Reviewerly/exHarmony huggingface dataset:
Analisis Model-model Blended Learning di Lembaga Pendidikan

The purpose of the study was to analyze the blended learning models used in various educational institutions. These blended models become references for educators who want to apply this model in their learning. The research method uses library research. The results of this research can be seen from many blended learning models used by various educational institutions. There are four MBL developed in US educational institutions, namely: rotation, flex, Self-blend and enriched-virtual models. There are also three possibilities for the development of MBL learning in education, namely web courses, web centric courses, and web enhanced courses. In addition, there are also five levels of ICT use in learning. Each model and type has advantages and disadvantages. Educators can combine or modify these models according to the conditions of their respective educational institutions..
DEVELOPING GRADED READING MATERIALS TO ENHANCE EXTENSIVE READING FOR STUDENTS OF SENIOR HIGH SCHOOL

Improving literacy and a reading habit remains a major challenge to the Indonesian education system. To achieve maximum fluency in reading, the students should not only rely on classroom activities. They need a considerable amount of exposure. Therefore, there is a need to develop self-access learning materials to enhance independent learning through extensive reading. The objectives of this research were to identify the students’ target needs, identify the learning needs, and develop self-access graded reading materials to enhance extensive reading. The study employed the procedure of materials development by Jolly and Bolitho (2011) with some adaptations. There are originally 7 main stages, however, the steps were adapted and reduced into 5 stages. The adapted stages include needs analysis, syllabus planning, materials production, materials evaluation, and revision for the final draft. The data of this research were collected by using questionnaires and interviews. The developed materials employ three levels of difficulty based on the number of headwords and readability ease. The materials were evaluated in terms of content, language, presentation, and layout appropriateness. The results of the materials evaluation showed a good outcome according to the expert judgment results.
Applying Machine Learning Classifiers in Argumentation Context

Group decision making is an area that has been studied over the years. Group Decision Support Systems emerged with the aim of supporting decision makers in group decision-making processes. In order to properly support decision-makers these days, it is essential that GDSS provide mechanisms to properly support decision-makers. The application of Machine Learning techniques in the context of argumentation has grown over the past few years. Arguing includes negotiating arguments for and against a certain point of view. From political debates to social media posts, ideas are discussed in the form of an exchange of arguments. During the last years, the automatic detection of this arguments has been studied and it’s called Argument Mining. Recent advances in this field of research have shown that it is possible to extract arguments from unstructured texts and classifying the relations between them. In this work, we used machine learning classifiers to automatically classify the direction (relation) between two arguments.
PEMODELAN PENYEBARAN INFEKSI COVID-19 DI KALIMANTAN, 2020

In the regional scale of the province in Kalimantan, the spread case appeared in West Kalimantan and East Kalimantan on 18th March 2020, Central Kalimantan on 20th March 2020, South Kalimantan on 22nd March 2020 and North Kalimantan on 29th March 2020. In this case the Covid-19 epidemic was caused by coronavirus disease 2019 (Covid-19). The prediction of the Covid-19 is currently sought. We use the SIR Model to perform basic reproductive value calculations (R0). This model is the mathematic language, interpreted as the number of nativity of a new case due to a person infected with Covid-19 into a fully healthy and potential population for illness or infection by the Covid-19. Using the linear regression, we estimate the value of R0. The value of R0 in Kalimantan region is West Kalimantan (R0 = 1.15), East Kalimantan (R0 = 1.17), Central Kalimantan (R0 = 1.09), South Kalimantan (R0 = 1.24), and North Kalimantan (R0 = 1.20). According to the SIR Model, the highest R0 value is in South Kalimantan, followed by North Kalimantan, East Kalimantan, West Kalimantan, and Central Kalimantan.
AN ITERATIVE APPROACH FOR SELF-CALIBRATING BUNDLE ADJUSTMENT

Abstract. Obtaining accurate image interior and exterior orientations is the key to improve 3D measurement accuracy besides reliable and accurate image matching. A majority of cameras used for those tasks are non-metric cameras. Non-metric cameras commonly suffer various distortions. Generally, there are two ways to remove these distortions: 1) conducting prior camera calibration in a controlled environment; 2) applying self-calibrating bundle adjustment in the application environment. Both approaches have their advantages and disadvantages but one thing is common that there is no universal calibration model available so far which can remove all sorts of distortions on images and systemic errors of image orientations. Instead of developing additional calibration models for camera calibration and self-calibrating adjustment, this paper presents a novel approach which applies self-calibrating bundle adjustment in an iterative fashion: after performing a conventional self-calibrating bundle adjustment, the image coordinates of tie points are re-calculated using the newly obtained self-calibration model coefficients, and the self-calibrating bundle adjustment is applied again in the hope that the remaining distortions and systematic errors will be reduced further within next a few iterations. Using a “virtual image” concept this iterative approach does not require to resample images or/and re-measure tie points during iterations, only costs a few additional iterations computational resource. Several trails under various application environments are conducted using this proposed iterative approach and the results indicate that not only the distortions can be reduced further but also image orientations become much stable after a few iterations.
IOT BASED BLOCKCHAIN SOLUTION: COVID-19 AND DEFENSE

