Reviewerly/exHarmony huggingface dataset:
Blockchain-Based eCommerce Warranty System Using NFTs

Blockchain-based e-commerce NFT warranty system is a new solution that allows customers to receive digital warranty for their purchases on e-commerce platforms. The system is built using the Ethereum blockchain and uses non-fiscal tokens (NFTs) to set up and execute transactions. This project aims to solve the physical certificate management problem and the challenge of warranty tracking for customers and companies. With the NFT warranty system, customers can store the warranty in their connected digital wallet and easily send it to other users when selling. Also, companies do not need to manage services separately, as authentication and authorization are tracked by a blockchain-based system. The advantage of this project is that it provides a secure and transparent warranty system that prevents fraud and counterfeit products. Additionally, the use of NFTs ensures the authenticity and authenticity of all warranties, thus preventing competition or abuse of warranties. The project works by allowing companies to join e-commerce sites and add their vendors to generate NFT security tokens. When customers make a purchase, they receive a digital NFT warranty that they can use for warranty service. Warranty claims are recorded on the blockchain and can be tracked by both the customer and the company. The system also adds value to customers' purchases by allowing them to resell NFT-warrantied products. All in all, the blockchain-based e-commerce NFT warranty system is a cutting-edge solution that will simplify the warranty process for clients and customers. company. It offers an easy-to-use, secure and transparent warranty system and provides added value to customers.
Determining the Accuracy of Resonate Demography Expedition Using Faster Regions Based on Novel K-Means Algorithm Comparing with Gradient Boosting Algorithm

Determining the accuracy of resonate demography expeditions using faster regions based on Novel K-means algorithm compared with Gradient Boosting algorithm for actual time resonate management. Materials and Methods: Novel K-Means algorithm and Gradient Boosting algorithms are replicated by multiple bounds to robotize sound management systems to optimize the pH. Accuracy is valued taking G power 80% to both algorithms and we have taken 10 samples in this work for every single of the algorithms. Results: Comparing on accomplished outcomes, the Novel K-Means algorithm has fundamentally greater accuracy (82.2070 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) on comparison with Gradient Boosting algorithm accuracy (79.2980%). Two groups were found to be statistically significant, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{p}=.001(\mathrm{p} &lt; 0.05)$</tex> . Conclusion: The K-Means algorithm gives good outcomes to detect audio statistics than Gradient Boosting.
Client-Level Monitoring Approach to Secure Data in a Cloud Environment

Cloud computing imparts huge storage at low cost, because the internet is so widely used around the world, massive amounts of data are stored on local devices and integrated and stored on a common platform for easy access, sharing, and, high availability. The cloud environment provides a convenient way for normal users and enterprises to migrate their apps, data, and storage to the cloud-based server for storage due to the high availability of its services and the flexibility of computing processes. Securing information is the primary challenge in cloud computing. Securing the data from unauthorized modification and access is one of the key challenges of the cloud environment. In order to secure user data, different security algorithms were adapted. This paper provides a client-level monitoring approach to secure data in a cloud environment. The monitoring approach consists of the user or client system, monitor, and cloud storage. The user uploads the encrypted form of documents onto the cloud storage and the cloud storage stores the encrypted form of user documents, and the monitor periodically monitors or the user directs the monitor to check the integrity of user data.
Speak to your Database: Streamlining Human-to-Database Communication with NLP

The paper demonstrates the successful integration of recent enhancements in Natural Language Processing (NLP) with the goal of generating accurate and meaningful Structured Query Language (SQL) queries from human language. The focus is on the methods that we have implemented in the project. The methodology we propose in this paper relies entirely on NLP techniques. Our system accepts user queries in English, which are then transformed into SQL statements using our NLP-based approach, before being executed against the database to retrieve the desired results. The student database was used in our project, as it provides a real-world example of how our NLP-based approach can be applied to retrieve data from educational institutions. The paper also outlines the various challenges and limitations faced by our system when converting human language into SQL. The paper concludes by discussing the broader implications of our approach for the field of database querying, and how it could lead to the development of more intuitive and user-friendly interfaces for interacting with databases and outlines potential directions for future improvements.
Deep Learning based Smart American Sign Language Detection in Alphabets

