Reviewerly/exHarmony huggingface dataset:
Decentralized Digital Identity Wallet using Principles of Self- Sovereign Identity Applied to Blockchain

Human dignity demands that personal information be hidden. Currently, due to the dominance of large internet companies/cloud service providers, the control over the identity is not with the identity holder, leading to privacy concerns. The need for decentralization of identity is because it gives back control of identity credentials to identity holders and allows them to control when, how, and with whom to share their credentials. Self-sovereign Identity is an arising concept, which provides a way for digital identification. It enables the entities to control their identity and data flow in the digital world while enhancing privacy and security. Major privacy concerns are because of the numerous attacks and data breaches that can happen when sensitive information like identity credentials is stored on a centralized system used by the existing systems. Therefore, we propose a blockchain based self-sovereign identity platform where the user’s mobile wallet application. The proof of the Identity credentials are kept in a decentralised storage system based on the blockchain. This platform provides a Zero-Knowledge Proof (ZKP) mechanism to verify the information.
Predicting Survival of People with Heart Failure Using Oversampling, Feature Selections and Dimensionality Reduction

Cardiovascular diseases are deadly and kill millions of people around the world every year. Heart failure is one of the unfortunate consequence where the heart is unable to pump enough blood for the body. A medical checkup of these patients with attributes including creatinine phosphokinase, ejection fraction, serum creatinine and serum sodium can be used for analysis. In this paper, we have analysed this clinical data and built machine learning models that can predict the survival rate of heart failure of a person. We have used various dimensionality reduction techniques to analyse the data with the aim of reducing the dimensions of the dataset. Finally, we reduced the overfitting of data using Synthetic Minority Oversampling Technique(SMOTE) and Adaptive Synthetic(ADASYN).
Design and Analysis of BFO-PBFO based Optimized Watermarking Algorithm for Medical Images

Day by day digital watermarking is getting more attention in medical image security related research area. As we know digital multimedia applications provide a robust and easy data modification and data alteration. However it requires a special attention related to owners privacy and authenticity of data while data transmission. Digital data can be easily copied and used by some-one else, which affects the copyright holders in safeguarding the intellectual property right of owner. Thus a lot of research has been carried in this area to secure the image. There are several methods in image security like Cryptography, Steganography and Digital Watermarking. The technique for embedding the data into some advanced digital media is digital watermarking. Image watermarking can be done in Spatial and Transform domain. Trans-form domain techniques are most frequently used. Transform domain techniques uses DWT (Discrete Wavelet Transform), DCT (Discrete Cosine Transform) and SVD (Singular Value Decomposition) etc. There are a few strategies have been discussed in the literature based on DWT and DCT. In this work, we have proposed a DWT-DCT based watermarking and BFO (Bacterial Foraging optimization) and PSO/PBFO (Particle Swarm optimization) based optimization technique for secure medical image transmission. The PBFO optimization improves the efficiency of watermarking scheme. It is used in several applications of optimization of algorithm. The imperceptibility and robustness of algorithm has been computed using PSNR (Peak-signal-Noise Ratio), NCC (Normalized Cross Correlations) and IF (Image Fidelity). The results show a best performance in case of DWT-DCT-PBFO watermarking.
GeoFencing and Cryptocurrency Exchange Platforms; with Blockchain Anonymity

Cryptocurrency is growing at the top pace, with a total market capitalization exceeding $2.2 trillion and around 100 million crypto users worldwide. After an investment is made, where and how to keep the tokens is determined by the user. The public and private keys of the wallet are used to identify the tokens as well as initiate the transactions. A hot wallet is just a tool that enables users to receive, store and send tokens, and the security of the same mainly depends on the user behavior. Moreover, the hot wallet is more vulnerable to thefts and hacks than the cold wallet as it is connected to the internet. Cryptocurrency uses the Blockchain as the underlying technology. Security issues can be alarming with the drastic rise in the number of apps that help even the novice to start investing in crypto. In this work, we aim to study the security issues faced by the exchange platforms and how well geofencing can mitigate the security risks of the wallet. Here, the coin mixing technique is implemented with user parameters to enforce anonymity and security in the Blockchain and obfuscates the attackers with the randomness in the block.
Deep Crowd Analysis to Spot Social Distancing Violations in Post-COVID 19 Lifestyle

