Reviewerly/exHarmony huggingface dataset:
Classifying text using machine learning models and determining conversation drift

Text classification helps analyse texts for semantic meaning and relevance, by mapping the words against this hierarchy. An analysis of various types of texts is invaluable to understanding both their semantic meaning, as well as their relevance. Text classification is a method of categorising documents. It combines computer text classification and natural language processing to analyse text in aggregate. This method provides a descriptive categorization of the text, with features like content type, object field, lexical characteristics, and style traits. In this research, the authors aim to use natural language feature extraction methods in machine learning which are then used to train some of the basic machine learning models like Naive Bayes, Logistic Regression, and Support Vector Machine. These models are used to detect when a teacher must get involved in a discussion when the lines go off-topic.
SUB-SPARC: Investigation of Imperfect Teachers

Early literacy and language skills are a significant precursor to children's later educational success.Using social robots as a learning companion is one method to augment efforts to support this aim.SPARC has been shown as a useful way to combine reinforcement learning with human experts to reduce the state search space and for human experts to intervene before any negative reward actions take place, however human expertise is not consistent.In this paper we investigate if SPARC can be counter-useful as expertise levels decline.Results from the study suggest that once a skill level below 90% of an expert is breached, alternative LfD methods maybe more suitable.
Smart wheelchairs: Visual perception pipeline to improve prediction of user intention

Wheelchairs aid people with physical disabilities by assisting with mobility, thus improving their independence.Autonomous assistance on wheelchairs are limited to prototypes that provide 'smart functionality', by completing tasks such as docking or terrain adaption.The biggest constraints are navigating within dynamic environments, such as the home.This paper describes the data pipeline to automate the wheelchair navigation process, from classifying an object, estimating the user's intention via verbal command (e.g.take me to the fridge) and navigating towards a goal.Object locations will be registered within a map whilst contextual meta data is calculated.A combination of object classification confidence and object instances is used to calculate the uniqueness of all identifiable objects.Thus, assisting in predicting the user's intention.For example, if a "go to the fridge" request is received, the wheelchair will know that the fridge is located within the kitchen, and therefore drive to the kitchen and then the fridge.Results show that utilising contextual data reduces the likelihood of false-positive object detections being registered by the navigation pipeline, thus is more likely to interpret the user intention more accurately.
Forgiveness Meaning in the Lyric of Gandong Song: Linguistics Point of View

This study aims to describe the memorable experience with the conflict, the feelings of the Maluku people, and the influence of the meaning of the lyrics of the Gandong song when listening to the Gandong song, in the context of sustainability of the community’s life Orang Basudara in Maluku. This study used the qualitative method. The primary data were collected using questionnaires with closed and open-ended questions. While literature reviews were about the meaning of the lyrics was collected as secondary data. There were 224 respondents in total, which consist of 53 male respondents and 165 female respondents in the gender category, with 6 people choosing not to identify their gender, and also all from other different backgrounds. Further, the Gandong song, which is a Malukan people's rhythm song, is very popular and frequently sung during conflict and post-conflict times. Subsequently, the result revealed that the respondent listened to the song on several occasions. The lyrics contain a range of word classes, including nouns, verbs, adverbs, adjectives, pronominal phrases, and numerical terms. The term Gandong appears more frequently than other terms. Linguistically means that the song gives stressing on the Gandong relationship as a pearl of important local wisdom for all people living in Maluku. The majority of respondents (99.6 % of 224 respondents) have ever listened to the Gandong song, and it has influenced their feelings about the conflict in Maluku as a reminder of a harmonious life (82. 2%), drawing back from revenge (9, 9%), and 4% of people accepted it to forget the conflict, while the other respondents will forget the feeling of mutual suspicion and forgive rudeness.
Pattern Detection of Economic and Pandemic Vulnerability Index in Indonesia Using Bi-Cluster Analysis

Bi-clustering is a clustering development that aims to group data simultaneously from two directions. The Iterative Signature Algorithm (ISA) is one of the bi-clustering algorithms that work iteratively to find the most correlated bi-cluster. Detecting economic and pandemic vulnerability using bi-cluster analysis is essential to get spatial patterns and an overview of Indonesia's economic and pandemic vulnerability characteristics. Bi-clustering using ISA requires setting the row and column threshold to form seventy combinations of thresholds. The best is chosen based on the average value of mean square residue to volume ratios. In addition, the similarity of the best bi-cluster with the other is also seen based on the Liu and Wang index values. The -1.0 row and -1.0 column threshold combinations were selected and produced the best bi-cluster with the smallest average value of mean square residue to volume ratios (0.00141). Based on Liu and Wang index values, it has more than 95% similarity with the combination of -1.0 row and -0.9 column thresholds and the -0.9 row and -1.0 column thresholds. These selected threshold combinations produce three bi-clusters with five types of spatial patterns and different characteristics because of the overlap between these three bi-clusters.
Freshwater Fish Classification Based on Image Representation Using K-Nearest Neighbor Method

