Model-Based Clustering in Gene Expression Microarrays: An Application to Breast Cancer Data In microarray studies, the application of clustering techniques is often used to derive meaningful insights into the data. In the past, hierarchical methods have been the primary clustering tool employed to perform this task. However attention is now turning to model-based clustering approaches. The hierarchical algorithms have been mainly applied heuristically to these cluster analysis problems. Further, a major limitation of these methods is their inability to determine the number of clusters. Thus there is a need for a model-based approach to these clustering problems. To this end, McLachlan et al. (2002) developed a mixture model-based algorithm (EMMIX-GENE) for the clustering of tissue samples. To further investigate the EMMIX-GENE procedure as a model-based approach, we present a case study involving the application of EMMIX-GENE to the breast cancer data as studied recently in van't Veer et al. (2002). Our analysis considers the problem of clustering the tissue samples on the basis of the genes which is a nonstandard problem because the number of genes greatly exceed the number of tissue samples in a typical study. We demonstrate how EMMIX-GENE can be useful in reducing the initial set of genes down to a more computationally manageable size. The results from this analysis also emphasise the difficulty associated with the task of separating two tissue groups on the basis of a particular subset of genes. These results also shed light on why supervised methods have such a high misallocation error rate for the breast cancer data. The complexity and magnitude of DNA microarray data have inundated researchers with a flood of new bioinformatic challenges. The data generated by these experiments necessitate the use of specialised statistical tools in order to make reliable inferences about the data. In this paper we discuss the application of a model-based approach to the cluster analysis for gene expression microarrays.. typical cluster analysis is to organise genes or tissue samples (data produced by separate hybridisations) into groups or clusters displaying similar patterns of gene expression. In the past, mainly hierarchical methods have been applied to cluster analysis problems. However attention is now turning to model-based approaches; see Ghosh and Chinnaiyan (2002) , McLachlan et al. (2002) , Pan et al. (2002) , Yeung et al. (2001) .. The majority of clustering algorithms that have been employed in the literature are largely heuristically motivated. There exist a number of unresolved issues associated with the use of these algorithms, including how to determine the number of clusters.. As commented by Yeung et al. (2001) , "in the absence of a well-grounded statistical model, it seems difficult to define what is meant by a 'good' clustering algorithm or the 'right' number of clusters." They have advocated a model-based approach to clustering by adopting a finite mixture model for the distribution of each observation. In their study, they were concerned with the clustering of the genes on the basis of the tissue samples. Here we consider the problem of clustering the tissues on the basis of the genes, which is a more challenging problem to consider in a mixture model framework, since the number of observations to be clustered (the tissue samples) is typically small relative to the number of genes in each tissue sample.. A recent application of microarray technology involves its use in the development of patient-tailored therapies to target complex, highly heterogeneous diseases. The work of van't Veer et al. (2002) used microarray experiments on three patient groups who had different classes of breast cancer tumours. The goal of the overall experiment was to identify a set of genes that could distinguish between the different tumour groups based on their gene expression information for a given tumour sample. We use the data set produced by this group as the basis for our analysis using a mixture model-based clustering algorithm called EMMIX-GENE .. The results of this analysis shed light on why it is such a difficult problem to distinguish between the two tissue groups (disease-free and metastases) and consequently why supervised methods have such a high error rate for this data set, as noted by Tibshirani and Efron (2002) . When we identified the clusters produced by EMMIX-GENE with the externally existing classes Π 1 (diseasefree group), Π 2 (metastases group), and Π 3 (BRCA), the error rate of this rule is not small.. However, it is consistent with the gene expressions as displayed in the heat maps for the 40 groups of similar genes. For example, in the first three groups given in Figures 8 to 10 , it can be seen that tissues of class Π 2 have similar gene expression patterns to those of the majority of the tissues in class Π 1 (Π 3 ) that have similar gene expression patterns to those of the majority of the tissues in class Π 1 (Π 3 ) . Likewise, the misallocated tissues of the class Π 1 have similar gene expression patterns to those of the majority of the tissues in class Π 2.

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


Restricted inflectional form generation in management of morphological keyword variation Abstract Word form normalization through lemmatization or stemming is a standard procedure in information retrieval because morphological variation needs to be accounted for and several languages are morphologically non-trivial. Lemmatization is effective but often requires expensive resources. Stemming is also effective in most contexts, generally almost as good as lemmatization and typically much less expensive; besides it also has a query expansion effect. However, in both approaches the idea is to turn many inflectional word forms to a single lemma or stem both in the database index and in queries. This means extra effort in creating database indexes. In this paper we take an opposite approach: we leave the database index un-normalized and enrich the queries to cover for surface form variation of keywords. A potential penalty of the approach would be long queries and slow processing. However, we show that it only matters to cover a negligible number of possible surface forms even in morphologically complex languages to arrive at a performance that is almost as good as that delivered by stemming or lemmatization. Moreover, we show that, at least for typical test collections, it only matters to cover nouns and adjectives in queries. Furthermore, we show that our findings are particularly good for short queries that resemble normal searches of web users. Our approach is called FCG (for Frequent Case (form) Generation). It can be relatively easily implemented for Latin/Greek/Cyrillic alphabet languages by examining their (typically very skewed) nominal form statistics in a small text sample and by creating surface form generators for the 3-9 most frequent forms. We demonstrate the potential of our FCG approach for several languages of varying morphological complexity: Swedish, German, Russian, and Finnish in well-known test collections. Applications include in particular Web IR in languages poor in morphological resources. Various methods for handling the morphological variation of keywords in information retrieval (IR) have been used already for decades. Some of them are more complex than others, while some are amazingly simple but produce still quite good results in IR. So far it has been shown among other things that even a quite simple rule-based non-lexical stemmer can improve precision and recall of textual searches for languages that are morphologically more complex than English or some times even very complex-as, e.g., Finnish and Slovene (cf. Popovič and Willett 1992; Hollink et al. 2004; Airio 2006) . Use of stemming has been a de facto standard in information retrieval, but in language technology use of full coverage lemmatization has been thought a necessity for languages that are morphologically complex, even in monolingual single term IR (Koskenniemi 1996) . This belief has been shared also by some IR researchers (Galvez et al. 2005; Galvez and de Moya-Anegon 2006; Jacquemin and Tzoukerman 1999) .. At the same time as simple conflation methods have been used in IR, not much attention has been given to heuristic linguistically motivated aids that do not even aim to cover all the inflection of the keywords but are based, for example, on the statistically most frequent word forms of the language in question. In Kettunen and Airio (2006) we showed that case form frequency based keyword generation competes quite