Reviewerly/exHarmony huggingface dataset:
Event Management Using Blockchain

The rapid advancement of blockchain-based technology, online booking of tickets has come as a great relief to most enthusiasts. Event management is a good fit for blockchain technology because of its attributes like transparency, security, and immutability. This project develops a system which provides a real time booking and transfer of tickets, events for customers and in turn examines the potential of blockchain technology in event management. People can book tickets. The controls are decentralized or controlled by algorithm. They can book, buy, and retrieve information and transfer tickets.
Food-Block: IoT–Blockchain-Based Framework for Agriculture Food Supply Chain

India has the second most massive agricultural food production in the world. However, due to the shortfall of traditional agriculture food supply chain management (SCM), India suffers significant food loss that affects the economy and limits farmer income. Recently, blockchain has emerged as a potential substitution of traditional food supply chain. Blockchain is a fully digitized technology that ensures end-to-end integrity with complete transparency of data. Due to the advancement of technology, traditional farming integrates with blockchain, Internet of Things (IoT), artificial intelligence (AI), and many other to bring significant innovation in the agricultural and food supply process. The technologies accumulate processes and analyze the information with minimum human intervention and with less error-prone data. This paper carries existing research on agriculture food supply and identified the potential of blockchain to address the issues. The paper proposed a blockchain-based Food-Block framework with technical specifications for future system development using IoT–blockchain integration.
Neural Transfer of Style Learning and Its Application

Neural transfer of style learning is the technique of gathering styling technique such as brush strokes, color palette, etc., in case of images and transferring it to another image creating a whole new image. Such a technique can be also used upon video and audio formats allowing users with very low artistic capabilities to create advanced styled content such as creating new paintings transferring style from famous painters, and similarly creating cool styled VFX backdrops without need of costly VFX artists or creating custom audio filters to sound like popular artists, etc. Neural transfer of learning technique has many applications which are yet to be explored by users. The primary problem with it is lack of awareness and good quality models, which generate excellent results to replace actual artists.
WhatsApp Chat Analyser

Among all communication channels, WhatsApp has been the most popular and effective. Numerous individual and group conversations make up this. As a result, they might contain some unstated facts. In this initiative, the data from those talks is thoroughly analysed. Regardless of the subject, this tool offers a thorough study of the information that WhatsApp provides. Matplotlib, seaborn, Pandas, word cloud, URL extract, emoji, collection, NLTK libraries, among others were used in its construction. Graphs can be efficiently plotted using them, as well as data frames. It is possible to apply this library to the greatest data set because it is used to build data frames and plot various graphs. Inspecting and modelling data is a technique that aims to find some valuable information before drawing certain conclusions. We have both sentiment and chat analysis in WhatsApp Chat Analyser. Also group and individual analysis can be done. Above that we have used 12h format according to the latest update of WhatsApp.
ChatGPT: An Advanceds Natural Language Processing System for Conversational AI Applications—A Comprehensive Review and Comparative Analysis with Other Chatbots and NLP Models

ChatGPT is an OpenAI-developed big-language model architecture based on the GPT-3.5 standard. It is aimed at engaging users in natural language talks on a variety of themes. With its comprehensive knowledge of English and the world, input from users can result in replies that resemble those of humans. It can understand and react to a variety of linguistic patterns and idioms because it was trained on a vast corpus of Internet content. Numerous programmes, such as customer support systems, virtual assistants, and chatbots have already implemented ChatGPT. Potentially useful for communication, education, and entertainment.
Two-Step Method for Extracting Emotion Cause Pairs from Texts

The main purpose of emotion cause extraction (ECE) is to find all the causes of certain emotions. This task has become an important topic for research in recent years since it may be used to examine the causes of distinct emotions in the text. However, annotated datasets are rare in research, particularly in languages other than Chinese. The main drawback of the ECE task is that emotions should be labeled before the real cause can be identified, which is practically challenging. To address this issue, emotional cause pair extraction (ECPE), a new work, has been suggested. It attempts to find potential emotion pairings and the causes of those pairings in a given document. Given a document, the first stage identifies all emotion and cause sentences, and the second stage identifies the correct emotion-cause pairs. This work demonstrates the task’s performance in extracting emotion-cause pairs and identifying the causes of emotions in texts.
A Secure and Practical Scientific Computation Outsourcing Application in Cloud Computing

