Feature engineering often plays a crucial role in building mining systems for tabular data, which traditionally requires experienced human experts to perform. Thanks to the rapid advances in reinforcement learning, it has offered an automated alternative, i.e. automated feature engineering (AutoFE). In this work, through scrutiny of the prior AutoFE methods, we characterize several research challenges that remained in this regime, concerning system-wide efficiency, efficacy, and practicality toward production. We then propose Catch, a full-fledged new AutoFE framework that comprehensively addresses the aforementioned challenges. The core to Catch composes a hierarchical-policy reinforcement learning scheme that manifests a collaborative feature engineering exploration and exploitation grounded on the granularity of the whole feature set. At a higher level of the hierarchy, a decision-making module controls the post-processing of the attained feature engineering transformation. We extensively experiment with Catch on 26 academic standardized tabular datasets and 9 industrialized real-world datasets. Measured by numerous metrics and analyses, Catch establishes a new state-of-the-art, from perspectives performance, latency as well as its practicality towards production. Source code1 can be found at https://github.com/1171000709/Catch.

Fingerprint recognition technology is the most widely used technology in the field of biometrics and is also the preferred solution for mobile devices and the financial industry. However, traditional fingerprint recognition technology requires expensive sensors on the one hand and risks leaking fingerprint images on the other hand. In this paper, we propose Fingersound, a low-cost and deployable authentication system that utilizes the sound generated by finger sliding as an identity feature. We use a microphone array to record continuous sliding sound and extract multiple features in the frequency and time domains. Then we use various algorithms including a deep neural network to perform user authentication. We also design a mobile phone application that can interact with the embedded system to manage users and record authentication history. In our experiments, 100 participants used this system and achieved an equal error rate of 5.2%. Additionally, we investigate the system’s robustness within different sliding materials, texturesand under noise disturbances. We further demonstrate the resistance of Fingersound to replay attack.

The access control architecture using state-transfer-based dynamic policy is proposed to make access control more flexible and dynamic. This architecture can dynamically change the policy according to a state-transfer composed of a previous state and a current state. It can indicate the tendency of the system by using both previous and current information in order to decide a more appropriate policy to the current system and future condition. And there are two steps in this dynamic policy method, one is deciding a partial-state and the other one is deciding a state, two dynamic policy decision layers are included into this architecture in order to manage these two steps.