well against the gold standard, FINTWOL lemmatizer, in best-match IR for Finnish, a highly inflectional and compound rich language. A similar but converse approach, stemming based on the statistical distribution of Hungarian noun suffixes, is reported in Tordai and de Rijke (2005) . Two other types of approaches can be seen as more remotely related to our approach: Xu and Croft's (1998) idea of using corpus-based word variant statistics in stemmer creation or modification and the use of a probabilistic (and thus language independent) model for stemmer generation (Bacchin et al. 2004; Di Nunzio et al. 2004 ). Our method is called FCG (for Frequent Case (form) Generation).. In this paper we shall further examine our method in monolingual IR of morphologically complex languages by testing three more languages, German, Russian and Swedish, with the methods developed in Kettunen and Airio (2006) . For Finnish we shall also show some new results with very short queries.. On a general level, our background motivations can be stated as follows:. The average precision and recall (P/R) of retrieval needs to be kept as high as possible without using excessively complex language technology tools; we believe that the need of large lexicon-based lemmatizers in basic monolingual IR is not as high as often thought even for a morphologically complex language.. Our research questions are following:. (1) Is the FCG approach viable across languages of varying morphological complexity? (1a) In order of increasing complexity, what is the performance of FCG in Swedish, German, Russian and Finnish as observed in generally available test collections? (1b) How many morphological surface forms are needed to achieve reasonable performance?. (1c) How does this performance compare to doing nothing at all, stemming and lemmatization?. (2) What is the effect of topic length on the performance of FCG as compared to doing nothing at all, stemming or lemmatization?. The main research question of the paper is, whether our FCG method can be shown to work with other languages that have non-trivial morphology. As the idea of the method is based on the skewed distributions of word form frequencies, it is supposed to work regardless of language in question, but verification for more than one language (Finnish) is also needed.. The performance of our new methods is compared to the state of art, usage of a lemmatizer, which is more challenging than use of raw words that has become all too common in IR (e.g., Hollink et al 2004; Braschler and Ripplinger 2004; Mayfield and McNamee 2003; Tomlinson 2004a, b) . We have argued in Kettunen et al. (2005) that the performance gained with raw words is quite meager and variable for a morphologically rich language like Finnish, and thus the performance gains attributed to different morphological processing methods are not as great as they are thought to be. If comparisons are made, they should be made with respect to the state of the art or gold standard, not with respect to the worst possible result, as now is done many times in IR. With morphologically complex languages the best retrieval result is usually attained through a lemmatizer, such as TWOL for different languages (Koskenniemi 1996) . This line of argumentation is taken in the present study.. The structure of our paper is following. First we discuss distributions of word forms in the light of linguistic corpus statistics and introduce our word form frequency based method and IR results of Kettunen and Airio (2006) . After this our frequency based keyword generation method is introduced, tested and discussed using three European languages of increasing level of morphological complexity, Swedish, German, and Russian. Morphological normalization is needed in IR because morphological variation needs to be accounted for and several natural languages are morphologically non-trivial. Several languages have a fairly complex or very complex morphology in the sense, that each nominal base form may have 8-26 productive variant forms (and even a few thousand grammatical forms). There are two basic and popular approaches to morphological normalization: lemmatization and stemming. Lemmatization is effective but often requires expensive resources. Stemming is also effective in most contexts, generally almost as good as lemmatization and typically much less expensive; besides it also has a query expansion effect. However, in both approaches the idea is to turn many inflectional forms to a single lemma or stem both in the database index and in queries. This means additional database normalization for indexes. In this paper we took an opposite approach, called FCG (for Frequent Case (form) Generation): the database index is left un-normalized and the queries are enriched to cover for surface form variation of keywords. We have shown that word form generation of 3-9 most frequent cases or forms is sufficient. Therefore the potential penalty of the approach in processing time remains negligible. By only covering such a negligible number of surface forms even in morphologically complex languages one may arrive at a performance that is just as good as or better than what stemming or lemmatization provide. It is also noteworthy, that for morphologically less complex languages, as e.g. Romance languages mostly are, this means that all the varying noun forms can be generated for retrieval.. The method has been tested with four languages, Finnish, German, Russian and Swedish, in IR laboratory settings using newspaper article collections of various sizes and queries of varying length. The results for Finnish, German and Swedish are clearly positive. In Finnish the best FCG methods achieve about 86-90% of the best lemmatization results. In Swedish the best FCG methods achieve about 90-93% of the best lemmatization results. In German the best FCG methods achieve about 96-97% of the best lemmatization or stemming results. The best FCG method in these languages was never significantly worse than the best lemmatization or stemming method. Furthermore, in these three languages at least one FCG procedure was significantly better than doing nothing to keyword variation. Russian results are ambivalent, and they should be retested in a better collection, when such becomes available. On the basis of these findings and common knowledge about word form distributions in texts of natural languages, it is to be expected, that the method will work for other languages of equal morphological complexity as well. Nevertheless the findings of this paper should be tested further with more languages and in different query environments to make sounder conclusions about the proposed method's applicability in IR. Applications include in particular Web IR in languages poor in morphological resources (sometimes called ''low density languages''). Also the multilinguality of a web index (Rasmussen 2003) can be dealt with the approach.. are also grateful to the FIRE research group for helpful comments. FINTWOL (morphological description of Finnish). Copyright Ó Kimmo Koskenniemi and Lingsoft plc. 1983-1993 Our research questions for this study were formulated in the introduction as testing of the suitability of the FCG method with new languages against lemmatization and stemming with both long and short queries. We had earlier shown with Finnish that the use of only the most frequent noun and adjective forms worked well, when compared to the best available morphological method, usage of a lemmatizer. We now tested our method with three more languages, Swedish, German, and Russian, which all have a fair degree of morphological variation. For all of the languages we tested both normal laboratory type long queries and more realistic very short queries taken out of the title field of the topics. Our Swedish results showed quite clearly that the FCG method works for Swedish both in long and short queries. In short queries differences between all the methods are smallest, but also the margin between non-processing and the best method increases, which emphasizes the meaning of some sort of keyword processing. Lemmatization with compound splitting is the best method in both long and short queries.. Our German results showed that the method works for German too, although the overlap of inflectional noun forms slightly disturbs results. The margin between non-processing and the best method is smaller than in Swedish, which is obviously due to inflectional homography. However, the differences to the gold standards were statistically insignificant. Our Russian results are partly counterintuitive. With both long and very short queries recall rises