Reviewerly/exHarmony huggingface dataset:
Secure Image Encryption using Playfair Cipher with Quadrant-Based Division

Numerous photo encryption techniques have been presented in the previous decade, thanks to the increased focus on image encryption within data security research. Image encryption is different from textual content encryption due to the intrinsic properties of images, such as their mass records capacity and excessive records redundancy, making it far more challenging to address them utilising standard encryption processes. A new photo encryption add-on that uses Playfair cypher to better securely encode image data. The image is divided into four equal quadrants, which is the method indicated. A different image is shown in each of the four corners. The first quadrant consists of the top left corner, the second of the top right corner, the third of the bottom left corner, and the fourth of the remaining space. In the suggested approach, each quadrant picture functions as a standalone image that is encrypted using a random key. Once the images have been encrypted, they are combined into a single entity, making decryption very challenging. The fundamental goal of our research is to protect images from unauthorised duplication and distribution by encrypting data based on them using a suitable key.
Deep Fusion: A CNN-LSTM Image Caption Generator for Enhanced Visual Understanding

Photo caption generators had gained huge recognition recently from the areas of NLP and computer vision. These generators produce captions that explain an image's content using deep learning algorithms. A sizable collection of image-caption pairs is used to train the algorithm. The photos are fed into a pre-trained CNN after the dataset has undergone preprocessing to extract its visual properties. Along with the picture features, the necessary captions are tokenized and supplied to the LSTM decoder. In order to accelerate the caption generation process, the LSTM decoder is trained to utilize beam search and maximum likelihood estimation methods. To assess the effectiveness of the suggested model, several metrics such as BLEU and METEOR are employed. The suggested CNN-LSTM image caption generator has a lot of promise for use in a variety of contexts, such as picture comprehension, information retrieval, and assistive technology for the blind. Combining CNN and LSTM models takes advantage of each architecture's capabilities, allowing the model to produce insightful and contextually appropriate captions for a variety of images. We also explore present difficulties and potential directions for additional investigation in this area. This research study concludes by presenting an innovative method for creating image captions that combines the strength of CNN and LSTM. The experimental outcomes demonstrate the efficacy and reliability of the suggested model, highlighting its potential to advance the field of image captioning and contribute to the creation of intelligent systems that can comprehend visual content and produce precise and insightful textual descriptions.
Multi-Factor Analysis of RSA Based on Variations in Primes Used for Modulus Generation

The RSA cryptosystem encompasses some of the most common and popular encryption-decryption algorithms. In conjunction with other encryption schemes, RSA encryption is often used as a method to prove the authenticity and integrity of messages. Due to its less efficient and resource-intensive nature, it is generally, not used to encrypt entire multi-line messages or files. In order to crack an RSA encrypted asset, it is important for attackers to be able to factorize humongous primes; which, with the advent of quantum computing is slowly turning into a possibility thereby causing the factoring problem to lose its relevance. The purpose of this paper is to examine the cryptosystem for potential correlations that might exist between factors such as “the number of primes used” and the algorithm's “level of security”. Classical RSA involves the use of a pair of prime numbers for the process of key generation. In this paper, we aim to propose a generalized algorithm for generating RSA keys using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> distinct prime numbers. Previously, papers have been published on the RSA algorithm subject to changing “the number of primes” used to generate the RSA modulus. However, the aim of this paper is to generalize this approach while simultaneously illustrating the changes in the classical algorithm's properties over a spectrum of different values of the factors involved.
Secure, Reliable and Transparent Patient-Centered Health Record Management Framework Using Blockchain Technology

The Internet of Things, widely known as IoT, has multitudinous applications in medical sciences. However, IoT does not support decentralized, immutable, distributed-ledger properties like Blockchain technology. The amalgamation of the up-to-the-minute healthcare sector with Blockchain not only enables the healthcare authorities but also the patients and other stakeholders like the insurance policymakers. Since Blockchain is a public distributed-ledger database, it makes the accessibility of healthcare-related data easier. The stringent security that Blockchain provides because of its strictly immutable property helps safeguard the privacy of healthcare records. This paper elucidates the application of Blockchain Technology in healthcare sectors. Besides, a framework for a Blockchain-based healthcare system is developed and implemented for uplifting the functioning of healthcare industries where the patients' healthcare records can be secured in a decentralized and distributed ledger platform hence minimizing the superfluous expenditure which occurs due to repeated diagnosis, re-prescribing and repurchasing of medicines. Our intent is also to pre-empt the wastage of time which occurs due to the same actions as that of the unnecessary outlay.
Comparative Analysis of Homography Technique based on RANSAC and Least Square Method

