var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"https://bibbase.org/network/files/zx6BQtfxmRBt8jGF5\",\"jsonp\":\"1\",\"noBootstrap\":\"1\",\"host\":\"bibbase.org\",\"ssl\":false},\n      data: [{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shawon\"],\"firstnames\":[\"Md.\",\"Tanvir\",\"Rouf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shahariar\"],\"firstnames\":[\"G.\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shah\"],\"firstnames\":[\"Faisal\",\"Muhammad\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alam\"],\"firstnames\":[\"Mohammad\",\"Shafiul\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mahbub\"],\"firstnames\":[\"Md.\",\"Shahriar\"],\"suffixes\":[]}],\"booktitle\":\"2023 5th International Conference on Natural Language Processing (ICNLP)\",\"title\":\"Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks\",\"year\":\"2023\",\"volume\":\"\",\"number\":\"\",\"pages\":\"12-16\",\"abstract\":\"This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.\",\"keywords\":\"\",\"url_link\":\"https://ieeexplore.ieee.org/document/10236810\",\"issn\":\"\",\"month\":\"March\",\"bibtex\":\"@INPROCEEDINGS{10236810,\\n  author={Shawon, Md. Tanvir Rouf and Shahariar, G. M. and Shah, Faisal Muhammad and Alam, Mohammad Shafiul and Mahbub, Md. Shahriar},\\n  booktitle={2023 5th International Conference on Natural Language Processing (ICNLP)}, \\n  title={Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks}, \\n  year={2023},\\n  volume={},\\n  number={},\\n  pages={12-16},\\n  abstract={This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.},\\n  keywords={},\\n  url_link = {https://ieeexplore.ieee.org/document/10236810},\\n  ISSN={},\\n  month={March},}\\n\\n\",\"author_short\":[\"Shawon, M. T. R.\",\"Shahariar, G. M.\",\"Shah, F. M.\",\"Alam, M. S.\",\"Mahbub, M. S.\"],\"key\":\"10236810\",\"id\":\"10236810\",\"bibbaseid\":\"shawon-shahariar-shah-alam-mahbub-bengalifakereviewdetectionusingsemisupervisedgenerativeadversarialnetworks-2023\",\"role\":\"author\",\"urls\":{\" link\":\"https://ieeexplore.ieee.org/document/10236810\"},\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Jamdani motif generation using conditional GAN\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shawon\"],\"firstnames\":[\"MD\",\"Tanvir\",\"Rouf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tanvir\"],\"firstnames\":[\"Raihan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shifa\"],\"firstnames\":[\"Humaira\",\"Ferdous\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Kar\"],\"firstnames\":[\"Susmoy\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Jubair\"],\"firstnames\":[\"Mohammad\",\"Imrul\"],\"suffixes\":[]}],\"booktitle\":\"2020 23rd International Conference on Computer and Information Technology (ICCIT)\",\"pages\":\"1–6\",\"year\":\"2020\",\"organization\":\"IEEE\",\"url_link\":\"https://ieeexplore.ieee.org/document/9392654\",\"abstract\":\"Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled, the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of a desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix on it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.\",\"bibtex\":\"@inproceedings{shawon2020jamdani,\\n  title={Jamdani motif generation using conditional GAN},\\n  author={Shawon, MD Tanvir Rouf and Tanvir, Raihan and Shifa, Humaira Ferdous and Kar, Susmoy and Jubair, Mohammad Imrul},\\n  booktitle={2020 23rd International Conference on Computer and Information Technology (ICCIT)},\\n  pages={1--6},\\n  year={2020},\\n  organization={IEEE},\\n  url_link = {https://ieeexplore.ieee.org/document/9392654},\\n  abstract= {Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled, the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of a desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix on it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.}\\n}\\n\\n\",\"author_short\":[\"Shawon, M. T. R.\",\"Tanvir, R.\",\"Shifa, H. F.\",\"Kar, S.\",\"Jubair, M. I.\"],\"key\":\"shawon2020jamdani\",\"id\":\"shawon2020jamdani\",\"bibbaseid\":\"shawon-tanvir-shifa-kar-jubair-jamdanimotifgenerationusingconditionalgan-2020\",\"role\":\"author\",\"urls\":{\" link\":\"https://ieeexplore.ieee.org/document/9392654\"},\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"A GAN-BERT Based Approach for Bengali Text Classification with a Few Labeled Examples\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Tanvir\"],\"firstnames\":[\"Raihan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shawon\"],\"firstnames\":[\"Md\",\"Tanvir\",\"Rouf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mehedi\"],\"firstnames\":[\"Md\",\"Humaion\",\"Kabir\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mahtab\"],\"firstnames\":[\"Md\",\"Motahar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rasel\"],\"firstnames\":[\"Annajiat\",\"Alim\"],\"suffixes\":[]}],\"booktitle\":\"International Symposium on Distributed Computing and Artificial Intelligence\",\"pages\":\"20–30\",\"year\":\"2022\",\"organization\":\"Springer International Publishing Cham\",\"url_link\":\"https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3\",\"abstract\":\"Basic machine learning algorithms or transfer learning models work well for language categorization, but these models require a vast volume of annotated data. We need a better model to tackle the problem because labeled data is scarce. This problem may have a solution in GAN-BERT. To classify Bengali text, we have developed a GAN-BERT based model, which is an adapted version of BERT. We have used two different datasets for this purpose. One is a hate speech dataset, while the other is a fake news dataset. To understand how the GAN-BERT and traditional BERT models behave with Bangla datasets, we have experimented with both. With a small quantity of data, we are able to get a satisfactory result using GAN-BERT. We have also demonstrated how the accuracy increases as the number of training samples increases. A comparison of performance between traditional BERT based Bangla-BERT and our GAN-Bangla-BERT model is also shown here, where we can see how these models react to a small number of labeled data.\",\"bibtex\":\"@inproceedings{tanvir2022gan,\\n  title={A GAN-BERT Based Approach for Bengali Text Classification with a Few Labeled Examples},\\n  author={Tanvir, Raihan and Shawon, Md Tanvir Rouf and Mehedi, Md Humaion Kabir and Mahtab, Md Motahar and Rasel, Annajiat Alim},\\n  booktitle={International Symposium on Distributed Computing and Artificial Intelligence},\\n  pages={20--30},\\n  year={2022},\\n  organization={Springer International Publishing Cham},\\n  url_link = {https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3},\\n  abstract= {Basic machine learning algorithms or transfer learning models work well for language categorization, but these models require a vast volume of annotated data. We need a better model to tackle the problem because labeled data is scarce. This problem may have a solution in GAN-BERT. To classify Bengali text, we have developed a GAN-BERT based model, which is an adapted version of BERT. We have used two different datasets for this purpose. One is a hate speech dataset, while the other is a fake news dataset. To understand how the GAN-BERT and traditional BERT models behave with Bangla datasets, we have experimented with both. With a small quantity of data, we are able to get a satisfactory result using GAN-BERT. We have also demonstrated how the accuracy increases as the number of training samples increases. A comparison of performance between traditional BERT based Bangla-BERT and our GAN-Bangla-BERT model is also shown here, where we can see how these models react to a small number of labeled data.