Zhuzhu Wang

Ruohan Gao, Hao Li, Gokul Dharan et al. · stanford

Developing embodied agents in simulation has been a key research topic in recent years. Exciting new tasks, algorithms, and benchmarks have been developed in various simulators. However, most of them assume deaf agents in silent environments, while we humans perceive the world with multiple senses. We introduce Sonicverse, a multisensory simulation platform with integrated audio-visual simulation for training household agents that can both see and hear. Sonicverse models realistic continuous audio rendering in 3D environments in real-time. Together with a new audio-visual VR interface that allows humans to interact with agents with audio, Sonicverse enables a series of embodied AI tasks that need audio-visual perception. For semantic audio-visual navigation in particular, we also propose a new multi-task learning model that achieves state-of-the-art performance. In addition, we demonstrate Sonicverse's realism via sim-to-real transfer, which has not been achieved by other simulators: an agent trained in Sonicverse can successfully perform audio-visual navigation in real-world environments. Sonicverse is available at: https://github.com/StanfordVL/Sonicverse.

Zhuo Ma, Haoran Ge, Zhuzhu Wang et al.

Android malware detection is a critical step towards building a security credible system. Especially, manual search for the potential malicious code has plagued program analysts for a long time. In this paper, we propose Droidetec, a deep learning based method for android malware detection and malicious code localization, to model an application program as a natural language sequence. Droidetec adopts a novel feature extraction method to derive behavior sequences from Android applications. Based on that, the bi-directional Long Short Term Memory network is utilized for malware detection. Each unit in the extracted behavior sequence is inventively represented as a vector, which allows Droidetec to automatically analyze the semantics of sequence segments and eventually find out the malicious code. Experiments with 9616 malicious and 11982 benign programs show that Droidetec reaches an accuracy of 97.22% and an F1-score of 98.21%. In all, Droidetec has a hit rate of 91% to properly find out malicious code segments.