Air pollution, particularly particulate matter 2.5 (PM2.5), poses a significant public health challenge in densely populated developing regions. Moreover, deploying an extensive ground sensor network to monitor PM2.5accurately is economically unfeasible in such regions. To address this problem, we utilize Sparse Variational Gaussian Process (SVGP) models to generate approximate data using the limited ground sensor data. Since SVGPs use computational approximators for Gaussian Process modeling, we hypothesize that their inducing points can be trained to adapt spatially, i.e., these points, when optimized, can spread over the region of interest. Hence, well-initialized inducing points allow SVGPs to model PM2.5data by capturing spatial variations of the region. We evaluate our hypothesis using PM2.5data from Lima, Peru, one of the most polluted cities in the Americas, and with very few PM2.5ground sensors. Our experiments qualitatively validate our hypothesis of spatial adaptation and provide a quantitative justification of improved performance over the baseline models.

In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimodal essence of these debates [27]. To address this gap, we introduce a comprehensive automated toolkit that employs advanced computer vision and speech-to-text techniques for large-scale multimedia analysis. Utilizing state-of-the-art computer vision algorithms and speech-to-text methods, we transcribe, diarize, and analyze thousands of YouTube videos of a prime-time television debate show in India. These debates are a central part of Indian media but have been criticized for compromised journalistic integrity and excessive dramatization [18]. Our toolkit provides concrete metrics to assess bias and incivility, capturing a comprehensive multimedia perspective that includes text, audio utterances, and video frames. Our findings reveal significant biases in topic selection and panelist representation, along with alarming levels of incivility. This work offers a scalable, automated approach for future research in multimedia analysis, with profound implications for the quality of public discourse and democratic debate. To catalyze further research in this area, we also release the code, dataset collected and supplemental pdf.1

With the widespread increase in hateful content on the web, hate detection has become more crucial than ever. Although vast literature exists on hate detection from text, images and videos, interestingly, there has been no previous work on hateful comment detection (HCD) from video pages. HCD is critical for comment moderation and for flagging controversial videos. Comments are often short, contextual and convoluted making the problem challenging. Toward solving this problem, we contribute a dataset, HateComments, consisting of 2071 comments for 401 videos obtained from two popular video sharing platforms. We investigate two related tasks: binary HCD and 4-class multi-label hate target-type prediction (HTP). We systematically explore the importance of various forms of context for effective HCD. Our initial experiments show that our best method which leverages rich video context (like description, transcript and visual input) leads to an HCD accuracy of ~78.6% and an ROC AUC score of ~0.61 for HTP. Code and data is at https://drive.google.com/file/d/1EUbWDUokv1CYkWKlwByUC6yIuBGUw2MN/.