Reviewerly/exHarmony huggingface dataset:
APPLICATION OF THE K-NEAREST NEIGHBOR (KNN) ALGORITHM IN SENTIMENT ANALYSIS OF THE OVO E-WALLET APPLICATION

Abstract— The OVO application can be downloaded on the Android platform via Google Play, Google play has a review feature on the application product to be downloaded, so that the review can be viewed or accessed by anyone, With these reviews, potential users of the application will see how important it is to consider using an application, problems regarding reviews or sentiment analysis of applications processed using text mining. The purpose of this study is to provide information to prospective OVO application users before using the application which can be seen from the results of giving reviews based on rating or stars (*) in the OVO application review column on Google Play and the authors categorize them into 3 classes, the first class ( 1 to 5 stars, second class (1 and 5 stars) third class by providing labeling grouping (1&amp;2 stars are negative labels, 3 stars are neutral labels and 4&amp;5 stars are positive labels) testing using the k-nearest neighbor method by finding the value of k from the k value of 1-10 to get the highest accuracy value, in order to obtain the highest accuracy value of 84.86% in the 2nd class test and giving a value of k 1 which means that the 1st and 5th star tests get positive values so that they can give a good impression to prospective application users OVO
Exploiting Reversible Computing for Verification

Today, the verification of classical circuits poses a severe challenge for the design of circuits and systems. While the underlying (exponential) complexity is tackled in various fashions (simulation-based approaches, emulation, formal equivalence checking, fuzzing, model checking, etc.), no "silver bullet" has been found yet which allows to escape the growing verification gap. In this work, we entertain and investigate the idea of a complementary approach which aims at exploiting reversible computing. More precisely, we show the potential of the reversible computing paradigm for verification, debunk misleading paths that do not allow to exploit this potential, and discuss the resulting consequences for the development of future, complementary design and verification flows. An extensive empirical study (involving more than 30 million simulations) confirms these findings. Although this work cannot provide a fully-fledged realization yet, it may provide the basis for an alternative path towards overcoming the verification gap.
ICT in inclusive education by AI blockchain technology

A new trend of digitalization of inclusive education for education in the framework of the UNESCO report "Futures of education. Learning to become". It was discussed how digitalization of education can make it more accessible for children with disabilities, studying in conditions of inclusion. The presented technology allows for a long time to consistently collect and analyze pedagogical, psychological, and medical information on the results of observations and examinations of each child for supervisory groups and consultations while maintaining full confidentiality of personal data. The information collected serves as the basis for drawing up individual educational routes for the students, and for the teacher, it makes it possible to work in the zone of the children's closest development without comparing them with each other. The presented development met with lively interest because the widespread introduction of inclusive practices in education is not always provided by the ergonomic technologies of the teacher, who has to work simultaneously with children with different educational needs.
Sentiment Analysis of Electric Vehicles using the Naïve Bayes Algorithm

Industrial resolutions that are increasingly developing make many transitions in all fields. The increasing use of fuel for vehicles makes Indonesia's fuel stocks run low. Electric vehicles are one type of vehicle that uses electricity. The presence of electric vehicles is expected to be a solution to save fuel oil. Another advantage of electric vehicles is that electric vehicles produce low emissions so that apart from irritating fuel stocks, air pollution will also be reduced. However, the presence of electric vehicles is still a pro and contra in society. One of the media where people express their opinions is the social media Youtube. Testing was carried out using the K-Fold Cross Validation and the Naïve Bayes algorithm. From this test, an accuracy value of 82% was obtained with the polarity of the sentiment obtained, namely negative sentiment of 82% while positive sentiment of 18%. The purpose of this study is to determine the polarity of sentiment and the accuracy value obtained. It is hoped that this research can be properly conveyed to related parties for future progress Keywords: Electric Vehicle , Nave Bayes , K-Fold Cross Validation
Digital transformations of the process of professionalization of socionomics specialists on the basis of innovative pedagogical technologies

The purpose of the study is identifying ways and providing conditions for the effective formation of professional competence specialists in the field of socionomics on the basis of innovative pedagogical technologies. The relevance of the study is due to the needs of practice in the constant professional and personal development of specialists socionomic sphere, given the demands of society. The problem of implementation is relevant for the modern development of vocational education innovative pedagogical technologies in providing personalized approach in the organization of the educational process of training socionomic spheres. It has established that education is becoming more and more innovative: the latest services of educational services are being created, there is an active digital transformation of the educational environment. The model of professional training of students for practical activities in the socionomic sphere is seen as a holistic system that includes a strategic goal - the formation of innovative thinking in the future specialist socionomic sphere who can effectively to carry out professional activity according to the requirements of international standards. The following components of professional competencies of socionomic specialists have distinguished: motivational, value-semantic, cognitive-professional, effective-professional, autopsychological, regulatory. It is proposed to focus on the development of skills of socio-professional interaction of the future specialist in the field of socionomics.
A Discerning Several Thousand Judgments: GPT-3 Rates the Article + Adjective + Numeral + Noun Construction

