Scalable and dynamic quorum systems Abstract. We investigate issues related to the probe complexity of quorum systems and their implementation in a dynamic environment. Our contribution is twofold. The first regards the algorithmic complexity of finding a quorum in case of random failures. We show a tradeoff between the load of a quorum system and its probe complexity for non adaptive algorithms. We analyze the algorithmic probe complexity of the Paths quorum system suggested by Naor and Wool in [29] , and present two optimal algorithms. The first is a non adaptive algorithm that matches our lower bound. The second is an adaptive algorithm with a probe complexity that is linear in the cardinality of the smallest quorum set. We supply a constant degree network in which these algorithms could be executed efficiently. Thus the Paths quorum system is shown to have good balance between many measures of quality. Our second contribution is presenting Dynamic Paths -a suggestion for a dynamic and scalable quorum system, which can operate in an environment where elements join and leave the system. The quorum system could be viewed as a dynamic adaptation of the Paths system, and therefore has low load high availability and good probe complexity. We show that it scales gracefully as the number of elements grows.

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The entropy of traces in parallel computation The following problem arises in the context of parallel computation: how many bits of information are required to specify any one element from an arbitrary (nonempty) k-subset of a set? We characterize optimal coding techniques for this problem. We calculate the asymptotic behavior of the amount of information necessary, and construct an algorithm that speci es an element from a subset in an optimal manner.

O O O B O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


An easy-to-use MATLAB program (MamLand) for the assessment of landslide susceptibility using a Mamdani fuzzy algorithm In this study, landslide susceptibility mapping using a completely expert opinion-based approach was applied for the Sinop (northern Turkey) region and its close vicinity. For this purpose, an easy-to-use program, ''MamLand,'' was developed for the construction of a Mamdani fuzzy inference system and employed in MATLAB. Using this newly developed program, it is possible to construct a landslide susceptibility map based on expert opinion. In this study, seven conditioning parameters characterising topographical, geological, and environmental conditions were included in the FIS. A landslide inventory dataset including 351 landslide locations was obtained for the study area. After completing the data production stage of the study, the data were processed using a soft computing approach, i.e., a Mamdani-type fuzzy inference system. In this system, only landslide conditioning data were assessed, and landslide inventory data were not included in the assessment approach. Thus, a file depicting the landslide susceptibility degrees for the study area was produced using the Mamdani FIS. These degrees were then exported into a GIS environment, and a landslide susceptibility map was produced and assessed in point of statistical interpretation. For this purpose, the obtained landslide susceptibility map and the landslide inventory data were compared, and an area under curve (AUC) obtained from receiver operating characteristics (ROC) assessment was carried out. From this assessment, the AUC value was found to be 0.855, indicating that this landslide susceptibility map, which was produced in a data-independent manner, was successful. According to a review of the international landslide literature (Gokceoglu and Sezer, 2009) , the most frequently used terms in this field are ''landslide susceptibility'', and ''Geographical Information Systems, GIS.'' Several possible reasons mainly include: (a) developments in computer techniques and Geographical Information Systems; (b) increasing awareness of the socio-economic significance of landslides; and (c) the complex nature of landslides. Due to the complex nature of landslides, several methods have been applied for the assessment of landslide susceptibility at a medium scale. In general, these methods can be classified into three main groups: statistical (e.g., Can et al., 2005; Gokceoglu et al., 2005; Lee, 2005; Clerici et al., 2006; Duman et al., 2006; Pradhan et al., 2006 Pradhan et al., , 2008 Lee and Pradhan, 2006; Akgun and Bulut, 2007; Akgun et al., 2008; Tunusluoglu et al., 2008; Lamelas et al., 2008; Gorum et al., 2008; Turer et al., 2008; Nefeslioglu et al., 2008a; Nefeslioglu and Gokceoglu, 2011) , soft computing (e.g., Ercanoglu and Gokceoglu, 2002, 2004; Arora et al., 2004; Lee et al., 2004; Ercanoglu, 2005; Lui et al., 2006; Kanungo et al., 2006; Melchiorre et al., 2008; Nefeslioglu et al., 2008b Nefeslioglu et al., , 2010 Pradhan et al., 2009 Pradhan et al., , 2010 Caniani et al., 2008; Yilmaz, 2009; Bedarnik et al., 2010; Pradhan, 2010; Sezer et al., 2011) , and the combination of index maps (e.g., Anbalagan and Singh, 1996; Gokceoglu and Aksoy, 1996; Turrini and Visintainer, 1998; Ayenew and Barbieri, 2005) . In the literature of this field, it is possible to find attempts to assess landslide susceptibility based on expert opinion Saboya et al., 2006; Ercanoglu et al., 2008) without constructing an inference system. However, all of the available methods for landslide assessment at a medium scale ( $ 1:25,000) are associated with uncertainties arising from a lack of knowledge and high variability (Ercanoglu and Gokceoglu, 2002) . For these reasons, certain generalizations Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/cageo and simplifications are applied during data-driven analyses for landslide susceptibility mapping at a medium scale. Additionally, these data-driven methods do not require an expert opinion. The data-driven methods include a training phase. After training, testing, and validation stages are employed for assessing the quality of the model. There are a number of disadvantages associated with this type of process. First, for selection of the training data and the modeling the system uses different training sets. Second, the results produced are dependent on the dataset employed. Third, the methods used, such as decision trees, support vector machines, or artificial neural networks, are black box approaches, and the user is completely isolated from the learning stage. To increase the role of expert opinion in such methods and to provide new insight related to reusable model production for landslide susceptibility assessment, the Mamdani fuzzy inference system (FIS) appears to be a potentially suitable approach.. FIS are capable of reducing uncertainties when solving complex problems, and the advantages of FIS are summarized by Alvarez Grima (2000) as follows:. (a) FIS allows explicit expression of knowledge of the system via fuzzy ''if-then'' rules; (b) FIS deals with the subjective uncertainty (fuzziness, vagueness, and imprecision) inherent in the way experts approach their problems; (c) FIS can combine numerical and categorical data; and (d) FIS provides a sound mathematical basis.. In the last decade, the Mamdani FIS has been used extensively to solve complex and nonlinear problems of engineering geology. An interesting and perhaps the most attractive characteristic of fuzzy models compared with other methods commonly used in geosciences, such as statistics, is that they are able to describe such problems in a transparent way (Setnes et al., 1998) . A Mamdani FIS (Mamdani and Assilian, 1975 ) is a transparent and expert opinion-based system. Although several studies using this type of system (i.e., Den Hartog et al., 1997; Alvarez Grima and Babuska, 1999; Gokceoglu, 2002; Kayabasi et al., 2003; Sonmez et al., 2003 Sonmez et al., , 2004 Nefeslioglu et al., 2003 Nefeslioglu et al., , 2006 Gokceoglu and Zorlu, 2004; Gokceoglu et al., 2009a, b; Yagiz and Gokceoglu, 2010) have been published related to various branches of engineering geology, a Mamdani FIS has been applied for a limited number of landslide susceptibility assessments to date Juang et al., 1992; Saboya et al., 2006) . Lee and Juang (1992) and Juang et al. (1992) proposed a quantitative assessment method to evaluate the slope failure potential. Using fuzzy sets and linguistic rules, they Juang et al., 1992) assessed the slope failure potential of relatively large areas in Hong Kong. More than 10 years later, Saboya et al. (2006) used the Mamdani FIS for assessment of failure susceptibility of soil slopes. Saboya et al. (2006) constructed a Mamdani FIS by considering the opinions of the experts. According to the authors (Saboya et al., 2006) , the conception of intelligent models by means of fuzzy logic in geotechnical engineering is quite appropriate as long as in this expertise area, the judgement, induction, deduction, and a sense of importance are always present. Although useful for assessment of