Reviewerly/exHarmony huggingface dataset:
An Evaluation of Web3 Concepts and Technologies and Relation with Digital Forensics

The World is experiencing a significant shift in how technology is being used and how new strategies are being developed to streamline and get around bureaucratic processes. Cannot continue without mentioning blockchain as a revolutionary decentralization concept that also serves as a foundational notion for Web 3. One must admit that this is a struggle we are going through and that will continue into this new period as we add more approaches and technologies. In this study, the Web3 principles (like decentralization, trust and security, privacy and data protection), methodologies, and technologies are evaluated, along with their connections to digital forensics. Various viewpoints emphasizing the difficulties and current condition of these issues are offered. Authors discuss their viewpoints while highlighting their extensive background in digital forensics, keeping in mind that digital forensics is a relatively new profession. Based on their viewpoint's conclusions are being set. This work is currently being done by the authors as part of their research in the field of digital forensics and Web3.
Visual Servoing in Door Opening Task for Omnidirectional Mobile Manipulator

The general door opening task takes special place among the mobile robot operation challenges. Research interest in the topic grows in various directions, from mathematical modelling to energy saving solutions. Still, door recognition and locating, and further robot/manipulator approaching algorithms are the main and must tasks have to be solved. The paper presents a study on a door opening task for an omnidirectional mobile manipulator based on visual information. Moreover, a custom configuration of mobile manipulator is going to be introduced.
Efficient Generative Adversarial DAG Learning with No-Curl

Causal structure learning from data is a challenging task as the search space is typically huge. In recent years, a series of methods have been proposed to reformulate causality learning into an optimization problem with a continuous acyclicity constraint to allow problem-solving with continuous optimization techniques. This paper further improves on the causality learning results and efficiency of the continuous optimization approach through the use of generative adversarial neural network learning, which overcomes the limitations of using maximum likelihood estimation in the existing methods. In addition, we adapt the recently proposed DAG-NoCurl framework to the generative causal structure learning to improve speed performance. In particular, our adapted method does not constrain causal structure discovery to its initial estimation, hence allowing further improvement of the learning results. The proposed method has been tested on several benchmarks to compare against the state-of-the-art.
Comparing Accuracy and Time of Support Vector Machine with Different Kernels for Handwritten Digits Classification

The aim of this publication is to compare the accuracy and time performance of the Support Vector Machine method with different kernels in image classification within the field of machine learning. In the classification of handwritten digits using the Support Vector Machine approach, we employed the following kernels: polynomial kernel, linear kernel, sigmoid radial kernel, and radial basis kernel. The results indicate that the algorithm employing the radial basis kernel achieved the highest accuracy, while the linear kernel algorithm demonstrated the fastest performance.
Compiler For Mathematical Operations Using English Like Sentences

This work revolves around the creation of a unique tool designed to interpret and execute mathematical operations articulated in English-like sentences. The tool developed using Lex and YACC, popular tools for creating lexical analyzers and parsers. The aim is to make mathematical computations more accessible and user-friendly. It bridges the gap between complicated mathematical grammar and everyday language by converting specified English words into executable code.
Wi-Fi Based Smart Attendance Monitoring System

The attendance monitoring system provides a convenient way to track the presence of faculty members. In the past, faculty attendance was marked using traditional methods under the supervision of higher authorities such as the principal or department head. However, these methods were often outdated and prone to errors, leading to discrepancies in attendance records over the years. To address these issues, a new system was implemented using modern technology, specifically finger-punching and face recognition. This system replaced the old record books and other outdated methods, offering a more efficient and accurate way to manage faculty attendance. Additionally, a mobile application is developed for faculty or staff members to mark their attendance using their smartphones. This application utilizes Wi-Fi and location services to record attendance on campus. The recorded attendance data is securely stored in a database, allowing the administrator or main authority to access and modify the records as needed. This integrated system not only simplifies the attendance process but also ensures data integrity and provides real-time monitoring capabilities.
Advanced Object Detection for Capsules and Tablets Identification Through Deep Learning

In this study, a novel deep learning framework has been created for the precise detection and identification of pills and capsules in medical photographs. In the pharmaceutical industry, there is always a danger that tablets and capsules will mix up inadvertently. A system has been devised where an alert mechanism is launched if a tablet is spotted in order to avoid such accidents from occuring. The warning system uses WhatsApp’ s messaging service to send a message to a pre-registered number and play a siren alert sound. This method recognises prescription pills with great accuracy and effectiveness using the object detection algorithm YOLO. The algorithm gains the ability to recognise distinguishing visual features and changes unique to particular pills through rigorous training on annotated datasets. The proposed framework could be used to improve automation and reliability in drug detection operations, as well as pharmaceutical manufacturing, quality control, and medicine identification systems.
Comparative Approach to Secure Data Over Cloud Computing Environment

