Major researches in the domain of complex systems are interdisciplinary, collaborative and geographically distributed. The purpose of our work is to explore a new methodology that facilitates scientist's interactions during the simulation process. Through the analysis of the collaboration and pluridisciplinary in a simulation project, we have identified the needs of a common representation about models and simulators. Then, we provide an extension of ODD protocol including new features dedicated to collaboration design. This new protocol associated with identified tools tackle an interesting way for defining exchanges in simulation.

Machine learning techniques have always been a strong candidate for solving complex recognition problems. Drone/Bird detection and classification is one of the most challenging tasks in recent years. Both drones and birds come in different sizes, velocities, and behaviors. The lack of bird images and videos is tackled in this work. Deep learning, classical machine learning techniques such as Support Vector Machines (SVM) and Random Forest (RF) in addition to shallow neural network (NN) learning methods are used. Combined open-source data sets and labeled bird images data sets are used in training and testing for detection and classification. In particular, several deep learning methods are used in the detection of RGB and IR drone images. They were compared with the new SSD-AdderNet which showed the best results in the detection of IR images while exhibiting the least complexity. The SVM proved to be the best in classification.

Major researches in the domain of complex systems are interdisciplinary, collaborative/participative and geographically distributed. Therefore, the simulation of complex systems is usually a collaborative/participative work that highly demands the exchange, from distance, of participants. However, most of the actual simulators cannot support this exchange. In this paper, we will present a new method to create a participatory simulator supporting the collaboration/participation from distance in the complex system participatory simulation. The method includes: (i) a collaborative software infrastructure, named PAMS, containing common collaborative tools (videoconferencing, instant messaging, and many other communication tools) and specific tools dedicated to the simulation domain (sharing experiments, results, experience exchange and various type of manipulation tools) and (ii) a role-based meta-model facilitating the integration of actual simulators into PAMS.