Catálogo de publicaciones - revistas

<jats:p> Argumentation has proven successful in a number of domains, including Multi-Agent Systems and decision support in medicine and engineering. We propose its application to a domain yet largely unexplored by argumentation research: computational linguistics. We have developed a novel classification methodology that incorporates reasoning through argumentation with supervised learning. We train classifiers and then <jats:italic>argue</jats:italic> about the validity of their output. To do so, we identify arguments that formalise prototypical knowledge of a problem and use them to correct misclassifications. We illustrate our methodology on two tasks. On the one hand, we address <jats:italic>cross-domain sentiment polarity classification</jats:italic> , where we train classifiers on one corpus, for example, Tweets, to identify positive/negative polarity and classify instances from another corpus, for example, sentences from movie reviews. On the other hand, we address a form of argumentation mining that we call <jats:italic>Relation-based Argumentation Mining</jats:italic> , where we classify pairs of sentences based on whether the first sentence attacks or supports the second or whether it does neither. Whenever we find that one sentence attacks/supports the other, we consider both to be argumentative, irrespective of their stand-alone argumentativeness. For both tasks, we improve classification performance when using our methodology, compared to using standard classifiers only. </jats:p>

<jats:p>Preserving users’ privacy is important for Web systems. In systems where transactions are managed by a single user, such as e-commerce systems, preserving privacy of the transactions is merely the capability of access control. However, in online social networks, where each transaction is managed by and has effect on others, preserving privacy is difficult. In many cases, the users’ privacy constraints are distributed, expressed in a high-level manner, and would depend on information that only becomes available over interactions with others. Hence, when a content is being shared by a user, others who might be affected by the content should discuss and agree on how the content will be shared online so that none of their privacy constraints are violated. To enable this, we model users of the social networks as agents that represent their users’ privacy constraints as semantic rules. Agents argue with each other on propositions that enable their privacy rules by generating facts and assumptions from their ontology. Moreover, agents can seek help from others by requesting new information to enrich their ontology. Using assumption-based argumentation, agents decide whether a content should be shared or not. We evaluate the applicability of our approach on real-life privacy scenarios in comparison with user surveys.</jats:p>

<jats:p>Internet of Things (IoT) has been commercially explored as Platforms as a Services (PaaS). The standard solution for this kind of service is to combine the Cloud computing infrastructure with IoT software, services, and protocols also known as CoT (Cloud of Things). However, the use of CoT in latency-sensitive applications has been shown to be unfeasible due to the inherent latency of cloud computing services. One proposal to solve this problem is the use of the computational resources available at the edge of the network, which is called Fog computing. Fog computing solves the problem of latency but adds complexity to the use of these resources due to the dynamism and heterogeneity of the IoT. An even more accentuated form of fog computing is Mist computing, where the use of the computational resources is limited to the close neighborhood of the client device. The decision of what computing infrastructure (Fog, Mist, or Cloud computing) is the best to provide computational resources is not always simple, especially in cases where latency requirements should be met by CoT. This work proposes an algorithm for selecting the best physical infrastructure to use the computational resource (Fog, Mist, or Cloud computing) based on cost, bandwidth, and latency criteria defined by the client device, resource availability, and topology of the network. The article also introduces the concept of feasible Fog that limits the growth of device search time in the neighborhood of the client device. Simulation results suggest the algorithm’s choice adequately attends the client’s device requirements and that the proposed method can be used in IoT environment located on the edge of the network.</jats:p>