Indonesia is a maritime and agricultural country with enormous world fishery potential. The large variety of fish is often confusing for ordinary people in recognizing types of fish, especially freshwater fish. It was stated that the types of freshwater fish often consumed by the Indonesian people are bawal (pomfret), betutu, gabus (cork), gurame (carp), mas (goldfish), lele (catfish), mujaer (tilapia), patin (asian catfish), tawes, and nila (tilapia nilotica). Some fish types have similar shapes, so it is tricky to tell them apart. Meanwhile, in the digitalization era today, Artificial Intelligence (AI)-based technology has become a demand in all areas of life. It is overgrowing, not apart from the fisheries sector. Therefore, in this study, the K-Nearest Neighbor (KNN) method was applied as one of the methods in AI to identify and classify freshwater fish species based on their images. The KNN method classifies new data into specific classes based on the distance between the new data and the closest k data through the learning process. This KNN model is built by preparing the dataset stages, separating the dataset into data-train and data-test with a ratio of 70%:30%, then building and testing the model. The dataset is freshwater fish images, totaling 100 images from 10 freshwater fish types. Model testing is done by measuring performance using a confusion matrix. Based on the test results, the model has an accuracy performance of 70%. Thus, KNN can be used as a model to identify freshwater fish species based on their image.
Model Structure of Fetal Health Status Prediction

One of the issues of pregnant mothers in Indonesia is their access speed and accuracy services availability towards the prediction of fetus or baby conceived during pregnancy. Thus, the research aimed to obtain the ability to predict three ranges of a fetal target, namely normal, risk, and abnormal condition. This research emphasized the modeling aspect of supervised learning using seven different algorithms to obtain an optimal working score. Those are Decision Tree, Gradient Boosting, Random Forest, SVM, k-NN, AdaBoost, and Stochastic Gradient Descent (SGD). The structure process is mainly divided into two steps, pre-process model and the prediction model. An early data pre-process is needed before executing. Prediction output indicated that dataset test is valid, and can be proven by comparing between the testing data table and prediction and testing table diagram. The resulting model has described the sequence for simulating the training and testing data model to produce the highest working score from the seven selected algorithms. The simulated data based on the model created is proved its validity thru three main filter processes, which are missing data solution, outlier data control, and data normalization. The result obtained a working score that has data proximity with a low score range of 0.063 from model evaluation, confusion matrix, and prediction output.
On the biological plausibility of orthogonal initialisation for solving gradient instability in deep neural networks

Initialising the synaptic weights of artificial neural networks (ANNs) with orthogonal matrices is known to alleviate vanishing and exploding gradient problems. A major objection against such initialisation schemes is that they are deemed biologically implausible as they mandate factorization techniques that are difficult to attribute to a neurobiological process. This paper presents two initialisation schemes that allow a network to naturally evolve its weights to form orthogonal matrices, provides theoretical analysis that pre-training orthogonalisation always converges, and empirically confirms that the proposed schemes outperform randomly initialised recurrent and feedforward networks.
Large Language Models Struggle to Learn Long-Tail Knowledge

The Internet contains a wealth of knowledge -- from the birthdays of historical figures to tutorials on how to code -- all of which may be learned by language models. However, while certain pieces of information are ubiquitous on the web, others appear extremely rarely. In this paper, we study the relationship between the knowledge memorized by large language models and the information in pre-training datasets scraped from the web. In particular, we show that a language model's ability to answer a fact-based question relates to how many documents associated with that question were seen during pre-training. We identify these relevant documents by entity linking pre-training datasets and counting documents that contain the same entities as a given question-answer pair. Our results demonstrate strong correlational and causal relationships between accuracy and relevant document count for numerous question answering datasets (e.g., TriviaQA), pre-training corpora (e.g., ROOTS), and model sizes (e.g., 176B parameters). Moreover, while larger models are better at learning long-tail knowledge, we estimate that today's models must be scaled by many orders of magnitude to reach competitive QA performance on questions with little support in the pre-training data. Finally, we show that retrieval-augmentation can reduce the dependence on relevant pre-training information, presenting a promising approach for capturing the long-tail.
Wireless CCTV, a workable tool for overcoming security challenges during elections in Nigeria

