by Ciarán Bryce, Daniel Celeny, André Charneca, Tugrul Daim, Marc Greuter, Andrei Kucharavy, Loïc Maréchal, Natalia Ostapuk, Dimitri Percia David, Alon Shahak, Maxime Würsch, Haydar Yalcin | Jun 23, 2023 | Podcasts
About Members of the Swiss Technology Observatory community met in Sachseln (Obwalden) in end of June 2023 and gave presentations on key topics such as scouting and technology and market monitoring. The event brought together many participants from the DDPS,...
by Eric Jollès, Sébastien Gillard, Dimitri Percia David, Martin Strohmeier, Alain Mermoud | Jun 8, 2023 | Publications, Scientific Articles
Abstract This article describes three collective intelligence dynamics observed on ThreatFox, a free platform operated by abuse.ch that collects and shares indicators of compromise. These three dynamics are empirically analyzed with an exclusive dataset provided by...
by Chi Thang Duong, Dimitri Percia David, Ljiljana Dolamic, Alain Mermoud, Vincent Lenders, Karl Aberer | May 11, 2023 | Publications, Scientific Articles
Abstract Mapping the technology landscape is crucial for market actors to take informed investment decisions. However,given the large amount of data on the Web and its subsequent information overload, manually retrievinginformation is a seemingly ineffective and...
by Sarah Ismail, Alain Mermoud, Loïc Maréchal, Samuel Orso, Dimitri Percia David | Apr 20, 2023 | Publications, Scientific Articles
Abstract This paper presents a novel science indicator to identify, analyze, and capture technology trends based on Wikipedia page views and OpenAlex presented at STI2022. Our webometric methodology is grounded in open science practices and applied to crowd-sourced,...
by Dimitri Percia David, Santiago Anton Moreno, Loïc Maréchal, Thomas Maillart, Alain Mermoud | Mar 28, 2023 | Publications, Scientific Articles
Abstract We use a unique database of digital, and cybersecurity hires from Swiss organizations and develop a method based on a temporal bi-partite network, which combines local and global indices through a Support Vector Machine. We predict the appearance and...