Development is an indispensable part in the information technology field.The Internet of Things (IoT) basically opens door for all devices, appliances, and software to share and communicate information over the Internet.The shared data contains a large amount of secretive information, thus preserving information security on the shared data is an important issue that cannot be neglected.To secure data linked with IOT, blockchain is combined with IOT.IOT when implemented with blockchain is of great advantage to us.Here, the various characteristics, challenges and applications related to IOT are studied.Also, the Blockchain architecture and challenges are discussed in brief.In this work, the two IOT cases are considered; one for COVID-19 and another for Defense System.In both the cases the security and privacy issues are discussed and the solution is proposed in context of the blockchain.
A Study of Connectionism Theory

The development of connectionism represents a paradigm shift in science. Connectionism has its root in cognitive and computational neuroscience. Likening the brain to a computer, connectionism tries to describe human mental abilities in terms of artificial neural networks. A neural network consists of a large number of nodes and units which are joined together to form an interconnection network. Within these interconnections, knowledge is distributed. Therefore learning is a processing by-product. This article is about the concept of connectionism, what it accounts for and what it doesn't take into account. Finally, different approaches to connectionism are discussed.
Integrating Gamification and Social Interaction into an AR-Based Gamified Point System

A loyalty program is an important link between consumers and merchants in daily consumption. While new technologies (e.g. gamification, social networks, augmented reality, and so on) make it possible to strengthen this bond, their potential has not yet been fully exploited. In our research, we explore a novel approach to integrate gamification and social interaction into a loyalty program based on augmented reality (AR). We propose an AR-based gamified point system which supports users in obtaining pet-based dynamic feedback on mobile devices and provides a multi-user environment for social interaction. Compared to traditional point systems, pet-based designs help to establish an emotional connection between users and the system. The multi-user environment is designed to increase the user’s motivation through the positive effects of social interactions. Our system provides interpersonal communication channels between users, including competitive and non-competitive interactions. We performed an evaluation consisting of two experiments to examine the effects of game elements (mission and feedback) and social cues (competitive and non-competitive interactions). In the first experiment, we analyze the change in online shopping behavior before and after adding game elements. The results show that gamification can increase user participation in online shopping. In the second experiment, we study the effects of social cues. The results show that social cues can motivate users to participate in the use of a gamified point system.
CPU Hardware Classification and Performance Prediction using Neural Networks and Statistical Learning

We propose a set of methods to classify vendors based on estimated CPU performance and predict CPU performance based on hardware components.For vendor classification, we use the highest and lowest estimated performance and frequency of occurrences of each vendor to create classification zones.These zones can be used to identify vendors who manufacture hardware that satisfy a given performance requirement.We use multi-layered neural networks for performance prediction, which account for nonlinearity in performance data.Various neural network architectures are analysed in comparison to linear, quadratic, and cubic regression.Experiments show that neural networks obtain low error and high correlation between predicted and published performance values, while cubic regression produces higher correlation than neural networks when more data is used for training than testing.An analysis of how the neural network architecture affects prediction is also performed.The proposed methods can be used to identify suitable hardware replacements.
THE EFFECT OF GAMIFICATION BASED ON BALINESE LOCAL STORY

This research aimed to know the significant effect of Gamification based on Balinese Local Story toward students’ listening comprehension. The implementation of one group pretest posttest was a design for collecting the data. The pretest and posttest was in the form of multiple choice tests. The research population consisted of 164 students of SD Negeri 2 Anturan. Then, the research sample was 5th grade students who consisted of 21 students. The result of the data presented that, there was significance different score between pretest and posttest. The mean score of pretest was 43.37. In contrast, the mean score of posttest was 79.74. Based on the results, the mean score of posttest was higher than pretest. The result of effect size was 3.190 that means to large effect. The result of effect size showed Gamification based on Balinse local story could improve students’ listening comprehension.&#x0D;
3D Sketching for Multi-Pose Products: An Interactive Showcase

