Reviewerly/exHarmony huggingface dataset:
A global blockchain for recording high rates of COVID-19 vaccinations

Blockchain has been recently proposed to securely record vaccinations against COVID-19 and manage their verification. However, existing solutions may not fully meet the requirements of a global vaccination management system. These requirements include the scalability required to support a global vaccination campaign, like one against COVID-19, and the capability to facilitate the interoperation between the independent health administrations of different countries. Moreover, access to global statistics can help to control securing community health and provide continuity of care for individuals during a pandemic. In this paper, we propose GEOS, a blockchain-based vaccination management system designed to address the challenges faced by the global vaccination campaign against COVID-19. GEOS offers interoperability between vaccination information systems at both domestic and international levels, supporting high vaccination rates and extensive coverage for the global population. To provide those features, GEOS uses a two-layer blockchain architecture, a simplified byzantine-tolerant consensus algorithm, and the Boneh-Lynn-Shacham signature scheme. We analyze the scalability of GEOS by examining transaction rate and confirmation times, considering factors such as the number of validators, communication overhead, and block size within the blockchain network. Our findings demonstrate the effectiveness of GEOS in managing COVID-19 vaccination records and statistical data for 236 countries, encompassing crucial information such as daily vaccination rates for highly populous nations and the global vaccination demand, as identified by the World Health Organization.
Recent Advancement in Education System Using AR Tools: A New Perspective

Technology in education has the potential to drive pupils to learn in a more interactive and perceptible manner. The application presented here is focused on the used materials during the teaching-learning process, such as text and photos, and displays a major 3-dimensional model or video on the screen. These applications provide the students by boosting them to peruse innovative ideas by using the graphical guide. Apart from utilizing it in school, it can be used for higher education in universities. The advantages of Augmented Reality are also discussed in comparison to old tactics such as chalkboards and textbooks. The findings demonstrate that, in general, Augmented Reality technologies offer good potential and benefits that can be applied in education. The paper also identifies AR's shortcomings, which could be solved through further research.
Fusion Techniques in Neural Network Model for Image Captioning

Unmanned generation of the caption of any given image can be defined as image captioning. It is still one of the most important and researched topics as it is used in many tools like virtual assistants, chatbots, visual question-and-answering systems, etc. In the past, many researchers have improved the captioning quality of an image. Recurrent Neural Network using long short-term memory (LSTM) units has contributed a lot despite their disadvantages. LSTM cells are very multiplex and intrinsically progressive at different intervals of time therefore to mark the problem we have tried to solve it using a convolutional neural network considering the advantages of this network. Motivated by past research we modeled an image captioning model using a masked convolutions layer and attention mechanism. MSCOCO datasets are used as a dataset for developing a model used in captioning.
Evaluation of the Effectiveness of a Secure Blockchain Framework for IoT-Based Data Transmission

The rise of the internet of things has revolutionized the way people work and live. It has also created a vast amount of data that can be collected and stored by various devices. Unfortunately, the centralization of this data has raised concerns about its security. With the help of blockchain technology, a secure and decentralized method of transmission can be established. The concept of blockchain technology is a globally distributed ledger that records and transactions using cryptography. Due to the rapid technological development taking place in the field, it is now considered a key component of Web 3.0. It allows applications to run on a secure and stable basis. In terms of tech terms, data stored on a blockchain is immutable. Various sectors where blockchain shows comprehensive success like e-learning Banking & Finance Online shopping. Reason for success of blockchain. These innovations have the potential to transform our daily lives. The Internet of Things is a vast network that collects and transmits vast data. This data is often sensitive and non-critical, raising questions about its ownership and protection of it. The goal of this paper is to explore the potential of blockchain technologies to address the security challenges of the Internet of Things (IoT). It will first introduce the concept of the secure blockchain framework and its various features. The paper will then introduce the framework for implementing secure and decentralized transmission of IoT data. It will discuss the various features of this technology and its selection of appropriate cryptographic methods and consensus algorithms. It will be subjected to a series of tests to evaluate its performance and security. The findings of the tests will be discussed and analyzed, and recommendations for future developments will be provided. The paper also concludes by summarizing the results. The paper will provide an evaluation of the various aspects of secure blockchain technology for the transmission of data from the Internet of Things. It will also explore its potential to improve the security of the networks that are connected to it.
Colour Image Cryptography using Chaotic Pixel Shuffling and 3D Logistic Diffusion

