Reviewerly/exHarmony huggingface dataset:
Protection from online falsehoods and manipulation act and the roles of internet intermediaries in regulating online falsehoods

This article proposes that intermediaries should adopt proactive roles in regulating online falsehoods. The relevant Protection from Online Falsehoods and Manipulation Act 2019 (Act 18 of 2019) (P...
Uji similarity artikel: Motivation and Learning Strategies: Student Motivation Affects Student Learning Strategies

Despite being a popular research subject internationally, self-regulated learning is relatively under-investigated in the Indonesian context. This article examined student learning motivation and its use as an indicator to predict student learning strategies in an Indonesian school context. This article applied quantitative research design, with Motivated Strategies for Learning Questionnaire (MSLQ) used to collect the data. This questionnaire was completed by 408 public high secondary students randomly selected from the population in Lampung Province schools, and multiple regression was used to analyze the obtained data. Results show that student motivation and learning strategies were positively and significantly correlated; three predictor variables of student motivation could significantly predict learning strategies; and value components of student motivation best predicted learning strategies. In conclusion, these findings indicate that, when teachers apply learning strategies, such variables as motivation including value, expectancy, and affective components should be strongly considered to be in place. It is hoped finally that the students will be self-regulated learners for their success.
FlowReg: Fast Deformable Unsupervised Medical Image Registration using Optical Flow

In this work we propose FlowReg, a deep learning-based framework that performs unsupervised image registration for neuroimaging applications. The system is composed of two architectures that are trained sequentially: FlowReg-A which affinely corrects for gross differences between moving and fixed volumes in 3D followed by FlowReg-O which performs pixel-wise deformations on a slice-by-slice basis for fine tuning in 2D. FlowReg-A warps the moving volume using gross global parameters to align rotation, scale, shear, and translation to the fixed volume. A correlation loss that encourages global alignment between the moving and the fixed volumes is employed to regress the affine parameters. The deformable network FlowReg-O operates on 2D image slices and is based on the optical flow CNN network that is adapted to neuroimaging with three loss components. The photometric loss minimizes pixel intensity differences, the smoothness loss encourages similar magnitudes between neighbouring vectors, and a correlation loss that is used to maintain the intensity similarity between fixed and moving image slices. The proposed method is compared to four open source registration techniques ANTs, Demons, SE, and Voxelmorph for FLAIR MRI applications. In total, 4643 FLAIR MR imaging volumes (approximately 255,000 image slices) are used from dementia and vascular disease cohorts, acquired from over 60 international centres with varying acquisition parameters. To quantitatively assess the performance of the registration tools, a battery of novel validation metrics are proposed that focus on the structural integrity of tissues, spatial alignment, and intensity similarity. Experimental results show FlowReg (FlowReg-A+O) performs better than iterative-based registration algorithms for intensity and spatial alignment metrics with a Pixelwise Agreement (PWA) of 0.65, correlation coefficient (R) of 0.80, and Mutual Information (MI) of 0.29. Among the deep learning frameworks evaluated, FlowReg-A or FlowReg-A+O provided the highest performance over all but one of the metrics. Results show that FlowReg is able to obtain high intensity and spatial similarity between the moving and the fixed volumes while maintaining the shape and structure of anatomy and pathology.
Authentication System Based on Fuzzy Extractors

In this paper, we research on client-server authentication system without local key storage. We take advantage of the available fuzzy extractor technology to design a client-server authentication system. Our authentication system is built from a fuzzy extractor and a digital signature scheme. Fuzzy extractor is in charge of key generation/reproduction during the client enrollment and client-server authentication stages. The client only stores some public information generated during enrollment procedure. When doing authentication, the extracted key can be reproduced with this public information. Then we use the challenge-response to implement the authentication, which is supported by the digital signature. Overall, our client-server authentication system relaxes the requirement of random sources, and gets rid of the risk of key leakage and key abuse since key storage is not needed.
Google Classroom Sebagai Alternatif E-Learning Pembelajaran SKI Pada Masa Pandemi Covid-19 di MA Hidayatullah

