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Image Captioning is an arduous task of producing syntactically and semantically correct textual descriptions of an image in natural language with context related to the image. Existing notable pieces of research in Bengali Image Captioning (BIC) are based on encoder-decoder architecture. This paper presents an end-to-end image captioning system utilizing a multimodal architecture by combining a one-dimensional convolutional neural network (CNN) to encode sequence information with a pre-trained ResNet-50 model image encoder for extracting region-based visual features. We investigate our approach's performance on the BanglaLekhaImageCaptions dataset using the existing evaluation metrics and perform a human evaluation for qualitative analysis. Experiments show that our approach's language encoder captures the fine-grained information in the caption, and combined with the image features, it generates accurate and diversified caption. Our work outperforms all the existing BIC works and achieves a new state-of-the-art (SOTA) performance by scoring 0.651 on BLUE-1, 0.572 on CIDEr, 0.297 on METEOR, 0.434 on ROUGE, and 0.357 on SPICE., Comment: Accepted in "IJCACI 2020: International Joint Conference on Advances in Computational Intelligence"

The rapid outbreak of COVID-19 has caused humanity to come to a stand-still and brought with it a plethora of other problems. COVID-19 is the first pandemic in history when humanity is the most technologically advanced and relies heavily on social media platforms for connectivity and other benefits. Unfortunately, fake news and misinformation regarding this virus is also available to people and causing some massive problems. So, fighting this infodemic has become a significant challenge. We present our solution for the "Constraint@AAAI2021 - COVID19 Fake News Detection in English" challenge in this work. After extensive experimentation with numerous architectures and techniques, we use eight different transformer-based pre-trained models with additional layers to construct a stacking ensemble classifier and fine-tuned them for our purpose. We achieved 0.979906542 accuracy, 0.979913119 precision, 0.979906542 recall, and 0.979907901 f1-score on the test dataset of the competition.

In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature. To pretrain BanglaBERT, we collect 27.5 GB of Bangla pretraining data (dubbed `Bangla2B+') by crawling 110 popular Bangla sites. We introduce two downstream task datasets on natural language inference and question answering and benchmark on four diverse NLU tasks covering text classification, sequence labeling, and span prediction. In the process, we bring them under the first-ever Bangla Language Understanding Benchmark (BLUB). BanglaBERT achieves state-of-the-art results outperforming multilingual and monolingual models. We are making the models, datasets, and a leaderboard publicly available at https://github.com/csebuetnlp/banglabert to advance Bangla NLP., Comment: Findings of North American Chapter of the Association for Computational Linguistics, NAACL 2022 (camera-ready)

Identifying cause-effect relations among variables is a key step in the decision-making process. While causal inference requires randomized experiments, researchers and policymakers are increasingly using observational studies to test causal hypotheses due to the wide availability of observational data and the infeasibility of experiments. The matching method is the most used technique to make causal inference from observational data. However, the pair assignment process in one-to-one matching creates uncertainty in the inference because of different choices made by the experimenter. Recently, discrete optimization models are proposed to tackle such uncertainty. Although a robust inference is possible with discrete optimization models, they produce nonlinear problems and lack scalability. In this work, we propose greedy algorithms to solve the robust causal inference test instances from observational data with continuous outcomes. We propose a unique framework to reformulate the nonlinear binary optimization problems as feasibility problems. By leveraging the structure of the feasibility formulation, we develop greedy schemes that are efficient in solving robust test problems. In many cases, the proposed algorithms achieve global optimal solutions. We perform experiments on three real-world datasets to demonstrate the effectiveness of the proposed algorithms and compare our result with the state-of-the-art solver. Our experiments show that the proposed algorithms significantly outperform the exact method in terms of computation time while achieving the same conclusion for causal tests. Both numerical experiments and complexity analysis demonstrate that the proposed algorithms ensure the scalability required for harnessing the power of big data in the decision-making process.