All types of multimedia data, including digital images, audio, text, and video, are now easily accessible on the internet due to the phenomenal growth of internet services and wireless telecommunication systems, including mobile and computer systems, in order to achieve a specific degree of security, integrity, and secrecy as well as to prevent malicious access to the information. In contrast, for data storage and transmission, cryptographic procedures are necessary. This study presents brand-new, very effective colour image encryption and decryption algorithms that use diffusion and pixel scrambling. At first Zig-Zag permutation was used to scramble the data. Then, by concealing the statistical makeup of the pixels, the two-dimensional Lozi map is used to distort further the connection between neighbouring pixels in the original image. Furthermore, the diffusion in three colour channels is accomplished using a 3D logistic map. During the decryption process, the image pixels are again moved back to their original positions. The chaotic mapping technique encrypts images to reduce the correlation between encrypted and unencrypted versions. As described in this study, the tested and examined encryption algorithm on certain colour photos showed substantial security and authenticity to resist statistical and differential cyber attacks.
Protecting customer databases to shield business data against ransomware attacks and effective disaster recovery in a hybrid production environment

Backup and recovery are the processes of making copies of data and storing it securely for V. Chang and G. Wills, “A model to compare cloud and non-cloud storage of big data, Future Gener,” Future Gener. Compute. Syst. Int recovery in case the original data gets damaged or lost due to computer or storage system failures. During recovery, the data is restored to its operating location from the backup copy, so it can once again be used in business operations. In order to safeguard against ransomware risk, these backup copies should be immutable, which means once written to the storage these copies cannot be modified. The category of onsite and cloud-based technology solutions known as ‘backup and recovery’ also automates and supports this process, enabling business firms to safeguard their data and keep it for operational and regulatory requirements. Multiple backup solutions are available to protect a database running in a production environment. Customers can choose a specific or hybrid solution based on the deployment and requirements. For example, databases running in a virtual environment on a hypervisor in a data center may have a different solution than a database running on bare metal servers. Each method has its own benefits and limitations. The goal of this paper is to discuss the different solutions available for backup and recovery and come up with a proposal for effective backup and disaster recovery solutions in the event of any system failures.
IT Security Frameworks: Risk Management Analysis and Solutions

The negative effects of information security events are growing globally due to the substantial growth and organizational reliance on information technology (IT). To resolve the information security attacks, vulnerabilities, threats and breaches, there‘s a need for standards that adapt the best practices so that appropriate security levels are accomplished and IT risk management frameworks demands a great change and thus need of new framework is paramount in current perspective. This paper examines most of the existing IT risk management frameworks by conducting the survey amongst various industries, their team leaders and their IT security team members. Following the detailed analytics of the existing models and discussion of various common control management techniques and challenges, this paper attempts to identify gaps in these models that can be resolved through a new framework that can enhance the security levels in IT sector
Keypoint Detection for Identifying Body Joints using TensorFlow

Keypoint Detection for the Body Joints is a process of finding and detecting all the important body joints of a human being. It takes an RGB image as input gives a list of keypoints as output. It can be applied for single-person body joints detection or multi-person body joints detection. The various architectures for detecting the human body joints can be compared using the Percentage of Detected Joints (PDJ), Object Keypoint Similarity (OKS) and mean Average Precision(mAP) evaluation metrics. The state-of-the-art body joint detection architectures like DeepPose, Higher Resolution Network and OpenPose architectures were studied and compared against each other. These architectures were evaluated on the COCO dataset for the 17 key body joints using the OKS and mAP evaluation metrics. The evaluation results on all the body joints were compared to each other for each architecture. The architectures were implemented using opensource implementations of the PyTorch and TensorFlow libraries. The HRNet architecture was the fastest and most accurate of all the architectures.
Cross-Platform Empirical Analysis of Mobile Application Development frameworks: Kotlin, React Native and Flutter

Mobile application development is currently seen as one of the most indispensable skills due to the recent boom in the population using smartphones and tablets. Due to the abundance and diversity of devices and platforms, developing applications for mobile devices is a complicated task involving multiple alternatives, technologies, and trade-offs. The development of applications in a cross-platform framework has become more prevalent in recent years. However, it is less clear which of the three major frame-works for android development, React Native, Flutter, and Kotlin is the most efficient in general comparisons.
Global and Indian Perspectives on Russia-Ukraine War using Sentiment Analysis