This study discusses the application of the google classroom as an alternative to e-learning for learning SKI (Islamic Cultural History) class XII during the Covid-19 pandemic at MA Hidayatullah. This study aims to determine how the implementation of google classroom, the inhibiting factors and supporting factors as well as the solutions taken to overcome the inhibiting factors.�This research is a descriptive qualitative research. The subjects of this study were students of class XII, teachers who used the google classroom application and principals of madrasah. Methods of data collection in this study using the method of observation, interviews and documentation. The data validity technique in this study used triangulation of sources and methods. The collected research data is analyzed using interactive analysis with data reduction steps, data display and verification (conclusion).�Based on the results of the research, the authors can conclude that the application of the google classroom application in learning SKI class xii ma hidayatullah is going well supported by several factors, namely the ease of finding reference sources, is not bound by time and place and is easy to understand while the inhibiting factors are found is a weak signal and data quota / packet is limited. These two inhibiting factors can be overcome by providing concessions on deadlines for sending assignments and submitting online learning facilities to the relevant government.�Key Word;�Google Classroom, E-learning, Islamic Cultural History��
Scalable Graph Synthesis with Adj and 1 — Adj

Graph synthesis is a long-standing research problem.Many deep neural networks that learn about latent characteristics of graphs and generate fake graphs have been proposed.However, in many cases their scalability is too high to be used to synthesize large graphs.Recently, one work proposed an interesting scalable idea to learn and generate random walks that can be merged into a graph.Due to its difficulty, however, the random walk-based graph synthesis failed to show state-of-the-art performance in many cases.We present an improved random walk-based method by using negative random walks.In our experiments with 6 datasets and 8 baseline methods, our method shows the best performance in almost all cases.We achieve both high scalability and generation quality.
Pengaruh Ketebalan Woven Fusible Interfacing Terhadap Kualitas Produk Lengan Spiral Pada Blus Berbahan Taffeta

Abstract Interfacing is an inside layer to supporting , strong holding, and keeping the form of a cloth or dress. Interfacing is applied on some certain part for example pocket, collar neck, cuff, and many more. Its function is to form and give a volume on the arm. this research is for knowing the thickness effect of wove fusible interfacing against spiral arm product quality on the blouse using Taffeta as the material, based on the flatness of spiral arms aspect, the form stability, volume, and the length of the spiral arms which its function to know the result of the best spiral arms from the appliance of the 3 type of woven fusible interfacing on the blouse that using Taffeta as the material, based on the flatness of spiral arms aspect, the form stability, volume, and the length of the spiral arms. This is an experimental research by using observation method in data gathering. By observing the object and the observer filling the questionnaire by check listing (√) on the observation form. The data analysis that being used in this research is non-pramectic kruskal wallis test with some help from the SPSS 23 program and P ≤ 0,05. The result of the test is showing that (1) there is an influence on the woven fusible interfacing against four aspects on the quality of the spiral arm product on the blouses using Tafetta as the material which is the flatness of the spiral arm aspect, the stability of the spiral arm form aspect, the volume of spiral arm aspect and the length of the spiral arm aspect. (2) the result in the quality of the spiral arm product as the best result is from woven fusible interfacing type M70 with 0,56 mm as the thickness. keywords : : spiral arm, blouse, Taffeta fabric, woven fusible interfascing type M10, M32, dan M70.
A Generalized Deep Learning Model for Denoising Image Datasets

Advance in technology world has lots of contributions from artificial intelligence which is a highly growing area. The failure of traditional algorithms has led to the employment of deep learning algorithms in various fields like pattern recognition, recommendation systems and classification systems. Removal of noise from images can be done using traditional noise removal filters. These filters can either remove more noise that wanted or leave unwanted noise than what is needed in the data. Utilization of Convolutional neural networks designed based on the dataset requirements along with the noise removal filter can yield better results. In this work, evaluation of the performance of convolutional neural network (CNN) against existing image denoising algorithms has been successfully executed . The proposed model is a generalized CNN model which can recognize and classify any type of noisy image given. Two types of model were compared where one model 1 uses the Adam optimizer and model 2 uses the Stochastic Gradient Descent (SGD) optimizer. The image dataset used here is MNIST handwritten dataset, which is trained, tested and validated with both the models by adding three different types of noise viz, Poisson, Salt and Pepper as well as Gaussian Noise. More accuracy and better results were given by the model 2 which uses the SGD optimizer.
Three-dimensional depth estimation of virtual objects in augmented reality