Knowledge of syntax includes knowledge of rare, idiosyncratic constructions. LLMs must overcome frequency biases in order to master such constructions. In this study, I prompt GPT-3 to give acceptability judgments on the English-language Article + Adjective + Numeral + Noun construction (e.g., "a lovely five days"). I validate the prompt using the CoLA corpus of acceptability judgments and then zero in on the AANN construction. I compare GPT- 3's judgments to crowdsourced human judgments on a subset of sentences. GPT-3's judgments are broadly similar to human judgments and generally align with proposed constraints in the literature but, in some cases, GPT-3's judgments and human judgments diverge from the literature and from each other.
Crawling the Internal Knowledge-Base of Language Models

Language models are trained on large volumes of text, and as a result their parameters might contain a significant body of factual knowledge. Any downstream task performed by these models implicitly builds on these facts, and thus it is highly desirable to have means for representing this body of knowledge in an interpretable way. However, there is currently no mechanism for such a representation. Here, we propose to address this goal by extracting a knowledge-graph of facts from a given language model. We describe a procedure for ``crawling'' the internal knowledge-base of a language model. Specifically, given a seed entity, we expand a knowledge-graph around it. The crawling procedure is decomposed into sub-tasks, realized through specially designed prompts that control for both precision (i.e., that no wrong facts are generated) and recall (i.e., the number of facts generated). We evaluate our approach on graphs crawled starting from dozens of seed entities, and show it yields high precision graphs (82-92%), while emitting a reasonable number of facts per entity.
A New Symmetric Homomorphic Functional Encryption over a Hidden Ring for Polynomial Public Key Encapsulations

This paper proposes a new homomorphic functional encryption using modular multiplications over a hidden ring. Unlike traditional homomorphic encryption where users can only passively perform ciphertext addition or multiplication, the homomorphic functional encryption retains homomorphic addition and scalar multiplication properties, but also allows for the user's inputs through polynomial variables. The proposed homomorphic encryption can be applied to any polynomials over a finite field, with their coefficients considered as their privacy. We denote the polynomials before homomorphic encryption as plain polynomials and after homomorphic encryption as cipher polynomials. A cipher polynomial can be evaluated with variables from the finite field, GF(p), by calculating the monomials of variables modulo a prime p. These properties allow functional homomorphic encryption to be used for public key encryption of certain asymmetric cryptosystems to hide the structure of its central map construction. We propose a new variant of MPKC with homomorphic encryption of its public key. We propose to use a single plaintext vector and a noise vector of multiple variables to be associated with the central map, in place of the secret plaintext vector to be encrypted in MPKC. We call this variant of encrypted MPKC, a Homomorphic Polynomial Public Key algorithm or HPPK algorithm. The HPPK algorithm holds the property of indistinguishability under the chosen-plaintext attacks or IND-CPA. The overall classical complexity to crack the HPPK algorithm is exponential in the size of the prime field GF(p). We briefly report on benchmarking performance results using the SUPERCOP toolkit. Benchmarking results demonstrate that HPPK offers rather fast performance, which is comparable and in some cases outperforms the NIST PQC finalists for key generation, encryption, and decryption.
An Overview of Cyber Security in Digital Banking Sector

In the 21st century the online technology as updated with high performance and used widely by all users. In top five the Digital Banking Sector is also one which makes use of online technology constantly such as NEFT, Googlepay, Phonepe etc. Though there are more usage of Online banking but still the cybercrime in the banking sector has been increasing over the years. As reported 50% of cybercrime is happened related to ATM, debit card and net banking. Banking sector is facing cyber attacks more times compared to other sectors. This paper view the cyber attacks in the banking sector and way of providing the cyber security to those attacks.
FER_ML: Facial Emotion Recognition using Machine Learning

