Haining Liang

Ilesanmi Olade, Christopher Champion, Haining Liang et al.

The popularity and projected growth of in-home smart speaker assistants, such as Amazon's Echo, has raised privacy concerns among consumers and privacy advocates. Notable questions regarding the collection and storage of user data by for-profit organizations include: what data is being collected and how is it being used, who has or can obtain access to such data, and how can user privacy be maintained while providing useful services. In addition to concerns regarding what the speaker manufacturer will do with your data, there are also more fundamental concerns about the security of these devices, third-party plugins, and the servers where they store recorded data. To address these privacy and security concerns, we introduce an intermediary device to provide an additional layer of security, which we call the \textit{smart, smart speaker blocker} or Smart\textsuperscript{2} for short. By intelligently filtering sensitive conversations, and completely blocking this information from reaching a smart speaker's microphone(s), the Smart$^2$ Speaker Blocker is an open-source, network-local (offline) smart device that provides users with decisive control over what data leaves their living room.

Likun Zhang, Sicheng Yang, Zhuo Wang et al.

In this paper, we present an end-to-end automated motion recognition (AutoMR) pipeline designed for multimodal datasets. The proposed framework seamlessly integrates data preprocessing, model training, hyperparameter tuning, and evaluation, enabling robust performance across diverse scenarios. Our approach addresses two primary challenges: 1) variability in sensor data formats and parameters across datasets, which traditionally requires task-specific machine learning implementations, and 2) the complexity and time consumption of hyperparameter tuning for optimal model performance. Our library features an all-in-one solution incorporating QuartzNet as the core model, automated hyperparameter tuning, and comprehensive metrics tracking. Extensive experiments demonstrate its effectiveness on 10 diverse datasets, achieving state-of-the-art performance. This work lays a solid foundation for deploying motion-capture solutions across varied real-world applications.

Boxuan Zhu, Sicheng Yang, Zhuo Wang et al.

Gesture recognition in resource-constrained scenarios faces significant challenges in achieving high accuracy and low latency. The streaming gesture recognition framework, Duo Streamers, proposed in this paper, addresses these challenges through a three-stage sparse recognition mechanism, an RNN-lite model with an external hidden state, and specialized training and post-processing pipelines, thereby making innovative progress in real-time performance and lightweight design. Experimental results show that Duo Streamers matches mainstream methods in accuracy metrics, while reducing the real-time factor by approximately 92.3%, i.e., delivering a nearly 13-fold speedup. In addition, the framework shrinks parameter counts to 1/38 (idle state) and 1/9 (busy state) compared to mainstream models. In summary, Duo Streamers not only offers an efficient and practical solution for streaming gesture recognition in resource-constrained devices but also lays a solid foundation for extended applications in multimodal and diverse scenarios.

Ilesanmi Olade, Haining Liang, Charles Fleming

We introduce SemanticLock, a single factor graphical authentication solution for mobile devices. SemanticLock uses a set of graphical images as password tokens that construct a semantically memorable story representing the user`s password. A familiar and quick action of dragging or dropping the images into their respective positions either in a \textit{continous flow} or in \textit{discrete} movements on the the touchscreen is what is required to use our solution. The authentication strength of the SemanticLock is based on the large number of possible semantic constructs derived from the positioning of the image tokens and the type of images selected. Semantic Lock has a high resistance to smudge attacks and it equally exhibits a higher level of memorability due to its graphical paradigm. In a three weeks user study with 21 participants comparing SemanticLock against other authentication systems, we discovered that SemanticLock outperformed the PIN and matched the PATTERN both on speed, memorability, user acceptance and usability. Furthermore, qualitative test also show that SemanticLock was rated more superior in like-ability. SemanticLock was also evaluated while participants walked unencumbered and walked encumbered carrying "everyday" items to analyze the effects of such activities on its usage.