Reviewerly/exHarmony huggingface dataset:
A Methodology for Selecting Smart Contract in Blockchain-Based Applications

In the era of e-commerce and digital economy where business parties as well as consumer-provider relationship are largely built on their agreement between them, agreement could comprise of several terms and policies on which both the parties are obligated, but in blockchain-based application, these terms and conditions are written in smart contract; smart contract is the executable code which can be executed in hardware or software without manual intervention. This paper reviews various practical applications of blockchain and challenges faced in implementing smart contract over blockchain. Second contribution of this paper is, we are proposing a methodology to be followed for designing and implementing smart contract in context of legal policies and difficulties encountered, as of right now there is no universal legal framework to be followed for smart contracts.
A Correction-Based Dynamic Enhancement Framework Towards Underwater Detection

To assist underwater object detection for better performance, image enhancement technology is often used as a pre-processing step. However, most of the existing enhancement methods tend to pursue the visual quality of an image, instead of providing effective help for detection tasks. In fact, image enhancement algorithms should be optimized with the goal of utility improvement. In this paper, to adapt to the underwater detection tasks, we proposed a lightweight dynamic enhancement algorithm using a contribution dictionary to guide low-level corrections. Dynamic solutions are designed to capture differences in detection preferences. In addition, it can also balance the inconsistency between the contribution of correction operations and their time complexity. Experimental results in real underwater object detection tasks show the superiority of our proposed method in both generalization and real-time performance.
Analysis of Reversible Data Hiding Techniques for Digital Images

With the rapid advancement of information technology, more data and images are now accessible through the Internet. As a consequence, some sort of authentication is required for such sensitive information. Intruders could be present in the transmission path between the transmitter and the receiver and capture the image/data. After the images have been taken, the intruder may be able to view its important content. In other cases, it may not be a problem. Access to such images or data, on the other hand, is not desired in industries such as medicine or the military. Data concealment is one of the most effective methods for avoiding this scenario. Data concealing is a technique in which secret data is integrated into the cover media and is accessible only to the sender and receiver. The method of data concealment has a wide range of applications, including copyright protection for films, videos, and other media. The data concealing method is more secure because attackers cannot alter the information concealed within the cover medium, even if any changes made by the intruder are visible to the sender and recipient. Steganography is perhaps the most effective data concealing methods. One of the uses of steganography is reversible data hide (RDH). After the embedded message has been removed, RDH is a method for retrieving the original cover without losing any data. The following is a synopsis of the research for this subject. A literature review is a brief overview of various techniques proposed by various researchers. The results show that the PSNR for the Lena image is 55.8 dB at a 0.05 bpp embedding rate, which is higher than the TBRDH technique. In terms of PSNR, the suggested LSWTRDH outperforms other embedding rates
Deep Learning Framework for Object Detection from Drone Videos

The extensive research in the space of deep learning has resulted in unimaginable solutions to various problems around us. Deep learning has spread its roots fairly into computer vision, speech, and text. Vision applications through CCTVs, mobiles, and cameras are dominating the market. Hence, this paper attempts to display that aerial-based videos which are possible to fetch through drones when coupled with computer vision-based object detection deep learning networks will give promising results leading to remarkable solutions benefiting the sectors of agriculture, surveillance, military, logistics, search, and rescue. Furthermore, drones have less battery and computational hardware; therefore, performing object detection on drones is highly challenging. It is very essential to understand how an object detection model would perform on certain architecture before deploying the model on to drone. Therefore, this paper implements object detection models on a drone-based images and makes an analysis that helps in choosing a best model to deploy by making a performance comparison.
Cross-Cultural Translation Studies in the Context of Artificial Intelligence: Challenges and Strategies

It is an enormous challenges and strategies in cross-cultural. As a result of the analysis in artificial intelligent, it isn’t a primary language for analysis teams. In society analysis, translation provides an additional challenge and strategy. In society analysis, the interpretation and comprehension of which suggest of data is concentrate. The aim of this text is to supply an overview of the interpretation methodology and explore variety of the challenges, like difficulties notice associate degree applicable translator, and also the importance of communication between the investigator and also the translator. A developing commercial center for dialect benefit on and rapidly developing innovation has introduced in an awfully unused wave of T&I computer program bundle advancement. In spite of interpretation tools’ wide choice of applications conjointly the current blast of AI, interpretation memory and interpretation tools’ machine learning calculations are expelled from palatable in giving dialect arrangements. Tragically, relate degree overreliance in this innovation. By dissecting cases wherever computational phonetics gave erroneous recommendations, this content points to investigate the association between rising innovation and antiquated expertness in T&I. The author believes that innovation and expertness aren’t in an awfully zero-sum amusement, and innovation doesn’t deny interpreters of work openings, in any case or maybe their expository aptitudes, fundamental considering, and stylish interests.
Community-Based Question Answering Site Using MVC Architecture for Rapid Web Application Development

