Reviewerly/exHarmony huggingface dataset:
Domain Adaptation of Pretrained Models for Telugu Wh-Questions

Question answering (QA) systems which can support the English language have been improvised in the past few years using several techniques. Presently, we are tackling this problem for one of the Indic languages, Telugu. Telugu is one of the majority languages used by the southern states of India. Literature suggested that a few techniques for solving the challenges of QA system for Telugu language have been pro- posed, with limited use. In QA, complex information retrieval methods are utilized to extract information from all documents, with some models focusing solely on extracting the most important data. As a result, two strategies are commonly used in any QA system, information retrieval and extraction. The main aim of this paper is to build a machine reading comprehension (MRC) model for Telugu corpus. Presently our ideology is to be identified by keyword matching technique including the additional structures from the question as well as the answer. The distance between word embedding is used to find similarity and finally suggest the right answer to the user. Limited availability of Telugu corpus over the internet is the first challenge to be faced apart from other challenges. However, in the Telugu language, we might face structural ambiguity compared to English, and hence, choosing an efficient technique is also a difficult task to get a precise answer to the given question. We have generated/augmented the existing Telugu QA dataset ‘TyDiQA’ and fine-tuned attention-based transformer model in extracting relevant answer from a given context to wh-questions.
CIA Tool Using Python and mypy for Python Applications

Nowadays, software is very dynamic and needs to be changed constantly as per the requirements of the clients and the business. The principle aim of maintenance of software is to update and modify the developed application after the production phase to correct any faults that may occur, this is done to improve performance or to enhance the functionality. The average cost of maintenance of the software is higher than 50% of all SDLC phases. In case the change request comes up with a need to modify the application, the software developers have the need to identify the modifying risk before making the change. This is where the proposed model is different from the existing IDE’s. Change impact analysis (CIA) is the process using which we can find the various effects of a change request in a real-time application before making the changes. This method focuses on developing a novel CIA approach for Python that can be extensively used by the application developer during the maintenance of software phase. The proposed 3-pass algorithm helps the developer/programmer to know “which segment of the code base (of a Python application) is affected if a modification is to be actually made to a specific class/method/variable”. It displays the methods and classes and shows the exact lines that are affected, we achieve this by using mypy for static type analysis, and we also propose a test client, test interface, and a sample set of test cases.
Change Impact Analysis for Evaluating Java and Python Applications Using Python

Software modification impact analysis could be a methodology for decisive in measuring the impacts of a change or estimating what has to be modified to work with the upcoming changes. This paper work is typically used in the maintenance phase since software programs must be adjusted to meet the needs of users or clients. This paper aims to evaluate the Java and Python applications using change impact analysis. The proposed approach helps developers to determine which parts of a Java/Python application is affected by specified change given as an input which is either class/variable/method name as change with 95–96% accuracy in the Python environment tested in visual studio code. The developed tool name is Anpert. It also displays the before and after change, the type of change whether it is variable change or method name change or class name change along with true positives, true negatives, false positives for specified change for a given Java or Python application, and it also determines how long it took to analyze the impact (i.e., impacted percentage for a given change for tested application) and time taken for change impact analysis.
CNN Depending Spectrum Sensing for Effective Data Transmission in Wireless Communication

This article is about convolutional neural network-dependent spectrum sensing for efficient data transfer in wireless communications. In present days, as the wireless devices users have been enormously growing, the serviceable spectrum is not sufficient and as a result scarcity has been arising, and there is a necessity to use the spectrum in a more dexterous way for increasing the improved wireless communication system. Spectrum sensing is one of the methods for efficiently utilizing the spectrum. There are conventional methods existing for spectrum sensing nowadays, but through the advent of machine learning, sensing of spectrum can be performed in a more effective way. As convolutional neural networks need a smaller number of computations as related to regular conventional neural networks, in this paper, CNN-based classifier using scaled exponential linear unit (SELU), exponential linear unit (ELU) along with activation functions has been proposed to find the signal presence in noisy environment for spectrum sensing to enable the secondary users (SU) for exploiting the channel when primary users (PU) are quiet. And comparison among these activation functions has been given away and it has been found that ELU using CNN gives the best performance while training the network.
Leveraging the Potentiality of Blockchain Technology for Waste Management in Smart City Development