landslide susceptibility, the Mamdani FIS has not been employed commonly due to the difficulties when applied in large areas without some proper computer program. For this reason, the purposes of the present study are to develop a MATLAB program for the construction of a Mamdani FIS and to apply it for landslide susceptibility evaluation at a medium scale. The present study includes four main stages such as (i) development of MATLAB program, (ii) data production; (iii) construction of the FIS and landslide susceptibility mapping; and (iv) assessment of the results. The major benefits to be obtained from the study are as follows:. (a) The Mamdani FIS can be constructed completely based on expert opinion without an exhaustive data analysis process. (b) An expert can reflect his/her opinions on landslides in a region in the model via fuzzy if-then rules. (c) The computational load is low, and results can be obtained in a short time. (d) Using the MATLAB program developed in this study, a Mamdani FIS can be constructed, and production of the degrees of landslide susceptibility can be easily obtained. A great number of landslide susceptibility assessment papers have been published in landslide literature. Several different approaches for the assessment of landslide susceptibility have been employed. Almost all of these approaches are data driven. Although the Mamdani FIS is an expert-based system, it had previously been applied to a limited number of landslide susceptibility assessment studies Juang et al., 1992; Saboya et al., 2006) . However, it is a suitable system for landslide susceptibility assessment because it has a considerable capacity to model complex and nonlinear systems. The main reason for its lack of use has been the absence of a suitable computer program in which to implement it. It is possible to construct a Mamdani FIS using MATLAB. However, it is not possible to run a model including a very large geographic dataset using existing MATLAB software. To eliminate this problem, the MATLAB program MamLand was developed in this study. This program will assist landslide experts in assessing regional landslide susceptibility by directly employing expert opinion. MamLand is designed to be easy to use.. Sinop (Northern Turkey), which is a landslide-prone area, was considered as a case study area. The landslides identified in the study area are generally of an active deep-seated rotational type, and they usually occur on gentle slopes. Shoreline landslides are also observed in the area. Considering the main characteristics of the landslides and the field observations, a total of 7 inputs were selected for the model: altitude, lithology, slope gradient, curvature, NDVI, SPI, and TWI. Except for lithology, all of the inputs are A. Akgun et al. / Computers & Geosciences 38 (2012) 23-34 formed by two membership functions, while lithology includes three input membership functions. To minimize the uncertainty, a 50% overlap is applied between the membership functions. The output, the landslide susceptibility degree, is formed by five membership functions (Fig. 9) . The FIS constructed for the study area has a total of 192 if-then rules. The if-then rules are described only using expert opinion. The AUC value of the ROC curve for the Mamdani FIS model constructed in the present study was found to be 0.855. Finally, we conclude that this methodology will present new opportunities for landslide researchers, as they can employ their knowledge in this model via membership functions and if-then rules.

O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


DISTRIBUTION OF MULTI-WORDS IN CHINESE AND ENGLISH DOCUMENTS As a hybrid of N-gram in natural language processing and collocation in statistical linguistics, multi-word is becoming a hot topic in area of text mining and information retrieval. In this paper, a study concerning distribution of multi-words is carried out to explore a theoretical basis for probabilistic term-weighting scheme. Specifically, the Poisson distribution, zero-inflated binomial distribution, and G-distribution are comparatively studied on a task of predicting probabilities of multi-words' occurrences using these distributions, for both technical multi-words and nontechnical multi-words. In addition, a rule-based multi-word extraction algorithm is proposed to extract multiwords from texts based on words' occurring patterns and syntactical structures. Our experimental results demonstrate that G-distribution has the best capability to predict probabilities of frequency of multi-words' occurrence and the Poisson distribution is comparable to zero-inflated binomial distribution in estimation of multi-word distribution. The outcome of this study validates that burstiness is a universal phenomenon in linguistic count data, which is applicable not only for individual content words but also for multi-words. In text mining and information retrieval, study on indexing terms, such as individual words, N-gram, and multi-word, has become an unavoidable even indispensable issue confronted with researchers. Of all themes concerning this topic, distribution of terms, which is a fundamental problem and focusing on word frequency prediction, has attracted great interest in textual information processing. Thus, various term distribution models have been proposed to capture the regularities of word occurrences and discover underlying mechanisms of terms (words' behavior) in texts.. On the one hand, a good understanding of term occurrence mechanisms is useful for information retrieval when we want to assess the likelihood of a certain number of occurrences of a specific term in a collection of texts. In this aspect, Teevan and Karger have proposed an exponential probabilistic model based on computational analysis of corpora and queries. They reported that their model can better describe text probability and consequently, a significant improvement on text retrieval was produced.. 1 Madsen et al. used the Dirichlet distribution to model word burstiness in texts with goal to remove heuristics from naive Bayes for text classification.. 2 However, their model is at document level and too complex for practice, especially in parameter estimation with corpus of a small number of documents. The Poisson distribution is utilized for term-dependent smoothing to estimate query likelihood and further document scores in information retrieval. 3 Their experiment shows that in comparison with other multinomial models for smoothing, the Poisson model has better performance for producing query results, especially with two-stage smoothing.. On the other hand, term-weighting is a crucial procedure when documents are transformed into numerical vectors. However, most term-weighting methods, such as term frequency and document frequency, are based on empirical observation and linguistic intuition, rather than theoretical analysis of term distribution and properties in documents. For this reason, term distribution is studied to shed light on distinguishing significant (content, topical, semantically focused) terms from insignificant (function, noncontent, semantically unfocused) terms in texts for practical applications such as Refs. 4, 5, and 6, according to explicit statistical characteristics of terms. For instance, in term-weighting using residual inverse document frequency 7 shown in Eq. (1), it is assumed that term occurrence in documents follows the Poisson distribution and if parameter λ i can be accurately estimated, then weighting of the indexing term will be precisely computed out to reflect its importance in a text collection:. Generally, indexing terms for texts are individual words during the process from textual information to numerical vectors. However, for some kinds of texts, such as technical and professional papers, a group of words are often considered as a feature to describe a special concept in a specific field. Multi-word features are not found too frequently in a document collection, but when they do occur they are often highly predictive and informative in explaining discovered patterns from learning methods.. 8 While "multi-word" is the fundamental notion of this paper, this notion had no satisfactory formal definition until now. It can only be intuitively characterized: it occurs only in specialized types of discourse, often specific to subsets of domains; when it occurs in general types of discourse or in a variety of domains it often has broader or more comprehensive meaning, such as name entities, terminological noun phrases (NP), etc. In recent studies on term distribution, two kinds of disciplines are actually followed by research with this topic. The first one is to use large-scale data to study mechanisms of word occurrence in a corpus such as Refs. 9 and 10. The concentration of this branch is on collective properties of natural utterances and population distribution for all words. The second one is trying to match underlying model assumptions to text through manual analysis of a small number of terms.