There are many social platforms that connect everyone across the globe. But still, we find a gap in connecting with our faculty, seniors, alumni, etc., who belong to the same college and community. It is important to know the perspective of others regarding any aspect related to the college. Making the students, faculty and alumni connected together will give a broader perspective of things that are relevant. We have developed a web-based application which allows students to ask questions and also give answers to the previously posted questions. And also giving special privileges to faculty and alumni to actively share their knowledge on a same platform enabling every person of the college to stay united and get updated with the current news. Any person belonging to the college can post questions and also answer the questions. There is no such application for any college in which everybody has the liberty to express their ideas, thoughts, and knowledge for other’s problems. This idea not only makes us knowledgeable but also united.
Sign Language Interpreter

This article addresses a design of an apposite system which provides a supportive hand for hearing and speaking challenged person to expediently communicate with normal people. Normally, a sign language is adopted by them for their communication which needs an interpreter to convert into user's understandable language. The proposed system is used for converting the sign language into voice and text and vice versa. The idea of the proposed project is to come up with a device that captures the gestures and converts it to voice output as well as in text output and also to capture the voice by speech recognition module and convert it to corresponding sign language by displaying on a screen with the help of various elements like microphone, camera, sign language database and display unit. For the general-purpose indoor implementation, a facial expression recognition system can also be additionally included.
Real-Time Indian Sign Language Recognition Using Image Fusion

According to a 2018 report by the World Health Organization, nearly 63 million people in India suffer from partial or complete hearing impairment. They communicate using sign language (SL), a language that uses a set of specific non-verbal gestures, mainly involving hands. One major barrier in their way of communication is that the majority of the non-hearing and speech impaired population does not understand sign language, which raises the necessity of developing sign language recognition systems that can be standardized across the nation. This paper aims at giving the best recognition model for Indian Sign Language. The system involves the Bag of Visual Words technique for real-time prediction of the ISL with a comparative study of feature detectors and descriptors like SIFT, SURF, ORB, STAR + BRIEF, and FAST + FREAK. The results indicate that using the SURF feature detector and descriptor along with SVM yields an accuracy of 99.94% and gives a rotation threshold of up to 5 degrees. Meanwhile, parallel experimentation and comparison with the concept of CNN and ASL dataset have shown promising results such as 100% accuracy for ISL with CNN, 65.41% accuracy for ASL with SURF and SVM, and 99.77% accuracy for ASL with CNN.
Fake Currency Detection: A Survey on Different Methodologies Using Machine Learning Techniques

In the present world, everyone seems to be specializing in technologies; however, they’re not worrying concerning security. Most users and researchers are giving more attention of the machine learning applications and technology itself, however less attention toward genuinely of the detection of currency notes. In this paper, we represent overview of the various methods and the techniques of various fake currency detection using machine learning techniques. This paper for the most part centers around the security examination of the innovation and uses of the machine learning algorithms, and condenses the vulnerabilities and anticipates potential assaults and furthermore outlines the present status of detection of fake currency in terms of security protection.
InterCloud: Utility-Oriented Federation of Cloud Computing Environments Through Different Application Services

This white paper presents the vision, challenges, and key components of InterCloud for value-driven organizations in cloud computing environment. The related cloud environments support application scaling and career clouds. This method has been validated by performing a series of rigorous performance assessments using the Cloud Sim toolkit. The diagram shows that the hybrid cloud plan has great potential as it provides remarkably fast response times and resource savings in high-volume scenarios.
Ensemble Learning Techniques and Their Applications: An Overview