Cloud computing permits customers with constrained computational capability and communication bandwidth to economically and feasibly outsource their massive-sized computations and operations to a public-oriented cloud having large-scale computation capacity. Cloud possesses the large storage, computational capability as well as software program which may be utilized by customers for the reduction of their overhead in terms of computing. Though, in the case scenario of the outsourcing, the privacy of customer’s private data must be preserved. In this research work, we developed a methodology for the outsourcing big-scale eigen count value computational threat to a malevolent-natured cloud that gives input/output information security, verifiability of results, and empirical performance. As far as the calculation approach to discover all the eigenvectors is of computationally high cost for huge-dimensional input data, we have utilized energy-iterative procedure for finding out the biggest eigenvalues well as the relatively corresponding eigen set vector of a certain matrix. In order to save the primitiveness, certain modifications are carried out to the certain input matrix in order to obtain its encrypted matrix that is transferred to the virtual cloud and further perform decryption of the result which is obtained back from the specific cloud entity for obtaining the accurate answer of eigen cost problem. We also provided end-result verification scientific mechanism to detect dishonest entities and supplied theoretical evaluation as well as experimental evaluation finding that explains excessive efficiency, feasibility, correctness, protection, and robust dishonest impending of the given protocol.
An Efficient Approach for Compression of Endoscopic Video Using MapReduce Technique

Endoscopic video storage is a major issue today in cloud-based health centre. The EHR must include full-length endoscopic surgeries for diagnosis and research. This paper presents MapReduce-based compression. Thus, MapReduce solves the storage issue. The first AI-based solution is employed as an intelligent video splitter to form the key value as “Map” stages to filter the endoscopic video into a group of frames based on redundancy. These outputs are passed to “reduce” to merge them into a single output. After mapping and reducing endoscopic video frames, lossless compression is applied with PSNR 30–40 dB, SSI 0.7–0.8, Bitrate 32.17, MSE 2.1.
Identity-Based Encryption (IBE)-Based Improved Data Possession Techniques

Asymmetric key cryptography is used by Provable Data Possession (PDP) schemes to confirm the accuracy of files that are stored remotely. The size and authenticity of the public key are the main drawbacks of asymmetric key cryptography based on the RSA algorithm. In this work, Identity-Based Encryption—Dynamic Provable Remote Data Possession (IBE-RDP) solutions have been proposed. IBE solves the issue as it uses the user’s personal identity (e.g., phone number) as a public key. Hence, the problems related to the public key size and certification management are solved. The proposed solutions are efficient in terms of key generation and verification in spite of the more number of users. In addition, the proposed solutions solve the threats like (k, $$\theta $$ )—cheatability and server unforgeability. Two alternatives have been proposed IBE-RDP-I and IBE-RDP-II. IBE-RDP-I is efficient but provides a weaker security guarantee whereas IBE-RDP-II is less efficient with stronger security. The proposed solutions are implemented and proved very efficient. Computational Diffie–Hellman (CDH) hardness assumption is taken as a base for the security of the algorithm.
Navigating the Complexities of Cryptography: Trends, Problems, and Solutions

Cryptography is a complex field that has evolved significantly over the years. It is essential for the secure communication of sensitive information online. However, with the increase in cyber-attacks, keeping up with the latest trends, and identifying potential problems has become more important than ever. In this paper, authors try to explore the latest trends in cryptography, highlighting the problems that come with them, and discussing potential solutions for each. From quantum computing to blockchain technology, authors will delve into the complexities of cryptography, providing you with the knowledge and tools to navigate through this essential field with confidence.
Systematic Review on Growth of E-Agriculture in Context of Android-Based Mobile Applications

About one-third population of India is dependent directly or indirectly on agriculture or allied activities. India is a leading producer of several commodities, but still, we lag behind in terms of agriculture productivity. There is enough potential to enhance the production of agricultural commodities. There are several constraints that hinder the process of enhancing agriculture production. One of the key factors is the lack of availability as well as the use of information technology in agriculture. Farmers need user-friendly technology. In this view, this paper is an attempt to review the available application for effective agriculture management through e-agriculture in the context of Android-based mobile applications. The findings of the paper revealed that there are several e-agriculture applications for pest and disease management, weather advisory, Fertilizer management, etc. This shows that mobile application for agriculture provides growth opportunities for e-agriculture. The findings also revealed that there is a need for an app that can provide holistic services to farmers. This kind of technology will help farmers in managing agriculture practices more effectively through e agriculture.
Quantum memory and entanglement dynamics induced by interactions of two moving atoms with a coherent cavity

