Reviewerly/exHarmony huggingface dataset:
Developing augmented reality application on komodo dragon for elementary school children during the new normal of covid-19 pandemic

The COVID-19 pandemic has brought much challenge and disruption to the lives of students in elementary through high schools, their families, and their communities. Most students cannot attend schools due to the increased spread of COVID-19, and they learn online from home. This paper aims to develop an augmented reality learning model that delivers information on Komodo dragons that can be accessed using smartphones. The developing augmented reality application uses the Augmented Reality Development Method. It can be that the augmented reality learning model would be easier, more fun, and interesting for students in conducting online learning the New Normal of COVID-19 Pandemic. The research findings show that the learning model is useful to help teachers since it can be used for independent learning and motivates children to learn in COVID-19 pandemic. © 2021 Little Lion Scientific. All rights reserved.
STUDENTS’ PERCEPTIONS AND ENGAGEMENTS IN LEARNING ENGLISH VIA KAHOOT

This study deals with how students perceived the use of Kahoot in learning English. The aims of this study were to investigate the students’ perceptions on the use of Kahoot and their engagements when Kahoot was being implemented in the English classroom. Kahoot in this research was the online gamification method that a teacher applied in the classroom which was used by the students. This research employed a descriptive qualitative research. The data were obtained through three major instruments which were questionnaire, interview and classroom observation. The result from the questionnaire and interview revealed that the students’ perceptions on learning English via Kahoot were positive as the students felt that the use of Kahoot in learning English helped them to learn better. Meanwhile, the result from the classroom observation presented that Kahoot helped the students to engage actively with one another. Therefore, the use of Kahoot in learning English was beneficial for the junior high school students as it helped the students’ learning environment in variety of ways.
Cryptocurrencies: Money Laundering &amp;amp; Terrorism Financing

This paper is a research to study the use of cryptocurrencies in money laundering and terrorism financing, these currencies are decentralized currencies, and they are also virtual and convertible and were created on the basis of cryptography. They have been gaining reputation as a means of payment offered to enterprises and customers, and as substitutes and competitors for the traditional centrally controlled fiat currencies issued by governments. Our first point was the fact that its increasing popularity has been attracting scholars and governments because of anti-money laundering concerns. We argue on the fact that there are high potential benefits for criminals, because of its known uses in money laundering and the neglected fact that these currencies are used as money laundering instruments. To study these issues, the paper will study these currencies as a whole and then will study the structure of money laundering and anti-money laundering processes, this will help us to get an idea on money laundering and its use in terrorism financing and information regarding the influence cryptocurrencies have on money laundering process and the incentives they offer to criminals for them to utilize crypto in money laundering. This aims to answer a very big question, whether cryptocurrencies are being used in money laundering and terrorism financing.
Textbooks based on local wisdom to improve reading and writing skills of elementary school students

Many elementary students still find it difficult to understand the literature content used and it automatically affects their reading and writing skills. However, the adjustment of literature with local wisdom-based content needs to be considered as a supporting tool. This study aimed to improve reading and writing skills using textbook based on local wisdom. The sample was grade 4 of elementary school students who selected using purposive random sampling. This research used a nonequivalent control group design through the experiment and control classes. Data were collected through writing and reading skills test of 32 students in each class and analyzed using N-gain to describe the treatment effect. The results showed that textbooks based on local wisdom were more effective than teaching materials that were not integrated by local wisdom at improving students' reading and writing skills. In the future, this study is a reference for teachers to apply local wisdom to other learning themes.
Indian Institute of Management Libraries web OPACs and Available Search Features: A Checklist based Approach

This paper is carried out as part of research work on Indian Institute of Management Libraries web OPACs. The data has been collected during 2016-17 academic years, where author used OPAC evaluation parameter checklist as tool for data collection. Which is sixty-seven parameter checklist designed by Ramesh Babu and Obrien. The study is limited to thirteen IIMs in India, who shared login credentials for their respective library web OPACs accessibility. The author himself observed each IIMs web OPAC by searching with various keywords and compared functionality search features into checklist and marked as scores for presence and absence of each parameter. IIM-Trichy secured highest score 47 out of 53, followed by Ahmedabad and IIM-Calcutta 46. And IIM-Rohtak recorded least score 29 followed by IIM-Ranchi-29 and IIM-Raipur 30. The study has also revealed availability of search features in web OPACs from each IIM libraries and discussed about non-customized search features such as ‘contentsandcoverageintheOPAC’, ‘Hypertext links in full bibliographic record display’, ‘search strategies and search limits’ links to electronic sources and external sources’ etc. Further it is recommended to all the IIMs to find out their scores and to do the modifications at their library OPACs
Investing during a Fintech Revolution: Ambiguity and Return Risk in Cryptocurrencies

