Reviewerly/exHarmony huggingface dataset:
The COVID'19 Dashboard with Implementation of Face Mask Detection and Social Distancing Detection Algorithm

With the outbreak of the global pandemic, India seemed to reach its peak with regard to the number of confirmed positive cases in the months of April and May. Hence, the decision was made to develop a data visualization project with one of the efficient visualization tools Tableau to help people analyze the scenario of the cases across the country. To contribute to state-wise and country-wise analysis of COVID cases in India, 2 dashboards have been developed. The first dashboard consists of the analysis of cases across the country giving a holistic and overall view of the number of deaths, positive cases, and density of cases in each state which is done through color variation. On the other hand, the second dashboard gives a detailed state-wise analysis of cases with the necessary parameters and details catering to every individual state as per the preference of the user. On merging these components, users can get an all-inclusive analysis based on different parameters on the COVID'19 cases across India at a glance. In order to prevent a further spike in cases, implementing a face mask detection system will also take place after conducting a thorough analysis of the possible machine learning algorithms. Two major object detection algorithms were taken into consideration and based on the conclusion drawn, the best algorithm – RCNN was used to implement the face mask detection system. This project is solely motivated by the current extreme situation in the world and as an attempt to provide a solution to combat the same.
Performance Analysis of an ANN-based model for Breast Cancer Classification using Wisconsin Dataset

Breast Cancer (BC) is counted as one of the most fatal diseases responsible for mortality in women. BC feels like a lump that can be felt different than other BC tissues. Cancer cells spread to other parts of the body by getting into the bloodstream or lymphatic system, which results in BC. It becomes crucial to develop and analyze methods to help understand BC and its trend affecting women. In this paper, we discussed different models to analyze BC tumors and classified them into benign and malignant. Benign tumors are referred to as ‘non-cancerous’ by many, and those that are ‘cancerous’ as malignant. The analysis was done on Wisconsin Breast Cancer Dataset (WBCD). We performed Logistic Regression (LR), Decision Tree(DT), Random Forest(RF), K-Nearest Neighbor (KNN), and Artificial Neural Network(ANN) on the dataset and compared their results. Our ANN model yielded the best result among many. All models were compared on various metrics which are Accuracy score, F1 score, Precision score, and Recall score.
Power of kernel functions, its benefits, and limitations

In today's realm of machine learning, the non-linearity of data is the problem often faced during data analysis. A well-known supervised learning algorithm that is known to efficiently handle this problem is the Support Vector Machine. The trick deployed in the background to overcome the problem of non-linearity is the transformation of data from the existing dimension to a higher dimension. To make this conversion of dimensions possible, kernel functions are utilized. These kernel functions are simply mathematical functions, which are available in the literature for a long ago. The present study briefly describes the mathematical background of the kernel functions with examples and a quick summarization of the recent applications of the renowned kernel functions across various domains. Further, the numerous benefits, a few limitations of kernel functions, and future directions are also highlighted in the work.
A Deep Analysis of Dilemma Devices which uses Neural Network

The majority of people in just this digital age strive to get the appropriate information from the internet, thus creates a demand for thought systems which can comprehend the participant's voice recognition inquiry and provide a prompt, accurate response. The problem process, or QA Framework, that provides responses for various question kinds and on a subject basis, is reviewed in this study utilising a variety of methodologies. The report also describes the deficiencies in the current knowledge that have been found.
Linearity-based Sensor Data Online Compression Methods for Environmental Applications

Environmental monitoring is a typical Internet of Things (IoT) application.Environmental monitoring plays a significant role, for example, in smart farming and smart city applications.Environmental magnitudes are usually measured using wireless sensor nodes, which are often battery-powered, and the number of sensing nodes can be large.One effective method for reducing the energy consumption of a sensor node is to use data compression to reduce the amount of data required for transmission via a wireless connection.Compressing the sensor data means fewer transmission periods, and thus, lower energy consumption.Compression methods should be effective for compressing environmental magnitudes and be computationally light to be suitable for constrained sensor nodes.A compression algorithm should be able to compress an online data stream.In this paper, we review some compression algorithms suitable for environmental monitoring and present two new versions of those algorithms.The algorithms were evaluated, tested, and compared.The main parameters used for the comparisons were compression ratio, root mean square error, and inherent latency.The simulation results obtained using real datasets demonstrate that simple linearity-based compression algorithms are effective and suitable for compressing environmental data.Two new compression algorithm versions proved to be effective for compressing sensor data with reasonable compression quality and predictable inherent latency.
COVID-19 Effect on India's Business Utilizing Online, Blockchain, or AI Algorithms

