This paper presents an agent based framework for modeling of the formation and dynamics of pirate networks. The framework consists of (1) development of network formation mechanism and (2) formulation of pirate attack dynamics. Accordingly, the paper attempts to define the characteristics of Pirate Networks and to formulate the rules that govern the operation and evolution of Pirate Networks. We discuss the clan based social system that facilitate pirate formation as well as the pirate network inter-action with the hosting clan system. Using published material, empirical data and surveys the paper attempts to establish credible formation mechanism and operational characterization of pirate attacks. The proposed framework accounts for clan dynamics and the interplay of social, ecological and physical spaces. Finally we conclude with a discussion on exploratory modeling for the refinement of the proposed framework and for empirically grounding proposed simulations.

We present a new method for clustering based on compression. The method doesn't use subject-specific features or background knowledge, and works as follows: First, we determine a universal similarity distance, the normalized compression distance or NCD, computed from the lengths of compressed data files (singly and in pairwise concatenation). Second, we apply a hierarchical clustering method. The NCD is universal in that it is not restricted to a specific application area, and works across application area boundaries. A theoretical precursor, the normalized information distance, co-developed by one of the authors, is provably optimal but uses the non-computable notion of Kolmogorov complexity. We propose precise notions of similarity metric, normal compressor, and show that the NCD based on a normal compressor is a similarity metric that approximates universality. To extract a hierarchy of clusters from the distance matrix, we determine a dendrogram (binary tree) by a new quartet method and a fast heuristic to implement it. The method is implemented and available as public software, and is robust under choice of different compressors. To substantiate our claims of universality and robustness, we report evidence of successful application in areas as diverse as genomics, virology, languages, literature, music, handwritten digits, astronomy, and combinations of objects from completely different domains, using statistical, dictionary, and block sorting compressors. In genomics we presented new evidence for major questions in Mammalian evolution, based on whole-mitochondrial genomic analysis: the Eutherian orders and the Marsupionta hypothesis against the Theria hypothesis.

We are presenting a method to recognise geographical references in free text. Our tool must work on various languages with a mi ni-mum of language-dependent resources, except a gazetteer. The main difficulty is to disa mbiguate these place names by distinguis hing places from persons and by selecting the mo st likely place out of a list of homographi c place names world-wide. The system uses a number of language-independent clues and he uristics to disambiguate place name homogra phs. The final aim is to index texts with the countries and cities they mention and to automatically visualise this information on geographical maps using various tools.

We demonstrate the effectiveness of multilingual learning for unsupervised part-of-speech tagging. The central assumption of our work is that by combining cues from multiple languages, the structure of each becomes more apparent. We consider two ways of applying this intuition to the problem of unsupervised part-of-speech tagging: a model that directly merges tag structures for a pair of languages into a single sequence and a second model which instead incorporates multilingual context using latent variables. Both approaches are formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo sampling techniques for inference. Our results demonstrate that by incorporating multilingual evidence we can achieve impressive performance gains across a range of scenarios. We also found that performance improves steadily as the number of available languages increases.

This article considers the task of automatically inducing role-semantic annotations in the FrameNet paradigm for new languages. We propose a general framework that is based on annotation projection, phrased as a graph optimization problem. It is relatively inexpensive and has the potential to reduce the human effort involved in creating role-semantic resources. Within this framework, we present projection models that exploit lexical and syntactic information. We provide an experimental evaluation on an English-German parallel corpus which demonstrates the feasibility of inducing high-precision German semantic role annotation both for manually and automatically annotated English data.

Computational techniques have shown much promise in the field of Finance, owing to their ability to extract sense out of dauntingly complex systems. This paper reviews the most promising of these techniques, from traditional computational intelligence methods to their machine learning siblings, with particular view to their application in optimising the management of a portfolio of financial instruments. The current state of the art is assessed, and prospective further work is assessed and recommended

Standard belief contraction assumes an underlying logic containing full classical propositional logic, but there are good reasons for considering contraction in less expressive logics. In this paper we focus on Horn logic. In addition to being of interest in its own right, our choice is motivated by the use of Horn logic in several areas, including ontology reasoning in description logics. We consider three versions of contraction: entailment-based and inconsistency-basedcontraction (e-contraction and i-contraction, resp.), introduced by Delgrande for Horn logic, and package contraction (p-contraction), studied by Fuhrmann and Hansson for the classical case. We show that the standard basic form of contraction, partial meet, is too strong in the Horn case. We define more appropriate notions of basic contraction for all three types above, and provide associated representation results in terms of postulates. Our results stand in contrast to Delgrande's conjectures that orderly maxichoice is the appropriate contraction for both e- and i-contraction. Our interest in p-contraction stems from its relationship with an important reasoning task in ontological reasoning:repairing the subsumption hierarchy in EL. This is closely related to p-contraction with sets of basic Horn clauses (Horn clauses of the form p -> q). We show that this restricted version of p-contraction can also be represented as i-contraction.

Possibilistic networks are useful tools for reasoning under uncertainty. Uncertain pieces of information can be described by different measures: possibility measures, necessity measures and more recently, guaranteed possibility measures, denoted by Delta. This paper first proposes the use of guaranteed possibility measures to define a so-called Delta-based possibilistic network. This graphical representation tries to express and to deal with the minimal (lower-bound) possibility degree guaranteed for each variable. We then establish relationships between graphical and logical-based representations of uncertain information encoded by guaranteed possibility measures. We show that possibilistic networks based on guaranteed possibility measures can be easily transformed, in a polynomial time, in Delta-based knowledge bases. Then we analyze propagation algorithms in Delta-based possibilistic networks. In fact, standard possibilistic propagation algorithms can be re-used since we show that a simple rewriting of the chain rule allows the transformation of the initial Delta-based possibilistic networks into standard min-based possibilistic networks.

The need for efficient monitoring of spatio-temporal dynamics in large environmental applications, such as the water quality monitoring in rivers and lakes, motivates the use of robotic sensors in order to achieve sufficient spatial coverage. Typically, these robots have bounded resources, such as limited battery or limited amounts of time to obtain measurements. Thus, careful coordination of their paths is required in order to maximize the amount of information collected, while respecting the resource constraints. In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths. In particular, we first develop eSIP (efficient Single-robot Informative Path planning), an approximation algorithm for optimizing the path of a single robot. Hereby, we use a Gaussian Process to model the underlying phenomenon, and use the mutual information between the visited locations and remainder of the space to quantify the amount of information collected. We prove that the mutual information collected using paths obtained by using eSIP is close to the information obtained by an optimal solution. We then provide a general technique, sequential allocation, which can be used to extend any single robot planning algorithm, such as eSIP, for the multi-robot problem. This procedure approximately generalizes any guarantees for the single-robot problem to the multi-robot case. We extensively evaluate the effectiveness of our approach on several experiments performed in-field for two important environmental sensing applications, lake and river monitoring, and simulation experiments performed using several real world sensor network data sets.

This paper describes a method for the automatic inference of structural transfer rules to be used in a shallow-transfer machine translation (MT) system from small parallel corpora. The structural transfer rules are based on alignment templates, like those used in statistical MT. Alignment templates are extracted from sentence-aligned parallel corpora and extended with a set of restrictions which are derived from the bilingual dictionary of the MT system and control their application as transfer rules. The experiments conducted using three different language pairs in the free/open-source MT platform Apertium show that translation quality is improved as compared to word-for-word translation (when no transfer rules are used), and that the resulting translation quality is close to that obtained using hand-coded transfer rules. The method we present is entirely unsupervised and benefits from information in the rest of modules of the MT system in which the inferred rules are applied.