Despite of important successes in knowledge discovery, conventional machine learning techniques may fail to get satisfactory achievements when dispensing with complex and noisy data, like high-dimensional, imbalanced, etc. This is because achieving many features and the underlying structure of data is difficult with these methods. Ensemble learning, on the other hand, tries to combine data fusion, modeling, and mining into a coherent framework as a single research hotspot. Specifically, ensemble model generates a set of diverse classifiers and then creates a robust prediction model by combining the diverse classifier and creates a robust and reliable prediction model. Therefore, this paper systematically discusses ensemble learning methods and analyzes them critically. This paper also reports their suitability in different applications and their classifications. The paper targets the beginners in ensemble learning to provide a precise and compact view about ensemble learning methods.
RETRACTED CHAPTER: VoteChain: Electronic Voting with Ethereum for Multi-region Democracies

We present a contemporary alternative to the traditional voting process during national and regional elections in a democratic country. Our proposal is to swap out the tiring and tedious process of voting at a polling booth with an easy-to-use ubiquitous mobile app. In regions where this may not be feasible, we suggest our platform be used hand-in-hand with the traditional system, allowing for more flexibility to the voters. The elections are controlled by the Electoral Commission who, when needed, will set up an election on a blockchain powered platform we present—VoteChain. Elections are set up with the use of an administrative panel that allows for the creation of customized elections with timeline control, voter restrictions based on region, locality, educational background, and so on. The VoteChain platform comes with a user-friendly web application along with Android and iOS mobile apps for the voters to cast their votes through. The voters are authenticated with standard security measures such as with the use of one time passwords (OTP) sent to the voter’s registered email and mobile number. Voters are then allowed to cast their vote for the eligible elections available. Once cast, the voter is sent a follow-up by email with information regarding the election and the timeline for the declaration of results. The voters may check on the mobile app to see the voting progress and can check back to see the winners on the declared dates.
Neck Gesticulate Based Vehicle Direction Movement Control System to Assist Geriatrics

Transportation is an essential need for every individual to reach their destination. People who are physically fit are really fortunate as they can make their own drive on the vehicle while physically challenged and/or partially disabled people cannot drive a vehicle with the assistance of steering. As a result, a suitable solution for such a population to make a livelihood is required. The quadriplegic patients move independently from one place to another place with the help of the neckband by tilt movement of their head. The person driving the car will be supplied with a gadget that is worn around their neck and allows them to move the steering forward and backward without exerting any mental or physical effort. The prototype vehicle is driven by two 60 RPM geared motors, and it can make fast turns to the left, right, front, and back. Here, ATmega328p MCU is used as controller and four switches with ON/OFF control in the prototype model. Depending on the person neck movement the switch is press ON which makes the vehicle to move either a forward or backward and left or right directions.
A Secure Multi-tier Framework for Healthcare Application Using Blockchain and IPFS

Information technology advancement and the requirements in health care are to provide a variety of solutions to solve different problems. The better utilization of the technological advances of cloud computing, sensor networks, distributed processing, and distributed storage, security, and privacy in healthcare applications will give real-time monitoring of the patient’s vital signs, storage of healthcare data, and safe and continuous access of health records. This paper implemented a multi-tier framework that comprises the best features like blockchain-based security of health records, JPPF-based parallel processing, and decentralized storage using IPFS. Also, this framework provides the facility for remote patient monitoring.
Design of Efficient High-Speed Low-Power Consumption VLSI Architecture for Three-Operand Binary Adders

The three-operand binary adder is a fundamental component in cryptography and pseudorandom bit generators (PRBGs). The carry-save adder (CS3A) is a popular method for performing three-operand addition. In the final stage of the carry saving adder, the ripple-carry adder is used, resulting in a significant critical path delay. For three-operand addition, other adder like Han-Carlson adder (HCA) can be employed, which greatly reduces the delay, but area will increase. Hence, a new high-speed and area-efficient three-operand binary adder is designed, which uses pre-computation logic and carry prefix calculation logic for designing three-operand binary adder with less time and area. In comparison with the previous adders such as the carry-save adder and the Han-Carlson adder, the proposed adder uses less space and time. Xilinx ISE 14.7 tool is used to verify the synthesis and simulation.
Sahaay—A Web Interface to Improve Societal Impact

In our society, there are numerous people who are in need of essential day to day survival needs. We also have providers who are willing to help. But the providers could not find the right collaboration with NGOs and other non-profit organizations to help the needy. This is due to a clear absence of efficient interface between the people in need and people willing to contribute. Our application intends to focus on providing a medium for the users to contribute to the NGOs, senior citizens and physically disabled in small scales towards the bigger cause. This application is a two-user interface for the NGOs and the user. The two-end users of this application are the people in society and the organizations. It is a user-friendly website created using AngularJs and Django. MySQL database was used for storing data and performing manipulation. The website provides a platform for posting needs and for posting information regarding events. It provides an option for volunteers to contribute monetary and other help as requested. It allows volunteers to check their past contributions and an invoice will be provided for their contributions.
Expressive and Intuitive Models for Automated Context Representation Learning in Credit-Card Fraud Detection

