Reviewerly/exHarmony huggingface dataset:
The Impact of Ethical Leadership on Employee Performance: The Mediating Role of Employee Engagement and Organizational Citizenship Behavior (OCB)

A leader is a role model or example for subordinates in the organization whose attitude and behavior greatly affect the employees.Ethical leadership is needed for the company sustainability.According to previous studies, ethical leadership affects employees and overall organizational performance.This study aims to examine the effect of ethical leadership on employee performance mediated by employee engagement and Organizational Citizenship Behavior (OCB).Specifically, it clarifies the relationship between Ethical Leadership and Employee Performance mediated by employee engagement and Organizational Citizenship Behavior (OCB).Quantitative research methods and the Structural Equation Model were used to test the hypothesis.The respondents comprised of 207 outsourcing employees of service companies, such as banking and construction in Jakarta.The results show that Ethical Leadership positively affects Employee Performance with Employee Engagement as mediation.Furthermore, the effect of Ethical Leadership on Employee Performance is mediated by Organizational Citizenship Behavior (OCB).This contradicts a previous study, which showed that OCB is a mediating variable for the effect of ethical leadership on employee performance.However, this study contributes to knowledge about the relationship between ethical leadership and employee performance and helps understand the role of Organizational Citizenship Behavior (OCB).
Dampak Pembelajaran Daring Selama Pandemic Terhadap Kemampuan Kognitif Peserta Didik

Lately, we faced with situations that we have never experienced before. Our country has been attacked by a very dangerous virus for humans, the virus called Covid-19. Covid-19 is a virus that can be transmitted from one individual to another in a relatively short time. In order to minimize the spread of COVID-19, various policies have been put in place. The existence of Covid-19, which never end, had impacts on all aspects, such as economic, social, cultural aspects, and education is no exception. In the implementation of education, all levels of educators which were initially carried out by conducting face-to-face learning must now be carried out in a different way, namely online learning. The aims of writing this article is to explain how the impact of online learning on children's cognitive abilities during the covid-19 pandemic. This study was conducted using literature studies from various journals, and scientific writings and field observations during the pandemic. In addition, it is also done by direct participation during the online learning process. The conclusion from this article is that online learning during this pandemic has both positive and negative impacts on students' cognitive abilities.
Collaborative Multi-user Augmented Reality Solutions in the Classroom

Several educational institutions routinely recommend and use Augmented Reality in learning and teaching, for its potential benefits regarding an increase in the students’ motivation and attention as well as their knowledge retention. Most of the time, though, augmented reality applications consist of rather static, single-user experiences which do not fuel collaborative learning. In this paper, we present a novel augmented reality platform whose aim is to facilitate collaboration and interaction between teachers and students by adding synchronisation and multi-user capabilities. We present a case study demonstrating the capabilities of this platform for student-teacher and student-student collaboration.
Application of the KNN Algorithm for Predicting Data Card Sales at PT. XL Axiata Makassar

XL Axiata Company, TBK. Is an internet access service provider from the many operators in Indonesia and has to provide the best service in communication in the ease of communication services. The mainstay of communication service products owned by XL Axiata, TBK for the wider community is XL Prioritas Unlimited which includes three types of packages, namely silver, gold and platinum. People using these three packages have different levels of demand. Order the number of products ordered every month by the company, should be made based on the prediction of the number of previous sales. This study was conducted to assess product sales in the form of K-Nearest Neighbor (KNN) classification modeling. The number of datasets used is 500 records with details of 90% training and 10% testing. The results obtained are predictions of 0.98%.
The Effects of Immersive Virtual Reality Environments on Students' Academic Achievement: A Meta-analytical and Meta-thematic Study

The objective of this study is to ascertain the effects of immersive virtual reality environments on students’ academic achievement utilizing quantitative and qualitative approaches. First, databases of Web of Science, Google Scholar, ERIC, Proquest, YÖK Thesis Center, and ULAKBİM of Turkey were searched, and 31 studies (23 journal papers and 8 theses) were selected for the meta-analysis. In this context, the overall effect size on students' academic achievement was analyzed with the CMA program using subject area and grade level moderator variables. In the second part, content analysis was performed using the ATLAS.ti program for 8 studies selected for the meta-thematic analysis. it was found that immersive virtual reality environments had a positive, moderate (d = 0.526) effect on students' academic achievement. After the meta-thematic analysis, it was observed that immersive virtual reality environments had many positive effects on students' cognitive, affective, and psychomotor skills and that created a perception of reality and sense of presence in students, facilitates learning, increases motivation, creates a safe and interactive learning environment and make many contributions to learning. In addition, it was determined that there may be problems such as technical problems and health problems during the environments, and it was stated that measures should be taken.
Semiparametric Information State Embedding for Policy Search under Imperfect Information

