Reviewerly/exHarmony huggingface dataset:
Prototaip Aplikasi Realiti Maya Panorama UNISEL Sebagai Alat Bantu Pemasaran

Virtual environments are a way for humans to imagine, manipulate and interact with computers or smartphones. In other words, the user can feel his presence in an artificial environment. Currently, virtual reality (VR) environments used for a variety of purposes such as creating virtual tours of museums, monuments, cities or places to visit that reflect events in a 360-degree view of the environment. The main challenge in the development of this virtual environment is to bring visitors into the delivery of information situations as if they were in a real-world environment. With the growing number of competitions between IPTS and UAs increasing, various efforts and ways to increase the number of students. Thus, the objective of this study is to provide an overview of the UNISEL Bestari Jaya Campus in a real-world setting, disseminate information about the facilities available at UNISEL through virtual reality technology and develop a UNISEL panorama mobile app in virtual reality as UNISEL marketing aids. Research methods used in this study are using the ADDIE Model for software development, which consists of phases of Analysis, Design, Development, Implementation, and Evaluation. Result of this research project will be able to develop a mobile marketing application that will help to visualize the real UNISEL environment with more easily and clearly.  Also to show the public in virtual realities such as campus conditions, facilities provided and as a platform to promote UNISEL economy as an educational destination for the future especially at the UNISEL Bestari Jaya Campus.
Towards a Complete Pipeline for Segmenting Nuclei in Feulgen-Stained Images

Cervical cancer is the second most common cancer type in women around the world. In some countries, due to non-existent or inadequate screening, it is often detected at late stages, making standard treatment options often absent or unaffordable. It is a deadly disease that could benefit from early detection approaches. It is usually done by cytological exams which consist of visually inspecting the nuclei searching for morphological alteration. Since it is done by humans, naturally, some subjectivity is introduced. Computational methods could be used to reduce this, where the first stage of the process would be the nuclei segmentation. In this context, we present a complete pipeline for the segmentation of nuclei in Feulgen-stained images using Convolutional Neural Networks. Here we show the entire process of segmentation, since the collection of the samples, passing through pre-processing, training the network, post-processing and results evaluation. We achieved an overall IoU of 0.78, showing the affordability of the approach of nuclei segmentation on Feulgen-stained images. The code is available in: this https URL.
Performance and Evaluation Measures

Performance and evaluation measures for clustering algorithms are described in this chapter. We discuss various evaluation measures in order to judge the quality of clustering solutions obtained by clustering algorithms. These measures include cluster validity indices, silhouette coefficients and plots, purity, and others. In addition, we introduce performance profiles to compare efficiency of different clustering algorithms using the accuracy of obtained solutions, the number of distance function evaluations, and computational time.
End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data

While theories postulating a dual cognitive system take hold, quantitative confirmations are still needed to understand and identify interactions between the two systems or conflict events. Eye movements are among the most direct markers of the individual attentive load and may serve as an important proxy of information. In this work we propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events between the two systems through the collection and processing of data related to eye movements. A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios. Moreover, we show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy and to investigate more in depth the gaze dynamics.
Pengaruh Penggunaan Jurnal Belajar Berbantuan Google Classroom Pada Materi Sistem Pencernaan Manusia Terhadap Motivasi dan Hasil Belajar Peserta Didik Kelas XI IPA SMAN 3 Sidra

