The relatively short timeframe of the data-oriented approach has made cloud architecture the basis for flexible and effective data analysis and data science projects. This paper presents the design strategies and considerations of cloud architectures for data science platforms that compliments modern analytics and machine learning workloads. Sub-processes like data acquisition, management, analysis, and coordination are discussed, as well as their part in supporting moment and science driven decision-making. Responsiveness is given on the use of tools and platforms that are built natively on cloud to help in getting better collaboration, better costs and high performance. Security and compliance issues are discussed in order to create a viable base for the protection of potentially private and confidential information in risky business sectors. Also, there are emerging trends like edge computing and AI analytics that describe in detail what may be expected in cloud computing for data science in the future. In the framework of this paper, the best practices and case examples serve as recommendations for developing and improving cloud architecture for innovative data capabilities within an organization.

PEBSA is a machine learning (ML) model that aims to analyze public sentiment towards America's economic performance and predict its future behavior. By collecting public comments and posts from several social media platforms, sentiment analysis techniques will be applied to classify comments based on positive or negative outlooks. The ML model will then incorporate macroeconomic reasoning to forecast future economic behavior. The project will focus on fair and unbiased testing by employing scientific sampling techniques during data collection processes. The outcomes of this research will provide valuable insights for policymakers, businesses, and investors, facilitating informed decision-making in the realm of economic performance analysis.

This article analyzes the vulnerabilities of the Content Security Policy (CSP) standard with the aim of improving website security. It highlights that this standard has vulnerabilities that enable attackers to successfully execute attacks by injecting malicious code into web pages. The relevance of studying CSP vulnerabilities to enhance website protection is substantiated. The primary aspects of implementing web resource protection using the CSP standard are discussed. The article outlines the most well-known techniques for bypassing the directives of this standard. It describes the process of executing an XSS attack using the "unsafe-inline" directive. It is revealed that such an attack organization allows the interception of user data without detection by users or security administrators. The conclusion is made that the use of the unsafe-inline directive by developers does not provide an adequate level of protection against XSS attacks. As an alternative, the implementation of a more effective CSP policy, configured in accordance with the recommendations of information security specialists, is proposed. 

This paper proposes a novel Lambda Authorizer Benchmarking Tool utilizing AWS Serverless Application Model (SAM) and Artillery framework. The tool evaluates the performance of serverless functions implementing Lambda Authorizers. We focus on three key performance parameters: cold start behaviour, programming language runtimes, and authorization types (e.g., token-based, claim-based). By employing load testing with Artillery, the tool measures the impact of these factors on authorization processing speed and resource consumption. The findings from the benchmarking experiments offer valuable insights for developers building secure and cost-effective serverless APIs with Lambda Authorizers. The tool empowers and developers to make informed decisions regarding programming language selection, authorization strategy, and optimization techniques for their serverless applications.

This article outlines the importance of cybersecurity for modern engine control systems. Much emphasis is placed on how fast-paced the technologies in the automotive industry are and the several risks involved with their use. Due to high connectivity and heavy dependency on software, serious vulnerabilities are present in trucks' engine control units (ECUs) that pose a severe threat regarding cybersecurity in the automotive industry. The article discusses how engine control systems have been developing over time, enumerates the most common cyber-attacks they face, and points out the consequences that may influence vehicle safety and performance, as well as environmental compliance. Attention is given to regulatory laws, industry standards, and strategies regarding how to handle these challenges. The article also discusses how artificial intelligence influences ECU security and the challenges and opportunities arising in this fast-changing sector. Overall, the article provides a broad overview of the relationship between cybersecurity and ECUs, underlining the fact that strong security is urgently needed to provide safety, reliability, and trustworthiness to modern vehicles.