UPLB ICS Peak One

Data collected from a Planetary Health Diet Mon- itoring Application could be utilized to assess the impacts of adopting the diet in terms of personal nutrition. A dashboard containing data visualizations and forecasting was successfully developed to allow researchers and policy makers from nutri- tion to better utilize the collected data.The study developed a dashboard, utilizing FastAPI, React, MongoDB and Plotly, for researchers and policy makers from nutrition to better utilize the collected data. The dashboard’s usability was evaluated by randomly selected students, faculty members, and registered dietitians of the Institute of Human Nutrition and Food through a System Usability Scale. The results were unsatisfactory as the system was deemed ’OK’ by the participants, and further improvements could be made. Nonetheless, the participants are interested in testing future iterations of the dashboard, containing different types of data visualizations in the future.

Capture-the-flag (CTF) challenges are word prob- lems that are presented to the player alongside a vaguely-worded hint. Past attempts to build a CTF solver with LLMs have relied on multiple prompts and human intervention. The current literature suggests it is possible to enhance a large language model’s reasoning capabilities using instruction fine-tuning and chain-of-thought (CoT) prompting. A Llama 3 8B model was fine-tuned to classify four types of hints over a CoT instruction dataset. The fine-tuned model outperformed GPT-4 and the base Llama 3 8B when tasked to classify real-world CTF hints. An agent was built around the model by connecting a GPT-4 critic and a CyberChef ”magic” toolkit.

The challenges surrounding maritime boundary dis- putes have significant implications for the livelihood of fisherfolk communities. The absence of proper navigation assistance exposes them to substantial risks, stemming not only from natural maritime hazards but also from legal conflicts over territorial boundaries. Considering these concerns, this research aims to create a mobile application that utilizes an alert and tracking system. A website application is also developed for administrators to efficiently manage data and promptly respond to distress calls. System Usability Testing (SUS) was conducted with 20 respondents. Ten users for testing each of the applications. The mobile application has 92 out of 100 SUS scores while the website application has 86.5 out of 100 SUS scores. This indicates that the developed applications are usable for the intended users.

Subtitles enhance video accessibility for people who are deaf or hard of hearing by providing a written version of the verbal dialogue. They lack, however, in conveying the emotional nuances of the speaker. A more comprehensive ap- proach that includes speech content and emotional context can promote an engaging viewing experience. This study proposes using Google Cloud Speech Application Programming Interface (API) for speech recognition and the fusion of two models for emotion recognition: a Convolutional Neural Network (CNN) model for Audio Emotion Recognition and transfer learning with the ResNet-50 model for Visual Emotion Recognition. A mobile application is developed as the interface for the users. The speech recognition API achieved a word error rate of 13.78%. For emotion recognition, the audio emotion recognition model achieved a top-1 accuracy of 65.17% and a top-3 accuracy of 88%, while the visual emotion recognition model achieved a top- 1 accuracy of 64.29% and a top-3 accuracy of 90.10%. The fusion of both models resulted in a top-1 accuracy of 76.33% and a top- 3 accuracy of 95.70%. The application got a mean SUS score of 81.60. Overall, the findings of the study highlight the potential of combining speech and emotion recognition with the use of CNNs.

SaveSmart is a mobile-based financial tracker ap- plication that can be used on both Android and iOS devices. It has essential functions in managing personal finances like adding wallets, recording transactions, showing transaction stats, setting financial goals, recording debts and money lent, and allocating budgets. The application was tested by 23 users and evaluated using the System Usability Scale (SUS), where it achieved a score of 84.58, indicating excellent user satisfaction.