Abstract: The low learning outcomes of students can be influenced by the lack of variations in the use of methods or learning media in the teaching and learning process and the lack of the ability of teachers to make variations in learning assessment. Based on the results of preliminary observations conducted in class XI IPA of SMAN 3 Sidrap, the learning process that is still focused on the teacher causes a lack of activity and interest in students, the learning atmosphere becomes less interesting and monotonous, thus affecting student learning activities and consequently the learning outcomes are low. The purpose of this study was (i) to determine the effect of using a Google Classroom Learning Journal on human digestive system material on learning motivation of students of class XI IPA of SMAN 3 Sidrap, (ii) to determine the effect of using the Google Classroom Assisted Learning Journal on human digestive system material towards learning outcomes of students of class XI IPA SMAN 3 Sidrap. This research is a Quasi-Experimental type of research. The research sample was selected using a random sampling technique, where two classes were taken as samples randomly. Data collection techniques used a closed questionnaire to measure learning motivation and tests in the form of multiple-choice questions to measure student learning outcomes. The data analysis technique uses descriptive and inferential analysis. The data obtained is then analyzed to answer the formulated problem statement. Previous research conducted by Hudiono & Astuti (2013) concluded that learning journals are not only oriented towards developing academic abilities but through the habit of writing learning experiences, students are also accustomed to expressing their feelings, thoughts or expectations about the learning given by the teacher. So that, through the use of google classroom assisted learning journals, it is hoped that it can be an alternative to increase students' motivation and learning outcomes. This research is a new study, where there were no previous researchers who combined the use of learning journals and e-learning learning models, namely google classroom. Keyword: Learning Journals, Google Classroom, Learning Motivation, and Learning Outcomes
Basic Geophysics - Physical Volcanology

How active is the Santiaguito volcano in Guatemala?Operation principle and combination of different measuring methods in volcanology, differentiation of earthquakes, explosive eruptions and falling boulders.
Pengujian Perangkat Lunak Sistem Informasi Penilaian Mahasiswa dengan Teknik Boundary Value Analysis Menggunakan Metode Black Box Testing

In this student assessment application, if there is an error program, there will be losses for students and universities. Losses for students, if this application error occurs, then the assessment received by students will experience delays due to the difficulty in the process of calculating student grades and students will be late to receive an assessment. Losses for the university, if there is an error program on this application, the university will suffer losses if students want a rapid decline in grades but the university cannot calculate quickly and accurately. The method used in testing this application uses the black box method with boundary value analysis techniques. Boundary value analysis techniques test the quality of the application by showing that there are still some errors when entering data to be tested in the column to determine whether the input value is valid or not with the lower limit and the upper limit that has been determined. So the problems that occur can cause data stored in the database does not match the expected data. The test is carried out in the Student Rating form by testing the upper and lower limit values through several predetermined stages for each column contained in the form. Test results can be used as input to improve the application. The results of this test can provide a reference for testing the quality of student assessment applications by analyzing the functionality of the program that has been made in accordance with the expected results.
PoET-BiN: Power Efficient Tiny Binary Neurons

The success of neural networks in image classification has inspired various hardware implementations on embedded platforms such as Field Programmable Gate Arrays, embedded processors and Graphical Processing Units. These embedded platforms are constrained in terms of power, which is mainly consumed by the Multiply Accumulate operations and the memory accesses for weight fetching. Quantization and pruning have been proposed to address this issue. Though effective, these techniques do not take into account the underlying architecture of the embedded hardware. In this work, we propose PoET-BiN, a Look-Up Table based power efficient implementation on resource constrained embedded devices. A modified Decision Tree approach forms the backbone of the proposed implementation in the binary domain. A LUT access consumes far less power than the equivalent Multiply Accumulate operation it replaces, and the modified Decision Tree algorithm eliminates the need for memory accesses. We applied the PoET-BiN architecture to implement the classification layers of networks trained on MNIST, SVHN and CIFAR-10 datasets, with near state-of-the art results. The energy reduction for the classifier portion reaches up to six orders of magnitude compared to a floating point implementations and up to three orders of magnitude when compared to recent binary quantized neural networks.
Internet Language: An Investigation into the Features of Textisms in an ESL/EFL Context

