Many children on autism spectrum have atypical behavioral expressions of engagement compared to their neurotypical peers. In this paper, we investigate the performance of deep learning models in the task of automated engagement estimation from face images of children with autism. Specifically, we use the video data of 30 children with different cultural backgrounds (Asia vs. Europe) recorded during a single session of a robot-assisted autism therapy. We perform a thorough evaluation of the proposed deep architectures for the target task, including within-and across-culture evaluations, as well as when using the child-independent and child-dependent settings. We also introduce a novel deep learning model, named Cul-tureNet, which efficiently leverages the multi-cultural data when performing the adaptation of the proposed deep architecture to the target culture and child. We show that due to the highly heterogeneous nature of the image data of children with autism, the child-independent models lead to overall poor estimation of target engagement levels. On the other hand, when a small amount of data of target children is used to enhance the model learning, the estimation performance on the held-out data from those children increases significantly. This is the first time that the effects of individual and cultural differences in children with autism have empirically been studied in the context of deep learning performed directly from face images.
Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as “meta-controllers” and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.
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