Reviewerly/exHarmony huggingface dataset:
Use Of Comic-Based Learning Media In Economics Subject

Learning media related to the world of education is a tool, an intermediary in delivering material related to messages or lesson information. Thus the learning media certainly has a purpose. The purpose of learning media is to increase new interest, understanding, motivation and stimulation in the learning process. One of the interesting and interactive learning media, especially in economics subjects, is comic-based learning media. This study uses a qualitative approach, namely to describe and describe the influence of the use of instructional media in the world of education. So the results obtained are that comics can make the teaching and learning process effective, increase student interest in learning, and generate interest in student appreciation. Thus the existence of comics as a learning medium can certainly help increase the potential of students, especially in economics subjects. Students can feel the emotion in the pictures presented and the comic language is light because there are not many sentences to read which tends to make the reader lazy to read. So with the existence of comics as a learning medium can increase interest in learning better and cultivate reading.
Teachers’ Technique in Teaching Speaking in SMAN 1 Kinali Academic Year 2021/2022

The purpose of this study was to find out the teachers’ technique in teaching speaking and how did the teacher apply during the classroom at SMAN 1 Kinali Academic Year 2021/2022. Researcher found problems related to teacher and students. The first problem was that teacher used the same variety technique to teach speaking in class. Second problem was the students become surfeited in the classroom. The third problem was controlling the class with several students’ character. From the phenomenon above, the researcher wanted to analyze what types of teachers’ technique in teaching speaking and how to apply the technique in teaching speaking.This research was qualitative descriptive study. Researcher conducted this research at SMAN 1 Kinali. The informant of this research was the English teacher SMAN 1 Kinali. Data collection techniques used in this study were interviews and observations checklist. Based on the findings of this study, it was found that there were four types of techniques of teaching speaking, namely: Pair and group technique, Drilling technique, Jigsaw technique, Simulation technique. In learning process during speaking material, the teacher used the same technique and students became surfeite in learning so it was necessary to know what were the techniques that used by the teacher in the learning process. The teacher has implemented principles of techniques in teaching speaking and how to apply teaching speaking at SMAN 1 Kinali. The techniques that used by the teacher was effective and motivating students. The researcher also found out why the teacher used variety techniques in teaching speaking to make students fluent, become active and build their self-confidence, and to master speaking skill as well. By using techniques in teaching speaking, it motivated students to speak and to help students improving their speaking ability.
Possible usefulness of Mindsponge Theory in the DishBrain project

This short piece represents an attempt to explore any possibility of Mindsponge Theory (MT) becoming useful for the DishBrain project. It was done by prompting the Google Bard AI to read the Guardian article. Then Bard was asked to evaluate the relevance of MT to the DishBrainproject.
PENGEMBANGAN PEMBERIAN CONTOH PENJUMLAHAN BILANGAN BULAT MENGGUNAKAN MULTI REPRESENTASI SEBAGAI MUATAN BAHAN AJAR

This study aims to describe: the feasibility level of teaching materials developed in the form of Student Worksheets, responses, and student learning outcomes after being given learning. The type of research used isResearch and Development (R&amp;D)with the development model of Thiagarajan . The subjects in this study were the seventh grade students of the Asoka Singkawang Private Junior High School, which amounted to 20 people. The instruments used are questionnaires on the feasibility of teaching materials, student response questionnaires, and posttest questions. Data collection techniques: the feasibility of teaching materials was validated by experts, student responses were given a questionnaire, and learning outcomes were given posttest questions. Data were analyzed descriptively quantitatively. The results obtained: the feasibility level of teaching materials is 87.26% with very feasible criteria for use, student response questionnaires are 87% with very high criteria, meaning that student responses are very good, and student learning outcomes are classically fulfilled, namely 85% of students who achieve grades. minimal completeness. Conclusion: the developed teaching materials can be used in learning. Suggestion: further researchers can develop giving examples in learning mathematics and other learning using more diverse representations.
Corpus Dialectology

Corpus Dialectology combines the fields of corpus linguistics and dialectological mapping. It concerns documentation of linguistic variation and mapping of linguistic spaces and boundaries, while ascribing renewed importance to the methodology and the material itself, especially data processing and statistical analysis. This approach considers phenomena that have received little attention to date, such as migration, language contact, mobility and educational level, as well as the differentiation between rural and urban spaces. Transparently described and intersubjectively comprehensible encodings permit the enhancement of dialectometry in the context of Digital Humanities and further development of linguistic theories of variation and change, as well as different levels of structure (phonology, morphosyntax, semantics). This book contains nine chapters on ongoing corpus dialectological research projects. They discuss current issues of data collection, for example the validity of crowdsourced data, explore challenges and possibilities of data analysis and offer theoretical reflections on virtual Romance geolinguistics.
Identifying Key Grammatical Errors of Japanese English as a Foreign Language Learners in a Learner Corpus: Toward Focused Grammar Instruction with Data-Driven Learning

