The effectiveness of static trading strategies is often undermined when markets undergo non-stationary concept drift, which, in turn, precipitates increased return uncertainty and persistent performance deterioration. To address this challenge, we present a drift-aware dynamic trading strategy portfolio framework for streaming financial environments, incorporating ADWIN-based drift detection and a semi-supervised classification mechanism. The proposed framework operates through two stages. During the offline stage, initial trading strategy portfolios are constructed by applying memetic algorithm to historical data, thereby generating a baseline trading strategy portfolio. In the online stage, ADWIN continually monitors streaming market indicators to identify abrupt changes in statistical behavior. Once drift is detected, a semi-supervised classifier is updated using a combination of limited labeled data and pseudo-labeled streaming instances, enabling it to reassess current market regimes and guide adaptive adjustments to strategy parameters or partial portfolio replacements. Experiments conducted using Taiwan stock market data demonstrate that the proposed framework delivers substantial performance improvements over baseline strategies.
This study develops a pilot-type multimodal framework for predicting purchase intention in digital environments by integrating facial-expression analysis and eye-movement dynamics. While prior research has often examined facial expressions or visual attention separately, purchase intention is likely to emerge from the interaction between affective response and visual-cognitive processing. To explore this possibility, the present study adopted a small-scale within-subject pilot design involving three Taiwanese participants who browsed four digital platform conditions: traditional online shopping websites, Facebook, Instagram, and TikTok. Facial Action Units (AUs) and eye-related variables were analyzed in relation to post hoc judgments of purchase intention. The findings suggest that purchase intention cannot be sufficiently explained by facial expressions alone. Instead, it is better understood as a multimodal behavioral state characterized by platform-specific combinations of facial activity and eye-movement patterns, including scanning behavior, attentional engagement, and depth-related visual focus. The study contributes by constructing an exploratory pilot-type analytical framework that integrates facial and ocular indicators for the behavioral assessment of purchase intention across digital platforms. This framework may serve as a foundation for future large-scale research in digital marketing and consumer behavior.
Airports are complex built environments where passengers rely on effective wayfinding systems to navigate time-sensitive processes. However, traditional navigation methods such as signage and digital displays often become insufficient in visually dense and spatially complex terminals. Kuala Lumpur International Airport 2 (KLIA2) exemplifies these challenges due to its large-scale layout and high passenger volume. This study presents AR-Port, an augmented reality (AR)-based indoor navigation system designed to support intuitive wayfinding through spatially anchored visual guidance. The system integrates on-device environment scanning, waypoint-based path definition, and AR rendering to provide real-time navigation cues aligned with the user’s physical surroundings. A user acceptance evaluation involving 86 participants was conducted to assess perceived navigation difficulty and openness toward AR-based guidance. Results indicate that a majority of participants experience challenges with existing navigation tools and show positive receptiveness toward mobile and AR-assisted navigation. While the evaluation focuses on user perception rather than system performance, the findings highlight the potential of AR-based navigation to enhance passenger experience and reduce cognitive load in complex airport environments. Future work will include empirical benchmarking of localization accuracy, anchor stability, and navigation efficiency in real-world settings.
With the proliferation of obesogenic environments for companion dogs, the objective measurement of the Body Condition Score (BCS) has become increasingly critical as a key health indicator. To overcome the limitations of conventional Convolutional Neural Network (CNN)-based unimodal approaches, this study proposes Meta-Cobi, a Vision-Language Model (VLM)-based framework that integrates multi-view visual data with biological metadata. The framework incorporates the Convolutional Block Attention Module (CBAM) to maximize the density of captured anatomical features and utilizes a Learned Query Pooling mechanism to globally compress information gathered from 11 distinct viewpoints. Furthermore, biological metadata such as breed and age is integrated into structured prompts, while a task-specific special token strategy is employed to enhance inference precision. Experimental results demonstrate that Meta-Cobi achieves superior Macro-F1 score and robustness compared to baseline models, notably maintaining high discriminatory performance even in data-sparse classes. This research expands the scope of AI-driven BCS assessment into the veterinary domain and contributes to the establishment of data-driven precision healthcare systems for companion animals.
Higher Education Institutions in Latin America face several challenges, the most significant being digital transformation and the efficient management of intellectual capital. Among the obstacles they encounter are the persistence of “data silos” that fragment institutional information and hinder strategic decision-making. Therefore, this article presents a conceptual work-in-progress proposal for an interoperable data governance framework. While the overall methodology encompasses three phases, the current focus is on defining the fundamental architecture designed to enable and strengthen Knowledge Management in public university environments.
