Shashwat Kumar

Arash Tavakoli, Shashwat Kumar, Mehdi Boukhechba et al.

Integrating driver, in-cabin, and outside environment's contextual cues into the vehicle's decision making is the centerpiece of semi-automated vehicle safety. Multiple systems have been developed for providing context to the vehicle, which often rely on video streams capturing drivers' physical and environmental states. While video streams are a rich source of information, their ability in providing context can be challenging in certain situations, such as low illuminance environments (e.g., night driving), and they are highly privacy-intrusive. In this study, we leverage passive sensing through smartwatches for classifying elements of driving context. Specifically, through using the data collected from 15 participants in a naturalistic driving study, and by using multiple machine learning algorithms such as random forest, we classify driver's activities (e.g., using phone and eating), outside events (e.g., passing intersection and changing lane), and outside road attributes (e.g., driving in a city versus a highway) with an average F1 score of 94.55, 98.27, and 97.86 % respectively, through 10-fold cross-validation. Our results show the applicability of multimodal data retrieved through smart wearable devices in providing context in real-world driving scenarios and pave the way for a better shared autonomy and privacy-aware driving data-collection, analysis, and feedback for future autonomous vehicles.

Debajyoti Datta, Shashwat Kumar, Laura Barnes et al.

A growing body of recent evidence has highlighted the limitations of natural language processing (NLP) datasets and classifiers. These include the presence of annotation artifacts in datasets, classifiers relying on shallow features like a single word (e.g., if a movie review has the word "romantic", the review tends to be positive), or unnecessary words (e.g., learning a proper noun to classify a movie as positive or negative). The presence of such artifacts has subsequently led to the development of challenging datasets to force the model to generalize better. While a variety of heuristic strategies, such as counterfactual examples and contrast sets, have been proposed, the theoretical justification about what makes these examples difficult for the classifier is often lacking or unclear. In this paper, using tools from information geometry, we propose a theoretical way to quantify the difficulty of an example in NLP. Using our approach, we explore difficult examples for several deep learning architectures. We discover that both BERT, CNN and fasttext are susceptible to word substitutions in high difficulty examples. These classifiers tend to perform poorly on the FIM test set. (generated by sampling and perturbing difficult examples, with accuracy dropping below 50%). We replicate our experiments on 5 NLP datasets (YelpReviewPolarity, AGNEWS, SogouNews, YelpReviewFull and Yahoo Answers). On YelpReviewPolarity we observe a correlation coefficient of -0.4 between resilience to perturbations and the difficulty score. Similarly we observe a correlation of 0.35 between the difficulty score and the empirical success probability of random substitutions. Our approach is simple, architecture agnostic and can be used to study the fragilities of text classification models. All the code used will be made publicly available, including a tool to explore the difficult examples for other datasets.