The COVID-19 illness primarily carried by a Corona viral, commonly known as SARS-CoV-2. The rapid pace of such a drug's growth over six of the seven planets, except South, was already reported. Ever since it appeared, it has caused serious damage for many sectors and didn't harm the luxury goods sector. A major gate as in transition is opened by the epidemic. The COVID-19's effects on the Indian economy are negative. A old company concept needs to evolve to meet the demands of present.Through a clever absorption of internet media, method, & aptitude in any point & activity, the change as in setting of online is witnessed in all areas, i.e., in society, organisation, or in the exactness like an organisation, only in companies, or in the environment. Technology is used in conjunction with industry 4.0 (also known as DX or DT) to generate value for key parties (users in the totality), and to develop and gain the capacity for quickly adapting to situations which are altering.Any item or service may now be marketed but also advertised to customers online thanks to change carried out in a data - driven method, which has done away with the need for external middlemen. A large number of firms are impacted by COVID-19 since it has an impact to the entire globe and causes social estrangement with customers. In light of social distance, a wide range of sectors and businesses will be impacted. In this research, the idea of digitisation is proposed as a projected way through. Social distance may alter the characteristics or the purchasing habits. This study is being done to determine how it COVID-19 problem will affect consumer purchasing habits. By using internet tactics and strategies, consumers are shifting the way that merchandise and services are traditionally purchased. As per research, the digital advertising framework has permeated our imagination for acquiring the ways that are best for selling from several top-performing businesses.Only thing changing in regard of consumer purchase intention following COVID-19 recuperation would be the sales for items, which will mostly stay same with. Consumers will develop the buying habit so as protection, physically or straight from creators.
Answer Selection Using Interactive Attention Mechanism

Answer selection is a crucial subtask of a question answering system that focuses on ranking candidate answer sentences from an answer pool based on how relevant and useful it is to answer the question. Conventional deep learning methods like RNNs and CNNs suffer from obtaining local and global context information from the sentence representation. Also, utilizing question context information to generate better answer sentence representation will also contribute to better learning. Thus, our proposed approach uses an interactive attention mechanism using both co-attention for learning question context and self-attention for learning global context. We also adopt the attentive pooling network for compressing features where each element in a question-answer sentence pair can influence the representation of the other. We evaluate our proposals on the TREC-QA dataset and compare using the metrics of MRR and MAP. Our proposed model shows better performance on these evaluation metrics compared to existing baseline models.
Zero-Knowledge and Identity-Based Authentication, Authorization, Access Control, and Key Exchange for Publish/Subscribe in Internet of Things

Clients and brokers have to communicate via secure channels in the broker-based publish/subscribe model which can be regarded as one of the communication models usually used in the Internet of Things (IoT). A secure channel setup needs an authentic key exchange which in turn requires an authentication process. Moreover, it is required to verify that clients publish or subscribe only to authorized topics managed by authorized brokers. Additionally, IoT includes its own security requirements. This paper introduces a novel handshake protocol providing authentication, authorization, and access control with key exchange for the broker-based publish/subscribe model in IoT. In contrast to related work, realizations of these security goals are not disjunct processes and are integrated with each other in our approach combining zero-knowledge and identitybased schemes while meeting the IoT security requirements. Thus, it supports autonomy in IoT and does not require any public data pre-distribution or secret pre-sharing between communicating entities. Moreover, no third party has to hold any entity-specific authentication and authorization data. While being independent of publish/subscribe protocols without requiring additional components and procedures, our handshake protocol is resistant to active man in the middle attacks and does not include costly cryptographic operations. This paper also evaluates the performance of our approach with regard to multiple affecting factors.
Block Chain Driven Marketing Resources in Mobile Edge Computing and System

