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Managing novelty in perception-based human activity recognition (HAR) is critical in realistic settings to improve task performance over time and ensure solution generalization outside of prior seen samples. Novelty manifests in HAR as unseen samples, activities, objects, environments, and sensor changes, among other ways. Novelty may be task-relevant, such as a new class or new features, or task-irrelevant resulting in nuisance novelty, such as never before seen noise, blur, or distorted video recordings. To perform HAR optimally, algorithmic solutions must be tolerant to nuisance novelty, and learn over time in the face of novelty. This paper 1) formalizes the definition of novelty in HAR building upon the prior definition of novelty in classification tasks, 2) proposes an incremental open world learning (OWL) protocol and applies it to the Kinetics datasets to generate a new benchmark KOWL-718, 3) analyzes the performance of current stateof-the-art HAR models when novelty is introduced over time, 4) provides a containerized and packaged pipeline for reproducing the OWL protocol and for modifying for any future updates to Kinetics. The experimental analysis includes an ablation study of how the different models perform under various conditions as annotated by Kinetics-AVA. The code may be used to analyze different annotations and subsets of the Kinetics datasets in an incremental open world fashion, as well as be extended as further updates to Kinetics are released. 

Complex neural network architectures are being increasingly used to learn to compute the semantic resemblances among natural language texts. It is necessary to establish a lower bound of performance that must be met in or- der for new complex architectures to be not only novel, but also worthwhile in terms of implementation. This paper focuses on the specific task of determin- ing semantic textual similarity (STS). We construct a number of models from simple to complex within a framework and report our results. Our findings show that a small number of LSTM stacks with an LSTM stack comparator produces the best results. We use Se- mEval 2017 STS Competition Dataset for evaluation.