Reviewerly/exHarmony huggingface dataset:
ANALISIS SENTIMEN OPINI PENGGUNA TWITTER PADA APLIKASI BIBIT MENGGUNAKAN MULTINOMIAL NAÏVE BAYES

The COVID-19 pandemic has increased the interest number of capital market investors in Indonesia. One of the factor in Indonesian’s investment interest is the emergence of fintech in the investment sector. One of the fintech companies in mutual fund investment is "Bibit". To find out user opinions on the Bibit application, a sentiment analysis was carried out on Twitter’s users. This study aims to analyze the sentiments of twitter users' opinions on the Bibit application using a combination of Lexicon-Based and Multinomial Naïve Bayes methods. The training data used were 2211 tweets and the validation data was 553 tweets. In the model training process, the training accuracy level is 91.50% and the validation accuracy rate is 85.35%. Model testing was carried out using 39 new tweet data and obtained an accuracy rate of 88%. Sentiment analysis using this method is visualized in the form of pie charts, graphs, and wordclouds. Based on the results of visualization of Twitter social media user sentiment towards the seedling application, it tends to be positive with a percentage of 52% positive and 48% negative.
On distribution of the number of semisimple rings of order at most x in an arithmetic progression

Let \ell and q denote relatively prime positive integers. In this article, we derive the asymptotic formula for the summation \begin{align*} \sum_{n\leq x\atop n\equiv \ell \pmod q}S(n), \end{align*} where S(n) denotes the number of non-isomorphic finite semisimple rings with n elements.
EVALUATION OF INFLUENCE OF DISPLAY OF INSTRUCTIONAL RESOURCES ON CHILDREN’S PERFORMANCE IN EARLY LITERACY IN PUBLIC PRE-SCHOOLS IN KANDUYI SUB-COUNTY, KENYA

Availability and utilization of instructional resources play an important role in the attainment of educational goals, objectives and learners’ performance. In this study, the researcher argues that many learners are still not proficient in early literacy skills despite going through pre-school education. The factors associated with the teaching and learning of early literacy skills may have been addressed but influence of instructional resources continues to be a concern. This study focused on the influence of display of instructional resources on children’s performance in literacy. The study was framed on the constructivism theory propounded by Jean Piaget in 1896-1980. The study adopted a descriptive survey design and targeted 90 public pre-schools comprising of 5460 pre-school learners and 180 pre-school teachers. A simple random sampling technique was used in selecting the pre-schools and the sample size for teachers and learners for the study was arrived at using Krejcie and Morgan (1970) formula. Through these methods, 27 pre-schools, 359 learners and 123 pre-school teachers were selected for the study. Data was collected using questionnaires and a literacy skill test administered to the learners. Questionnaires were administered to pre-school teachers to seek information on pre-school teachers’ training skills and use of instructional resources. Content validity of the instruments was determined by employing the expertise of my supervisors and lecturers in the Department of Education while reliability was determined through split-half technique. Data was analyzed using descriptive and inferential statistics such as means, percentages, frequencies and Pearson Correlation analysis. The findings were presented in tables, pie charts and bar graphs. The study found that there was a positive correlation between display of instructional resources and learners’ achievement in writing skills (r = .646; p = .000) and reading skills (r = .780; p = .000). The study concluded that instructional resources play a significant role in imparting of reading and writing skills to the pre-school learners. The study recommended that there is need for provision of audio and audio-visual materials to pre-schools since these materials are known to positively affect the acquisition of writing and reading skills among pre-school learners. It also recommends that the government of Kenya through the Ministry of Education ensure that pre-schools acquire instructional resources equitably distributed in the various pre-schools across the country to facilitate timely implementation of the curriculum especially in reading and writing skills for good educational foundation of the learners.
INFLUENCE OF AVAILABILITY OF INSTRUCTIONAL RESOURCES ON CHILDREN’S PERFORMANCE ON EARLY LITERACY IN PUBLIC PRE-SCHOOLS IN KANDUYI SUB-COUNTY, KENYA