}\\n}\\n\\n\",\"author_short\":[\"Tanvir, R.\",\"Shawon, M. T. R.\",\"Mehedi, M. H. K.\",\"Mahtab, M. M.\",\"Rasel, A. A.\"],\"key\":\"tanvir2022gan\",\"id\":\"tanvir2022gan\",\"bibbaseid\":\"tanvir-shawon-mehedi-mahtab-rasel-aganbertbasedapproachforbengalitextclassificationwithafewlabeledexamples-2022\",\"role\":\"author\",\"urls\":{\" link\":\"https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3\"},\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Mandal\"],\"firstnames\":[\"Nibir\",\"Chandra\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shahariar\"],\"firstnames\":[\"GM\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shawon\"],\"firstnames\":[\"Md\",\"Tanvir\",\"Rouf\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022\",\"pages\":\"125–137\",\"year\":\"2023\",\"organization\":\"Springer Nature Singapore Singapore\",\"url_link\":\"https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10\",\"abstract\":\"The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or “things” that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the “IoT Security Dataset”, a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset “Opiner”, supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security. The dataset can be found at—https://anonymous.4open.science/r/IoT-Security-Dataset-8E35.\",\"bibtex\":\"@inproceedings{mandal2023effectiveness,\\n  title={Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions},\\n  author={Mandal, Nibir Chandra and Shahariar, GM and Shawon, Md Tanvir Rouf},\\n  booktitle={Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022},\\n  pages={125--137},\\n  year={2023},\\n  organization={Springer Nature Singapore Singapore},\\n  url_link = {https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10},\\n  abstract= {The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or “things” that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the “IoT Security Dataset”, a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset “Opiner”, supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security. The dataset can be found at—https://anonymous.4open.science/r/IoT-Security-Dataset-8E35.}\\n}\\n\\n\",\"author_short\":[\"Mandal, N. C.\",\"Shahariar, G.\",\"Shawon, M. T. R.\"],\"key\":\"mandal2023effectiveness\",\"id\":\"mandal2023effectiveness\",\"bibbaseid\":\"mandal-shahariar-shawon-effectivenessoftransformermodelsoniotsecuritydetectioninstackoverflowdiscussions-2023\",\"role\":\"author\",\"urls\":{\" link\":\"https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10\"},\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Automatic back transliteration of Romanized Bengali (Banglish) to Bengali\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shibli\"],\"firstnames\":[\"GM\",\"Shahariar\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shawon\"],\"firstnames\":[\"Md\",\"Tanvir\",\"Rouf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nibir\"],\"firstnames\":[\"Anik\",\"Hassan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Miandad\"],\"firstnames\":[\"Md\",\"Zabed\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Mandal\"],\"firstnames\":[\"Nibir\",\"Chandra\"],\"suffixes\":[]}],\"journal\":\"Iran Journal of Computer Science\",\"volume\":\"6\",\"number\":\"1\",\"pages\":\"69–80\",\"year\":\"2023\",\"publisher\":\"Springer International Publishing Cham\",\"url_link\":\"https://link.springer.com/article/10.1007/s42044-022-00122-9\",\"abstract\":\"Back transliteration of Romanized Bengali to Bengali is the process of converting text written in the Latin alphabet back into the Bengali script. This is often done in order to improve the readability of Bengali text for Bengali speakers using a simple rules-based system, or an interactive transliteration tool. There are many ways to back transliterate from Romanized Bengali to Bengali, but most of them are either grapheme or phoneme based. This paper introduces a unique pipeline that uses nine open source back transliteration tools to automatically back transliterate Romanized Bengali to Bengali. The pipeline consists of seven steps: (1) processing the Romanized Bengali input; (2) acquiring human transliteration for performance comparison; (3) employing transliteration tools; (4) generating candidate transliterations; (5) post-processing the candidate transliterations; (6) selecting best candidate transliteration, and (7) evaluating the quality of the transliterations through several performance metrics. Experimental results reveal that our approach produced the highest BLEU-1 score of 81.28, BLEU-2 score of 60.75, BLEU-3 score of 44.45, BLEU-4 score of 30.46, and the lowest average Word Error Rate and Word Information Lost of 29.21 and 43.68, respectively, on 1000 Romanized Bengali texts. In terms of recall, we achieved a Rouge-L score of 0.7190.\",\"bibtex\":\"@article{shibli2023automatic,\\n  title={Automatic back transliteration of Romanized Bengali (Banglish) to Bengali},\\n  author={Shibli, GM Shahariar and Shawon, Md Tanvir Rouf and Nibir, Anik Hassan and Miandad, Md Zabed and Mandal, Nibir Chandra},\\n  journal={Iran Journal of Computer Science},\\n  volume={6},\\n  number={1},\\n  pages={69--80},\\n  year={2023},\\n  publisher={Springer International Publishing Cham},\\n  url_link = {https://link.springer.com/article/10.1007/s42044-022-00122-9},\\n  abstract= {Back transliteration of Romanized Bengali to Bengali is the process of converting text written in the Latin alphabet back into the Bengali script. This is often done in order to improve the readability of Bengali text for Bengali speakers using a simple rules-based system, or an interactive transliteration tool. There are many ways to back transliterate from Romanized Bengali to Bengali, but most of them are either grapheme or phoneme based. This paper introduces a unique pipeline that uses nine open source back transliteration tools to automatically back transliterate Romanized Bengali to Bengali. The pipeline consists of seven steps: (1) processing the Romanized Bengali input; (2) acquiring human transliteration for performance comparison; (3) employing transliteration tools; (4) generating candidate transliterations; (5) post-processing the candidate transliterations; (6) selecting best candidate transliteration, and (7) evaluating the quality of the transliterations through several performance metrics. Experimental results reveal that our approach produced the highest BLEU-1 score of 81.28, BLEU-2 score of 60.75, BLEU-3 score of 44.45, BLEU-4 score of 30.46, and the lowest average Word Error Rate and Word Information Lost of 29.21 and 43.68, respectively, on 1000 Romanized Bengali texts. In terms of recall, we achieved a Rouge-L score of 0.7190.}\\n}\\n\\n\",\"author_short\":[\"Shibli, G. S.\",\"Shawon, M. T. R.\",\"Nibir, A. H.\",\"Miandad, M. Z.\",\"Mandal, N. C.\"],\"key\":\"shibli2023automatic\",\"id\":\"shibli2023automatic\",\"bibbaseid\":\"shibli-shawon-nibir-miandad-mandal-automaticbacktransliterationofromanizedbengalibanglishtobengali-2023\",\"role\":\"author\",\"urls\":{\" link\":\"https://link.springer.com/article/10.1007/s42044-022-00122-9\"},\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"Bengali Handwritten Digit Recognition using CNN with Explainable AI\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Shawon\"],\"firstnames\":[\"Md\",\"Tanvir\",\"Rouf\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tanvir\"],\"firstnames\":[\"Raihan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Alam\"],\"firstnames\":[\"Md\",\"Golam\",\"Rabiul\"],\"suffixes\":[]}],\"booktitle\":\"2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)\",\"pages\":\"1–6\",\"year\":\"2022\",\"organization\":\"IEEE\",\"url_link\":\"https://ieeexplore.ieee.org/document/10103341\",\"abstract\":\"Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily under-stand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.