Wen Shen

Wen Shen, Qihan Ren, Dongrui Liu et al.

In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing. We propose a method to disentangle the overall model vulnerability into the sensitivity to the rotation, the translation, the scale, and local 3D structures. Besides, we also propose metrics to evaluate the spatial smoothness of encoding 3D structures, and the representation complexity of the DNN. Based on such analysis, experiments expose representation problems with classic DNNs, and explain the utility of the adversarial training.

Wen Shen, Zhihua Wei, Shikun Huang et al.

The reasonable definition of semantic interpretability presents the core challenge in explainable AI. This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable compositional CNN, in order to learn filters that encode meaningful visual patterns in intermediate convolutional layers. In a compositional CNN, each filter is supposed to consistently represent a specific compositional object part or image region with a clear meaning. The compositional CNN learns from image labels for classification without any annotations of parts or regions for supervision. Our method can be broadly applied to different types of CNNs. Experiments have demonstrated the effectiveness of our method.

Henger Li, Wen Shen, Zizhan Zheng

Moving target defense has emerged as a critical paradigm of protecting a vulnerable system against persistent and stealthy attacks. To protect a system, a defender proactively changes the system configurations to limit the exposure of security vulnerabilities to potential attackers. In doing so, the defender creates asymmetric uncertainty and complexity for the attackers, making it much harder for them to compromise the system. In practice, the defender incurs a switching cost for each migration of the system configurations. The switching cost usually depends on both the current configuration and the following configuration. Besides, different system configurations typically require a different amount of time for an attacker to exploit and attack. Therefore, a defender must simultaneously decide both the optimal sequences of system configurations and the optimal timing for switching. In this paper, we propose a Markov Stackelberg Game framework to precisely characterize the defender's spatial and temporal decision-making in the face of advanced attackers. We introduce a relative value iteration algorithm that computes the defender's optimal moving target defense strategies. Empirical evaluation on real-world problems demonstrates the advantages of the Markov Stackelberg game model for spatial-temporal moving target defense.

Wen Shen, Binbin Zhang, Shikun Huang et al.

This paper proposes a set of rules to revise various neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs). We find that when a neural network uses quaternion features under certain conditions, the network feature naturally has the rotation-equivariance property. Rotation equivariance means that applying a specific rotation transformation to the input point cloud is equivalent to applying the same rotation transformation to all intermediate-layer quaternion features. Besides, the REQNN also ensures that the intermediate-layer features are invariant to the permutation of input points. Compared with the original neural network, the REQNN exhibits higher rotation robustness.

Wen Shen, Alanoud Al Khemeiri, Abdulla Almehrezi et al.

Highly automated robot ecologies (HARE), or societies of independent autonomous robots or agents, are rapidly becoming an important part of much of the world's critical infrastructure. As with human societies, regulation, wherein a governing body designs rules and processes for the society, plays an important role in ensuring that HARE meet societal objectives. However, to date, a careful study of interactions between a regulator and HARE is lacking. In this paper, we report on three user studies which give insights into how to design systems that allow people, acting as the regulatory authority, to effectively interact with HARE. As in the study of political systems in which governments regulate human societies, our studies analyze how interactions between HARE and regulators are impacted by regulatory power and individual (robot or agent) autonomy. Our results show that regulator power, decision support, and adaptive autonomy can each diminish the social welfare of HARE, and hint at how these seemingly desirable mechanisms can be designed so that they become part of successful HARE.