Language-independent fake news detection: English, Portuguese, and Spanish mutual features

Online Social Media (OSM) have been substantially transforming the process of spreading news, improving its speed, and reducing barriers toward reaching out to a broad audience. However, OSM are very limited in providing mechanisms to check the credibility of news propagated through their structure. The majority of studies on automatic fake news detection are restricted to English documents, with few works evaluating other languages, and none comparing language-independent characteristics. Moreover, the spreading of deceptive news tends to be a worldwide problem; therefore, this work evaluates textual features that are not tied to a specific language when describing textual data for detecting news. Corpora of news written in American English, Brazilian Portuguese, and Spanish were explored to study complexity, stylometric, and psychological text features. The extracted features support the detection of fake, legitimate, and satirical news. We compared four machine learning algorithms (k-Nearest Neighbors (k-NN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB)) to induce the detection model. Results show our proposed language-independent features are successful in describing fake, satirical, and legitimate news across three different languages, with an average detection accuracy of 85.3% with RF

Big Data Analytic Framework for Organizational Leverage

fals

Stimulating Personal Development and Knowledge Sharing

Koper, R., Stefanov, K., & Dicheva, D. (Eds.) (2009). Proceedings of the 5th International TENCompetence Open Workshop "Stimulating Personal Development and Knowledge Sharing". October, 30-31, 2008, Sofia, Bulgaria: TENCompetence Workshop.The fifth open workshop of the TENCompetence project took place in Sofia, Bulgaria, from 30th to 31st October 2008. These proceedings contain the papers that were accepted for publication by the Program Committee.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Intelligent decision support for maintenance: an overview and future trends

The changing nature of manufacturing, in recent years, is evident in industry’s willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable ‘Human in the loop’ interactions

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

ATEE Spring Conference 2020-2021

This book collects some of the works presented at ATEE Florence Spring Conference 2020-2021. The Conference, originally planned for May 2020, was forcefully postponed due to the dramatic insurgence of the pandemic. Despite the difficulties in this period, the Organising Committee decided anyway to keep it, although online and more than one year later, not to disperse the huge work of authors, mainly teachers, who had to face one of the hardest challenges in the last decades, in a historic period where the promotion of social justice and equal opportunities – through digital technologies and beyond – is a key factor for democratic citizenship in our societies. The Organising Committee, the University of Florence, and ATEE wish to warmly thank all the authors for their commitment and understanding, which ensured the success of the Conference. We hope this book could be, not only a witness of these pandemic times, but a hopeful sign for an equal and inclusive education in all countries