Visible to the public Biblio

Filters: Author is Vea, Larry A.  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
Espinosa, Floren Alexis T., Guerrero III, Guillermo Gohan E., Vea, Larry A..  2016.  Modeling Free-form Handwriting Gesture User Authentication for Android Smartphones. Proceedings of the International Conference on Mobile Software Engineering and Systems. :3–6.

Smartphones nowadays are customized to help users with their daily tasks such as storing important data or making transactions through the internet. With the sensitivity of the data involved, authentication mechanism such as fixed-text password, PIN, or unlock patterns are used to safeguard these data against intruders. However, these mechanisms have the risk from security threats such as cracking or shoulder surfing. To enhance mobile and/or information security, this study aimed to develop a free-form handwriting gesture user authentication for smartphones. It also tried to discover the static and dynamic handwriting features that significantly influence the recognition of a legitimate user. The experiment was then conducted by asking thirty (30) individuals to draw or swipe using their fingertip their desired free-form security pattern ten (10) times. These patterns were then cleaned and processed, and extracted seven (7) static and eleven (11) dynamic handwriting features. By means of Neural Network classifier of the RapidMiner data mining tool, these features were used to develop, validate, and test a model for user authentication. The model showed a very promising recognition rate of 96.67%. The model is further tested through a prototype, and it still gave a very satisfactory result.

Uy, Francis Aldrine A., Vea, Larry A., Binag, Matthew G., Diaz, Keith Anshilo L., Gallardo, Roy G., Navarro, Kevin Jorge A., Pulido, Maria Teresa R., Pinca, Ryan Christopher B., Rejuso, Billy John Rudolfh I., Santos, Carissa Jane R..  2020.  The Potential of New Data Sources in a Data-Driven Transportation, Operation, Management and Assessment System (TOMAS). 2020 IEEE Conference on Technologies for Sustainability (SusTech). :1–8.
We present our journey in constructing the first integrated data warehouse for Philippine transportation research in the hopes of developing a Transportation Decision Support System for impact studies and policy making. We share how we collected data from diverse sources, processed them into a homogeneous format and applied them to our multimodal platform. We also list the challenges we encountered, including bureaucratic delays, data privacy concerns, lack of software, and overlapping datasets. The data warehouse shall serve as a public resource for researchers and professionals, and for government officials to make better-informed policies. The warehouse will also function within our multi-modal platform for measurement, modelling, and visualization of road transportation. This work is our contribution to improve the transportation situation in the Philippines, both in the local and national levels, to boost our economy and overall quality of life.