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Pagerank based clustering of hypertext document collections Clustering hypertext document collection is an important task in Information Retrieval. Most clustering methods are based on document content and do not take into account the hyper-text links. Here we propose a novel PageRank based clustering (PRC) algorithm which uses the hypertext structure. The PRC algorithm produces graph partitioning with high modularity and coverage. The comparison of the PRC algorithm with two content based clustering algorithms shows that there is a good match between PRC clustering and content based clustering. Clustering hypertext document collection is an important task in Information Retrieval. Some examples of efficient clustering methods are K-means clustering, Information Bottleneck based clustering and Contextual Document Clustering (CDC) [3] . These clustering methods are based on the content of the documents and do not take into account the hyperlink structure of the document collection. In the present work we suggest clustering hypertext document collection using the graph formed by hyperlinks. We propose a novel graph clustering algorithm and compare the graph based clustering with the content based clustering. Our algorithm essentially uses PageRank popularity measure [10] and therefore we call our algorithm PageRank based Clustering (PRC algorithm).. We note that our method clusters directed graphs. The clustering of directed graphs is a young research area. To the best of our knowledge, there are only very few recent works [1, 7, 8] . We mention that the MCL method [4] can be used for clustering directed graphs as well as undirected ones. As is shown later in the paper, the proposed PRC algorithm surpasses the above algorithms in terms of efficiency and centroid detection.

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A log-Birnbaum-Saunders Regression Model with Asymmetric Errors The paper by Leiva et al. (2010) introduced a skewed version of the sinh-normal distribution, discussed some of its properties and characterized an extension of the Birnbaum-Saunders distribution associated with this distribution. In this paper, we introduce a skewed log-BirnbaumSaunders regression model based on the skewed sinh-normal distribution. Some influence methods, such as the local influence and generalized leverage are presented. Additionally, we derived the normal curvatures of local influence under some perturbation schemes. An empirical application to a real data set is presented in order to illustrate the usefulness of the proposed model. The two-parameter Birnbaum-Saunders (BS) distribution, also known as the fatigue life distribution, was introduced by Birnbaum and Saunders (1969a,b) . It was originally derived from a model for a physical fatigue process where dominant crack growth causes failure. A more general derivation was provided by Desmond (1985) based on a biological model and relaxing several of the assumptions made by Birnbaum and Saunders (1969a) . Desmond (1986) investigated the relationship between the BS distribution and the inverse Gaussian distribution. The author established that the BS distribution can be written as a mixture equally weighted from an inverse Gaussian distribution and its complementary reciprocal.. The random variable T is said to have a BS distribution with parameters α, η > 0, say BS(α, η), if its cumulative distribution function (cdf) is given by F (t) = Φ(v), t > 0, where Φ(·) is the standard normal distribution function, v = ρ(t/η)/α, ρ(z) = z 1/2 − z −1/2 and α and η are shape and scale parameters, respectively. Also, η is the median of the distribution: F (η) = Φ(0) = 1/2. For any constant k > 0, it follows that kT ∼ BS(α, kη). It is noteworthy that the reciprocal property holds for the BS distribution: T −1 ∼ BS(α, η −1 ); see Saunders (1974) . The BS distribution has received considerable attention over the last few years. Kundu et al. (2008) discussed the shape of the hazard function of the BS distribution. Results on improved statistical inference for the BS distribution are discussed in Wu and Wong (2004) and Lemonte et al. (2007 Lemonte et al. ( , 2008 . Some generalizations and extensions of the BS distribution are presented in Díaz-García and Leiva (2005) , Gómes et al. (2009) , Guiraud et al. (2009) and Castillo et al. (2009) . This distribution has been applied in reliability studies (see, for example, Balakrishnan et al., 2007) and outside this field; see Leiva et al. (2008) and Leiva et al. (2009) . Additionally, based on the BS distribution, Bhatti (2010) introduced the BS autoregressive conditional duration model. Xu and Tang (2010) presented estimators for the unknown parameters of the BS distribution using reference prior. From Rieck (1989) , if. then Y has a four-parameter sinh-normal (SHN) distribution, denoted by Y ∼ SHN(α, γ, σ, ν), where ν ∈ ℜ and α > 0 are the shape parameters, and γ ∈ ℜ and σ > 0 correspond to the location and scale parameters, respectively. According to Rieck (1989) , the parameter ν is also the noncentralty parameter. If ν = 0, the notation is reduced simply by Y ∼ SHN(α, γ, σ), and this distribution has a number of interesting properties. For example, it is symmetric around the mean E(Y ) = γ, it is unimodal for α ≤ 2 and bimodal for α > 2 and if. follows the BS distribution with shape parameter α and scale parameter η = exp(γ), i.e. T = exp(Y ) ∼ BS(α, η). For this reason, according to Leiva et al. (2010) , the SHN distribution is also called the log-Birnbaum-Saunders (log-BS) distribution. Additionally, according to these authors, the SHN and BS models corresponding to a logarithmic distribution and its associated distribution, respectively (Marshall and Olkin, 2007, Ch. 12) . Rieck and Nedelman (1991) introduced a log-BS regression model based on the SHN(α, γ, 2) distribution. Their regression model has been studied by several authors. Some important references are Tisionas (2001) , Galea et al. (2004) , Leiva et al. (2007) , Desmond et al. (2008) , Lemonte et al. (2010) , Xiao et al. (2010) and Cancho et al. (2010) , among others. Generalizations of the log-BS regression model introduced by Rieck and Nedelman (1991) are presented in Xi and Wei (2007, § 4) and Lemonte and Cordeiro (2009) . Leiva et al. (2010) introduced a skewed SHN distribution by replacing the standard normal distribution in equation (1) by the skew-normal (SN) distribution (Azzaline, 1985) , i.e. they consider the random variable. where λ ∈ ℜ is the shape parameter which determines the skewness. Now, the notation used is Y ∼ SSN (α, γ, σ, ν, λ) . From now on, we shall consider ν = 0 and σ = 2 and hence the notation is given by Y ∼ SSN(α, γ, 2, λ). The random variable T = exp(Y ) follows the extended Birnbaum-Saundres (EBS) distribution, with shape parameters α > 0 and λ ∈ ℜ, and scale parameter η = exp(γ). Now,. The density function of Y = log(T ) is given by (Leiva et al., 2010) . where φ(·) is the standard normal density function, and, as before, we write Y ∼ SSN(α, γ, 2, λ). The sth (s = 1, 2, . . .) moment of Y can be written as. Thus, the mean of Y is given by E(Y ) = γ + c(α, λ), with. Plots of the SSN(α, γ, 2, λ) distribution are illustrated in Figure 1 for selected parameter values. The chief goal of this paper is to introduce a skewed log-BS regression model based on the SSN(α, γ, 2, λ) distribution, recently proposed by Leiva et al. (2010) . The proposed regression model is convenient for modeling asymmetric data, and it is an alternative to the log-BS regression model introduced by Rieck and Nedelman (1991) when the data present skewness. The article is organized as follows. Section 2 introduces the class of skewed log-BS regression models. The score functions and observed information matrix are given. Section 3 deals with some basic calculations related with local influence. Derivations of the normal curvature under different perturbation schemes are presented in Section 4. Generalized leverage is derived in Section 5. Section 6 contains an application to a real data set of the proposed regression model. Finally, concluding remarks are offered in Section 7. In this paper we have introduced a log-Birnbaum-Saunders regression model with asymmetric errors, extending the usual log-BS regression model. The random errors of the regression model follow a skewed sinh-normal distribution, recently derived by Leiva et al. (2010) . The estimation of the model parameters is approached by the method of maximum likelihood and the observed information matrix is derived. We also consider diagnostic techniques that can be employed to identify influential observations. Appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes are obtained. The expressions derived are simple, compact and can be easily implemented into any mathematical or statistical/econometric programming environment with numerical linear algebra facilities, such as R (R Development Core Team, 2009) and Ox (Doornik, 2006) , among others, i.e. our formulas related with this class of regression model are manageable, and with the use of modern computer resources, may turn into adequate tools comprising the arsenal of applied statisticians. Finally, an application to a real data set is presented to illustrate the usefulness of the proposed model. As future research, it should be noticed that some generalizations of the proposed model could be done. For example, a skewed log-BS regression model that allows us consider censored samples could be introduced. Following Xi and Wei (2007) , one could introduce a skewed log-BS regression model in which the parameter α is considered different for each observation, i.e. to propose an heteroscedastic skewed log-BS regression model. Also, a skewed log-BS nonlinear regression model could be proposed, and so forth.

O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Average-Optimal String Matching The exact string matching problem is to find the occurrences of a pattern of length m from a text of length n symbols. We develop a novel and unorthodox filtering technique for this problem. Our method is based on transforming the problem into multiple matching of carefully chosen pattern subsequences. While this is seemingly more difficult than the original problem, we show that the idea leads to very simple algorithms that are optimal on average. We then show how our basic method can be used to solve multiple string matching as well as several approximate matching problems in average optimal time. The general method can be applied to many existing string matching algorithms. Our experimental results show that the algorithms perform very well in practice. We consider several classical string matching problems, given a text T [0 . . . n− 1] over some finite alphabet Σ, of size σ. The exact string matching problem is to find the occurrences of a given pattern P Numerous efficient algorithms solving the exact matching problem have been obtained. The first linear time algorithm (KMP) 1 was given in [30] , and the first sublinear average time algorithm (BM) in [7] . Many practical variants of the BM family have been suggested, see e.g. [24, 36] . An average optimal O(n log σ (m)/m) time algorithm is obtained e.g. in [13] (BDM). Recently bitparallelism has been shown to lead to the most efficient algorithms for relatively short patterns, in practice. The first algorithm in this class was Shift-Or [15, 5, 37] , which runs in O(n m/w ) time, where w is the number of bits in a computer word (typically 32 or 64). Shift-Or is extremely simple to implement, and can be easily adapted to more complex search problems; common properties for most of the bit-parallel algorithms. Currently, among the fastest algorithms in practice (for m ≤ w) are BNDM [33] and SBNDM [32, 35] . BNDM is bit-parallel version of BDM, and SBNDM is a simplified version of BNDM. Their common feature is combining bit-parallelism with skipping characters, in the manner of the BM family of algorithms [7] .. For multiple matching, the Aho-Corasick algorithm [1] (which can be seen as a generalization of KMP) achieves O(n) (or O(n log(σ)), depending on implementation details) time. The Commentz-Walter algorithm [10] is a generalization of the BM algorithm for multiple patterns. This is also sublinear on average, for small pattern sets. BDM algorithm can also be generalized for multiple patterns, resulting in an algorithm that is optimal in both worst and average cases [13] .. For the problem of matching under Hamming distance, the asymptotically most efficient algorithms are based on convolutions and Fast Fourier Transform, and one of them achieves O(n √ k log k) worst case time [4] . This is a major improvement over a trivial brute-force algorithm which solves the problem in O(nm) worst case time. Still, more practical algorithms for this problem are based on bit-parallelism or filtering. A classic example based on bit-parallelism is the Shift-Add algorithm [5] , which achieves O(n m log(k)/w ) worst case time, where w is the number of bits in a computer word (typically 32 or 64). This algorithm was recently refined to achieve O(n m/w ) worst case time, i.e., an optimal parallelization [21] . Besides the algorithms that have good worst case complexity, there is a number of filtering based algorithms [31] that achieve good average case time. These are usually developed for a different model, namely searching under