steadily when more case forms are put in to the query. Anyhow, the mean precision of long queries does not get any better, when forms are added, but rather decreases. The best mean average precision with long queries is gained with process Ru-FCG_3 with the singular forms only. But the inflected queries, where query words are taken as such from the topics, are the third best method in terms of mean average precision with long queries. Overall it seems that short Russian queries show some advantage for FCGs, but as the collection is small and has very few relevant documents, the interpretation of the Russian results remains uncertain.. In very short Russian queries the difference between doing nothing (inflected queries) and use of different number of case forms is quite clear. Anyhow, the differences between different case form procedures in terms of mean average precision are very small, and only recall gets clearly better when more forms are used.. The main reason for using stemming, lemmatization or any kind of morphological processing with IR is improvement in precision and recall of searches. Although the gains of morphological processing are varying, they are real. The usual way to estimate the performance gains is relative percentage improvement of mean average precisions between different methods. For comparison purposes of methods a slightly different point of view could also be used: the difference between doing nothing for the query words and the best mean average precision shows the need of morphological processing for the language in question. The bigger the discrepancy between these figures, the bigger the need to do something for the keywords.. In Figs. 9-12 we show P-R curves of Finnish, Swedish, German and Russian short queries for the best normalization method, best FCG method and no processing at all. Short queries are shown here, because they represent more realistically real user searches. As can be seen from figures and Tables 2, 9, 12 and 15, the largest difference between nonprocessing and best normalization method is in Finnish (20.4%) and smallest in Swedish (4.1%). German and Russian have slightly greater differences than Swedish, 5.7% and 6.9%, respectively. Figures also show that the FCG method gives clear gains for Finnish and smaller gains for German, Swedish and Russian. However, for three languages FCG works well in comparison to lemmatization; for Finnish 88% of the performance of lemmatization is achieved and 95% for Swedish and German. The graphs also show that the FCG method pushes close to normalization even when the gap between normalization and non-processing is narrow. It may be foreseen that a major application area for the FCG-approach is Web searching. The present state of language specific search capabilities of general search engines, such as Google, Alltheweb or Altavista, does not seem satisfying from the user point of view. Very few search engines seem to offer e.g. stemming, and search term truncation has been omitted almost totally (Search Engine Showdown 2006) . The status of language specific search capabilities of general search engines seems thus bad. Bar-Ilan and Gutman (2005) , it would be also time to get the most popular search tools more sensitive to requirements of specific languages. Otherwise the huge information potential of the non-English web cannot be utilized effectively.. The method we have presented in this paper and Kettunen and Airio (2006) provides one effective solution to the problem of web searches in various languages. So far we have shown that it competes well with other morphological programs in languages that are of varying morphological complexity. The basic idea of the method is easily adaptable to other languages and testing of the effects of FCG style of search can be implemented relatively easily with the present state of language technology tools and search engines.. We have generated in each FCG case mechanically all inflectional forms of nouns, no matter what their semantic category (e.g. a person's name, a word denoting to a place etc.). Some mileage may be gained by partitioning the generation to different case sets by the semantic noun category because the case distributions vary by the noun category. The mileage is however gained only through the cost of identifying the noun categories and therefore we do not further consider this possibility in the present paper.. Why, then, use this kind of approach when full morphological analysis programs are available? There are several reasons for this.. First, the generation approach works with inflected indexes of search systems and no base form processing is needed for the index. This is mainly of practical value, but an important issue: as especially web indexes are very large, separate base form runs for them would take much time. As indexes also need constant updating, making of base form indexes does not sound a very good option. Searching the index with a few most frequent inflected forms of keywords should not take too much time, when the usual web search consists only of one to three keywords regardless of the language (Jansen et al. 2000, Jansen and Spink 2005) . Secondly, our approach, generation of only the most frequent word forms, is simple and could be easier to implement, if (usually) commercial morphological analyzers are not available for IR in a specific language. Earlier, in Kettunen et al. (2005) , we tried out inflectional stem generation based retrieval. Performance of the runs was good, but the queries constructed with this method (by harvesting the inflectional index with inflectional stems) were large, up to circa 2-54 * the original query length in query terms.. In Kettunen (2006) this problem was remedied by using regular expressions to enhance inflectional stems to make queries smaller. This method worked better with respect to query size, but P/R declined somewhat. Our FCG method, then, can be seen as an optimized compromise out of these earlier generative efforts.. Thirdly, while word based IR is quite effective, it requires handling of word form variation in some way. Although full-scale morphological programs perform well, as e.g. Galvez et al. (2005) and Galvez and de Moya-Anegón (2006) argue, they might be unnecessarily elaborate and resource consuming for this purpose.. Fourthly the generation approach is not as dependent on large lexicons as full-scale morphological analyzers are, because for many languages use of lexicon in generation is not necessary. The main advantage of not using large lexicons is that out-of-vocabulary words should not affect retrieval results, as they evidently do with lemmatizers.. For the languages tested so far with the FCG approach, realistically long web-style searches would mean longer searches than with one form. In Table 16 we present the mean number of word forms per lexeme that are maximally generated for our short queries for each language's best FCG procedure. From this the number of required search forms can be approximated realistically.. As we can see in the table, the figures for German and Swedish are not prohibitively high. In a typical one to three word web search, these figures would mean about 3-10 keyword forms for German and Swedish. For Russian searches the number of generations for Ru_FCG_8 would mean already about 5-16 keyword forms, which is getting slightly high. Ru-FCG_6 would generate 4-12 keyword forms. For Finnish the number of keyword forms, 9-36, might approach impractical, but good index packaging and retrieval algorithms might make even this possible. Also smaller number of generated keyword forms, six, (cf. Kettunen and Airio 2006) could be enough for Finnish. In Kettunen and Airio (2006) we also evaluated, how the increasing number of keyword variants affects query runtime. When the maximal 12 Finnish keyword variants were used in long queries, the increase in mean CPU time was only about 20% in comparison to minimum, three forms or plain unprocessed keywords. An explanation is that often the different inflectional forms of a keyword reside on the same index page, which means that additional disk accesses are not required so often. This shows that processing time of queries does not increase prohibitively at least in a laboratory retrieval environment of moderate size when number of keyword variants is increased 3-12 fold. The consequences of the FCG approach for a real multi-user search system's search times are matter of implementation and can not be evaluated here. Results of the tests for the three new tested languages are shown in this section. For all the languages both long and short queries were tested.

