Experiences in Using a Multiparadigm and Multiprogramming Approach to Teach an Information Systems Course on Introduction to Programmi

In the current literature, there is limited evidence of the effects of teaching programming languages using two different paradigms concurrently. In this paper, we present our experience in using a multiparadigm and multiprogramming approach for an Introduction to Programming course. The multiparadigm element consisted of teaching the imperative and functional paradigms, while the multiprogramming element involved the Scheme and Python programming languages. For the multiparadigm part, the lectures were oriented to compare the similarities and differences between the functional and imperative approaches. For the multiprogramming part, we chose syntactically simple software tools that have a robust set of prebuilt functions and available libraries. After our experiments, we found that the students were strongly biased towards memorizing the syntax of these languages, jeopardizing their ability to learn to think algorithmically and logically in order to solve the given problems. We believe that teaching students using multiparadigm and multiprogramming techniques could be discouraging, especially for those students with no programming experience. In this research study, we present the results of applying this approach together with the achievements, failures, and trends of the students who were taught with this multipath system

Steganography and Data Loss Prevention: An overlooked risk?

Steganography is the art or science of hiding information into a carrier in such a way that the hidden data could not be detected at first sight. Steganography techniques have broadened their scope of action, from hiding information into picture media, to audio steganography and to the field of network steganography. All these methods entail a potential threat to the information security policies of any business; having into the data leakage threats its likely focus. In this scenario, business corporations cannot remain blind to these types of threats and should consider adequate policies and prevention techniques to avoid these risks. We have analyzed in this article the potential dangers that an organization could face in the light of these types of steganography techniques along with a review of current commercial software vendors to analyze their offers and mishaps on Data Leakage Prevention regarding steganography risks

Are information systems and computer science overlapping more and more?

In this research, we posit the importance of including Computer Science topics in undergraduate Information Systems courses. A review of the existing literature has mentioned the importance of learning some Computer Science topics for an Information Systems career. Unfortunately, we have not found a consensus that could push this initiative. There is a set of concepts that Information Systems students should acquire from Computer Science or at least have a solid background in these areas. Therefore, we propose a set of courses from Computer Science that, we believe, should be considered in an Information Systems education

Matching System for Animal-Assisted Therapy Based on the Levenshtein and Gale-Shapley Algorithms

This current research is based on the implementation of an algorithm that assigns pets, cats, or dogs to persons with depressive disorders such as low self-esteem. We found that even though different institutions have made the assignments of pets to patients, we were not able to find one that uses an IT tool for this task. Because of this situation, we decided to adapt to the well-known Gale-Shapley algorithm that has been used successfully in different situations in which it needs a perfect match between two parties. The results obtained have been validated by experts in the field of animal and person psychology. Because the Gale-Shapley algorithm needs a preference array between the parts involved and due that an animal cannot establish this set of preferences, we aimed to use a string similarity-based algorithm for obtaining preferences arrays based on the behavioral traits of an animal or person

One-class models for validation of miRNAs and ERBB2 gene interactions based on sequence features for breast cancer scenarios

One challenge in miRNA–genes–diseases interaction studies is that it is challenging to find labeled data that indicate a positive or negative relationship between miRNA and genes. The use of one-class classification methods shows a promising path for validating them. We have applied two one-class classification methods, Isolation Forest and One-class SVM, to validate miRNAs interactions with the ERBB2 gene present in breast cancer scenarios using features extracted via sequence-binding. We found that the One-class SVM outperforms the Isolation Forest model, with values of sensitivity of 80.49% and a specificity of 86.49% showing results that are comparable to previous studies. Additionally, we have demonstrated that the use of features extracted from a sequence-based approach (considering miRNA and gene sequence binding characteristics) and one-class models have proven to be a feasible method for validating these genetic molecule interactions

Automated classification system of giant white corn using image processing and supervised techniques