Disabled people are of different kinds like physically challenged people, mentally challenged etc. To help the people who cannot convey their thoughts and who cannot hear others thoughts, sign language is being used to communicate with them. Sign language is something that is used to communicate through body movements mainly through hands and arms.American Sign Language(ASL) is an authorized sign language that is taught and used widely. ASL is difficult to teach and learn for them because it is completely new and they have no knowledge about it. In this research paper the ASL is recognized and converted into text through data mining techniques. The study uses a dataset of ASL alphabet images and applies various data mining techniques, including feature selection, classification, and clustering. The features extracted from the images include shape, size, color, and texture. The use of neural networks, decision trees, and support vector machines speeds up the data categorization process. Techniques such as k-means clustering and hierarchical grouping are used to facilitate entity grouping. The results show that the best performance is achieved using a combination of feature selection and classification techniques. The decision tree algorithm with the selected features achieves an accuracy of 96.5% on the test dataset. The system can accurately recognize all the ASL alphabets and convert them into text or speech. The system developed can be used to improve communication between deaf and hearing individuals and can be extended to recognize other ASL gestures and phrases. Future research can focus on real-time detection and recognition of ASL gestures using data mining techniques. CNN algorithm is used to recognize the image based on the images collected. The accuracy rate gets low and the detection is inaccurate. Therefore, VGG16 algorithm is used to recognize the images and it is unique in convolutional models.
Research on fusion evaluation of numerical and real tests based on prior information reliability

In view of the characteristics of multiple test types and evaluation indexes of information equipment performance, this paper studies the reliability measurement of prior information from different sources of test information, such as physical test, semi-physical test, simulation test and physical substitution test, starting from the distribution difference of test data, and carries out the research on the fusion evaluation method of numerical and real tests based on prior information, to improve the test efficiency, shorten the development period, reduce the test cost, and provide technical and theoretical support for obtaining more reliable equipment test evaluation results.
Aspect-level sentiment classification based on aspect-oriented information and inter-aspect relations

Aspect-level sentiment classification aims to determine the sentiment polarity of a given target aspect in a sentence. To solve the problem of ignored the impact of noise in sentences and sentiment relations between different aspects on the sentiment classification performance of models in the current studies, this paper proposes an aspect-oriented syntactic dependency graph and an inter-aspect dependency tree. Based on it, an interactive graph attention network model is proposed to extract sentiment features of the target aspect by exploiting aspect-oriented and inter-aspect information. Experimental results on SemEval-2014 and Twiter datasets show that the sentiment classification ability of the model is superior to the baseline models, and the accuracy of sentiment classification on restaurant review dataset (Rest14) reach 83.36%.
Implementation of the Smart Method in Determining Organic Fertilizer in Palm Oil Plantations At PTPN IV Bah Jambi

Organic fertilizer is fertilizer composed of living creature material, such as weathered remains of plants, animals and humans. Organic fertilizer can be in solid or liquid form and is used to improve the physical, chemical and biological properties of soil. Organic fertilizer contains more organic material than its nutrient content. Sources of organic material can be compost, green manure, manure, harvest residues (straw, stover, corn cobs, sugar cane bagasse, and coconut husks), livestock waste, industrial waste that uses agricultural materials, and municipal waste (garbage). One solution for choosing the best organic fertilizer is to use a decision support system for selecting the best organic fertilizer using the Specific, Measurable, Achievable, Relevant and Timely (SMART) method. Where the author carries out calculations using the formulas available in the SMART method. A web-based application that has been created with a decision support system can make it easier for workers to choose the best organic fertilizer. The method used, namely the SMART method, has quite accurate results by going through several calculation processes.
A STUDY ON STRATEGIES AND CHALLENGES OF SUSTAINABLE DEVELOPMENT POLICY IN INDIA

Aim of the study: The study on strategies and challenges of sustainable development policy in India investigates the multifaceted aspects of sustainable development within the country. It aims to understand the various strategies employed and the obstacles faced in pursuing a sustainable development agenda Design/Methodology: The study embodied a literature reviews as well as a descriptive investigation to achieve its objectives. It comprehensively analyses the effectiveness and obstacles of sustainable development policies in India. The methodology for locating, selecting, going over and summarizing significant literature and research studies for the review is described in the report. Data have been taken from online papers, journals, and other relevant sources. Findings: It highlights key challenges and obstacles hindering the effective execution of sustainable development policies at various levels of governance, analysis of how different policy areas intersect and influence each other within the context of sustainable development, and identification of potential areas for collaboration with global partners and leveraging international best practices to enhance India's sustainable development efforts. Practical Implications: Provides specific recommendations for refining existing sustainable development policies in India to better address environmental, social, and economic challenges. Identifies key areas where targeted interventions and resource allocation can have the most significant impact on sustainable development outcomes. Originality/value: It offers a thorough examination of the multifaceted dimensions of sustainable development policies in India, considering economic, social, and environmental aspects. Contribute to the existing body of knowledge on sustainable development policies, particularly within the context of India, filling gaps in the literature. Serve as a valuable resource for policymakers, guiding evidence-based decision-making for sustainable development initiatives in India. Keywords: Sustainable Development, Policy, Climate Change, Poverty Alleviation, Environmental Conservation, Challenges and Obstacles Paper Type: Review Article
A Numerical Approach to Predict Breast Cancer Using Different Machine Learning Algorithms

