Abstract
The main goal of the conference is to promote the interest in the current digital culture and its intersection with art and technology as an important research field, and also to create a common space for discussion and exchange of new experiences. It seeks to foster greater understanding about digital arts and culture across a wide spectrum of cultural, disciplinary, and professional practices. To this end, many scholars, teachers, researchers, artists, comput-er professionals, and others who are working within the broadly defined areas of digital arts, culture and education across the world, submitted their innovative work to the conference.

Abstract
In this paper we discuss the incompatibility between the notions of validity and impreciseness in the context of Dynamic Logics. To achieve that we consider the Lukasiewicz action lattice and its interval counterpart, we show how some validities fail in the context of intervals. In order to capture the properties of action lattices that remain valid for intervals we propose a new structure called Quasi-action Lattices which generalizes action lattices and is able to model both: The Lukasiewicz action lattice, L, and its interval counterpart, (sic). The notion of graded satisfaction relation is extended to quasi-action lattices. We demonstrate that, in the case of intervals, the relation of graded satisfaction is correct (cf. Theorem 3) with respect to the graded satisfaction relation on the Lukasiewicz action lattice. Although this theorem guarantees that satisfiability is preserved on intervals, we show that validity is not. We propose, then, to weaken the notion of validity on action lattices to designated validity on quasi-action lattices. In this context, Theorem 4 guarantees that the dynamic formula which are valid with respect to L will be designated valid with respect to (sic).

Abstract
Taking into consideration a current legal Portuguese framework about accessibility requirements for websites and mobile applications of the public sector, we present an e-Learning project aiming to enhance awareness about the need of the development of accessible Web contents, thus promoting digital inclusion. In this context, in this paper we present a description of a Massive Open Online Courses (MOOC) on Web accessibility entitled: "Inclusive Web - How to develop inclusive web contents?" In this paper, we described the creation of this MOOC, not only the program contents, learning methodologies and evaluation descriptions but also present satisfaction and learning results of this project. This first edition counted on 174 students, among them, 2 with special needs, from 4 different countries, mostly professionals who develop web content, such as: students, professionals and enthusiasts in the areas of Informatics, Multimedia, Information and Communication Technologies (ICT), Digital Media, Web Design, Teaching and Special Education. The feedback results showed that despite there were a few features that must be improved, overall the applicants enjoyed and stated that the course was a good learning resource to start in the Web Accessibility topic.

Abstract
This paper proposes an optimal bidding strategy for autonomous residential energy management systems. This strategy enables the system to manage its domestic energy production and consumption autonomously, and trade energy with the local market through a novel hybrid interval-stochastic optimization method. This work poses a residential energy management problem which consists of two stages: day-ahead and real-time. The uncertainty in electricity price and PV power generation is modeled by interval-based and stochastic scenarios in the day-ahead and real-time transactions between the smart home and local electricity market. Moreover, the implementation of a battery included to provide energy flexibility in the residential system. In this paper, the smart home acts as a price-taker agent in the local market, and it submits its optimal offering and bidding curves to the local market based on the uncertainties of the system. Finally, the performance of the proposed residential energy management system is evaluated according to the impacts of interval optimistic and flexibility coefficients, optimal bidding strategy, and uncertainty modeling. The evaluation has shown that the proposed optimal offering model is effective in making the home system robust and achieves optimal energy transaction. Thus, the results prove that the proposed optimal offering model for the domestic energy management system is more robust than its non-optimal offering model. Moreover, battery flexibility has a positive effect on the system's total expected profit. With regarding to the bidding strategy, it is not able to impact the smart home's behavior (as a consumer or producer) in the day-ahead local electricity market.

Abstract

Abstract
O tratamento de conjuntos de dados de grande dimensão é uma questão que é recorrente nos dias de hoje. Uma das abordagens possíveis passa por realizar uma seleção de atributos que permita diminuir, consideravelmente, a dimensão dos dados sem aumentar a inconsistência dos mesmos. A Análise Lógica de Dados Inconsistentes (LAID) é uma metodologia sistematizada, robusta, sendo fácil de interpretar e consegue lidar com dados inconsistentes. O paradigma, relativamente ao manuseamento de grandes volumes de dados, tem-se alterado. Antes, o tratamento dos dados era efetuado num único computador e o acesso era realizado depois do seu carregamento em memória. A tendência atual é aceder aos dados em disco, num ambiente cloud. Este trabalho pretende validar o novo paradigma, com recurso ao sistema de dados HDF5 e ao ambiente remoto disponibilizado pela. Pelo facto de o HDF5 ser o sistema adotado pela comunidade Python para lidar com dados de grande dimensão, esta linguagem foi escolhida para implementação do LAID.;The treatment of large datasets is an issue that is often addressed today and whose task is not simple, given the computational limitations that still exist.One possible approach is to perform a feature selection that allows a considerably reduction of data size without increasing inconsistency. Logical Analysis of Inconsistent Data (LAID) is a systematic, robust methodology that is easy to interpret and can handle inconsistent data.The paradigm regarding the handling of large data has hasbeen changing over. Previously, data processing was performed on a single computer, with in-memory data access. The current trend is to access data on disk, in a cloud environment. The present work intends to validate this new paradigm, using HDF5 data system and remote environment provided by INCD. Because HDF5 is the system adopted by Python’s community to handle large datasets, this language was chosen for LAID algorithm implementation.