In this paper, we introduce a novel framework following an upstreamdownstream paradigm to construct user and item (Pin) embeddings from diverse data sources, which are essential for Pinterest to deliver personalized Pins and ads effectively.Our upstream models are trained on extensive data sources featuring varied signals, utilizing complex architectures to capture intricate relationships between users and Pins on Pinterest.To ensure scalability of the upstream models, entity embeddings are learned, and regularly refreshed, rather than real-time computation, allowing for asynchronous interaction between the upstream and downstream models.These embeddings are then integrated as input features in numerous downstream tasks, including ad retrieval and ranking models for CTR and CVR predictions.We demonstrate that our framework achieves notable performance improvements in both offline and online settings across various downstream tasks.This framework

We develop a novel probabilistic model for interpersonal coordination as a latent phenomenon explaining statistical temporal influence between multiple components in a system. For example, the state of one person can influence that of another at a later time, as indicated by their observed behaviors. We characterize coordination as the degree to which the distributions for such states at one time point are merged for the next salient time point. We evaluate our model in the context of three-person teams executing a virtual search and rescue (SAR) mission. We first use synthetic data to confirm that our technical definition of coordination is consistent with expectations and that we can recover generated coordination despite noise. We then show that captured coordination can be predictive of team performance on real data. Here we use speech vocalics and semantics to infer coordination for 36 teams carrying out two successive SAR missions. In two different datasets, we find that coordination is generally predictive of team score for the second mission, but not for the first, where teams are largely learning to play the game. In addition, we found that including a semantic modality improves prediction in some scenarios. This shows that our intuitive technical definition can capture useful explanatory aspects of team behavior.

We present a rich, multimodal dataset consisting of data from 40 teams of three humans conducting simulated urban search-and-rescue (SAR) missions in a Minecraft-based testbed, collected for the Theory of Mind-based Cognitive Architecture for Teams (ToMCAT) project. Modalities include two kinds of brain scan data---functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), as well as skin conductance, heart rate, eye tracking, face images, spoken dialog audio data with automatic speech recognition (ASR) transcriptions, game screenshots, gameplay data, game performance data, demographic data, and self-report questionnaires. Each team undergoes up to six consecutive phases: three behavioral tasks, one mission training session, and two collaborative SAR missions. As time-synchronized multimodal data collected under a variety of circumstances, this dataset will support studying a large variety of research questions on topics including teamwork, coordination, plan recognition, affective computing, physiological linkage, entrainment, and dialog understanding. We provide an initial public release of the de-identified data, along with analyses illustrating the utility of this dataset to both computer scientists and social scientists.

Monitoring and evaluating distributed systems at runtime is a difficult effort. Network traffic analysis may be used to analyse distributed systems, but the capacity to analyse large amount of traffic is a challenge. This work proposes the use of Map Reduce for deeply inspecting distributed systems network traffic, evaluating the effectiveness and the processing capacity of Map Reduce for this inspection.