Reviewerly/exHarmony huggingface dataset:
Evaluating Plan-Property Dependencies: A Web-Based Platform and User Study

The trade-offs between different desirable plan properties -- e.g. PDDL temporal plan preferences -- are often difficult to understand. Recent work addresses this by iterative planning with explanations elucidating the dependencies between such plan properties. Users can ask questions of the form ``Why does the plan not satisfy property p?'', which are answered by ``Because then we would have to forego q''. It has been shown that such dependencies can be computed reasonably efficiently. But is this form of explanation actually useful for users? We run a large crowd-worker user study (N=100 in each of 3 domains) evaluating that question. To enable such a study in the first place, we contribute a Web-based platform for iterative planning with explanations, running in standard browsers. Comparing users with vs. without access to the explanations, we find that the explanations enable users to identify better trade-offs between the plan properties, indicating an improved understanding of the planning task.
Drug Data Clustering Based on Total Inventory and Total Demand for Drugs Using the K-means Clustering Method at Pajar Bulan Health Center

At Pajar Bulan Health Center, drug supply data processing is already using office application packages, namely Microsoft Word and Excel. The application package is used for making monthly usage reports, requests and drug supplies. Constraints that often occur are that it takes a long time to manage drug inventory data because they have to record one by one the amount of drug use and the number of drug requests to be made. The number of requests is carried out every month by looking at the latest drug supply, if the stock starts to run low then a request is made. However, it is possible that the stock has run out before making a request, this results in a lack of drug supply management. Drug data clustering is carried out based on the amount of supply and the number of requests for drugs at the Pajar Bulan Health Center through the K-Means Clustering Method approach. To help cluster the drug data, an application was built using the Visual Basic .Net programming language and SQL Server 2008r2 database. Clustering of drug data is carried out in units of pcs in 2021 where the amount of inventory is reduced by the number of requests for drugs, so that the results obtained are 2 drugs enter cluster I and 27 drugs enter cluster II. good and the application can help the Pajar Bulan Health Center in knowing the grouping of drug data based on 2 groups, namely the few clusters and the large clusters
TextBrew: Automated Model Selection and Hyperparameter Optimization for Text Classification

In building a machine learning solution, algorithm selection and hyperparameter tuning is the most time-consuming task.Automated Machine Learning is a solution to fully automate the process of finding the best model for a given task without actually having to try various models.This paper introduces a new AutoML system, TextBrew, explicitly built for the NLP task of text classification.Our system provides an automated method for selecting transformer models, tuning hyperparameters, and combining the best models into one by ensembling.Keeping in mind that new state-of-the-art models are being constantly introduced, TextBrew has been designed to be highly flexible and thus can support additional models easily.In our work, we experiment with multiple transformer models, each with numerous different hyperparameter settings, and select the most robust models.These models are then trained on multiple datasets to obtain accuracy scores, which are then used to build the metadataset to train the meta-model.Since text classification datasets are not as abundant, our system generates synthetic data to augment the meta-dataset using CopulaGAN, a deep generative model.The meta-model is an ensemble of five models, which predicts the best candidate model with an accuracy of 78.75%.The final model returned to the user is an ensemble of all the best models that can be trained under the given time constraint.Experiments on various datasets and comparisons with existing systems demonstrate the effectiveness of our system.
Color Image Encryption Algorithm Using DNA Encoding and Fuzzy Single Neurons

In order to hide image information more efficaciously, improve the radio of workpiece and quality of encryption, this paper proposes delayed chaotic neuron dynamical system, extracts a single neuron research model to determine its dynamic properties, analyzes the model’s sensitivity, stability and the randomness of the chaotic sequence, and verifies its dynamics feasibility of combining the system with DNA encoding. The single delay neuron can simulate the unknown dynamic behavior and capability in the model in a certain sense which can effectively improve optimization ability and con-vergence speed of the neural to make the network more practical. DNA encoding also offers new train of thoughts and directions for the study of cryptography. In addition, by introducing the functional form of fuzzy number can further enhance the stability and adaptability of chaotic system in un-certain environment, and get more distinct chaotic phenomena. The experimental simulation and comprehensive data analysis of the algorithm are carried out in this paper. The data manifest that the system has a large chaotic range, can pass the randomness test of NIST and TestU01, has high complexity and sensitivity which could effectively avoid noise attacks. The encrypted information entropy of classical images can reach 7.9998, which confirms that it can effectively and safely realize the encryption of digital images.
Dampak Game Online Terhadap Motivasi Belajar Peserta Didik