We consider the problem of policy search in sequential decision making problems with imperfect information as encapsulated by a partially observed Markov Decision Process (POMDP) over possibly continuous state-spaces. In general, the optimal policy is history-dependent and the objective is non-convex in the policy parameters, making even stationary point policies challenging to ascertain. To address this problem class, we develop a constructive way to succinctly represent the history as an approximate information state, using Semiparametric Information State Embedding (SISE). SISE alternates between conditional kernel density estimation and fitting the parameters of an Echo State Network (ESN), a one-layer recurrent neural model. Based upon constructing SISE, we develop an actor-critic scheme for policy search over the approximate information states. Our main technical contributions are to (i) establish the convergence and generalization performance of SISE, and (ii) derive the convergence to stationary points of our policy search scheme. Experimentally, our fusion of SISE and actor-critic yields favorable performance in practice on the canonical POMDPs of Tiger, LightDark, and a partially observed variant of CartPole.
Fresa: A Plant Propagation Algorithm for Black-Box Single and Multiple Objective Optimization

Fresa implements a nature inspired plant propagation algorithm for the solution of single and multiple objective optimization problems. The method is population based and evolutionary. Treating the objective function as a black box, the implementation is able to solve problems exhibiting behaviour that is challenging for mathematical programming methods. Fresa is easily adapted to new problems which may benefit from bespoke representations of solutions by taking advantage of the dynamic typing and multiple dispatch capabilities of the Julia language. Further, the support for threads in Julia enables an efficient implementation on multi-core computers.
Virtual Reality as a Training Tool for the Development of Ice Hockey Thinking

Virtual reality is becoming an important part of sports training in all age and performance categories.The research group consisted of 197 ice hockey players aged thirteen to fifteen.All probands performed eight tests in virtual reality, the initial four focusing on cognitive functions and the remaining four on ice hockey-specific game skills.The strongest correlations were found between variables best solution and correct decision (0.87) and accurate pass 2 and pass success (0.91).The strongest correlation between main variables was discovered between correct decision and looking for open lanes (0.60).Strong relationships of partial correlations were also found between correct decision and looking for open lanes (0.58) and time to play the puck and time movement anticipation (0.43).Based on the findings in five key variables, three being game skills (time to play the puck, spatial orientation, looking for open lanes) and two being cognitive skills (time movement anticipation, multiple object tracking), it is possible to say that virtual reality can be used as a training tool for specific ice hockey training, especially for measuring specific ice hockey thinking.
Assessment of Heavy Metal Pollution in Clay Fraction of the Euphrates River Sediments, AlQaim, Haditha Area, Western Desert Iraq

Clay fractions (&lt;2µm) from ten stream sediments were collected from the Euphrates River between AlQaim-Haditha area in the western desert of Iraq. The clay fractions were analyzed for Cd, Co, Cr, Cu, Ni, Pb, Sr, and Zn using an Atomic Absorption Spectrophotometer. The aim of this research is to study the extent to which the industrial waste liquids from chemical complex of phosphates pollute stream sediments of Euphrates River. The total metal concentrations in the clay fraction are placed in the following descending order: Sr&gt; Zn&gt; Cr&gt; Ni&gt; Co&gt; Pb&gt; Cu&gt; Cd. Contamination factor pollution load index, geo-accumulation index, the metal contamination index, and potential ecological risk index were calculated to evaluate the environmental impacts of these heavy metals. The study area was found to be highly contaminated with Cd and Ni, moderately contaminated with Co, Pb and Sr, and uncontaminated with Cr, Cu and Zn. 100% of Cd and 93% of Sr were concentrated in the extracted part of the clay fraction, suggesting that Cd is totally derived from the industrial and human activities in the studied area, and Sr which can be attributed to the carbonates of Euphrates Formation as its source which dominate the geology of the area.
An underactuated parallel-link gripper for a multicopter capable of plane perching