In current era, breast cancer is one of the serious issues for women as compared to all other cancers. Breast cancer occurs when cancer cells form in breast tissues. It can be fat or connective tissue in the breast and difficult to detect at early age. But, as technology grows, number of detection methods have been put forwarded to diagnose breast cancer at early stage such as Mammography. Apart from clinical application, the diagnosis of breast cancer is also been done by using new Data Mining and Machine Learning approaches. ML been exclusively used in medical application especially for the prediction-based detection. Therefore, in proposed work we have opted novel machine learning approach to predict breast cancer disease. The implementation is done by using 4 machine learning algorithms which are (SVM, Naïve Bayes, Random Forest, XGBoost) classifier. The numerical analysis depicts that XGBoost algorithm is most efficient and effective because of the maximum achieved accuracy of 99.12%.
Data Sharing Enabled Through Internet of Things (IoT) in Wireless Communication Networks

A variety of difficulties while attempting to transmit a file including multimedia data through the many peers of a wireless IoT network. The system's centralization, which increases the risk of a security breach and makes it harder for users to access their information, is one of the biggest issues. It would be possible to address the issues with security and user accessibility, but doing so would forfeit scalability, low latency, and lengthy response times. The project in issue uses the Interplanetary File System and an original block chain approach as a direct result of this. Additionally, we discussed the system's workings and the ideas that led to its development. This research looks at a broad range of uses for economic and pricing models, which are often known as methods for intelligent, logical decision-making. Building adaptive protocols and algorithms that are designed expressly for use with WSNs is the ultimate objective of this study. Our objective is to create an atmosphere that inspires smartphone users to engage. The findings show that SATS has the potential to lower communication and computation costs, enabling it to provide affordable data transfer. The SATS system's calculation costs are as follows: AN: 14%, 23%, and 59%; Fog-Node: 6.5%,21.5%, and 51%; and Sensor Node and AN: 6%, 3%, and 4%, respectively. This is a reasonable price when compared to the costs of PPDAS, IDAP, and ASAS, which are 14%, 23%, and 59%, respectively. According to the findings, the suggested SATS has a greater storage capacity, lower computation costs, lower communication costs, and more effective energy utilization when compared to other systems that belong to the same category. When compared to other existing systems.
Analysis of Credit Card Fraud Transaction Detection using Machine Learning Algorithms

Credit cards are now used in daily life. The need for online business transactions is rising as quickly as the internet. Markets are expanding thanks to the internet, which influences customers to buy the things they need. The inability to raise enough funds can occasionally prevent you from getting the things you need. For a limited time, businesses are also offering good discounts on their products. It forces the user to need to buy things. Use of credit card is necessary to satisfy the user needs. Additionally, credit card companies provide numerous discounts and reward points for using credit cards to make purchases. When the number of people increases in using credit cards, then the chance of fraudulent transaction is also increases. Finding out about fraudulent credit card transactions becomes urgently necessary. Unauthorised and illegal actions are seriously hindering the expansion of the credit card industry. Financial losses result from fraud in many circumstances. Credit cards are utilised on websites. Numerous methods are offered by machine learning algorithms which can be used for determining that whether a transaction is fraud or not. This study compares various machine learning algorithms and demonstrates that the KNN method performs best, with a 99.9% accuracy rate. This provides a thorough review of the effectiveness of various machine learning methods, which is helpful in creating applications for detecting credit card fraud. This paper also shows that lowest accuracy of 98.6% is achieved using Naive Bayes algorithm.
CompressorWI software for calculating the parameters of a compressor with water injection into its path