\",\"bibtex\":\"@inproceedings{shawon2022bengali,\\n  title={Bengali Handwritten Digit Recognition using CNN with Explainable AI},\\n  author={Shawon, Md Tanvir Rouf and Tanvir, Raihan and Alam, Md Golam Rabiul},\\n  booktitle={2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)},\\n  pages={1--6},\\n  year={2022},\\n  organization={IEEE},\\n  url_link = {https://ieeexplore.ieee.org/document/10103341},\\n  abstract= {Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily under-stand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.}\\n}\\n\\n\",\"author_short\":[\"Shawon, M. T. R.\",\"Tanvir, R.\",\"Alam, M. G. R.\"],\"key\":\"shawon2022bengali\",\"id\":\"shawon2022bengali\",\"bibbaseid\":\"shawon-tanvir-alam-bengalihandwrittendigitrecognitionusingcnnwithexplainableai-2022\",\"role\":\"author\",\"urls\":{\" link\":\"https://ieeexplore.ieee.org/document/10103341\"},\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"downloads\":2,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"title\":\"Bengali fake reviews: A benchmark dataset and detection system\",\"journal\":\"Neurocomputing\",\"volume\":\"592\",\"pages\":\"127732\",\"year\":\"2024\",\"issn\":\"0925-2312\",\"doi\":\"https://doi.org/10.1016/j.neucom.2024.127732\",\"url\":\"https://www.sciencedirect.com/science/article/pii/S0925231224005034\",\"author\":[{\"firstnames\":[\"G\",\"M\"],\"propositions\":[],\"lastnames\":[\"Shahariar\"],\"suffixes\":[]},{\"firstnames\":[\"Md.\",\"Tanvir\",\"Rouf\"],\"propositions\":[],\"lastnames\":[\"Shawon\"],\"suffixes\":[]},{\"firstnames\":[\"Faisal\",\"Muhammad\"],\"propositions\":[],\"lastnames\":[\"Shah\"],\"suffixes\":[]},{\"firstnames\":[\"Mohammad\",\"Shafiul\"],\"propositions\":[],\"lastnames\":[\"Alam\"],\"suffixes\":[]},{\"firstnames\":[\"Md.\",\"Shahriar\"],\"propositions\":[],\"lastnames\":[\"Mahbub\"],\"suffixes\":[]}],\"keywords\":\"Bengali fake reviews detection, Ensemble learning, Transformers, Deep learning, Augmentation, Transliteration\",\"abstract\":\"The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.\",\"bibtex\":\"@article{SHAHARIAR2024127732,\\ntitle = {Bengali fake reviews: A benchmark dataset and detection system},\\njournal = {Neurocomputing},\\nvolume = {592},\\npages = {127732},\\nyear = {2024},\\nissn = {0925-2312},\\ndoi = {https://doi.org/10.1016/j.neucom.2024.127732},\\nurl = {https://www.sciencedirect.com/science/article/pii/S0925231224005034},\\nauthor = {G M Shahariar and Md. Tanvir Rouf Shawon and Faisal Muhammad Shah and Mohammad Shafiul Alam and Md. Shahriar Mahbub},\\nkeywords = {Bengali fake reviews detection, Ensemble learning, Transformers, Deep learning, Augmentation, Transliteration},\\nabstract = {The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.}\\n}\\n\",\"author_short\":[\"Shahariar, G M\",\"Shawon, M. T. R.\",\"Shah, F. M.\",\"Alam, M. S.\",\"Mahbub, M. S.\"],\"key\":\"SHAHARIAR2024127732\",\"id\":\"SHAHARIAR2024127732\",\"bibbaseid\":\"shahariar-shawon-shah-alam-mahbub-bengalifakereviewsabenchmarkdatasetanddetectionsystem-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.sciencedirect.com/science/article/pii/S0925231224005034\"},\"keyword\":[\"Bengali fake reviews detection\",\"Ensemble learning\",\"Transformers\",\"Deep learning\",\"Augmentation\",\"Transliteration\"],\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"downloads\":3,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"title\":\"A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Elahi\"],\"firstnames\":[\"Kazi\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rahman\"],\"firstnames\":[\"Tasnuva\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shahriar\"],\"firstnames\":[\"Shakil\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sarker\"],\"firstnames\":[\"Samir\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shawon\"],\"firstnames\":[\"Md.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Shahariar\"],\"firstnames\":[\"G.\",\"M.\"],\"suffixes\":[]}],\"editor\":[{\"propositions\":[\"van\",\"der\"],\"lastnames\":[\"Goot\"],\"firstnames\":[\"Rob\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bak\"],\"firstnames\":[\"JinYeong\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Müller-Eberstein\"],\"firstnames\":[\"Max\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Xu\"],\"firstnames\":[\"Wei\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ritter\"],\"firstnames\":[\"Alan\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Baldwin\"],\"firstnames\":[\"Tim\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024)\",\"month\":\"March\",\"year\":\"2024\",\"address\":\"San Ġiljan, Malta\",\"publisher\":\"Association for Computational Linguistics\",\"url\":\"https://aclanthology.org/2024.wnut-1.5\",\"pages\":\"44–57\",\"abstract\":\"While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts\",\"bibtex\":\"@inproceedings{elahi-etal-2024-comparative,\\n    title = \\\"A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy {B}angla Texts\\\",\\n    author = \\\"Elahi, Kazi  and\\n      Rahman, Tasnuva  and\\n      Shahriar, Shakil  and\\n      Sarker, Samir  and\\n      Shawon, Md.  and\\n      Shahariar, G. M.\\\",\\n    editor = {van der Goot, Rob  and\\n      Bak, JinYeong  and\\n      M{\\\\\\\"u}ller-Eberstein, Max  and\\n      Xu, Wei  and\\n      Ritter, Alan  and\\n      Baldwin, Tim},\\n    booktitle = \\\"Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024)\\\",\\n    month = mar,\\n    year = \\\"2024\\\",\\n    address = \\\"San {\\\\.G}iljan, Malta\\\",\\n    publisher = \\\"Association for Computational Linguistics\\\",\\n    url = \\\"https://aclanthology.org/2024.wnut-1.5\\\",\\n    pages = \\\"44--57\\\",\\n    abstract = \\\"While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts\\\",\\n}\\n\\n\\n\\n\\n\\n\",\"author_short\":[\"Elahi, K.\",\"Rahman, T.\",\"Shahriar, S.\",\"Sarker, S.\",\"Shawon, M.\",\"Shahariar, G. M.\"],\"editor_short\":[\"van der Goot, R.\",\"Bak, J.\",\"Müller-Eberstein, M.\",\"Xu, W.\",\"Ritter, A.\",\"Baldwin, T.\"],\"key\":\"elahi-etal-2024-comparative\",\"id\":\"elahi-etal-2024-comparative\",\"bibbaseid\":\"elahi-rahman-shahriar-sarker-shawon-shahariar-acomparativeanalysisofnoisereductionmethodsinsentimentanalysisonnoisybanglatexts-2024\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://aclanthology.org/2024.wnut-1.5\"},\"metadata\":{\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\"downloads\":1,\"html\":\"\"}],\n      metadata: {\"owner\":\"shawon, m\",\"authorlinks\":{\"shawon, m\":\"https://shawon-tanvir.github.io//publications/\"}},\n      ownerID: \"dL88vr4ACAxNXEc85\"\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"zx6BQtfxmRBt8jGF5\" href=\"data:text/bibtex;base64,@INPROCEEDINGS{10236810,
  author={Shawon, Md. Tanvir Rouf and Shahariar, G. M. and Shah, Faisal Muhammad and Alam, Mohammad Shafiul and Mahbub, Md. Shahriar},
  booktitle={2023 5th International Conference on Natural Language Processing (ICNLP)}, 
  title={Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks}, 
  year={2023},
  volume={},
  number={},
  pages={12-16},
  abstract={This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.},
  keywords={},
  url_link = {https://ieeexplore.ieee.org/document/10236810},
  ISSN={},
  month={March},}



@inproceedings{shawon2020jamdani,
  title={Jamdani motif generation using conditional GAN},