This study explores the dynamics of atomic entanglement of formation and entropic uncertainty relation (EUR) of two moving atoms interacting with an even coherent cavity during a two-photon interaction. We investigate the impact of the initial even coherent amplitude, the atom–photon coupling strengths, and the atomic motion coupling on the dynamics of quantum memory (QM) entropic uncertainty and entanglement. The dynamics of the entropic uncertainty and entanglement of formation are significantly altered by the atom-photon-atom interaction parameters. By increasing the initial mean photon number, the QM entropic uncertainty and the atomic entanglement can be improved. Bob’s capacity to limit his uncertainty regarding Alice’s measurement results depends on the initial even coherent state, the atom–photon couplings, and the atomic motion parameter. The atomic location couplings produce QM entropic uncertainty and entanglement with regular oscillatory behavior. The system parameters control the sudden death-birth entanglement phenomenon, which is improved by increasing the atomic location coupling.
Geochemical contamination of heavy metals and health risk assessment of coastal sediments along the North Chennai to Pondicherry, India using total reflection X-ray fluorescence spectroscopy (TXRF)

The present work aimed to assess the contamination and human health risk assessment of heavy metals (HMs) in 21 sediment samples collected from the North Chennai to Pondicherry coastal area of Tamil Nadu using total reflection X-ray fluorescence spectroscopy (TXRF). Enrichment factor (EF), contamination factor (CF), and geo-accumulation index (Igeo) were calculated to estimate sediment contamination. The average concentrations of HMs (mg kg-1 dry weight) were: Al (4305.12), V (25.77), Cr (15.08), Mn (83.39), Fe (4539.77), Ni (2.89), Cu (2.67), Zn (9.46), As (2.81), Hg (0.05), and Pb (0.92). Results of EF indicated no enrichment with Al, Ni, and Pb, moderate enrichment with V, and severe enrichment with As and Hg. Based on Igeo, all sediment samples showed unpolluted with HMs (except As and Hg). Based on total lifetime cancer risk (LCR), there are no significant health risks for people in the study area from carcinogenic Cr, As, and Pb.
Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

Most existing domain adaptation (DA) methods aim to explore favorable performance under complicated environments by sampling. However, there are three unsolved problems that limit their efficiencies: i) they adopt global sampling but neglect to exploit global and local sampling simultaneously; ii) they either transfer knowledge from a global perspective or a local perspective, while overlooking transmission of confident knowledge from both perspectives; and iii) they apply repeated sampling during iteration, which takes a lot of time. To address these problems, knowledge transfer learning via dual density sampling (KTL-DDS) is proposed in this study, which consists of three parts: i) Dual density sampling (DDS) that jointly leverages two sampling methods associated with different views, i.e., global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information; ii) Consistent maximum mean discrepancy (CMMD) that reduces intra- and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS; and iii) Knowledge dissemination (KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target domain. Mathematical analyses show that DDS avoids repeated sampling during the iteration. With the above three actions, confident knowledge with both global and local properties is transferred, and the memory and running time are greatly reduced. In addition, a general framework named dual density sampling approximation (DDSA) is extended, which can be easily applied to other DA algorithms. Extensive experiments on five datasets in clean, label corruption (LC), feature missing (FM), and LC&FM environments demonstrate the encouraging performance of KTL-DDS.
Efektivitas Model Pembelajaran GQGA Dan ICM Terhadap Kemandirian Belajar Dan Prestasi Belajar Peserta Didik

This study aims to determine (1) whether the learning independence and learning achievement of students using the Giving Questions and Getting Answers (GQGA) learning model is better than the Index Card Match (ICM) learning model. (2) Is the learning model of Giving Questions and Getting Answers effective for class VIII students of SMP Muhammadiyah Nunukan? The data collection technique used is the method of documentation, observation, and tests. The research instrument was in the form of observation sheets and student achievement tests. The hypothesis test used was a multivariate test and continued with a separate univariate test. Based on the calculations, it can be concluded: (1) learning independence and learning achievement of students who are subjected to the Giving Question and Getting Answer learning model are better than learning independence and student achievement with the Index Card Match learning model. (2) learning with the Giving Question and Getting Answers learning model is effectively used for class VIII students of SMP Muhammadiyah Nunukan.&#x0D; &#x0D;
Penerapan Model Snowball Throwing dalam Upaya Meningkatkan Keaktifan Siswa pada Pembelajaran IPS Kelas V di MI NU Miftahul Huda 01 Karangmalang