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


Energy efficiency with QoS control in dynamic optical networks with SDN enabled integrated control plane The paper presents energy efficient routing algorithms based on a novel integrated control plane platform. The centralized control plane structure enables the use of flexible heuristic algorithms for route selection in optical networks. Differentiated routing for various traffic types is used in our previous work. The work presented in this paper further optimizes the energy performance in the whole network by utilizing a multi-objective evolutionary algorithm for route selection. The trade-off between energy optimization and QoS for high priority traffic is examined and results show an overall improvement in energy performance whist maintaining satisfactory QoS. Energy savings are obtained on the low priority traffic whilst the Quality of Service for the high priority traffic is not degraded. Increasing energy consumption has become one of main concerns for the network development. Research in energy efficient networking has considered various network domains (e.g. core, metro and access networks) and network layers. Various effective solutions have been identified, e.g. node/link sleeping during off-peak hours [1] , low-power idle [2] and traffic engineering [3] . However, there are fewer works proposed to maintain a high level of Quality of Service (QoS) at the same time, which is one of main concerns of operators and service providers. The techniques used in node/link sleeping or traffic engineering are usually accompanied by a certain degree of QoS compromise. In this paper, in order to maintain a high QoS whilst building an energy efficient optical network, a flexible routing mechanism is proposed using a novel integrated control platform.. The proposed novel integrated control plane structure is to improve the traditional network operations by increasing the communication efficiency across multiple network domains. The network control mechanisms today are horizontal separations between core and access networks, and vertical separations between application, transport and physical layers. These separations can lead to high operational and energy costs, low network efficiency and high latency in path provisioning. The emergence of applications with high data rate and low delay demands, i.e. HDTV and VoIP, have posed new challenges for bringing down the energy costs whilst maintaining the QoS. However, it is not an easy task to provision an end-to-end QoS for such applications with the current separated control mechanisms across domains. A more unified, intelligent and cross domain controlling mechanism is needed to meet these challenges. Under the circumstances, Software Defined Networking (SDN) [4] concepts have been considered as alternatives. One of SDN features is to decouple the control plane and data plane, which gives a possibility to build an integrated control plane across domains. A programmable SDN control plane can provide flexibility in designing new functionalities and services, which is independent of the underlying physical infrastructure. Designing such an integrated control plane using SDN concepts is discussed in details in our previous work [5] . The proposed structure operates on the top of a translucent Generalized Multi-protocol Label Switching (GMPLS) controlled optical core network, together with Optical Network Units (ONU) Management and Control Interface (OMCI) [6] controlled Gigabit Passive Optical Network (GPON) access networks. The integrated control plane enables a sharing of network information across these network domains. To support an end-to-end (from access network to core network to access network) QoS control under the integrated control plane, the traffic from the access network is differentiated and the information is exchanged between the access and core networks by assistances of the control plane. Furthermore, differentiated routing can be applied with a more flexible routing structure from a path computation function in the integrated control plane. In the case considered in our previous work [5] , the tailored energy efficient rout-ing algorithm can be applied mainly to low priority traffic, while delay and blocking sensitive traffic is not influenced. We choose a centralized, overlay model [5] [7] to design the integrated control plane.. Within the context of centralized control, it is possible to accomplish more complex or computational heavy tasks without increasing the resource consumption. Traditionally, the most used dynamic routing algorithms, e.g.Shortest Path (SP) algorithm or Load Balancing (LB) algorithm, can only evaluate one parameter at a time. By simply applying energy efficient routing algorithms, e.g. Least Consumption (LC) algorithm [5] on the part of network traffic that is not delay or blocking sensitive, certain energy savings can be achieved without influencing other types of traffic. Such routing strategies are limited in that only one criterion (i.e. energy consumption, delay or blocking) is considered at a time. The idea presented in this work aims to improving routing techniques by utilizing the centralized control as well as a multi-objective routing mechanism. Instead of considering hop count as the only objective for the delay sensitive traffic, energy consumption is also considered as a secondary objective.. In this paper, a Multi-Objective Evolutionary Algorithm (MOEA) is used for improving the route selection in a dynamic network context. A nondominated sorting-based algorithm called Strength Pareto Evolutionary Algorithm 2 (SPEA2) [8] is used for assigning the fitness value to each candidate solution. The algorithm is tested in a dynamic simulation environment using OPNET [9] . Results from the multi-objective algorithm are compared against the results obtained from the single objective dynamic routing algorithms (SP, LB and LC algorithms [5] ). Note that a certain time is required to execute the heuristic algorithm because of the complexity of the algorithm, which is a dilemma when it supports time sensitive services in the network. The proposed algorithm's execution is based on the assumptions that a suitable computational environment exists and a certain delay tolerance is acceptable when setting up an optical core network path. The results provide input into improved schemes for real world dynamic scenarios.. An MOEA is to solve a problem involving multiple conflicting objectives using evolutionary mechanisms, such as mutation, crossover and selection. The problem search space is usually intractably large and highly complex. There is no single solution that is able to simultaneously optimize all the conflicting objectives. Therefore, the outcome of an MOEA is a set of trade-offs or compromises called Pareto optimal solutions. A solution is Pareto optimal when none of the objective values can be improved without degrading some of the other objective values. MOEAs work on a group of candidate solutions called a population. MOEAs typically start with a population generated by a random method or a predefined scheme. The population is then refined iteratively by employing two basic principles: selection and reproduction. The selection mechanism mimics the fierce competition for survival in the natural world. The fittest ones survive and have a chance to produce offspring. A reproduction mechanism, including mutation and crossover genetic operators, imitates the process of producing offspring, sharing genetic information between parents and changing genes accidentally. When MOEAs reach the stopping criterion, for example, a predefined maximum generation, MOEAs stop and a group of good solutions are obtained.. MOEA is a generic methodology and has to be customized to the particular problem. In our route selection problem, we have three types of traffic: VoIP, IPTV and web-surfing. VoIP and IPTV traffic have different pairs of conflicting objectives. The solutions require consideration of both objectives. Therefore, an MOEA based algorithm is applied to identify a group of good solutions. The proposed algorithm is invoked each time a connection request arrives at the ingress point. In this paper, a centralized integrated control plane architecture is proposed to enable a unified control across core and access domains in optical network. Such a centralized control plane not only enables a more efficient information exchange across different domains, but also gives a possibility of implementing more powerful algorithms. The work aims to deal with the conflict between energy savings and QoS maintenance. The traffic type information can be kept in the integrated control plane and used over the entire optical network domain. When employing single objective routing