Accurate three-dimensional (3D) depth perception of virtual objects is important for immersive augmented reality (AR). However, it is unclear whether/how 3D depth perception of virtual objects in AR differ from real objects as previous studies have reported mixed results showing both the underestimation and overestimation of an virtual object’s 3D depth. These discrepant results might be due to individual differences in inter-pupillary distances (IPDs), different devices used for research, and the interaction between those two factors. Here, we used a commercial AR device (Epson Moverio BT-35E) and measured 3D depth estimation of virtual objects rendered at reaching (34-50 cm) and room-scale (100-300 cm) distances in a perceptual depth matching task. Specifically, we compared the pixel disparity between the left and the right display calculated from the stereo camera model with the reported pixel disparity during the depth matching task at each distance. Next, we measured pixel offsets due to the mismatch between the inter-display distance (IDD; distance between each display on the device, 6.5 cm) and the individual IPD, and tested whether the calibration based on the individual IPD improve 3D depth estimation. Our results show that our observers (n= 11) overestimated the 3D depth of virtual objects at 34-50 cm and underestimated at 150-300 cm with near-perfect estimation at ~100 cm. Furthermore, our calibration method improved 3D depth estimation at 150-300 cm, progressively reducing underestimation as distance increases (by an average of 45%). Our findings suggest that the 3D depth estimation of virtual objects in AR changes from overestimation to underestimation as the rendered distance increases from reaching to room-scale distances, and that a simple calibration by compensating the mismatch between IDD and the individual IPD may help each individual’s immersive AR experience especially for commercial AR devices without IPD adjustment functionality.
Model Sistem Pendukung Keputusan Penentuan Strategi Peningkatan Kinerja Guru Menggunakan Metode Analytic Network Process

All parties are aware that teacher performance is directly proportional to improving the quality of education. Not a few teachers work under predetermined work standards, conditions like this are caused by low work enthusiasm which results in decreased performance. If we observer the passion of work in the form of sine graph which one day will meet a saturation point if there are no preventive and curative efforts eithers form himself or guidance form his superior. One of the efforts taken is to impose teacher performance assessment to ensure a quality learning process at levels of education. Teacher performance appraisal needs to be carried out so that the functions and duties in the functional teacher positions and duties in the functional teacher positions are carried out by the applicable rules and code of ethics. On that basis, a decision support system was created using the Analytical Network Process method which can determine teacher performance improvement strategies, based on objective performance appraisals and make decisions that become more efficient.
The digital transformation of fall prevention at the home environment

IntroductionThe digitalisation of the health sector has brought new opportunities as well as challenges for the ehealth. However, to be able to achieve a successful implementation there is a need to change the way of handling its data for a fruitful use and implementation of the big data approach. But not least, it is crucial to understand how to best elicit and understand the needs of data for the system from the elderly to achieve a fall risk prevention system. Theory/MethodsThe study is based on a project where both academia and industry are brought together to develop a platform for fall prevention. To understand the data needed workshops were arranged and test periods to understand the efficiency of the sensors. Thus, in a 24-hour test the sensors were tested and implemented. Also, data was produced by the use of a diary where the elderly had to fill in different activities done during the day. The approach used was the co-design, which emphasises on the users' involvement during the development's life cycle. ResultsThe results show that the integration of the data involves the transfer of the data into opensource alternatives that consider the characteristics of the big data, such as, its variety, volume and velocity. Thus, the variation of the data concerns the different kind of data that needs to be handled, stored and processed, such as, document, figures and data that the elderly fill into the system manually. While the volume and velocity concern much the sensor data that is continuously fed into the system.DiscussionThe aim with the use of these technologies is to support the elderly and to provide them with the possibilities to live longer at home with increased quality of life. Thus, for a successful implementation, the elderly use and acceptance of the technology becomes crucial. The workshops highlighted the importance of co-design and modelling since the data needs and the interaction with the system of both the elderly and the technology have to be understood by all parts involved to be able to advance successfully in the development process. Also, the use of reliable and light sensors is essential since the elderly should be motivated to wear them.Conclusions To be able to achieve successful use of the emergent technologies in the area there is a need to change the way of handling data, its structures as well as storage. Also, its acceptance and motivation of the elderly people become crucial for its successful implementation. Therefore, behavioural factors like motivation and attitude are essential factors to consider for its success. Lessons learned The importance of the different stakeholders’ interests, i.e. the elderly, owner of the technology, etc. are factors that should not be disregarded in order to achieve a successful fall prevention system.Limitations The content of the current work is based on a project, which diminishes its generalisation aspects.
Secured Cloud Computing for Data Management Using Big Data for Small and Medium Educational Institutions

