AdNext: A Visit-Pattern-Aware Mobile Advertising System for Urban Commercial Complexes

As smartphones have become prevalent, mobile advertising is getting significant attention as being not only a killer application in future mobile commerce, but also as an important business model of emerging mobile applications to monetize them. In this paper, we present AdNext, a visit-pattern-aware mobile advertising system for urban commercial complexes. AdNext can provide highly relevant ads to users by predicting places that the users will next visit. AdNext predicts the next visit place by learning the sequential visit patterns of commercial complex users in a collective manner. As one of the key enabling techniques for AdNext, we develop a probabilistic prediction model that predicts users ’ next visit place from their place visit history. To automatically collect the users ’ place visit history by smartphones, we utilize Wi-Fi-based indoor localization. We demonstrate the feasibility of AdNext by evaluating the accuracy of the prediction model. For the evaluation, we used a dataset collected from COEX Mall, the largest commercial complex in South Korea. Also, we implemented an initial prototype of AdNext with the latest smartphones, and deployed it in COEX Mall

MobiCon: A mobile context-monitoring platform

User context is defined by data generated through everyday physical activity in sensor-rich, resource-limited mobile environments.</jats:p

Quantitative local probing of polarization with application on HfO 2 ‐based thin films

Owing to their switchable spontaneous polarization, ferroelectric materials have been applied in various fields, such as information technologies, actuators, and sensors. In the last decade, as the characteristic sizes of both devices and materials have decreased significantly below the nanoscale, the development of appropriate characterization tools became essential. Recently, a technique based on conductive atomic force microscopy (AFM), called AFM‐positive‐up‐negative‐down (PUND), is employed for the direct measurement of ferroelectric polarization under the AFM tip. However, the main limitation of AFM‐PUND is the low frequency (i.e., on the order of a few hertz) that is used to initiate ferroelectric hysteresis. A significantly higher frequency is required to increase the signal‐to‐noise ratio and the measurement efficiency. In this study, a novel method based on high‐frequency AFM‐PUND using continuous waveform and simultaneous signal acquisition of the switching current is presented, in which polarization–voltage hysteresis loops are obtained on a high‐polarization BiFeO3 nanocapacitor at frequencies up to 100 kHz. The proposed method is comprehensively evaluated by measuring nanoscale polarization values of the emerging ferroelectric Hf0.5Zr0.5O2 under the AFM tip

Correlation between Geometrically induced oxygen octahedral tilts and multiferroic behaviors in BiFeO3 films

The equilibrium position of atoms in a unit cell is directly connected to crystal functionalities, e.g., ferroelectricity, ferromagnetism, and piezoelectricity. The artificial tuning of the energy landscape can involve repositioning atoms as well as manipulating the functionalities of perovskites (ABO3), which are good model systems to test this legacy. Mechanical energy from external sources accommodating various clamping substrates is utilized to perturb the energy state of perovskite materials fabricated on the substrates and consequently change their functionalities; however, this approach yields undesired complex behaviors of perovskite crystals, such as lattice distortion, displacement of B atoms, and/or tilting of oxygen octahedra. Owing to complimentary collaborations between experimental and theoretical studies, the effects of both lattice distortion and displacement of B atoms are well understood so far, which leaves us a simple question: Can we exclusively control the positions of oxygen atoms in perovskites for functionality manipulation? Here the artificial manipulation of oxygen octahedral tilt angles within multiferroic BiFeO3 thin films using strong oxygen octahedral coupling with bottom SrRuO3 layers is reported, which opens up new possibilities of oxygen octahedral engineering

Advancements in Korean Emotion Classification: A Comparative Approach Using Attention Mechanism

Recently, the analysis of emotions in social media has been considered a significant NLP task in digital and social-media-driven environments due to their pervasive influence on communication, culture, and consumer behavior. In particular, the task of Aspect-Based Emotion Analysis (ABEA), which involves analyzing the emotions of various targets within a single sentence, has drawn attention to understanding complex and sophisticated human language. However, ABEA is a challenging task in languages with limited data and complex linguistic properties, such as Korean, which follows spiral thought patterns and has agglutinative characteristics. Therefore, we propose a Korean Target-Attention-Based Emotion Classifier (KOTAC) designed to utilize target information by unveiling emotions buried within intricate Korean language patterns. In the experiment section, we compare various methods of utilizing and representing vectors of target information for the attention mechanism. Specifically, our final model, KOTAC, shows a performance enhancement on the MTME (Multiple Targets Multiple Emotions) samples, which include multiple targets and distinct emotions within a single sentence, achieving a 0.72% increase in F1 score over a baseline model without effective target utilization. This research contributes to the development of Korean language models that better reflect syntactic features by innovating methods to not only obtain but also utilize target-focused representations