Abstract
The non-portability of Prolog programs is widely considered one of the main problems facing Prolog programmers. Although since 1995, the core of the language is covered by the ISO standard 13211-1, this standard has not been sufficient to support large Prolog applications. As an approach to address this problem, since 2007, YAP and SWI-Prolog have established a basic compatibility framework. The aim of the framework is running the same code on Edinburgh-based Prolog systems rather than having to migrate an application. This article describes the implementation and evaluates this framework by studying how it can be used on a number of libraries and an important application.

Abstract
In this work we present XMuSer, a multi-relational framework suitable to explore temporal patterns available in multi-relational databases. XMuSer's main idea consists of exploiting frequent sequence mining, using an efficient and direct method to learn temporal patterns in the form of sequences. Grounded on a coding methodology and on the efficiency of sequential miners, we find the most interesting sequential patterns available and then map these findings into a new table, which encodes the multi-relational timed data using sequential patterns. In the last step of our framework, we use an ILP algorithm to learn a theory on the enlarged relational database that consists on the original multi-relational database and the new sequence relation. We evaluate our framework by addressing three classification problems. Moreover, we map each one of three different types of sequential patterns: frequent sequences, closed sequences or maximal sequences.

Abstract
Crowdsourcing is an emergent trend for general-purpose classification problem solving. Over the past decade, this notion has been embodied by enlisting a crowd of humans to help solve problems. There are a growing number of real-world problems that take advantage of this technique, such as Wikipedia, Linux or Amazon Mechanical Turk. In this paper, we evaluate its suitability for classification, namely if it can outperform state-of-the-art models by combining it with active learning techniques. We propose two approaches based on crowdsourcing and active learning and empirically evaluate the performance of a baseline Support Vector Machine when active learning examples are chosen and made available for classification to a crowd in a web-based scenario. The proposed crowdsourcing active learning approach was tested with Jester data set, a text humour classification benchmark, resulting in promising improvements over baseline results. © 2011 IEEE.

Abstract
Jokes classification is an intrinsically subjective and complex task, mainly due to the difficulties related to cope with contextual constraints on classifying each joke. Nowadays people have less time to devote to search and enjoy humour and, as a consequence, people are usually interested on having a set of interesting filtered jokes that could be worth reading, that is with a high probability of make them laugh. In this paper we propose a crowdsourcing based collective intelligent mechanism to classify humour and to recommend the most interesting jokes for further reading. Crowdsourcing is becoming a model for problem solving, as it revolves around using groups of people to handle tasks traditionally associated with experts or machines. We put forward an active learning Support Vector Machine (SVM) approach that uses crowdsourcing to improve classification of user custom preferences. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results.

Abstract
Humour classification is one of the most interesting and difficult tasks in text classification. Humour is subjective by nature, yet humans are able to promptly define their preferences. Nowadays people often search for humour as a relaxing proxy to overcome stressful and demanding situations, having little or no time to search contents for such activities. Hence, we propose to aid the definition of personal models that allow the user to access humour with more confidence on the precision of his preferences. In this paper we focus on a Support Vector Machine (SVM) active learning strategy that uses specific most informative examples to improve baseline performance. Experiments were carried out using the widely available Jester jokes dataset, with encouraging results on the proposed framework. © 2011 Springer-Verlag.

Abstract
Computer networks are highly dynamic environments in which the meaning of normal and anomalous behaviours can drift considerably throughout time. Behaviour-based Network Intrusion Detection System (NIDS) have thus to cope with the temporal normality drift intrinsic on computer networks, by tuning adaptively its level of response, in order to be able to distinguish harmful from harmless network traffic flows. In this paper we put forward the intrinsic Tunable Activation Threshold (TAT) theory ability to adaptively tolerate normal drifting network traffic flows. This is embodied on the TAT-NIDS, a TAT-based Artificial Immune System (AIS) we have developed for network intrusion detection. We describe the generic AIS framework we have developed to assemble TAT-NIDS and present the results obtained thus far on processing real network traffic data sets. We also compare the performance obtained by TAT-NIDS with the well known and widely deployed signature-based snort network intrusion detection system. © 2011 Springer-Verlag.