Rationally justifying Bitcoin's immense price fluctuations has remained a persistent challenge for both investors and researchers in this field. A primary reason is our potential weakness toward robustly quantifying unquantifiable risks or ambiguity in Bitcoin returns. This paper introduces a behavioral channel to argue that the degree of ambiguity aversion is a prominent source of abnormal returns from investment in Bitcoin markets. Using data over a ten-year period, we show that Bitcoin investors exhibit, on average, an increasing aversion to ambiguity. Furthermore, investors are found to earn abnormal returns only when ambiguity is low. Robustness exercises reassure the validity of our results.
원격 수업 환경에서 학습자 주도성의 발현 가능성과 한계 - 국어과 원격 수업 사례를 중심으로 -

This study focused on student agency, which has been identified as a core competency in future education amid rapid changes as part of the digital age, and attempts made to conceptualize it. In addition, this study aims to explore the possibilities and limitations of student agency, focusing on the case of learning the Korean language via distance education.In this study, learners independently selected learning topics; searched for materials; and selected, assessed, and refined their topic, thereby being able to learn about content related to their own lives. It is clear, therefore, that the online classroom environment has the potential to act as a useful medium for the manifestation of student agency.However, this possibility of student agency may not materialize if the learner does not initiate it. Therefore, in a distance learning environment, the role of teachers in promoting and supporting learning and interactions between learning topics should be more strongly emphasized.If online tools are used to strengthen student agency, the experience of distance lessons experienced by the COVID-19 pandemic could be an important turning point and represent a leap toward future education.
Termination Factor for Iterative Noise Reduction in MRI Images Using Histograms of Second-order Derivatives

Histograms of second-order derivatives are generated from the pixel data of MRI images. The histograms are then used to calculate a factor that is to be used for iterative processing. The factor is intended to limit the number of iterations, with the goal of preventing further loss of detail. The factor uses two conditions that depend on the profiles of the histograms. The methodology uses sample MRI images and versions of these images with Rician noise introduced into them. The noisy images are subjected to iterative noise reduction with a recursive averaging filter. The control tests in the methodology use the ground truth images to limit the number of iterations, with PSNR and SSIM peaks used as the measurements for determining when the iterations stop. The other tests use the proposed termination factor for the limitation. The results of the tests are compared to determine the effectiveness of the termination factor. The proposed termination factor does not cause divergence, but there are still different numbers of iterations in the case of MRI images with image subjects that have discrete regions and details resembling noise. The tests also reveal that differences between the histograms of derivatives and Laplace curves have to be retained in order to prevent loss of information.
LibGuides: LibrarySkills@UCL: Communicating and sharing information: Further help

A guide to scholarly communication, including sharing information through the principles of Open Research
An Exploration Model for A Gold Deposit in Kervian Area, Kurdistan Province, Iran, using a Combination of Geophysical Results with Geological Information and Other Exploratory Data

As many gold deposits are associated with sulfide zones, and the direct exploration of gold deposits using the geophysical methods is very difficult due to its low amount in the sub-surface, the direct exploration of sulfide zones by the geophysical electrical resistivity and induced polarization (IP) methods may lead to the indirect exploration of gold deposits. The gold deposit in the Kervian area is located in the Kurdistan shear zone, and is directly related to the sulfide, silica, and carbonate alteration units. After acquiring the resistivity and IP data, 2D modeling of the data is made in order to indirectly identify the gold-bearing zones in the surveyed area. As some of the identified geophysical anomalies indicating the sulfide zones may not be associated with the economic amounts of gold, in order to obtain an exploration pattern for the gold deposit in the studied area, a combination of the geophysical data modeling and interpretation results with the geological information and other exploratory data is used to reduce the uncertainty in identifying the gold-bearing zones in the studied area. Thus, modeling and interpretation of the geophysical data lead to identify the sub-surface anomalies as the locations of possible gold mineralization in the area, and then the drilling points are suggested in the area. Considering the geological studies and chemical analysis of the samples taken from the drilled boreholes crossing some of the geophysical anomalies, we conclude that the geophysical anomalies occurring inside the phyllite and carbonate units in the area can contain an economic amount of gold, and thus are recommended as the top priority for further exploration.
Decision Support System for Car Selection with Fuzzy Analytical Hierarchy Process (F-AHP) on Auto2000 Kenjeran Surabaya to Minimize Service Time

