Ziming Li

Ziming Li, Hongji Li, Jialin Wang et al.

The recent emergence and popularity of consumer-grade augmented reality (AR) glasses from major technology companies highlight their potential to become the next daily computing platform. A dominant design trend in this context is the integration of a front-facing camera to deliver a first-person perspective. While this approach is intuitive, there is limited evidence that it is optimal (or sufficient) for supporting users in daily tasks. This paper explores a more effective camera interaction technique for AR glasses, which we term ``FlexiCamAR." This novel method aims to enhance both efficiency and the range of applications for AR glasses by offering flexible and comfortable secondary camera viewpoints. To investigate the applicability and usability of this approach, we developed a ring camera prototype that can be attached to users' fingers. We then conducted a user study with 12 participants, comparing FlexiCamAR against the baseline, a traditional front-facing AR camera setup, across two common tasks: taking photos and scanning QR codes. Our findings show that FlexiCamAR significantly reduces physical load. We also explore potential scenarios where the additional viewpoint afforded by FlexiCamAR proves valuable, such as capturing low-angle perspectives or navigating confined spaces. Participant feedback further suggests strong potential for additional applications, including selfie taking, video conferencing, and object scanning. Overall, FlexiCamAR presents a novel interaction approach that can serve as a powerful supplement or alternative to the first-person perspective, significantly improving the adaptability of AR glasses for everyday use.

Ziming Li, Qianbo Zang, David Ma et al.

Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric framework that assists data scientists in completing daily data pipelines through a collaborative multi-agent system. AutoKaggle implements an iterative development process that combines code execution, debugging, and comprehensive unit testing to ensure code correctness and logic consistency. The framework offers highly customizable workflows, allowing users to intervene at each phase, thus integrating automated intelligence with human expertise. Our universal data science toolkit, comprising validated functions for data cleaning, feature engineering, and modeling, forms the foundation of this solution, enhancing productivity by streamlining common tasks. We selected 8 Kaggle competitions to simulate data processing workflows in real-world application scenarios. Evaluation results demonstrate that AutoKaggle achieves a validation submission rate of 0.85 and a comprehensive score of 0.82 in typical data science pipelines, fully proving its effectiveness and practicality in handling complex data science tasks.