Keyword spotting is often implemented by keyword classifier to the encoder in acoustic models, enabling the classification of predefined or open vocabulary keywords. Although keyword spotting is a crucial task in various applications and can be extended to call-for-help detection in emergencies, however, the previous method often suffers from scalability limitations due to retraining required to introduce new keywords or adapt to changing contexts. We explore a simple yet effective approach that leverages off-the-shelf pretrained ASR models to address these challenges, especially in call-for-help detection scenarios. Furthermore, we observed a substantial increase in false alarms when deploying call-for-help detection system in real-world scenarios due to noise introduced by microphones or different environments. To address this, we propose a novel noise-agnostic multitask learning approach that integrates a noise classification head into the ASR encoder. Our method enhances the model’s robustness to noisy environments, leading to a significant reduction in false alarms and improved overall call-for-help performance. Despite the added complexity of multitask learning, our approach is computationally efficient and provides a promising solution for call-for-help detection in real-world scenarios.

Conventional password-based authentication has been widely used due to its simplicity, familiarity, and cost effectiveness. However, the conventional password-based authentication has a fundamental weak-point that cleartext passwords are kept on client-side devices and networks. In order to acquire a user's password securely, we suggest a novel method that splits the roles of user interface onto two devices. With our method, cleartext passwords are neither stored on any devices nor transmitted over communication channels. Finally, we implement a demo application and analyze our method in the aspects of usability, deployability, and security.

QR code, for its recognition robustness and data capacity, has been often used for Augmented Reality applications as well as for other commercial applications. However, it is difficult to enable tangible interactions through which users may change 3D models or animations. It is because QR codes are automatically generated by the rules, and are not easily modifiable. Our goal was to enable QR code based Augmented Reality interactions. By analysis and through experiments, we discovered that some parts of a QR code can be altered to change the text string that the QR code represents. In this paper, we introduced a prototype for QR code based Augmented Reality interactions, which allows for Rubik's cube style rolling interactions.

QR code, for its recognition robustness and data capacity, has been often used for Augmented Reality applications as well as for other commercial applications. However, it is difficult to enable tangible interactions through which users may change 3D models or animations. It is because QR codes are automatically generated by the rules, and are not easily modifiable. Our goal was to enable QR code based Augmented Reality interactions. By analysis and through experiments, we discovered that some parts of a QR code can be altered to change the text string that the QR code represents. In this demo, we introduced a prototype for QR code based Augmented Reality interactions, which allows for Rubik's cube style rolling interactions.