Recently, facial recognition has been one of the most crucial technologies people need. Facial recognition has attracted a lot of the crowd; for example, it has been used in security on most modern devices. Using machine and deep learning, overall performance will be improved, and the identification accuracy will be more precise. We aim to discover how well these algorithms perform in classifying human facial expressions and whether or not we can depend on them. The steps are as follows. First, we embed the images from the dataset, then split the dataset into 70% training data and 30% testing data; after that, we apply five different algorithms: Support Vector Machine, K-nearest Neighbor, Logistic Regression, Naive Bayes, and Random Forest. Support Vector Machine achieved an accuracy rate of 36%, K-nearest Neighbor achieved an accuracy rate of 52.3%, Logistic regression achieved an accuracy rate of 64.2%, and Naive Bayes achieved an accuracy rate of 38.1%. Random Forest achieved an accuracy rate of 51.7%. The dataset used was a cleaned version of the FER13 dataset, which contains 16,780 images divided into five classes (angry, happy, neutral, disgust, and fear). The results show that Logistic Regression proved to be the most accurate classifier among the presented ones, with an F1-Score of 63.8% and an accuracy of 64.2%.
Real time evolution and a traveling excitation in SU(2) pure gauge theory on a quantum computer.

The Hamiltonian approach can be used successfully to study the real time evolution of a non-Abelian lattice gauge theory on the available noisy quantum computers.In this work, results from the real-time evolution of SU(2) pure gauge theory on IBM hardware are presented.The long real-time evolution spanning dozens of Trotter steps with hundreds of CNOT gates and the observation of a traveling excitation on the lattice were made possible by using a collection of error mitigation techniques.Self-mitigation is our novel tool, which consists of using the same physics circuit as a noise-mitigation circuit.
Bibliometric Analysis: Sofware Usage Trends GeoGebra in Mathematics Learning From 2017 - 2022

The use of GeoGebra Software in mathematics learning is the application of ICT-based learning that can be done in schools. This is done to make learning interactive. This study aims to determine and analyze trends in article writing with the theme of using GeoGebra software in mathematics learning between 2017-2022, articles, countries of origin, authors, and institutions that have the most citations and link strengths using databases. dimensions. The method used is bibliometric analysis. The sample used is 180 articles that were searched using the database dimensions according to the specified keywords. The data is stored in CSV format to be used in the VOSviewer software to obtain a mapping in search of publication trends. The trend of writing articles with the theme of using GeoGebra software experienced a significant increase between 2017 and 2021 by 279%. Analysis based on software VOSviewer has 5 themes related to the use of GeoGebra software in mathematics learning, namely: “control group”¸“concept”, “medium”, “subject”, “analysis”, and “ article” which is still rarely researched and is a theme in the renewal of research. This theme is used as reference material for further research.
The Effectiveness of Online Learning during the Covid-19 Pandemic in Malaysia

The Movement Control Order (MCO) was implemented in Malaysia on 18 March 2020. The MCO has changed people’s lifestyle to some extent because they have been advised to work from home. Due to this situation, the Malaysian Ministry of Education (MOE) implemented online learning methods throughout the MCO period to prevent the spread of the Covid-19 virus. Therefore, all schools have implemented online teaching and learning with the instructions of the MOE as a new norm to ensure that learning sessions are not delayed. The purpose of this study was to determine the level and factors of effectiveness of online learning for secondary school students studying economics subjects during the Covid-19 pandemic. This study used a quantitative research design employing a questionnaire as data collection instrument. The results show that the effectiveness of online learning for economics students is at a high level. The results of the survey illustrate that most students use mobile phones and laptops to attend classes that take place online. Furthermore, the study found that teacher factors are the main factors that influence the effectiveness of online learning among students. This research has implications for educators to apply online learning methods as a new normative teaching method to ensure compliance with the National Education Philosophy and the objectives of the Malaysia Education Development Plan 2013–2025.
THE USE OF INTERACTIVE ONLINE BOARDS IN THE TEACHING OF MATHEMATICAL DISCIPLINES IN INSTITUTIONS OF HIGHER EDUCATION

In recent years, the higher education system of Ukraine has faced significant upheavals: the introduction of quarantine due to the Covid-19 pandemic, martial law, and active hostilities. These upheavals were the reason for the transition from the traditional form of education to the distance form. To ensure a high level of distance learning, teachers actively use online learning platforms. The peculiarity of teaching mathematics is the high level of abstraction of educational information. A critical component of the organization of distance learning in institutions of higher education is the provision of conditions for students' maximum comfortable perception of the educational material. With the transition to distance learning, the question arose as to how to make the process of learning mathematical disciplines visible and understandable for applicants. Among all pedagogical technologies, interactive learning technology occupies an important place in the implementation of distance learning in the educational process. Interactive technologies are implementation of in the educational process through online tools and services. One of the main means of interactivity is an interactive online board. The use of online boards in the educational process of higher education institutions solves the problems of ensuring visibility, interactivity, availability of quick feedback, and organization of work in a single virtual space. In the work modern online whiteboards are analyzed: Miro, CleverMaths, LiveBoard, IDroo, Drawchat, Jamboard, Scribblar, Classroomscreen, Microsoft Whiteboard, and Twiddla. The main criteria for the analysis were chosen: the possibility of online use of the board, access to participants via the link, the existence of an application for Android and IOS, and free service. Miro, LiveBoard, Jamboard, and Microsoft Whiteboard meet all the requirements of interactivity, but unfortunately have limited functionality for teaching mathematical disciplines. CleverMaths has the largest visualization functionality for teaching math disciplines. At the same time, CleverMaths lacks interactivity. The combination of CleverMaths in a complex with additional communication channels, namely programs for video conferences, allows you to achieve the necessary level of visibility and interactivity for distance learning of mathematical disciplines in higher education institutions. Key words: Interactive technologies, online boards, institutions of higher education, educational process, teaching mathematics.
Violent Human Behavior Detection from Videos using Machine Learning