The fraud detection literature unanimously shows that the use of a cardholder’s transaction history as context improves the classification of the current transaction. Context representation is usually performed through either of two approaches. The first, manual feature engineering, is expensive, restricted, and hard to maintain as it relies on human expertise. The second, automatic context representation, removes the human dependency by learning new features directly on the fraud data with an end-to-end neural network. The LSTM and the more recent Neural Feature Aggregate Generator (NAG) are examples of such an approach. The architecture of the NAG is inspired by manual feature aggregates and addresses several of their limitations, primarily because it is automatic. However, it still has several drawbacks that we aim to address in this paper. In particular, we propose to extend the NAG in the following two main manners: (1) By expanding its expressiveness to model a larger panel of functions and constraints. This includes the possibility to model time constraints and additional aggregation functions. (2) By better aligning its architecture with the domain expert intuition on feature aggregates. We evaluate the different extensions of the NAG through a series of experiments on a real-world credit-card dataset consisting of over 60 million transactions. The extensions show comparable performance to the NAG on the fraud-detection task, while providing additional benefits in terms of model size and interpretability.
A Comparison of Different Machine Learning Techniques for Sentiment Analysis in Education Domain

For any educational institution, students are one of the most significant and important stakeholders. The role of teacher is to improve the intellectual level of the students. Teacher, student, and academic institution perform better when they use innovative teaching strategies. In the realm of education, the primary need of today is to monitor students understanding about learning and to improve or change teaching methodologies. Several research used various approaches to collect student’s opinions in order to analyze feedback and present the results to teacher. To do this, we must investigate the application of various sentiment analysis techniques in order to determine student opinions from their comments. Thus, the purpose of this work is to investigate several approaches or mechanism for collecting students opinions in the form of feedback, via various platforms, analyzing and evaluating opinions using various machine learning and deep learning techniques. The paper also addresses the needs of a different stakeholders, like students, educators, and researchers, to improve teaching effectiveness by using sentiment analysis in education domain.
Timestamp Prediction Using Enhanced Adaptive Correlation Clustering-Based Recommender System for the Long Tail Items

This is the study of recommender systems. Timestamps for the items in the long tail are predicted based on the way the items are clustered. The long tail Items have lesser ratings and rarely appear in the recommendations in contrast to the popular items. This work divides the items into two parts according to their popularities, head part and the tail part namely. Long tail items are adaptively correlation clustered. The head part items are grouped according to their popularity. In this paper, the authors propose the prediction of the timestamp of the long tail items using the Enhanced adaptive correlation-based clustering.
Role of Punctuation in Semantic Mapping Between Brain and Transformer Models

Modern neural networks specialised in natural language processing (NLP) are not implemented with any explicit rules regarding language. It has been hypothesised that they might learn something generic about language. Because of this property much research has been conducted on interpreting their inner representations. A novel approach has utilised an experimental procedure that uses human brain recordings to investigate if a mapping from brain to neural network representations can be learned. Since this novel approach has been introduced, more advanced models in NLP have been introduced. In this research we are using this novel approach to test four new NLP models to try and find the most brain aligned model. Moreover, in our effort to unravel important information on how the brain processes text semantically, we modify the text in the hope of getting a better mapping out of the models. We remove punctuation using four different scenarios to determine the effect of punctuation on semantic understanding by the human brain. Our results show that the RoBERTa model is most brain aligned. RoBERTa achieves a higher accuracy score on our evaluation than BERT. Our results also show for BERT that when punctuation was removed a higher accuracy was achieved and that as the context length increased the accuracy did not decrease as much as the original results that include punctuation.
A Game Theoretic Flavoured Decision Tree for Classification

A game theoretic flavoured decision tree is designed for multi-class classification. Node data is split by using a game between sub-nodes that try to minimize their entropy. The splitting parameter is approximated by a naive approach that explores the deviations of players that can improve payoffs by unilateral deviations in order to imitate the behavior of the Nash equilibrium of the game. The potential of the approach is illustrated by comparing its performance with other decision tree-based approaches on a set of synthetic data.