In today's world, social media has become a platform through which people express their opinions and thoughts regarding various topics. Twitter is one such platform wherein people resort to expressing their opinions or portraying sentiments to the world. Today it has become easier to analyze mass opinion by using sentiment analysis. This paper investigates the ongoing Russia-Ukraine war by analyzing opinionated tweets, and it seeks to understand the sentiments from a global and Indian perspective. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. Multinomial Naive Bayes classifier classified the tweets into positive, neutral, and negative categories. The paper employed NRCLex for emotion classification and aspect-based sentiment analysis to divide opinions into aspects and determine the sentiment associated with each element. For the study, 4,31,857 tweets were extracted, and the results of sentiment analysis depict that 44.09% users had negative sentiments followed by 33.378% users expressing positive sentiment and remaining 22.53% people were neutral in their tweets. Fear, anger and sadness were amongst the top emotions expressed in the negative tweets whereas the positive tweets expressed trust and anticipation that the war would end soon. Operation Ganga was carried out to evacuate Indian citizens from the war-hit region. An analysis was performed on 1542 tweets that were obtained for Operation Ganga. 74.5% of the users had positive sentiments about Operation Ganga, whereas 16.67% and 8.5% had negative and neutral sentiments respectively. The people trusted this evacuation process resulting in more positive sentiments. Fear of losing near and dear ones and fear of safety was the topmost concern for Indians and leadership was one of the topmost aspects tweeted in the positive sentiments. Thus, the overall results depict that the common man does not prefer war and is fearful of the outcomes. The government should hear the voice of the common man and plan strategies and decisions considering the common man's sentiments.
Investigation of Algorithms for Task Scheduling and Resource Distribution in the Cloud

The term "cloud computing" refers to a paradigm of distributed computing that allows software developers to automatically install programs throughout the process of allocating tasks and storing data. The term "cloud computing" refers to the practice of sharing a pool of virtual computer resources together with the capacity of the gadget, computing power, and processing capacity. In a cloud computing environment, one of the most important challenges is task scheduling and resource allocation. This problem is responsible for selecting the resources of cloud computing that are most suitable for user tasks while taking into account a number of constraints on user tasks. The NP-complete problem which is the task scheduling algorithm is an essential component of cloud computing and plays a vital role. The purpose of this study is to investigate the use of work scheduling and resource allocation algorithms in cloud computing environments, compare and assess a variety of traditional methods and optimization algorithms, and describe the benefits and drawbacks of each technique.
Revolutionizing Aerospace and Defense: The Impact of AI and Robotics on Modern Warfare

Integrating artificial intelligence (AI) and robotics in the aerospace and defense industry has revolutionized how things are done. This has led to cost savings, faster design and prototyping, and improved manufacturing efficiency. The growth in air traffic has prompted industry to explore new ways of automating production. This includes the development of unmanned autonomous aircraft to reduce time and cost, human error and environmental pollution. The application of AI in security strategies such as perimeter protection, threat detection, and automated response to network attacks has significantly enhanced defense capabilities. This article reviews the impact of AI and robotics on the aerospace and defense sector, highlighting the key technological advancements and inventions shaping military operations' future.
A Scalable Data Processing Framework for BigData Using Hadoop and MapReduce

Everything around us is being created in this day and age. Data generation rates are so frightening that there is a need for simple and cost-effective data storage, as well as a push to implement recovery processes. Furthermore, the relationship between understanding and assets must be examined in large datasets, which can lead to good decision-making and business strategies. The goal of this paper is to create an algorithm for the map reduce application, which includes a ballet count application that describes how to manage large amounts of data using various information mappers and how to distribute it using the map () function. Mapper outputs and retenders have been added, as well as map () to simplify the function. Set the execution input key/value pair as well as the output key/value pair. Maps receives user input and continuously implements and collects key/value pairs. The map passes through the libraries group with all of the intermediate quality and low function. Low Function assigns a value to the intermediate key and value pair key. It adds all associated values together to form a smaller value. As a result, when we perform the pre-process that is responsible for taking the internet key and its associated value, Reducer generates only one value pair or zero value pair.
Making a long video short: A Systematic Review

