Study to determine potential flight applications and human factors design guidelines for voice recognition and synthesis systems

A study was conducted to determine potential commercial aircraft flight deck applications and implementation guidelines for voice recognition and synthesis. At first, a survey of voice recognition and synthesis technology was undertaken to develop a working knowledge base. Then, numerous potential aircraft and simulator flight deck voice applications were identified and each proposed application was rated on a number of criteria in order to achieve an overall payoff rating. The potential voice recognition applications fell into five general categories: programming, interrogation, data entry, switch and mode selection, and continuous/time-critical action control. The ratings of the first three categories showed the most promise of being beneficial to flight deck operations. Possible applications of voice synthesis systems were categorized as automatic or pilot selectable and many were rated as being potentially beneficial. In addition, voice system implementation guidelines and pertinent performance criteria are proposed. Finally, the findings of this study are compared with those made in a recent NASA study of a 1995 transport concept

End-to-End Differentiable Proving

We introduce neural networks for end-to-end differentiable proving of queries to knowledge bases by operating on dense vector representations of symbols. These neural networks are constructed recursively by taking inspiration from the backward chaining algorithm as used in Prolog. Specifically, we replace symbolic unification with a differentiable computation on vector representations of symbols using a radial basis function kernel, thereby combining symbolic reasoning with learning subsymbolic vector representations. By using gradient descent, the resulting neural network can be trained to infer facts from a given incomplete knowledge base. It learns to (i) place representations of similar symbols in close proximity in a vector space, (ii) make use of such similarities to prove queries, (iii) induce logical rules, and (iv) use provided and induced logical rules for multi-hop reasoning. We demonstrate that this architecture outperforms ComplEx, a state-of-the-art neural link prediction model, on three out of four benchmark knowledge bases while at the same time inducing interpretable function-free first-order logic rules.Comment: NIPS 2017 camera-ready, NIPS 201

Finite Countermodel Based Verification for Program Transformation (A Case Study)

Both automatic program verification and program transformation are based on program analysis. In the past decade a number of approaches using various automatic general-purpose program transformation techniques (partial deduction, specialization, supercompilation) for verification of unreachability properties of computing systems were introduced and demonstrated. On the other hand, the semantics based unfold-fold program transformation methods pose themselves diverse kinds of reachability tasks and try to solve them, aiming at improving the semantics tree of the program being transformed. That means some general-purpose verification methods may be used for strengthening program transformation techniques. This paper considers the question how finite countermodels for safety verification method might be used in Turchin's supercompilation method. We extract a number of supercompilation sub-algorithms trying to solve reachability problems and demonstrate use of an external countermodel finder for solving some of the problems.Comment: In Proceedings VPT 2015, arXiv:1512.0221