167 research outputs found
Reading habits of students from secondary and higher secondary schools in Patrasayer block of Bankura district, West Bengal, India
This paper has focused on reading habits of school students in Patrasayer block of Bankura district in the state of West Bengal of India. This reading habits include the time spent by the students in school library. We are taken 380 students as sample from nineteen secondary and higher secondary schools at the Patrasayer block. The methodology includes data collection and data analysis. A structured questionnaire has been provided to all 380 students of those nineteen schools. There, lots of parameters have been study and criticized for the purpose of knowing reading habits of the students
Near-Optimal Target Learning With Stochastic Binary Signals
We study learning in a noisy bisection model: specifically, Bayesian
algorithms to learn a target value V given access only to noisy realizations of
whether V is less than or greater than a threshold theta. At step t = 0, 1, 2,
..., the learner sets threshold theta t and observes a noisy realization of
sign(V - theta t). After T steps, the goal is to output an estimate V^ which is
within an eta-tolerance of V . This problem has been studied, predominantly in
environments with a fixed error probability q < 1/2 for the noisy realization
of sign(V - theta t). In practice, it is often the case that q can approach
1/2, especially as theta -> V, and there is little known when this happens. We
give a pseudo-Bayesian algorithm which provably converges to V. When the true
prior matches our algorithm's Gaussian prior, we show near-optimal expected
performance. Our methods extend to the general multiple-threshold setting where
the observation noisily indicates which of k >= 2 regions V belongs to
BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
The dramatic increase in the use of social media platforms for information
sharing has also fueled a steep growth in online abuse. A simple yet effective
way of abusing individuals or communities is by creating memes, which often
integrate an image with a short piece of text layered on top of it. Such
harmful elements are in rampant use and are a threat to online safety. Hence it
is necessary to develop efficient models to detect and flag abusive memes. The
problem becomes more challenging in a low-resource setting (e.g., Bengali
memes, i.e., images with Bengali text embedded on it) because of the absence of
benchmark datasets on which AI models could be trained. In this paper we bridge
this gap by building a Bengali meme dataset. To setup an effective benchmark we
implement several baseline models for classifying abusive memes using this
dataset. We observe that multimodal models that use both textual and visual
information outperform unimodal models. Our best-performing model achieves a
macro F1 score of 70.51. Finally, we perform a qualitative error analysis of
the misclassified memes of the best-performing text-based, image-based and
multimodal models.Comment: EMNLP 2023 (main conference
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