Waste following is turning into a significant worry for created nations as well as creating areas. Smart urban communities can conquer ecological issues brought about by ill-advised waste removal by working on human well-being, safeguarding the amphibian biological system, and diminishing air contamination. Waste following is significant not just for a straightforward revealing framework viable with natural guidelines yet additionally for financially suitable waste assortment and recuperation arrangements. Nevertheless, the present frameworks, approaches, and advancements utilized for waste management are manually incorporated. Likewise, an enormous piece of the current waste management frameworks inside smart urban communities misses the mark in giving functional straightforwardness, discernibility, review, security, and believed information provenance highlights. Regular innovations don’t give an adequate degree of straightforwardness and synchronization among various elements. The aim of introducing blockchain can improve the effectiveness of waste management. This paper investigates the vital job of blockchain technology in overseeing waste inside smart urban communities as it can provide discernibility, changelessness, straightforwardness, and review highlights in a distributed, reliable, and safe way. This paper also examines the open doors of utilizing blockchain in different waste management applications. Besides, we present wise conversations on a few continuous blockchain-based SWM use cases to feature the feasibility of blockchain in waste management. At last, we discuss open problems that go about as upcoming exploration headings.
A Comprehensive Review on the Subject of Securing Web Application Data in Cloud Environments

Computing in the cloud is an extremely significant component of the Internet, as it is now provided to the commercial sector, the educational sector, and private consumers. Data protection is an extremely important concern in modern society. Data privacy and data security are becoming more important in the context of the computer environment for the sake of future growth in areas such as the government, industry, and business. As a result of the new security and privacy challenges that will emerge as a result of this, greater attention will be paid to issues relating to the confidentiality, authenticity, and integrity of data stored in the cloud. This paper provides an overview of the research that has been done on cloud computing and security research that is still underway. For the purpose of this inquiry, we have collected close to fifty current publications that have been published in authoritative journals such as Springer, IEEE, Elsevier, and Taylor & Francis. Each study article details a unique set of procedures and methods for enhancing cloud computing security with today's technologies.
TNM Breast Cancer Staging and Survival Rate Analysis

Breast cancer disease has become very common in women’s life and most dangerous as it leads to life threatening; the first breast cancer subject was reported in Egypt country. The causes of breast cancer can be due to family history, abnormal increase in the breast size, hormonal therapy after menopause, tobacco intake, obesity and gene mutation which are general reasons for BC disease. Different treatment helps to supper and kill the cancer cells. The treatment plan depends upon the stage of the cancer. Breast cancer stage is categorised using TNM breast staging. The survival rate depends upon the stage of the cancer. In this paper, the case studies are on the breast cancer staging and artefacts used to classify. The graphical and statistical analysis of survival rate is based on the age and stage of the cancer.
Graph Key-Based Encryption Technique for IoT Networks: Graph-Affine Cipher

The Internet of things (IoT) is a cutting-edge paradigm that is getting a lot of attention in the realm of contemporary wireless telecommunications. However, in other circumstances, energy-constrained devices and security issues restrict the IoT network’s deployment. Symmetric ciphers provide better protection over asymmetric ciphers, but a major challenge is to deliver a session key to other or both communicating parties. A variety of authentication-based public-key or symmetric key mechanisms are developed to address this issue. In this paper, we present a novel and simple graph-based session key generation technique, which eliminates complexity of the usual encryption process. Affine cipher is enhanced by deriving a unique pair of keys from the graph for enciphering each character of the message. The proposed graph-affine cipher presents a tough challenge for cryptanalysts to derive graph or session key from the available cipher text and single node number that is shared for each session.
Controllable and Abstractive Summarization of Clinical Trial Descriptions Using LEX-LDA Model

Clinical trials ensure development in a healthcare system that is evidence based and holds enormous potential for benefiting patients. Despite their importance, with an increased population it is difficult to find relevant information about the patients which may lead to delays in clinical trials. The awareness of potentially relevant trials and access to clinical trial information is required so that physicians can efficiently identify patients and enroll them into clinical trials. Toward tackling this challenge, we offer two ways to choose one approach over another. In this paper, we propose a model for better summarization control, which has a reward function in addition to the efficient summarization control. In addition, we proposed a LDA topic modeling text summarization approach to compare with the existing stochastic graph-based algorithms, such as LexRank and TextRank approaches. BLEU and ROUGE evaluation metrics are used to evaluate the efficiency of the generated text summaries. The focus of this study is not solely on the automated text summarization but also on controllable text for comparing the effectiveness of automated text summarization. We have demonstrated the potential of the proposed model; we have simulated the experiments using publicly accessible Mendeley datasets and the COVID-19 corpora. LEX-LDA, a proposed approach has been emerged and has a better performing model with better performance in ROUGE-1 F1-score of 0.45 and in ROUGE-L with 0.65, followed by the CMDP framework has been proven to improve the ROUGE score by a significant margin.
N-Gram based Convolutional Neural Network Approach for Authorship Identification

