Reviewerly/exHarmony huggingface dataset:
Penerapan Algoritma CART Dalam Menentukan Jurusan Siswa di MAN 1 Inhil

MAN 1 Inhil is a school that applies ministerial regulations to determine the direction of student majors at the beginning of entry, namely in class X. Determination of majors is done by considering several indicators, namely the results of academic tests, interviews, and student interest. The calculation in determining this course is very simple, namely by adding up the values of each indicator and dividing them together so that an average value is obtained. If the value is fulfilled then the student is grouped based on their interests. This can lead to errors in decision making by the school because it can be subjective because it prioritizes student interests. Therefore we need methods and algorithms to help make decisions well, the decision tree method. One algorithm that can be used is CART algorithm to classify majors with three indicators, namely Natural Sciences, Social Sciences and Religion. The results of this study indicate that the CART algorithm is able to predict correctly, from 360 data classified using the CART algorithm, it can be concluded that 71 data majoring in religion and correctly classified by CART. 144 data majoring in Natural Sciences, 119 data correctly classified and 24 data classified as IPS, and 1 data classified as religion. Of 146 data majoring in social studies, 129 were classified correctly, 16 data were classified as natural sciences. Therefore it can be concluded that CART algorithm has an 80% accuracy so that it can be used in decision making
Effectiveness of Mobile Learning Implementation in Increasing Student Competence and Preventing the Spread and Impact of COVID-19

To prevent the spread and impact of COVID-19, and effective learning, one attempt is to conduct m-learning.The purpose of this study is to analyze: student supporting and inhibiting factors, the effectiveness of m-learning, preventing the spread and impact of COVID-19.The research was conducted through quasi-experimental.A sample of 116 Surabaya State University students.Data on supporting and inhibiting factors, data on the prevention of the spread-impact were obtained through online surveys, and data on the effectiveness of M-Learning using the pretest-posttest results.The data analysis used descriptive, t-test, gain tests.The novelty of this research involves supporting and inhibiting factors, and variables preventing the spread-impact of COVID-19.The results of the study: Supporting factors the suitability of mlearning-RPS-material-video-interactive modules-evaluationtime duration have adequate supporting tools, the ability to access information-online materials and use collaborative software, the discipline of the COVID-19 protocol, parental support, happy, e-learning platform compatible with mobile phones, smooth internet, mobile screen size, ability to communicate via email-WhatsApp, accuracy in completing assignments-evaluations, relax, ability to plan-use time policies, conducive environment.Factors that hinder the ability to understand English content; M-learning can prevent the spread and impact of COVID-19, 100% of students are not exposed and are not affected by COVID-19; M-Learning is effective in improving student competence in terms of the gain is quite high 0.49, the mean pretest 38.41 is significantly different from posttest 76.38.The research results can contribute to developing a distance-learning model in abnormal conditions.
Blog Writing for Learning

Blog has played a major role in enhancing writing among second language learners.However, at times, the messages written are confusing and not being delivered appropriately especially among second language learners.Therefore, this study focuses on the use of blog writing in learning the Malay language.The objectives of the study are to identify and analyze the adjectives and verbs in the blog.The samples of the study consist of six volunteered female subjects from a class of Malay language at a university in Korea.The subjects are given a task as in writing a story about their stories, experiences and feelings towards the autumn season.The subjects are told to write at least a hundred words of writing in the blog.The writings from the subjects are analyzed by using the discourse analysis theory.The results of the study revealed that each subject managed to write at least five adjectives and five verbs in the writing.The subjects also managed to write interesting experiences and stories in the blog.The result of the study also revealed that the subjects managed to write more than one story and show that they are happy with the task given to them.This showed that blog writing helps in the process of learning the second language.It is hoped that future studies will focus on blog writing for other means of grammar usage.
CULTURAL HERITAGE AND INTEROPERABLE OPEN PLATFORMS: STRATEGIES FOR KNOWLEDGE, ACCESSIBILITY, ENHANCEMENT AND NETWORKING