algorithms for different types of traffic, QoS can be maintained for high priority traffic and energy savings gained for low priority traffic by energy efficient routing algorithms. However, compared with such single objective dynamic routing algorithms, a multi-objective evolutionary algorithm is proposed to further optimize the energy performance across the whole network. Results show that through using the proposed MOEA algorithm, there is an opportunity to further improve the energy performance of high priority traffic without degrading QoS by taking energy saving as the secondary objective. Moreover, parameter adjustments play an essential role in the trade-off between energy savings and QoS. However, unsurprisingly, it is also observed that the performance of low priority traffic, with which energy minimization is the only objective, performance degrades when the high priority traffic puts more weights on energy minimization. This is due to more traffic connections competing for energy efficient routes across the network. Energy efficiency in optical networks has been widely considered. In [10] , the authors compare energy efficiency with network topologies and architectures. The authors in [11] use power-aware provisioning methods to achieve energy savings in optical networks including grooming operations. There are other similar works, but few of them take QoS requirements into consideration. In order to enable a differentiated service in the core network, similar to the concept in this work, the authors in [12] explored the possibilities of using MPLS Traffic Engineering (MPLS-TE) in the core network. However, the method has limited use in an optical network environment. Similar to the integrated control plane raised in this work, a unified control plane concept has been discussed by a number of researchers [13] , [14] and [15] , where end-to-end QoS is the driver. Similar concepts are developed with SDN technologies where [16] and [17] proposed an unified control plane to use with cloud services and optical burst switching. However, these works do not consider the impact on energy efficiency. The Internet Engineering Task Force (IETF) Internet draft proposed in 2013 [18] , has defined details for network control functions for transport SDN. The concept is similar to our proposal, with a more general and broader scope. However, there is no actual implementation discussed in the work. Evolutionary Algorithms (EA) have been widely used in solving the Routing and Wavelength Assignment problems in optical networks. In [19] , the author discusses using an EA approach to solve static RWA problems, and the author in [20] uses EA in forming both single and multi-objective problems. Neither of these has tried to apply the solutions in a dynamic environment, nor designs tailored to the needs of different applications.

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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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


Saving space by algebraization The Subset Sum and Knapsack problems are fundamental N P-complete problems and the pseudo-polynomial time dynamic programming algorithms for them appear in every algorithms textbook. The algorithms require pseudopolynomial time and space. Since we do not expect polynomial time algorithms for Subset Sum and Knapsack to exist, a very natural question is whether they can be solved in pseudo-polynomial time and polynomial space. In this paper we answer this question affirmatively, and give the first pseudo-polynomial time, polynomial space algorithms for these problems.Our approach is based on algebraic methods and turns out to be useful for several other problems as well. Then we show how the framework yields polynomial space exact algorithms for the classical Traveling Salesman, Weighted Set Cover and Weighted Steiner Tree problems as well. Our algorithms match the time bound of the best known pseudo-polynomial space algorithms for these problems. In the 1950's, Richard Bellman published his book "Bottleneck Problems and Dynamic Programming" [1] , initiating the systematic study of Dynamic Programming (DP). Nowadays, DP is considered to be one of the most prominent techniques for designing algorithms, and many algorithmic results obtained by DP are not known to be obtainable by any other technique. Conceptually, a DP algorithm can be seen as a combination of a table and an algorithm computing table entries, usually formalized as a recurrence. An inherent property of DP algorithms is that they require a relatively big amount of working memory. This is because often, many previously computed table entries are required for efficient computation of new entries. For an elementary introduction to DP, see for example [7] .. In this paper we identify sufficient conditions for being able to turn DP algorithms into algorithms with roughly the same running time and significantly lower space usage. In particular, we show that if a DP algorithm can be formalized as an expression using specific operators, then one can use algebraic transforms to evaluate the expression in a spaceefficient manner.. Two of the canonical applications of DP are the Subset Sum and Knapsack problems. In the Subset Sum problem one is given a set of integers S = {w1, . . . , wn} and an integer w. The question is whether there exist of subset S ⊆ S such that w i ∈S wi = w. In the Knapsack problem one is given an additional set of values v1, . . . , vn and an integer v. Here the objective is to find a subset S of items whose total weight is at most w and total value is at least v. The Subset Sum problem is the special case of Knapsack where w = v and all items have weight equal to value. The classical O(nw) time 1 for Subset Sum of Bellman [2] uses O(w) space. Hence, the algorithm uses pseudo-polynomial time and space. A natural and fundamental question is whether it is possible to have a pseudo-polynomial time algorithm for Subset Sum using only polynomial space. We answer this question affirmatively by showing that Bellman's DP algorithm can be encoded as an expression with a restricted set of operators. In particular, we obtain an algorithm with running timeÕ(n 3 t log t) time andÕ(n 2 ) space. For the more general Knapsack problem we give an algorithm running iñ O(n 4 log 2 (vw)vw) time andÕ(n 2 log(vw)) space. Our methodolody enables us to make many exponentialtime and space DP algorithms run in polynomial space in-stead. This is useful because algorithms using both exponential time and space tend to run out of space long before they run out of time. Notable results in the direction of polynomial space exponential-time algorithms for N Phard problems include Karp [19] . Our method unifies these resukls, and gives improved algorithms for the weighted variants of these problems. An important open problem is whether Traveling Salesman can be solved in O * (2 n ) time and polynomial space [21] . We make progress towards resolving this problem by giving a polynomial space algorithm for Traveling Salesman running in O * (2 n w) time. For the two other problems, Weighted Set Cover and Weighted Steiner Tree we make similar improvements. In particular our algorithms match the time bound of the best known pseudopolynomial space algorithms for these problems.. The paper is organized as follows: In Section 2 we introduce the necessary notation. Then we briefly explain our approach in Section 3. In 4 we give sufficient conditions to be able to save space for DP on tables indexed by integers. In 5 we give sufficient conditions to be able to save space for DP on tables indexed by subsets. Finally, in Section 6 we combine the results from the two previous sections. All the latter three sections will be concluded by a few applications. A typical dynamic programming algorithm computes some basic tables, and then combines them using some operations in order to get an output table. Often we are only interested in a single element of the output table. In this paper we will formalize a dynamic programming algorithm as circuit C over a ring (R[S]; ⊕, O2), where O2 is a "complicated" convolution operator. Since S is very big, we are not allowed to store a single table. Thus, we cannot explicitely store the constants -the inputs to our circuit. Instead we will require the constants of our circuits to be singletons, and represent them implicitely -as the only key whose value is non-zero, together with the value associated to that key.. The main idea is to apply a linear transform on the circuit, such that the "complicated" operator O2 is transformed into the much simpler . This allows us to use C over (R, +, ·) to compute an entry of the transformed output table fast. Now we can obtain an entry of the original output of C by applying the inverse transformation.