Finding the author of a writing among a group of authors whose authorship is unknown or questioned is known as “authorship identification.” Finding the proper author attributes to obtain stylistic data is the major component of this task. From the standpoint of machine learning, it can be viewed as a multiclass, single-label text classification problem in which the author’s name represents a text document's class label. This type of problem, which identifies patterns in the writings of the same author, is solved using stylistic elements. Many aspects, including vocabulary, syntactic, semantic, and n-grams, can be utilised to record the style information of writers. The authors of this study developed convolutional neural network (CNN) models that use character-level N-grams, such as 1-g, 2-g, and 3-g, to identify the authors of a given text. Experimental studies on three different datasets: novels extracted from Project Gutenberg of different authors and genres and Stamatatos 06 Author Identification: C10-Attribution—confirm that this model facilitates author identification.
Leveraging Cloud-Native Microservices Architecture for High Performance Real-Time Intra-Day Trading: A Tutorial

Day trading has been gaining attention from prospective investors over the past decades, even more so in the last decade due to a plethora of factors such as instantaneous availability and accessibility to information such as social media, news, Internet of Things (IoT), availability of market’s sentiment data associated with them, and increased broker discounts. This tutorial aims at providing a framework for intra-day trading that supports scalability, easily maintainable by creating a low coupling, high cohesion, and stateless architecture between client and server, considering the time-sensitive nature of transactions involved. This provides the benefits of additional business value for Software as a Service (SaaS) providers based on high productivity as well as enhanced end-user experience. To achieve these objectives, a combination of cloud-native architectural components, such as microservices and event streaming using Kafka, is used in this tutorial to provide a near real-time experience to end users. Additionally, to ensure security, robust authentication management is used in the proposed solution to control the access of read and write operations on the Firebase cloud database.
A Systematic Review on Compatibility Requirements for Communication in Hetero-Multi Blockchain Systems

Hetero-Multi Blockchain (HMBC) communication systems is a method of conveying the information between one or more blockchains. HMBC communication is encouraged by two necessities: retrieving data and retrieving functionality which exists in other organizations. The dispersed nature of HMBC technology can be exploited to create a unique data sharing and access system that is protected, flexible, and more reliable. It allows swapping and moving data between systems, but understanding and interconnecting with the swapped data depends on the compatibility level. This article is an attempt to systematically summarize the compatibility requirements as consensus mechanisms and smart contract protocol for communication across the borders of HMBC systems. Various limitations and issues of consensus mechanisms and smart contracts of HMBC is also identified along with the possible solutions. Finally, this article reviews the existed HMBC solutions and related issues.
Role of AI for Data Security and Privacy in 5G Healthcare Informatics

The encouraging prospects of 5G and Internet of things (IoT) have brought significant advancement in the Healthcare domain. Medical IoT primarily uses cloud computing approaches for real-time remote monitoring of patient’s health by employing cyborg-automated techniques such as tele-ultrasound, telestenting and cardiac catheterization. As a result, hospital services have become more convenient and cost-effective. However, the dispersed environment of the sensor-cloud based services poses an enormous threat to patient data privacy and security. Moreover, in a generation dictated by cyber-attacks, data breaches can provide full access to patients’ sensitive data such as personally identifiable information and medical history. The necessity to yield new measures for Data Security and Privacy in the epoch of 5G Healthcare Informatics stems from the shortcomings of the prevailing security methodologies like data encryption, third party auditing, data anonymization, etc. In order to address the above challenges and explore the most promising use cases of 5G in the healthcare sector, we discuss the role of Artificial Intelligence (AI), Machine Learning (ML)/Deep Learning (DL) techniques and Blockchain applications that coalesce in overcoming existing hurdles.
Extractive Text Summarization Using Topic Modelling and Entropy