Nowadays, the use of artificial vision for classification in agricultural products has proven to have a great impact on this field. The exportation of agricultural goods has risen all over the world, consequently, that is the reason why exporting companies are looking to automate their processes and artificial vision techniques seems a great niche. This automation will allow an improvement in their production performance by diminishing the time and cost of their processes. While having a sound quality product in less time, improved precision and with no extensive manipulation of the product. In this article, we aim to offer a low cost alternative to this procedure oriented to the classification of Peruvian white corn by proposing an algorithm for the segmentation and recognition of images using computer vision techniques

Content-Based Learning Object Recommendation System Using a User Profile Ontology for High School Students

The lack of quality of education in Peruvian schools has caused young people to look for other ways to obtain information, of which the web stands out. However, this tool is made up of billions of web pages, which affects the time each student takes to search. To address this situation, we propose the development of a content-based recommendation system that uses ontologies for data storage. Our recommender system allows the user profile data to be integrated into the model to consider its characteristics as part of the recommendation. We carried out two sets of validations for the evaluation of our proposal, one with expert judgment and the other by gathering the opinion of the end-users. As a result of the first evaluation, we found that 76.25% of the items were highly related to the search. For the second evaluation, we found that our system obtained a usability of 78.67%, considering the opinion of the students tested

One-class SVM and supervised machine learning models for uncovering associations of non-coding RNA with diseases

The study of MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and gene interactions may be expected to provide new technologies to serve as valuable biomarkers for personalized treatments of diseases and to aid in the prognosis of certain conditions. These molecules act at the genome level by regulating or suppressing their protein expression functions. The primary challenge in the study of these non-coding molecules involves the necessity of finding labeled data indicating positive and negative interactions when predicting interactions using machine-learning or deep-learning techniques. However, usually we end up with a scenario of unbalanced data or unstable scenarios for using these models. An additional problem involves the extraction of features derived from the binding of these non-coding RNAs and genes. This binding process usually occurs fully or partially in animal genetics, which leads to considerable complexity in studying the process. Therefore, the main objective of the present work is to demonstrate that it is possible to use features extracted for miRNAs sequences in the development of diseases such as breast cancer, breast neoplasms, or if there is any influence with immune genes related to the SARS-COV-2. We performed experiments focusing on the erb-b2 receptor tyrosine kinase 2 (ERBB2) gene involved in breast cancer. For this purpose, we gathered miRNA-mRNA information from the binding between these two genetic molecules. In this part of our research, we applied a One-Class SVM and an Isolation Forest to discriminate between weak interactions, outliers given by the one-class model, and strong interactions that could occur between miRNA and mRNA (messenger RNA). Additionally, this study aimed to differentiate between breast cancer cases and breast neoplasm conditions. In this section we used the information encoded in lncRNAs. The additional feature used in this part was the frequency of k-mers, i.e., small portions of nucleotides, along with the data from the energy released in miRNA folding. The models used to discriminate between these diseases were One-Class SVM, SVM, and Random Forest. In the final part of the present work, we described a subset of probable miRNA binding with SARS-COV-2 RNA, focusing on those miRNAs with a relationship with genes involved in the immunological system of the human body. The models used as classifiers were One-Class SVM, SVM, and Random Forest. The results obtained in the present study are comparable to those found in the current literature and demonstrate the feasibility of using one-class models combined with features from the coupling of non-coding genes or mRNAs and their relationships with forms of breast cancer and viral infections. This work is expected to establish a basis for future avenues of research to apply one-class machine-learning models with feature extraction based on genomic sequences to the study of the relationship between non-coding RNAs and various diseases.School of ComputingPh. D. (Computing

Steganography Application Using Combination of Movements in a 2D Video Game Platform

Steganography represents the art of hiding information within a harmless medium such as digital images, video, audio, etc. Its purpose is to embed and transmit a message without raising suspicion to a third party or attacker who wishes to obtain that secret information. This research aims to propose a methodology with steganography using as a cover object a 2D platform video game. The experimentation model followed consists of using the combination of horizontal and vertical movements of the enemies by applying the numbering in base 5 or quinary where each character of the message is assigned a quinary digit. In the proposal for improvement the video game is set with 20 enemies per level along the map. The concealment is divided into 3 phases from the choice of the message, allocation of quinary values and generation of the videogame level. Finally, the limitations found will be presented based on experimentation