2D perspective sketching is an essential tool for designers during the early stage of design. However, for products that have moving parts and take different poses during usage, 2D perspective sketching can be painstaking and time-consuming. In this interactive showcase, we present a 3D sketching system for multi-pose products. Our system lets designers easily sketch 3D curves, and part, rig, and pose them. We showcase that, with interactions that closely resemble traditional 2D perspective sketching and the physical manipulation of an articulated object, designers can quickly try many different form and movement ideas in 3D during the early stage of design.
EFFICIENT COMPUTATION OF POSTERIOR COVARIANCE IN BUNDLE ADJUSTMENT IN DBAT FOR PROJECTS WITH LARGE NUMBER OF OBJECT POINTS

Abstract. One of the major quality control parameters in bundle adjustment are the posterior estimates of the covariance of the estimated parameters. Posterior covariance computations have been part of the open source Damped Bundle Adjustment Toolbox in Matlab (DBAT) since its first public release. However, for large projects, the computation of especially the posterior covariances of object points have been time consuming.The non-zero structure of the normal matrix depends on the ordering of the parameters to be estimated. For some algorithms, the ordering of the parameters highly affect the computational effort needed to compute the results. If the parameters are ordered to have the object points first, the non-zero structure of the normal matrix forms an arrowhead.In this paper, the legacy DBAT posterior computation algorithm was compared to three other algorithms: The Classic algorithm based on the reduced normal equation, the Sparse Inverse algorithm by Takahashi, and the novel Inverse Cholesky algorithm. The Inverse Cholesky algorithm computes the explicit inverse of the Cholesky factor of the normal matrix in arrowhead ordering.The algorithms were applied to normal matrices of ten data sets of different types and sizes. The project sizes ranged from 21 images and 100 object points to over 900 images and 400,000 object points. Both self-calibration and non-self-calibration cases were investigated. The results suggest that the Inverse Cholesky algorithm is the fastest for projects up to about 300 images. For larger projects, the Classic algorithm is faster. Compared to the legacy DBAT implementation, the Inverse Cholesky algorithm provides a performance increase by one to two orders of magnitude. The largest data set was processed in about three minutes on a five year old workstation.The legacy and Inverse Cholesky algorithms were implemented in Matlab. The Classic and Sparse Inverse algorithms included code written in C. For a general toolbox as DBAT, a pure Matlab implementation is advantageous, as it removes any dependencies on, e.g., compilers. However, for a specific lab with mostly large projects, compiling and using the classic algorithm will most likely give the best performance. Nevertheless, the Inverse Cholesky algorithm is a significant addition to DBAT as it enables a relatively rapid computation of more statistical metrics, further reinforcing its application for reprocessing bundle adjustment results of black-box solutions.
The Influence of Jigsaw Cooperative Model toward Writing Exposition Text Ability of Class X At SMK Negeri 1 Pematang Siantar

The purpose of this study is: (1) To know that Jigsaw Cooperative Model can improve the students’ writing Exposition ability and (2) to describe the process ofJigsaw Cooperative Model in improving students’ writing Exposition ability. This study was conducted at class X at SMK Negeri 1 Pematang Siantar.The population were 70 students. The qauntitative data were taken from the scores of the pre-test and post-test. The total score is 4894, the mean 69, 9 thus, in the post test found the total score is 5981, and the mean 85, 4.Jigsaw Cooperative Model inspired both the students and the studyer in improving the ability of English especially on writing. The students could practice their skill of English in the different situation and treatment. They could express their idea or opinion freely as the studyer only became mediator during the activities of Jigsaw Cooperative Model as it was student-centred. In fact, the students’ cooperation increased. They helped each other, communicated using English, and built the idea, imagination and opinion that they wanted to compose in their writing during applying Jigsaw Cooperative Model.
Enhanced Authentication for Web-Based Security using Keystroke Dynamics

Current password authentication system was proven not secure enough to protect the information from intruders.However, various research has been done and the results show the value of FRR still low and the value of FAR still high.Thus, one of the methods suggests, is enhancing the current system using keystroke dynamics.Keystroke dynamics is a type of biometric authentication that does not require any special hardware, easy to use as the same routine as normal password authentication.Therefore, this research proposed an authentication system using keystroke dynamics to prevent the system from intruders.A system is developed that consist of two parts which are enrolment and verification.Then, a prototype is developed for testing process that consists of 3 main modules, namely Enrolment, Client/Server Connection and, Verification and Retraining.Based on the testing, the system proved that the keystroke dynamic authentication system was able to implement in client/server environment and shows the value of EER is low that indicates it provide a better system authentication.In future, the system can be improved by enhancing the security, performance, and user interface.
STUDY OF DIGITAL CAMERA CALIBRATION ON A FLAT TEST OBJECT

