This work demonstrates DISCERN (Detection Image System with Commonsense Efficient Ranking Network), a novel generalizable task-ranking approach to improve human-robot collaboration via "discern"-ing with commonsense knowledge (CSK) derived from huge data repositories, augmented with image models and other everyday premises. It is an explainable, efficient solution useful to dynamic multipurpose robots.

Information-theoretic or unconditional security provides the highest level of security --- independent of the computational capability of an adversary. Secret-sharing techniques achieve information-theoretic security by splitting a secret into multiple parts (called shares ) and storing the shares across non-colluding servers. However, secret-sharing-based solutions suffer from high overheads due to multiple communication rounds among servers and/or information leakage due to access-patterns ( i.e. , the identity of rows satisfying a query) and volume ( i.e. , the number of rows satisfying a query). We propose S 2 , an information-theoretically secure approach that uses both additive and multiplicative secret-sharing, to efficiently support a large class of selection queries involving conjunctive, disjunctive, and range conditions. Two major contributions of S 2 are: ( i ) a new search algorithm using additive shares based on fingerprints, which were developed for string-matching over cleartext; and ( ii ) two row retrieval algorithms: one is based on multiplicative shares and another is based on additive shares. S 2 does not require communication among servers storing shares and does not reveal any information to an adversary based on access-patterns and volume.