edit distance (allowing also insertions and deletions of symbols), but they work for Hamming distance as well. In general these algorithms work well for large alphabets and small k/m. The filtering algorithms are based on simple techniques to quickly eliminate the text positions that cannot match with k differences, and the rest of the text is verified using some slower algorithm. The average complexity of this prob-lem is O(n(k + log σ (m))/m), which was shown by Chang and Marr [9] (their result for the edit distance can trivially be translated also to this problem), and a real algorithm with this average complexity was given in the same work. Another average-optimal algorithm was presented in [19] .. For more references, see e.g. [13, 14, 34] . We have presented an extremely simple filtering technique which has a surprising number of applications in string matching algorithms. The resulting new algorithms often have optimal running times on average, and have simple implementations, which helps them achieve very competitive speeds in practice. The simplicity comes from a novel forward matching technique (as opposed to backward matching as in most competing algorithms) and from the fact that the pattern shifts are constant. This also leads to simple unrolling trick that boosts the search in modern hardware. This trick cannot be applied so successfully to more complex backward matching algorithms.. We started with the classic exact string matching problem, and showed how our technique can be used to modify the well-known bit-parallel algorithm, Shift-Or, to achieve the optimal running time on average, for short patterns. Interestingly, the same idea can be used for other exact string matching algorithms, as we showed on the example of modifying the brute-force algorithm. Our best result for exact matching, in asymptotic terms, is based on building the Aho-Corasick automaton for a number of subsequences of the given pattern. The algorithm achieves the optimal O(n log σ (m)/m) average time, without any limitation on the pattern length. Generalizing this algorithm for multiple patterns is straightforward and again optimal average search time is achieved for this problem. We note now that another application along these lines can be modifying the Karp-Rabin algorithm to improve its average time from O(n) (for a "reasonable" pattern length; see [29] and [11, Sec. 34.2] for details) to the optimal O(n log σ (m)/m). The key idea of the original algorithm is to calculate a signature (hash) from all pattern symbols, and compare it to a respective signature for a text substring. Matches have to be verified (usually with a brute-force algorithm), but mismatches with equal signatures are very rare. An important property of the algorithm is that the hash function can be calculated incrementally, paying O(1) time per a text character. Our technique of skipping characters in regular intervals can also be applied to the KR algorithm. The only problem is that matching a signature built over m/q text symbols against all q signatures for subsequences of the pattern cannot be done, in brute-force manner, as it would not lead to any improvement in the average time complexity. Instead, we can use a hash table with signatures as keys, and restrict the signatures to, e.g., min(m, log 2 (n)/2) bits, instead of the standard whole machine word (which has at least log n bits). In this way, the hash table gets quite cheap both in space and initialization time, and the average lookup time remains O(1).. We also consider several approximate matching problems. One is matching under Hamming distance, where we present two algorithms, the first based on the Shift-Add algorithm, the other on brute-force; the former of them achieves the optimal average case complexity for short patterns. Other models are pattern matching with swaps, and pattern matching with all circular shifts, where again our technique easily allows to achieve optimal average case complexity, for short patterns in case of the former problem, and for any patterns in case of the latter one. Finally, the (δ, γ)-matching problem, motivated by music information retrieval, is discussed. The obtained average case time is again optimal for short patterns. Our technique could be used with the classic pattern partitioning technique [31] as well, to solve string matching under Levenshtein distance (allowing k insertions, deletions or substitution of characters) in O(nk log σ (m)/m) average time, but this is optimal only for log σ (m) = O(1).. Some of the proposed algorithms have been shown in thorough experiments, with various real-world and artificial texts, to be very competitive in practice. In modern pipelined CPUs branching is costly. In Alg. 1 there are two conditionals in the search code; to detect the matches, and to check the end of the input. A simple way to avoid these to some degree is to unroll the line 5, i.e. repeat the code. inline several, say U , times (with increasing offsets for the variable i). This means that the bit m − 1 of D, indicating an occurrence, will be overflowed due to the repeated shifts, and hence in line 6 we must detect if any of the bits m − 1 . . . m + U − 1 is zero. This means that we need U − 1 extra bits, and the pattern length is therefore limited to m ≤ w − U + 1.. The second optimization involves detecting the matches. Line 6 in Alg. 1 involves a variable mm. This can be avoided if the bit-vectors are aligned so that the highest bit is in position w − U + 1, instead of in position m + U − 1. This means that the matches can be detected with ∼D >> (w − U ) = 0, which is efficient if U is constant.. These two simple optimizations (shown in Alg. 4) give about 2 − 5× speed-up for standard Shift-Or (Alg. 1), depending on the architecture. The line 5 in Alg. 4 is automatically inlined by compilers, for small constant U . Although the speed-up is considerable, note that this can depend on the architecture.. Unrolling speeds-up also the Optimal Shift-Or, but the second optimization cannot be applied in this case, since the bit positions indicating the matches are not consecutive. The unrolling technique uses U − 1 extra bits per pattern, Alg. 5 Fast-Average-Optimal-Shift-Or(T, n, P, m, q, U ).. with the optimal q. Note that we must conceptually add U − 1 wild cards to the end of each piece P i , so that the bits denoting the matches are preserved; i.e., the symbols P. should match every symbol in the alphabet. This is implemented by zeroing the corresponding U − 1 bits in the B table. Alg. 5 gives the code.. Finally, observe that while unrolling is well suited to Shift-Or, the benefits are negligible e.g. for BNDM algorithm, since the more complex control logic cannot be avoided.