In the area of cloud computing ensuring the utmost security for sensitivity data is of paramount significance. This look at undertakes a complete journey via a comparative analysis of four broadly diagnosed classical ciphers: the Caesar Cipher, Columnar Transposition Cipher, Hill Cipher, and Rail Fence Cipher. The cognizance is to meticulously assess their efficacy in encrypting and decrypting data within the dynamic and difficult panorama of cloud computing. While the simplicity of the Caesar Cipher belies its vulnerability, the Columnar Transposition Cipher reinforces security via key-based totally column permutations, albeit with susceptibility to frequency analysis attacks. Leveraging the electricity of linear algebra, the Hill Cipher affords a robust safety paradigm that demands great key control and matrix inversion. Conversely, the Rail Fence Cipher expedites encryption but compromises protection. Through meticulous evaluation, the Hill Cipher emerges with the highest encryption and decryption instances, even as the Rail Fence Cipher lingers at the opposite end of the spectrum. The Caesar and Columnar Transposition Cipher maintain their floors inside the centre. The profound insights gleaned from this analysis empower selection-makers to judiciously select encryption techniques tailored to the multifaceted eventualities inherent in Cloud Computing. Thus, this challenge appreciably strengthens information protection and propels the frontiers of cryptographic studies in the cloud domain. It is well worth citing that in the abstract, the Rail Fence Cipher demonstrates efficacy, particularly in the context of Cloud Computing.
Binary Voting Protocol Using Quantum Secret Sharing

In a traditional voting system, voters have to reach the voting system to cast their votes. It is difficult for the elderly and for those living in remote localities to cast a vote. In the era of digital advancements, remote electronic voting has emerged as an efficient means of engaging citizens in decision-making processes. However, ensuring fairness and mitigating fraud in elections remain significant challenges. This research paper proposes a novel approach called Quantum Secret Sharing (QSS) combined with the quantum binary voting protocol, leveraging the capabilities of the IBM Quantum Experience platform. By incorporating established security features and introducing new criteria, this protocol aims to surpass the limitations of classical voting systems. The research involves the creation of communication circuits using IBM Quantum Experience and subsequent analysis through quantum state tomography. By integrating the principles of QSS and the quantum binary voting protocol, the proposed approach addresses the limitations of classical voting systems. The protocol satisfies the standards of traditional voting systems while introducing new criteria to overcome their shortcomings. Through the utilization of IBM Quantum Experience, secure communication circuits are established, and the outcomes are analyzed using quantum state tomography, ensuring the fairness and accuracy of the voting process. It integrates quantum cryptography, quantum communication, and classical cryptography techniques to create a robust and tamper-resistant voting protocol. By employing quantum superposition, the protocol enables voters to cast their votes in multiple states simultaneously, making it extremely difficult for a malicious person to intercept or alter individual votes. Furthermore, quantum entanglement ensures that any unauthorized attempt to measure or manipulate the quantum states would result in detectable changes, enhancing the overall security of the voting process.
Classification of Human Poses Using Deep Learning Techniques

In numerous applications, such as tracking physical activity, decoding sign language, and controlling full-body gestures, human position estimation from the video is crucial. Human pose classification is challenging yet well-researched area. The particular way that human joints are arranged is referred to as pose. The localization of human joints or predetermined markers in images and videos can therefore be defined as the topic of “Human Pose Estimation.” Pose estimate is required for applications like human activity detection, sports, yoga positions, sign language applications, motion. The Mediapipe API is utilized in this study for human classification. This model categorizes human poses while identifying and tracking the actions of individual body parts. This paper demonstrates the adaptability of deep learning techniques and highlights the efficiency and accuracy of the BlazePose API in providing reliable pose landmarks.
Performance Analysis of Object Detection Algorithms for Multi-Person Identification