Surveillance and security cameras are becoming much more common in city streets, shopping malls, private homes and many other places. In recent years terrorist attacks in shopping malls, schools and public places have increased. Such places, if equipped with a computer vision-based system which can effectively identify abnormal or violent human behavior, can help us in saving many human lives in due time. In this paper, we present such a system which can detect abnormal or violent human behavior in a video using machine learning. The system first identifies every human present in a video, extracts some vital data for that person and then, based on those data points, trains machine learning models to perform the classification. We have found that the system works reasonably well under certain conditions. DUJASE Vol. 7(1) 22-28, 2022 (January)
Analysis of Utilization of the Silampari PGRI University Library as a Student Learning Resource

The problem in this research is how to use the library at PGRI Silampari University for learning activities and self-development. In its use, the role of the library is not only used by students, but also by lecturers. The purpose of this study was to determine the analysis of the utilization of the PGRI Silampari University Library as a source of student learning. This research method is descriptive qualitative. This method discusses research based on field data or direct observation. To find out the level of usefulness of the PGRI Silampari University Library, the researcher described his research using various reference sources and interviews. The results of this study are the PGRI Silampari University library as a learning resource, that is, all groups, both lecturers and students, can use the library to support the learning system on the UNPARI Campus. Various references can provide valuable benefits both in learning and research. It is very clear that the library has various kinds of references that can support campus activities. The conclusion from this research is that the library as a reference source must be prioritized. Lecturers can invite students to visit the UNPARI library both to support materials and research.
A Feature Selection Approach Based on Archimedes’ Optimization Algorithm for Optimal Data Classification

Feature selection is an active research area in data mining and machine learning, especially with the increase in the amount of numerical data.FS is a search strategy to find the best subset of features among a large number of subsets of features.Thus, FS is applied in most modern applications and in various domains, which requires the search for a powerful FS technique to process and classify high-dimensional data.In this paper, we propose a new technique for dimension reduction in feature selection.This approach is based on a recent metaheuristic called Archimedes' Optimization Algorithm (AOA) to select an optimal subset of features to improve the classification accuracy.The idea of the AOA is based on the steps of Archimedes' principle in physics.It explains the behavior of the force exerted when an object is partially or fully immersed in a fluid.AOA optimization maintains a balance between exploration and exploitation, keeping a population of solutions and studying a large area to find the best overall solution.In this study, AOA is exploited as a search technique to find an optimal feature subset that reduces the number of features to maximize classification accuracy.The K-nearest neighbor (K-NN) classifier was used to evaluate the classification performance of selected feature subsets.To demonstrate the superiority of the proposed method, 16 benchmark datasets from the UCI repository are used and also compared by well-known and recently introduced meta-heuristics in this context, such as: sine-cosine algorithm (SCA), whale optimization algorithm (WOA), butterfly optimization algorithm (BAO), and butterfly flame optimization algorithm (MFO).The results prove the effectiveness of the proposed algorithm over the other algorithms based on several performance measures used in this paper.
A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm

Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure.Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem.The score and search technique is one Bayesian Network structure learning strategy.In Bayesian network structure learning the Falcon Optimization Algorithm (FOA) is presented and evaluated by the authors.Inserting, Reversing, Moving, and Deleting, are used in the method to create the FOA for finding the best structural solution.The FOA algorithm is based on the falcon's searching technique during drought conditions.The suggested technique is compared to the score metric function of Pigeon Inspired search algorithm, Greedy Search, and Antlion optimization search algorithm.The performance of these techniques in terms of confusion matrices was further evaluated by the authors using a variety of benchmark data sets.The Falcon optimization algorithm outperforms the previous algorithms and generates higher scores and accuracy values, as evidenced by the results of our experiments.