Reviewerly/exHarmony huggingface dataset:
Making Requirements for Karanglo Asri Waste Bank Using the User-Centered-Design Method

Since 2017, the Karanglo Asri Waste Bank has been operating with three core managers, including the chairman, secretary, and treasurer, as well as 32 active customers. Residents have been educated on identifying waste that can be resold to collectors or transformed into fertilizers and handicrafts using a waste bank maintained by a local government agency. The staff of the waste bank has continued to write by hand, and each has his or her notebook. Nonetheless, administrative books include hurdles—for instance, errors in recording, destruction, loss, improper computations, and unstructured records. Therefore, the customers must resolve these issues by matching the terms and criteria. The User-Centered-Design method included three phases for determining the requirements of the Karanglo Asri Waste Bank. They generated four user journey maps, four use case diagrams, six activity diagrams, and eighty-four user stories. It was completed for testing by the managers and customers based on requirements derived from the needs of the Karanglo Asri Waste Bank’s staff and customers.
Distance learning as a progressive information technology in teaching foreign languages

International scientific conference «Innovative aspects of the development of philological sciences» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 216 pages.
Physics, Biology and Human Faculties: A Structural Stepwise Approach to Modeling Human Behavior

Seeking a better understanding of human behavior and social phenomena were the primary pursuits of Richard Day’s scholarship, and of the Journal he co-founded and edited. In that pioneering spirit of adventure, we structure approaches to modeling human behavior in three steps with respect to scientific disciplines, their associated principles, and subject matters: human faculties, biology and physics. We notice that the extant practice of modeling human behavior in social sciences starts from psycho-sociological features and principles before moving to biological ones, and finally using physical laws for formalization. These formalizations are often presented with the disclaimer: of course, human behavior extends beyond our physical existence. Alternatively, but less often, they are defended in a reductionist spirit. In the present contribution we propose reversing this extant order of deploying scientific principles and argue that this exercise will help link social sciences to biology and physics, without reductionism.
Scholarship Acceptance Selection Using Neural Network Method

Muhammadiyah University of Yogyakarta is one of the private tertiary institutions that provides scholarship programs. UMY provides scholarship programs for outstanding students and middle-lower class students. In the process of receiving a scholarship, UMY has two stages, namely registration and selection. In order to obtain a scholarship, students who register must be declared to pass administrative selection and meet the assessment component as a condition for students to be eligible for scholarships. In order to know the scholarship information received by students, data processing is needed, data processing is often referred to as data mining. This writing is done to predict prospective scholarship recipients using the Neural network algorithm. The methodology at this writing begins with searching for literature studies, choosing data mining methods, data collection, data processing, application and testing of models, results and conclusions. The data used at this writing are 2019 general scholarship data, GPA data, and organizational active information data. The attributes used are parental income, GPA, scholarships, qualification and non-qualification, and active organizational information. The caption attribute qualification and non-qualification is used as a label. At this writing the authors get an accuracy of 72.62%..
Application of Reactive Artificial Intelligence Model to Predict Malicious Activities

The critical infrastructure in the United States have been attacked countless time, and the federal, state, and private entities are concerned with securing their network infrastructure and sensitive data to prevent revenue loss and intellectual properties. Billion dollars are lost to cyberattacks globally, and the United States tops the list of countries that recorded more cyber-attacks. The healthcare industries have lost revenue due to litigation from compromised patient data and revenue paid as a ransom to cyber attackers. Integrating a reactive artificial neural network model into their cybersecurity architecture will monitor the activities of users' activities internally or externally accessing the organization's resources. The Newton method algorithm was applied to predict the malicious activities and the performance of the model support derivative matrix from the input variables that were used to train the model. The validation result of the algorithm has proven that an artificial neural network model can be deployed and integrated into the cybersecurity solution to predict the likelihood of an attack. Keywords: Healthcare, Cybersecurity, Artificial Intelligence, Threat Actors, Risk Assessment. Yamcharoen P., Folorunsho O.S., Bayewu A. &amp; Ojo T.P. (2021): Application of Reactive Artificial Intelligence Model to Predict Malicious Activities. Journal of Advances in Mathematical &amp; Computational Science. Vol. 9, No. 2. Pp 61-68. DOI: dx.doi.org/10.22624/AIMS/MATHS/V9N2P5 Available online at www.isteams.net/mathematics-computationaljournal.
Coyote Optimization Algorithm with Hybrid SVD-EVD Approach for Digital Image Watermarking