In the prospect of computer vision, the accurate recognition of individuals and objects constitutes a pivotal pursuit with far-reaching implications. It has enormous significance for a variety of uses, including retail analytics, public transportation, event management, the hospitality industry, workplace utilization, education facilities, public safety and many more. This paper delves into the intricacies of object detection algorithms for multi-person identification. It involves detecting and recognizing individuals within images containing multiple persons. Object detection algorithms locate and draw bounding boxes around objects of interest in an image in this case it is people. We have implemented HOG-SVM, YOLOv8 and RetinaN et algorithms to count people in varied-sized gatherings in different contexts with multiple postures. The performance of these algorithms is analyzed, where recall of 0.97, 0.94, and 1.0 are observed for HOG-SVM, YOLOv8 and RetinaN et algorithms respectively demonstrating their ability as people counting algorithms.
Design and Development of Automotive Domain Needs Using Rhapsody Model Manager

The automotive domain has unique and complex requirements for software and system development because of the need for safety, reliability and compliance with industry standards. Engineering Lifecycle Management integrates product development tools to manage requirements, modelling, test and workflow management in one toolset built for global collaboration. Rhapsody Model Manager (RMM) is well-suited to cater these needs and has become a popular tool in the automotive industry. It enables engineers to model the system architecture, design the software components, generate code, and validate the system using simulation and testing. The tool also provides traceability, version control and reporting features required for compliance with safety and regulatory standards. From literature survey it is found that Rhapsody Model Manager is used as a collaborative tool that supports agile and DevOps practices, allowing automotive teams to work efficiently and deliver high-quality software faster. It integrates with other tools that are mostly used in the automotive industry, such as Engineering Requirements Management DOORS and Rational Team Concert, to provide a seamless development experience. Therefore, the requirement for safety, reliability, compliance with industry standards and collaboration in the automotive domain makes Rhapsody Model Manager an ideal tool for managing complex software and system development projects. The paper gives a brief overview about Engineering Lifecycle Management, its benefits, key components and describes the architecture and workflow of Rhapsody Model Manager. It also explains the procedure of creating and managing the architectural models in local environment and in server.
Comparative Analysis of the Bernoulli and Multinomial Naive Bayes Classifiers for Text Classification in Machine Learning

The purpose of the report is a comparative analysis of the Bernoulli and Multinomial Naive Bayes classifiers in text classification for machine learning. The conducted research demonstrates that when classifying English user comments, the accuracy and precision of the Multinomial Naive Bayes classifier are higher than those of the Bernoulli Naive Bayes classifier, with the difference increasing with the volume of data. Both the Bernoulli and Multinomial Naive Bayes classifiers have identified a higher percentage of negative comments as belonging to their respective classes and a lower percentage of positive comments as belonging to their respective classes when classifying English user opinions. The Multinomial Naive Bayes classifier has higher values for the recall parameter of positive comments compared to the Bernoulli Naive Bayes classifier, whereas the Bernoulli Naive Bayes classifier has higher values for the recall parameter of negative comments compared to the Multinomial Naive Bayes classifier.
College Club Activity Management System

A college club management system can be useful for organising and managing various aspects of student clubs, including membership, events, communication, and records. It can help streamline administrative tasks and improve the overall efficiency and effectiveness of the club. Additionally, it can provide a centralised platform for students to connect with each other and collaborate on club activities. Currently, the different clubs in an institution maintain their own web sites. Each of these websites usually only contain information about the past events conducted by the club. Absence of features such as member login can cause inefficiency in management of club activities. Further, as there is no single integrated application for management of activities of different clubs, events conducted by the clubs may be scheduled on the same day which can be a loss to students interested in participating in these events. The proposed system overcomes these drawbacks by providing a single web application that can be used by a college or university to maintain all its clubs and their activities. The application can be used by core team members to plan and organise events, schedule meetings and analyse feedback received for various events using natural language processing, while club members can effectively participate and contribute to the club while gaining extra curricular skills.
Performance Driven Experimental Analysis of Apache Spark Configuration Parameters

Big Data refers to huge volume of data in the range of peta bytes or higher. In order to store and process such large volume of data there is a need for distributed processing platforms as conventional stand-alone platforms cannot store or process bigdata. Distributed bigdata analytics frameworks helps in storage and processing of bigdata. The most popularly used open source bigdata analytics framework provided by Apache foundation includes Hadoop and Spark. Apache Spark is widely considered for processing bigdata because of its in-memory computation capability. The major challenges involved in big data analytics are managing the huge volume and variety of data originating from different sources in different formats. Apache Spark has around 180 configurable parameters and all of these parameters are set to some default values. It is very difficult for the normal user to identify the optimal values for these configuration parameters. And also, not all parameters may have impact on the application performance. Therefore, the proposed work aims at conducting an experimental research for identifying the subset of key apache spark configuration parameters that impacts on the execution time of an application and its optimal values to maximize performance by analyzing the effect of various parameters and its values. In the proposed work the Execution Time of the spark job/application is considered as the performance indicator. The result section of the proposed work presents the various key parameters and its optimal values.
A Novel Approach for Developing Inclusive Real-Time Yoga Pose Detection for Health and Wellness Using Raspberry pi