The factors associated with the teaching and learning of early literacy skills may have been addressed but influence of instructional resources continues to be a concern. This study focused on how availability of instructional resources influences children’s performance in early literacy in public pre-schools in Kanduyi Sub-County, Kenya. The study was framed on the constructivism theory propounded by Jean Piaget in 1896-1980. The study adopted a descriptive survey design and targeted 90 public pre-schools comprising of 5460 pre-school learners and 180 pre-school teachers. A simple random sampling technique was used in selecting the pre-schools and the sample size for teachers and learners for the study was arrived at using Krejcie and Morgan (1970) formula. Through these methods, 27 pre-schools, 359 learners and 123 pre-school teachers were selected for the study. Data was collected using questionnaires and a literacy skill test administered to the learners. Questionnaires were administered to pre-school teachers to seek information on pre-school teachers’ training skills and use of instructional resources. Content validity of the instruments was determined by employing the expertise of my supervisors and lecturers in the Department of Education while reliability was determined through split-half technique. Data was analyzed using descriptive and inferential statistics such as means, percentages, frequencies and Pearson Correlation analysis. The findings were presented in tables, pie charts and bar graphs. The study found out that concrete materials, were used by majority of the pre-school teachers in the study area. However, audio and audio-visual resources were lacking in all the schools in the study area hindering the acquisition of writing and reading skills. The study concluded that instructional resources play a significant role in imparting of reading and writing skills to the pre-school learners. The study recommended that there is need for provision of audio and audio-visual materials to pre-schools since these materials are known to positively affect the acquisition of writing and reading skills among pre-school learners. It also recommends that the government of Kenya through the Ministry of Education ensure that pre-schools acquire instructional resources equitably distributed in the various pre-schools across the country to facilitate timely implementation of the curriculum especially in reading and writing skills for good educational foundation of the learners.
PATTERN RECOGNITION FROM IMAGES AND VIDEOS USING MACHINE LEARNING

Pattern recognition is a data analysis method that uses machine learning algorithms to automatically recognize patterns and regularities in data. This data can be anything from text and images to sounds or other definable qualities. Pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach structural and new has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have deserved increasing attention. There are three main types of pattern recognition, dependent on the mechanism used for classifying the input data. Those types are: statistical, structural (or syntactic), and neural. Based on the type of processed data, it can be divided into image, sound, voice, and speech pattern recognition.
ANALSYING THE PARALLEL FUSION LOGIC AND FUSION K-MEANS (FKM) WITH LANGUAGE DOCUMENT

Text summarization is a method of retrieving information, and it is very effective. Text summarization approach techniques fall into two categories: abstract and abstract. This white paper focuses on extraction techniques so that the output of the application is the key sentence taken verbatim from the original text without verbal modification. There are four main processes in this research. These are the preprocessing phase, the scoring function, the optimization of summarization by two methods (logic and fused K-Means), and the extraction of summarization results. In this study, seven features are used to calculate a score for each sentence. The core of this study is to compare the reliability of fused logic and fused k-means methods in optimizing the aggregation results process. The results showed that the Fusion Logic method outperformed the Fusion K-Means method and had the highest similarity to human summarization, with a percentage accuracy of 48.25% for Fusion Logic and 44% for Fusion K-Means. It was %.33%. Accuracy improves with the additional compression of 0.67% for Fusion Logic Method and 3.5% for Fusion K-Means. Keywords: Language, Text Features, Scores Sentences, Fusion Logic, Fusion K–Means
Expanding Protection and Value Promotion of Cultural Routes to Promote Rural Revitalization—A Case Study of Ya’ an Section of the Sichuan-Tibet Ancient Tea-Horse Road

As a new theory of cultural heritage protection and research, cultural routes emphasize the integrity of culture and pay more attention to the interaction and integration between cultural communities along the route. The Ancient Tea-Horse Road is an important cultural, economic and religious exchange link between the various ethnic groups in the Han and Tibetan two places. As the starting point of the line, Ya ‘an section plays an extremely important role in history. Under the concept and connotation of cultural routes, this paper explores the cultural value of the Ya’an section of the Sichuan-Tibet Tea Horse Ancient Road with the idea of cultural integrity along the line, integrates the cultural elements along the line to form an integrated cultural industry cluster along the line, establishes the tea-horse ancient road cultural route heritage system and the management system of overall coordination and overall protection, deeply excavates the multiple values of the cultural industry along the line, promotes the coordinated development of the line, and empowers the rural revitalization along the line.
ARTICLE SENTENCE ANALYSIS: USE OF CLAUSES AND PHRASES IN SCIENTIFIC SENTENCES

