Trauma lurking in the shadows: A Reddit case study of mental health issues in online posts about Childhood Sexual Abuse

Childhood Sexual Abuse (CSA) is a menace to society and has long-lasting effects on the mental health of the survivors. From time to time CSA survivors are haunted by various mental health issues in their lifetime. Proper care and attention towards CSA survivors facing mental health issues can drastically improve the mental health conditions of CSA survivors. Previous works leveraging online social media (OSM) data for understanding mental health issues haven't focused on mental health issues in individuals with CSA background. Our work fills this gap by studying Reddit posts related to CSA to understand their mental health issues. Mental health issues such as depression, anxiety, and Post-Traumatic Stress Disorder (PTSD) are most commonly observed in posts with CSA background. Observable differences exist between posts related to mental health issues with and without CSA background. Keeping this difference in mind, for identifying mental health issues in posts with CSA exposure we develop a two-stage framework. The first stage involves classifying posts with and without CSA background and the second stage involves recognizing mental health issues in posts that are classified as belonging to CSA background. The top model in the first stage is able to achieve accuracy and f1-score (macro) of 96.26% and 96.24%. and in the second stage, the top model reports hamming score of 67.09%. Content Warning: Reader discretion is recommended as our study tackles topics such as child sexual abuse, molestation, etc

Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks

Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields. A current trend in methods used for SER is to leverage embeddings from pre-trained models (PTMs) as input features to downstream models. However, the use of embeddings from speaker recognition PTMs hasn't garnered much focus in comparison to other PTM embeddings. To fill this gap and in order to understand the efficacy of speaker recognition PTM embeddings, we perform a comparative analysis of five PTM embeddings. Among all, x-vector embeddings performed the best possibly due to its training for speaker recognition leading to capturing various components of speech such as tone, pitch, etc. Our modeling approach which utilizes x-vector embeddings and mel-frequency cepstral coefficients (MFCC) as input features is the most lightweight approach while achieving comparable accuracy to previous state-of-the-art (SOTA) methods in the CREMA-D benchmark.Comment: Accepted to Interspeech 202

Multi-objective Reinforcement Learning based approach for User-Centric Power Optimization in Smart Home Environments

Smart homes require every device inside them to be connected with each other at all times, which leads to a lot of power wastage on a daily basis. As the devices inside a smart home increase, it becomes difficult for the user to control or operate every individual device optimally. Therefore, users generally rely on power management systems for such optimization but often are not satisfied with the results. In this paper, we present a novel multi-objective reinforcement learning framework with two-fold objectives of minimizing power consumption and maximizing user satisfaction. The framework explores the trade-off between the two objectives and converges to a better power management policy when both objectives are considered while finding an optimal policy. We experiment on real-world smart home data, and show that the multi-objective approaches: i) establish trade-off between the two objectives, ii) achieve better combined user satisfaction and power consumption than single-objective approaches. We also show that the devices that are used regularly and have several fluctuations in device modes at regular intervals should be targeted for optimization, and the experiments on data from other smart homes fetch similar results, hence ensuring transfer-ability of the proposed framework.Comment: 8 pages, 7 figures, Accepted at IEEE SMDS'202