  author={Shawon, MD Tanvir Rouf and Tanvir, Raihan and Shifa, Humaira Ferdous and Kar, Susmoy and Jubair, Mohammad Imrul},
  booktitle={2020 23rd International Conference on Computer and Information Technology (ICCIT)},
  pages={1--6},
  year={2020},
  organization={IEEE},
  url_link = {https://ieeexplore.ieee.org/document/9392654},
  abstract= {Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled, the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of a desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix on it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.}
}



@inproceedings{tanvir2022gan,
  title={A GAN-BERT Based Approach for Bengali Text Classification with a Few Labeled Examples},
  author={Tanvir, Raihan and Shawon, Md Tanvir Rouf and Mehedi, Md Humaion Kabir and Mahtab, Md Motahar and Rasel, Annajiat Alim},
  booktitle={International Symposium on Distributed Computing and Artificial Intelligence},
  pages={20--30},
  year={2022},
  organization={Springer International Publishing Cham},
  url_link = {https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3},
  abstract= {Basic machine learning algorithms or transfer learning models work well for language categorization, but these models require a vast volume of annotated data. We need a better model to tackle the problem because labeled data is scarce. This problem may have a solution in GAN-BERT. To classify Bengali text, we have developed a GAN-BERT based model, which is an adapted version of BERT. We have used two different datasets for this purpose. One is a hate speech dataset, while the other is a fake news dataset. To understand how the GAN-BERT and traditional BERT models behave with Bangla datasets, we have experimented with both. With a small quantity of data, we are able to get a satisfactory result using GAN-BERT. We have also demonstrated how the accuracy increases as the number of training samples increases. A comparison of performance between traditional BERT based Bangla-BERT and our GAN-Bangla-BERT model is also shown here, where we can see how these models react to a small number of labeled data.}
}



@inproceedings{mandal2023effectiveness,
  title={Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions},
  author={Mandal, Nibir Chandra and Shahariar, GM and Shawon, Md Tanvir Rouf},
  booktitle={Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022},
  pages={125--137},
  year={2023},
  organization={Springer Nature Singapore Singapore},
  url_link = {https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10},
  abstract= {The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or “things” that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the “IoT Security Dataset”, a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset “Opiner”, supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security. The dataset can be found at—https://anonymous.4open.science/r/IoT-Security-Dataset-8E35.}
}



@article{shibli2023automatic,
  title={Automatic back transliteration of Romanized Bengali (Banglish) to Bengali},
  author={Shibli, GM Shahariar and Shawon, Md Tanvir Rouf and Nibir, Anik Hassan and Miandad, Md Zabed and Mandal, Nibir Chandra},
  journal={Iran Journal of Computer Science},
  volume={6},
  number={1},
  pages={69--80},
  year={2023},
  publisher={Springer International Publishing Cham},
  url_link = {https://link.springer.com/article/10.1007/s42044-022-00122-9},
  abstract= {Back transliteration of Romanized Bengali to Bengali is the process of converting text written in the Latin alphabet back into the Bengali script. This is often done in order to improve the readability of Bengali text for Bengali speakers using a simple rules-based system, or an interactive transliteration tool. There are many ways to back transliterate from Romanized Bengali to Bengali, but most of them are either grapheme or phoneme based. This paper introduces a unique pipeline that uses nine open source back transliteration tools to automatically back transliterate Romanized Bengali to Bengali. The pipeline consists of seven steps: (1) processing the Romanized Bengali input; (2) acquiring human transliteration for performance comparison; (3) employing transliteration tools; (4) generating candidate transliterations; (5) post-processing the candidate transliterations; (6) selecting best candidate transliteration, and (7) evaluating the quality of the transliterations through several performance metrics. Experimental results reveal that our approach produced the highest BLEU-1 score of 81.28, BLEU-2 score of 60.75, BLEU-3 score of 44.45, BLEU-4 score of 30.46, and the lowest average Word Error Rate and Word Information Lost of 29.21 and 43.68, respectively, on 1000 Romanized Bengali texts. In terms of recall, we achieved a Rouge-L score of 0.7190.}
}



@inproceedings{shawon2022bengali,
  title={Bengali Handwritten Digit Recognition using CNN with Explainable AI},
  author={Shawon, Md Tanvir Rouf and Tanvir, Raihan and Alam, Md Golam Rabiul},
  booktitle={2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)},
  pages={1--6},
  year={2022},
  organization={IEEE},
  url_link = {https://ieeexplore.ieee.org/document/10103341},
  abstract= {Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily under-stand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.}
}



@article{SHAHARIAR2024127732,
title = {Bengali fake reviews: A benchmark dataset and detection system},
journal = {Neurocomputing},
volume = {592},
pages = {127732},
year = {2024},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2024.127732},
url = {https://www.sciencedirect.com/science/article/pii/S0925231224005034},
author = {G M Shahariar and Md. Tanvir Rouf Shawon and Faisal Muhammad Shah and Mohammad Shafiul Alam and Md. Shahriar Mahbub},
keywords = {Bengali fake reviews detection, Ensemble learning, Transformers, Deep learning, Augmentation, Transliteration},
abstract = {The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.}
}


@inproceedings{elahi-etal-2024-comparative,
    title = "A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy {B}angla Texts",
    author = "Elahi, Kazi  and
      Rahman, Tasnuva  and
      Shahriar, Shakil  and
      Sarker, Samir  and
      Shawon, Md.  and
      Shahariar, G. M.",
    editor = {van der Goot, Rob  and
      Bak, JinYeong  and
      M{\"u}ller-Eberstein, Max  and
      Xu, Wei  and
      Ritter, Alan  and
      Baldwin, Tim},
    booktitle = "Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024)",
    month = mar,
    year = "2024",
    address = "San {\.G}iljan, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.wnut-1.5",
    pages = "44--57",
    abstract = "While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts",
}