The main problem of this research is the learning process that still uses the lecture method in social studies learning at MI NU Miftahul Huda 01 Karangmalang, Gebog District, Kudus Regency, Central Java so that it influences student activity. This study aims to apply the Snowball Throwing model to social studies learning about the Indonesian Community's Economic Business for class V at MI NU Miftahul Huda 01 Karangmalang. Classroom Action Research (CAR) was carried out in two cycles. The research model uses the Kemmis &amp; MC Taggart research model which consists of four stages, namely planning, implementing, observing, and reflecting. The subjects in this study were class V for the 2022/2023 academic year with a total of 31 students. Data collection techniques through observation, interviews, and documentation. The type of data used is qualitative data. The results showed that the application of the Snowball Throwing learning model could increase student activity. The increase occurred in each cycle experiencing an increase of 60.08% in cycle I. In cycle II students were very active in increasing an average of 93.33%. This proves that the Snowball Throwing learning model can increase social studies learning activity at MI NU Miftahul Huda 01 Karangmalang Class V for the 2022/2023 academic year.
An Ensemble approach of Pretrained CNN models for Recognition of Handwritten Characters in Bangla

Bangla is a rich language with a variety of characters, which includes numerals, basic characters, compound, and modifier characters. This makes it more challenging to recognize handwritten characters in Bangla than in other languages. Pretrained convolutional neural network (CNN) architectures are effective because they can learn complex features from images, which is significant considering variations in handwriting styles. This paper uses an ensemble model consisting of pretrained CNN architectures to classify numerals, and basic and compound bangla handwritten characters. The ensemble model comprises four pretrained convolutional neural network architectures - ResNet50, DenseNet121, Xception, and EfficientNetB0. The proposed model is trained on BanglaLekha-Isolated, Ekush, and MatrrivaSha datasets. To keep each image in the dataset noise-free, they are all preprocessed using a bilateral filter. The image size is reduced to 75X75 pixels, which has been used as input in several CNN architectures. The results state that the proposed model attained an accuracy of 97.83% on the BanglaLekha-Isolated dataset, 97.78% on Ekush, and 97.01% on the MatrrivaSha dataset. The model is also evaluated on several characters that people could find confusing, yet the proposed approach showed an equivalent ability for recognition of these images.
Solar Photovoltaic Generation Forecasting using LSTM and SARIMAX model

In several areas of load forecasting and throughout the years, energy management has concentrated on artificial intelligence (AI). In several circumstances, time series forecasting models have been developed. In this study, two separate models, Seasonal Autoregressive Integrated Moving Average with Exogenous factors (SARIMAX) and Long Short-Term Memory Loss (LSTM), are used to forecast solar photovoltaic (PV) generation, which involves several variables. As a result, the data utilized in this work is real-time data from NREL. To solve these issues, the data has been used to train a range of deep learning and machine learning models, and the outcomes provide novel insights on the benefits and need of integrating exogenous components in a time series model. Additionally, forecasting mistakes are evaluated using short-term prediction updates. Their analysis demonstrates how accurate their model is.
3D Avatar Animation Optimization in Metaverse by Differential Evolution Algorithm

Metaverse is an online platform that combines a wide range of technologies. It integrates the physical and virtual worlds to connect with other users in real-time. The application and development of the metaverse platform are growing in a broad range. For such platforms, an avatar represents the user to explore the metaverse platform. Therefore, the avatar, a 3D digital twin representing the user's expression and action in the metaverse, is essential to metaverse development. Creating a realistic 3D avatar animation is the next level of avatar development. The rigging step, an integration process of bones and joints to an object, influences the avatar animation. This study shows the concept of vertex weight value optimization adapted by the Differential Evolution meta-heuristic algorithm in the rigging process to create a realistic avatar animation. The fitness function represents how much a mesh driven by the corr. vertex weights is offset to a goal position under the influence of multiple joints. The proposed methodology is investigated for the challenging use case of animating face expressions, and shows the fitness function value of representing an animation could be minimized by 54.5%, from 1.49 of the initial design down to 0.68.
An Enhanced Blockchain Consensus Mechanism Using Proof-of-Work and Proof-of-Stake

Blockchain is a composite technology that combines cryptography and consensus algorithms to solve traditional distributed database synchronization problem. Due to the features like immutability and traceability, blockchain is considered to be a reliable platform to store shared information. It is an integral part of various modern multi-field infrastructures, including cryptocurrency. The popularity of blockchain gained spectacularly after the introduction of Bitcoin in the financial world. The consensus mechanism used in Bitcoin, called Proof-of-Work (PoW), also received a similar acceptance and even used in the present blockchain technologies. However, PoW has faced criticism for being less responsive to transaction throughput and for demanding huge computational power. In this paper, we make an attempt to improve the PoW mechanism by incorporating it with another popular consensus mechanism called Proof-of-Stake (PoS). From our experiments, it is observed that such a collaboration can greatly improve the block creation time in the blockchain network and also increase fairness as both higher computational power owners and higher stakeholders get a fair chance to contribute to the chain.