Cryptographic protocols often make honesty assumptions---e.g., fewer than t out of n participants are adversarial. In practice, these assumptions can be hard to ensure, particularly given monetary incentives for participants to collude and deviate from the protocol.

A Single Secret Leader Election (SSLE) enables a group of parties to randomly choose exactly one leader from the group with the restriction that the identity of the leader will be known to the chosen leader and nobody else. At a later time, the elected leader should be able to publicly reveal her identity and prove that she is the elected leader. The election process itself should work properly even if many registered users are passive and do not send any messages. SSLE is used to strengthen the security of proof-of-stake consensus protocols by ensuring that the identity of the block proposer remains unknown until the proposer publishes a block. Boneh, Eskandarian, Hanzlik, and Greco (AFT'20) defined the concept of an SSLE and gave several constructions. Their most efficient construction is based on the difficulty of the Decision Diffie-Hellman problem in a cyclic group. In this work we construct the first efficient SSLE protocols based on the standard Learning With Errors (LWE) problem on integer lattices, as well as the Ring-LWE problem. Both are believed to be post-quantum secure. Our constructions generalize the paradigm of Boneh et al. by introducing the concept of a re-randomizable commitment (RRC). We then construct several post-quantum RRC schemes from lattice assumptions and prove the security of the derived SSLE protocols. Constructing a lattice-based RRC scheme is non-trivial, and may be of independent interest.

Robots have to take highly responsive real-time actions, driven by complex decisions involving a pipeline of sensing, perception, planning, and reaction tasks. These tasks must be scheduled on resource-constrained devices such that the performance goals and the requirements of the application are met. This is a difficult problem that requires handling multiple scheduling dimensions, and variations in computational resource usage and availability. In practice, system designers manually tune parameters for their specific hardware and application, which results in poor generalization and increases the development burden. In this work, we highlight the emerging need for scheduling CPU resources at runtime in robot systems. We use robot navigation as a case-study to understand the key scheduling requirements for such systems. Armed with this understanding, we develop a CPU scheduling framework, Catan, that dynamically schedules compute resources across different components of an app so as to meet the specified application requirements. Through experiments with a prototype implemented on ROS, we show the impact of system scheduling on meeting the application's performance goals, and how Catan dynamically adapts to runtime variations.

The availability of vast video collections and the accuracy of ML models has generated significant interest in video analytics systems. Since naively processing all frames using expensive models is impractical, researchers have proposed optimizations such as selectively using faster but less accurate models to replace or filter frames for expensive models. However, these optimizations are difficult to apply on queries with multiple predicates and models, as users must manually explore a large optimization space. Without significant systems expertise or time investment, an analyst may manually create an execution plan that is unnecessarily expensive and/or terribly inaccurate. We propose Relational Hints , a declarative interface that allows users to suggest ML model relationships based on domain knowledge. Users can express two key relationships: when a model can replace another (CAN REPLACE) and when a model can be used to filter frames for another (CAN FILTER). We aim to design an interface to express model relationships informed by domain specific knowledge and define the constraints by which these relationships hold. We then present the VIVA video analytics system that uses relational hints to optimize SQL queries on video datasets. VIVA automatically selects and validates the hints applicable to the query, generates possible query plans using a formal set of transformations, and finds the best performance plan that meets a user's accuracy requirements. VIVA relieves users from rewriting and manually optimizing video queries as new models become available and execution environments evolve. We evaluate VIVA implemented on top of Spark and show that hints improve performance up to 16.6X without sacrificing accuracy.

We introduce the novel and challenging task of answering Points-of-interest (POI) recommendation questions, using a collection of reviews that describe candidate answer entities (POIs). We harvest a QA dataset that contains 47,124 paragraph-sized user questions from travelers seeking POI recommendations for hotels, attractions and restaurants. Each question can have thousands of candidate entities to choose from and each candidate is associated with a collection of unstructured reviews. Questions can include requirements based on physical location, budget, timings as well as other subjective considerations related to ambience, quality of service etc. Our dataset requires reasoning over a large number of candidate answer entities (over 5300 per question on average) and we find that running commonly used neural architectures for QA is prohibitively expensive. Further, commonly used retriever-ranker based methods also do not work well for our task due to the nature of review-documents. Thus, as a first attempt at addressing some of the novel challenges of reasoning-at-scale posed by our task, we present a task specific baseline model that uses a three-stage cluster-select-rerank architecture. The model first clusters text for each entity to identify exemplar sentences describing an entity. It then uses a neural information retrieval (IR) module to select a set of potential entities from the large candidate set. A reranker uses a deeper attention-based architecture to pick the best answers from the selected entities. This strategy performs better than a pure retrieval or a pure attention-based reasoning approach yielding nearly 25% relative improvement in [email protected] over both approaches. To the best of our knowledge we are the first to present an unstructured QA-style task for POI-recommendation, using real-world tourism questions and POI-reviews.