Reviewerly/exHarmony huggingface dataset:
An improved point feature‐based sparse stereo vision

Since the limitation on the onboard equipment, the sparse stereo vision is becoming a suitable choice for the deployment of micro air vehicles (MAV) and small robots. However, for the point feature-based sparse stereo, most of the current stereo algorithms ignore the similarity between feature points, so it is hard to achieve high accuracy. In addition, the problem of clustered feature distribution will still affect the performance of point feature-based algorithms in the application. To make up for these deficiencies, the authors propose an improved features from accelerated segment test (FAST) feature detector to suppress the point detection in complex texture regions. Most importantly, the authors present a novel census transform (CT)-based algorithm that contains two encoders ‘texture orientation’ and ‘texture gradient’ to get a more efficient census bit string for the feature point. Instead of randomly selecting pixels to calculate the bit string, we combine the texture characteristics of the census windows where feature points are located. Compared with the original CT, the processing speed of our method is improved, and the average error of our method is reduced by 18.05%. The evaluation results show the presented improved point feature-based sparse stereo algorithm has a great value in engineering applications.
ONLINE SCIENCE LABORATORY-BASED ACTIVITIES ON STUDENT’s METACOGNITION AND PERFORMANCE AMIDST PANDEMIC

This study determined the effects of online science laboratory-based activities on student's metacognition and performance amidst pandemic of the respondents from Gov. Felicisimo T San Luis Integrated Senior High School.It aimed to answer the questions such as the level of online Science laboratory based activities in terms of manipulative online activities, online videos, and interactive simulations.Furthermore, this also sought to determine the mean level of the student's metacognition in terms of cognitive process, thinking process, self-regulations, self-reflection, and appropriate strategies utilization, and the mean level of the student's performance as to Pre-Test and Post-Test.Lastly to sought to answer the question about the significant effect of online science laboratory-based activities on the students' metacognition and performance.The descriptive method was utilized in this study since it yields valid and reliable results for a manageable number of respondents and can be accomplished with limited resources.The research also used the survey questionnaire and Pretest and Post-test to obtain data from the selected grade 7 students from different public junior high school in Sta Cruz Laguna specifically Gov. Felicisimo T. San Luis Integrated Senior High School and Pedro Guevara National High School.The process of descriptive survey research went beyond mere gathering and tabulation of data.It involved an element of interpretation of the meaning or significance of the result since the investigation is concerned with determining the effect of online science laboratory-based activities on the students' metacognition and performance.It presents the significant effect of the science laboratory-based activities on the level of students' metacognition.Specifically, it shows the effect of manipulative online activities, online videos, and interactive simulations on the cognitive process, thinking process, self-regulation, self-reflection, and appropriate strategies utilization.The researcher then came up that online science laboratory-based activities have no significant effect on the students' metacognition and performance" is rejected and calls for the acceptance of the alternative, which incites a significant effect.
Credit Card Fraud Detection

Abstract: Now a day’s online transactions have become an important and necessary part of our lives. It is vital that credit card companies are able to identify fraudulent credit card transactions so that customers are not charged for items that they did not purchase. As frequency of transactions is increasing, number of fraudulent transactions are also increasing rapidly. Such problems can be tackled with Machine Learning with its algorithms. This project intends to illustrate the modelling of a data set using machine learning with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem includes modelling past credit card transactions with the data of the ones that turned out to be fraud. This model is then used to recognize whether a new transaction is fraudulent or not. Our objective here is to detect 100% of the fraudulent transactions while minimizing the incorrect fraud classifications. Credit Card Fraud Detection is a typical sample of classification. In this process, we have focused on analyzing and preprocessing data sets as well as the deployment of multiple anomaly detection algorithms such as Local Outlier Factor and Isolation Forest algorithm on the PCA transformed Credit Card Transaction data. Keywords: Credit Card, Python, Detection
Accuracy of Kidney Disease Expert System Based On Certainty Factor and Dempster Shafer Algorithm

The information technology developments today resulted in several innovations, including the existence of an Expert System. The systems help diagnose diseases without directly meeting with experts/doctors. Many researchers have proposed algorithms to improve the accuracy of the expert system to approach the diagnosis by experts/doctors, including certainty factor and dempster shader. This study compares the algorithm's accuracy with the results of expert diagnosis of kidney disease. The expert system was developed using UML and a web-based version. From the comparison results, the dempster shaver algorithm has an accuracy rate of 80%, while the certainty factor is 60% compared to expert diagnoses.
Ingredient-enriched Recipe Generation from Cooking Videos