Yoga, an ancient practice with myriad physical and mental benefits, has gained popularity across the globe. Accurate pose detection and guidance are crucial for practitioners to maximize the benefits and minimize the risk of injury. This study introduces a novel approach to real-time yoga pose identification and instruction using Raspberry Pi. Traditionally, machine learning algorithms have been utilized for pose detection; however, this study uses MediaPipe library and OpenCV for pre-trained pose estimation model from the MediaPipe library to detect and track the key landmarks of a person's body. Based on the detected landmarks, it calculates various angles between the joints and classifies the pose accordingly. The proposed system's core advantage lies in its utilization of Mediapipe, an open-source framework developed by Google, which efficiently identifies and tracks essential body landmarks over time. These landmarks, alongside TensorFlow's capabilities, are pivotal in extracting pertinent features from images and videos. OpenCV, a renowned computer vision library, further enhances the system by processing visual data. The user interface is designed to offer user convenience and efficient interaction. By providing real-time feedback through voice commands, the system assists users in achieving proper yoga poses. The Raspberry Pi, equipped with its camera module, serves as the hardware foundation. Its affordability, adaptability, and compact size make it an ideal choice for on-device processing, ensuring low latency and responsive performance. Additionally, it provides some simple stretches and recommendations for alleviating muscular stiffness and soreness brought on by inadequate yoga or other wrongdoing movements and finally provides a searching option for yoga pose.
Using Big Data and Hadoop in the Student Learning Process - Enhancing the Educational Process through Real Experience

In recent years, with the development of IoT technologies, digitization in all spheres of life, the generation of data of any nature in huge quantities, the organization, storage, processing and analysis of big data is becoming more and more relevant. Hadoop as an ecosystem provides very powerful tools for working with big data. The environment itself is free, and with the availability of appropriate hardware for its installation, familiarization with such frameworks is very useful for students. This report presents the advantages and disadvantages of using Hadoop in student education and how the use of these modern environments can improve the educational process, expand the practical skills of students and prepare them for their future professional challenges in the field of processing and analysis of big data.
Secure, Private and Transparent Messaging on a Public Blockchain

This research paper delves into the crucial aspects of data security, privacy concerns, lack of transparency, and avoiding monopolistic anti-competitive practices in modern communication platforms. This paper proposes an innovative approach that involves unbreakable encryption, interoperability between platforms, no data spoofing by the middleman and limiting monopolies. By leveraging asymmetric cryptography to encrypt data while in transit and stored, and a self-maintained blockchain to store messages, user data is safeguarded against exploitation such as for serving targeted ads or AI model training. Unlike traditional platforms, this system promotes collective ownership, preventing any single entity from monopolising user information. Instead, the platform acts as a networking bridge, enabling secure and decentralized data management. This paper advocates for a transformative communication ecosystem that prioritizes user protection and empowerment while fostering transparency and accountability.
Application of Deep Learning Techniques for Detection and Classification of Human Disease

Machine learning is used practically everywhere from technologies to healthcare. Many researchers predict that disease diagnosis through machine learning is cost efficient and time efficient. In traditional methods, an expert is needed to detect disease and sometimes, it may cause human error. So, there is a need of automated diagnosis tool for detection of disease. In this paper, a methodology for detection of malaria and breast cancer is proposed. In malaria detection, data is preprocessed, trained and tested using CNN, Resnet50 and VGG16. Accuracy of CNN, VGG16 and Resnet50 are 94.73%, 90.83% and 92.96% respectively. CNN has highest accuracy when compared to VGG16 and Resnet50. In malaria classification, YOLOv5 was used for classification of malaria which classified malaria parasite species and its stages. In Breast cancer detection, data is preprocessed, trained and tested using CNN, ResNet50 and VGG16. Accuracy of CNN, VGG16 and Resnet50 are 95.13%, 94.04% and 95.53% respectively. Resnet50 has highest accuracy when compared to VGG16 and CNN.