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    ABSTRACT: The work presented in this papers is directed at mechanisms where by 3D surfaces can be represented to support the generation and application of classification techniques. Three different mechanisms are presented to allow for the representation of 3D surfaces in such a way that key features are retained while at the same time ensuring compatibility with prediction (classification) techniques. The three representation techniques are: (i) Local Geometry Matrices (LGMs) founded on the concept of local binary patterns, (ii) Local Distance Measure (LDM) founded on the idea that distances from edges (critical points) may be significant, and (iii) Point Series (PS) whereby local geometries are represented in terms of a linearisation of space. The representations are designed to capture the nature of 3D surfaces in terms of their local geometry and predict class labels associated with such local geometries. To act as a focus for the work the prediction of “springback” within the context of sheet metal forming is considered, where springback is a form of deformation that occurs (in a non-uniform manner) across a manufactured 3D surface as a result of the application of some sheet metal forming process. The evaluation of each of the techniques, and variations thereof, using sheet metal parts that have been manufactured especially for the purpose, is fully described. The paper also reports on a statistical significance test concerning the results.
    Expert Systems with Applications 01/2015; 42(1):79–93.
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    ABSTRACT: We investigate the computational complexity of the empire colouring problem (as defined by Percy Heawood in 1890) for maps containing empires formed by exactly $r > 1$ countries each. We prove that the problem can be solved in polynomial time using $s$ colours on maps whose underlying adjacency graph has no induced subgraph of average degree larger than $s/r$. However, if $s \geq 3$, the problem is NP-hard even if the graph is a forest of paths of arbitrary lengths (for any $r \geq 2$, provided $s < 2r - \sqrt{2r + 1/4+ 3/2). Furthermore we obtain a complete characterization of the problem's complexity for the case when the input graph is a tree, whereas our result for arbitrary planar graphs fall just short of a similar dichotomy. Specifically, we prove that the empire colouring problem is NP-hard for trees, for any $r \geq 2$, if $3 \leq s \leq 2r-1$ (and polynomial time solvable otherwise). For arbitrary planar graphs we prove NP-hardness if $s<7$ for $r=2$, and $s < 6r-3$, for $r \geq 3$. The result for planar graphs also proves the NP-hardness of colouring with less than 7 colours graphs of thickness two and less than $6r-3$ colours graphs of thickness $r \geq 3$.
    Discrete Mathematics 06/2013; 313(11):1248–1255.
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    ABSTRACT: When modelling realistic systems, physical constraints on the resources available are often required. For example, we might say that at most N processes can access a particular resource at any moment, exactly M participants are needed for an agreement, or an agent can be in exactly one mode at any moment. Such situations are concisely modelled where literals are constrained such that at most N, or exactly M, can hold at any moment in time. In this paper we consider a logic which is a combination of standard propositional linear time temporal logic with cardinality constraints restricting the numbers of literals that can be satisfied at any moment in time. We present the logic and show how to represent a number of case studies using this logic. We propose a tableau-like algorithm for checking the satisfiability of formulae in this logic, provide details of a prototype implementation and present experimental results using the prover.
    Journal of Applied Logic 03/2013; 11(1):30–51.
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    ABSTRACT: The mining frequent itemsets plays an important role in the mining of association rules. Frequent itemsets are typically mined from binary databases where each item in a transaction may have a different significance. Mining Frequent Weighted Itemsets (FWI) from weighted items transaction databases addresses this issue. This paper therefore proposes algorithms for the fast mining of FWI from weighted item transaction databases. Firstly, an algorithm for directly mining FWI using WIT-trees is presented. After that, some theorems are developed concerning the fast mining of FWI. Based on these theorems, an advanced algorithm for mining FWI is proposed. Finally, a Diffset strategy for the efficient computation of the weighted support for itemsets is described, and an algorithm for mining FWI using Diffsets presented. A complete evaluation of the proposed algorithms is also presented.
    Expert Systems with Applications 03/2013; 40(4):1256–1264.