In recent years, the prevalence of mobile communication has given rise to a new writing style that is an amalgam of oral and written modes. The rapid growth of Textisms arouses curiosity, fear, and perplexity among people. In Lebanon, some educators complain about the propagation of texting and claim that it is becoming harmful for their students. Thus, this study examines the nature of this new trend by describing its linguistic features and comparing it with oral speech through the application of Carter and McCarthy’s Model of Spoken Grammar. A detailed analysis of 1,890 messages obtained from 240 university students shows that Textisms are mainly characterized by unconventional spelling through the use of contractions and expressive. Similar to the oral language, texted sentences are weakly structured due to their briefness, and common features entail frequent use of deictic references, ellipsis, discourse markers, and others. Unlike the spoken language, vagueness, swearing and taboo expressions, and sign-offs are rarely used by texters. Finally, punctuation rules are violated by texters and hold major signification in texting.
Limitations in an Imitation Game: Lessons from another Bitcoin Copycat

We find strong evidence that imitation to the leader in an uncertain environment provides short-term early success without enhancing long-term survival. Using a unique setting in cryptocurrencies where Bitcoin has been the centre focus, we define copycats as cryptocurrencies that have a name similar to Bitcoin. Our results show copycats earn higher returns in the first four weeks of trading but have a lower survival rate comparing to non-copycats and are robust to an alternative definition of copycat and an instrumental variable. Our results highlight that in an age of information, coupled with an uncertain market created by a novel technology, the name of an organization may be a strong signal to investors when other signals are unclear.
Quality Management of Pedagogical Personnel as a Driver for the Development of the Education System

In this work, we conducted a study in the field of modifying the competencies of faculty and students, carried out in accordance with the Concept of Modernization of Russian Education and the signed agreements at the Bologna Process, during its deep integration into the international community with the assistance of the European Union. The conditions, nature, and problems of teacher quality management and the degree of humanization are considered. Goals/objectives. The goal is to discuss the results of a study of the parameters of a complex phenomenon; management, degree of humanization, conditions, and quality of work of “faculty at the Higher School.” To achieve the designated goal, the following tasks were solved: (1) A comparative assessment of the criteria for working conditions of various concepts and the degree of their impact on the work of employees of the Higher School; (2) discussion of methods of managing labor processes in a higher educational institution that have a direct impact on the working conditions of the faculty; (3) the possibility of the impact of humanization on the degree of effectiveness and appropriateness of application for evaluating the above process, and the concept of humanization. Methodology. The traditional methodology of scientific research, that is, the formulation of a scientific hypothesis followed by argumentation, both in favor of confirmation and disavowal, was used. Conclusion. The article proposes an interaction algorithm through the modification of methods, techniques, approaches, and forms of management of the administration-faculty-department-teacher-student, which demonstrates the following. The management tools used make it possible to achieve a specific set of competencies required by the teacher and student during the period of integration of education, in order to ensure the breakthrough development of the individual and the highly skilled worker as creative persons. To increase the effectiveness of the educational process and ensure that the necessary set of student competencies is obtained, it is necessary to create conditions conducive to the disclosure of the teacher’s creative abilities. This is possible if the quality management system is modified. The application of the concept of working conditions for teachers of the Higher School, the degree of humanization, which is determined by the factors of the external environment, can positively affect the management of the quality of work of the teaching staff and its professional competencies, in the context of increased digitalization of the economy. Novelty. The leading proposal is the recommendation of the author of the article on the need to reform the quality management system of the labor management of the teaching staff, based on the principles of humanization, as well as the creation of a separate system of approved methods for improving the level and development of teachers’ competencies at the initial stage of a career, depending on the specifics and profile of the institution.
Distributed Consensus Reduced Support Vector Machine

Nowadays, machine learning performs astonishingly well in many different fields. In general, the more data we have, our machine learning methods will show better results. However, in many situations, the data owners may not want to or not allow to share their data because of legal issues or privacy concerns. However, if we can pool all the data together as the training data for the machine learning task we will have a better result. In the other situation, we encounter an extremely large dataset, which is difficult to store in a single machine. We may utilize more computing units to solve it. To deal with these two problems, we propose the distributed consensus reduced support vector machine (DCRSVM), which is a nonlinear model for binary classification. We apply the ADMM, Alternating Direction Method of Multipliers, to solve the DCRSVM. In each iteration, the local worker will update their model by incorporating the information shared by the master. The local workers only share their models in each iteration but never share their data. The master will fuse the local models reported by the local workers. At the end, the master will generate the consensus model that almost identical to the model generated by pooling all data together. Pooling all data together is not allowed in many real world applications.
Multivariate q-Polya and inverse q-Polya distributions