O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Navigation in degree of interest trees We present an experiment that compares how people perform search tasks in a degree-of-interest browser and in a Windows-Explorer-like browser. Our results show that, whereas users do attend to more information in the DOI browser, they do not complete the task faster than in an Explorer-like browser. However, in both types of browser, users are faster to complete high information scent search tasks than low information scent tasks. We present an ACT-R computational model of the search task in the DOI browser. The model describes how a visual search strategy may combine with semantic aspects of processing, as captured by information scent. We also describe a way of automatically estimating information scent in an ontological hierarchy by querying a large corpus (in our case, Google's corpus). Visualizing large hierarchical information structures is a pervasive task in the real world. Whether the structures are organization charts, web pages, chronological information, or even file systems, we need to see as much as possible on a limited size screen. With that aspiration in mind, one successful and inventive technique was focus + context [2] . Focus + context is a name that covers several visualization methods (e.g., fisheye views [2] , hyperbolic trees [6] , degreePermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. of-interest trees [3] ) aiming to increase the amount of information displayed to a user. These visualizations generally present in greater detail the information around the user's (assumed) point of focus and offer a more schematic view of the other parts of the information structure. However, in spite of their intuitive efficacy, it is not clear that focus + context techniques present a real advantage to the everyday user. Performance in focus + context visualization appears to be largely a function of the relation between the semantics of the navigation cues and the user's tasks. (This relation is called information scent and is described in greater detail below.) For instance, [12] examines users' performance on search tasks performed with a hyperbolic tree browser and a more common, Windows Explorer-based browser. They conclude that, although in some cases (e.g., expert users or low information scent tasks) the hyperbolic tree may produce faster results, the interaction between the semantic qualities of the labels and the visual layout is quite complex and a clear advantage cannot be predicted for the hyperbolic tree.. The purpose of this paper is to understand how the combination between visual and semantic information in the focus + context browser affects users' performance in a search task. To achieve this goal, we first collected experimental data about how users perform simple search tasks. We looked at two kinds of search tasks: high-scent tasks, in which the target item was on a highly predictable, common-sense path, and low-scent tasks, in which the target item was hidden under less plausible labels. We collected the times that people needed to complete the search tasks for two different browsers, as well as finer grain eye-movement data such as number of nodes looked at, number of revisitations per node, or direction of eye gaze. Then, based on these data, we built a detailed cognitive model of navigation, that took into account visual search phenomena, as well as semantic aspects of the information processing.. One of the problems in evaluating interfaces or in devising computational models of browsing is an automatic measure of information scent: how do we know which nodes look highly relevant to people? This paper also describes a way of automatically determining scent in an ontological hierarchy. Our method involves extracting category relationships from a large corpus (in our case the corpus that Google makes accessible through their API ). We have presented an experiment and a computational model of how people navigate through large hierarchical information structures such as DOI trees. The experiment studied how people complete search tasks in a DOI tree browser and a Windows-Explorer like browser. The results suggested that, in terms of task completion speed, the two browsers are not different. What really made a difference in the task completion speed was the semantic aspect captured by the information scent: how well chosen were the labels for the nodes. People tend to visit more new nodes in the DOI tree than in the Explorer browser, whereas in the Explorer browser they tend to backtrack and revisit the same nodes more. By displaying more information, the DOI browser seems to encourage users to wander more off the solution path and to gather more knowledge than necessary for task completion. Thus, the DOI browser may prove beneficial over long term usage because it allows users to gather more information about the hierarchy structure. The DOI browser also allows users to recover more easily when they made certain types of errors; however, it also can distract the participants with highly salient but irrelevant items that are placed close to the users' focus of attention.. We also sketched an ACT-R computational model of navigation in DOI trees. The model combined a detailed visual strategy in which a region of the screen was located as relevant with a semantic process that made the selection of which node to click next. The semantic part of the model was based on an automatic estimation of the information scent of the nodes in the tree. In fact, one of the lessons from this model is that, to capture the human performance even in the roughest detail (e.g., which tasks get completed in a useful time) we need a good measure of information scent. We presented a measure that captures the hypernymy relationship: to what degree the search target A is member of the category denoted by the node label B? That measure was computed by querying a large corpus (in our case Google's database) with queries describing category membership.. Our goal was to understand the interplay between visual search and semantic information. We noted earlier that information scent plays the most important part in search. However, even good information scent will not be able to salvage a poor search strategy. Simple facts (such as the fact that people know that if they clicked a node, they should attend to new parts of the screen first) are not trivial to capture in a visual attention model when the visual environment is so rich in items.. Stepping back into the world of focus + context visualizations, our results with the DOI tree browser corroborated those in [12] : cramming more information on the screen does not necessarily improve performance. Putting the right labels on the information that you display is a better way to speed up the task.. of the six participants who completed that task clicked on that node.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Theoretical clarity is not 'Manicheanism': A reply to Marcia Bates It is argued that in order to establish a new theoretical approach to information science it is necessary to express disagreement with some established views. The "social turn" in information science is not just exemplified in relation to the works of Marcia Bates but in relation to many different researchers in the field. Therefore it should not be taken personally, and the debate should focus on the substance. Marcia Bates has contributed considerably to information science. In spite of this some of her theoretical points of departure may be challenged. It is important to seek theoretical clarity and this may involve a degree of schematic confrontation that should not be confused with theoretical onesidedness, "Manicheanism" or lack of respect. In a number of papers I argue that a "paradigm shift" is needed in information science and that it may be developing. In [1] I described three major theoretical turns in psychology (behaviorism, cognitivism and the discursive view/the social turn). I argued that these theoretical turns are also important in other fields such as linguistics and information science and that we as information scientists may learn about our own field by considering the arguments which have been put forward in the debate about these positions. Such philosophical clarification is also relevant in relation to the study of major concepts such as browsing, information retrieval and indexing , relevance, etc. such as I have described in former papers [1, 2, 3] and which I am also preparing new articles on. I consider it very important for information science to develop a coherent theoretical view of our domain and have the implications worked out in every corner of our discipline.. In her paper professor Bates [4] continues the controversy about the concept of information [5] [6] [7] [8] [9] [10] and also addresses my paper on browsing [1] . Although I think it is best to address one issue at a time, I'll reply to both issues. As the point of departure I find that debates are important in science and scholarship: criticism is a vital element in the growth of science (cf. [11] ). We have too few debates in information science. Philosopher of science Karl Popper found that scientific theories compete with each other and that the best scientific theories are those that can withstand the most criticism. As a consequence a theory should be formulated in a strict way that allows it to be falsified. Unclear theories are bad science because there are no clear criteria by which they can be tested. Although I share Thomas Kuhn's (1922 Kuhn's ( -1996 criticism of Popper, I still believe that we should try to formulate our scientific understandings in ways which makes it clear which theories they are in opposition to. If the collective clarification of scientific theories should be possible, it is necessary that a scientific culture exist in which discussions are encouraged and in which scientific arguments are not taken personally. As I wrote [12, p. 1303] : "I find that a schematic approach is necessary to compare different positions. Such a schematic confrontation should not be confused with theoretical one-sidedness, but should be understood as an attempt to achieve clarity.". A key argument made in Bates' new article is: " [Hjørland] has set up a Manichean opposition between key ideas in my work and his. Apparently, from his standpoint, if his ideas have value then mine cannot; the two perspectives stand in inalterable opposition to each other. In his view, his ideas represent the light, and mine are misguided, lost in the darkness." [4, p. 1] . It is correct that I try to formulate theoretical positions in a clear way that allows us to distinguish between them and to choose the most fruitful position. My analysis is not personal, however. Instead, it is focused on core theoretical issues. It is correct that I hereby tend to neglect valuable substantive elements in Bates' (and other's) work in order to place analytic attention on their theoretical underpinnings and assumptions. Bates has made many important contributions to information science. For example her presentation of metatheories in information science [13] is still the best published overview, her metaphor "berrypicking" [14] has been a great inspiration for many information professionals and in numerous ways her research has challenged established models of information retrieval. Her research into the online searching of humanists [15] remains a landmark domain analytic case study. In the field of Browsing Dr. Bates is the most cited author (cf. [1, p. 595]). As already said it is still necessary to try to identify which theoretical views are in conflict with each other and which are complementary. I found her paper [16] useful in order to confront "behavioral views" with "discursive views". In fact, it is an acknowledgement of the importance and popularity of Dr. Bates' ideas that I place them in the spotlight for serious critical engagement and scrutiny. I read her new paper [4] as recognizing that I did contribute a new valuable perspective to the understanding of browsing based on the discursive view. What is here disputed by Bates is that the new discursive view is in contrast with the view formulated in [16] based on "behavioural science research". This issue: whether behaviorism, cognitivism and the discursive view are in conflict or should be seen as supplementary approaches is itself a controversial issue. Bates is arguing that they are supplementary while I am arguing that they are in conflict. Information science needs theoretical work very much. We should encourage discussions and not portray proponents of other views in negative ways. We should avoid ad hominem arguments. I believe that many of the arguments that Bates has raised against me and my way of arguing can be turned against herself. I shall abstain from providing further examples of my own and just end with an ironic quote that might indicate that this may be the case:. "Manichean means "Pertaining to a strongly dualistic worldview," and in this article Bates complains that Birger Hjørland misinterprets her work by saying he is right and she is wrong. After a long and desultory explanation, Bates then concludes that she is right and Hjørland is wrong" [25] .