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    ABSTRACT: Model checking is a well-established technique for the formal verification of concurrent and distributed systems. In recent years, model checking has been extended and adapted for multi-agent systems, primarily to enable the formal analysis of belief–desire–intention systems. While this has been successful, there is a need for more complex logical frameworks in order to verify realistic multi-agent systems. In particular, probabilistic and real-time aspects, as well as knowledge, belief, goals, etc., are required. However, the development of new model checking tools for complex combinations of logics is both difficult and time consuming. In this article, we show how model checkers for the constituent temporal, probabilistic, and real-time logics can be re-used in a modular way when we consider combined logics involving different dimensions. This avoids the re-implementation of model checking procedures. We define a modular approach, prove its correctness, establish its complexity, and show how it can be used to describe existing combined approaches and define yet-unimplemented combinations. We also demonstrate the feasibility of our approach on a case study.
    Theoretical Computer Science 01/2013; 503:61–88.
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    ABSTRACT: Mining social media for opinions is important to governments and businesses. Current approaches focus on sentiment and opinion detection. Yet, people also justify their views, giving arguments. Understanding arguments in social media would yield richer knowledge about the views of individuals and collectives. Extracting arguments from social media is difficult. Messages appear to lack indicators for argument, document structure, or inter-document relationships. In social media, lexical variety, alternative spellings, multiple languages, and alternative punctuation are common. Social media also encompasses numerous genres. These aspects can confound the extraction of well-formed knowledge bases of argument. We chart out the various aspects in order to isolate them for further analysis and processing.
    Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management; 10/2012
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    ABSTRACT: We consider the following online scheduling problem in which the input consists of n jobs to be scheduled on identical machines of bounded capacity g (the maximum number of jobs that can be processed simultaneously on a single machine). Each job is associated with a release time and a completion time between which it is supposed to be processed. When a job is released, the online algorithm has to make decision without changing it afterwards. We consider two versions of the problem. In the minimization version, the goal is to minimize the total busy time of machines used to schedule all jobs. In the resource allocation maximization version, the goal is to maximize the number of jobs that are scheduled under a budget constraint given in terms of busy time. This is the first study on online algorithms for these problems. We show a rather large lower bound on the competitive ratio for general instances. This motivates us to consider special families of input instances for which we show constant competitive algorithms. Our study has applications in power aware scheduling, cloud computing and optimizing switching cost of optical networks.
    Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation; 05/2012
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    ABSTRACT: This paper describes an approach to multi-agent classification using an argumentation from experience paradigm whereby individual agents argue for a given example to be classified with a particular label according to their local data. Arguments are expressed in the form of classification rules which are generated dynamically. As such each local database can be conceptualised as an experience repository; and the individual classification rules, generated from this repository, as describing generalisations drawn from this experience. The argumentation process and the supporting data structures are fully described. The process has been implemented in the PISA (Pooling Information from Several Agents) multi-agent framework which is fully described. Experiments indicate that the operation of PISA is comparable with other classification approaches and that, when operating groups or in the presence of noise, PISA outperforms such comparable approaches.
    Data & Knowledge Engineering. 05/2012; 75:34–57.
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    ABSTRACT: An alternative to deploying a single robot of high complexity can be to utilize robot swarms comprising large numbers of identical, and much simpler, robots. Such swarms have been shown to be adaptable, fault-tolerant and widely applicable. However, designing individual robot algorithms to ensure effective and correct overall swarm behaviour is actually very difficult. While mechanisms for assessing the effectiveness of any swarm algorithm before deployment are essential, such mechanisms have traditionally involved either computational simulations of swarm behaviour, or experiments with robot swarms themselves. However, such simulations or experiments cannot, by their nature, analyse all possible swarm behaviours. In this paper, we will develop and apply the use of automated probabilistic formal verification techniques to robot swarms, involving an exhaustive mathematical analysis, in order to assess whether swarms will indeed behave as required. In particular we consider a foraging robot scenario to which we apply probabilistic model checking.
    Robotics and Autonomous Systems. 01/2012; 60:199-213.
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    ABSTRACT: We consider the problem of characterising relational con-straints under which TBox reasoning in EL is tractable. We obtain P vs. coNP-hardness dichotomies for tabular constraints and constraints imposed on a single reflexive role.
    Proceedings of the 24th International Workshop on Description Logics (DL 2011), Barcelona, Spain, July 13-16, 2011; 01/2011
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