The proliferation of digitalized media on the Internet has assisted in enhancing digitalization, which has increased copyright issues.To solve these copyright issues, digital watermarking methods were used.Digital image watermarking refers to a method in which watermark data is embedded into multimedia products and can be detected or extracted from the watermarked product later.Such techniques ensure content verification, tamper-resistance, integration, and image authentication.Therefore, this study develops a coyote optimization algorithm-based hybrid (COA-hybrid) model for digital Image watermarking.The presented COA-hybrid technique focuses on designing an effective image watermarking approach to fulfil the requirements of imperceptibility and robustness.In addition, the COA-hybrid model integrates the concepts of Singular Value Decomposition (SVD) and Eigen Value Decomposition (EVD).Here, the COA-hybrid technique embeds the watermarking on the DWT sub-band of the cover image.In addition, the watermarking's placed on the particular value components of the DWT sub-band of the cover imaging.Finally, the COA is utilized to adjust the parameters related to the hybrid model to increase fitness function.The simulation results of the COA-hybrid model are tested on different images, and the results reported the improvements of the COA-hybrid model over other models.
Robust Sarcasm Detection using Artificial Rabbits Optimizer with Multilayer Convolutional Encoder-Decoder Neural Network on Social Media

Nowadays, posting sarcastic comments on media platforms developed a general trend.People to pester or taunt others frequently utilize sarcasm.It is regularly stated that with tonal stress, inflexion from the speech or in the procedure of hyperbolic, lexical, and pragmatic aspects occur from the textual data.Sarcasm Detection (SD) utilizing Deep Learning (DL) on media platforms is an active study field in Natural Language Processing (NLP).Sarcasm is a figurative language method frequently exploited on social networks like Reddit, Twitter, and Facebook.Detecting sarcasm is essential to various applications like Sentiment Analysis (SA), opinion mining, and social network monitoring.DL techniques are demonstrated that effectual at sarcasm detection on media platforms.This study presents a robust sarcasm detection using Artificial Rabbits Optimizer with Multilayer Convolutional Encoder-Decoder Neural Network (ARO-MCEDNN) technique on social media-the presented ARO-MCEDNN technique concentrations on detecting sarcasm in social networking sites.Primarily, the ARO-MCEDNN technique follows a series of pre-processing data levels for transforming the input data into a compatible format.Followed by, Glove approach is applied for word embedding purposes.Moreover, the MCEDNN model is applied as a classification model to identify and categorize distinct kinds of sarcasm.Furthermore, the ARO algorithm is chosen as a hyperparameter optimizer of the MCEDNN model, enhancing the sarcasm detection performance.To highlight the advanced performance of the ARO-MCEDNN system, a sequence of simulations was performed.
RISC: Generating Realistic Synthetic Bilingual Insurance Contract

This paper presents RISC, an open-source Python package data generator 1 .RISC generates look-alike automobile insurance contracts based on the Quebec regulatory insurance form in French and English.Insurance contracts are 90 to 100 pages long and use complex legal and insurance-specific vocabulary for a layperson.Hence, they are a much more complex class of documents than those in traditional NLP corpora.Therefore, we introduce RISCBAC, a Realistic Insurance Synthetic Bilingual Automobile Contract dataset based on the mandatory Quebec car insurance contract.The dataset comprises 10,000 French and English unannotated insurance contracts.RISCBAC enables NLP research for unsupervised automatic summarisation, question answering, text simplification, machine translation and more.Moreover, it can be further automatically annotated as a dataset for supervised tasks such as NER.
The Effects of Reading and Concept Map Teams Games Tournament Model Assisted by Picture Card and Snake Ladder on Students' Critical Thinking Skills

This study aims to determine the significant effect of the application of the reading and map concept learning models teams games tournament with the help of picture cards and snake ladders on students' critical thinking skills on the material excretion system. This type of research is quasi experimental with a nonequivalent control group design using purposive sampling in class VIII D and class VIII E from the population in class VIII A to class VIII H. The research data were obtained from pretest and posttest scores and questionnaire sheets. The results of the t-test analysis related to critical thinking skills showed -0.13 in the initial data and 10.47 in the final data. Analysis of N-gain on increasing students' critical thinking skills results 0.80 (high) for the experimental class and 0.38 (moderate) for the control class. The results of the questionnaire sheet analysis showed a percentage of 89.30% with a very good category. Based on this it was concluded that learning using the reading and map concept team games tournament model with the help of picture cards and snake ladder on the excretion system material affects the improvement of students' critical thinking skills.
ENGLISH-FOR-TOURISM TRAINING TO IMPROVE THE ENGLISH CAPABILITY OF TOURISM WORKERS AND BUSINESS OWNERS IN THE DEPARTMENT OF TOURISM, CULTURE, YOUTH, AND SPORTS OF BLITARREGENCY

