TL;DR: Introduces a high-resolution dataset to detect mosquitoes, swarms, and breeding sites using deep learning. Top 5% paper in ICLR 2024 Tiny Papers Track.

TL;DR: Presents a multimodal dataset and benchmark for detecting Islamophobic content in memes using both image and text signals.

TL;DR: Introduces ElectroCom61, a labeled dataset of common electronic components for object detection and classification tasks.

TL;DR: Evaluates various ML model compression strategies for efficient deployment in IoT and embedded systems.

TL;DR: Introduces a low-cost, autonomous rover with computer vision to locate and treat mosquito breeding sites.

TL;DR: Proposes a CNN-based approach for recognizing Braille characters, enabling assistive technology for the visually impaired.

TL;DR: Proposes a feminism quantification scale using NLP and survey data to measure individual gender-progressive attitudes.

TL;DR: A comprehensive survey on mental health AI research and the vital role of explainability and clinicians in closing diagnostic gaps.

TL;DR: Introduces a synthetic prompt generation framework and explainable detection pipeline for jailbreaking attempts in LLMs.