Cooking video captioning aims to generate the text instructions that describes the cooking procedures presented in the video. Current approaches tend to use large neural models or use more robust feature extractors to increase the expressive ability of features, ignoring the strong correlation between consecutive cooking steps in the video. However, it is intuitive that previous cooking steps can provide clues for the next cooking step. Specially, consecutive cooking steps tend to share the same ingredients. Therefore, accurate ingredients recognition can help to introduce more fine-grained information in captioning. To improve the performance of video procedural caption in cooking video, this paper proposes a framework that introduces ingredient recognition module which uses the copy mechanism to fuse the predicted ingredient information into the generated sentence. Moreover, we integrate the visual information of the previous step into the generation of the current step, and the visual information of the two steps together assist in the generation process. Extensive experiments verify the effectiveness of our propose framework and it achieves the promising performances on both YouCookII and Cooking-COIN datasets.
CRIMINAL LEGAL CHARACTERISTIC OF GENOCIDE IN THE REPUBLIC OF UZBEKISTAN

In this article, based on the norms of international law, the Constitution and the Criminal Code of the Republic of Uzbekistan, as well as the legislation of some foreign countries, the features of the criminal law characteristics of genocide as a crime are analyzed, a legal analysis of Article 153 of the Criminal Code of the Republic of Uzbekistan is carried out, relevant conclusions and recommendations are made to improve the norms of criminal legislation.
PENYUSUNAN NASKAH SOAL BESERTA KISI-KISI DAN KARTU SOAL DI SMA NEGERI 7 LUBUKLINGGAU

This community service program is motivated by the teacher's obligation to prepare learning tools including question scripts that will be given to students. The preparation of the question script must be accompanied by a question grid and question card so that the questions used are in accordance with the criteria. Therefore, the PGRI Silampari University service team carried out the preparation of the question script along with the grid and question cards. Question papers, grids and question cards are used to give tests to students according to level. The output target to be achieved by the proposing team is the improvement of values, knowledge and publications. In its implementation, it uses theoretical and practical methods starting with the preparation stage, providing material and the practice of preparing question texts, question grids and question cards.
Situational Interest and Engagement of Public Junior High School Science Students in Modular Distance Learning

Due to the ongoing global health crisis, the Philippine Basic Education opted to implement modular distance learning (MDL) as an alternative mode of learning for the current school year.This modality depends highly on the use of self-learning modules (SLMs) with the goal of bringing about learning and continuing students' education amidst the pandemic.The present study employed mixed methods research to describe and examine the relationship between situational interest and engagement (behavioral, emotional, and cognitive) of public junior high school science students in modular distance learning.Participants in the online survey were 380 students from eight different public secondary schools while the interviews involved 19 junior high school students who were determined based on their initial responses on the survey.Results revealed the significant correlation between situational interest and the three dimensions of engagement (behavioral, emotional, and cognitive).Emotional engagement obtained the lowest mean among the engagement dimensions while novelty attained the lowest mean score among the situational interest dimensions.Results showed that although most students in the current study are interested and engaged in MDL, the format and content presentation of the SLMs do not always necessarily interest and engage students into learning.
A NEW MULTI-PARTY PRIVATE SET INTERSECTION PROTOCOL BASED on OPRFs

In many crucial real-world applications, parties must jointly perform some secure multi-party computation (MPC) while keeping their inputs hidden from other parties. Private Set Intersection (PSI), the specific area of Multi-Party Computation, let the parties learn the intersection of their private data sets without sharing their secret data with others. For instance, a smartphone user downloads a messaging application, naturally, he wants to discover who are the other contacts that are using the same application. The naive and insecure solution is to send all contacts to the server to discover them. However, the user does not want to share his contacts with the application for privacy issues. To handle this, in recent years, companies and organizations start to use PSI to enhance privacy and security with a little cost of communication and computation. In this paper, we introduce a novel method to compute Private Set Intersection with multi parties where there are at least three or more parties participating in the protocol. By employing the Zero-Secret Sharing scheme and Oblivious Pseudo-Random Functions (OPRFs), parties securely calculate the intersection with computational and communication complexities which are both linear in the number of parties.
A Deep Neural Network-Based Yoruba Intelligent Chatbot System