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T. R.</a>; Shah, F. M.; Alam, M. S.;  and Mahbub, M. S.\n</span>\n\n  \n\n</span>\n\n  <i>Neurocomputing</i>, 592: 127732.  2024.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.sciencedirect.com/science/article/pii/S0925231224005034\"\n     onclick=\"log_download('shahariar-shawon-shah-alam-mahbub-bengalifakereviewsabenchmarkdatasetanddetectionsystem-2024', 'https://www.sciencedirect.com/science/article/pii/S0925231224005034', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bengali fake reviews: A benchmark dataset and detection system [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/https://doi.org/10.1016/j.neucom.2024.127732\"\n     onclick=\"log_download('shahariar-shawon-shah-alam-mahbub-bengalifakereviewsabenchmarkdatasetanddetectionsystem-2024', 'http://doi.org/https://doi.org/10.1016/j.neucom.2024.127732', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shahariar-shawon-shah-alam-mahbub-bengalifakereviewsabenchmarkdatasetanddetectionsystem-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>3 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shahariar-shawon-shah-alam-mahbub-bengalifakereviewsabenchmarkdatasetanddetectionsystem-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{SHAHARIAR2024127732,\ntitle = {Bengali fake reviews: A benchmark dataset and detection system},\njournal = {Neurocomputing},\nvolume = {592},\npages = {127732},\nyear = {2024},\nissn = {0925-2312},\ndoi = {https://doi.org/10.1016/j.neucom.2024.127732},\nurl = {https://www.sciencedirect.com/science/article/pii/S0925231224005034},\nauthor = {G M Shahariar and Md. Tanvir Rouf Shawon and Faisal Muhammad Shah and Mohammad Shafiul Alam and Md. Shahriar Mahbub},\nkeywords = {Bengali fake reviews detection, Ensemble learning, Transformers, Deep learning, Augmentation, Transliteration},\nabstract = {The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.}\n}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The proliferation of fake reviews on various online platforms has created a major concern for both consumers and businesses. Such reviews can deceive customers and cause damage to the reputation of products or services, making it crucial to identify them. Although the detection of fake reviews has been extensively studied in English language, detecting fake reviews in non-English languages such as Bengali is still a relatively unexplored research area. The novelty of this study unfolds on three fronts: (i) a new publicly available dataset called Bengali Fake Review Detection (BFRD) dataset is introduced, (ii) a unique pipeline has been proposed that translates English words to their corresponding Bengali meaning and also back transliterates Romanized Bengali to Bengali, (iii) a weighted ensemble model that combines four pre-trained transformers model is proposed. The developed dataset consists of 7710 non-fake and 1339 fake food-related reviews collected from social media posts. Rigorous experiments have been conducted to compare multiple deep learning and pre-trained transformer language models and our proposed model to identify the best-performing model. According to the experimental results, the proposed ensemble model attained a weighted F1-score of 0.9843 on a dataset of 13,390 reviews, comprising 1339 actual fake reviews, 5,356 augmented fake reviews, and 6695 reviews randomly selected from the 7710 non-fake instances.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"elahi-rahman-shahriar-sarker-shawon-shahariar-acomparativeanalysisofnoisereductionmethodsinsentimentanalysisonnoisybanglatexts-2024\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://aclanthology.org/2024.wnut-1.5\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'elahi-rahman-shahriar-sarker-shawon-shahariar-acomparativeanalysisofnoisereductionmethodsinsentimentanalysisonnoisybanglatexts-2024', 'https://aclanthology.org/2024.wnut-1.5', event);\">\n    A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Elahi, K.; Rahman, T.; Shahriar, S.; Sarker, S.; <a class=\"bibbase author link\" href=\"https://shawon-tanvir.github.io//publications/\">Shawon, M.</a>;  and Shahariar, G. M.\n</span>\n\n  \n\n</span>\n\n  In van der Goot, R.; Bak, J.; Müller-Eberstein, M.; Xu, W.; Ritter, A.;  and Baldwin, T., editor(s), <i>Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024)</i>, pages 44–57, San Ġiljan, Malta, March 2024. Association for Computational Linguistics\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://aclanthology.org/2024.wnut-1.5\"\n     onclick=\"log_download('elahi-rahman-shahriar-sarker-shawon-shahariar-acomparativeanalysisofnoisereductionmethodsinsentimentanalysisonnoisybanglatexts-2024', 'https://aclanthology.org/2024.wnut-1.5', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/elahi-rahman-shahriar-sarker-shawon-shahariar-acomparativeanalysisofnoisereductionmethodsinsentimentanalysisonnoisybanglatexts-2024\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('elahi-rahman-shahriar-sarker-shawon-shahariar-acomparativeanalysisofnoisereductionmethodsinsentimentanalysisonnoisybanglatexts-2024')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{elahi-etal-2024-comparative,\n    title = &quot;A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy {B}angla Texts&quot;,\n    author = &quot;Elahi, Kazi  and\n      Rahman, Tasnuva  and\n      Shahriar, Shakil  and\n      Sarker, Samir  and\n      Shawon, Md.  and\n      Shahariar, G. M.&quot;,\n    editor = {van der Goot, Rob  and\n      Bak, JinYeong  and\n      M{\\&quot;u}ller-Eberstein, Max  and\n      Xu, Wei  and\n      Ritter, Alan  and\n      Baldwin, Tim},\n    booktitle = &quot;Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024)&quot;,\n    month = mar,\n    year = &quot;2024&quot;,\n    address = &quot;San {\\.G}iljan, Malta&quot;,\n    publisher = &quot;Association for Computational Linguistics&quot;,\n    url = &quot;https://aclanthology.org/2024.wnut-1.5&quot;,\n    pages = &quot;44--57&quot;,\n    abstract = &quot;While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts&quot;,\n}\n\n\n\n\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  While Bangla is considered a language with limited resources, sentiment analysis has been a subject of extensive research in the literature. Nevertheless, there is a scarcity of exploration into sentiment analysis specifically in the realm of noisy Bangla texts. In this paper, we introduce a dataset (NC-SentNoB) that we annotated manually to identify ten different types of noise found in a pre-existing sentiment analysis dataset comprising of around 15K noisy Bangla texts. At first, given an input noisy text, we identify the noise type, addressing this as a multi-label classification task. Then, we introduce baseline noise reduction methods to alleviate noise prior to conducting sentiment analysis. Finally, we assess the performance of fine-tuned sentiment analysis models with both noisy and noise-reduced texts to make comparisons. The experimental findings indicate that the noise reduction methods utilized are not satisfactory, highlighting the need for more suitable noise reduction methods in future research endeavors. We have made the implementation and dataset presented in this paper publicly available at https://github.com/ktoufiquee/A-Comparative-Analysis-of-Noise-Reduction-Methods-in-Sentiment-Analysis-on-Noisy-Bangla-Texts\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2023\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2023')\"\n      onkeypress=\"toggleGroup('group_2023')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2023</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"shawon-shahariar-shah-alam-mahbub-bengalifakereviewdetectionusingsemisupervisedgenerativeadversarialnetworks-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/10236810\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shawon-shahariar-shah-alam-mahbub-bengalifakereviewdetectionusingsemisupervisedgenerativeadversarialnetworks-2023', 'https://ieeexplore.ieee.org/document/10236810', event);\">\n    Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://shawon-tanvir.github.io//publications/\">Shawon, M. T. R.</a>; Shahariar, G. M.; Shah, F. M.; Alam, M. S.;  and Mahbub, M. S.\n</span>\n\n  \n\n</span>\n\n  In <i>2023 5th International Conference on Natural Language Processing (ICNLP)</i>, pages 12-16, March 2023. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/10236810\"\n     onclick=\"log_download('shawon-shahariar-shah-alam-mahbub-bengalifakereviewdetectionusingsemisupervisedgenerativeadversarialnetworks-2023', 'https://ieeexplore.ieee.org/document/10236810', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shawon-shahariar-shah-alam-mahbub-bengalifakereviewdetectionusingsemisupervisedgenerativeadversarialnetworks-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shawon-shahariar-shah-alam-mahbub-bengalifakereviewdetectionusingsemisupervisedgenerativeadversarialnetworks-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@INPROCEEDINGS{10236810,\n  author={Shawon, Md. Tanvir Rouf and Shahariar, G. M. and Shah, Faisal Muhammad and Alam, Mohammad Shafiul and Mahbub, Md. Shahriar},\n  booktitle={2023 5th International Conference on Natural Language Processing (ICNLP)}, \n  title={Bengali Fake Review Detection using Semi-supervised Generative Adversarial Networks}, \n  year={2023},\n  volume={},\n  number={},\n  pages={12-16},\n  abstract={This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.},\n  keywords={},\n  url_link = {https://ieeexplore.ieee.org/document/10236810},\n  ISSN={},\n  month={March},}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper investigates the potential of semi-supervised Generative Adversarial Networks (GANs) to fine-tune pretrained language models in order to classify Bengali fake reviews from real reviews with a few annotated data. With the rise of social media and e-commerce, the ability to detect fake or deceptive reviews is becoming increasingly important in order to protect consumers from being misled by false information. Any machine learning model will have trouble identifying a fake review, especially for a low resource language like Bengali. We have demonstrated that the proposed semi-supervised GAN-LM architecture (generative adversarial network on top of a pretrained language model) is a viable solution in classifying Bengali fake reviews as the experimental results suggest that even with only 1024 annotated samples, BanglaBERT with semi-supervised GAN (SSGAN) achieved an accuracy of 83.59% and a f1-score of 84.89% outperforming other pretrained language models - BanglaBERT generator, Bangla BERT Base and BanglaElectra by almost 3%, 4% and 10% respectively in terms of accuracy. The experiments were conducted on a manually labeled food review dataset consisting of total 6014 real and fake reviews collected from various social media groups. Researchers that are experiencing difficulty recognizing not just fake reviews but other classification issues owing to a lack of labeled data may find a solution in our proposed methodology.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mandal-shahariar-shawon-effectivenessoftransformermodelsoniotsecuritydetectioninstackoverflowdiscussions-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mandal-shahariar-shawon-effectivenessoftransformermodelsoniotsecuritydetectioninstackoverflowdiscussions-2023', 'https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10', event);\">\n    Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Mandal, N. C.; Shahariar, G.;  and <a class=\"bibbase author link\" href=\"https://shawon-tanvir.github.io//publications/\">Shawon, M. T. R.</a>\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022</i>, pages 125–137,  2023. Springer Nature Singapore Singapore\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10\"\n     onclick=\"log_download('mandal-shahariar-shawon-effectivenessoftransformermodelsoniotsecuritydetectioninstackoverflowdiscussions-2023', 'https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mandal-shahariar-shawon-effectivenessoftransformermodelsoniotsecuritydetectioninstackoverflowdiscussions-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mandal-shahariar-shawon-effectivenessoftransformermodelsoniotsecuritydetectioninstackoverflowdiscussions-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{mandal2023effectiveness,\n  title={Effectiveness of Transformer Models on IoT Security Detection in StackOverflow Discussions},\n  author={Mandal, Nibir Chandra and Shahariar, GM and Shawon, Md Tanvir Rouf},\n  booktitle={Proceedings of International Conference on Information and Communication Technology for Development: ICICTD 2022},\n  pages={125--137},\n  year={2023},\n  organization={Springer Nature Singapore Singapore},\n  url_link = {https://link.springer.com/chapter/10.1007/978-981-19-7528-8_10},\n  abstract= {The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or “things” that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the “IoT Security Dataset”, a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset “Opiner”, supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security. The dataset can be found at—https://anonymous.4open.science/r/IoT-Security-Dataset-8E35.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The Internet of Things (IoT) is an emerging concept that directly links to the billions of physical items, or “things” that are connected to the Internet and are all gathering and exchanging information between devices and systems. However, IoT devices were not built with security in mind, which might lead to security vulnerabilities in a multi-device system. Traditionally, we investigated IoT issues by polling IoT developers and specialists. This technique, however, is not scalable since surveying all IoT developers is not feasible. Another way to look into IoT issues is to look at IoT developer discussions on major online development forums like Stack Overflow (SO). However, finding discussions that are relevant to IoT issues is challenging since they are frequently not categorized with IoT-related terms. In this paper, we present the “IoT Security Dataset”, a domain-specific dataset of 7147 samples focused solely on IoT security discussions. As there are no automated tools to label these samples, we manually labeled them. We further employed multiple transformer models to automatically detect security discussions. Through rigorous investigations, we found that IoT security discussions are different and more complex than traditional security discussions. We demonstrated a considerable performance loss (up to 44%) of transformer models on cross-domain datasets when we transferred knowledge from a general-purpose dataset “Opiner”, supporting our claim. Thus, we built a domain-specific IoT security detector with an F1-Score of 0.69. We have made the dataset public in the hope that developers would learn more about the security discussion and vendors would enhance their concerns about product security. The dataset can be found at—https://anonymous.4open.science/r/IoT-Security-Dataset-8E35.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shibli-shawon-nibir-miandad-mandal-automaticbacktransliterationofromanizedbengalibanglishtobengali-2023\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/article/10.1007/s42044-022-00122-9\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shibli-shawon-nibir-miandad-mandal-automaticbacktransliterationofromanizedbengalibanglishtobengali-2023', 'https://link.springer.com/article/10.1007/s42044-022-00122-9', event);\">\n    Automatic back transliteration of Romanized Bengali (Banglish) to Bengali.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Shibli, G. S.; <a class=\"bibbase author link\" href=\"https://shawon-tanvir.github.io//publications/\">Shawon, M. T. R.</a>; Nibir, A. H.; Miandad, M. Z.;  and Mandal, N. C.\n</span>\n\n  \n\n</span>\n\n  <i>Iran Journal of Computer Science</i>, 6(1): 69–80.  2023.