This study focuses on knowing the impact of online games on the learning motivation of students at MI Al-Anshar, Hulonthalangi District. This study uses a qualitative research type. Furthermore, the data collection technique used is through observation, interviews, and documentation. While the data analysis technique is carried out through three stages, namely: data reduction, data presentation, and drawing conclusions. The results obtained are: online games have an impact on students' learning motivation. The impact of online games is divided into two, namely the negative impact and the positive impact.
Instructional Design of Online Courses in Mozambique: The Use of eBooks as a Strategy to Improve Learning

The potential of electronic books (ebooks) has been discussed widely, due mainly to their role in helping the user transform passive reading into interactive and dynamic learning, while promoting better retention and appropriation of materials. Taking this premise, the present study follows an exploratory and qualitative approach, discussing how the use of ebooks can improve learning, motivation, dynamism and interactivity in Distance Education. This study was supported by a bibliographic review and a survey method through an online questionnaire used as data collection instruments. The sample consisted of students who attended distance courses taught by the Open University ISCED – UnISCED in Mozambique. The scientific literature studied, and the results from the field research, show the practicality of using ebook as a learning resource and they facility the interactivity.
A Facial Expression Discrimination System Using the Expansion and Contraction of Face Masks and the Conductive Thread

In recent years, wearable interfaces have attracted research attention. As wearing masks has become common worldwide after the appearance of the coronavirus disease 2019, we devised a system to measure facial information from a mask, focusing on facial expressions in this study. Specifically, the expansion and contraction of the mask was measured to classify facial expressions. We conducted classification experiments under various conditions, achieving high classification accuracy, especially for facial expressions in the non-woven fabric mask to which a conductive thread was attached. We found that conductive threads are affordable and easy to sew into masks, and they can be used to construct a wearable interface.
Evaluation of Context-Aware Language Models and Experts for Effort Estimation of Software Maintenance Issues

Reflecting upon recent advances in Natural Language Processing (NLP), this paper evaluates the effectiveness of context-aware NLP models for predicting software task effort estimates. Term Frequency–Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT) were used as feature extraction methods; Random forest and BERT feed-forward linear neural networks were used as classifiers. Using three datasets drawn from open-source projects and one from a commercial project, the paper evaluates the models and compares the best performing model with expert estimates from both kinds of datasets. The results suggest that BERT as feature extraction and classifier shows slightly better performance than other combinations, but that there is no significant difference between the presented methods. On the other hand, the results show that expert and Machine Learning (ML) estimate performances are similar, with the experts’ performance being slightly better. Both findings confirmed existing literature, but using substantially different experimental settings.
Digital-Based Physics Pocket Book Design with Short Counting Methods for Junior High School Students

This study aims to design a digital-based physics pocket book with a short calculation method for junior high school students. This type of research and development is used in this study. At the design stage, we selected the ADDIE model. The design phase includes: the analysis phase, the stage design, the stage of development. Research data obtained by reviewing the curriculum, interviews, and validation questionnaires. Class VII, VIII, IX students, as many as 9 people, became the subject of a limited trial in this study. Data analysis was carried out by reviewing curriculum content, descriptive interview results, and quantitative analysis for validation. The results showed that the design of a digital-based physics pocket book with a short calculation method was valid and could be used by Junior high school students. Keywords: digital book, pocket book, short count method, physics learning. Abstrak: Penelitian ini bertujuan untuk merancang buku saku fisika berbasis digital disertai cara hitung singkat bagi siswa SMP. Jenis penelitian dan pengembangan digunakan dalam penelitian ini. Pada tahap perancangan, model ADDIE dipilih. Tahap perancangan meliputi: tahap analisis, tahap desain, tahap pengembangan. Data penelitian diperoleh dengan telaah kurikulum, wawancara, dan angket validasi. Siswa kelas VII, VIII, IX, sebanyak 9 orang, menjadi subyek uji coba terbatas dalam penelitian ini. Analisis data dilakukan dengan telaah isi kurikulum, deskriptif hasil wawancara, dan analisis kuantitatif untuk hasil validasi. Hasil penelitian menunjukkan bahwa hasil rancangan buku saku fisika berbasis digital disertai cara hitung singkat dinyatakan valid dan dapat digunakan siswa Junior high school. Kata kunci: buku digital, buku saku, metode hitung pendek, pembelajaran fisika. DOI: http://dx.doi.org/10.23960/jpmipa/v23i3.pp1030-1037
Analyzing COVID-related Stance and Arguments using BERT-based Natural Language Inference

