Abstract
Web Search Engine Results Pages (SERP) are one of the most well-known and used web pages. These pages have started as simple "10 blue links"pages, but the information in SERP currently goes way beyond these links. Several features have been included in these pages to complement organic and sponsored results and attempt to provide answers to the query instead of just pointing to websites that might deliver that information. In this work, we analyze the appearance and evolution of SERP features in the two leading web search engines, Google Search and Microsoft Bing. Using a sample of SERP from the Internet Archive, we analyzed the appearance and evolution of these features. We found that SERP are becoming more diverse in terms of elements, aggregating content from different verticals and including more features that provide direct answers.

Abstract
Federated learning (FL) is a collaborative, decentralized privacy-preserving method to attach the challenges of storing data and data privacy. Artificial intelligence, machine learning, smart devices, and deep learning have strongly marked the last years. Two challenges arose in data science as a result. First, the regulation protected the data by creating the General Data Protection Regulation, in which organizations are not allowed to keep or transfer data without the owner's authorization. Another challenge is the large volume of data generated in the era of big data, and keeping that data in one only server becomes increasingly tricky. Therefore, the data is allocated into different locations or generated by devices, creating the need to build models or perform calculations without transferring data to a single location. The new term FL emerged as a sub-area of machine learning that aims to solve the challenge of making distributed models with privacy considerations. This survey starts by describing relevant concepts, definitions, and methods, followed by an in-depth investigation of federated model evaluation. Finally, we discuss three promising applications for further research: anomaly detection, distributed data streams, and graph representation.This article is categorized under:Technologies > Machine LearningTechnologies > Artificial Intelligence

Abstract
Fast and precise identification of minerals in geological samples is of paramount importance for the study of rock constituents and for technological applications in the context of mining. However, analyzing samples based only on the extrinsic properties of the minerals such as color can often be insufficient, making additional analysis crucial to improve the accuracy of the methods. In this context, Laser-induced breakdown spectroscopy mapping is an interesting technique to perform the study of the distribution of the chemical elements in sample surfaces, thus allowing deeper insights to help the process of mineral identification. In this work, we present the development and deployment of a processing pipeline and algorithm to identify spatial regions of the same mineralogical composition through chemical information in a fast and automatic way. Furthermore, by providing the necessary labels to the results on a training sample, we can turn this unsupervised methodology into a classifier that can be used to generalize and classify minerals in similar but unseen samples. The results obtained show good accuracy in reproducing the expected mineral regions and extend the interpretability of previous unsupervised methods with a visualization tool for cluster assignment, thus paving for future applications in contexts requiring high-throughput mineral identification systems, such as mining.

Abstract
Seven years after the seminal paper on FAIR was published, that introduced the concept of making research outputs Findable, Accessible, Interoperable, and Reusable, researchers still struggle to understand how to implement the principles. For many researchers, FAIR promises long-term benefits for near-term effort, requires skills not yet acquired, and is one more thing in a long list of unfunded mandates and onerous requirements for scientists. Even for those required to, or who are convinced that they must make time for FAIR research practices, their preference is for just-in-time advice properly sized to the scientific artifacts and process. Because of the generality of most FAIR implementation guidance, it is difficult for a researcher to adjust to the advice according to their situation. Technological advances, especially in the area of artificial intelligence (AI) and machine learning (ML), complicate FAIR adoption, as researchers and data stewards ponder how to make software, workflows, and models FAIR and reproducible. The FAIR+ Implementation Survey Tool (FAIRIST) mitigates the problem by integrating research requirements with research proposals in a systematic way. FAIRIST factors in new scholarly outputs, such as nanopublications and notebooks, and the various research artifacts related to AI research (data, models, workflows, and benchmarks). Researchers step through a self-serve survey process and receive a table ready for use in their data management plan (DMP) and/or work plan. while gaining awareness of the FAIR Principles and Open Science concepts. FAIRIST is a model that uses part of the proposal process as a way to do outreach, raise awareness of FAIR dimensions and considerations, while providing timely assistance for competitive proposals. © 2023, Ubiquity Press. All rights reserved.

Abstract
O Exame Nacional de Desempenho de Estudantes (ENADE) compõe uma das bases avaliativas do Sistema Nacional de Avaliação da Educação Superior e tem como principal propósito, avaliar o rendimento dos estudantes a partir da sua formação nos cursos de graduação. Por isso, este trabalho identificou a possibilidade de se basear nesses resultados para aprimorar os processos de ensino-aprendizagem. Para tanto, este artigo apresenta o planejamento, criação e avaliação de uma plataforma de aprendizagem Web responsiva, baseada no ENADE e que utiliza elementos de gamificação com o propósito de aumentar o engajamento dos estudantes. O software foi avaliado, por meio de questionários de aceitação tecnológica, aplicados com 38 usuários e que apresentou resultados promissores, com média geral de 4,71 entre 5 pontos possíveis.

Abstract
This study intends to understand whether hotels should choose to surprise through a discount or a surprise gift. The experiment consisted in identifying whether there were differences in satisfaction and delight, according to the associated treatment (no surprise, surprise discount, or gift). With this purpose, a fictional hotel website was created for participants to simulate a reservation. Through the analysis of the experiment, the impact of surprise on customer satisfaction was confirmed. It was also found that, in the hospitality industry, a gift has a higher impact on satisfaction than a discount. When analyzing the guest delight, the results differ from what is stipulated in the literature (which points to the significant impact of surprise in this measure). It was concluded that between the two promotion tools, only the gift can significantly increase customer delight. This study demonstrates the importance of understanding the concept of surprise according to different industries. It also points to the importance of identifying the best methods to surprise customers, as different methods may lead to different results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.