As a consequence of the great expansion in technology excessive amount of digitized data is produced daily. Data could be in any form, either images, videos, or text. In this paper, our focus is on large videos generated by various online devices. As per the Cisco networking report, an individual requires 5 million per year to watch all uploaded videos on the internet till the year 2021. To handle such massive videos is tough. These videos require large memory to store, high computational speed, and require lots of time to process. But nowadays, no one has time to deal with such long videos without knowing the interestingness of the content. So here, we will discuss various tools, techniques, and frameworks proposed by researchers to make the long video short by identifying the most prominent features and objects in the entire video. We will discuss various video summarization techniques based on domains, datasets available in this direction, applications, and challenges. We categorized summarization techniques using deep learning, machine learning, and computer vision approaches.
Secure Online Voting System: Blockchain and other Approaches

Online voting platforms are security-focused and have the latest accessibility features. Online voting systems help you to protect your vote by not allowing for multiple votes. They also eliminate the need for people to gather in person to cast their votes, as well as using paper or other means of voting. A voting system is a tool that allows an organization to reference how their group feels before coming to a decision. This can be done through an online or electronic election system, sometimes referred to as e voting systems. Verification and traceability of multistep transactions require blockchain. It can provide secure transactions, reduce compliance costs, and speed up data transfer processing. In addition to helping manage contracts, blockchain technology can be used to audit the origin of products. This paper reviews the various researches by the authors for the secure voting system using blockchain and other secure algorithm, for the purpose of authentic and secure voting.
An analysis of Blockchain-based IoT solutions to overcome current challenges

With the ongoing boom in IoT technology its usage has increased drastically and that took over a vast range of use cases. As the use of IoT increases over time so does the need to solve the challenges which come with it. Security, Data Privacy & management, and single point of failure are some of those major challenges. A decentralized & distributed technology called blockchain emerges as a workable remedy to this problem. The goal of this paper is to give a review of published papers that provides possible blockchain-based solutions to overcome challenges faced by IoT. This paper analyses the multiple papers to provide a comprehensive survey on security & other improvements achieved in IoT systems using blockchain and the challenges that originate during this integration.
RDF-ML: A Proposed SPARQL Tool for Machine Learning on Semantic Web Data

Semantic web data is a core data representation format in different technologies over web like linked open data, social networks, and knowledge graph databases. It will give the strength to these technologies while incorporating machine learning power on those data. W3C community is also working in this direction while constructing a group of SPARQL-ML. RDF-ML is a proposed web based tool which contains the capability of SPARQL processing engine. Through this tool machine learning can be applied on the built-in as well as user defined datasets in the form of RDF. This tool will produce faster results for the research community, working in the domain of semantic web and wants to produce results while applying machine learning and interpret them. This paper brief about the tool, it's utility with the aspect for SPARQL query processing along with machine learning. This paper proposed a framework for RDF-ML tool and represents the working of tool with snapshots and comparison for machine learning model on RDF data.
Using Machine Learning to detect abnormalities on Modbus/TCP Networks

Modbus/TCP is a communication protocol extensively used in machine-to-machine communications. Therefore, anomaly detection on this protocol is of paramount importance in the industry as it helps protect cyber assets and monitor IoT devices created by manufacturing plants across the globe. This detection is achieved by looking at standard patterns and then comparing them with patterns of data found unknown to the system itself. Today, catapulted by rapid developments in machine learning, there has been a marked reduction in the time it takes to identify anomalies in Modbus/TCP networks. Anomaly detection methodologies, leveraging neural networks trained through numerous machine-learning algorithms, further provide a system that could be effectively applied to many IoT device marketplaces, such as consumer-level applications. This paper proposes a framework for evaluating feature sets and learning-based models to detect Modbus/TCP anomalies efficiently and accurately.
Crime Alert Through Smart Surveillance Using Deep Learning Techniques

In this paper, we proposed a way to detect different categories of abnormal activities happening in India such as fight, explosion and shootout. The goal is to build a model that can identify signs of violence and aggression in videos and separates out anomalies from normal patterns. The problem statement of crime detection is achieved in two broad steps: One by building a deep learning model (ConvLSTM) to categorize different crimes and then deploying this model to an interface where live stream footage is connected to the server for any crime alert. The model is evaluated on UCF-crime dataset and if any frame in live stream captures a criminal act, the tool will issue a detection warning for a danger situation, signaling suspicious actions at a certain point in time. The suggested ConvLSTM2D approach outperforms the traditional convolutional neural networks-long short-term memory (CNN-LSTM) algorithms in terms of accuracy. The performance evaluation of training data is based on Area under the Receiver Operator Characteristic (ROC) curve.