Improving the accessibility of information on cultural heritage is a key step to achieve sustainable and inclusive valorisation actions recognising knowledge as a valuable and operable resource.This paper addresses the issue from the point of view of digital technologies, in particular interoperable digital platforms for the storage, processing and visualization of open and interoperable data available in different formats (3D models, images, texts, etc.) and collected from different sources (traditional archives, digital libraries, real-time content, IoT, sensors, social networks, etc.).The comparison among three projects with different focuses -the H2020 INCEPTION project (Inclusive Cultural Heritage in Europe through 3D semantic Modelling -G.A. No. 665220), the H2020 ROCK project (Regeneration and Optimization of Cultural heritage in creative and Knowledge -G.A. No. 730280) and the Italian national project PON PNR IDEHA (Innovation for Data Elaboration in Heritage Areas -No.ARS01_00421) -shows possible strategies led by the knowledge of data usage models and information databases, focused on the common goal of improving accessibility, knowledge, enhancement and conservation of cultural heritage.The results show how information management through open and interoperable platforms represents a useful decision and monitoring support system related to cultural heritage: further, the provision of data and digital sources to different users, the use of this tool can create collaborative networks between all stakeholders involved at various levels and to allow new potential knowledge scenarios.
A Novel Parent Centric Crossover with the Log-Logistic Probabilistic Approach Using Multimodal Test Problems for Real-Coded Genetic Algorithms

In this paper, a comprehensive empirical study is conducted to evaluate the performance of a new real-coded crossover operator called Fisk crossover (FX) operator. The basic aim of the proposed study is to preserve population diversity as well as to avoid local optima. In this context, a new crossover operator is designed and developed which is linked with Log-logistic probability distribution. For its global performance, a realistic comparison is made between FX versus double Pareto crossover (DPX), Laplace crossover (LX), and simulated binary crossover (SBX) operators. Moreover, these crossover operators are also used in conjunction with three mutation operators called power mutation (PM), Makinen, Periaux, and Toivanen mutation (MPTM), and nonuniform mutation (NUM) for inclusive evaluation. The performance of probabilistic-based algorithms is tested on a set of twenty-one well-known nonlinear optimization benchmark functions with diverse features. The empirical results show a substantial dominance of FX over other crossover operators with authentication of performance index (PI). Moreover, we also examined the significance of the proposed crossover scheme by administrating ANOVA and Gabriel pairwise multiple comparison test. Finally, the statistically significant results of the proposed crossover scheme have a definite edge over the other schemes, and it is also expected that FX has a great potential to solve complex optimization problems.
Realization and application of geological cloud platform

In recent years, with the progress of computer technology, some traditional industries such as geology are facing changes in industrial structure and application mode. So we try to apply big data and virtualization technology in the field of geoscience. This study aims at addressing the existing problems in geological applications, such as data sharing, data processing and computing performance. A Geological Cloud Platform has been designed and realized preliminarily with big data and virtualization technology. The application of the Geological Cloud Platform can be divided into two parts: 1) to nest the geological computing model in cloud platform and visualize the results and 2) to use relevant software to conduct data analysis and processing in virtual machines of Windows or Linux system. Finally, we prospect Carlin-type deposits in Nevada by using the spatial data model ArcSDM in the virtual machine.
A Scientometric Overview of Educational Development Literature

Educational development is essential to enhance the standard of education with the collaboration of educators, students, and parents.This research aims to map the status of international educational development publication literature indexed by Scopus using scientometric overview.The study has carried out scientometric methods and analysed research data using the analysis search results service from Scopus and the VOSviewer application.The research data of 5,807 academic documents published from 1910 to 2019 were obtained from the Scopus database.The results showed an increasing trend in the number of educational development literature at the international level each year.The most productive countries, research institutions, and individual researchers in educational development literature were the United States; UCL Institute of Education; and Watson, K.The greatest number of funding sponsors in the international educational development literature was the Economic and Social Research Council.The most intensive subject areas and sources of publications in educational development literature were social science and International Journal of Educational Development.There were three patterns of collaborative researchers and five networks of research keywords and in the educational development literature.
Clustering based feature selection using Partitioning Around Medoids (PAM)

High-dimensional data contains a large number of features. With many features, high dimensional data requires immense computational resources, including space and time. Several studies indicate that not all features of high dimensional data are relevant to classification result. Dimensionality reduction is inevitable and is required due to classifier performance improvement. Several dimensionality reduction techniques were carried out, including feature selection techniques and feature extraction techniques. Sequential forward feature selection and backward feature selection are feature selection using the greedy approach. The heuristics approach is also applied in feature selection, using the Genetic Algorithm, PSO, and Forest Optimization Algorithm. PCA is the most well-known feature extraction method. Besides, other methods such as multidimensional scaling and linear discriminant analysis. In this work, a different approach is applied to perform feature selection. Cluster analysis based feature selection using Partitioning Around Medoids (PAM) clustering is carried out. Our experiment results showed that classification accuracy gained when using feature vectors' medoids to represent the original dataset is high, above 80%.
Information System Security Risk Management E-Learning Using FMEA in University

