SDE-2 @ AI Startup | Backend, ML & Analytics

github

Anubhab Das  • Peerlist

Administered two models to solve the task -  One based on Dependency Trees to exploit underlying sentence structure thus eliminating the need for any training data & the other one is based on LSTM networks with attention-mechanism. The models have been trained & evaluated on abstract-title pairs collected from arXiv.org

TitleAssist : Automatic Title Generation from

4 in 5 US adults seek healthcare advice online, making social media and online searches common tools to study health conditions’ prevalence, outbreaks, and evolution. We study the suitability of 2 types of online data &, difference b/w between different online data and official prevalence of diseases for public health research. We propose a unified framework that allows us to 1) compare active (Reddit posts/ comments) and passive (Google Searches) engagement across conditions, revealing when and why one outperforms the other, and 2) explain the biases inherent in both types of online health data. Combing the largest online derived medical taxonomy from Reddit and Google Trend Searches for a set of 16 equivalent conditions in the two data sources, we show that there is implicit knowledge to be gained from looking at cases where official, active, and passive derived online scores diverge (i.e., higher or lower online engagement in health discussions about conditions compared to their official prevalence). Specifically, we find that: 1) online searches (passive engagement) more closely resemble true prevalences than social media discussions (active engagement) for most but not all conditions, 2) the divergence between official scores and those derived from active engagement on social media discussions is explained by state socio-demographic, personality & cultural indicators to a much higher degree than the divergence between official scores and those derived from passive engagement, i.e. online searches. Drawing from our results, certain guidelines emerge for interpreting online health data. Firstly, while search trends seem to normally outperform discussion based scores, caution is required, and comparative approaches recommended. Divergences can illuminate potential issues, including the stigma associated with conditions, the severity of conditions, and other variables impacting how individuals actively discuss their medical conditions.

Understanding the divergence between digital 

Abstract: Dreaming is a fundamental but not fully understood part of human experience that can shed light on our thought patterns. Traditional dream analysis practices, while popular and aided by over 130 unique scales and rating systems, have limitations. Mostly based on retrospective surveys or lab studies, they struggle to be applied on a large scale or to show the importance and connections between different dream themes. To overcome these issues, we developed a new, data-driven mixed-method approach for identifying topics in free-form dream reports through natural language processing. We tested this method on 44,213 dream reports from Reddit's r/Dreams subreddit, where we found 217 topics, grouped into 22 larger themes: the most extensive collection of dream topics to date. We validated our topics by comparing it to the widely-used Hall and van de Castle scale. Going beyond traditional scales, our method can find unique patterns in different dream types (like nightmares or recurring dreams), understand topic importance and connections, and observe changes in collective dream experiences over time and around major events, like the COVID-19 pandemic and the recent Russo-Ukrainian war. We envision that the applications of our method will provide valuable insights into the intricate nature of dreaming.

Dream Content Discovery from Social Media Usi

Trained DCGAN, ACGAN, and WGAN-GP on a dataset of Bangla characters and evaluated performance using Inception Score & Frechet Inception Distance

Bangla Character Generation using Generative 

CRUD app with user authentication, data storage in MongoDB & a basic search feature. The search feature computes a search score which scores distinct word occurrences higher than occurrences of the same term in the search string; thus offering an improved search experience.