Color homography is the main component of geometric approaches used in computer vision and is utilized in camera calibration, stereoscopic vision, 3D views, and many other applications. It links the visual components of two images with the help of the Root Polynomial and Least Square Method. These methods are helpful when there is no change in the viewing conditions of images. In this paper, we propose that colors across a change in viewing condition can also be related by homography such as changing light color, shading, camera, etc. This study presents an integrated framework that describes the various phases of the homography estimation method. This study also provides an overview of two approaches i.e. direct and feature-based used for homography estimation. In this research paper, a comparative analysis of Root Polynomial, Least Square, and other techniques are performed on the different datasets with parameters of Precision, Recall, F-measure, Mean, and Median. These parameters are used to determine the simulated results.
Blockchain Design Elicitation for Healthcare: A VFT Based Approach

The healthcare system in India, like in many other countries, is complex and influenced by various factors such as population size, the presence of structured and unstructured healthcare artifacts, social, governance and commercial disparities and structures. Existing research has highlighted several facets that are crucial for stimulating and improving the Indian healthcare system. These include primarily the triple t aspect of traceability, transparency, and trust along with aspects of identity management, incentive management, and standardization of processes and Blockchain technology, as a recent innovation, offers potential solutions for many of these facets as inherent features to the adopting systems. Furthermore, its implementation requires careful consideration and alignment with the specific needs, challenges, and regulatory frameworks of the Indian context. We use the Value-Focused Thinking framework in addressing the research context of adopting Blockchain Technology from a stakeholder viewpoint to elicit design-based recommendations.
Body- and Pose-tracking for Augmented Reality Applications in the Field of Sports

This paper presents the concept for a system that utilizes a single RGB camera to track and evaluate a human body. The system aims to support office workers in improving their posture, by doing simple exercises. This should reduce the risk of health issues like back, shoulder and neck pain which will lead to an increase in the overall productivity. By creating an augmented reality mirror this system will give instructions for doing simple exercises. During this workout the user will be evaluated and the user will be notified about wrong stances. This paper discusses related works on body tracking systems, including systems that utilize a single camera as well as those systems that utilize RGBD cameras. It also presents a basic system design and ideas for pose comparison. Additionally a prototype that is based on the MediaPipe pose library for JavaScript has been created, which will be discussed and evaluated in the course of this paper. This paper concludes by highlighting possible future developments, such as additional features and improved usability. This whole work demonstrates the feasibility of using body tracking for sports applications.
Logical Decision Networks Based on the Optical Coloroids

This work is dedicated to the creation of efficient systems based on the use of light radiation of a certain color directly as a fuzzy variable - the carrier of logical information and the basis for building logical decision networks by transforming light radiation with appropriate light filters. The optical processing of color information, which reflects different values of the input data, is carried out by the proposed structural construction of fuzzy logic gates and is significantly simplified, compared to the existing systems, due to the implementation of the properties of additive and subtractive color processing using simple light filters. The concept of applying the rule of idempotence and decision blocking, as well as the criterion for the maximum reliability of estimates based on the minimum standard deviation from the average weighted values of the corresponding combinations of color information, has been formed. The advantages of the proposed approach in relation to decision-making systems with big data are substantiated.
Quantum-Inspired Computing: Shor's Algorithm and Euler's Totient Function

In this paper, an attempt is made to apply the Euler's Totient function and Euler's theorem to organizing calculations of the period of the modular exponentiation function inspired by Shor's quantum algorithm. A particular method for determining the period by a classical computer is described and a general method for its determination is proposed. For the first 10,000 prime numbers, semiprime numbers are compiled and the distribution of periods of exponentiation functions modulo a semiprime number is studied. The relationship between the Euler's Totient function and the period of the modular exponentiation function is established.
Distance Impact on Collaboration Quality in Smart Glasses Applications