There have been upheavals in areas where elections have been conducted lately in Nigeria. The latest being in Ekiti and Osun States where there were cases of ballot box snatching, electoral result fraud, sporadic shootings and killings which call for electoral security carefulness and concern. The government of the day has done well in trying to computerize the electoral process by introducing BVAS in all polling units, but has failed to implement a non-human monitoring mechanism which could watch over electoral conducts and support result collation in all voting points without loopholes, intricacies and injuries to members of the voting communities. Wireless Close Circuit Television (CCTV) is an electronic monitoring system that combines network Software and hardware subsystems which are installed with Wireless channels and other monitoring mechanisms to cover and record activities in a particular domain. Data recorded in this device are immediately transmitted from the remote database in real-time, to a cloud-based central server where all happenings and events are stored for future retrieval and decision making. This paper looked at various reports on security challenges and happenings in Nigerian elections for eight (8) years and recommended that all polling units in Nigeria should have a Wireless CCTV system installed before the 2023 presidential election, irrespective of the cost, instead of the always compromised human monitors. Data were gathered using the author’s lens as a Supervisory Presiding Officer (SPO) from 2011 to 2019, and from related literatures.
Survey on Intelligent Accident Detection System and Emergency Services Alert System

In past few years the rate of accidents are increasing day by day and many people die in this accidents due to late treatments, so our paper works to solve this issue in our life. By our paper we could save many lives and save many loved once happiness that they deserve in their life. Also our paper also work on the issue many insurance companies faces that some time they cannot tell or determine that what was the time and date of the accident because they just need the date n time to proceed their pending insurance claims. Our paper will help them to get the exact information about the collusion.
PROJECT APPROACH TO LEARNING: EXPERIENCE IN DIFFERENT COUNTRIES

Different countries in their educational systems have accumulated extensive experience in using project technology in education. This technology is an alternative to traditional lecture teaching: it helps to connect theoretical material with practice in real life; to change the roles of students and teachers: students have the opportunity to become an active subject in the educational process, and teachers are engaged in organizing, managing and directing this process, and not just broadcasting ready-made information. The purpose of the article is an analytical review of the options for implementing project-based learning for students in Finland, France, Australia, China and the USA described in foreign peer-reviewed journals. A brief excursion into the history of the origin and development of the project-based learning method is given. The main characteristics of different approaches to its application are highlighted, which differ in the attitudes towards solving certain problems, the choice of goals and means of implementing educational projects. The article is an attempt to comprehend and generalize the world experience in the application of project technology in education. The materials of the publication provide an opportunity for managers of the education system of different levels and practicing teachers to get acquainted with the potential and diversity of project-based learning practices in order to adopt the best of them and the most acceptable for Ukrainian realities, adapting them to the conditions of specific educational institutions.
An Analysis on the Operation Performance of Alternative Classrooms in Schools

Objectives In this study, as part of the school interruption prevention policy, the operation status and performance of alternative classes in schools, which have been implemented since 2013, were analyzed and future improvement measures were presented. Methods To this end, first, a system model-based logic model was derived to evaluate the performance of the project, second, the status and status of alternative classes in schools of Gangwon-do Office of Education in 2018-2022, and third, based on the performance analysis model, a survey (32 school managers, 40 teachers) and an interview survey (7 teachers, 5 students). Results As a result of the analysis, more than 50% of the students who participated in the alternative class returned to school, and less than 10% of the students stopped studying, and most of the students did not stop or give up and were emotionally satisfied. Conclusions In addition to improving the perception of school members, it was found that the ultimate operation goal of alternative classes, “the ratio of school maladjusted students to stop studying,” was improved based on student satisfaction and attitude changes. However, as it can be seen that more than 50% of schools have stopped operating their businesses after a year since the first participation in the school, it was confirmed that policy improvement is needed in the direction of continuous management and operation of alternative classes in the school.
METHODOLOGICAL COMPETENCE DEVELOPMENT OF PRESCHOOL TEACHERS IN THE SYSTEM OF CONTINUOUS EDUCATION

