Cognitive visual tracking and camera control

Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft

Using Natural Language as Knowledge Representation in an Intelligent Tutoring System

Knowledge used in an intelligent tutoring system to teach students is usually acquired from authors who are experts in the domain. A problem is that they cannot directly add and update knowledge if they don’t learn formal language used in the system. Using natural language to represent knowledge can allow authors to update knowledge easily. This thesis presents a new approach to use unconstrained natural language as knowledge representation for a physics tutoring system so that non-programmers can add knowledge without learning a new knowledge representation. This approach allows domain experts to add not only problem statements, but also background knowledge such as commonsense and domain knowledge including principles in natural language. Rather than translating into a formal language, natural language representation is directly used in inference so that domain experts can understand the internal process, detect knowledge bugs, and revise the knowledgebase easily. In authoring task studies with the new system based on this approach, it was shown that the size of added knowledge was small enough for a domain expert to add, and converged to near zero as more problems were added in one mental model test. After entering the no-new-knowledge state in the test, 5 out of 13 problems (38 percent) were automatically solved by the system without adding new knowledge

Argumentación y educación: apuntes para un debate

This is an Author's Original Manuscript of an article published by Taylor & Francis in Infancia y Aprendizaje on 02/01/2016 available online at http://www.tandfonline.com/10.1080/02103702.2015.1111607The objective of the present article is twofold. On the one hand, it aims to analyse the relationship between argumentation and education with a special emphasis on the difficulties that occur when defining and assessing argumentative skills. These difficulties are related to the thinking patterns underlying the argumentation models and, at the same time, are reflected in the educational models used to train and to assess students’ argumentative skills. On the other hand, this article presents and discusses common and distinctive aspects of the papers selected for this monographEl artículo que se presenta tiene un doble objetivo. Por un lado, pretende analizar cuáles son las relaciones entre argumentación y educación, poniendo énfasis en las dificultades para definir en qué consisten las competencias argumentativas y en los debates que esta indefinición ocasiona. Estas dificultades se relacionan con los modelos normativos de pensamiento que subyacen más o menos explícitamente a los modelos de argumentación y, al mismo tiempo, se reflejan en los modelos educativos que quieren formar a los estudiantes en las competencias argumentativas o que analizan las habilidades de estos estudiantes. Por otro lado, en este artículo se presentan y comentan los aspectos comunes y diferenciadores de los artículos seleccionados en la convocatoria ‘Argumentación y Educación’ y que constituyen este número de la revistaWe would like to express our gratitude to the general editors who helped us in all of the decision making processes and to Anna Sala, for her technical help. This research was funded by the Ministerio Español de Economía y Competitividad [EDU2013-47593-C2-1-P], y [EDU2013-47593-C2-2-P

Website Architecture, Information Flows and Cognitive Models

This paper explores the relation between new digital genres configuration and their users’ previous knowledge patterns from an interlinguistic perspective. More precisely, first we analyse two models that underlie the formal architecture of websites. For that purpose we introduce diverse pieces of software that allow for visualization of website organization in terms of nodes and links. Then, we show the most entrenched metaphoric models that provide cognitive tools for users to understand website configuration and usage, in an English speaking culture. Finally, we discuss to what extent these models can be transferred and learned by users from other cultures, particularly in Spanish speaking communitie