MERCAT: Mediated, Encrypted, Reversible, SeCure Asset Transfers

For security token adoption by financial institutions and industry players on the blockchain, there is a need for a secure asset management protocol that enables condential asset issuance and transfers by concealing from the public the transfer amounts and asset types, while on a public blockchain. Flexibly supporting arbitrary restrictions on financial transactions, only some of which need to be supported by zero-knowledge proofs. This paper proposes leveraging a hybrid design approach, by using zero-knowledge proofs, supported by restrictions enforced by trusted mediators. As part of our protocol, we also describe a novel transaction ordering mechanism that can support a flexible transaction workflow without putting any timing constraints on when the transactions should be generated by the users or processed by the network validators. This technique is likely to be of independent interest

Object persistence in 3D for home robotics

This document presents an interactive pipeline for collecting, labelling and re-recognizing movable objects in home-like environments. Inspired by the fact that a human child learns about a movable object by observing it moving from time to time, and memorizes its name once a name is associated with it, we have designed an object persistence system which performs similar tasks based on change detection. We utilize 3D registration and change detection systems in order to distinguish foreground from the background. Focusing on the dynamic objects (from different vantage points) and interactively asking the user for labels, endows our system with a database of labeled object segments, which further is used for multi-view instance recognition. We have expanded the temporal interval logic to 3D bounding boxes in order to aggregate regions that contain foreground dynamic objects, and simultaneously update our model of the background. The object segments are extracted by removing the background. Finally the objects are matched to the existing database of objects and if no match is present the user will be prompted to provide a label. To demonstrate the capabilities of our system, an inexpensive RGB-D sensor (Kinect) is used to collect 3D point clouds. Results show that for tabletop scenes, our approach is able to effectively separate object regions from background, and that objects can be successfully modelled and recognized during system operation.Science, Faculty ofComputer Science, Department ofGraduat