Social media sites such as YouTube and Facebook have become an integral part of everyone's life and in the last few years, hate speech in the social media comment section has increased rapidly. Detection of hate speech on social media websites faces a variety of challenges including small imbalanced data sets, the findings of an appropriate model and also the choice of feature analysis method. further more, this problem is more severe for the Bengali speaking community due to the lack of gold standard labelled datasets. This paper presents a new dataset of 30,000 user comments tagged by crowd sourcing and varified by experts. All the comments are collected from YouTube and Facebook comment section and classified into seven categories: sports, entertainment, religion, politics, crime, celebrity and TikTok & meme. A total of 50 annotators annotated each comment three times and the majority vote was taken as the final annotation. Nevertheless, we have conducted base line experiments and several deep learning models along with extensive pre-trained Bengali word embedding such as Word2Vec, FastText and BengFastText on this dataset to facilitate future research opportunities. The experiment illustrated that although all deep learning models performed well, SVM achieved the best result with 87.5% accuracy. Our core contribution is to make this benchmark dataset available and accessible to facilitate further research in the field of in the field of Bengali hate speech detection., Comment: 13 pages, 02 figures. To appear on International Joint Conference on Advances in Computational Intelligence, 20-21 November 2020

Automatic detection and recognition of banknotes can be a very useful technology for people with visual difficulties and also for the banks itself by providing efficient management for handling different paper currencies. Lightweight models can easily be integrated into any handy IoT based gadgets/devices. This article presents our experiments on several state-of-the-art deep learning methods based on Lightweight Convolutional Neural Network architectures combining with transfer learning. ResNet152v2, MobileNet, and NASNetMobile were used as the base models with two different datasets containing Bangladeshi banknote images. The Bangla Currency dataset has 8000 Bangladeshi banknote images where the Bangla Money dataset consists of 1970 images. The performances of the models were measured using both the datasets and the combination of the two datasets. In order to achieve maximum efficiency, we used various augmentations, hyperparameter tuning, and optimizations techniques. We have achieved maximum test accuracy of 98.88\% on 8000 images dataset using MobileNet, 100\% on the 1970 images dataset using NASNetMobile, and 97.77\% on the combined dataset (9970 images) using MobileNet., Comment: 4 pages

In today's world, Neural Style Transfer (NST) has become a trendsetting term. NST combines two pictures, a content picture and a reference image in style (such as the work of a renowned painter) in a way that makes the output image look like an image of the material, but rendered with the form of a reference picture. However, there is no study using the artwork or painting of Bangladeshi painters. Bangladeshi painting has a long history of more than two thousand years and is still being practiced by Bangladeshi painters. This study generates NST stylized image on Bangladeshi paintings and analyzes the human point of view regarding the aesthetic preference of NST on Bangladeshi paintings. To assure our study's acceptance, we performed qualitative human evaluations on generated stylized images by 60 individual humans of different age and gender groups. We have explained how NST works for Bangladeshi paintings and assess NST algorithms, both qualitatively \& quantitatively. Our study acts as a pre-requisite for the impact of NST stylized image using Bangladeshi paintings on mobile UI/GUI and material translation from the human perspective. We hope that this study will encourage new collaborations to create more NST related studies and expand the use of Bangladeshi artworks., Comment: 6 pages

Sentiment analysis (SA) in Bengali is challenging due to this Indo-Aryan language's highly inflected properties with more than 160 different inflected forms for verbs and 36 different forms for noun and 24 different forms for pronouns. The lack of standard labeled datasets in the Bengali domain makes the task of SA even harder. In this paper, we present manually tagged 2-class and 3-class SA datasets in Bengali. We also demonstrate that the multi-lingual BERT model with relevant extensions can be trained via the approach of transfer learning over those novel datasets to improve the state-of-the-art performance in sentiment classification tasks. This deep learning model achieves an accuracy of 71\% for 2-class sentiment classification compared to the current state-of-the-art accuracy of 68\%. We also present the very first Bengali SA classifier for the 3-class manually tagged dataset, and our proposed model achieves an accuracy of 60\%. We further use this model to analyze the sentiment of public comments in the online daily newspaper. Our analysis shows that people post negative comments for political or sports news more often, while the religious article comments represent positive sentiment. The dataset and code is publicly available at https://github.com/KhondokerIslam/Bengali\_Sentiment., Comment: 5 pages