Two Artificial Intelligence software systems, Bot and Chatbot have recently debuted on the internet. This initiate a communication between the user and a virtual agent. The modeling and performance in deep learning (DL) computation for an Assistant Conversational Agent are presented in this research (Chatbot). The deep neural network (DNN) technique is used to respond to a large number of tokens in an input sentence with more appropriate dialogue. The model was created to do Yoruba-to-Yoruba translations. The major goal of this project is to improve the model's perplexity and learning rate, as well as to find a blue score for translation in the same language. Kares is used to run the experiments, which is written in Python. The data was trained using a deep learning-based algorithm. With the use of training examples, a collection of Yoruba phrases with various intentions was produced. The results demonstrate that the system can communicate in basic Yoruba terms and that it would be able to learn simple Yoruba words. The study result when evaluated showed that the system had 80% accuracy rate. Keywords: Chatbot, Natural Language Processing, Deep Learning, Artificial Neural Network, Yoruba Language
PERAMALAN INFLASI DENGAN METODE PARTICLE SWARM OPTIMIZATION – EXTREME LEARNING MACHINE

The Extreme Learning Machine (ELM) method is a learning method on an artificial neural network that is used to solve non-linear problems. In this article, the Particle Swarm Optimization (PSO) method is combined with the Extreme Learning Machine (ELM) method to determine the number of neurons in the hidden layer, so that the results obtained are quite accurate and more efficient in terms of time. Furthermore, the PSO-ELM method was applied to inflation data in Indonesia, and the results showed that the Roots Mean Square Error (RMSE) value in the training process was 0.005487935, and the RMSE value in the testing process was 0.4124935. These results indicate that the PSO-ELM method is suitable for forecasting inflation in Indonesia.
Industrial Revolution Era 4.0: Digital Literacy of Accounting Education Students

This study aims to find out the different profile of digital literacy skills of four batches of Accounting Education students in the 4th industrial revolution era. As almost all human works will be replaced by robots and only those who have particular skills will be able to survive, it is essential to examine students’ digital literacy skills. A survey employed quantitative approach was conducted with a sample of 175 of four batches Accounting Education students of Universitas Negeri Yogyakarta. The data gathered with questionnaires technique were analysed using descriptive analysis followed with multivariate analysis. The results show that the four batches Accounting Education students have different profile of digital literacy skills in terms of selecting, addressing, and evaluating information. It is suggested for the students to be aware of the changes in digital technology and its contribution to the field of education and consider it as the weapon for them to improve their digital literacy skills.
Violence among Nigerian Students: A Doom to Nigerian Educational System

The study examined different types of violence prevalent among Nigerian students. It also examined the major causes, the damages caused, and effects of violence in the Nigerian tertiary educational system. It finally examined the effect of violence as perceived by undergraduates based on gender, academic level and age. These were with the view to providing information on how violence could be curbed among students in higher institutions of learning. The study employed survey research design. The population consisted of students of the Obafemi Awolowo University, Ile-Ife, Osun State. Proportionate Stratified sampling technique was employed in selected samples size of 200 respondents from the 13 faculties in Obafemi Awolowo University, Ile-Ife. An instrument was used for the collection of data. Data collected were analyzed using frequency counts, simple percentages and ANCOVA. The results among others showed that overt physical assault of one on others representing 87.5% is the most prevalent type of violence. Result also showed that there is no significant difference in the effect of violence as perceived by students on the basis of gender, academic level and age at (F=0.056, p&gt;0.05). The study concluded that violence should be discouraged as much as possible among undergraduates.
"Strategic Implementation of Effective Marketing Plan for Indonesian National Sports Institute"

The Indonesian National Sports Institute (IONI) is a private university that will produce skilled and competent coaches to create the best athletes and educate the public to exercise properly. In an effort to gain customers, IONI will conduct marketing through promotions and increasing followers on social media in the form of youtube, tiktok, Instagram, facebook, and linked in. In addition, in an effort to introduce itself to the public, IONI will organize sports events that can reach the community, such as Fun Run or Fun Bike. IONI will also cooperate with the Central KONI, the military, and 63 sports in Indonesia through the signing of an MoU as a selling point that adds to IONI's image.
Structure and Complexity of 2-Intersection Graphs of 3-Hypergraphs

