additional effort.Researchers can also build their versions of existing datasets by adjusting various trade-off parameters.