Vikram Pande

Tanisha Khurana, Kaushik Pillalamarri, Vikram Pande et al.

This paper explores humor detection through a linguistic lens, prioritizing syntactic, semantic, and contextual features over computational methods in Natural Language Processing. We categorize features into syntactic, semantic, and contextual dimensions, including lexicons, structural statistics, Word2Vec, WordNet, and phonetic style. Our proposed model, Colbert, utilizes BERT embeddings and parallel hidden layers to capture sentence congruity. By combining syntactic, semantic, and contextual features, we train Colbert for humor detection. Feature engineering examines essential syntactic and semantic features alongside BERT embeddings. SHAP interpretations and decision trees identify influential features, revealing that a holistic approach improves humor detection accuracy on unseen data. Integrating linguistic cues from different dimensions enhances the model's ability to understand humor complexity beyond traditional computational methods.

Shashank Sripad, Sekar Kulandaivel, Vikram Pande et al.

Electric Vehicles (EVs), like all modern vehicles, are entirely controlled by electronic devices embedded within networks that are exposed to the threat of cyberattacks. Cyber vulnerabilities are magnified with EVs due to unique risks associated with EV battery packs. Current batteries have well-known issues with specific energy, cost and fire-related safety risks. In this study, we develop a systematic framework to assess the impact of cyberattacks on EVs. While the current focus of automotive cyberattacks is on short-term physical safety, it is crucial to consider long-term cyberattacks that aim to cause financial losses through accrued impact, especially in the context of EVs. Faulty components of battery management systems such as a compromised voltage regulator could lead to cyberattacks that can overdischarge or overcharge the battery. Overdischarge could lead to failures such as internal shorts in the timescale of minutes through cyberattacks that compromise energy-intensive EV subsystems like auxiliary components. Attacks that overcharge the pack could shorten the lifetime of a new battery pack to less than a year. Further, such attacks also pose physical safety risks via the triggering of thermal (fire) events. Attacks on auxiliary components lead to battery drain, which could be up to 20% of the state-of-charge per hour. Lastly, we develop a heuristic for the stealthiness of a cyberattack to augment traditional threat models. The methodology presented here will help in building the foundational principles of electric vehicle cybersecurity: a nascent but critical topic in the coming years.