The concept of digital transformation involves exploiting digital technologies to generate new ways of doing things in organizations, including the creation of new processes, models, and services that produce value based on the digitization of data and processes. The application of digital technologies enables organizations to develop capabilities for innovation, automation, etc., utilizing both established and emerging technologies, including the widespread use of artificial intelligence. In this paper is proposed the implementation of a fuzzy cognitive map based on experts for the evaluation of the level of digital transformation in MSMEs (Micro, Small & Medium Enterprises). The main variables of digital transformation used to define our fuzzy cognitive map were classified into five types: i) Organization and Culture variables related to strategies, way of working, and ecosystems, ii) Customer variables related to services and digital channels and products, iii) Operations and Internal Processes variables related to supply chain, suppliers, business model, iv) Information Technologies variables related to innovation, digitization, data and analytic. Finally, the fifth type of variable is the target, which indicates the level of digital transformation of the organization. Our model managed to specify with 99.4% the level of digital transformation of the organization. Thus, our fuzzy cognitive map can predict and analyze the factors associated with digital transformation in MSMEs.

A growing trend in information technology is not just reacting to changes, but anticipating them as much as possible. This paradigm is the base of modern applications, such as recommendation systems, the context-aware applications, among others. Anticipatory systems extend the idea to the communication systems, by studying patterns and periodicity in human behavior and network dynamic, to optimize the network performance. Particularly, for context-awareness applications is very important to extract autonomously contextual information. This work proposes a set of autonomic cycles of data analysis tasks to provide context awareness using the network traffic data, which gives information about the behavior of the traffic flow in a given context. This information about the network (links, users, type of traffic, etc.) is used to extract useful knowledge about the context using data analysis tasks.

The difficulty in finding the most suitable recommendation algorithm for all requests is a common challenge in the recommendation system context, regardless of the domain. Although the combination of multiple techniques in a hybrid approach is often a good solution, the majority of implementations follow a static hybridization scheme, which does not consider the available data, the user specificities at a given time, or the changing environment. Thus, another challenge is the definition of adaptive hybrid recommenders that are capable to adjust the combination of the recommendation algorithms based on the properties of the dynamic inputs. For that purpose, it is necessary to define meta-features that contain information to effectively differentiate recommendation algorithm behaviors over time, and capture how properties of the user/items are related to their performance. This work studies these meta-features in order to analyze their characteristics, relevance, behavior, redundancy and their abilities to represent the system dynamics over time. The paper presents several experiments that can be used as a meta-feature’s evaluation guide, and briefly propose its utilization in a hybrid fuzzy system for recommendation.

In this work is proposed a parameter adaptive system for emerging serious games, which allows the emerging of properties in a game, in order to adapt it to the educational context in which it is being used. Previous articles have proposed an engine for emerging serious games based on the ant colony optimization algorithm, which allows the initial emergence of a serious game. In this paper, another component of the game adaptive sub-system of the engine is specified, which allows its dynamic adaptation while the game is running, in this case, in order to adapt its properties to the educational context-domain. Particularly, the property emergence system is based on the use of cultural algorithms, which establishes a learning process to modify its parameters. This system is tested in the context of a smart classroom.

This paper proposes a redefinition of the traditional concepts of T-Learning in which the television set is the most important component and the only device able to deploy applications and services. It is presented an IDTV-based learning system which allows different Interactive Television platforms and different final user devices (Television Set, PC, Mobile Phones, Tablets, etc.) to connect and access learning contents, applications and services under the concepts of multiplatform. The platform considers a second dimension that includes the technical requirements dependent on the learning requirements made up by learning objects based on video. Those objects are articulated within the constructivist pedagogical currents, and they can be applied in instructional design environments in formal and informal education.