Abstract Given a 3-uniform hypergraph H having a set V of vertices, and a set of hyperedges $$T\subset \mathcal {P}(V)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>T</mml:mi> <mml:mo>⊂</mml:mo> <mml:mi>P</mml:mi> <mml:mo>(</mml:mo> <mml:mi>V</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , whose elements have cardinality three each, a null labelling is an assignment of $$\pm 1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>±</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> to the hyperedges such that each vertex belongs to the same number of hyperedges labelled $$+1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> and $$-1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:math> . A sufficient condition for the existence of a null labelling of H (proved in Di Marco et al. Lect Notes Comput Sci 12757:282–294, 2021) is a Hamiltonian cycle in its 2-intersection graph. The notion of 2-intersection graph generalizes that of intersection graph of an (hyper)graph and extends its effectiveness. The present study first shows that this sufficient condition for the existence of a null labelling in H can not be weakened by requiring only the connectedness of the 2-intersection graph. Then some interesting properties related to their clique configurations are proved. Finally, the main result is proved, the NP-completeness of this characterization and, as a consequence, of the construction of the related 3-hypergraphs.
Analysis of the visualizing changes in radar time series using the REACTIV method through satellite imagery

&lt;span&gt;A visualizing temporal stack of synthetic aperture radar (SAR) images are presented in this work, the method is called REACTIV, which enabled us to highlight color zones that have undergone change over the detected period of time. This work has been widely tested using Google earth engine (GEE) platform, this method depends on the hue-saturation-value (HSV) of visualizing space and supports estimation only in the time domain; the method does not support the spatial estimation. The coefficient of temporal coefficient variation is coded depending on the saturation color, of which several statistical properties are described. The limitations are studied, and some applications are implemented in this study.&lt;/span&gt;
Detection of Breast Cancer Using Machine Learning Algorithms

Abstract: Breast Cancer is one of the most frequently occurring cancers in women. Cancer rates are increasing in almost every region around the world. Early detection of cancer is the most efficient way to prevent the deaths caused due to it. Currently, the most commonly used tests for the detection of cancer are Mammograms, Breast Ultrasound and Breast MRI. These techniques have their own disadvantages which include the risk of false detection or no guarantee that all cancers will be detected. It is crucial to use alternative methods that are easier to implement and produce more reliable results. The solution is to use various Machine Learning algorithms to overcome the disadvantages of traditional techniques. This paper aims to propose a prediction model using Machine Learning classifier algorithms like Naïve Bayes (NB), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The performance of the various classifiers is compared in terms of accuracy, precision, and recall and the best classifier for the detection of cancer.
THE DEVELOPMENT OF "ACCESS" ANDROID BASED LEARNING MEDIA IN THE ACCOUNTING CYCLE MATERIAL

This research is intended to produce "Access" Android Based Learning Media in The Service Company Accounting Cycle. This is a Research and Development type of research that includes four phases, namely: Analysis, Design, Development, and Implementation. The feasibility of the learning media was assessed by material expert, media expert, accounting learning practitioner and students, using questionnaires. The quantitative research data were analyzed using the quantitative-descriptive analysis technique. The final product of this research is a learning media in the form of Android Application. The media includes material, questions, and game. The results of this research indicate; (1) “Access” android-based learning media development through 5 phases, they were: Analysis, Design, Development, and Implementation. (2) The development of “Access” android based learning media on expert evaluation: a) Material expert got score 3,66 with “Very feasible” category, b) Media expert got score 2,89 with category “Feasible”, c) Accounting learning practitioner got score 3,79 with the category “Very Feasible”. (3) Students assessment to media got average score 3,46 with the category “Very feasible”. The developed “Access” android-based learning media shows that it is very feasible as the learning media.
Investigating the PCA Effect on the 3D Face Recognition System Speed

In this paper, we discuss increasing the speed of facial recognition using 3-dimensional data. The use of this data makes facial recognition safer because it is difficult to imitate compared to using 2-dimensional data. 2-dimensional data can be obtained from photos, but 3-dimensional data cannot be done because it requires a third dimension, namely facial depth data which can only be taken from 3-dimensional objects. The three-dimensional (3D) face recognition process in this study did not go through the stages of the facial reconstruction process into 3D form, but directly used data taken from the Kinect Xbox camera system, unlike most previous research studies. So that it can increase the speed and is not expected to reduce the level of accuracy. Data from the camera is directly inputted into the backpropagation algorithm. This algorithm was chosen because it is simpler than CNN or other algorithms, and also this research is only to prove the method used can increase the speed of facial recognition. In addition to eliminating the reconstruction stage, the PCA method is also used. PCA has a function to simplify the amount of data, thereby reducing the amount of computing which can increase system speed. Testing is done in two ways. The first test uses a combination of Backpropagation and PCA, while the second test uses only Backpropagation. The results showed that the system using a combination of Backpropagation and PCA achieved a speed increase of up to 34.2 times, but the accuracy was reduced by 8.5%. Further research is needed to answer the results of this study.