English plays a vital role in the tourism field since it supports tourism workers and business owners to provide excellent services. Numerous tourism destinations in Indonesia including the ones in Blitar Regency have become the center of attention for foreign tourists. Located in Blitar, East Java, Kampung Coklat and Karanganyar Coffee Plantation have also been continuously preparing their workers and business owners to provide efficient services for both domestic and foreign visitors. In accordance with providing excellent services to foreign tourists, tourism workers and business owners are expected to have good English mastery. Objectives: This community service aims to provide a support for the tourism workers and business owners under the supervision of the Department of Tourism, Culture, Youth, and Sports and the Department of Tourism Promotion of Blitar Regency in terms of improving their English mastery, especially their communication skills in English. Methods: The activity was carried out in three stages, namely two material sessions, speaking activities, and feedback. Results: This activity gave the participants an opportunity to practice serving a customer in English. Even though it was quite challenging for some of the participants, they felt encouraged to actively participate in the speaking activities and get familiar with expressions used in tourism and hospitality services. Conclusion: The supervision and training sessions in speaking English was very enlightening for the participants to improve their English mastery in tourism services. It needs a continuation to provide greater impact to the targeted participants.
SISTEM INFORMASI PELATIHAN PADA LEMBAGA ILMU PENGETAHUAN INDONESIA PUSAT PENGEMBANGAN TEKNOLOGI TEPAT GUNA BERBASIS WEB

In 2017, services at the Indonesian Institute of Sciences Center for Appropriate Technology Development, especially in the training registration administration section, had not been computerized so that data was piling up and difficult when the data was needed. Based on the existing problems, the approach taken is to analyze and design a training information system and implement it into an application form that is used to support the delivery and management of training and education programs in helping plan, develop and provide training to employees, communities, and other stakeholders. other interests. So it is necessary to develop an information system that can be personalized, and adaptive so that it can be adjusted for prospective training participants. The development method uses the PHP programming language, the CodeIgniter Framework with the concept of Model, View, Controller (MVC), modeling using the Unified Modeling Language (UML), MySQL database, and the Unified Process (UP) work method. The modules contained in the training information system include the Participant module, the Institution module, the Resource Person module, the Service User module, and the External Services module.
PENGGUNAAN APLIKASI GOOGLE MEET SEBAGAI MEDIA PERKULIAHAN DARING

The rapid spread of the Covid-19 infection requires all learning to switch from face-to-face learning to online learning to break the chain of infection transmission. This learning does not fully run smoothly due to limitations and the availability of supporting media. Not all educators and students have an understanding of using internet media. only google meet is fast to use for distance learning. The Google Meet application provides features that support the implementation of bold learning, is free and uses web quota that is more efficient or more stable. Which aims to be a place for learning that is carried out online learning during the covid. This of course makes it easier for students (students and students) to access Google Meet compared to other media platforms. This study aims to determine the effectiveness of using Google Meet as a learning medium during the Covid-19 pandemic. The method used is a qualitative method, namely by obtaining an in-depth understanding, developing theory, describing how the Google Meet application works. The data collected beforehand was directly in class, while the case of Covid 19 could not collect data directly. Research shows that Google Meet was very effective in being used in the teaching process during the Covid 19 period, because it was very easy to access and there was no need to pay and can be used anywhere and anytime.
PENGARUH METODE READING GUIDE TERHADAP MOTIVASI BELAJAR DAN HASIL BELAJAR SISWA PADA MATA PELAJARAN IPS DI SMP NEGERI 1 GEMPOL KABUPATEN CIREBON

The purpose of this study is to determine the influence of the reading guide method on learning motivation in social studies subjects, to determine the influence of the reading guide method on student learning outcomes in social studies subjects, and to determine the influence of the reading guide method on learning motivation and student learning outcomes in social studies subjects at SMP Negeri 1 Gempol, Cirebon Regency.This research is a quantitative research. The design of this study Quasi experimented using the nonequivalent posttest-onty control group design. The study population was 15 students from class IX H and 15 students from class IX I, totaling 30 students selected through random sampling. The instruments used in this study were in the form of tests, questionnaires, observations of learning activities and documentation. The results of this study show that, there is an influence of the reading guide method on learning motivation in social studies subjects with a Sig value of 0.003, there is an influence of the reading guide method on student learning outcomes in social studies subjects with a sig value of 0.012, and there is an influence of the reading guide method on learning motivation and student learning outcomes in social studies subjects with a p value (sig) of 0.451. this means that student learning motivation and learning outcomes are influenced by the reading guide method.
Student training program of building drawing and augmented reality in SMKN 2 Pangkalpinang