Abstract The need for a perching robot is increasing in the field of rescue and transportation. Accordingly studies on perching an object by attaching a robot arm to a perching robot have been conducted. However, almost all the studies related to perching have been conducted using an actuated or electric device. However, perching by using an electric device has several disadvantages, such as additional power consumption and an increase in the mass of the multicopter used to load the electric source. Instead of using an electric device, perching by using an underactuated gripper can effectively avoid these disadvantages. Accordingly, we developed an underactuated passive gripper that has the advantage of nonconsumption of electric power for perching. A method to confirm the available range for stable perching is one of the problems of using an underactuated passive gripper. Therefore, in this study, we analyze a multicopter carrying an underactuated parallel-link passive gripper for available plane perching. To enable perching on planes with different thicknesses and being embedded at different depths, we summarize the available perching range and limitations based on the friction cone theory. Our conclusion is supported by both theoretical and experimental results.
Quantum Density Peak Clustering Algorithm

A widely used clustering algorithm, density peak clustering (DPC), assigns different attribute values to data points through the distance between data points, and then determines the number and range of clustering by attribute values. However, DPC is inefficient when dealing with scenes with a large amount of data, and the range of parameters is not easy to determine. To fix these problems, we propose a quantum DPC (QDPC) algorithm based on a quantum DistCalc circuit and a Grover circuit. The time complexity is reduced to O(log(N2)+6N+N), whereas that of the traditional algorithm is O(N2). The space complexity is also decreased from O(N·⌈logN&rceil;) to O(⌈logN&rceil;).
An Object Detection Algorithm for Rotary-Wing UAV Based on AWin Transformer

The increasing use of rotary-wing UAVs poses security risks, which makes image detection of rotary-wing UAVs a critical issue.This paper proposes an object detection algorithm for rotary-wing UAVs based on a transformer network.A self-attention mechanism is used to utilize the local contextual information to extract the features of the rotary-wing UAV more effectively, which improves the accuracy of object detection.Meanwhile, a new self-attention mechanism is designed, in which the query vector and the key vector of the surrounding annular area are calculated separately and then concatenated by different heads of attention.Experimental results show that, compared with existing algorithms, the proposed algorithm improves the mean average precision by 1.7% on the proposed rotary-wing UAV dataset.
Review of Facial Recognition Techniques

Abstract: Facial Recognition, the biggest breakthrough in Biometric identification and security since fingerprints, uses an individual’s facial features to identify and recognize them. A technology that seems too farfetched taken straight from a science fiction novel is now available in smartphones in the palm of our hands. Facial Recognition has gained traction as the primary method of identification whether its mobile phones, smart security systems, ID verification or something as simple as login in a website. Recent strides in facial recognition technologies have made it possible to design, build and implement a facial recognition system ourself. Using Computer Vision and machine learning libraries like Facial Recognition and Dlib, we can create a robust system that can detect faces and then match and identify it with a database of pre-loaded facial data to successfully recognize them. This study conducted a literature review of these aforementioned technologies and various other advancements in the field of computer vision facial recognition by other scholars in their research papers. This paper analyzes domains to understand the working of these machine learning models and their different implementations in facial recognition systems. The research conducted by us during this review will be paramount in creating a proof-of-concept prototype facial recognition system. Keywords: DLib, Facial _Recognition, Machine Learning (ML), Deep Learning (DL), CNN, KNN, Face Detection, HOG, Support Vector Machine (SVM), Face Recognition.
Remote Monitoring System Dedicated To COVID-19 Patients Healthcare

In 2019, COVID-19 posed a challenge to the whole world. Till today, it confuses the scientists as it has no defined medication. This paper aims to design a cost-efficient system that can monitor mild and serious cases of COVID-19 patients by continuously measuring and recording their health status throughout studying the obvious symptoms of COVID-19. The collected data can then be evaluated by nearby health centers and proper medical intervention will take place in times of emergency. This system shall protect doctors from too much contact with the patients. It will also alleviate the financial and emotional pressure that patients face during their stay in hospitals. In addition, it can act as one of the solutions to improve the low-standard healthcare system in Egypt [1]. In comparison to the prior applied systems, it has been found that the designed system is cost efficient, simpler in structure and more dedicated to COVID-19 virus. The proposed system is designed to include a transmitter, a receiver, and a virtual communication channel adopting IoT technology. The implemented part of the system is centered on the sensory and the patients’ interactions with the system. The obtained results revealed its efficiency compared to the state of the art systems.
Overview of One-Dimensional Continuous Functions with Fractional Integral and Applications in Reinforcement Learning

One-dimensional continuous functions are important fundament for studying other complex functions. Many theories and methods applied to study one-dimensional continuous functions can also be accustomed to investigating the properties of multi-dimensional functions. The properties of one-dimensional continuous functions, such as dimensionality, continuity, and boundedness, have been discussed from multiple perspectives. Therefore, the existing conclusions will be systematically sorted out according to the bounded variation, unbounded variation and ho¨lder continuity. At the same time, unbounded variation points are used to analyze continuous functions and construct unbounded variation functions innovatively. Possible applications of fractal and fractal dimension in reinforcement learning are predicted.
The Determinants of the Volatility in Cryptocurrency Markets: The Bitcoin Case