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/article/10.1007/s42044-022-00122-9\"\n     onclick=\"log_download('shibli-shawon-nibir-miandad-mandal-automaticbacktransliterationofromanizedbengalibanglishtobengali-2023', 'https://link.springer.com/article/10.1007/s42044-022-00122-9', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Automatic back transliteration of Romanized Bengali (Banglish) to Bengali [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shibli-shawon-nibir-miandad-mandal-automaticbacktransliterationofromanizedbengalibanglishtobengali-2023\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shibli-shawon-nibir-miandad-mandal-automaticbacktransliterationofromanizedbengalibanglishtobengali-2023')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{shibli2023automatic,\n  title={Automatic back transliteration of Romanized Bengali (Banglish) to Bengali},\n  author={Shibli, GM Shahariar and Shawon, Md Tanvir Rouf and Nibir, Anik Hassan and Miandad, Md Zabed and Mandal, Nibir Chandra},\n  journal={Iran Journal of Computer Science},\n  volume={6},\n  number={1},\n  pages={69--80},\n  year={2023},\n  publisher={Springer International Publishing Cham},\n  url_link = {https://link.springer.com/article/10.1007/s42044-022-00122-9},\n  abstract= {Back transliteration of Romanized Bengali to Bengali is the process of converting text written in the Latin alphabet back into the Bengali script. This is often done in order to improve the readability of Bengali text for Bengali speakers using a simple rules-based system, or an interactive transliteration tool. There are many ways to back transliterate from Romanized Bengali to Bengali, but most of them are either grapheme or phoneme based. This paper introduces a unique pipeline that uses nine open source back transliteration tools to automatically back transliterate Romanized Bengali to Bengali. The pipeline consists of seven steps: (1) processing the Romanized Bengali input; (2) acquiring human transliteration for performance comparison; (3) employing transliteration tools; (4) generating candidate transliterations; (5) post-processing the candidate transliterations; (6) selecting best candidate transliteration, and (7) evaluating the quality of the transliterations through several performance metrics. Experimental results reveal that our approach produced the highest BLEU-1 score of 81.28, BLEU-2 score of 60.75, BLEU-3 score of 44.45, BLEU-4 score of 30.46, and the lowest average Word Error Rate and Word Information Lost of 29.21 and 43.68, respectively, on 1000 Romanized Bengali texts. In terms of recall, we achieved a Rouge-L score of 0.7190.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Back transliteration of Romanized Bengali to Bengali is the process of converting text written in the Latin alphabet back into the Bengali script. This is often done in order to improve the readability of Bengali text for Bengali speakers using a simple rules-based system, or an interactive transliteration tool. There are many ways to back transliterate from Romanized Bengali to Bengali, but most of them are either grapheme or phoneme based. This paper introduces a unique pipeline that uses nine open source back transliteration tools to automatically back transliterate Romanized Bengali to Bengali. The pipeline consists of seven steps: (1) processing the Romanized Bengali input; (2) acquiring human transliteration for performance comparison; (3) employing transliteration tools; (4) generating candidate transliterations; (5) post-processing the candidate transliterations; (6) selecting best candidate transliteration, and (7) evaluating the quality of the transliterations through several performance metrics. Experimental results reveal that our approach produced the highest BLEU-1 score of 81.28, BLEU-2 score of 60.75, BLEU-3 score of 44.45, BLEU-4 score of 30.46, and the lowest average Word Error Rate and Word Information Lost of 29.21 and 43.68, respectively, on 1000 Romanized Bengali texts. In terms of recall, we achieved a Rouge-L score of 0.7190.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2022\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2022')\"\n      onkeypress=\"toggleGroup('group_2022')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2022</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"tanvir-shawon-mehedi-mahtab-rasel-aganbertbasedapproachforbengalitextclassificationwithafewlabeledexamples-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'tanvir-shawon-mehedi-mahtab-rasel-aganbertbasedapproachforbengalitextclassificationwithafewlabeledexamples-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3', event);\">\n    A GAN-BERT Based Approach for Bengali Text Classification with a Few Labeled Examples.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Tanvir, R.; <a class=\"bibbase author link\" href=\"https://shawon-tanvir.github.io//publications/\">Shawon, M. T. R.</a>; Mehedi, M. H. K.; Mahtab, M. M.;  and Rasel, A. A.\n</span>\n\n  \n\n</span>\n\n  In <i>International Symposium on Distributed Computing and Artificial Intelligence</i>, pages 20–30,  2022. Springer International Publishing Cham\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3\"\n     onclick=\"log_download('tanvir-shawon-mehedi-mahtab-rasel-aganbertbasedapproachforbengalitextclassificationwithafewlabeledexamples-2022', 'https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"A GAN-BERT Based Approach for Bengali Text Classification with a Few Labeled Examples [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/tanvir-shawon-mehedi-mahtab-rasel-aganbertbasedapproachforbengalitextclassificationwithafewlabeledexamples-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('tanvir-shawon-mehedi-mahtab-rasel-aganbertbasedapproachforbengalitextclassificationwithafewlabeledexamples-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{tanvir2022gan,\n  title={A GAN-BERT Based Approach for Bengali Text Classification with a Few Labeled Examples},\n  author={Tanvir, Raihan and Shawon, Md Tanvir Rouf and Mehedi, Md Humaion Kabir and Mahtab, Md Motahar and Rasel, Annajiat Alim},\n  booktitle={International Symposium on Distributed Computing and Artificial Intelligence},\n  pages={20--30},\n  year={2022},\n  organization={Springer International Publishing Cham},\n  url_link = {https://link.springer.com/chapter/10.1007/978-3-031-20859-1_3},\n  abstract= {Basic machine learning algorithms or transfer learning models work well for language categorization, but these models require a vast volume of annotated data. We need a better model to tackle the problem because labeled data is scarce. This problem may have a solution in GAN-BERT. To classify Bengali text, we have developed a GAN-BERT based model, which is an adapted version of BERT. We have used two different datasets for this purpose. One is a hate speech dataset, while the other is a fake news dataset. To understand how the GAN-BERT and traditional BERT models behave with Bangla datasets, we have experimented with both. With a small quantity of data, we are able to get a satisfactory result using GAN-BERT. We have also demonstrated how the accuracy increases as the number of training samples increases. A comparison of performance between traditional BERT based Bangla-BERT and our GAN-Bangla-BERT model is also shown here, where we can see how these models react to a small number of labeled data.