Biblio

There are many everyday situations in which users need to enter their user identification (user ID), such as logging in to computer systems and entering secure offices. In such situations, contactless passive IC cards are convenient because users can input their user ID simply by passing the card over a reader. However, these cards cannot be used for successive interactions. To address this issue, we propose AccelTag, a contactless IC card equipped with an acceleration sensor and a liquid crystal display (LCD). AccelTag utilizes high-function RFID technology so that the acceleration sensor and the LCD can also be driven by a wireless power supply. With its built-in acceleration sensor, AccelTag can acquire its direction and movement when it is waved over the reader. We demonstrate several applications using AccelTag, such as displaying several types of information in the card depending on the user's requirements.

Embodied conversational agents are changing the way humans interact with technology. In order to develop humanlike ECAs they need to be able to perform natural gestures that are used in day-to-day conversation. Gestures can give insight into an ECAs personality trait of extraversion, but what factors into it is still being explored. Our study focuses on two aspects of gesture: amplitude and frequency. Our goal is to find out whether agents should use specific gestures more frequently than others depending on the personality type they have been designed with. We also look to quantify gesture amplitude and compare it to a previous study on the perception of an agent's naturalness of its gestures. Our results showed some indication that introverts and extraverts judge the agent's naturalness similarly. The larger the amplitude our agent used, the more natural its gestures were perceived. The frequency of gestures between extraverts and introverts seem to contain hardly any difference, even in terms of types of gesture used.

Smartphones nowadays are customized to help users with their daily tasks such as storing important data or making transactions through the internet. With the sensitivity of the data involved, authentication mechanism such as fixed-text password, PIN, or unlock patterns are used to safeguard these data against intruders. However, these mechanisms have the risk from security threats such as cracking or shoulder surfing. To enhance mobile and/or information security, this study aimed to develop a free-form handwriting gesture user authentication for smartphones. It also tried to discover the static and dynamic handwriting features that significantly influence the recognition of a legitimate user. The experiment was then conducted by asking thirty (30) individuals to draw or swipe using their fingertip their desired free-form security pattern ten (10) times. These patterns were then cleaned and processed, and extracted seven (7) static and eleven (11) dynamic handwriting features. By means of Neural Network classifier of the RapidMiner data mining tool, these features were used to develop, validate, and test a model for user authentication. The model showed a very promising recognition rate of 96.67%. The model is further tested through a prototype, and it still gave a very satisfactory result.