The number of studies on data-driven learning (DDL) has increased in recent years, and DDL’s overall effectiveness as an L2 (second language) teaching methodology has been reported to be high. However, the degree of its effectiveness in grammar instruction, particularly for the goal of correcting errors in L2 writing, is still unclear. To provide guidelines for focused grammar instruction with DDL in the Japanese classroom setting, we aimed to identify the typical grammatical errors made by Japanese learners in the Cambridge Learner Corpus First Certificate in English (CLC FCE) dataset. The results revealed that three error types (nouns, articles, and prepositions) should be addressed in DDL grammar instruction for Japanese English as a foreign language (EFL) learners. In light of the findings, pedagogical implications and suggestions for future DDL research and practice are discussed.
Transisi Akuntansi Menuju Digitalisasi

The issue of the end of accounting in the digital era will threaten the existence of the accounting profession itself. The purpose of this study is to provide an overview of the accounting transition process towards digitization both in terms of profession, work assignments, learning process, and other related matters. As well as the strategy of the accounting profession in the digital era. The research method uses descriptive qualitative methods with interview data collection techniques involving accountant educators and accounting students from various public and private universities in Indonesia. The results of this study indicate that digitalization requires the accounting profession to adapt itself to the digital era. The role of the accounting profession, which is no longer focused on journalizing as it used to be, requires them to prepare themselves more maturely to face their main competitors, namely technology and programmers. Currently, at the tertiary level, learning information systems in the accounting major is urgently needed by simultaneously increasing the number of case studies to train creative thinking in decision making to prepare prospective accounting professions in the digital era.
PEMBELAJARAN MASA PENDEMI

The Covid-19 virus outbreak has a very broad impact on all aspects of life, including in the field of education. This causes the government to implement education policies that raise various problems. This research method uses the method of literature study. Nazir said that library research also means data collection techniques by consulting books, literature, notes, and various reports relating to the problem to be solved. The final consequence is that learning done in schools and universities can be studied online at home. Learning is carried out from home using a laptop, computer, or smartphone along with various learning support programs. As a result, to deal with this and produce an effective and fast learning process, a strategy is needed. Teachers must make learning implementation strategies before starting teaching so that they are successful and efficient in achieving learning goals.
PELAKSANAAN PEMBELAJARAN PADA MASA COVID 19

The Covid-19 virus outbreak has a very broad impact on all aspects of life, including in the field of education. This causes the government to implement education policies that raise various problems. This research method uses the method of literature study. Nazir said that library research also means data collection techniques by consulting books, literature, notes, and various reports relating to the problem to be solved. The final consequence is that learning done in schools and universities can be studied online at home. Learning is carried out from home using a laptop, computer, or smartphone along with various learning support programs. As a result, to deal with this and produce an effective and fast learning process, a strategy is needed. Teachers must make learning implementation strategies before starting teaching so that they are successful and efficient in achieving learning goals.
Design and build a Point of Sales (POS) application model using the agile method for super mama frozen food

The Point of Sales (POS) system refers to a system which supports sales transactions where POS is currently growing since it has the ability not only to record sales, record inventory, print invoices, calculate profits but also improve services for business people and entrepreneurs. Super Mama Frozen Food is a store that is currently developing in Malang. In addition, the simple of access to buying products through online services has made the increasing in transactions. However, at Super Mama Frozen Food, most of the data management, data processing, and sales transaction processing have so far relied on a manual or paper-based recording system. As a result, this condition may open up risks in both data management and security. By designing a website-based Point of Sales System, recording sales, collecting inventory, printing invoices, and calculating profits can be done using the PHP programming language with the CodeIgniter framework and MySQL. This system was developed using the Agile Method with the reason that short-term system design can increase client satisfaction
SENTIMENT CLASSIFICATION OF TWEETS WITH EXPLICIT WORD NEGATIONS AND EMOJI USING DEEP LEARNING