Abstract. The paper presents a method for calibration of digital cameras based on the use of a flat test object. The main distinctive feature of this method is that the camera is fixed and does not change its position in space. A series of images of a flat test object (for example, a chessboard) is taken at various distances and inclination of the test object. One should tilt the test object relative to the image plane in order to avoid ambiguity in solving the problem. All these images are measured separately, and processing is performed together, counting them as one image with common exterior orientation elements. Experimental studies have shown sufficient efficiency of such calibration, which is easily implemented and gives positive results in comparison with the classical calibration of the camera on a spatial test object. The results of experimental studies on real images have shown that proposed calibration method gives the comparable with conventional method accuracy. Thus, it is possible to use simple and cheap flat calibration test object instead of spatial one. To achieve the maximum result in camera calibration accuracy, use this method to tilt the chessboard at angles in the range of 30–40 degrees to the optical axis of the camera being calibrated.
Analytic Hierarchy Process-based Fuzzy Measurement to Quantify Vulnerabilities of Web Applications

Much research has been conducted to detect vulnerabilities of Web Applications; however, these never proposed a methodology to measure the vulnerabilities either qualitatively or quantitatively.In this paper, a methodology is proposed to investigate the quantification of vulnerabilities in Web Applications.We applied the Goal Question Metrics (GQM) methodology to determine all possible security factors and subfactors of Web Applications in the Department of Transportation (DOT) as our proof of concept.Then we introduced a Multi-layered Fuzzy Logic (MFL) approach based on the security sub-factors' prioritization in the Analytic Hierarchy Process (AHP).Using AHP, we weighted each security sub-factor before the quantification process in the Fuzzy Logic to handle imprecise crisp number calculation.
Low-Sulfidation Epithermal Carbonate-Basemetal-Gold Mineralization Hosted by Tertiary Sedimentary Rocks in Bastem Prospect, Luwu District, Sulawesi Island, Indonesia: A Preliminary Study

In Indonesia, gold is typically mined out from epithermal, porphyry and skarn deposit types occurred within volcanic belts along magmatic arc or active continental margin setting. Numerous gold prospects, however, are recently discovered in association with metamorphic and sedimentary rocks. This paper is aimed to discuss a preliminary study on the occurrences and characteristics of the sedimentary rock-hosted low-sulfidation (LS) epithermal gold mineralization in Bastem (Bessengan Tempe) prospect, Luwu district, Sulawesi Island, Indonesia. Detailed geological and hydrothermal alteration mapping was performed to understand the distribution and characteristics of ore mineralization. Representative ore samples taken were analysed for ore chemistry by means of Fire Assay – Atomic Absorption Spectrometry (FA-AAS) and Acid Geochemical Digest - Atomic Absorption Spectrometry (GA-AAS). The results from this work that the Bastem prospect is stratigrapically occupied by Tertiary sedimentary rocks of Toraja formation, which is adjacent to volcanic rocks of Lamasi formation. Gold-bearing quartz±carbonate veins are hosted by mudstone and siltstone of Toraja formation. The quartz-carbonate veins show a typical LS epithermal open space filling texture containing erratic gold grade of up to 7.16 g/t with relatively high besemetals (Pb and Zn) grades of up to &gt;0.4 and &gt;1%, respectively. Based on those various features, the LS epithermal deposit is categorized as “carbonate-basemetal-gold mineralization type”, which might be originated in back arc rift/basin setting. This ‘unconventional’ sedimentary rock hosted-gold mineralization type would be the new target of gold exploration in Indonesia.
Exploring the Readability of Terms of Service of Social Networking Sites

Terms of service of social networking sites provides relevant information for end-users to make inform decision regarding their use of social networking site.The purpose of this paper was to evaluate the readability of the top five social networking sites namely Facebook, WhatsApp, Messenger, YouTube, and WeChat.Using Flesch reading ease and grade level indexes, the authors measured the readability of the selected terms of service.Measures of central tendencies, and one-way analysis of variance, with bootstrapping, were used to analyse the data.The results showed that the ToS were difficult to read when compared to the standard score of public documents.Moreover, the readability levels of the ToS required end users to have attained at least 13 years of formal education, which is equivalent to first year college student, in order for the users to find the ToS readable.In addition, no statistical difference existed among ToS of the five social networking sites meaning all the ToS of the five networks were unilaterally difficult for the average reader (end user).It is recommended that the ToS of these networks be revised to make them more readable since substantial number of their audience are young with low number of years of education.