The security of information systems in the learning process during the Covid-19 pandemic is very important for educational institutions today, which are based on government regulations that require learning to be done online.In order to prevent the transmission of covid-19, people are advised to stay at home, but the learning process must continue.In the process of learning system risk is a point that must be considered.In online learning, information system security risk management can use the FMEA platform to analyze data security and minimize risks that occur.
The Phonetic Bases of Vocal Expressed Emotion: Natural versus Acted

Can vocal emotions be emulated? This question has been a recurrent concern of the speech community, and has also been vigorously investigated. It has been fueled further by its link to the issue of validity of acted emotion databases. Much of the speech and vocal emotion research has relied on acted emotion databases as valid proxies for studying natural emotions. To create models that generalize to natural settings, it is crucial to work with valid prototypes -- ones that can be assumed to reliably represent natural emotions. More concretely, it is important to study emulated emotions against natural emotions in terms of their physiological, and psychological concomitants. In this paper, we present an on-scale systematic study of the differences between natural and acted vocal emotions. We use a self-attention based emotion classification model to understand the phonetic bases of emotions by discovering the most 'attended' phonemes for each class of emotions. We then compare these attended-phonemes in their importance and distribution across acted and natural classes. Our tests show significant differences in the manner and choice of phonemes in acted and natural speech, concluding moderate to low validity and value in using acted speech databases for emotion classification tasks.
Observed quantum dynamics: classical dynamics and lack of Zeno effect

We examine a case study where classical evolution emerges when observing a quantum evolution. By using a single-mode quantum Kerr evolution interrupted by measurement of the double-homodyne kind (projecting the evolved field state into classical-like coherent states or quantum squeezed states), we show that irrespective of whether the measurement is classical or quantum there is no quantum Zeno effect and the evolution turns out to be classical.
Simultaneous Translation

Simultaneous translation, which performs translation concurrently with the source speech, is widely useful in many scenarios such as international conferences, negotiations, press releases, legal proceedings, and medicine. This problem has long been considered one of the hardest problems in AI and one of its holy grails. Recently, with rapid improvements in machine translation, speech recognition, and speech synthesis, there has been exciting progress towards simultaneous translation. This tutorial will focus on the design and evaluation of policies for simultaneous translation, to leave attendees with a deep technical understanding of the history, the recent advances, and the remaining challenges in this field.
On plane subgraphs of complete topological drawings

Topological drawings are representations of graphs in the plane, where vertices are represented by points, and edges by simple curves connecting the points. A drawing is simple if two edges intersect at most in a single point, either at a common endpoint or at a proper crossing. In this paper we study properties of maximal plane subgraphs of simple drawings D n of the complete graph K n on n vertices. Our main structural result is that maximal plane subgraphs are 2 -connected and what we call essentially 3 -edge-connected . Besides, any maximal plane subgraph contains at least ⌈3 n /2⌉ edges. We also address the problem of obtaining a plane subgraph of D n with the maximum number of edges, proving that this problem is NP-complete. However, given a plane spanning connected subgraph of D n , a maximum plane augmentation of this subgraph can be found in O ( n 3 ) time. As a side result, we also show that the problem of finding a largest compatible plane straight-line graph of two labeled point sets is NP-complete.
Virtual Prototyping-Based Path Planning of Unmanned Aerial Vehicles for Building Exterior Inspection

Virtual Prototyping-Based Path Planning of Unmanned Aerial Vehicles for Building Exterior Inspection Zhenjie Zheng, Mi Pan and Wei Pan Pages 16-23 (2020 Proceedings of the 37th ISARC, Kitakyushu, Japan, ISBN 978-952-94-3634-7) Abstract: Proper building maintenance is critical to ensuring the well-being of citizens and sustainable development of the urban environment. For high-rise high-density cities, one challenge to building maintenance concerns the inspection of building exteriors, which has been mostly done manually but is of highly risky, labor-intensive, and error-prone. Unmanned aerial vehicles (UAVs) have the potential to support building exterior inspection in a safe and efficient way. However, the effective application of UAVs for exterior inspection is hindered by path planning problems in high-rise high-density urban areas. Previously developed path planning algorithms are constrained by algorithmic complexity and cannot be implemented in a large three-dimensional space. Besides, path planning assisting tools available in the market that supported by Google Earth Pro can hardly assure high accurate results. In this paper, a new virtual prototyping-based path planning system is developed for UAVs for building exterior inspection in high-rise high-density urban areas. Unreal Engine as a widely-used game engine was applied. The system provides a realistic game world to simulate real-world activities, which enables operators to design UAV paths like playing games. The flight paths and viewpoints can be repeatedly tested in a gaming context until requirements are satisfied. The system integrates expert knowledge through "human in the loop" and realistic information on the virtual environment featured with repeatability. The system thereby greatly alleviates the risks that are induced by physical constraints in the real world when adopting UAV, and thus should significantly improve the effectiveness and efficiency of building exterior inspection. Keywords: Unmanned aerial vehicles (UAVs); Path planning; Building exterior inspection; Virtual prototyping; Human in the loop DOI: https://doi.org/10.22260/ISARC2020/0004 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
Text classification and sentiment analysis

