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Mobile beacon application leverages the beacon technology as location-based or proximity sensors to notify or interact with user via mobile apps. There are many instances of these applications especially as an engagement platform across many domains like marketing, retails, exhibitions, or public services. Mobile beacon application holds potential as an engagement tool for empowering co-curricular engagement in higher education. Integrated with personalized mobile notification, users can be notified and interact with co-curricular events within their proximity in the campus. However, literature indicates limited insights on the design applicability and empirical studies of mobile beacon application for higher education context. This new application context poses its own unique challenges, but imperative in helping researchers to discover the extents of mobile beacon potentials. This concept paper introduces a preliminary mobile beacon application framework for supporting co-curricular engagement in higher education. The framework aims to enable design applicability of a mobile beacon application integrated with personalized notification for education engagement purposes. Characterization of mobile beacon application components along with the deliberation of mobile notification features are highlighted in the framework. The presented conceptual framework offers the foundation for application designers in building a mobile beacon application for higher education engagement.

Vehicular traffic flow anomaly detection is crucial for traffic management, public safety, and transportation efficiency. It assists experts in responding promptly to abnormal traffic conditions and making decisions to improve the traffic flow. This survey paper offers an overview of the application of machine learning to detect anomalies in the traffic flow. Through an extensive review of the literature from the Scopus database, this paper explores the technical aspects of traffic flow anomaly detection using machine learning, including data sources, data processing approaches, machine learning algorithms, and evaluation metrics. Additionally, the paper highlights the emerging research opportunities for researchers in enhancing traffic flow anomaly detection using machine learning.

3D animators commonly employ facial expressions to convey emotions, yet this method has limitations in fostering audience immersion. Existing guidelines prioritize storytelling, offering limited insight into character construction for immersive experiences. Our investigation seeks to enhance the lifelike movement of animated characters, focusing on audience engagement at specific points. This paper presents empirical findings highlighting the importance of facial and body movements in authentically portraying animated characters’ emotions. Drawing on Shapiro’s 15 controllers for character animation, we conducted an empirical study, examining distinct elements associated with each emotion. Data collection via Likert-scale assessments determined the average agreement for each controller concerning specific emotions. Our results indicate that varied emotions demand unique controllers for optimal realism. Although facial and gaze controllers are integral to all emotions, their intensity differs across emotional states. In response, we propose a preliminary model rooted in basic emotions, offering guidance to animators crafting realistic 3D characters. This model addresses the nuanced requirements of diverse emotions, providing a valuable resource for those seeking to enhance the authenticity of animated character expressions.

Scattered data interpolation plays an important role in computer graphics and scientific visualization. This method can be used to interpolate any regular or irregular data sets. For instance, rainfall distribution is an example of an irregular data that usually appear in statistics. In this study, cubic Bézier-Like triangular patches defined on triangular domain is used to interpolate rainfall data sets. From graphical results, we obtain a very smooth surface. Therefore, the proposed scheme can be used to interpolate and provide a good visualization to the given irregular data sets.