Reviewerly/exHarmony huggingface dataset:
Creation of a Platform for Artisans to Promote Their Product Using Blockchain as NFT

Before blockchain technologies, the idea of securing digital asset is remained susceptible to tampering but after few many years of studies and improvements in blockchain brought about the development of Non-Fungible Tokens (NFTs), that are tokens which might be virtual property and have evidence of possession embedded. The Blockchain Platform for Artisans (BPFA) is an internet platform in which customers/artists can buy or promote non-fungible paintings portions/products and earn cryptocurrencies. Therefore, by this technology, promoting art using NFT and creating revenue by earing cryptocurrencies are easy and authorized creating and selling NFTs. In this paper, we are presenting an idea for developing a platform for artisans to promote their art using blockchain and with security assurance also how they can make NFT as an investment option. Blockchain Platform For Artisans (BPFA) uses interactive UI so that local artisans can also use platform easily and promote their art like image in the form of NFT and blockchain provides security as it has immutable property. Moreover, our paper will also help to know the key demanding situations and challenges in edition of blockchain from the attitude of privateness, safety, and selling.
Leafy: Enhancing Home Energy Efficiency through Gamified Experience with a Conversational Smart Mirror

In the next few years, people will be called upon to try to slow climate change and achieve carbon neutrality collectively. The use of persuasive digital tools and engaging mechanisms can play an important role in matching such objectives. In our research, we explore the usage of a Multimodal Conversational Agent embedded in a Smart Mirror and connected to home automation appliances to help users reduce their energy consumption. The agent employs a variety of gamification techniques to encourage short and long-term sustainable behavior, and it is designed to be an enjoyable and non-intrusive experience. It informs householders about their energy consumption and encourages them regularly to reduce and optimize their electric usage. In order to keep the user engaged, the mirror contains visually appealing components and recommendations for daily challenges.
On Using Twitter to Understand the Stablecoin Terra Collapse

Stablecoins have emerged as a solution to the legal status and volatile exchange rate issues surrounding cryptocurrencies like Bitcoin. Terra is a blockchain protocol that enables the creation of stablecoins pegged to fiat currencies, including UST and KRT, which have gained popularity for their use in DeFi applications. However, in May 2022, TerraUSD (UST) lost its peg to the USD, collapsing to a value of 0.091 USD due to a massive withdrawal of UST from the Anchor Protocol, leading to a sharp drop in price. In this paper we analyze people's opinions and thoughts about the topic using Twitter data and we propose a method to identify the accounts that acted as opinion leaders. We observed an increase in negativity in the month leading up to the collapse and identified the United States as the most prolific country in terms of tweets; we identified opinion leaders accounts and we showed that the number of followers is not an important metric to identify opinion leaders. The failure of the Terra project highlights the intrinsic fragility of algorithmic stablecoins, which can fail due to sudden fluctuations in demand and supply, and their potential for success and stability remains uncertain.
Security Models of cloud computing using Machine Learning &amp; Network Security Application

The paper's objective is to improve cloud security and enhance information literacy levels for cloud re-appropriation. Since the protection of cloud data is a major concern in cloud re-appropriating, valid computation is important. An instrument for cloud security has been suggested in this paper. The system proposes two calculations, one depends on AI and another depends on neural system. The AI base calculation depends on the KNN calculation, and the neural system procedure depends on information discontinuity and hashing innovation. Both methods are effective in improving cloud security by encrypting cloud data in cloud workers.
Anomaly detection in mixed high-dimensional molecular data

Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly.We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest.All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire.
Computational facial analysis for rare Mendelian disorders

With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next-generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state-of-the-art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra-rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.
Distribution constraining for combating mode collapse in generative adversarial networks

Image synthesis is a critical technique in the image processing field. Recently, generative adversarial networks (GANs) have played a significant role in synthesis tasks. However, the issue of mode collapse remains a major challenge in GANs, which limits their potential applications. We propose a method to address the mode collapse problem. Our approach focuses on minimizing the divergence between the distributions of real and generated features, thereby reducing the learning pressure on the discriminator. An advantage of our method is that it does not require prior knowledge or manual design. Additionally, it can be easily incorporated into state-of-the-art frameworks across various domains. Experimental results demonstrate the effectiveness and competitive performance of our proposed method.
Current and Future Trends of Augmented and Mixed Reality Technologies in Construction