SMK Negeri 2 Pangkalpinang is a public vocational high school in Pangkalpinang, which has a Construction and Property Technology Program and a Computer and Informatics Engineering Program. To support the improvement of the hard skills of the students, implementing a training program is one way that is quite solutive. The student training program is devoted to building drawing technique training for the Building Modeling and Information Design Department, which hones students' drawing skills. In addition, the students are also given training related to augmented reality for the Multimedia Department. This activity also produced works in the form of design building drawings and augmented reality products.
Assessment design, learning strategies and obstacles in facing Computer-Based Madrasah Exam on the English subject

The objectives of this study were to describe how teachers design the Computer-Based Madrasah Exam (CBME) questions for English subjects, to investigate learning strategies students use, and to explore the obstacles the students face in this exam. Questionnaires and interviews were used as the instruments to collect data for this study. A number of 95 ninth-grade students and two English teachers of Madrasah Tsanawiyah Negeri No. 1 (MTsN 1; this school level is equivalent to junior high schools) in South Aceh, Indonesia, participated in this study. The data were analyzed quantitatively and qualitatively using thematic analysis. The results revealed that in designing the assessment of CBME, the teachers used four strategies which were preparing for the change in the final exam model, selecting sources in designing the exam questions, preparing students to face the exam in the new model, and resolving obstacles in developing the exam questions. Meanwhile, the students used five strategies in facing the exam, they are managing time, motivating themselves, creating study groups, practicing, and strengthening basic abilities. Furthermore, the obstacles that the students faced in the exam are a lack of skill in using a computer for the exam, slow internet access, limited computer availability, limitation of vocabulary mastery, taking time to understand questions, and peers’ interventions. Thus, this study suggests that more training is still needed for teachers in designing the assessment model of CMBE and the school also need sufficient facilities before they enforce CMBE to their students.
Clustered and Multi-Tasked Federated Distillation for Heterogeneous and Resource Constrained Industrial IoT Applications

Service heterogeneity, the diversity of services provided by different devices and systems in the Industrial Internet of Things (IIoT), makes communication and data exchange difficult and affects the IIoT performance. Artificial intelligence, particularly machine learning (ML), can address this challenge by analyzing data, predicting behavior, and developing self-configuring autonomic systems. However, incorporating ML into IIoT faces challenges such as high data complexity and variability, communication costs, lack of data privacy and security, and scalability. Federated multitask learning (FML) is a promising solution to tackle most of these challenges by training ML models locally and exchanging only the updated parameters. However, it still faces challenges in device and system compatibility, data heterogeneity, and scalability, especially for hierarchical heterogeneous IIoT environments. To address all these challenges, this article proposes a novel hybrid framework dubbed Clustered Multitask Federated Distillation (CMFD). In conjunction with the FML, CMFD combines clustered FL to address the data heterogeneity and knowledge distillation to address the system compatibility and scalability. To show the efficiency of CMFD for intelligent IIoT applications, we present a case study of heterogeneous IIoT environments using a realistic dataset. We conclude with useful insights emerging from our work and highlight promising future research directions.
Vision and Voice Based Hand Gesture Recognition

Abstract: The development of sign language over time has been astounding. It's unfortunate that this language has some unpleasant side effects. Not everyone can understand spoken sign language when using sign language among a mute or deaf person. For deaf-dumb people, hand gestures and sign language are important forms of communication. Communication is difficult without an interpreter, hence it is necessary to translate sign language so that it is understandable to the general public. The goal is to increase the participation of the deaf and the mute in communication. In order to incorporate colour, depth, and trajectory information to increase performance, the CNN is fed numerous channels of video streams that include body joint locations in addition to depth cues and colour information. We demonstrate how the proposed model outperforms conventional approaches based on hand-crafted features by testing it on a real dataset gathered using Microsoft Kinect.
A novel multi-objective learning-to-rank method for software defect prediction

Search-Based Software Engineering (SBSE) is one of the techniques used for software defect prediction (SDP), in which search-based optimization algorithms are used to identify the optimal solution to construct a prediction model. As we know, the ranking methods of SBSE are used to solve insufficient sample problems, and the feature selection approaches of SBSE are employed to enhance the prediction model?s performance with curse-of-dimensionality or class imbalance problems. However, it is ignored that there may be a complex problem in the process of building prediction models consisting of the above problems. To address the complex problem, two multi-objective learning-to-rank methods are proposed, which are used to search for the optimal linear classifier model and reduce redundant and irrelevant features. To evaluate the performance of the proposed methods, excessive experiments have been conducted on 11 software programs selected from the NASA repository and AEEEM repository. Friedman?s rank test results show that the proposed method using NSGA-II outperforms other state-of-the-art singleobjective methods for software defect prediction.