This article presents a proposal of conceptual model for public administrations that allows analyzing the level of IT management capacity as an enabler of smart cities from a multidimensional and dynamic approach taking into account technological, institutional and innovation aspects. The model includes five domains (e-government strategy, Public Innovation, Data Management, IT Services and Infrastructure) that are described in terms of key domain areas, objectives and questions. The model was conceptually validated with four IT offices in Colombia and a pilot test was developed in the Atlantic department. A profile of information technology management capabilities was obtained from public data of the Ministry of ICT in Colombia.

Smart University is an emerging concept, strongly anchored to smart technologies and considered by different authors in the literature. Organizations, including universities, need to incorporate smart technologies to take advantage of their capabilities to transform their processes and drive them toward new organizational models. A Smart University focuses on improving its technological infrastructure for achieving its quality educational goals. This paper presents the integration of the key factors for adopting four smart technologies: Cloud Computing, Big Data, Artificial Intelligence, and the Internet of Things. This characterization and integration allow us to conclude on the need to align digital technologies with the organization's processes, requiring greater interaction with the company’s senior management.

Una ciudad inteligente es un área geográfica o territorio que se caracterizapor el uso intensivo de las tecnologías con el objetivo, de manera general, de mejorar la calidad de vida de los ciudadanos y el desarrollo sostenible de las ciudades bajo los supuestos de la colaboración y la innovación. El propósito de este artículo es presentar una revisión sistemática de literatura sobre el concepto y las características de las ciudades inteligentes publicados en la literatura científica, a partir de los cuales se hace énfasis en el rol de lastecnologías de información y comunicación, como un elemento fundamentalen la concepción e implementación de proyectos e iniciativas que inciden en el desarrollo exitoso de las ciudades inteligentes.

Traffic accidents are one of the leading causes of death around the world. One wellestablished strategy to deal with this public health issue is the design and deployment of road safety systems, which are in charge of predicting traffic crashes to promote safer roads. Increasing data availability has supported Machine learning (ML) to address the prediction of crashes and their severity. Transportation literature reports various methods for such purposes; however, there is no single method that achieves competitive results in all crash prediction problems. In this context, Automated machined learning (AutoML) arises as a suitable approach to automatically address the model selection problem in areas wherein specialized ML knowledge is not always available or affordable, such as road safety. AutoML has been successfully used in other areas; nevertheless, extensive analysis to determine their strengths and weaknesses has not been done in very diverse learning tasks, such as crash severity forecasting. Thus, this paper aims to examine to what extent AutoML can be competitive against ad hoc methods (Gradient Boosting, Gaussian Naive Bayes, k-Nearest Neighbors, Multilayer Perceptron, Random Forest) on crash severity prediction modeled from a supervised learning perspective. We test 3 state-of-the-art AutoML methods (Auto-Sklearn, TPOT, AutoGluon). Results show that AutoML can be considered a powerful approach to support the model selection problem in crash severity prediction.