The article examines the features and capabilities of the software developed by the authors for the calculation of compres-sor parameters at different variants of liquid (water) injection into the path of axial vane compressor of a GTE or GTU in the Visual Studio 2022 environment in the 64-bit version in the C# (C Sharp) programming language
Low Dimensional Relevance Coding for Personalized Tag Recommendation in Image Tagging Applications

An approach of image coding for tag recommendation based on feature clustering and weighted coding is presented in this paper. The existing tag recommendation approach develops a decision based on correlation of image features and their tag annotated. The descriptive feature of the image sample defines the content of an image and is correlated with database features for tag recommendation. The feature dimension and its representation have a greater impact on the recommendation performance. The recent method tag recommendation developed CNN based visual features and proposed a tag recommendation based on weight factor. The dimensional feature and the isolated weight allocation limit the performance of presented tag recommendation system. This paper presents a new weight allocation and feature clustering method for tag recommendation. An approach of integral coding for weighted image-tag is presented to improve recommendation accuracy. The proposed recommendation system performance is tested on Flickr dataset for retrieval and recommendation accuracy.
The Study on Multivariate Time Series Anomaly Detection via Convolutional Variational Autoencoder

Monitoring is essential for managing and maintaining industrial equipment by identifying abnormal areas in multivariate time series. This work presents CNN-VAEAT, a new technique for identifying anomalies in multivariate time series data from complex systems. The approach uses an upgraded Variational Autoencoder (VAE) algorithm with CNNs to reconstruct the data and calculate an anomaly score. To select anomaly detection threshold, an adaptive threshold approach based on extreme value distribution theory is used. The approach reconstructs a normal multivariate time series model using a convolutional variational autoencoder. It then derives the system’s multivariate time series state index from the new signal. Finally, it uses the extended extreme value distribution theory to discover anomalous conditions early. Studies on the SCADA data set of real wind turbine faults show that the CNN-VAE-AT algorithm outperforms existing wind turbine fault omen detection techniques.
SSC3OD: Sparsely Supervised Collaborative 3D Object Detection from LiDAR Point Clouds

Collaborative 3D object detection, with its improved interaction advantage among multiple agents, has been widely explored in autonomous driving. However, existing collaborative 3D object detectors in a fully supervised paradigm heavily rely on large-scale annotated 3D bounding boxes, which is labor-intensive and time-consuming. To tackle this issue, we propose a sparsely supervised collaborative 3D object detection framework SSC3OD, which only requires each agent to randomly label one object in the scene. Specifically, this model consists of two novel components, i.e., the pillar-based masked autoencoder (Pillar-MAE) and the instance mining module. The Pillar-MAE module aims to reason over high-level semantics in a self-supervised manner, and the instance mining module generates high-quality pseudo labels for collaborative detectors online. By introducing these simple yet effective mechanisms, the proposed SSC3OD can alleviate the adverse impacts of incomplete annotations. We generate sparse labels based on collaborative perception datasets to evaluate our method. Extensive experiments on three large-scale datasets reveal that our proposed SSC3OD can effectively improve the performance of sparsely supervised collaborative 3D object detectors.
Accelerating Column Generation Algorithm Using Machine-Learning-Based Column Elimination

The column generation (CG) algorithm is widely used in large-scale optimization problems. However, a large amount of columns in the restricted master problem (RMP) makes the computing process very time-consuming. This paper proposes a machine learning based column elimination strategy to accelerate the CG algorithm. Our approach represents the RMP by a bipartite graph and applies a learned Graph Neural Network model to predict redundant columns to be eliminated from the RMP, so as to reduce the time cost of solving the RMP and iterations required for convergence. Our approach is tested on cutting stock problem instances. Compared with the vanilla CG algorithm, the iterations and time required for convergence are reduced by up to 31 % and 48%, respectively. Furthermore, our approach shows great generalization to cutting stock problem instances of different sizes.
Lossless Compression of Raw Images by Learning the Prediction and Frequency Decomposition

This paper presents a lossless color filter array (CFA) image compression method that attempts to maximize the use of image correlations from two perspectives. First, we adopt a hierarchical approach for decomposing an input into subimages and proceed with the subimage encoding with a new encoding order. The new encoding scheme is shown to provide better prediction performances compared to conventional raster scan orders. Secondly, for each subimage, we compress the low-frequency components first and use them as additional input for encoding the remaining high-frequency components. In this scenario, the high-frequency components show improved compression efficiency since they use the low-frequency ones as strong prior. Experiments show that the proposed method achieves state-of-the-art performance for 8∼16 bits raw images.