The Linear Ordering Problem Revisited

The Linear Ordering Problem is a popular combinatorial optimisation problem which has been extensively addressed in the literature. However, in spite of its popularity, little is known about the characteristics of this problem. This paper studies a procedure to extract static information from an instance of the problem, and proposes a method to incorporate the obtained knowledge in order to improve the performance of local search-based algorithms. The procedure introduced identifies the positions where the indexes cannot generate local optima for the insert neighbourhood, and thus global optima solutions. This information is then used to propose a restricted insert neighbourhood that discards the insert operations which move indexes to positions where optimal solutions are not generated. In order to measure the efficiency of the proposed restricted insert neighbourhood system, two state-of-the-art algorithms for the LOP that include local search procedures have been modified. Conducted experiments confirm that the restricted versions of the algorithms outperform the classical designs systematically. The statistical test included in the experimentation reports significant differences in all the cases, which validates the efficiency of our proposal.

Deep learning review and its applications

Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.

We can work it out: an enactive look at cooperation

The past years have seen an increasing debate on cooperation and its unique human character. Philosophers and psychologists have proposed that cooperative activities are characterized by shared goals to which participants are committed through the ability to understand each other’s intentions. Despite its popularity, some serious issues arise with this approach to cooperation. First, one may challenge the assumption that high-level mental processes are necessary for engaging in acting cooperatively. If they are, then how do agents that do not possess such ability (preverbal children, or children with autism who are often claimed to be mind-blind) engage in cooperative exchanges, as the evidence suggests? Secondly, to define cooperation as the result of two de-contextualized minds reading each other’s intentions may fail to fully acknowledge the complexity of situated, interactional dynamics and the interplay of variables such as the participants’ relational and personal history and experience. In this paper we challenge such accounts of cooperation, calling for an embodied approach that sees cooperation not only as an individual attitude toward the other, but also as a property of interaction processes. Taking an enactive perspective, we argue that cooperation is an intrinsic part of any interaction, and that there can be cooperative interaction before complex communicative abilities are achieved. The issue then is not whether one is able or not to read the other’s intentions, but what it takes to participate in joint action. From this basic account, it should be possible to build up more complex forms of cooperation as needed. Addressing the study of cooperation in these terms may enhance our understanding of human social development, and foster our knowledge of different ways of engaging with others, as in the case of autism.

Impacto de la web 2.0 en los perfiles profesionales

En pleno siglo XXI, el uso de Internet y los avances no sólo afectan a las personas sino que las empresas también deben evolucionar al mismo ritmo y adaptar todas sus prácticas a dichos avances. Con la aparición de la Web 2.0, ciertos aspectos de las empresas han quedado obsoletos y se han debido adaptar a la nueva era: la era de la comunicación e de la interacción a través de Internet. Se han creado nuevos modelos de negocio, se han mejorado actividades de la cadena de valor, han surgido nuevas estrategias de marketing y comunicación corporativa y se han creado unos nuevos canales de venta, alrededor del fenómeno e-Commerce. En cuanto a los trabajadores, las empresas han comenzado a valorar nuevas competencias relacionadas con el uso de Internet y la Web 2.0. Dichas competencias pueden ser comunes para muchos puestos de trabajo, por ejemplo el uso de redes sociales o la gestión de la información, otras son más específicas y dependen del puesto de trabajo que consideremos. Finalmente, la aparición de la Web 2.0 ha exigido a las empresas a crear nuevas áreas y puestos de trabajo o modificar los actuales para adecuarse a los nuevos tiempos y tendencias. Así surgen los diferentes perfiles profesionales de las áreas de Estrategia Digital, Marketing Digital, Contenido Digital, Social Media, Análisis Big Data, e-Commerce y Mobile Marketing. Estos perfiles gozan de mucha popularidad y demanda por parte de las empresas y se estima que va a crecer aún más el número de puestos relacionados con el ámbito digital, ya que son las profesiones del futuro.