Abstractive summarization is the process of generating novel sentences based on the information extracted from the original text document while retaining the context. Due to abstractive summarization's underlying complexities, most of the past research work has been done on the extractive summarization approach. Nevertheless, with the triumph of the sequence-to-sequence (seq2seq) model, abstractive summarization becomes more viable. Although a significant number of notable research has been done in the English language based on abstractive summarization, only a couple of works have been done on Bengali abstractive news summarization (BANS). In this article, we presented a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences with noteworthy information of the original document. We also prepared a dataset of more than 19k articles and corresponding human-written summaries collected from bangla.bdnews24.com1 which is till now the most extensive dataset for Bengali news document summarization and publicly published in Kaggle2. We evaluated our model qualitatively and quantitatively and compared it with other published results. It showed significant improvement in terms of human evaluation scores with state-of-the-art approaches for BANS., Comment: 10 Pages, 2 figures, 4 tables, 2nd International Conference on Trends in Computational and Cognitive Engineering(TCCE-2020)

Due to the breathtaking growth of social media or newspaper user comments, online product reviews comments, sentiment analysis (SA) has captured substantial interest from the researchers. With the fast increase of domain, SA work aims not only to predict the sentiment of a sentence or document but also to give the necessary detail on different aspects of the sentence or document (i.e. aspect-based sentiment analysis). A considerable number of datasets for SA and aspect-based sentiment analysis (ABSA) have been made available for English and other well-known European languages. In this paper, we present a manually annotated Bengali dataset of high quality, BAN-ABSA, which is annotated with aspect and its associated sentiment by 3 native Bengali speakers. The dataset consists of 2,619 positive, 4,721 negative and 1,669 neutral data samples from 9,009 unique comments gathered from some famous Bengali news portals. In addition, we conducted a baseline evaluation with a focus on deep learning model, achieved an accuracy of 78.75% for aspect term extraction and accuracy of 71.08% for sentiment classification. Experiments on the BAN-ABSA dataset show that the CNN model is better in terms of accuracy though Bi-LSTM significantly outperforms CNN model in terms of average F1-score., Comment: 11 pages,2 figures, 8 tables Included in proceedings of International Joint Conference on Advances in Computational Intelligence (IJCACI) 2020

Smartwatches or fitness trackers have garnered a lot of popularity as potential health tracking devices due to their affordable and longitudinal monitoring capabilities. To further widen their health tracking capabilities, in recent years researchers have started to look into the possibility of Atrial Fibrillation (AF) detection in real-time leveraging photoplethysmography (PPG) data, an inexpensive sensor widely available in almost all smartwatches. A significant challenge in AF detection from PPG signals comes from the inherent noise in the smartwatch PPG signals. In this paper, we propose a novel deep learning based approach, BayesBeat that leverages the power of Bayesian deep learning to accurately infer AF risks from noisy PPG signals, and at the same time provides an uncertainty estimate of the prediction. Extensive experiments on two publicly available dataset reveal that our proposed method BayesBeat outperforms the existing state-of-the-art methods. Moreover, BayesBeat is substantially more efficient having 40-200X fewer parameters than state-of-the-art baseline approaches making it suitable for deployment in resource constrained wearable devices., Comment: IMWUT March 2022, Vol 6 Article 8 (UbiComp 2022)

In this paper, we envision a web-based framework that can help anyone, anywhere around the world record a short speech task, and analyze the recorded data to screen for Parkinson's disease (PD). We collected data from 726 unique participants (262 PD, 38% female; 464 non-PD, 65% female; average age: 61) -- from all over the US and beyond. A small portion of the data was collected in a lab setting to compare quality. The participants were instructed to utter a popular pangram containing all the letters in the English alphabet "the quick brown fox jumps over the lazy dog..". We extracted both standard acoustic features (Mel Frequency Cepstral Coefficients (MFCC), jitter and shimmer variants) and deep learning based features from the speech data. Using these features, we trained several machine learning algorithms. We achieved 0.75 AUC (Area Under The Curve) performance on determining presence of self-reported Parkinson's disease by modeling the standard acoustic features through the XGBoost -- a gradient-boosted decision tree model. Further analysis reveal that the widely used MFCC features and a subset of previously validated dysphonia features designed for detecting Parkinson's from verbal phonation task (pronouncing 'ahh') contains the most distinct information. Our model performed equally well on data collected in controlled lab environment as well as 'in the wild' across different gender and age groups. Using this tool, we can collect data from almost anyone anywhere with a video/audio enabled device, contributing to equity and access in neurological care.