Auto2000 Kenjeran Surabaya is one of the authorized dealers that operate as a sales, repair, maintenance, and supplier service network for Toyota parts. Location Kenjeran Auto2000 is located on Jalan Kenjeran No.516, Surabaya City, East Java. The process of buying a car on Auto2000 Kenjeran is carried out when the customer comes to the Kenjeran Auto2000 dealer or online via social media. When making the purchase process, sometimes customers are confused about choosing a car due to a lack of information about the car, besides that there are also new salesmen who find it difficult to know what the customer wants, making the service process that initially takes ± 30 minutes to offer and provide information about the car can be ± 45 minutes due to finding out what car the customer wants.Based on the existing problems, a solution is proposed for the Auto2000 Kenjeran in the form of a car selection decision support system using the fuzzy analytical hierarchy process method that can help customers find a car, because there is a car finder feature that can help customers find a car according to customer preferences by simply selecting and give a score for each criterion and the desired alternative, and the car results will come out immediately. In addition, there are also vehicles and recommendation car features that provide information about cars to customers.Based on the test results, there are several features in the application that can provide information about cars, make car reservations, and can search for cars according to the criteria and alternatives that have been provided, besides that the application can print car order data reports. So it can be concluded that the application is in accordance with the functions and needs of the user.
Promoting reproductive isolation through diversity in on-line collective robotics

We present a behavioral diversity selection scheme that favors reproductive isolation to promote the learning of multiple task in online embodied evolutionary robotics (EER). The scheme estimates the behavior of the controllers without the need to access the agent experience, respecting thus the online, distributed properties EER. Reproductive isolation is assessed through coalescence trees and task specialization is tested on a concurrent foraging setting.
Preserving Privacy of Co-occurring Keywords over Encrypted Data

The indexes of ranked searchable encryption contain encrypted keywords and their encrypted relevance scores. The encryption scheme of relevance scores must preserve the plaintext order after encryption so as to enable the cloud server to determine ranks of the documents directly from the encrypted keywords’ scores for a given trapdoor. Existing schemes such as Order Preserving Encryption (OPE) and One-to-Many OPE preserve the plaintext order. However, they leak the distribution information, i.e., the frequency of ciphertext values, due to the insufficient randomness employed in these schemes. The cloud server uses frequency analysis attack to infer plaintext keywords of the indexes based on the frequency leakage. In this paper, an Enhanced One-to-Many OPE scheme is proposed to minimize the frequency leakage. The proposed scheme reduces not only the frequency leakage of individual keywords but also the co-occurring keywords of the phrases like “computer network”, and “communication network”.
Impact of Video Compression on Object Tracking Performance

Object detection is a computer vision task that can recognize objects and classify objects from digital images. Object tracking is also a computer vision task on top of object detection, and this task performs unique identification of each object across the sequence of images in the video. Object tracking in computer vision has various applications such as surveillance, gesture recognition, and Unmanned Aerial Vehicles (UAVs), etc. Video compression is universal in our visual processing pipelines, and as an example, any video we access from the internet is pre- compressed. Without video compression, uncompressed video size will be huge, and it is impractical to transmit the raw video and save it to the storage devices. Since there has not been much work between video compression and object tracking, this raises the question of how video compression will impact object tracking performance. This work will involve designing the object tracking pipeline and analysis of tracking performance with compressed and uncompressed video.
Management and Rationalization of Costs in the Space Work Area through the Cloud in Educational Institutions

Importance for attempt manages and rationalizes costs in space work area to provide best services in educational institutions through cloud.And forcalculating using cost management tools for purpose of access to rationalize public spending. Problemis use traditional methods in (management, conservation, archiving, etc.) its taking very large ground and storage areas, wasting time, effort; High cost of services.Aimtomanage and rationalize costs of space work area through cloud in harmony with cost management tools, using ABC-M systems. Hypothesis is use cloud in space work area to manage and rationalize cost for providing services in cooperation with cost management tools in administration and calculation of rationalization, using ABC-M systems. ResultsUnder use cloud in the space work area we can manage and rationalize cost and provide services in educational institutions in cooperationin additional it we will be able to reduce total cost for activities and then down to per unit cost.
Network Variability Impacts Mobile Application Performance

