The Dynamics of Interfirm Networks along the Industry Life Cycle: The Case of the Global Video Games Industry 1987-2007

In this paper, we study the formation of network ties between firms along the life cycle of a creative industry. We focus on three drivers of network formation: i) network endogeneity which stresses a path-dependent change originating from previous network structures, ii) five forms of proximity (e.g. geographical proximity) which ascribe tie formation to the similarity of actors' attributes; and (iii) individual characteristics which refer to the heterogeneity in actors capabilities to exploit external knowledge. The paper employs a stochastic actor-oriented model to estimate the - changing - effects of these drivers on inter-firm network formation in the global video game industry from 1987 to 2007. Our findings indicate that the effects of the drivers of network formation change with the degree of maturity of the industry. To an increasing extent, video game firms tend to partner over shorter distances and with more cognitively similar firms as the industry evolves.network dynamics, industry life cycle, proximity, creative industry, video game industry, stochastic actor-oriented model

Edge AI on a Deep-Learning based Real-Time Face Identification and Attributes Recognition System

There is another way of understanding how a customer service office works, and Everis is developing it in its new generation of spaces designed to offer easy and personalized attention to its customers. Some of the technologies implemented in this space to offer a better experience range from voice recognition or facial identification to the detection of hand gestures. The purpose of the project is to incorporate into the Everis customer e-Motion HUB a new computer vision-based system to extend its abilities and to improve the user experience.Face recognition systems are nowadays being used in a variety of settings, including surveillance systems and human-computer interactions. Different approaches have been used for face recognition throughout the years, but recent research has shown that Deep Learning models along with Convolutional Neural Networks, or \gls{CNN}s, provide better results than any other methods. However, these more complex \gls{CNN} models have several limitations, including the need for extensive training data or high computational requirements in some cases. Fields such as robotics and embedded systems that deploy face recognition systems have significantly less power on board and limited heat dissipation capacity. Therefore, it can be difficult to deploy deep learning models on them. Additionally, and to counter these issues, the classical approach in some industries has been to rely on cloud computing or other third companies paid services. Edge computing devices, such as the NVIDIA Jetson Nano proposed in this approach, can bridge this gap by providing certain advantages in many different areas. In this thesis, we explore the Edge Artificial Intelligence or Edge AI capabilities by developing and implementing a real-time face recognition system along with multiple feature extraction namely age, gender, emotions, and paid attention. Additionally, we provide a data storing approach into a relational database so that all the gathered information can be further exploited. Although this work has certain areas that can be improved, mainly with regards to its efficiency, it has served as a proof of concept for the ideas behind it. Consequently, research in this direction will surely be continued