Crowd analysis is a new field of study that involves processing a large group of people to examine one or more of their behaviors. Deep learning is an appropriate technique for crowd analysis using a convolutional neural network. To calculate the distance between crowd members and to identify social distance violations, a deep crowd analysis is proposed in this study. Pre-trained in a single class To discover the region of interest, CNN is utilised to classify people (RoI). The people in the picture are then localized using a density map. The reference point used to calculate the distance between the people is the centroid of the isolated areas in the density map. A social distance violation is reported if the estimated distance is less than the specified threshold distance (3 meters). Between the two ROIs, a distance measured in pixels is determined.
Investigation on Impact of Fast Moving Cumulus Clouds on Power Quality in Deregulated Powergrid with Large Scale Solar Integration

Emerging deregulation policies together with the distributed renewable resource had led to the restructuring of power grid operation as several interconnected self-reliant clusters. Forecasting models play a vital role to handle its inherent variability so that the safe and reliable operation of the grid can be ensured. However, the irradiance variation caused by a fast-moving cumulus cloud in the nowcasting horizon results in a fairly higher forecast error. This in turn leads to fluctuating power injections with considerable ramp rates. In a deregulated grid with weaker reactive power support, these fluctuating injections can cause severe voltage quality issues. Since the availability of the resources is uncertain in the deregulated grid, conventional decentralized control may not be sufficient to handle these voltage quality issues. Though much research is happening on these issues, the characteristics of fast-moving cumulus cloud-induced power quality issues are not well investigated. In this regard, this work investigates the voltage quality issues caused by the grid tied solar farms. Real-world data of irradiance variation under fast-moving cumulus clouds are considered for the investigation. The investigation carried out and the results demonstrated can benefit the power quality remedial measures in emerging deregulated grids.
Data Regeneration From Poisoned Datasets

The administration of data in soft copies is growing increasingly popular as organizations switch from keeping documents in hard copies to more contemporary information handling systems that enable faster sharing and exchanging of data. Unfortunately, as the data has gotten easier to manage, modify, and store, it has also become easier to lose or corrupt them. It is well known that many approaches, including Autoencoders, K means, and generative models, can identify noise and regenerate data in response. Using the unique modified Adamic Adar technique, which was previously used for link prediction in the context of graphs, we tackled the aforementioned issue in this study. On the same medium-sized dataset, our suggested solution with an accuracy of 78% outperformed more established techniques like K means, which had an efficiency of only 50%. We further regenerated them using several regression and deep learning models. The research also discusses the extension of GANs, which are well renowned for their effectiveness in image generation, to lost data regeneration.
Offline Signature verification using CNN and SVM classifier

An efficient signature verification method using both CNN and SVM is proposed. Cedar dataset consisting of a total of 2640 signature images of 55 individuals is used here as the dataset. Since it is a writer-dependent method, the genuine and forged signature images of each individual are used separately for training and testing. CNN (Convolutional Neural Network) is used here only for feature extraction and SVM is used for classification. The input images are supplied to the CNN input layer and the feature extracted, which is the downsampled convolution of the 9X9 kernel initialized to 1, sliding across the image. The extracted features are supplied to the Support Vector Machine (SVM) classifier in the 80 : 20 ratio and the classification result is found to have the best accuracy of 93.63%. The same features are used with CNN itself for classification to compare the performance. It gives a lower accuracy than expected because of the overfitting problem of the deep neural network. Thus use of CNN for feature extraction and SVM for classification yields good results in offline signature verification.
A Blockchain Based Patient Consent Management Technique for Electronic Health Record Sharing

Rapid advancements to digitization in Electronic Health Records (EHR) systems and record sharing have happened at the cost of uncontrolled access and insecure control to the patient’s sensitive health information. This is due to existing centralized systems and intermediaries sharing the medical records with multiple stakeholders causing single point of failure, data tampering, correlation attack, loss of privacy and transparency. Besides this, patient’s consent for personal health record sharing is neglected or restricted to one-time consent policy that fails to produce tamper-proof evidence in an irrefutable way. With the emergence of promising distributed ledger technology like blockchain makes it easy to realize the trust factors required for patient-controlled record sharing without relying on a centralized third parties.This work proposes a smart contract based blockchain solution/framework for patient consent management to achieve a secure health record exchange and its access in automated way. The proposed solution is implemented on a public blockchain called as Ethereum. The security analysis of smart contracts for potential vulnerabilities is conducted using auditing tools like Oyente and Surya. Furthermore, the implemented solution is evaluated in terms of throughput, average latency using a Hyperledger Caliper benchmarking tool. Finally, the security and performance analysis illustrates that proposed framework is suitable for real-world patient-centric health record sharing in EHRs.
An Efficient Feature Selection Scheme for Big Data Based on GOA and LSTM