The article deals with methodological competence as one of the important components of preschool teachers’ professional activity the system of continuous education. The content of methodical competence’s is considered within the system of three components: scientific-andtheoretical, personal, practical. The objectives of methodological competence development in preschool teachers are achieved in the process of professional training and retraining the system of continuous education. The development of methodical competence by the educators from the pre-school educational institutions in the developed structural-and-functional model is grounded on the basis of developing methodical readiness and educator’s personality traits. Two groups of pedagogical conditions were distinguished for the development of methodical competence of the educator in the course of his pedagogical activity: organizational and download. The technology of methodical competence of teachers of preschool the system of continuous education is based on productive professional experience; which will reflect the system level of functioning of methodological, methodical and research knowledge, skills, motivation, abilities and readiness for creative self-realization in scientific-methodical and pedagogical activity as a whole, provides for an optimal combination of methods of professional pedagogical activity.
Silence — A web framework for an agile generation of RESTful APIs

Silence is a web framework made in Python that allows users to automatically generate a REST-compliant API and a set of test skeletons given a relational database. Silence provides the user with a number of console commands to create, manage and run projects. In contrast with other existing web frameworks, Silence makes it quick and straightforward to publish existing data available online, and it is flexible enough to be used for both research and general-purpose applications. Silence is published as a Python package, making it easy to install and operate, and does not require knowledge of any specific programming language to be used.
COMMUNICATION COMPETENCE IN UKRAINIAN AND FOREIGN LITERATURE CLASSES

The priority direction of the educational informatization process is introducing information and communication technologies (ICT) in the educational sector. When developing digital information and educational space, certain questions arise regarding the expediency and effectiveness, the place and the role of ICT in the educational process, which is quite natural. The aim of the research is to prove the expediency of using ICT at the lessons of Ukrainian and foreign literature. The relevance of the issue is influenced by many contradictions between the potential possibilities of multimedia technologies as a means of multimodal and multichannel perception of artistic information from literature and the technology uncertainty of conducting a literature lesson with ICT technologies, as well as insufficient development of computerbased literature lessons, regarding the specifics of perception of an artistic literary work in the system of activity-value learning. Incorporating ICT into the educational process provides opportunities for further differentiation of general and professional education, comprehensive implementation of creative, searching, person-centered and communicative forms of education, increasing its effectiveness, mobility and compliance with practice requests. Modern life demands searching for new forms of the educational process arrangement, especially, lessons of foreign literature. The use of computer technologies and the Internet is one of innovative means of studying literature, which allows stimulating students’ interest in artistic works and saves time for creative cooperation between the teacher and the student. The implementation of ICT at the lessons of Ukrainian and foreign literature is considered to be one of the urgent methodological problems.
Credit Card Fraud Detection Using Machine Learning

Abstract: Credit card fraud has existed ever since credit cards were introduced, resulting in financial losses, identity theft, severe security threatas, and misuse of personal information. Such a situation already dire at an individual level only worsens when an organization gets involved. With the COVID-19 pandemic taking over the world and the introduction of quarantine, online transactions have surged exponentially. Naturally, credit cards have become one of the main means to process these transactions. With an extraordinary number of people making transactions every second, it becomes difficult to keep track of fraudulent ones. An increase in online transactions also increases the risk of cybercrimes, in this case, fraudulent transactions. In this paper, we proposed a fast yet light system to detect fraudulent transactions in the IEEE-CIS dataset. We used feature engineering and data mining techniques to analyze the dataset and make it usable for the model. Then, we fit the dataset to LightGBM and XGBoost to classify the transactions as fraud or non-fraud. Finally, we compared the performance of the two models. Since we were dealing with a heavily skewed dataset, we gauged the performance of our model with the F1 score and ROC AUC score. The obtained ROC AUC score was 95% for our proposed model.
Analysis on perspectives of récepteur and its implication in classic literature education: focused on &lt;Along With the Gods: The Two Worlds&gt;

Objectives I would like to in-depth understand the perceptions, attitudes, and perspectives of today's public who have experienced video media based on classical narratives. This is to emphasize that the aspect of ‘reception’ as well as the aspect of ‘production’ should be actively considered in the media conversion of classical literature in the content development process, the study of classical literature and the study of classical technical education. Methods The review of the movie “Along With the Gods: The Two Worlds” was analyzed by the method of qualitative content analysis. ‘Blue Bird 2.0’, developed as a domestic CAQDAS, visually showed the specific codes and categories extracted according to the review analysis stage, and helped to clearly reveal the process of deriving the topic. Results The production of a new medium based on classical literature did not go as far as making the public familiar with classical literature or instilling a positive perception. It is positive that classical literature has been “read” again as a method of media conversion, but a basic understanding of the source material is essential. Conclusions In order to transform the medium of classical literature, it is necessary to explore the essential and universal value inherent in the source material. In addition, it is necessary to try to look at classical literature education from a popular and popular perspective, away from the perspective of contemporary classics or canon.