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Gender discrimination is a universal issue that results in women’s predicament. From time immemorial women become victims of gender disparities. It starts with one’s own family. The present paper explores the crisis of gender discrimination and marital discord in the life of the protagonist in particular and Indian women in general. As a Child Sarita, the Protagonist tolerates gender discrimination and as a grown-up after her marriage due to marital discontentment. In this paper, many gender issues are explored which in due course of time decrease the moral spirit of women and make them weak and subordinate. Although Sarita is an educated and economically independent woman still she silently suffers. When it crosses limits she raises her voice to assert her self-esteem.

Authorship Attribution is the task of creating an appropriate characterization of text that captures the authors’ writing style to identify the original author of a given piece of text. With increased anonymity on the internet, this task has become increasingly crucial in various security and plagiarism detection fields. Despite significant advancements in other languages such as English, Spanish, and Chinese, Bangla lacks comprehensive research in this field due to its complex linguistic feature and sentence structure. Moreover, existing systems are not scalable with the increasing number of authors, and performance drops with the small number of samples per author. In this paper, we propose the use of Average-Stochastic Gradient Descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) architecture and an effective transfer learning approach that addresses the problem of complex linguistic features extraction and scalability for authorship attribution in Bangla Literature (AABL). We analyze the effect of different tokenization, such as word, sub-word, and character level tokenization, and demonstrate the effectiveness of these tokenizations in the proposed model. Moreover, we introduce the publicly available Bangla Authorship Attribution Dataset of 16 authors (BAAD16) containing 17,966 sample texts and 13.4+ million words to solve the standard dataset scarcity problem and release six variations of pre-trained language models for use in any Bangla NLP downstream task. For evaluation, we used our developed BAAD16 dataset as well as other publicly available datasets. Empirically, our proposed model outperformed state-of-the-art models and achieved 99.8% accuracy in the BAAD16 dataset. Furthermore, we showed that the proposed system scales much better with the increasing number of authors, and performance remains steady even with few training samples.

Due to the breathtaking growth of social media or newspaper user comments, online product reviews comments, sentiment analysis (SA) has captured substantial interest from the researchers. With the fast increase of domain, SA work aims not only to predict the sentiment of a sentence or document but also to give the necessary detail on different aspects of the sentence or document (i.e. aspect-based sentiment analysis). A considerable number of datasets for SA and aspect-based sentiment analysis (ABSA) have been made available for English and other well-known European languages. In this paper, we present a manually annotated Bengali dataset of high quality, BAN-ABSA, which is annotated with aspect and its associated sentiment by three native Bengali speakers. The dataset consists of 2619 positive, 4721 negative and 1669 neutral data samples from 9009 unique comments gathered from some famous Bengali news portals. In addition, we conducted a baseline evaluation with a focus on deep learning model, achieved an accuracy of 78.75% for aspect term extraction and accuracy of 71.08% for sentiment classification. Experiments on the BAN-ABSA dataset show that the CNN model is better in terms of accuracy though Bi-LSTM significantly outperforms CNN model in terms of average F1-score.

Violence has been directed at Rohingya refugees seeking asylum to Bangladesh since their arrival from Myanmar during the seventies. In Myanmar, the Rohingyas were denied citizenship which rendered them stateless. Bangladesh is one of the world’s most densely populated countries with congested urban areas and villages, making it difficult to cope with the burden of Rohingya refugees. Since 10 years, Bangladesh has quietly adopted a policy to push the refugees to Myanmar, yet the Rohingyas have consistently managed to return, slipping through the porous border, usually by river crossings. The worst influx entered is nearly close to a million of Rohingya people since 25 August 2017. Nearly 60% of these new refugees are children below the age of 18; among these Rohingya refugees, 14,000 are unaccompanied children. These unaccompanied children are at particular risk of human trafficking, sexual abuse, child labour and child marriage. Moreover desperate families are selling their children into bonded labour, most commonly in the fish drying industry that dominates the nearest city, providing a fertile hunting ground for traffickers looking for young girls to recruit as maids etc. Also thousands of Rohingya children are at risk of an agonizing death from malnutrition after reaching the safety of refugee camps in Bangladesh.

Language models are at the core of natural language processing. The ability to represent natural language gives rise to its applications in numerous NLP tasks including text classification, summarization, and translation. Research in this area is very limited in Bangla due to the scarcity of resources, except for some count-based models and very recent neural language models being proposed, which are all based on words and limited in practical tasks due to their high perplexity. This paper attempts to approach this issue of perplexity and proposes a subword level neural language model with the AWD-LSTM architecture and various other techniques suitable for training in Bangla language. The model is trained on a corpus of Bangla newspaper articles of an appreciable size consisting of more than 28.5 million word tokens. The performance in comparison with various other models depicts the significant reduction in perplexity the proposed model provides, reaching as low as 39.84, in just 20 epochs.

A frequently sought output from a shotgun proteomics experiment is a list of proteins that we believe to have been present in the analyzed sample before proteolytic digestion. The standard technique to control for errors in such lists is to enforce a preset threshold for the false discovery rate (FDR). Many consider protein-level FDRs a difficult and vague concept, as the measurement entities, spectra, are manifestations of peptides and not proteins. Here, we argue that this confusion is unnecessary and provide a framework on how to think about protein-level FDRs, starting from its basic principle: the null hypothesis. Specifically, we point out that two competing null hypotheses are used concurrently in today's protein inference methods, which has gone unnoticed by many. Using simulations of a shotgun proteomics experiment, we show how confusing one null hypothesis for the other can lead to serious discrepancies in the FDR. Furthermore, we demonstrate how the same simulations can be used to verify FDR estimates of protein inference methods. In particular, we show that, for a simple protein inference method, decoy models can be used to accurately estimate protein-level FDRs for both competing null hypotheses.

Every writer has his/her personal writing style. In the era of technology, authorship attribution is a big problem in natural language processing because fake writers can publish other writers' contents and it is difficult to identify the real author. Various kinds of features such as frequently used words, word length, sentence length, WH words, Number, etc. were analyzed to identify and specify a writer’s writing style. A statistical analysis of different articles by different writers was created that can identify the real author. An artificial neural network model was developed to identify a writer from an unknown document and it achieved above 85% accuracy rate for each writer. In this article, writings of five Bangladeshi authors named Imon Jubayer (IJ), Humayun Ahmed (HA), Muhammed Zafar Iqbal (MZI), Kazi Nazrul Islam (KNI) and Hassan Mahbub (HM) are observed.

Quantum computation, the ultimate goal of future computing, is an interesting field for researchers. The concept of quantum computation is based on basics of quantum mechanics. A quantum computer is a device for computation that makes direct use of quantum mechanical phenomena such as superposition and entanglement, to perform operations on data. The basic principle behind quantum computation is that quantum properties can be used to represent data and perform operations on these data. A quantum computer operates by manipulating the qubits with a fixed sequence of quantum logic gates. The sequence of gates to be applied is called a quantum algorithm. The field of quantum computation algorithm is fast moving and the scope is vast. Major quantum algorithms are summarized in this paper along with their applications. General Terms Quantum Algorithms.