Internet connectivity became a mandatory on mobile device in recent days. Due to memory constrain and dynamic data management developers were going for the web application or hybrid application development approach for the morden mobile applications. Web applications completely depend on the internet and hybrid applications partially depend on internet. For both cases the network connectivity is mandatory. This paper aim to compare the different speed of the network and based on that what is the impact of the mobile application performance has been analysed.
Image and Video Dehazing based on Gamma Correction Method Contrast Enhancement

As the revolution in computational photography and computer vision applications facilitates fast and reliable information, quality of the scene and visual perception and is being increasingly used in various fields like public safety, traffic accident analysis, crime forensics, remote sensing area and military surveillance. We make an investigation of the dehazing effect of scenes affected by weather phenomena. ‘Dehazing’ has emerged as a promising technology to recover the clear image and video from an input hazy scene, such that the quality can be significantly enhanced. A scene captured in the outdoor environment affected by haze like fog mist and dust particles in the atmosphere. We are utilizing a ‘Dehazing algorithm, to remove this unwanted haze from videos and Real-time video. Also, remove haze from images, for this, we use a novel method of video dehazing based on contrast enhancement. From our observation, it is concluded that hazy image and video has low contrast, so we estimate transmission map to maximize the contrast of output scene. And we use the depth estimation process to detect or identify hidden parameters from the scene, and also creates a corresponding haze scene with high fidelity. Finally, we reconstruct or restore the seen without changing its originality. Hence dehazing performance with fewer artifacts and better coding efficiency and demonstrate that the proposed algorithm can remove haze efficiently and recover the parameters of the original scene.
Cloud Computing Applications, Security and Challenges – A Review

Cloud computing, a rising model worldwide, empowers clients to distantly store their information in a cloud, in order to gain benefits on-request. Moving information from the On-prem applications to the cloud offers extraordinary accommodation to clients, since they can get information in the cloud whenever and wherever, utilizing any gadget, without thinking often about the capital Investments on assets, Security of data is the greatest test to enterprise applications cloud computing. Security of information is the biggest challenge to cloud computing. Cloud security has many benefits such as centralized security, reduced costs, reduced administration and reliability. Cloud security challenges are control over cloud data, misconfiguration, ever changing workload, access management and disaster recovery. In this paper, a detailed review of the security-related challenges in cloud computing is presented. After discussing the issues in cloud computing security, various emerging and existing technologies focused.
Speaking Back: Comparing Australia's Civil Racial Laws to Counterspeech Alternatives

This article examines two versions of the argument that speaking back is an appropriate response by the state to instances of vilification, or hate speech. It finds that civil anti-vilification laws are preferable to counterspeech responses for two reasons. First, these laws enable victims of vilification to seek legal redress for the wrong committed against them. Second, civil laws provide public assurance that all members of society are entitled to be treated with dignity in public discourse.
Penerapan Pelatihan Edulead Oleh Manajemen Sekolah dan Dampaknya Pada Stake Holder Sekolah

The purpose of this research was to gain preliminary insights on how the participants of Edulead Training implemented the training in their respective institutions and the impacts perceived after the implementation. Research method used to measure the implementation was survey method using online questionnaire, with open and closed questions. The data analise were from 25 out of 28 training participants participated in the data collection. The results showed that all respondents reported that they had implement the training on their job. There were three channels reported on how the training materials being implemented: by conducting training to teachers and other members of school management, applying the materials themselves, and conducting socailization/knowledge sharing to teachers, other members of school management, and parents. After implementing training materials on their respective institutions, all participants reported positive impact. The positive impact reported was seen in teachers, students, other members of school management, working environment, and parent
Training Agents to Play 2D Games Using Reinforcement Learning

This chapter provides an important step in the field of reinforcement learning, and their agent outperforms all human benchmarks on Atari games. Effective emulation, simulation and agent-based application of learning is a very sophisticated criterion of establishing the supremacy and applicability of visual and spatial parameters. The advent of artificial intelligence can be effectively used to create advanced systems that are capable of decision-making, solve complex issues and emulate human processes of learning and decision-making virtually and comparably accurate. A special type of algorithm called reinforcement learning enables an agent to learn within an environment and earn the maximum reward available. AI-based training of a CPU agent to simulate and play games in diverse environments has been thought of for quite some time but has been relatively paused due to the unavailability of GPUs and fast processors owing to its high computation requirement.