Augmented reality (AR) and mixed reality (MR) technologies have gained significant interest throughout the past two decades in the Architecture, Engineering, and Construction (AEC) Industry. However, despite the rapid growth of these technologies, their effective implementation in the AEC industry is still in its infancy. Therefore, a comprehensive investigation of the state-of-the-art applications and categories of AR/MR in the construction industry can guide researchers and industry experts to choose the most suitable AR/MR solution for research and implementation. This paper provides a comprehensive overview of 103 AR/MR articles published in credible journals in the field of the AEC industry within the years 2013–2021. Typically, review-type papers assess articles primarily based on their application areas. However, this classification approach overlooks some other critical dimensions, such as the article's technology type, the maturity level of technology used in the research, and the project phase in which technology is implemented. Accordingly, this paper classifies articles based on ten dimensions and their relevant categories: research methodology, improvement focus, industry sector, target audience, project phase, stage of technology maturity, application area, comparison role, technology type, and location. The results reveal that AR/MR literature has increasingly focused on simulation/visualization applications during construction and maintenance/operation phases of the project, emphasizing improving the performance of workers/technicians. Additionally, the increasing trend in AR/MR articles was identified as using self-contained headsets (e.g., Microsoft HoloLens). Markerless tracking systems show a significant trend among the articles. Moreover, the target location of implementing AR/MR primarily found to be in on-site and in outdoor spaces. The trend indicates an increase in immersive and mobile AR/MR applications in outdoor job sites such as construction sites to aid workers/technicians in assembly works during the construction phase.
Graph-based learning for automated code checking – Exploring the application of graph neural networks for design review

Although automated code checking (ACC) has been a subject of interest for many years, we have not yet seen significant breakthroughs in the field that may lead to the development of generic, comprehensive tools for ACC. Hard-coded rules are the backbone of all emerging platforms for ACC. These rules require a significant amount of engineering, which often requires manual labor; and the resulting rule sets are strict and difficult to scale to other building models. On the other hand, approaches relying purely on classic machine learning (e.g. SVM) are too coarse and unable to accurately express building information. In our hope to come up with a more scalable solution, we investigate here a novel workflow that relies on graph-based learning algorithms instead of processing rule sets. We illustrate the suggested workflow by checking accessibility requirements in residential houses, which we believe is one of the more promising rule sets that can be checked using graph-based learning methods. The high accuracy of the obtained results is encouraging to continue exploring Graph Neural Networks (GNN) for this type of ACC, yet rule-based and classic ML-based approaches show other advantages as well (rigor and speed, respectively). The main contribution of this work is therefore its identification of meaningful limitations and directions for future research, including alternative graph structures and GNN architectures.
A Novel Masked Facial Recognition System Using CNN

The present work consists of the elaboration of models for facial recognition of individuals with a mask and to present suggestions of different methodologies that can be applied to adapt existing models. There is a need to evaluate the accuracy of these architectures in real scenarios, especially when it comes to applications that help prevent COVID-19. In this work convolutional neural network is applied to facial recognition. In addition, the ability of transfer learning to assist in the creation of new models will be evaluated. So, the steps include pre-processing of the chosen database, Model development, Training of models with hyperparameter variation, and Validation of models. In a problem of deep convolutional networks with multiclasses, the obtained results were able to classify the vast majority of images and be consistent with the current state of the art.
Elimination of External Power Source for Solar Trackers in Agriculture Fields

As the world continues to prioritize renewable energy sources, solar energy has emerged as a key player in the transition towards a sustainable future. India has taken significant steps towards becoming a global leader in solar energy manufacturing, with both domestic companies and foreign investments establishing the country as a hub for solar equipment production. However, the efficiency of solar panels can be further optimized through the use of solar trackers, which can increase power output by up to 30-40%. While solar trackers have proven to be effective, they often require separate power supplies or consume significant amounts of energy themselves. In the proposed research, a novel solar tracker system that utilizes energy from earth batteries, a green and renewable energy source that is non-toxic and harmless to the soil. This system not only conserves energy but also reduces pollution, making it an ideal solution for a wide range of markets and applications. The earth batteries used in the system are composed of Aluminum and graphite electrodes, which are connected in both parallel and series combinations to achieve the necessary voltage and current levels for charging the secondary batteries and powering the solar tracker. The proposed system demonstrates that by combining clean energy, earth batteries, and solar technology, the proposed topology can create an effective and sustainable solution for agriculture and other industries. The use of earth batteries not only reduces the environmental impact of solar trackers but also provides a reliable and cost-effective energy source. The system has the potential to significantly improve the competitiveness and adoption of solar energy in various markets, while also contributing to the global effort towards a more sustainable future.
Implementation of Industry 4.0 in Supply Chain Management in the Healthcare Industry

The implementation of Industry 4.0 will assist the healthcare sector in building reliable and sustainable supply chains. This research discusses about how the current technologies can lead to improve the existing supply chain management systems and how the drugs- one of the main component circulated in the supply chain be safeguarded from being replaced by counterfeits. As a result, this research study proposes a centralized system, which includes gathering and sending purchase orders, tracking the transportation of drugs, implementing a programme for drug exchanges in collaboration with numerous hospitals, and using AI models for demand forecasting to guard against shortage or oversupply conditions. To prevent an inflow of counterfeit supplies, blockchain technology is used to monitor pharmaceuticals and other medical products. To monitor the state of pharmaceutical products in storage facilities or warehouses, IIoT is employed. This improvement guarantees improved healthcare facilities and more transparency in the procurement procedures.
Securing Data in the Image Using SHA &amp; ECC