This research investigates the effect of the distance between users within the virtual space on collaboration quality in social smart glasses based virtual environments. We developed a Vuzix smart glasses application to enable users to work together on assembling furniture models. Afterwards, we conducted a user study to assess the impact of distance between users on collaboration quality, and the results showed that distance significantly affects collaboration quality. Our prototype could be used as a remote aftersale service, and our findings could aid in designing low-cost Smart Glasses social solutions for productive teamwork.
Being alone, together: How does design shape social interactions and relationships of young adults in Singapore

Studying the social interactions and relationships of young adults in Singapore, this paper seeks to understand the issue of social disconnection and how design shapes them. Young adults face the challenge of navigating the complex lifestyle of a smart city while balancing their ever expanding and fluctuating social network. As such, it causes them to feel disconnected in their social interactions and relationships. By considering these questions: What does it mean to be connected? What is the role of product design in people’s relationships? Why do young adults feel disconnected in a technologically connected society? How can design establish a truly connected society? it will evaluate how products could be designed and created for genuine interactions and fulfilling relationships. From the surveys, interviews, observations, case studies and literature readings in this study, it reveals the social disconnection young adults face in a connected city, how they navigate in a technological environment, and how experiences are designed for connection. It also shows that design plays a vital role in tackling the social disconnection young adults face. Designing with a human-centric approach not only creates an opportunity for people to create meaningful relationships using technology, but also maintains the spontaneous and fluid elements of social connections through prompting interactions. Additionally, it has been found that design is only a platform where they can initiate these interactions and relationships, the decision to disconnect remain with the human user as much as devices have been designed to lead to certain decisions being made.
Innovation and Technology For Making Indian Cooperatives Future-Ready

India has 8.5 lakh cooperative units, out of which 20 per cent are credit cooperatives. An invention encourages the innovator to observe, orient, thoughts, and develop new ideas. Cooperatives have long been an integral part of India’s socio-economic fabric, playing a crucial role in empowering rural communities and promoting inclusive growth. However, in the era of rapid technological advancements, it is imperative for Indian cooperatives to embrace innovation and leverage technology to enhance their efficiency, competitiveness, and sustainability. In this backdrop, this article makes an attempt to examine key areas where innovation and technology can be applied to empower Indian cooperatives and enable them to thrive in the digital age. Further, it also discusses the challenges and opportunities associated with implementing innovative solutions in the cooperative sector.
Evaluation of the Effectiveness of Online Learning Platforms for the Improvement of Academic Performance in Higher Education Institutions

A documentary review was carried out on the production and publication of research papers related to the study of the variable ONLINE LEARNING, ACADEMIC PERFORMANCE, HIGHER EDUCATION. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period 2017-2022, achieving the identification of 643 publications. The information provided by this platform was organized through graphs and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors on the proposed topic is referenced through a qualitative analysis. Among the main findings made through this research, it is found that the United States, with 97 publications, was the country with the highest scientific production registered in the name of authors affiliated with institutions in that country. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material related to the study of ONLINE LEARNING, ACADEMIC PERFORMANCE, HIGHER EDUCATION was Social Sciences with 380 published documents, and the most used Publication Type during the period indicated above were Conference Articles with 69% of the total scientific production.
K-means and Support Vector Machines for Thai Language IT News Retrieval System

The proliferation of information technology news websites on the internet has resulted in an abundance of content that can be quickly and easily published. There are numerous websites in Thailand that offer information technology news, which provides great benefits to both students and IT employees in the country. These websites are valuable resources for keeping up with the latest technological advancements and for sharing knowledge among IT professionals. However, searching through this massive amount of material to find relevant news in the Thai language can be a challenging task. Traditional search engine approaches rely on general keyword searches to locate pertinent information, but this method can prove problematic when dealing with extensive content in the Thai language. In response, this paper proposes an information retrieval system designed and developed specifically for Thai-language information technology news. The system employs the K-means algorithm and support vector machine for classification, serving as a pre-processing step for text classification used in the indexing process of the information technology news retrieval system. Experimental results indicate a recall value of 68% and precision of 95%. The F-measure performance was found to be 79%.
Estimation of Camera Parameters via Gray Wolf Optimization Algorithm