In Natural Language Processing, text summarization is one of the prominent applications in digital trend for extracting information from a single or multiple documents and making a summary of the document/documents. There has been an extensive study on extractive summarization process. Here, in the proposed model, topics are generated by using topic modelling techniques like LDA or HDP, Probabilities are generated after topics are classified with selected optimal number of topics, using the probabilities of the term for each topic the sentence probability is calculated using term probability and entropies are calculated for topic and sentence, Sentence scores are calculated by taking Sentence entropy in topic spaces of the topic which has highest entropy in term space and similarity score between the sentences. The top sentences that are having score which is more than threshold score those sentences are considered and if any duplicates are present in the list of sentences, those duplicates are removed, remaining sentences are joined to form a summary and evaluated against the target summary using ROUGE metrics. The LDA model achieved the accuracy of 75% as compared with state-of-the art methods by considering the WikiHow dataset.
GPU Based AI for Modern E-Commerce Applications: Performance Evaluation, Analysis and Future Directions

Every year in the United States, the 4th Thursday of November is commemorated as Thanksgiving Day. The next day which is a Friday is known as Black Friday. This day is the busiest day in terms of shopping because all major retailers and e-commerce websites offer massive amounts of discounts and deals. Hence, this sale is termed the Black Friday Sale. There is a lot of potentials to make a profit even after such discounts if the sales patterns from previous years’ data are analyzed properly. Investigating various demographics of customers and analyzing the purchase amount spent by each customer on various products, there is a need to find out patterns for this behavior. Therefore, we utilized classical and modern artificial intelligence and machine learning techniques such as Linear Regression, Neural Networks, Gradient Boosting Trees and AutoML, to make predictions on the available test data to find a model for the most accurate predictions. We used a Graphical Processing Unit (GPU)-based high-performance computing environment to analyze the performance of various artificial intelligence and machine learning techniques for e-commerce applications. Since the dataset contains information about various demographics and backgrounds of the customers, we encoded the data in an easy-to-understand format and reduce the bias for each algorithm. Furthermore, various techniques of feature engineering are used, and new features are generated from existing features by grouping the target variable purchase, for each sub-category of that feature. Erroneous predictions are also handled; ultimately, the model performed well on unseen test data. Finally, this study can help researchers to find the best model with more accurate predictions for e-commerce applications.
Automatic Multiple-Choice Question and Answer (MCQA) Generation Using Deep Learning Model

The ability to extract useful information from very large data sources is known as data mining. Nowadays, semantic web is increasing its user attention in several research inventions especially in the field of artificial intelligence and neural networks. As the world is using AI and ML for all types of applications, there is a huge demand for these domains. In this current work, we are taking MCQA as the main problem statement and trying to propose a novel model which can automatically generate MCQA with more accuracy and less time delay. All the primitive models are either manual approach or ML-based generation models which are not completely successful in designing MCQS without any errors. As the data size increases, the primitive methods try to get a problem like overfitting, and all the data is not going to generate MCQA properly. Hence in this current work, we try to design a deep learning model for generating multiple-choice questions and answers automatically for a given phrase or paragraph with more accuracy and efficient way. By conducting various experiments on our proposed model by taking sample Stanford question answering dataset, which consists of more than one lakh question, some answered and some are not answered. The proposed model is trained on this dataset and finally able to generate MCQA for any type of context which is given by the end-user.
Blockchain-Based Medical Report Management and Distribution System

Generally, the Hospital operations contain loads of scientific reviews which can be a crucial part of operations. As a result of integrating pathology and other testing labs within the medical center, hospitals today have improved their business operations while also achieving greener and faster diagnoses. Many different strategies are used in hospital operations, from patient admission and control to health center cost management. This will raise operational complexity and make it more challenging to manage, especially when combined with newly introduced offerings like pathology and pharmaceutical control. In order to overcome this issue, we employ the Hyperledger notion and a blockchain era to retain the data of each individual transaction with 100% authenticity. Instead of using a centralized server, all transactions are encrypted and kept as blocks, which are then used to authenticate within a network of computers. Additionally, we employ the hyper ledger concept to associate and store all associated scientific files for each transaction with a date stamp. This makes it possible to confirm the legitimacy of each document and identify any changes made by someone else. This consultation defines that affected person’s clinical record is personal and every affected person has his very own privacy. To guard the reviews from hackers or enemies, who will make changes on clinical reviews and additionally saving the statistics without lacking any content material which performs an important position to shape a life. To study reviews, we are using a block chain method which splits the information into modules. Using this method hackers or enemies can’t get the right information. “To bring forward a secure, safe, efficient, and legitimate medical report management system” is the primary goal of this project.