We introduce Semantic Recall, a novel metric to assess the quality of approximate nearest neighbor search algorithms by considering only semantically relevant objects that are theoretically retrievable via exact nearest neighbor search. Unlike traditional recall, semantic recall does not penalize algorithms for failing to retrieve objects that are semantically irrelevant to the query, even if those objects are among their nearest neighbors. We demonstrate that semantic recall is particularly useful for assessing retrieval quality on queries that have few relevant results among their nearest neighbors---a scenario we uncover to be common within embedding datasets. Additionally, we introduce Tolerant Recall, a proxy metric that approximates semantic recall when semantically relevant objects cannot be identified. We empirically show that our metrics are more effective indicators of retrieval quality, and that optimizing search algorithms for these metrics can lead to improved cost-quality tradeoffs.

The past year has been mentally and physically testing because of the COVID19 pandemic. Because of the pandemic, conferences have moved to a remote style which has really hampered the social aspect of conferences. To increase the social aspect of conferences in a remote time, we propose doing an online Trivia as an event at HotOS.

New applications enabled by personal smart devices and the Internet-of-Things (IoT) require communication in the context of periods of spatial co-location. Examples of this encounter-based communication (EbC) include social exchange among individuals who shared an experience, and interaction among personal and IoT devices that provide location-based services. Existing EbC systems are limited to communication among participants that share a direct encounter. This paper is inspired by two insights: (1) encounters also enable group communication among devices connected by paths in the encounter graph that is contextual, spontaneous, secure, and does not require users to reveal identifying or linkable information; and (2) addressing communication partners using encounter closures subject to causal, spatial, and temporal constraints enables powerful new forms of group communication. We present the design of enClosure, a service providing group communication based on encounter closures for mobile and IoT applications, and a prototype implementation for Android and the Microsoft Embedded Social Cloud platform. Using real-world traces, we show that enClosure provides a privacy-preserving, secure platform for a wide range of group communication applications ranging from connecting attendees of a large event and virtual guest books to disseminating health risk warnings, lost-and-found, and tracing missing persons.

New applications enabled by personal smart devices and the Internet-of-Things (IoT) require communication in the context of an encounter (a period of spatial co-location). However, existing encounter-based communication (EbC) systems are limited to communication among participants that share a direct encounter. This work is inspired by two insights: (1) encounters also enable group communication among devices connected by paths in the encounter graph that is contextual, spontaneous, secure, and privacy-preserving; and (2) addressing communication partners using encounter closures subject to causal, spatial, and temporal constraints enables powerful new forms of group communication. We present the design of enClosure, a service providing group communication based on encounter closures for mobile and IoT applications, and a prototype implementation for Android and the Microsoft Embedded Social Cloud platform. Using real-world traces, we show that enClosure provides a privacy-preserving, secure platform for a wide range of group communication applications ranging from connecting attendees of a large event to disseminating health risk warnings and tracing missing persons.