A Secure and Decentralized Authentication Mechanism Based on Web 3.0 and Ethereum Blockchain Technology

Over the past decade, there has been significant evolution in the security field, specifically in the authentication and authorization part. The standard authentication protocol nowadays is OAuth 2.0-based authentication. This method relies on a third-party authentication service provider with complete control over the users’ data, which it can filter or modify at will. Blockchain and decentralization have generated much interest in recent years, and the decentralized web is considered the next significant improvement in the world wide web (also known as Web 3.0). Web3 authentication, also known as decentralized authentication, allows for the secure and decentralized authentication of users on the web. The use cases for this technology include online marketplaces, social media platforms, and other online communities that require user authentication. The advantages of Web3 authentication include increased security and privacy for users and the ability for users to have more control over their data. The proposed system implementation uses Ethereum as the blockchain and a modern web stack to enhance user interaction and usability. The solution brings benefits both to the private and the public sector, proving that it has the capability of becoming the preferred authentication mechanism for any decentralized web application

An assessment of feature extraction methods for SENTINEL-1 images on urban areas

This paper makes a comparative assessment of the observable landcover classes visible in the data provided by the newly launched SENTINEL-1 (S-1) satellite. The analysis focuses on two feature extraction methods previously reported in the literature to be able to distinguish between a relatively large number of classes in high and very high resolution SAR data. The analysis of the S-1 medium resolution data makes use of the Gabor filtering and Fourier spectral coefficients. Moreover, we consider the opportunity of speckle reduction before feature extraction, considering that the texture analysis is sensitive to correlated speckle. Furthermore, the comparison takes into account the adaptation of the window size to the resolution and pixel spacing of the data. In order to make a quantitative assessment of the results, we perform a joint evaluation of the detection probability at pixel and patch level, with respect to an expert annotated dataset

Analysis and Interpretation of Visual Scenes through Collaborative Approaches

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