In the recent past, the cloud computing plays vital role in educational institutions for improving business and service to students efficiently. The cloud computing is a standard methodology that gives accurate and efficient network for computational resources that can be useful for various educational institutions. The educational institutions should keep the data confident and higher range of security with privacy preservation. The management of big data in cloud is difficult to secure from risk and threats due to data exposure to many networks such as web, mobile communication, Internet of Things (IoT) and social media. The data storage in cloud safely and efficiently is the major challenging task in big data cloud computing. The major attention on cloud computing is the security issues could be associated with big data of educational institutions. In this paper, we have proposed data security approaches and methods in cloud to ensure the protection by highly minimizing risks and threats. We have discussed on efficient solution for cloud computing security issues to provide network security, data security and privacy preservation. The proposed methodology permits educational institutions to store data in the cloud assures safety and efficiency. It provides the solution to problem of big data security issues based on encryption and compression methodologies. The experimentation results show the proposed methodologies performance is accurate and efficient than existing systems.
Communication and Service Aspects of Smart Mobility: Improving Security, Privacy and Efficiency of Mobility Services by Utilizing Distributed Ledger Technology

Innovative information and communication technologies make it possible to use existing transport infrastructures optimally and expand them with new mobility concepts such as car sharing, semi-autonomous, fully autonomous vehicles and new transport options, thus, intelligently improving traffic through new communication and service aspects. Smart mobility requires a high level of communication, both between vehicles and between vehicles and the surrounding infrastructure. Increasing urbanization and the associated new challenges are leading to a change in mobility. While there used to be plenty of room for individual activities and energy consumption, modern society demands a more efficient interaction in order to ensure greater road safety, smoother traffic and ecological sustainability. Overall, the following challenges can be formulated from the previous discussion: Communication and Service Aspects of Smart Mobility: Improving Security, Privacy 6.2.2.3 As you can see, from the aforementioned points that blockchain oers inadequate solutions for some applications.
DEVELOPMENT OF TEACHING BASED TEACHING MATERIALS TO IMPROVE STUDENTS 'DEVELOPMENT AND ACHIEVEMENT (Study on computer subjects and basic networks in Class X in Bengkulu City Vocational School)

This research aims to describe, first the development of website-based teaching materials can improve student learning independence, secondly the development of website-based teaching materials can improve the learning achievement of computer lessons and basic student networks. This research uses Research and Development (R & D) research with Borg & Gall development model in class X TKJ Vocational High School 3 of Bengkulu City, Vocational High School 3 of Bengkulu City and Muhammadiyah 9th Vocational High School in the City of Bengkulu. This research uses steps, namely research and information collecting, planning, develop preliminary form of product, preliminary field testing, main product revision, main field testing, operational product revision, operational field testing, final product revision, implementation. The results of the research show that the development of website-based teaching materials can improve student independence and learning achievement.
Resource integrity-aware flexible resource scaling approach over sensor-cloud

Massive internet of things (IoT) framework deployments increase edge devices usage and dependently increase the generation of data. The traditional elastic asset scheduling approach is phenomenally...
유아교사의 코칭역량 척도

The purpose of this study is to help early childhood teachers objectively recognize their teaching competency and to measure coaching competence more reliably to enable various program development and educational approaches based on coaching competence in the future. For this purpose, two research questions were established. First, the item composition of the early childhood teacher's coaching competency scale was conducted through three steps: preliminary item selection process, scale development process, and test reliability and validity. Six components with 36 questions were selected: competency, questioning competency, listening competency, and recognition/feedback competency. Second, reliability and validity were verified through Cronbach's α coefficient and model fit index of the early childhood teacher's coaching competency scale. Conclusion, the six components developed as a result of this study are important factors constituting the coaching competency of early childhood teachers based on positive psychology as a theoretical background.
Factors Affecting Covid 19 Information Needs of Teachers in Oguta, LGA Imo State, Nigeria

The general purpose of this study is to investigate the factors affecting Covid 19 information needs of teachers in Oguta, LGA Imo State, Nigeria. The survey research design was used for this study using questionnaire as an instrument for data collection. This study covered the entire population of 178. Out of these, 155 copies of the questionnaire representing 87.1% were duly completed and returned for analysis. The findings this study showed that teachers need Covid 19 information to keep them abreast of new developments in the society. The findings also showed that age, gender, academic discipline and rank significantly affects Covid 19 information needs of teachers in Oguta LGA. Imo State, Nigeria. This study recommended that there should be budgetary allocation by the government for the development of school libraries in the state. If the money is made available to the school libraries, they will be able to buy as many current books and journals as expected. The Internet services as an area in the school libraries should be adequately equipped for active utilization by the teachers in meeting their information needs. This study also recommended that teachers should be allowed to participate in selection of school library materials for acquisition.