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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
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Posts
Unveiling the Dream Continuum: My Mind’s Epic Journey Across Days and Nights
Published:
Recently, I was experiencing something dreams very differently and I was wondering if that happens to others. I found that it does and it is called dream sequencing. Dream sequencing refers to a phenomenon where an individual experiences a series of dreams that are interrelated or connected as part of a larger narrative. Rather than having disjointed and unrelated dreams, these sequences feel like they are part of a larger story or plotline. In my personal experience, I have had a few nights where I experienced this phenomenon, and it was spine-chilling to be the main character in the dream. I invite you to read on and immerse yourself in my experience.
Is it true that “Shiva” Chooses his own Devotees ?
Published:
For the past few days, I have been enveloped in a mystical aura surrounding Lord Shiva. It all began on a Mahashivratri night when I experienced an overwhelming feeling while meditating. Since then, my curiosity about Lord Shiva has grown immensely, and every new discovery makes me fall in love with him even more. As I pondered over this experience, a friend mentioned that Lord Shiva chooses his devotees, which intrigued me. I delved into research to find out if this was true, and to my amazement, it was! Keep reading to discover the fascinating details of my findings.
publications
Explainable and High-Performance Hate and Offensive Speech Detection
Published in HCI International 2022, 2022
The spread of information through social media platforms can create environments possibly hostile to vulnerable communities and silence certain groups in society. To mitigate such instances, several models have been developed to detect hate and offensive speech. Since detecting hate and offensive speech in social media platforms could incorrectly exclude individuals from social media platforms, which can reduce trust, there is a need to create explainable and interpretable models. Thus, we build an explainable and interpretable high performance model based on the XGBoost algorithm, trained on Twitter data. For unbalanced Twitter data, XGboost outperformed the LSTM, AutoGluon, and ULMFiT models on hate speech detection with an F1 score of 0.75 compared to 0.38 and 0.37, and 0.38 respectively. When we down-sampled the data to three separate classes of approximately 5000 tweets, XGBoost performed better than LSTM, AutoGluon, and ULMFiT; with F1 scores for hate speech detection of 0.79 vs 0.69, 0.77, and 0.66 respectively. XGBoost also performed better than LSTM, AutoGluon, and ULMFiT in the down-sampled version for offensive speech detection with F1 score of 0.83 vs 0.88, 0.82, and 0.79 respectively. We use Shapley Additive Explanations (SHAP) on our XGBoost models outputs to makes it explainable and interpretable compared to LSTM, AutoGluon and ULMFiT that are black-box models.
Recommended citation: https://arxiv.org/abs/2206.12983
talks
Time Series Analysis in Python Workshop
Published:
Time is precious so is Time Series Analysis”