In the previous chapter, we focused on using image data to learn more about machine learning and build an image classifier. In this chapter, we are going to talk about using text data, cover a few concepts of natural language processing, and build a few experiments around sentiment analysis.
Google Cloud Platform Adoption for Teaching in HEIs: A Qualitative Approach

Higher educational institutions (HEIs) are continuing to struggle with mounting fiscal problems due to economic downturn, which are increasingly forcing information technology (IT) departments to do more with same or less budgets.One area of growing interest for meeting the high technology demands and minimizing costs is the use of Google Cloud Platform to centralize computing and information management functions.Google Cloud Platform is a rapidly growing, yet evolving model that offers significant advantages to these institutions and is being widely used by large & small corporate organizations to manage their IT provisions and reduce their operational as well as IT maintenance costs.The use of Google Cloud Platform could also possibly allow HEIs to shift resources normally allocated for procurement, infrastructure, development, maintenance, and staffing to pay-as-you-go services to minimize operational & maintenance costs.However, despite such potential benefits of Google Cloud Platform higher educational institutions are yet to reap the benefits of this emerging technology as can be witnessed by snail pace adoption of Google Cloud Platform solutions.This paper thus intends to investigate the factors which could affect the acceptance and use of Google Cloud Platform in HEIs especially in developing economies like Malaysia.
Cultivating a Self-Centered Sociality?

Abstract The present study uses texting interactions from different digital contexts to reconstruct the structural features of everyday digital interaction between young people in order to examine the socializing tendencies of growing up in the digital age. Secondly, it will be reflected which of these characteristics is cultivated by the structure of the digital communication platforms, that is, stimulated and encouraged by the architecture of the platforms. Third, in order to further differentiate this question, it is considered in a cultural-historical perspective which technically cultured structures of interaction build on and promote long-lasting cultural tendencies. Finally, all three focuses on the impact of these interaction structures and tendencies on the experience, attitude and development of adolescents.
Improving End-to-End Bangla Speech Recognition with Semi-supervised Training

Automatic speech recognition systems usually require large annotated speech corpus for training.The manual annotation of a large corpus is very difficult.It can be very helpful to use unsupervised and semi-supervised learning methods in addition to supervised learning.In this work, we focus on using a semi-supervised training approach for Bangla Speech Recognition that can exploit large unpaired audio and text data.We encode speech and text data in an intermediate domain and propose a novel loss function based on the global encoding distance between encoded data to guide the semisupervised training.Our proposed method reduces the Word Error Rate (WER) of the system from 37% to 31.9%.
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game

Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait.In this work, we introduce a novel formulation for PIC based on a communication game between a speaker and a listener.The speaker attempts to generate natural language captions while the listener encourages the generated captions to contain discriminative information about the input images and personality traits.In this way, we expect that the generated captions can be improved to naturally represent the images and express the traits.In addition, we propose to adapt the language model GPT2 to perform caption generation for PIC.This enables the speaker and listener to benefit from the language encoding capacity of GPT2.Our experiments show that the proposed model achieves the state-of-the-art performance for PIC.
MCMH: Learning Multi-Chain Multi-Hop Rules for Knowledge Graph Reasoning

Multi-hop reasoning approaches over knowledge graphs infer a missing relationship between entities with a multi-hop rule, which corresponds to a chain of relationships.We extend existing works to consider a generalized form of multi-hop rules, where each rule is a set of relation chains.To learn such generalized rules efficiently, we propose a twostep approach that first selects a small set of relation chains as a rule and then evaluates the confidence of the target relationship by jointly scoring the selected chains.A gametheoretical framework is proposed to this end to simultaneously optimize the rule selection and prediction steps.Empirical results show that our multi-chain multi-hop (MCMH) rules result in superior results compared to the standard single-chain approaches, justifying both our formulation of generalized rules and the effectiveness of the proposed learning framework.