Neural Machine Translation from Bengali Language to English language and vice-versa

Bengali ranks among the first ten spoken languages in the world with a native speaker numbering about 230 million people.  With UNESCO declaring 21st February as International Mother Language Day to commemorate the laying down of lives by five Bangladeshi students for the cause of their mother tongue, Bengali has come into the radar of worldwide  attention . Though significant amount of prose, poetry have been written in Bengali language and large number of newspapers in Bengali get published daily, technically it is still considered a Low Resource Language (LRL) unlike English or French which are High Resource Language (HRL). The reason is not far to seek as corpora in varied domains such as short stories, sports, politics, agriculture etc is less in number and even when they are available, the size is less. Machine translation (MT) is difficult to perform in Bengali as parallel corpora from Bengali to other languages and vice versa is few and far between and when they are available they suffer from the problems of size and quality. This work is aimed at implementing one state of the art model in Neural Machine Translation (NMT) which is called the self-attention transformer model to perform translation from English to Bengali and vice versa. Though a couple of research work has been published in the recent years on MT from English to Bengali, they are mostly domain specific. This paper does not focus on any specific domain for NMT from English to Bengali and as such may be conceived as a more of general domain NMT from English to Bengali which is more difficult than domain specific NMT. Performance evaluation of the model was done  using BLEU version-4  vis-à-vis translations of well known English-Bengali MTsystems

On A Quiet Evening At Home

With Ukulele arrangement. Contains advertisements and/or short musical examples of pieces being sold by publisher.https://digitalcommons.library.umaine.edu/mmb-vp/7028/thumbnail.jp

Comments on Information Erasure in Black Hole

We analyze the Kim, Lee & Lee model of information erasure by black holes and find contradictions with standard physical laws. We demonstrate that the erasure model leads to arbitrarily fast information erasure; the proposed physical interpretation of information freezing at the event horizon as observed by an asymptotic observer is problematic; and information erasure, whatever the process may be, near the black hole horizon leads to contradictions with quantum mechanics if Landauer's principle is assumed. The later part of the work demonstrates the significance of the "erasure entropy." We show that the erasure entropy is the mutual information between two subsystems.Comment: 13 pages, clarified some issues in detai

Improving Service Delay in Smart Parking System in Smart Cities with 5G

Rapid urbanization and population growth leads to substantial increase in vehicular traffic while reducing empty spaces. This necessitates an enhanced parking systems to efficiently address the parking space management. The smart city and Internet of Things (IoT) holds a very high potential to solve this problem. However, the data traffic generation at the edge is increasing rapidly. Tremendous growth in IoT is putting a high congestion on cloud services. To ease up the congestion issue, we have proposed an efficient fog-based communication model for the smart city parking management. In this work, we have proposed a parking system management model based on 5G with low latency and green communication. Our model reduces congestion at the cloud and enable faster processing by addressing the parking system locally. Furthermore, our model finds out the nearby parking without putting additional load to the cloud. Simulation result shows the performance efficiency of the proposed model in terms of response time, average cost and service delay

Functional Environmental Screening of a Metagenomic Library Identifies stlA; A Unique Salt Tolerance Locus from the Human Gut Microbiome

Functional environmental screening of metagenomic libraries is a powerful means to identify and assign function to novel genes and their encoded proteins without any prior sequence knowledge. In the current study we describe the identification and subsequent analysis of a salt-tolerant clone from a human gut metagenomic library. Following transposon mutagenesis we identified an unknown gene (stlA, for “salt tolerance locus A”) with no current known homologues in the databases. Subsequent cloning and expression in Escherichia coli MKH13 revealed that stlA confers a salt tolerance phenotype in its surrogate host. Furthermore, a detailed in silico analysis was also conducted to gain additional information on the properties of the encoded StlA protein. The stlA gene is rare when searched against human metagenome datasets such as MetaHit and the Human Microbiome Project and represents a novel and unique salt tolerance determinant which appears to be found exclusively in the human gut environment