The traditional feature selection models are assumed with all the instances of big data and features are used for learning. Therefore, in the case of real-life applications, it was not likely to face the data streams with feature streams or both. The present streaming feature selection algorithms focused mainly on eliminating redundant and irrelevant features. However, while selecting the most relevant features, the interacting features were ignored in the existing approaches. These features are essential for the proposed work that uses the Grasshopper Optimization Algorithm (GOA) for the relevant feature selection and Long Short Term Memory (LSTM) for the classification of diseases. The proposed work uses 4 types of datasets Leukemia, Colon, Prostate, and Breast Cancer. The GOA optimization problems in various domains find input parameters or the arguments for functioning. The minimum and maximum output functions have been obtained from the LSTM model which provides a large range of parameters like input biases, output biases, learning rate, and complexity. The results show that the Leukemia dataset which is 99.17% of accuracy with a execution time of 0.5472s better when compared to the existing streaming feature selection method obtains 82.26% of accuracy with a execution time of 2.4077s.
Modelling and Simulation of Fast Autonomous Exploration and Mapping System for GPS Denied Unknown Environment Using ROS

This paper describes a multi-robot coordinated heterogeneous vehicle which employs cooperative unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to explore and map a GPS-denied unknown terrain. Exploration processes like cave exploration, tunnel inspections required an accurate and quick mapping of the environment in emergencies for rescue or maintenance operations. This multi-robot system’s main objective is to use the Dijkstra algorithm to produce a 2D and 3D map of an unexplored region. Each robot features a border identification system that uses image processing to derive border locations. The autonomous exploration problem is a partially observable Markov decision process (POMDP) to the local planner exploration component. The robot is directed to the uncharted region in the map after recovering boundary points in the region separating known from unknown space. The global planner graph, introducing flexible values, is constructed during the discovery phase to examine the robot’s border positions at the end. The two methodologies are combined with speeding up the search for border sites. Closed-loop control ensures that the robot is always pushed to its greatest advantage following achieving the desired advantages. Finally, the 2D and 3D maps obtained from each robot are combined using conventional map merging techniques to form a map.
BDOSN: Privacy-aware Blockchain based Decentralized OSNs

Online Social Networks (OSN) have progressed exponentially in terms of both participants and data being shared on the OSN. The OSN is more prevalent, and user profile on OSN has become the second identity in users’ life. The most popular and widely used OSN (i.e., Facebook, Google+, Twitter, Instagram, etc.) is built on a centralized architecture. The centralized OSN is designed around a centralized server, and users’ data are stored on the server. The centralized OSN poses the risk of a single point of failure, disclosure of sensitive information, and the user has little control over their sensitive data. The distributed OSNs are designed as a solution to single-point-of-failure and data privacy problems. However, it adduces inefficiencies in delivering essential functionalities such as unique identity distribution, data availability and access control services. This research work proposes a novel blockchain-based decentralized OSN (BDOSN) framework. The proposed BDOSN framework ensures data solitude and integrity of data. It also attains confirmable identity for the user and provides friends’ recommendations. Two smart contracts are designed to achieve useful functionalities in this research work. The potentiality of the proposed BDOSN scheme is implemented as an exemplification on solidity-Ethereum-blockchain using our dataset and publicly available data sets. The outcome of experiment illustrate the efficiency and validity of the proposed BDOSN.
Target selection in handheld Augmented Reality for distant and occluded object

As the current advancement of the technologies evolves, handheld devices are able to fulfil the AR requirements, allowing handheld AR potential to be further discovered, especially for interaction in handheld AR. Other than target selection on faraway object, target selection on obscured object is another remaining issue that still need to be addressed. These issues are often addressed separately in previous research. However, as the target object is distant, it becomes even harder to complete the selection when the distant target object is occluded. Therefore, the gesture of a real hand is implemented with raycasting allowing target selection in handheld AR. We utilized simultaneous localization and mapping (SLAM) tracking to allow AR space. This paper concludes with the recommendations of the future work.
Weighted Robinson Compass Gradient and Charbonnier Penalty Function as a Loss Function

Loss function quantifies how well a machine learning model is performing, it computes the difference between the ground truth and the reconstructed outcomes. In image processing tasks such as image dehazing, image deblurring and image denoising the most popular loss functions used are Mean Squared error(MSE) and perceptual loss. MSE is less computationally expensive but fails to recreate precise boundaries whereas perceptual loss is more computationally expensive as they use feature vectors from a pre-trained model like VGG-net. In smaller on the edge systems like autonomous navigation systems, one of the most critical tasks is relying on visual cues, the take in the video feed from the environment and make decision on it after a few prepossessing steps to eliminate things like physical barriers, blurr, and smoke that clutter a robot’s environment are one of the several factors that affect autonomous navigation. We need systems that aren’t computationally expensive but also have a good reconstruction. For this we propose a loss function that incorporates gradients along with MSE for faster and better reconstruction.
Fine Food Products Recommender using Sentiment Analysis based on Twitter and Amazon reviews