O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Generating Reliable Code from Hybrid-Systems Models Abstract-Hybrid systems have emerged as an appropriate formalism to model embedded systems as they capture the theme of continuous dynamics with discrete control. Under this paradigm, distributed embedded systems can be modeled as a network of communicating hybrid automata. Several techniques for code generation from these models have also been proposed and commercially implemented. Providing formal guarantees of the generated code with respect to the model, however, has turned out to be a hard problem. While the model is set in continuous time with concurrent execution and instantaneous switching, the code running on an inherently discrete platform, can be affected by the sampling interval, round-off errors, and communication delays between the sensor, controller, and actuators. Consequently, semantic differences between the model and its code can arise with potentially different system behavior. This paper proposes a criterion for faithful implementation of the hybrid-systems model with a focus on its switching semantics. We discuss different techniques to ensure a faithful implementation of the model, and test the feasibility of our concepts by implementing a model heater system. In this heater case study, we successfully eliminate all fault transitions and, thereby, generate code with correct behavior complying with the specification. M ODERN real-time embedded systems are complex, distributed, feature-rich applications. For example, a car incorporates 30 to 60 microcontroller units [1] , [2] and desired functionality includes automatic parking, automatic car coordination, and automatic collision avoidance. The development of such functionality is time consuming and difficult, since faults in the temporal or value domain may lead to system failures, which in turn can lead to catastrophes with possibly human losses. Model-based development of real-time embedded systems promises to simplify and accelerate the implementation process. This is because of its promises such as formal guarantees and code generation. Several mathematical models such as Timed Automata [3] , Hybrid Systems [4] , and State charts [5] have been successfully applied to such systems. Hybrid-systems-based design is a promising yet challenging approach for producing reliable embedded software. Providing formal guarantees is difficult due to the semantic differences between the model and the code arising as a result of discretization and communication delays. This paper has presented an approach to guarantee faithful switching semantics that involves preventing missed and faulty transitions. In contrast to related and prior work, this work has defined a notion of relative faithful implementation for systems. A runtime instrumentation technique to prevent faulty transitions has been proposed and a sufficient condition to prevent missed transitions in the generated code has been identified. These concepts have been illustrated via an implementation of a model heater system. In the future, the focus will be to fully integrating the infrastructure into the CHARON development environment and provide comprehensive techniques to detect missed transitions in the code at runtime.. In conclusion, we discuss some aspects of the techniques introduced here, and their applicability to large-scale realworld systems. First off, is the issue of scalability. The work here mainly focuses on preventing faulty transitions and detecting missed transitions. The main requirement for instrumentation is the bounds on the errors, which depends on the system at hand and might be tricky. However, Instrumentation itself is a fast operation. Checking missed transitions depends on computing the intersection of sets, which may be hard for complex sets. This could potentially be the only performance bottleneck of this framework. However, it must be noted that there exist techniques for efficient computation of the intersection (see [35] ) and the procedure is performed offline, and therefore, it is only a one-time cost.. The abstractions for platform and code consider only the basic of all the actual implementation effects and make several assumptions. These effects can potentially weaken some of the results presented in this work (e.g., instrumentation bounds). However, we do expect that similar techniques would be sufficient to handle these aspects of real-world implementations. The aim of this work is to establish a sound theory at categorizing some of the implementation effects. More artifacts of the implementation can be incorporated into the model as deemed necessary.. Instrumentation can cause tardier control as shown in the case study. We consider this a positive result as it provides motivation to study optimization and trade-offs between incorrect implementation (missed or faulty transitions) and faster controller response. It is not part of this framework to characterize this trade-off, since our motivation was to produce a sound implementation of a given model.. Finally, the faithful implementation criteria introduced in this work may not be enough to enforce a strict correlation between the model and its implementation, especially when one considers the introduced tardiness. There have been a number of metrics introduced in literature (e.g., [36] , [38] ) that relate to approximating continuous systems. Currently, many of these metrics have yet to be fully extended to hybrid systems. We expect that metrics developed for hybrid systems could be incorporated into our framework. . For more information on this or any other computing topic, please visit our Digital Library at www.computer.org/publications/dlib. Model-based automatic code generation has been an extensive research initiative in recent years and already successfully applied in industry [13] . Commercial modeling tools such as RationalRose [14] , TargetLink [15] , and SIMULINK [16] also support code generation and address the effect of errors in the code. However, their concerns are largely limited to numerical errors occurring each step during simulation, and the effect of such errors on discrete behavior is not addressed rigorously. Synchronous languages for reactive systems, such as STATECHARTS [5] , ESTEREL [17] , and LUSTRE [18] , [19] also support code generation. However, they do not explicitly support continuous time modeling. SHIFT [20] is a language for hybrid automata that also supports code generation, but it concentrates on dynamic networks.. Model-based development of embedded systems is also promoted by other projects with orthogonal concerns: Ptolemy supports integration of heterogeneous models of computation [21] and GME supports metamodeling for development of domain-specific modeling languages [22] .. Girard et al. [23] also consider hybrid-systems modeling of embedded applications; however, their focus is on verification of safety properties and not code generation. There also exist other efforts toward model-driven development of embedded software from models other than hybrid systems [24] . In a closely related work, Stauner [25] discusses at length, the discrete refinement of hybrid automata, considering implementation effects such as sampling errors and its impact on verification.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The role of moderating factors in user technology acceptance Along with increasing investments in new technologies, user technology acceptance becomes a frequently studied topic in the information systems discipline. The last two decades have seen user acceptance models being proposed, tested, refined, extended and unified. These models have contributed to our understanding of user technology acceptance factors and their relationships. Yet they have also presented two limitations: the relatively low explanatory power and inconsistent influences of the factors across studies. Several researchers have recently started to examine the potential moderating effects that may overcome these limitations. However, studies in this direction are far from being conclusive. This study attempts to provide a systematic analysis of the explanatory and situational limitations of existing technology acceptance studies. Ten moderating factors are identified and categorized into three groups: organizational factors, technological factors and individual factors. An integrative model is subsequently established, followed by corresponding propositions pertaining to the moderating factors. r Driven by market competitiveness, business enhancement, service improvement and work