In this paper we present our approach for stance detection and premise classification from COVID-related messages developed for the RuArg-2022 evaluation. The methods are based on so-called NLI-setting (natural language inference) of BERT-based text classification (Sun et al., 2019), when the input of a model includes two sentences: a target sentence and a conclusion (for example, positive to masks). We also use translating Russian messages to English, which allows us to leverage COVID-trained BERT model. Besides, we use additional marking techniques of targeted entities. Our approach achieved the best results on both RuArg-2022 tasks. We also studied the contribution of marking techniques across datasets, tasks, models and languages of RuArg evaluation. We found that "&lt;A:ASPECT&gt; keyword &lt;/A:ASPECT&gt;” gave the highest average increase over corresponding basic methods.
USING PADLET VIRTUAL BOARD IN THE PROCESS OF DISTANCE LEARNING IN HIGHER EDUCATION INSTITUTIONS

The article deals with the issue of organizing educational process in a distance format in institutions of higher education in a way that ensures the transfer of educational information, effective interaction of students and opportunities for independent work at a convenient time. The following two ways of organizing distance learning are highlighted and described: synchronous and asynchronous. Special attention is paid to the problem of organizing distance learning using various web resources, particularly Padlet virtual board as an effective interactive tool for storing educational content and organizing active interaction between students and teachers during synchronous and asynchronous learning, with the ability to download various types of files, independently control its design and content, choose the layout, change privacy settings, etc. The following advantages of Padlet virtual whiteboard are defined and explained: availability and free access to the web resource; multimedia capabilities; possibility to adjust privacy; mobility; ability to work in synchronous and asynchronous modes; possibility of regular feedback, correction and editing; easy access etc. Its functions (visual, informative, interactive) and opportunities during the implementation in the educational process are characterized. The experience of using Padlet in the process of distance learning of future social workers is represented using the example of the subject "Social Prevention". The author highlights peculiarities of preparing virtual boards with general information about the subject, tasks for independent work, a list of references and recommended literature, an individual creative task, as well as using the virtual board for organizing group work in all lectures and practical classes. The following algorithm for teaching students to use the possibilities of a virtual board in educational activities is presented: creating a board, choosing its type, filling it with content, commenting, evaluating, adding links, presentations, photos and videos, configuring user access, etc. It is determined that Padlet became not only a web repository of educational materials prepared by the teacher, but also allowed to save the results of joint activities of students themselves, which can be accessed at any time by all students using the appropriate link.
Performance analysis of various segmentation algorithms for microarray images

Image segmentation deals with partitioning of images into separate constitute in order to report a particular problem. In this project, different input images are taken. The identification of noise in the input images is done by calculating the probability density function of the images and a sharpening filter is used to remove the noises presented in the input images. Then various segmenting algorithms are applied on the different input images. The performance parameters like (PSNR), (MSE) and intensity (gray level) are calculated through simulation for various segmenting algorithms and the results are compared.
Load Prediction Techniques in Cloud Environment

Businesses and websites have rapidly increased their energy consumption, necessitating the development of data centres tailored to the cloud. Predicting when a system's resources will be needed means you can allocate them more efficiently and save money in the cloud. Predictive accuracy may be increased by classifying loads first. In this research, we offer a new method for predicting future demand for cloud-centric data centres. The Phase Space Reconstruction (PSR) and Extended Approximation-Group Method of Data Handling (EA-GMDH) methods are compared to the Bayesian model for predicting the mean load over a long-term time period. Multi-step ahead CPU load prediction using Support Vector Regression is very stable, i.e., its prediction error increases quite slowly as the predicted steps increase; this is in contrast to a neural network, which predicts the future load based on the past historical data and is distinguished by the presence of hidden layers
Predicting Key Predictors of Project Desertion in Blockchain: Experts’ Verification Using One-Sample T-Test

Aim/Purpose: The aim of this study was to identify the critical predictors affecting project desertion in Blockchain projects. Background: Blockchain is one of the innovations that disrupt a broad range of industries and has attracted the interest of software developers. However, despite being an open-source software (OSS) project, the maintenance of the project ultimately relies on small core developers, and it is still uncertain whether the technology will continue to attract a sufficient number of developers. Methodology: The study utilized a systematic literature review (SLR) and an expert review method. The SLR identified 21 primary studies related to project desertion published in Scopus databases from the year 2010 to 2020. Then, Blockchain experts were asked to rank the importance of the identified predictors of project desertion in Blockchain. Contribution: A theoretical framework was constructed based on Social Cognitive Theory (SCT) constructs; personal, behavior, and environmental predictors and related theories. Findings: The findings indicate that the 12 predictors affecting Blockchain project desertion identified through SLR were important and significant. Recommendations for Practitioners: The framework proposed in this paper can be used by the Blockchain development community as a basis to identify developers who might have the tendency to abandon a Blockchain project. Recommendation for Researchers: The results show that some predictors, such as code testing tasks, contributed code decoupling, system integration and expert heterogeneity that are not covered in the existing developer turnover models can be integrated into future research efforts. Impact on Society: This study highlights how an individual’s design choices could determine the success or failure of IS projects. It could direct Blockchain crypto-currency investors and cyber-security managers to pay attention to the developer’s behavior while ensuring secure investments, especially for crypto-currencies projects. Future Research: Future research may employ additional methods, such as a meta-analysis, to provide a comprehensive picture of the main predictors that can predict project desertion in Blockchain.
IMPLEMENTASI ALGORITMA LINEAR CONGRUENT METHOD (LCM) PADA MEDIA PEMBELAJARAN BAGIAN-BAGIAN BUNGA BERBASIS VIRTUAL REALITY (Studi Kasus : SMP Negeri 2 Ciawigebang)