In this day and age, online entertainment has turned into a significant stage where individuals are allowed to offer their viewpoint on any item. This can be useful for others on the off chance that they will realize other’s perspectives prior to purchasing anything. Individuals will more often than not visit the majority of the E-trade sites where item surveys are accessible. Likewise many tweets are additionally being made on twitter, a social media site, connected with various item audits. In this venture we mean to make a web application in which we can accept a fine food items as a contribution from customers and aid them by recommending or not recommending the product based on the reviews using Random Forest Classifier with 85 percent accuracy. The current systems employ recommendations based on data from single source of reviews. In our system, we plan to verify the viability of reviews across different platforms and then the product is recommended so that the customer can make an informed decision.
Security System for Digital Land Certificate Based on Blockchain and QR Code Validation in Indonesia

In this research, we developed a land certificate security system based on blockchain technology and a qr code validation scheme to prevent counterfeiting of land certificates and illegal levies in the process of making land certificates in Indonesia. The process of making land certificates is currently still being carried out by involving various related parties so that it requires a complex and long process. This triggers cases of illegal levies carried out by related parties on the grounds of speeding up the process of obtaining land certificates, thereby reducing the transparency aspect. Conventional paper certificates are prone to forgery and easy to lose, besides that electronic certificates designed by the government still use the concept of a centralized system, so they are vulnerable to being hacked. Another problem is the falsification of ownership of land certificates by the land mafia. Therefore, a land certificate security system is needed for the online land certificate data storage process. From the test results, it was found that the system built with the implementation of blockchain technology guarantees data validity, based on verification by smart contracts and consensus mechanisms, and ensures that processes that occur in blockchain-based systems are guaranteed transparency, data is stored globally in a peer-topeer and immutable.
Real-Time Face Recognition System in Smart Classroom using Haar Cascade and Local Binary Pattern Model

The time-saving classroom technology solution is one of the main features of a smart classroom. The traditional student attendance system opens for negligence as the students can cheat by asking their friends to sign on the attendance paper. Besides, taking students’ attendance manually such as calling out names and passing out papers to be signed is time-consuming. This paper is to propose a face recognition framework for students’ attendance that can be taken in real-time using a webcam in the classroom and to develop a system where both students and lecturers can save time at the same time to smooth out the learning process. On top of that, parents or guardians also get informed about the attendance status. The face recognition system is proposed to use the Haar Cascades algorithm to detect individual faces of students while using Local Binary Pattern (L B P) is used to identify and verify students’ identity by using their facial features. This, this framework also emphasizes real-time capturing of students’ attendance, and the attendance can be taken automatically when students’ faces are detected and identified. Using 35 individuals as a sample, this paper aspires to present an effective and efficient model for the proposed system.
Legal challenges facing blockchain-based peer-topeer energy trading

Blockchain-based peer-to-peer energy trading epitomizes multilateral goals, values, and interests vis-à-vis environmental, economic, and social pursuits. It drives innovative complementarities and facilitates energy access and reliability of supply, thus effectively improving the living quality of citizens and communities. However, there is a growing pessimism concerning the deployment of blockchain-based peer-to-peer energy trading across various jurisdictions, mainly due to the rigorous regulatory framework tailored to conventional energy structures. Implementing conventional licensing requirements is ineffective and non-viable for the rapid proliferation of blockchain systems in energy sectors. Due to complex administrative procedures, overbearing bureaucracy, arcane licensing procedures, and rigid requirements, the rate of adoption of peer-to-peer energy trading models has stagnated despite the tapestry of benefits. The prodigious rise of blockchain and advanced metering infrastructures necessitates regulators to apply a variegation of dynamic pricing mechanisms to enable real-time economic gains, increase system flexibility, and facilitate blockchainbased peer-to-peer energy trading. However, the contemporary practices of imposing network charges and the lack of a prosumer-centric regulatory framework pose critical issues and hinder the transition from passivity to agility. This article adopts an explorative and eclectic approach to engage the regulatory gap of blockchain-based peer-to-peer energy trading. By identifying these foundational challenges, countries can implement salient institutional and governance regimes to address these impediments.