In response to the challenges posed by the "new normal," this study introduces WeBarangay, a website designed to serve as a communication platform and deliver crucial barangay services. The primary goal is to offer an alternative means of interaction between barangay officials and residents in the Philippines. Utilizing the Technology Acceptance Model (TAM), the study evaluates the viability and acceptance of WeBarangay, focusing on key elements: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Towards Use (ATU), and Intention to Use (ITU). The positive outcomes of the TAM evaluation affirm that users perceive WeBarangay as useful and easy to use and express a strong intention to adopt it. The Cronbach's alpha test ensures the reliability of survey responses, highlighting consistent positive attitudes across all TAM elements. With successful development and implementation, WeBarangay emerges as a promising tool to enhance communication and service delivery in barangays, contributing to more efficient and effective community engagement.
In the process of online shopping, when consumers encounter products or services with both positive and negative reviews, they experience attitudinal ambivalence, making it challenging for them to make prompt decisions. Existing studies have not effectively integrated consumers' ambivalent attitudes to accurately recommend reviews. Therefore, this paper proposes a review recommendation method that considers consumers' ambivalent attitudes. To achieve this, we first combine sentiment analysis with an ambivalence measurement formula to categorize consumers into different ambivalence groups. Next, we employ a combination of LDA and Word2vec models to extract the concerns of these distinct ambivalence groups. Finally, we utilize a Logistic regression model to match the ambivalence groups with users' basic attribute indicators and offer review recommendations tailored to address the challenge of consumers facing difficulty in making quick decisions due to attitudinal ambivalence.The example results of mafengwo platform data show that this study can divide ambivalence groups through sentiment analysis and develop differentiated recommendation strategies.
Assessing the engagement of students in online classroom is crucial to meet their learning objectives. Many machine learning and deep learning models have been proposed to handle this problem using a variety of sensors, with videos cameras being the most prominent. However, most of these approaches are not interoperable because different datasets use different labeling protocols. As a result, the classification models range from binary, multi-class to regression problems. Another problem is the lack of rigor and definition of engagement to annotate the data. In this paper, firstly we showed inconsistencies in the labeling of a popular student engagement DAiSEE dataset. Then, we re-labeled more than 7000 videos of this dataset using a methodical engagement annotation protocol, HELP, to convert it from four class to binary classification problem. Further analysis highlights issues in DAiSEE annotation in comparison to the HELP protocol. Lastly, we tested three state-of-the-art deep learning and feature-based methods and discussed their performance. Data imbalance in the newly and previously annotated data was found to be the main issue in developing predictive models.
Online learning is currently developing rapidly, but the separation between teachers and students' physical spaces leads to a lack of emotional communication, which is a huge challenge that online learning currently faces. This study aims to design a multimodal emotion recognition system for online learning, to achieve real-time monitoring and feedback on the emotional states of online learners, in order to facilitate teachers' real-time adjustment and improvement of online teaching resources. This article first reviews and evaluates relevant research in the field of online learning, and then proposes a design scheme based on a multimodal emotion model, which is implemented and validated. Finally, this article verifies the effectiveness and superiority of the system through experiments.
This study introduces the Auto-Gardener, a smart gardening system designed to enhance urban gardening practices. The Auto-Gardener incorporates an impact sensor and real-time data analysis to automate critical aspects of plant care, providing users with a user-friendly and efficient gardening assistant. The study evaluates the system's usability through the System Usability Scale (SUS) and the Post-Study System Usability Questionnaire (PSSUQ), resulting in an impressive 80.6 SUS score and a 1.84 PSSUQ Overall Mean. These scores reflect users' high satisfaction and acceptance of the Auto-Gardener. The innovative features of the Auto-Gardener contribute to achieving study objectives, showcasing the potential of technology to revolutionize urban gardening. The positive outcomes underscore the importance of user-centric design in smart gardening technology. As we continue to embrace technological advancements, the Auto-Gardener exemplifies a transformative tool that can make urban gardening more accessible, efficient, and enjoyable for users.