SMP Negeri 2 Ciawigebang is one of the schools that also uses books as the main source of learning references. The use of books has limitations, especially in the content of the existing 2-dimensional images, the images on the textbook will fade over time, which makes it difficult for students to understand and has limited visualization of the material. Learning that is carried out online due to the impact of the COVID-19 pandemic and books as independent learning media result in ineffective understanding of the material to students. Therefore, we need media with the use of technology that can visualize materials, visualizations and objects that help students understand in independent learning such as virtual reality (VR). Virtual reality is a sophisticated human-computer interface that simulates a realistic environment and allows the user to interact directly with the simulated object. To find out the extent to which students are able to absorb the material given, an evaluation is carried out in the form of the questions that are given, therefore a Linear Congruent Method algorithm is used to randomize the questions, so that there is no repetition of the same questions that are raised. While the system development method used is the Multimedia Development Life Cycle (MDLC) as the right method for developing AR-based learning media. The final result of this research is an application that is compatible with smartphone devices with a minimum Android operating system Nougat version. which displays objects in the form of 3-dimensional VR where users feel like they are in a virtual world and questions are given to evaluate the extent to which students can absorb the material. it is hoped that this application can facilitate the learning and teaching process through virtual reality media and make it easier for teachers when delivering material and conducting 3D visual-based evaluations. Keywords: Learning Media, Virtual Reality, Algorithm Linear Congruent Method (LCM)
Analisis User Interfaces Pada Website Kampiun ITTP Dengan Metode Heuristik dan System Usability Scale (SUS)

Kampiun Digital Learning is a website-based Massive Open Online Course (MOOC) owned by the Telkom Purwokerto Institute of Technology (ITTP), which aims to improve the quality of free non-formal education. Unfortunately, since its initial release until now there has never been an analysis of the user interface itself. Even though there are several problems related to the user interface, such as problems with navigation (search filters), notifications, registration instructions, and basic website guidelines. Based on these problems, it is necessary to analyze the user interfaces on the Kampiun website to see how the sustainability of Kampiun is based on the perspectives of experts and users, the combination of Heuristics and System Usability Scale (SUS) methods in this study was carried out because in their functions they have different but complementary, heuristic focuses. has the perspective of the experts, while SUS has the perspective of the user. This study uses 10 heuristic aspects and 10 SUS questionnaire questions. The purpose of this research is to be able to produce recommendations for improvement from both existing perspectives and identify aspects that need improvement, as well as being useful in helping to find problematic heuristic aspects that must be addressed. The SUS score from the Kampiun website after the calculation is done is 58.4. The results of this study are recommendations on aspects that need to be improved in order to provide a significant increase in the SUS score, several recommendations for improvements that need to be made include clarity of information, use of images, appropriate colors and grammar, consistency of writing, standard writing standards, error message, there is an online guide and a help button.
Research on Semi-Supervised Text Classification Based on Graph Attention Network

Text classification is a basic task in natural language processing. Due to the sparsity of text data and the finiteness of tags, the feature construction method and the neural network used to learn features in text affect the subsequent classification effect. In order to make full use of text label data and improve the accuracy of text classification, we propose a semi-supervised text classification method combining topic information and graph attention network. Firstly, we construct text heterogeneous information graph by combining text self-information and additional information to better mine text semantics. Then, the Heterogeneous Graph Convolutional Network(HGCN) is used to learn the text graph. In particular, a two-layer attention mechanism is added to our method, including type-level attention and node-level attention. Through the attention mechanism, the model can understand the importance of different adjacent nodes and the importance of various types of information in the graph to the current node. A large number of experimental results show that our proposed model is significantly superior to other methods in three benchmark data sets.