A paradigm-shifting architecture for device-to-device edge computing and networking is proposed by the authors of this study in the abstract of their recently published work (D2D-ECN). By contracting out the processing of data, this architecture makes it feasible to execute real-time Internet of Things applications with a bare minimum of lag time at a price that is affordable. During the process of formulating our plan, we made sure to take into account the significant problems that exist with the organizational effectiveness of the D2D-resource ECN. Furthermore, we took into consideration the following security issues that are caused when there is a lack of cooperation between task managers and asset suppliers. It is strongly advised to use an infrastructure that is based on blockchain technology in order to build smart contracts for commodities exchange and job allocation. IoT devices that are limited in their resource capacity are unable to employ the Proof-of-Work (PoW) algorithm that is currently in use since the mining process has a high computational dimensionality. As a result, we provide a proof-of-reputation (PoR) consensus technique, in which the piece of machinery that has the highest reputation score is tasked with the responsibility of monitoring the whole process. The blockchain records both the history of transactions and data relating to reputation. The popularity score for each device is calculated using a formula that takes into account both its current performance in calculations and its past reputation. As evidenced by our security analysis, feasibility research, and numerical results, the compute offloading approach that we have presented for usage in a decentralized D2D-ECN system is both secure and effective for deployment in that environment.
An Efficient Consensus Algorithm for Blockchain-Based Federated Learning

Machine learning techniques have proved to be an inevitable tool for data analysis and prediction in various domains. Federated learning has enhanced it by allowing localized training of datasets where the owners need not share their data. The participating parties only need to share their locally trained model weights and biases to accomplish a federated learning task. This sharing mechanism can become significantly secure and tamper-proof on a blockchain network. In this paper, we have proposed a consensus algorithm to ensure efficient and economical utilization of blockchain to perform federated learning activities. The proposed model is tested in a simulated test bed to present a practical implementation of our algorithm and to reflect upon its efficiency.
A Risk Assessment Study: Encircling Docker Container Assets on IaaS Cloud Computing Topology

The articulate utilization of docker container assets in Cloud Computing is one strategic pivotal aspect of the underlying rationale for customers to conjecture on their ventures. It can be standardized under the organization’s viewpoints toward its purposes and objectives, given the facilities provided by a cloud service provider. IaaS (Infrastructure-as-a-Service) topology offers many opportunities, including serverless capability in a docker container through software abstraction, and magnifying customization. Techniques such as intensifying the quality of streamlined services, and uplifts productivity, become sufficient evidence of underlying equal responsibilities between provider and customer, which need to be evaluated its risks periodically to improve the resilience of containerized objects. This study contributes alternate insight concerning risk assessment methodology in a docker container environment to mitigate the risk rating and propose risk treatment recommendations for cloud providers and customers. To grasp unified security perception in analyzing assets, threats, and vulnerabilities, harnessing several methods and international standards such as the Analytical Hierarchy process, Partial Dependency, ISO 27K Family, MITRE ATT& CK, EBIOS Risk Manager, and NIST 800-30. The experiment uses a realworld threat group from MITRE ATT& CK called TeamTNT.
Security Enhancement using Vectoring, Cryptography and Steganography

Secure transmission of data is one of the most challenging goals. Generally, users avoid the use of a secure channel due to its high cost. Various schemes have been proposed to solve this issue. Some of the schemes are based only on encryption or steganography alone, which is not able to provide the desired level of security in a modern scenario. In this work, a security scheme has been proposed to address the challenge of secure communication at a low cost in three phases: vectoring, cryptography, and steganography. The proposed scheme has a flexibility factor that allows for different levels of security and secrecy. Different types of messages require different levels (high/low) of security, considering the circumstantial robustness on demand. A high level of security ensures security against attacks from the attacker for sensitive information. Our proposed security scheme is capable of providing the required level of security with decreased complexity. The proposed scheme is of a dynamic nature, as per the requirement.
The Future Era of Computing: Automatic Computing

The paper aim to describe the futuristic perspective of technology that will govern the aspects of our lives. The computers of today would soon start to become decapacitated as the needs, requirements and our dependency on technology continues to evolve. The study makes an attempt to look for the possible futuristic ideas that would aid this massive technological dependence. The ever-growing technology has proved beneficial to mankind however, we should have a glimpse of the next big thing. With the fall in Moore’s law and the rise of quantum computing it has now become a necessity that the new options and opportunities must be examined. The term "quantum computing" refers to a sort of computing whose processes may exploit the processes of quantum physics, such as superposition, interference, and entanglement. Quantum computing is a relatively new field of research. Moore's Law is a model of technical economics that has enabled the information technology sector to roughly double the functionality and performance of digital devices every two years while retaining the same cost, power, and space. This paper is merely a small cog in this long chain of futuristic possibilities.
A Decentralized Cryptocurrency Infrastructure for Edge-Based IoMT Systems