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


Using Articulated Models for Tracking Multiple C. elegans in Physical Contact Abstract We present a method for tracking and distinguishing multiple C. elegans in a video sequence, including when they are in physical contact with one another. The worms are modeled with an articulated model composed of rectangular blocks, arranged in a deformable configuration represented by a spring-like connection between adjacent parts. Dynamic programming is applied to reduce the computational complexity of the matching process. Our method makes it possible to identify two worms correctly before and after they touch each other, and to find the body poses for further feature extraction. All joint points in our model can be also considered to be the pseudo skeleton points of the worm body. It solves the problem that a previously presented morphological skeleton-based reversal detection algorithm fails when two worms touch each other. The algorithm has many applications in the study of physical interactions between C. elegans. The nematode Caenorhabditis elegans is widely used for studies of nervous system function and behavior. The C. elegans nervous system has a small number of neurons, all of which have been individually identified and characterized. Moreover, the ease of genetic manipulation in these animals makes it straightforward to isolate mutant strains with abnormalities in behavior and rapidly identify the mutant gene by molecular cloning. However, in order to rigorously study the relationship between genes and behavior in C. elegans, precise quantitative assays for behaviors such as locomotion, feeding and egg-laying are required. Because some of these behaviors occur over long time scales that are incompatible with real-time scoring by a human observer, automated systems consisting of a tracking microscope and image processing software have been developed and used to follow and analyze animals. Some of these systems [1] [2] [3] [4] perform tracking of individual worms at high magnification, while others have been designed to track multiple worms at lower magnification [5, 6] .. Both types of system currently in use have disadvantages for the collection of behavioral data. Single-worm systems can provide a considerable amount of information about each animal that is recorded, but since statisticallysignificant characterization of any worm type requires the analysis of multiple animals, collecting data one animal at a time is often frustratingly slow. On the other hand, multiple-worm recordings do not typically provide as much information as single worm recordings due to their lower magnification. In addition, in most existing multi-worm systems, any time two individuals touch, segmentation of separate animals is difficult and so their individual identities are lost by the system. When the animals separate, the system is unable to determine the correspondence between individuals before and after touching. This is the problem we solve in this paper. The inability to separately segment and track individual animals when they touch seriously limits the ability of a multi-worm system to characterize the behavior of an individual in a population over time. Moreover, some behaviors of significant interest to researchers, such as mating and social feeding [6, 7] , by their very nature involve physical interaction between animals. For any automated system to be useful in characterizing these behaviors, it is essential that the position and body posture of a worm can be followed during and after physical contact with another animal.. Here we describe a new method for tracking multiple C. elegans that makes it possible to accurately resolve the individual body postures of two worms in physical contact with one another by using a modeling algorithm. Model matching algorithms can be roughly divided into two categories [8, 9] . The first class is "contour based" which represents objects in terms of their boundaries. One popular approach is called active contours or "snakes", which deform elastically to fit the contour of the target structure [10] . Another method uses combinations of trigonometric functions with variable coefficients to represent objects [11, 12] .. The second class consists of "appearance-based" approaches. In this case a model is used to simulate the complete appearance (shape, color, texture, etc.) of the target object in the image. Bajcsy and Kovacic [13] describe a volume model that deforms elastically to cover the object region. In [14] , a model composed of a collection of parts is used to represent objects in terms of a constellation of local features. All parts in this model are constrained with respect to a central coordinate system. There have been other part-based modeling algorithms. Fischler and Elschlager [15] introduce an articulated model with all parts arranged in a deformable configuration which is represented by spring-like connections between pairs of parts. This articulated model has recently been used for tracking an individual person in videos [16, 17] . In [18] , this method is further improved with an efficient algorithm for finding the best match of a person and a car in images. A number of methods to track a 3D human figure using articulated models also have been proposed. [19] uses a 3D articulated model combined with a modified particle filter to recover full articulated human body motion. [20] tracks a 3D human body with 2D contours using information from multiple cameras with different viewpoints. [21] presents an algorithm which projects 3D motion of a figure onto an image plane instead of using multiple cameras.. Multiple object tracking has been an intensive area of research due to its many applications [22] [23] [24] . However, traditional methods fail when the objects are in close proximity or present occlusions. A snake, for example, can be used to track individual deformable moving binary objects. But when two binary objects touch each other, the boundary between them is not represented in the image data, and so a snake algorithm would likely fail without usable image information at the boundary. Tracking multiple objects separately in videos is achievable by using appearance-based models. Some methods track and separate people with occlusions by using cues such as appearance (color and texture of clothing, etc.) from frame to frame [25] [26] [27] [28] . Some approaches are able to track multiple rigid objects in simple interactions. In [29] , Khan uses particle filtering combined with a pairwise penalty function that only depends on the number of pixels which overlap between the appearance templates associated with the two targets to track multiple ants. In [30] , Qu uses the "magnetic potential" and "inertia potential" to solve the "error merge" and "false object labeling" problems after severe occlusion of targets.. Although there have been numerous algorithms for multiple-object tracking, additional issues arise when tracking C. elegans worms. The worm tracking problem differs from previous tracking experiments in several respects: (1) The worms are not rigid bodies; they are highly deformable, (2) the actual worm body is transparent, and (3) the worm moves almost entirely in a 2D plane. The fact that the worm body is transparent means that color information is completely unavailable and even grayscale can be unreliable. Therefore we cannot use the color or texture to easily distinguish the animals from the background of transparent bacteria layers. To solve this technical problem, the illumination of the microscope is chosen to make the worm body look dark and the background look bright in grayscale images. This means the grayscale images are very nearly binary, and in fact we convert them to binary as a pre-processing step. While the binary nature of the images makes it relatively easy to compute body posture features for single-worm videos, it is a difficult problem to track and distinguish touching deformable binary blobs when there is more than one object in the scene. The fact that the worm