The widespread use of social media platforms such as Twitter, Instagram, Facebook, and LinkedIn have had a huge impact on daily human interactions and decision-making. Owing to Twitter's widespread acceptance, users can express their opinions/sentiments on nearly any issue, ranging from public opinion, a product/service, to even a specific group of people. Sharing these opinions/sentiments results in a massive production of user content known as tweets, which can be assessed to generate new knowledge. Corporate insights, government policy formation, decision-making, and brand identity monitoring all benefit from analyzing the opinions/sentiments expressed in these tweets. Even though several techniques have been created to analyze user sentiments from tweets, social media engagements include negation words and emoji elements that, if not properly pre-processed, would result in misclassification. The majority of available pre-processing techniques rely on clean data and machine learning algorithms to annotate sentiment in unlabeled texts. In this study, we propose a text pre-processing approach that takes into consideration negation words and emoji characteristics in text data by translating these features into single contextual words in tweets to minimize context loss. The proposed preprocessor was evaluated on benchmark Twitter datasets using four deep learning algorithms: Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN). The results showed that LSTM performed better than the approaches already discussed in the literature, with an accuracy of 96.36%, 88.41%, and 95.39%. The findings also suggest that pre-processing information like emoji and explicit word negations aids in the preservation of sentimental information. This appears to be the first study to classify sentiments in tweets while accounting for both explicit word negation conversion and emoji translation.
Workshop Pemrograman Android menggunakan AppInventor untuk Tim Musyawarah Guru Mata Pelajaran (MGMP) TIK/Informatika Kota Padang

Android is a platform for smartphones that can be used anytime and anywhere. Smartphones can be felt in various aspects of human life, especially in education, as a medium of learning material between teachers and students. The development of Android-based applications needs special basic Android programming skills. The Informatics Subject Teacher Consultation of Padang (MGMP) is interested in developing a simple Android-based application but needs direction and guidance to learn Android programming. Therefore, a workshop was needed to develop an Android-based application using AppInventor for simple applications. The workshop provided more steps, essential knowledge, and new experience to be developed by MGMP Team to support duties as teachers.
Peningkatan Sanitasi Masjid di Kelurahan Mata Air Kota Padang Tahun 2022

The mosque is one of the most visited public places. Currently, the sanitary conditions of the mosque are still not a concern of the community. There are still mosques that do not meet the sanitation requirements of public places. Poor mosque sanitation can cause various diseases in society caused by a polluted environment. This dedication aimed to present an overview of the sanitation condition of mosques in Mata Air, increase knowledge, and increase public awareness of the importance of mosque sanitation. The method used was data collection with sanitation inspection forms and personal hygiene questionnaires, interviews, dissemination of information through focus group discussion, and administration of the intervention of the book of pocket guides, poster, sanitary mosque SOPs, and garbage disposal in the Masjid of the Mata Air Village. The results of the sanitation assessment found that four of the eight mosques still have not met the sanitation requirements. Through this community dedication, an increase in the knowledge and awareness of mosque administrators regarding improving mosque sanitation was obtained. In this case, improved sanitation is essential to avoid the potential for disease transmission due to environmental pollution.
Design of a User Comment Management System Based on Text Mining

Presently, text mining in e-commerce reviews predominantly focus on singular sentiment analysis, yet constraints persist in sentiment score computation, semantic inclination discernment, and lexicon construction. To address these limitations, this study establishes an e-commerce user comment management system based on text mining. It performs part-of-speech tagging and dependency grammar analysis on the historical corpus of e-commerce, unveiling collocations that potentially convey users' emotive predispositions. Subsequently, a dependency grammar rule table is formulated for the extraction of emotional words. The enhanced BiGRU model is employed for bidirectional extraction of textual features, which are subsequently fused with the TextCNN model. Test results evince that the system effectively accomplishes the desired objectives, with positive comments attaining accuracy and recall rates of 93.49% and 96.98%, respectively, thereby mitigating the drawbacks associated with laborious operations and inadequate precision inherent in extant e-commerce comment analysis systems.
Aplikasi Pengukuran Tingkat Stres Pada Mahasiswa Tingkat Akhir Dengan Metode Certainty Factor

Introduction: This stress level measurement application for final year students provides information about the level of stress experienced by students based on the symptoms felt by students, by displaying the results of the diagnosis in the form of the selected symptoms, the level of stress experienced along with the percentage of beliefs, and the solutions recommended. Purpose:to build a web-based expert system to measure stress levels in final year students and find out the level of stress experienced by students and provide recommendations for solutions to these students by applying the certainty factor method. Method: This study uses a quantitative method by carrying out a calculation process using the ceratinty factor, namely the calculation process is obtained from the quantitative value of the selected symptoms and then calculated based on the CF formula. Results: This application has an accuracy rate between the system and experts worth 90% based on 20 cases that have been tested, which shows that the certainty factor method can be applied in measuring stress levels in final year students. Conclusion: this application can be applied to measure stress levels in final year students with an accuracy value of 90%.
Adaptive Microservices for Dynamic E-commerce: Enabling Personalized Experiences through Machine Learning and Real-time Adaptation