Networks have expanded globally, and information is now available digitally. Due to the volume of data being exchanged online, data security has become a difficult problem. Cryptography provides integrity, confidentiality, non-repudiation, and authenticity to the secret data. Symmetric & asymmetric cryptographic techniques are used today for hiding any confidential information from unauthorized persons. The combination proposed is the asymmetric algorithm (ECC) and a hash function (SHA). This research study introduces a new technique for encrypting and decrypting text that uses elliptical curve cryptography (ECC) to improve data security and a secure hash function (SHA-3) to ensure data integrity. When compared to the previous method for text encryption (RSA), the suggested solution of SHA with ECC is more effective. Compared to other cryptographic algorithms, it offers a higher level of security with a smaller key size.
BLIP-NLP Model for Sentiment Analysis

The abstract highlights the close relationship between sentiment analysis and natural language processing (NLP), emphasizing their shared techniques and applications. Sentiment analysis, a subset of NLP, employs machine learning algorithms to automatically detect and categorize subjective information in text, focusing on determining the emotional tone or attitude conveyed. NLP, on the other hand, is concerned with enabling computers to comprehend, interpret, and generate human language, utilizing computational techniques to analyze and manipulate natural language data. The integration of sentiment analysis and NLP has resulted in the advancement of more advanced algorithms and techniques for the analysis and understanding of human language. For instance, sentiment analysis can be incorporated into an NLP pipeline to evaluate customer feedback datasets, allowing companies to monitor real-time brand reputation and customer satisfaction. Similarly, NLP techniques can enhance the precision of sentiment analysis by considering the context, syntax, and identification of named entities and other crucial features within the text. The combined capabilities of sentiment analysis and NLP find applications in various domains, including social media monitoring, customer service, market research, and healthcare. Researchers and practitioners are continuously refining algorithms and tools by leveraging the strengths of both fields, leading to the development of innovative approaches for the analysis and comprehension of human language. This, in turn, opens possibilities for novel applications in the future.
Enhancing Product Authentication and Counterfeit Detection Using QR Codes and Blockchain Technology

Counterfeit products have been a persistent challenge for businesses and consumers, leading to financial losses, brand damage, and even risks to public health and safety. In recent years, blockchain technology has emerged as a promising solution to address this problem. By leveraging the decentralized and immutable nature of blockchain, businesses can create a tamper-evident and auditable record of a product's journey throughthe supply chain, from its origin to the point of sale. This allows consumers to verify the authenticity of a product andtrust that it has not been counterfeited or compromised. This research explores the potential of blockchain technology for product authentication, reviews the current state of the art, and discusses the challenges and opportunities for future research and development and also provides a case study of a real-world implementation of blockchain-based product authentication, highlighting its benefits and limitations. Overall, it is argued that blockchain technology hasthe potential to revolutionize product authentication and createa more secure and trustworthy supply chain ecosystem.
Hybrid Approach for Handwritten Digit Recognition using Deep Learning and ESRGAN-based Image Super-Resolution

This paper presents a novel approach for handwritten digit recognition and image super-resolution using deep learning and ESRGAN (Enhanced Super-Resolution Generative Adversarial Network). The study utilizes the MNIST dataset for digit recognition tasks and the DIDA dataset for image super-resolution. For handwritten digit recognition, a convolutional neural network (CNN) architecture is employed. The MNIST dataset has been pre-processed, and stochastic gradient descent (SGD) and Adam optimizer are used to train the model. The trained model achieves promising results in accurately recognizing handwritten digits. In parallel, the ESRGAN technique is applied to enhance image resolution. The DIDA dataset is pre-processed, and an ESRGAN model comprising a generator and discriminator network is designed. The model is trained using adversarial training techniques, such as generative adversarial networks (GANs), to generate high-resolution images. The evaluation of the trained ESRGAN model demonstrates its effectiveness in significantly improving the resolution of low-resolution images. The combined approach of handwritten digit recognition and image super-resolution provides a comprehensive system capable of recognizing handwritten digits and enhancing the resolution of images. The system can find applications in various domains, including document processing, image analysis, and computer vision. Experimental results and comparative analyses validate the efficacy and performance of the proposed methodology.
Sustainable and Intelligent Control Strategies for Electric Vehicle Systems

Electric utilities have a number of difficulties when integrating electric vehicles (EVs) into distribution networks without a communication infrastructure. To overcome these obstacles, it is essential to design reliable autonomous controllers that can handle charging operations efficiently while ensuring adherence to grid standard restrictions. This research suggests an innovative method based on fuzzy logic for an autonomous charging controller for EVs in distribution systems. The fuzzy inference system is made to meet three main goals: eliminating under-voltage problems in the grid, optimizing the system loading profile, and ensuring equitable charging among numerous connected EVs. To do this, it takes into account both the system voltage profile and the EV's battery state of charge (SOC). The findings reveal that the communication-free autonomous charging controller operates better than existing ones, displaying quicker and better charging performance while successfully reducing voltage violations. The controller can dynamically adjust to shifting system conditions thanks to the incorporation of fuzzy logic, enabling efficient and dependable charging operations for EVs. This research advances EV integration in distribution systems by offering a reliable and communication-free approach to autonomous charging regulation.