An urn containing specified numbers of balls of distinct ordered colors is considered. A multiple q-Polya urn model is introduced by assuming that the probability of q-drawing a ball of a specific color from the urn varies geometrically, with rate q, both with the number of drawings and the number of balls of the specific color, together with the total number of balls of the preceded colors, drawn in the previous q-drawings. Then, the joint distributions of the numbers of balls of distinct colors drawn (a) in a specific number of q-drawings and (b) until the occurrence of a specific number of balls of a certain color, are derived. These two distributions turned out to be q-analogues of the multivariate Polya and inverse Polya distributions, respectively. Also, the limiting distributions of the multivariate q-Polya and inverse q-Polya distributions, as the initial total number of balls in the urn tends to infinity, are shown to be q-multinomial and negative q-multinomial distributions, respectively.
Sentimental Analysis Using Twitter to Predict the 2019 PM Elections

Internet-based life is a stage that comprises of individuals from around the globe. It has changed how the data is accessible to everybody. Individuals through Twitter, Facebook and different other social stages uncover their data that can be utilized to enhance basic leadership. In this paper, a few information was separated utilizing wistful examination (also referred to as opinion extraction, opinion mining, sentiment mining and subjective analysis) which is an information mining arrangement calculation. Estimation investigation is a method used to quantify the feelings of web-based life clients in online content. This paper centered in removing the assumptions toward PM applicant in India for 2019 Elections. The informational index has been gathered utilizing Twitter for about fourteen days, and astute information pre-handling system has been used for evacuating clamor present in the dataset.
Analysis of Multivariate Adaptive Regression Spline (MARS) Model in Classifying factors affecting on Student the Study Period at FKIP Darussalam University of Ambon

Abstract This study aims to determine the applicative model that can be revealed through the Multivariate Adaptive Regression Spline (MARS) classification Model for the problems alumni study period of the FKIP Darussalam University of Ambon. To find out the applicative model that can be revealed through the MARS classification model, parameter estimation is first performed to find the best MARS model. The best MARS model is chosen based on the minimum generalized cross validation (GCV) value.The research result indicates that the parameter estimation of MARS model using Ordinary Least Square (OLS) method obtained were convergent. This is indicated by the smallest of value Mean Square Error (MSE). In addition, the application of the MARS model using the OLS method on data alumni study period of the FKIP Darussalam University of Ambon, result indicates that there is an influential base function, BF3 which contains two predictor variable, that is first semester achievement index and family economics conditions. This is caused by BF3 has a significance value t count &gt; t table so the decision taken is to reject H 0 which means that the BF3 basis function parameter has a significant effect on the model.
Estimating the Cost of Implementing Virtual Desktops as a Stage of Project Management in the Field of Cloud Technologies

The article considers the problem of estimate the project in the field of cloud technologies: value determination virtual desktops in the activity of the educational organization. The introduction of desk-top virtualization leads to the appearance of many effects in the functioning of the enterprise: financial, technical, social, organizational. But the introduction of virtualization is associated with significant upfront investments. This often stops organizational leaders from implementing cloud projects. Therefore, it is important to correctly evaluate investments in such IT initiatives. The grouping of costs depends on the goals of the organization. The separation of costs into fixed and variable helps to calculate the break-even point. Grouping, which involves the division into capital and operational allows you to understand the amount of one-time investments in the project. Grouping costs according to the product development and sales allows you to calculate the planned taxes. In IT projects under different conditions, various grouping methods and costing methods are relevant. At the design stage, preparation for implementation, most are interested in capital and operating expenses. Which in turn are also divided into groups. The capital costs of implementing cloud technologies can be divided into: costs of equipment, infrastructure, installation. Operating costs: energy costs, operating costs of the virtualization system.