In the rapidly evolving landscape of e-commerce, the need for personalized experiences and real-time adaptation has become paramount. This article proposes a framework for adaptive microservices in the ecommerce domain, leveraging machine learning techniq
Efficient Fall Detection using Bidirectional Long Short-Term Memory

Falls are one of the most common causes of injury among the elderly. As a result, fall detection has received in the last decade considerable attention from both academia and the healthcare industry. Accelerometer data, collected from simulated falls, were widely used with classical machine learning (ML) algorithms as well as with threshold-based methods to identify fall situations that can be used to launch an alert for help. As collecting real fall data is challenging, most of the research papers on fall detection have used limited data which do not reflect the complexity of real fall situations. Fortunately, a comprehensive fall dataset called “Simulated Falls and Daily Living Activities Dataset” has recently become available. This dataset includes 1827 simulated falls of 20 different types. In this paper, we use this dataset to evaluate the possibility of fall detection, more precisely, impact and pre-impact which correspond to fall and pre-fall, respectively. Unlike the classical ML algorithms and threshold-based methods commonly used in previous research works, in this paper, we implement a bidirectional long short-term memory (Bi-LSTM) algorithm which we believe better reflects the impact and pre-impact context as it takes into consideration both backward and forward sequence information at every time step. Our experimental results showed that Bi-LSTM achieves an accuracy of 99.97% and 99.95%, with 99.80% and 99.30% sensitivity, and 100% and 99.99% specificity for fall and pre-fall detections, respectively. These results largely exceed previously published results.
21 IoMT-based data aggregation using quantum learning

In this chapter, quantum-assisted learning is introduced to aggregate data from the IoMT sensors for possible data processing and storage. The model is designed in such a way that it reduces redundant information and improves the flexibility of processing data. The entire modeling is conducted in a simulation environment to test the effectiveness of data aggregation from IoMT and its associated complexity. Experimental validation shows a reduced complex data aggregation task than with other methods.
Promoting Originality in Online Swarm Robotics

We address the problem of promoting diversity in online embodied evolution of heterogeneous robot swarms. We argue that it is not easy to adapt existing diversity algorithms from traditional evolutionary robotics to this context and describe a method in which selection is based on originality and which allows a swarm of heterogeneous agents to maintain a high degree of diversity in behavioral space. We also describe a behavioral distance measure that compares behaviors in the same conditions to provide reliable measurements in online and distributed contexts. We test the selection scheme on an open-ended survival task and show its effectiveness. Without any other pressure besides that of the environment, the evolved strategies tend toward simplicity, exploiting the existing affordances. An additional external pressure enables the emergence of rich and diverse behaviors.
Perspectives on anomaly resolution

A bstract We examine in detail the process of resolving ’t Hooft anomalies by extending the symmetry of a theory. Specifically, we interpret the ingredients of existing prescriptions for anomaly resolution as the addition of topological operators with designated mixed anomalies, which can be interpreted as coupling our original field theory to a topological one. We show that, upon gauging, the presence of such mixed anomalies leads to a modified version of the original symmetry which now acts on the newly introduced operators, allowing for an overall anomaly-free action. We also show that the original, anomalous symmetry remains present in the theory. This analysis is applied to anomaly-resolving extensions by both ordinary and higher-form symmetries, leading to related but qualitatively distinct stories.
Constraint-Handling Techniques used with Evolutionary Algorithms

tutorial Share on Constraint-Handling Techniques used with Evolutionary Algorithms Author: Carlos Artemio Coello Coello Department of Computer Science, CINVESTAV-IPN, Mexico, Mexico City, Mexico Department of Computer Science, CINVESTAV-IPN, Mexico, Mexico City, Mexico https://orcid.org/0000-0002-8435-680XView Profile Authors Info & Claims GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary ComputationJuly 2023Pages 1248–1270https://doi.org/10.1145/3583133.3595032Published:24 July 2023Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks10 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Assessing the impact of computational resources to the Quality of Experience provided by vRANs

&lt;p&gt;This work aims to assess the impact of computational resources on the Quality of Experience (QoE) of video streaming services delivered over vRAN-based networks. The obtained results prove that inadequate resource assignment to vRAN instances leads to degradation of the QoE, even if the allocation of radio resources is adequate for the service&lt;/p&gt;