moves almost entirely in a 2D plane also makes this tracking problem somewhat different from many prior studies. Although portions of a worm's body can cross over/under its own body or the body of another worm, the worm's body is sufficiently flat that such crossings do not involve appreciable curvature up and out of the plane of the agar plate. Thus the projection of the worm's body onto the plane of the plate has a roughly constant length.. These three significant differences make the wormtracking problem quite unique compared to other multiple object tracking problems, such as those involving people and cars. Prior research on tracking multiple C. elegans is limited to [31] and [32] , of which the latter is a preliminary version of the research presented here. In [31] , a deform-able worm model and several motion patterns are used to define an overall locomotory space of C. elegans and to describe its general dynamic movements. Using a combination of "predicted energy barrier" and multiple-hypothesis tracking, multiple touching worms can be identified and separated with a high success rate. The algorithm presented in [31] focuses on the crawling mechanism, which means sudden position-shift of the worm bodies caused by the swimming mechanism of the worm or even the movement of the stage may lead to loss of a track. Our work differs from [31] in that the algorithm presented here can accommodate both crawling and swimming mechanisms or other causes of sudden shifts such as stage movement. Our work also differs in that we introduce various pixelbased, feature-based, and human observation based methods for evaluating the accuracy of the body matching algorithm, and we evaluate some features (angle change rate, reversals) extracted from the matched body positions.. In this paper, we also choose an appearance-based method to match the bodies of C. elegans. We combine a part-based articulated model [18, 33] with a dynamic programming algorithm to determine the correct location of the individual worm bodies. Our work accurately resolves the individual body postures of two worms in physical contact with one another and identifies them correctly before and after they touch each other, and can still maintain track of the worms when their bodies have non-crawling displacement. Furthermore, we solve the problem that the skeleton-based reversal detection algorithm in [34] fails when two worms touch each other because of the difficulty of obtaining morphological skeletons. The basic algorithms described here appeared in abbreviated form in a preliminary paper [32] . This longer version includes an expanded exposition of the algorithm, as well as completely new results (in Tables 2 and 4 ) and expansions to the earlier results presented in Tables 1 and 3 . In particular, we now show that a parameter such as angle change rate (related to body curvature) can be extracted from the model, verifying that our algorithm accurately simulates worm body poses, and we also examine the rate of movement before and after touching, and we extract parameters during the time the animals touch (reversal rate, duration of touching). Our algorithm shows good performance in the analysis of real worm videos, and should have many applications in the study of C. elegans behavior. This paper presents a method that combines articulated models and dynamic programming for simulating the body poses of C. elegans in multi-worm videos. The models are composed of a number of rectangular parts arranged in a deformable configuration. For each video, we begin by using a dynamic programming algorithm to generate many worm body poses in every frame. For those portions of the video where the two worms are clearly separated, a dynamic programming algorithm is used again to find the best sequences over time for both worms. For those portions of the video where the two worms are close or touching each other, we find the best match configuration for the two worms based on the Euclidean distances between pairs of body poses in adjacent image frames.. There are several contributions in this paper. First, the presented method allows us to identify two worms correctly before and after they touch each other in 90% of our videos. Second, we can use these models to accurately resolve the individual body postures of two worms in physical contact with one another. We note that this tracking algorithm for two worms is fully automated and requires no human annotation at any point (including no need for human annotation of the first frame). When this algorithm was combined with a previously described algorithm for detection of reversals, human annotation was required to identify head and tail once per video, which was needed to identify reversals. However the tracking algorithm itself involves no human intervention, so it is suitable for analyzing large numbers of videos where human annotation would be tedious. We also showed that reversal behaviors of multiple worms can be accurately detected by using our model even when their bodies are in physical contact with one another. This algorithm will provide many applications towards characterizing physical interactions between animals. Previous research on automated analysis of C. elegans videos has shown that large numbers of biologically relevant features can be automatically extracted. These features include, for example, body length and width, average speed, curvature, depth of body bends, and frequency and duration of reversals. These features and others have been shown to be important in classifying and characterizing many different mutant strains [4] . Using the algorithm described in this paper, the body poses of two worms can be identified, so the various features extracted in prior research for single-worm videos can now be extended to videos with two worms. In this paper, we examined two of these features (angle change rate and reversals) in these two-worm videos. In addition, new features characterizing the interactions themselves such as average duration of bodily contact can be extracted across different mutant strains and different environmental conditions. We intend to characterize how interactions between animals affect various features of body movement and posture in future work. Finding the best sequence for the second worm OBTAIN the image sequence with the first worm being removed OBTAIN the best image sequence for the second worm using the above method END FOR Pseudo code for finding the best sequences within one close/touching subsection FOR subsection 1 to Index2 FOR each image in the subsection SET the primary worm differently from the previous image frame OBTAIN the 10 best worm body poses for the primary worm FOR each primary worm body pose OBTAIN the best worm body pose for the secondary worm END FOR OBTAIN the best set of two worm body poses END FOR END FOR Gaussian smoothing SHOW new video The experiments were performed using Matlab on a 2.33 GHz Pentium-IV desktop computer. The algorithm was run on 29 different videos which contained 10,579 frames in total and each sequence varies from 131 to 498 frames. K, M, and H were set to be 3,000, 1,000, and 10 respectively, where K is the number of sampled pixel positions, M is the number of sampled worm body poses for each image, and H is the number of the candidate body poses for each worm in each frame as previously described. The weights W x , W y , and W q in Eq. 1 were chosen in the ratio 4:4:3 based on limited subjective evaluation over a set of values. The choice of relative weight W q should allow the worm body pose to have appropriate bending but still agree with the worm body anatomy which can be verified by human observers. Binary images are obtained from the First, we evaluate how well the algorithm performs on the three major goals: (1) to find the best body poses for both worms for further extraction of motion related features, (2) to identify two worms correctly before and after they touch each other in every video and (3) to detect reversals even when morphological skeletons are not available due to the two worms touching.