Sentence analysis in scientific articles is a literature study that can improve students' ability to think critically. Especially in articles that use English. Syntax analysis is the first step in reading writing before the reader can understand the meaning implied in an English sentence as a whole. This research is literature review research that aims to analyse sentence structure and determine the types of sentences, clauses, and phrases. By selecting the title of the article from the accredited journal Sinta, a search is carried out on each sentence and then classifying sentences and word combinations according to the character and type of sentences, clauses, and phrases.
Covid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN MethodsCovid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN Methods

Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things that are of concern to social media users. One of the information shared is information about COVID-19, which is known that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility. Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used as a means of receiving information to assess the source of communication. The method used in this research is Information Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection (crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation value between credibility and hoax of 0.115, and a p-value &lt;0.005.&#x0D;
MORPHOSEMANTIC FEATURES OF MEMBUAT ‘MAKE’ IN THE LIGHT VERB CONSTRUCTIONS OF INDONESIAN

Light verb constructions (LVCs) are combinations of a verb and a noun that operate as a referee for a distinctive idiomatic-like meaning in which the noun is the central focus of meaning deduction. This study aimed to identify and describe Indonesian LVCs as morphosemantic phenomena. The dataset comprises Indonesian sentences containing featured LVCs. Three corpora were utilized as data sources: Leipzig Corpora Collection – Indonesian (LCCI), SEAlang Library Indonesian Text Corpus (SEAlang), and Kamus Besar Bahasa Indonesia (KBBI). In this study, a framework from Morphosemantics has been employed to provide the deep analysis technique. The results revealed the following two folds. First, at the morphological level, the Indonesian verb membuat 'make' is a clear indicator of the existence of LVCs. Second, on a semantic level, the verb membuat ‘make’ in Indonesian LVCs tends to produce ACTION type verbs, AGENT paired argument of verbs and RESULTATIVE grammatical meaning. This study suggested that further research be done on the morphosyntactic features of the Indonesian LVCs verb membuat 'make.'
On the Need for Standardizing Industrial Internet of Things (IIoT) Forensics Processes

The growth of the Internet of Things (IoT) has resulted in the emergence of the Industrial Internet of Things (IIoT), which has greatly impacted various industrial sectors, including manufacturing, energy, and healthcare. However, the increased use of connected devices and systems has also led to the introduction of new security challenges and the need for forensics investigations. Despite the growing need for IIoT forensics, there is currently a lack of standardization in this field, which creates challenges for conducting investigations and preserving evidence. This research paper aims to address the need for standardization in IIoT forensics, highlighting the current challenges and limitations, benefits, industry and government roles, and impact of IIoT forensic standardization. The ultimate aim of this research is to provide insights into the importance of standardization in IIoT forensics and the benefits it could bring to the industry.
Deepfake Video Detection Based on Improved CapsNet and Temporal–Spatial Features

Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms, presenting risks for numerous countries, societies, and individuals, and posing a serious threat to cyberspace security.To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection, we propose a detection method based on improved CapsNet and temporal-spatial features (iCapsNet-TSF).First, the dynamic routing algorithm of CapsNet is improved using weight initialization and updating.Then, the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal-spatial features.Finally, the iCapsNet model is employed to fully learn the temporal-spatial features of facial videos, and the results are fused.Experimental results show that the detection accuracy of iCapsNet-TSF reaches 94.07%, 98.83%, and 98.50% on the Celeb-DF, FaceSwap, and Deepfakes datasets, respectively, displaying a better performance than most existing mainstream algorithms.The iCapsNet-TSF method combines the capsule network and the optical flow algorithm, providing a novel strategy for the deepfake detection, which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.
TeST: Test-time Self-Training under Distribution Shift

Despite their recent success, deep neural networks continue to perform poorly when they encounter distribution shifts at test time. Many recently proposed approaches try to counter this by aligning the model to the new distribution prior to inference. With no labels available this requires unsupervised objectives to adapt the model on the observed test data. In this paper, we propose Test-Time Self-Training (TeST): a technique that takes as input a model trained on some source data and a novel data distribution at test time, and learns invariant and robust representations using a student-teacher framework. We find that models adapted using TeST significantly improve over baseline test-time adaptation algorithms. TeST achieves competitive performance to modern domain adaptation algorithms [4], [43], while having access to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines on two tasks: object detection and image segmentation and find that models adapted with TeST. We find that TeST sets the new state-of-the art for test-time domain adaptation algorithms.
Composite Relationship Fields with Transformers for Scene Graph Generation