Observing the damages that can be done by the rapid propagation of fake news in various sectors like politics and finance, automatic identification of fake news using linguistic analysis has drawn the attention of the research community. However, such methods are largely being developed for English where low resource languages remain out of the focus. But the risks spawned by fake and manipulative news are not confined by languages. In this work, we propose an annotated dataset of ~50K news that can be used for building automated fake news detection systems for a low resource language like Bangla. Additionally, we provide an analysis of the dataset and develop a benchmark system with state of the art NLP techniques to identify Bangla fake news. To create this system, we explore traditional linguistic features and neural network based methods. We expect this dataset will be a valuable resource for building technologies to prevent the spreading of fake news and contribute in research with low resource languages., Comment: LREC 2020

Randomized Kaczmarz (RK), Motzkin Method (MM) and Sampling Kaczmarz Motzkin (SKM) algorithms are commonly used iterative techniques for solving a system of linear inequalities (i.e., $Ax \leq b$). As linear systems of equations represent a modeling paradigm for solving many optimization problems, these randomized and iterative techniques are gaining popularity among researchers in different domains. In this work, we propose a Generalized Sampling Kaczmarz Motzkin (GSKM) method that unifies the iterative methods into a single framework. In addition to the general framework, we propose a Nesterov type acceleration scheme in the SKM method called as Probably Accelerated Sampling Kaczmarz Motzkin (PASKM). We prove the convergence theorems for both GSKM and PASKM algorithms in the $L_2$ norm perspective with respect to the proposed sampling distribution. Furthermore, we prove sub-linear convergence for the Cesaro average of iterates for the proposed GSKM and PASKM algorithms.From the convergence theorem of the GSKM algorithm, we find the convergence results of several well-known algorithms like the Kaczmarz method, Motzkin method and SKM algorithm. We perform thorough numerical experiments using both randomly generated and real-world (classification with support vector machine and Netlib LP) test instances to demonstrate the efficiency of the proposed methods. We compare the proposed algorithms with SKM, Interior Point Method (IPM) and Active Set Method (ASM) in terms of computation time and solution quality. In the majority of the problem instances, the proposed generalized and accelerated algorithms significantly outperform the state-of-the-art methods.

Characters are the smallest unit of text that can extract stylometric signals to determine the author of a text. In this paper, we investigate the effectiveness of character-level signals in Authorship Attribution of Bangla Literature and show that the results are promising but improvable. The time and memory efficiency of the proposed model is much higher than the word level counterparts but accuracy is 2-5% less than the best performing word-level models. Comparison of various word-based models is performed and shown that the proposed model performs increasingly better with larger datasets. We also analyze the effect of pre-training character embedding of diverse Bangla character set in authorship attribution. It is seen that the performance is improved by up to 10% on pre-training. We used 2 datasets from 6 to 14 authors, balancing them before training and compare the results., Comment: 5 pages

Language models are generally employed to estimate the probability distribution of various linguistic units, making them one of the fundamental parts of natural language processing. Applications of language models include a wide spectrum of tasks such as text summarization, translation and classification. For a low resource language like Bengali, the research in this area so far can be considered to be narrow at the very least, with some traditional count based models being proposed. This paper attempts to address the issue and proposes a continuous-space neural language model, or more specifically an ASGD weight dropped LSTM language model, along with techniques to efficiently train it for Bengali Language. The performance analysis with some currently existing count based models illustrated in this paper also shows that the proposed architecture outperforms its counterparts by achieving an inference perplexity as low as 51.2 on the held out data set for Bengali., Comment: 6 pages