Digital Signature Security in Data Communication

Authenticity of access in very information are very important in the current era of Internet-based technology, there are many ways to secure information from irresponsible parties with various security attacks, some of technique can use for defend attack from irresponsible parties are using steganography, cryptography or also use digital signatures. Digital signatures could be one of solution where the authenticity of the message will be verified to prove that the received message is the original message without any change, Ong-Schnorr-Shamir is the algorithm are used in this research and the experiment are perform on the digital signature scheme and the hidden channel scheme.Comment: 6 pages, Paper presented at the International Conference on Education and Technology (ICEduTech2017), Novotel Hotel, Balikpapan, Indonesi

A New Diversity Technique for Imbalance Learning Ensembles

Data mining and machine learning techniques designed to solve classification problems require balanced class distribution. However, in reality sometimes the classification of datasets indicates the existence of a class represented by a large number of instances whereas there are classes with far fewer instances. This problem is known as the class imbalance problem. Classifier Ensembles is a method often used in overcoming class imbalance problems. Data Diversity is one of the cornerstones of ensembles. An ideal ensemble system should have accurrate individual classifiers and if there is an error it is expected to occur on different objects or instances. This research will present the results of overview and experimental study using Hybrid Approach Redefinition (HAR) Method in handling class imbalance and at the same time expected to get better data diversity. This research will be conducted using 6 datasets with different imbalanced ratios and will be compared with SMOTEBoost which is one of the Re-Weighting method which is often used in handling class imbalance. This study shows that the data diversity is related to performance in the imbalance learning ensembles and the proposed methods can obtain better data diversity

Philosophy of Education in Ethical Decision Making in the Classroom

The goal of this study is to provide educators a deeper understanding of how to apply ethical theories in practical classroom settings to help them make morally sound decisions. The author uses a literature study approach. The author carries out the analysis process by exploring the application of various ethical theories developed by prominent philosophers such as Immanuel Kant, John Stuart Mill, and Aristotle in the context of education. Ethical decision-making in the classroom is a key element in education that has a long-term impact on the development of student character. Ethical decisions made by teachers, whether based on deontology, utilitarianism, or virtue ethics, influence a learning environment that is not only fair but also supports the formation of students' morals and character. An educational philosophy that integrates ethical principles provides teachers with a solid foundation in dealing with everyday ethical dilemmas, both in the context of assessment, discipline management, and interactions with students. Therefore, the role of teachers as moral guides is very important in creating an environment that not only prioritizes academic results but also the moral character of students, which will guide them in their social lives (Jones & Brooks, 2020; Smith & Thomas, 2021). In order to create more equitable, inclusive, and humane learning environments, we need to further research on the application of educational philosophy to ethical decision-making in the classroom (Tatum, 2022)

Indigenous approach in organic solid waste management in Guyana (South America)

Organic waste posses a serious environmental problem globally. This can be solved by combination of effective technologies like Biodung composting and Vermitech (incorporating earthworms for the production of vermicompost). The present work was carried out during the year 2006-2007 at University of Guyana, Georgetown to recycle grass clippings, water hyacinth and cattle dung by using Eisenia fetida the locally available surface species of earthworm. The results indicated that the organic waste (grass clipping s and water hyacinth) were successfully processed through partial biodung composting and vermicomposting during the period of 60 days. The temperature study during biodung composting showed two peak rise of temperature resulting in destruction of harmful microbes. Subsequent vermicomposting resulted in production of vermicompost confirming to the excellent nutrient status recorded in earlier experiments. The temperature study during vermicomposting showed that fluctuation was restricted to +0.83

The Circular Restricted Four-body Problem With Triaxial Primaries and Variable Infinitesimal Mass

This paper investigates the circular restricted four-body problem in which three primaries are taken as triaxial rigid body which are placed at the vertices of an equilateral triangle and the fourth infinitesimal body is varying its mass with time. We used the Jeans law to determine equations of motion and then evaluated the Jacobi integral. In the next section, we have performed the computational work to draw the graphs of the equilibrium points in different planes, zero velocity curves, surfaces and the Newton-Raphson basins of attraction with the variations of the triaxiality parameters. Finally, we have examined the linear stability of the equilibrium points with the help of Meshcherskii space-time inverse transformation and found that all the equilibrium points are unstable

An OpenMP Based Approach for Parallelization and Performance Evaluation of k-Means Algorithm

In today’s digital world, the volume of data is drastically increasing due to the continuous flow of data from various heterogenous sources such as WWW, social media, environmental sensors, huge enterprise data warehouses, bioinformatic labs etc. to name a few. This results in creation of many high-volume datasets in various domains. Processing such large datasets is a tedious task, therefore they need to be categorized into smaller subsets using various supervised or unsupervised classification techniques. Clustering is the process of statistically analyzing and categorizing data objects with similarity, into substantially homogeneous groups, called data clusters. k-Means is the most common, simple and popular clustering technique, due to its ease of implementation, usability and wide range of applications. One of the issues associated with the k-Means algorithm is that it suffers from the scalability problem due to which, its performance degrades as the dataset sizes grow.  In order to address this issue, we have presented an OpenMP based parallelized k-means algorithm which results in better computational cost as compared with its sequential counterpart. Computational performance results of both sequential and OpenMP based k-means algorithms are illustrated and compared