efficiency, organizations have invested heavily in information technology with the likelihood of continuing this investment pattern into the foreseeable future (Chau and Hu, 2002) . Some estimates show that since the 1980s, 50% of all new capital investment in organizations has been in information technology (Venkatesh et al., 2003) . Understanding the factors that influence user technology acceptance and adoption in different contexts continues to be a focal interest in information systems (IS) research.. Several models and theories have been developed to explain user technology acceptance behavior. However, these models have some limitations. The first limitation concerns the explanatory power of the models. Most of the existing studies account for less than 60% of variance explained, especially those using field studies with professional users. Although there may be many other factors that are beyond researchers' reach, the differences in explanatory power between laboratory studies and field studies, and between studies using students and using professionals, imply some complex contextual factors in the real world that should be taken into account (e.g., the influence of organizational factors such as the voluntariness of IT usage). The second limitation of these models is the inconsistent relationships among constructs, making researchers question the generalizability of these models across differing contexts (e.g., Lee et al., 2003; Legris et al., 2003) . These limitations call for improvement and refinement of existing studies.. Moderating factors may account for both the limited explanatory power and the inconsistencies between studies. In an early study, Adams et al. (1992) called for more examination of moderating factors. Several recent studies continue to call for the inclusion of some moderating factors (e.g., Lucas and Spitler, 1999; Venkatesh et al., 2003) . Agarwal and Prasad (1998) explicitly criticized the absence of moderating influences in technology acceptance model (TAM), and called for more research to investigate moderating effects. Venkatesh et al. (2003) tested eight models and found that the predictive validity of six of the eight models significantly increased after the inclusion of moderating variables. Furthermore, they argued, ''it is clear that the extensions (moderators) to the various models identified in previous research mostly enhance the predictive validity of the various models beyond the original specifications'' (Venkatesh et al., 2003, p. 21 ). In addition, Chin et al. (2003) empirically examined and confirmed the significant influence of moderating factors in existing models of user technology acceptance.. While stating that ''the extensive prior empirical work has suggested a large number of moderators'', Venkatesh et al. (2003, p. 21) included only four in their study: experience, voluntariness, gender and age. Based on a careful literature review, we believe that there are more moderating factors with empirical evidence than the four studied. For example, the nature of the tasks may affect users' acceptance of technology, as does the nature of the technology. Few of these moderators were examined either conceptually or empirically in recent efforts. A systematic examination of significant moderating factors should contribute to our better understanding of the dynamics of the user technology acceptance phenomenon.. This study examines the moderating effects in user technology acceptance. It adds to the few studies that take into account the individual and contextual factors in technology acceptance (i.e., Igbaria et al., 1997) . The objectives of this paper are three-fold. It first provides an overview of prior literature to disclose the limitations of explanatory powers and the inconsistencies between prior studies. Then the paper highlights the moderating factors that account for both the limitations of the explanatory power and the inconsistencies. Ten moderating factors that have strong empirical evidence are identified and categorized into three groups: organizational factors, technological factors and individual factors. And, finally, the paper proposes a new model with propositions pertaining to the effects of the moderating factors. Readers interested in other aspects of user technology acceptance research summaries, such as research emphases and evolutions, empirical sample sizes and characteristics, most influential authors, and critical comments from several major researchers, are encouraged to read a recent meta analysis by Lee et al. (2003) , which lacks discussion of the effects of the moderating factors.. This study calls for more research attention to individual and contextual factors that are often neglected in technology acceptance studies but can be critical in applying theoretical models to specific situations in organizations. The study also provides a basis for further empirical investigation in this research area. Although they have received considerable empirical validation and confirmation, existing user acceptance models still have room for improvement. Their limited explanatory power and inconsistent relationships call for taking additional factors into account. Researchers have suggested models be tested in field settings with organizational and technological factors considered (Lucas and Spitler, 1999; e.g., Sun and Zhang, 2004) . This present study is an attempt to move in this direction. By including the moderators in user acceptance models, we hope to lessen the limitations of low explanatory power and inconsistencies existing in prior studies. It is noteworthy that the influence of including moderating factors on R 2 is statistically limited (Chin et al., 2003) . However, by taking moderating factors into account, we are more confident in explaining and describing the meanings of existing models. This paper draws several implications for both researchers. First, this study suggests that research on moderating factors is of great value. This is consistent with suggestions from existing studies that contexts could play an important role in user technology acceptance Taylor and Todd, 1995a; Szajna, 1996) . It is noteworthy that the major function of moderating factors is explaining the inconsistencies by identifying the situational differences. Its effect in enhancing R 2 is modest. This observation is consistent with prior empirical study (Chin et al., 2003) . Second, research should pay more attention to less studied issues. For instance, few studies have empirically examined cultural issues associated with user technology acceptance. The mechanisms through which the culture exerts its influence are still unclear (e.g., Straub et al., 1997) . Therefore, future research may focus on ''how'' questions by identifying the major cultural dimensions and their corresponding relationships with user technology acceptance.

O O O B I O B I I O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I I O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I


Declarative extensions of XML languages We present a set of XML language extensions that bring notions from functional programming to web authors, extending the power of declarative modelling for the web. Our previous work discussed expressions and user-defined events. In this paper, we discuss how one may extend XML by adding definitions and parameterization; complex data and data types; and reactivity, events and continuous "behaviours". We consider these extensions in the light of World Wide Web Consortium standards, and illustrate their utility by a variety of use cases. Many Web authors make use of W3C language standards [10] as powerful yet simple to use authoring tools. These standards promote a declarative approach to defining complex document manipulations, permitting the author to describe what is to happen rather than how the effect is to be achieved. Similarly, functional programming embodies a declarative approach to programming. Our work examines how features from functional programming may extend the declarative authoring model of XML-based languages, in particular addressing situations when authors, needing additional capabilities not provided in the XML language [3] , are forced to work outside the declarative dictum in an imperative scripting or programming language. In [6] we showed how expressions and user-defined events could be added to XML. In this paper we discuss a variety of further extensions, including a generalized type mechanism, expression evaluation, and a more powerful model of dynamic behaviours.

B O O B O O O O O O B O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O