This article analyzes the volatility in the cryptocurrency markets.Cryptocurrencies have come to the fore especially in recent years.They have no global border and are easy to use.The earliest example of a cryptocurrency was Bitcoin, first introduced in an article by Satoshi Nakamoto ( 2008).This study examines symmetries and asymmetries in the Bitcoin price and the expected leverage effect.The GARCH (1,1) model is statistically significant at the 1%.EUR/USD buying rate, GOLD price, USD10 Year Bond Yield, US Dollar Index and VIX variables are significant.The interaction between international financial variables and Bitcoin volatility emerge in the period of study.The EGARCH model indicates that the leverage effect is not valid in the cryptocurrency markets.
Perspectives of digital identity – the case of online education during the COVID-19 pandemic

Abstract Each individual is unique, and the digital identity associated with a person should model and guarantee this uniqueness and the ability to reliably recognize him/her. In this paper, we review the challenges posed by the sudden transfer of a significant part of the social activity in the online realm, due to the COVID-19 pandemic, with unforeseen consequences on the educational process in particular. If there was a fairly large spectrum of economic activities that were taking place or were facilitated via a digital medium prior to the COVID-19 pandemic, the education system worldwide proved to not have been prepared to provide an effective and consistent response to the going online approach, which was merely a choice, but rather the only embraceable option. In the context of online education, this paper can design a comprehensive and integrated framework for assisting the process of students’ digital identity validation, particularly when attending an examination or any other form of academic evaluation conducted remotely. We also consider the integration of digital identity validation with platforms that offer educational content, knowledge evaluation tests, and other elements to which access must be ensured only for authorized users, based on a strict identity identification process to eliminate those who want to defraud or disrupt the functionality of the system.
Haptic play

Increasingly, urban environments are becoming spaces for forms of hybrid play that merge material and digital contexts. Hybridity is of particular relevance to our experience of augmented reality (AR) games such as Pokémon GO, in which real-time pedestrian movement is integrated with mobile location-based functionality and network information. At the same time, mobile touchscreen devices are haptic interfaces that turn our attention to the embodied and proprioceptive aspects of playful media. In this chapter, we consider the coalescence of hybrid and haptic play in augmented reality mobile games, and revisit notions of hybridity in terms of the concept of digital wayfaring. The discussion draws on ethnographic work conducted in Badalona, Spain on the uptake of Pokémon GO by older adults, and explores the benefits of hybrid-haptic play in facilitating intergenerational connection and social and physical wellbeing.
Vibrating Tilt Platform Enhancing Immersive Experience in VR

One of the disadvantages of virtual reality systems of the past was the fact that they had visual-only interfaces. However, with the development of haptic technology, peripheral solutions to enhance the VR experience are gaining momentum, and many haptic systems are being developed for deepening VR immersion. This paper deals with the concept of a vibrating tilt platform allowing the change of three angles of inclination to thus reinforce the VR experience. The proposed system is flexible and adaptable to different sports, health, and education applications. In this paper, we present the mechanical and mechatronics aspects of the platform and usability testing results based on an immersive geological experience application. The first tests were studied in terms of the cyber sickness, perceived realism, and anxiety through both subjective (questionnaires) and objective (electroencephalogram) measurements. The results indicate that our platform increased anxiety levels and was perceived as realistic. Adding vibrations and tilting had a considerable impact on the immersion level and brain activity.
Replication study challenges and new number formats for chaotic pseudo random number generators

Abstract Chaotic Pseudo Random Number Generators have been seen as a promising candidate for secure random number generation. Using the logistic map as state transition function, we perform number generation experiments that illustrate the challenges when trying to do a replication study. Those challenges range from uncertainties about the rounding mode in arithmetic hardware over chosen number representations for variables to compiler or programmer decisions on evaluation order for arithmetic expressions. We find that different decisions lead to different streams with different security properties, where we focus on period length. However, descriptions in articles often are not detailed enough to deduce all decisions unambiguously. To address similar problems in other replication studies for security applications, we propose recommendations for descriptions of numerical experiments on security applications to avoid the above challenges. Moreover, we use the results to propose the use of higher-radix and mixed-radix representations to trade storage size for period length, and investigate if exploiting the symmetry of the logistic map function for number representation is advantageous.