Significant shifts in e-health services have occurred as a result of the healthcare industry’s use of edge of the network (MEC) and Blockchain technology for improving efficiency in healthcare operations. While some MEC-Blockchain based systems have been established, there are still significant technical challenges, including insufficient service quality (QoS), privacy laws, and security concerns. Across this research, we introduce BEdgeHealth, an innovative different level of the health architecture that integrates MEC and Blockchain for offloading and sharing data in distributed hospital networks. First, a data offloading approach is released, which lets mobile devices transmit healthcare information to a close MEC server for efficient computation in a secure environment. In addition, we design a data-sharing system for the healthcare industry that employs Blockchain technology and an alien file system. Specifically, a sage Collective bargaining agreement authentication technique is paired with MEC to allow local access permissions certification at the edge nodes without the requirement for a central authority. The results of such real-world trials and evaluations demonstrate the effectiveness of the proposed method BEdge Health architecture in delivering higher quality of service and guarantees that data privacy and security when compared to the existing systems.
Technology Works as A Medicine for Crime Scene Evidence

The legal framework has to face new challenges everyday related to protection of crime scene evidences, as a result of which the technological interference helps to protect them and help in the protection of victim rights also. The combination of legal and digital intervention into normal functioning of courts will be a great help to prosecutors also for defend the case properly in order to attain justice and protecting the rights of victim. The present study is significant as there is no such significant research in the legal evidences to imply AI, blockchain and cloud computing on crime evidences which however, impacts the crime evidence to protect the rights of victim. In the particular study, we have premeditated the challenges of technological intervention in legal domain related to crime scene evidences, as victim rights should be protected. The study reviews the research exist now to address the concern of victim rights protection through crime scene evidences protected. At last, study concluded with way forward for future work to be adopted.
Block Chain Technology Application to the Banking Sector

Around the world, digital technology is a new disruptive factor that is changing business paradigms and becoming more significant. In India, block chain technology is attracting a lot of attention from a variety of industries. As the number of uses for block chain technology grows, industry leaders are adapting and customizing the technology to fit a range of use cases. The next development in the decentralized method for designing applications is due to the Block chain technology. This essay seeks to describe both the structure and operation of the block chain technology. Along with the Block chain 's numerous features, its advantages are also explored. For a select few banking transactions, the use cases and block chain suitability assessments have also been completed. We also look at the Block chain 's security features in the final segment.
Automated Scene Text Detection Systems: An Imminent Progress

In this techno-savvy world, digitalization has become a part of our daily lives and has various applications in every field of work. For example, an automatic scene text detection system utilizes a video camera to capture the frames and then detect and recognize the text on the frames to help in different applications like navigation for blind people, sign detection, text retrieval, etc. So, it is essential to understand the current state of the work done in this field. Therefore, in this paper, with the same objective, the theoretical analysis includes the details of the present benchmark datasets, existing systems, and approaches. Moreover, the current methods' shortcomings are also discussed in this paper, along with the future requirement which will help the researchers to target their objectives.
A Recommendation System to Envision Movie Ranking Using Collaborative Filtering

This paper discusses about recommendations of the movies. A movie recommendation system is beneficial for entertaining quotient. This system suggests movies to users based on user’s interest, movie’s popularity. It narrows down the database of information and helps users in finding their appropriate need or interest in movies. A recommendation is an abstract of information that filters the data on the basis on "ratings" or "preferences". They are widely used in all sorts of movie platforms like Netflix, amazon prime video etc.
Performance analysis of Sentiment Classification using Optimized Kernel Extreme Learning Machine

Experts’ attention has recently shifted to sentiment classification because of its applications in a variety of fields. In the past two decades, scientists have developed various multidisciplinary methodologies for sentiment analysis (SA) to enhance classification accuracy. Initial studies in sentiment analysis concentrated mostly on structured customer product and service reviews. However, academics have abruptly shifted their focus to unstructured data because of the availability of real-time messages via Twitter. Information considered to be more valuable is taken from tweets that express a point of view. There are a variety of programs, such as sentimentor, designed expressly for assessing the feelings of tweets. This paper provides a comprehensive overview of the several available SA methodologies, from the earliest to the most recent and accurate. Further the paper presents emotion detection in political tweets about an individual using various supervised machine learning (ML) approaches under different training rates. Kernel Extreme Learning Machine (KELM) by incorporating it with the Salp swarm optimization algorithm (SSA) is investigated under different training rates and with different data sets. Experimental results depicts that proposed KELM optimized by SSA attains improved performance compared to KNN, SVM, and Ensemble Classifiers.