Abstract
As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain’s specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource’s content, user’s preferences, users’ social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources’ semantic information. The recommendation engine allows the promotion and discovery of unknownunknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources. © Springer International Publishing AG 2016.

Abstract
As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. In order to aid users in the resource-sharing process, the adoption of an entity that recommends users with access policies for their resources is proposed, by the analysis of (i) resource content, (ii) user preferences, (iii) users' social networks, (iv) semantic information, (v) user feedback about recommendation actions and (vi) provenance/ traceability information gathered from action sensors. A hybrid recommendation engine capable of performing collaborative-filtering was adopted and enhanced to use semantic information. Such recommendation engine translates user and resources' semantic information and aggregates those with other content, using a collaborative filtering technique. Recommendation of access policies over resources promotes the discovery of known-unknown and unknown-unknown resources to other users that could not even know about the existence of such resources. Evaluation to such recommender system is performed.

Abstract
This work presents a recommender system of economic news articles. Its objectives are threefold: (i) managing the vocabulary of the economic news domain to improve the system based on the seamlessly intervention of the documentalist (ii) automatically multi-classify the economic new articles and users profiles based on the domain vocabulary, and (iii) recommend the articles by comparing the multiclassification of the articles and profiles of the users. While several solutions exist to recommend news, multi-classify document and compare representations of items and profiles. They are not automatically adaptable to provide a mutual answer to previous points. Even more, existing approaches lacks substantial correlation with the human and in particular with the documentalist perspective. © Springer International Publishing Switzerland 2015.

Abstract
AEMOS is a simulator which aims to support the development of agent-based electronic markets capable of dealing with the natural semantic heterogeneity existent in this kind of environment. AEMOS simulates a marketplace which provides ontology matching services, enhanced with the exploitation of emergent social networks, enabling an efficient and transparent communication between agents, even when they use different ontologies. The system recommends possible alignments between the agents' ontologies, and lets them negotiate and decide which alignment should be used to translate the exchanged messages. In this paper we propose a new ontology alignment negotiation process, which promotes the reutilisation and combination of already existing alignments, as well as the involvement of business agents in the alignment composition process. With this new model, we aim to achieve a higher adequacy of the used alignments, as well as a more accurate and trustful evaluation of the alignments. Copyright © 2015 Inderscience Enterprises Ltd.

Abstract
Since the advent of artificial intelligence, researchers have been trying to create machines that emulate human behaviour. Back in the 1960s however, Licklider (IRE Trans Hum Factors Electron 4-11, 1960) believed that machines and computers were just part of a scale in which computers were on one side and humans on the other (human computation). After almost a decade of active research into human computation and crowdsourcing, this paper presents a survey of crowdsourcing human computation systems, with the focus being on solving micro-tasks and complex tasks. An analysis of the current state of the art is performed from a technical standpoint, which includes a systematized description of the terminologies used by crowdsourcing platforms and the relationships between each term. Furthermore, the similarities between task-oriented crowdsourcing platforms are described and presented in a process diagram according to a proposed classification. Using this analysis as a stepping stone, this paper concludes with a discussion of challenges and possible future research directions.

Abstract
Breast cancer is the most frequently diagnosed cancer in women. Using historical patient information stored in clinical datasets, data mining and machine learning approaches can be applied to predict the survival of breast cancer patients. A common drawback is the absence of information, i.e., missing data, in certain clinical trials. However, most standard prediction methods are not able to handle incomplete samples and, then, missing data imputation is a widely applied approach for solving this inconvenience. Therefore, and taking into account the characteristics of each breast cancer dataset, it is required to perform a detailed analysis to determine the most appropriate imputation and prediction methods in each clinical environment This research work analyzes a real breast cancer dataset from Institute Portuguese of Oncology of Porto with a high percentage of unknown categorical information (most clinical data of the patients are incomplete), which is a challenge in terms of complexity. Four scenarios are evaluated: (I) 5-year survival prediction without imputation and 5-year survival prediction from cleaned dataset with (II) Mode imputation, (Ill) Expectation-Maximization imputation and (IV) K-Nearest Neighbors imputation. Prediction models for breast cancer survivability are constructed using four different methods: K-Nearest Neighbors, Classification Trees, Logistic Regression and Support Vector Machines. Experiments are performed in a nested ten-fold cross-validation procedure and, according to the obtained results, the best results are provided by the K-Nearest Neighbors algorithm: more than 81% of accuracy and more than 0.78 of area under the Receiver Operator Characteristic curve, which constitutes very good results in this complex scenario.