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Basic machine learning algorithms or transfer learning models work well for language categorization, but these models require a vast volume of annotated data. We need a better model to tackle the problem because labeled data is scarce. This problem may have a solution in GAN-BERT. To classify Bengali text, we have developed a GAN-BERT based model, which is an adapted version of BERT. We have used two different datasets for this purpose. One is a hate speech dataset, while the other is a fake news dataset. To understand how the GAN-BERT and traditional BERT models behave with Bangla datasets, we have experimented with both. With a small quantity of data, we are able to get a satisfactory result using GAN-BERT. We have also demonstrated how the accuracy increases as the number of training samples increases. A comparison of performance between traditional BERT based Bangla-BERT and our GAN-Bangla-BERT model is also shown here, where we can see how these models react to a small number of labeled data.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"shawon-tanvir-alam-bengalihandwrittendigitrecognitionusingcnnwithexplainableai-2022\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/10103341\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shawon-tanvir-alam-bengalihandwrittendigitrecognitionusingcnnwithexplainableai-2022', 'https://ieeexplore.ieee.org/document/10103341', event);\">\n    Bengali Handwritten Digit Recognition using CNN with Explainable AI.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://shawon-tanvir.github.io//publications/\">Shawon, M. T. R.</a>; Tanvir, R.;  and Alam, M. G. R.\n</span>\n\n  \n\n</span>\n\n  In <i>2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)</i>, pages 1–6,  2022. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/10103341\"\n     onclick=\"log_download('shawon-tanvir-alam-bengalihandwrittendigitrecognitionusingcnnwithexplainableai-2022', 'https://ieeexplore.ieee.org/document/10103341', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Bengali Handwritten Digit Recognition using CNN with Explainable AI [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shawon-tanvir-alam-bengalihandwrittendigitrecognitionusingcnnwithexplainableai-2022\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>2 downloads</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shawon-tanvir-alam-bengalihandwrittendigitrecognitionusingcnnwithexplainableai-2022')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{shawon2022bengali,\n  title={Bengali Handwritten Digit Recognition using CNN with Explainable AI},\n  author={Shawon, Md Tanvir Rouf and Tanvir, Raihan and Alam, Md Golam Rabiul},\n  booktitle={2022 4th International Conference on Sustainable Technologies for Industry 4.0 (STI)},\n  pages={1--6},\n  year={2022},\n  organization={IEEE},\n  url_link = {https://ieeexplore.ieee.org/document/10103341},\n  abstract= {Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily under-stand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Handwritten character recognition is a hot topic for research nowadays. If we can convert a handwritten piece of paper into a text-searchable document using the Optical Character Recognition (OCR) technique, we can easily under-stand the content and do not need to read the handwritten document. OCR in the English language is very common, but in the Bengali language, it is very hard to find a good quality OCR application. If we can merge machine learning and deep learning with OCR, it could be a huge contribution to this field. Various researchers have proposed a number of strategies for recognizing Bengali handwritten characters. A lot of ML algorithms and deep neural networks were used in their work, but the explanations of their models are not available. In our work, we have used various machine learning algorithms and CNN to recognize handwritten Bengali digits. We have got acceptable accuracy from some ML models, and CNN has given us great testing accuracy. Grad-CAM was used as an XAI method on our CNN model, which gave us insights into the model and helped us detect the origin of interest for recognizing a digit from an image.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2020\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2020')\"\n      onkeypress=\"toggleGroup('group_2020')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2020</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"shawon-tanvir-shifa-kar-jubair-jamdanimotifgenerationusingconditionalgan-2020\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://ieeexplore.ieee.org/document/9392654\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'shawon-tanvir-shifa-kar-jubair-jamdanimotifgenerationusingconditionalgan-2020', 'https://ieeexplore.ieee.org/document/9392654', event);\">\n    Jamdani motif generation using conditional GAN.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  <a class=\"bibbase author link\" href=\"https://shawon-tanvir.github.io//publications/\">Shawon, M. T. R.</a>; Tanvir, R.; Shifa, H. F.; Kar, S.;  and Jubair, M. I.\n</span>\n\n  \n\n</span>\n\n  In <i>2020 23rd International Conference on Computer and Information Technology (ICCIT)</i>, pages 1–6,  2020. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://ieeexplore.ieee.org/document/9392654\"\n     onclick=\"log_download('shawon-tanvir-shifa-kar-jubair-jamdanimotifgenerationusingconditionalgan-2020', 'https://ieeexplore.ieee.org/document/9392654', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Jamdani motif generation using conditional GAN [link]\"\n       title=\" link\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\"> link</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/shawon-tanvir-shifa-kar-jubair-jamdanimotifgenerationusingconditionalgan-2020\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('shawon-tanvir-shifa-kar-jubair-jamdanimotifgenerationusingconditionalgan-2020')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{shawon2020jamdani,\n  title={Jamdani motif generation using conditional GAN},\n  author={Shawon, MD Tanvir Rouf and Tanvir, Raihan and Shifa, Humaira Ferdous and Kar, Susmoy and Jubair, Mohammad Imrul},\n  booktitle={2020 23rd International Conference on Computer and Information Technology (ICCIT)},\n  pages={1--6},\n  year={2020},\n  organization={IEEE},\n  url_link = {https://ieeexplore.ieee.org/document/9392654},\n  abstract= {Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled, the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of a desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix on it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.}\n}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Jamdani is the strikingly patterned textile heritage of Bangladesh. The exclusive geometric motifs woven on the fabric are the most attractive part of this craftsmanship having a remarkable influence on textile and fine art. In this paper, we have developed a technique based on Generative Adversarial Network that can learn to generate entirely new Jamdani patterns from a collection of Jamdani motifs that we assembled, the newly formed motifs can mimic the appearance of the original designs. Users can input the skeleton of a desired pattern in terms of rough strokes and our system finalizes the input by generating the complete motif which follows the geometric structure of real Jamdani ones. To serve this purpose, we collected and preprocessed a dataset containing a large number of Jamdani motifs images from authentic sources via fieldwork and applied a state-of-the-art method called pix2pix on it. To the best of our knowledge, this dataset is currently the only available dataset of Jamdani motifs in digital format for computer vision research. Our experimental results of the pix2pix model on this dataset show satisfactory outputs of computer-generated images of Jamdani motifs and we believe that our work will open a new avenue for further research.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);