UPLB ICS Peak One

Households and establishments have been reliant on their internet service connection for their everyday needs to the point where they cannot afford to tolerate long duration of service interruptions. With that, a mobile application was developed in order to assist in the operational activities of professionals who are in charge of resolving such issues. The respondents evaluated the application as Good and Acceptable in the SUS Scale proving that the application is effective enough to use in their operations.

As people spend more time indoors and as door-to- door delivery services become more prevalent, there is a need for people to see their location and to navigate indoors. Due to obstructions, GPS results to unreliable location approximation and there is no existing mapping up to the room-level. With that, a user application was developed in order to compute the user’s location and to aid the user in navigation which made use of existing Wi-Fi infrastructure within the specified building. While getting the current location resulted to a high error, the pathfinder is still able to accurately show the optimal path by inputting room names.

The aim of the study is to design and develop a 2D Android game in GDevelop that teaches Christian values while providing entertainment.

Network security reports are important tools in assessing the security level of any given computer network. These reports are often long and cumbersome, rendering them unintelligible to clients. In this study, a desktop application caled SwiftVuln, which utilizes OpenVAS and a weighted role- based mathematical model, is used to quantify the security of a network by producing one scalar value (from 0 to 10) and a description of that value to describe the state of a network. The resulting information is straightforward and uncomplicated, enabling the dissemination of information to have better reach. This, in turn, would trigger interventions that would ultimately lead to increasing the overall security of a network.

This study presents and implements a Convolutional Neural Network(CNN) that will be used to classify steganographic materials. Image is one of the most used form of data in the current technological generation and in this study, the goal is to classify JPEG images whether these images contain suspicious data. Training the CNN involves two separate image datasets. The first dataset contains 6000 images and uses F5, JSteg and Outguess from Salgado’s work and the second set of images uses BOSS(Break Our Steganograpy System) dataset which contains HUGO, UNIWARD, and WOW. Results show that the images that use F5, JSteg, and Outguess can only hide relatively small amount of data which makes the CNN more confused and thus reduce the accuracy. The same model was applied to the BOSS dataset and shows that the model is not capable of beating the accuracy obtained by Couchot et al.’s model.