In machine vision for industrial growth, camera calibration accuracy is essential. In this paper, we propose an algorithm for camera calibration that combines an enhanced version of the two-step approach with Gray Wolf Optimization algorithm (GWO). First, the two-step process is enhanced to lessen distortion's impact on camera calibration. After employing the principle of slope invariance to determine the camera's distortion parameters, we employ Zhang's calibration technique to acquire the camera's internal and exterior parameters. The above-mentioned camera parameters are then optimized using a GWO approach that combines a dynamic inertia weight and a variation idea. Finally, the proposed algorithm is subjected to a series of comparative experiments, the results of which demonstrate that it achieves higher accuracy, overcomes the problem of slipping into local optimal more effectively, and achieves faster convergence in parameter optimization.
Exploiting COVID-19 Tweets for Sentiment Analysis Using Chameleon Optimization with Ensemble Learning

In the initial stage of the COVID-19 pandemic, assessing public opinion is the most critical problem to combat the epidemic by enforcing a national lockdown, rendering health services, and presenting quarantine procedures. One popular social media platform that provides public sentiment towards events that happened at the time of the COVID-19 period is Twitter. Since the Twitter post is short and continuously generated. In the domain of Natural Language Processing (NLP), Sentiment Analysis (SA) came up for mining public judgement, while Machine Learning (ML) methods are commonly used for examining sentiments. This research presents a Chameleon Optimization Algorithm with Ensemble Deep Learning Enabled Sentiment Analysis (COAEDL-SA) on COVID-19 Twitter Data. The presented COAEDL-SA technique identifies diverse types of sentiments posted by the user on Twitter. Firstly, data preprocessing is performed to transform the data into a useful format. Next, the COAEDL-SA technique uses the FastText technique for feature extraction. For SA, an ensemble of DL models such as Recurrent Neural Network (RNN), Deep Belief Network (DBN), and Stacked Autoencoder (SAE) is used. Finally, COA is used for the effectual hyperparameter selection of the DL models. The performance assessment of the COAEDL-SA approach takes place on the Twitter dataset. The widespread comparison study highlighted that the COAEDL-SA technique has resulted in improved SA over other DL models.
Golden Tortoise Beetle Optimizer with Deep Learning Based Emotion Detection in Social Media

Social networking sites like Twitter developed as social media platform that impart a vast knowledge base for an individual to express their perspectives and exclusive ideologies on several subjects and problems with families and friends. In recent times, the two methods that were commonly used in Natural Language Processing (NLP) domain are sentiment analysis (SA) and emotion classification. Emotion analysis can be defined as the process of detecting the feature against targets or topics. The attitude can be polarity either negative/positive or an emotional condition like anger, miserable, or bliss. Hence, opinion mining and classifying posts manually will be complex. In this article, we focus on the design of Golden Tortoise Beetle Optimizer with Deep Learning based Emotion Detection (GTBO-DLED) technique on Social Media. The presented GTBO-DLED technique identifies various kinds of emotions expressed by people on social media. Primarily, the GTBO-DLED technique undergoes a series of preprocessing to transform the text into useful format. For emotion detection, the preprocessed data gets analyzed by deep belief network (DBN). Since trial and error hyperparameter tuning will be a tedious task, automated procedure using GTBO method was designed in this study. The simulation values of the GTBO-DLED approach was executed on benchmark emotion detection database. The GTBO-DLED technique shows promising performance over other recent algorithms with respect to various measures.
Automated Modulation Classification of Communication Signals Using Hybrid Deep Learning

For the recognition of automated modulation, algorithms based on frequency, amplitude, and signal phase were extensively used. But the algorithm is affected considerably by noise, and the performance is degraded significantly in low signal-to-noise ratio (SNR) conditions. Deep learning (DL) is an effective mechanism that has accomplished greater attainment in various applications. But the usage in transmission systems has not been well-studied. This study explores an algorithm for modulation classification and multi-signal detection that is substantial in different transmission techniques. In this aspect, this study develops an Automated Modulation Classification of Communication Signals using Metaheuristics with Deep Learning (AMCCS-MDL) model. The presented AMCCS-MDL technique focuses on identifying and classifying different types of modulations in communication signals. To attain this, the presented AMCCS-MDL technique follows the SqueezeNet-based transfer learning technique to extract features from the communication signals. In addition, the AMCCS-MDL technique uses an attention-based bidirectional long short-term memory (ABiLSTM) model for classification purposes. At last, improved artificial bee colony optimization (IABC) algorithm is employed for the hyperparameter optimization process. A wide-ranging experimental validation process is made to demonstrate the enhanced efficacy of the AMCCS-MDL technique.