Scene graph generation (SGG) methods extract relationships between objects. While most methods focus on improving top-down approaches, which build a scene graph based on detected objects from an off-the-shelf object detector, there is a limited amount of work on bottom-up approaches, which jointly detect objects and their relationships in a single stage.In this work, we present a novel bottom-up SGG approach by representing relationships using Composite Relationship Fields (CoRF). CoRF turns relationship detection into a dense regression and classification task, where each cell of the output feature map identifies surrounding objects and their relationships. Furthermore, we propose a refinement head that leverages Transformers for global scene reasoning, resulting in more meaningful relationship predictions. By combining both contributions, our method outperforms previous bottom-up methods on the Visual Genome dataset by 26% while preserving real-time performance.
DESIGN EMPLOYEE ATTENDANCE INFORMATION SYSTEM USING BARCODE CODE 39

The development of information and Communication Technology at this time has touched all sides of life. There is almost no side of human life that is not touched by information technology, not least also with computerization that is popular in the corporate environment. When in ancient times everything was still done manually. However, at this time all the leaders and management of the company have realized the importance of this information technology product that can facilitate them in carrying out Daily company functions. Many a design that uses computerized attendance but not integrated with attendance recording. Attendance recording is still manual so that it takes a long time in the process of making attendance because they have to do the counter manually. In this study will be designed a computerized attendance information system where the attendance process using database processing applications ysng assisted by using the barcode method code 39. The final result of the design of attendance information system is expected to help companies to manage attendance data effectively and efficiently.
RELATIVE RISK ANALYSIS OF THE SPREAD OF COVID-19 VIRUS IN MEDAN CITY BY SPATIAL AND NON-SPATIAL APPROACHES

&lt;span lang="EN"&gt;The city of Medan is the city with the highest cases of COVID-19 virus among cities in North Sumatra. This study was conducted to analyze the relative risk level for the spread of the COVID-19 virus. Estimation of relative risk is a statistic in disease mapping that is used to determine the distribution of disease. Relative risk estimation can be estimated using a direct estimator model or Standardized Morbility ratio and a small area estimation model using Bayesian Conditional Autoregressive (CAR) with the Poisson-Gamma model. The Poisson-Gamma model is one of the models in estimating small areas in the form of count data which is suitable for use in disease mapping cases. This study aims to find the relative risk value as the basis for mapping the spread of the COVID-19 virus in the city of Medan using the Standardized Morbility Ratio and Bayesian Condition Autoregressive models. And look for the value of the Central Error Squared (KTG) / Mean Squared Error (MSE) as a comparison which model is more efficient in estimating this research. Condition Autoregressive models. And look for the value of the Central Error Squared (KTG) / Mean Squared Error (MSE) as a comparison which model is more efficient in estimating this research.&lt;/span&gt;
Keyframe Extraction and Process Recognition Method for Assembly Operation Based on Density Clustering

A keyframe extraction and process recognition method for assembly operation is proposed based on density clustering to solve the problems of data redundancy and difficulty in obtaining valid data frames from the process of continuous assembly operation. A standard operation gesture set including dynamic and static action was constructed through assembly operation decomposing. Finger feature variables and gesture comprehensive feature quantized function were defined according to finger joint structure. Based on searching for local extreme points of the function, density clustering method was used to extract the keyframes of the assembly operation sequence to eliminate the redundant data. Finally, the support vector machine algorithm model and the Levinstein distance were determined to complete the keyframe recognition and assembly operation matching. A case study showed that the method presented can effectively discretize the assembly operation sequence, remove approximately 84% of redundant data frames, and achieve a comprehensive recognition rate of 92%.
Decoherence: A Numerical Study

We study quantum decoherence numerically in a system consisting of a relativistic quantum field theory coupled to a measuring device that is itself coupled to an environment. The measuring device and environment are treated as quantum, non-relativistic particles. We solve the Schr\"odinger equation for the wave function of this tripartite system using exact diagonalization. Although computational limitations on the size of the Hilbert space prevent us from exploring the regime where the device and environment consist of a truly macroscopic number of degrees of freedom, we nevertheless see clear evidence of decoherence: after tracing out the environment, the density matrix describing the system and measuring device evolves quickly towards a matrix that is close to diagonal in a subspace of pointer states.