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


Digimatge, a rich internet application for video retrieval from a multimedia asset management system This paper describes the integration of two new services aimed at assisting into the retrieval of video content from an existing Multimedia Asset Manager (MAM) of a TV broadcaster archive. The first tool suggests tags after a first textual query, and the second ranks the keyframe of retrieved assets according to their visual similarity. Both applications were integrated as web services that are accessed from a Rich Internet Application via REST calls. Video retrieval from archives is nowadays a challenging need for TV broadcasters. The growing amount of data daily ingested into their repositories has made no longer feasible to rely only on the manual search and indexing of expert documentalists. This paper presents the integration of state of the art retrieval tools in the Multimedia Asset Manager (MAM) of the Catalan Broadcast Corporation (CCMA), a media group that produces four TV stations. The main goal of the new systems is the assist journalists in a fast retrieval of video assets to be edited and included in the clips for the news programs.. The starting point of the presented work is Digition, the MAM previously developed by the CCMA whose search system is based only on matching query keywords and the textual metadata manually generated by the documentalists. Digimatge is a new search system that adds two new services to the ones previously offered by Digition: a tag suggestion for text-based queries and a ranking of retrieved keyframes based on image similarity.. The addition of these new plug-ins into a MAM in exploitation requires a modular architecture of multiple systems interconnected with a variety of network protocols. Web services coming from the Internet offer nowadays mature technologies designed to be deployed in distributed systems, offering user experiences on the cloud as rich as if they were being run at the client side. This application are referred as Rich Internet Applications (RIA) and their technologies are also valid in the framework of a corporate network. A second contribution of the presented work is the adoption of this paradigm to extend the features of the existing MAM.. The paper is structured as follows. Section 2 presents the new services added to the existing system and how to exploit them from a graphical user interface. Section 3 introduces the concept of Rich Internet Application and describes the system architecture and communication protocols. Finally Section 4 draws the future research lines and provides the project conclusions. This paper has presented the implementation of two additional video retrieval services in an existing MAM. Their integration in the existing system required the design of an architecture where data and applications are distributed in heterogeneous servers running different technologies. The access to the new services was implemented as a RIA, allowing simple access to all users through a conventional web browser. The additional services running in external hosts were successfully connected to the corporate systems, providing guidelines for the future deployment of similar extensions. After these first successful steps, the system will continue evolving to introduce new methods for results clustering and navigation, efficient indexing for visual descriptors and connectivity to external news sources.. Video demos of the system are available online. 1 .

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


Application of the fuzzy Failure Mode and Effect Analysis methodology to edible bird nest processing This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. The focus of this paper is on the production processes of Edible Bird Nest (EBN) in Sarawak, Malaysia. Sarawak and Sabah (two states of Malaysia in the Borneo Island) are known as the second ranked resource area (after Indonesia) of the world for EBN production. In spite of the popularity of EBN as a food source and the important economic status of the EBN industry, the use of a quality and risk assessment tool for the production of EBN is new. As such, the implementation of an advanced quality and risk assessment tool, i.e., the fuzzy Failure Mode and Effect Analysis (FMEA) methodology, for EBN processing is described in this paper. Data and information are gathered from several EBN production sites, and fuzzy FMEA is adopted to analyze the collected data/information. It is worth mentioning that the EBN production in Sarawak is relatively traditional. As such, this work makes an important contribution to modernization of the EBN production industry in Sarawak, i.e., to improve the production process and ensure the quality of EBN via the use of a formal quality and risk assessment tool. Besides, this paper contributes to a new application of fuzzy FMEA to the agriculture and food domain. Edible Bird Nest (EBN) (or known as ''the Caviar of the East'') is the nest of swiftlets which is edible and consumed by humans as (healthy) food (Hobbs, 2004; Marcone, 2005) . EBN is made up of saliva produced by cave-nesting swiftlets of two genera, i.e., glossy swiftlets (genus Collocalia Gray 1840) and echolocating swiftlets (genus Aerodramus Oberholser 1906) (Lim and Earl of Cranbrook, 2002) . The white-nest swiftlets (Aerodramus fuciphagus) and the black-nest swiftlets (Aerodramus maximus) are heavily exploited for commercial purposes (Lim and Earl of Cranbrook, 2002) . Traditionally, raw EBN originates from the natural limestone caves (Jordan, 2009) . However, with a high demand of EBN from China, the traditional caves are insufficient to produce enough EBN to support the increasing need of the market (Jordan, 2009) . Thus, in Malaysia, swiftlets farming has appeared as an alternative industry to supplement raw EBN. This helps preserve swiftlets species and avoid the over exploitation of the raw EBN resources (Lim and Earl of Cranbrook, 2002) . Today, swiftlets farming and EBN processing have emerged as a popular urban industry among Southeast Asia countries, including Malaysia (Lim and Earl of Cranbrook, 2002; Jordan, 2009) .. It is generally believed by the Chinese community that EBN has a high medical value. Hobbs (2004) listed a number of claimed benefits of consuming EBN soup, which includes dissolving phlegm, relieving gastric troubles, aiding renal functions, raising libido, enhancing complexion, alleviating asthma, suppressing cough, curing tuberculosis, strengthening the immune system, speeding recovery illness and surgery, increasing energy and metabolism, and improving concentration. Recent researches have also shown that extracts from EBN have a significant effect for inhibiting the infection of influenza (Guo et al., 2006; Yagi et al., 2008) , and avoiding bone loss (Matsukwa et al., 2011) .. Despite the popularity of EBN as a food source, it is challenging to ensure the quality of EBN. From the literature, many activities on ensuring and enhancing the quality of EBN have been reported. Lin et al. (2009) developed a method based on an analysis of cytochrome b gene in mitochondrial deoxyribonucleic acid (DNA) for genetic identification of EBN. Given a sample of EBN, the proposed method was able to help identify the species of birds that produced the sample; hence distinguishing between authentic and counterfeit EBN. A combination of observational and analytical investigative technique to determine the authenticity of EBN from bioprocessed food was proposed by Marcone (2011) . A combination of DNA polymerase chain reaction (PCR) and protein-based two dimensional gel electrophoresis-based method for rapid and reliable identification of genuine EBN products was reported by Wu et al. (2010) . In short, the investigations in Lin et al. (2009) , Marcone (2011) and Wu et al. (2010) focused on developing reliable methods to determine the authenticity of EBN.. In addition to establishing the authenticity of EBN, to the best of our knowledge, there are relatively few reports on the implementation of a quality and risk assessment tool for the food industry, not to mention EBN production. In this paper, the EBN production processes (i.e., farming, harvesting, processing, and packaging) are explained as a series of systematic manufacturing or engineering operations. It is important to implement a quality and risk assessment tool for EBN production in order to (i) identify problems and solutions systematically, (ii) improve quality, reliability, and safety, (iii) collect data/information for reducing future failures as well as capturing engineering knowledge, (iv) reduce production time and cost, (v) improve production yield. In this paper, the focus is on the use of an advanced quality and risk assessment tool, i.e., the fuzzy Failure Mode and Effect Analysis (FMEA), for improving the EBN production processes.. FMEA is a popular and practical quality and risk assessment tool. It is useful to define, identify, and eliminate known and/or potential failures, problems, errors from a system, design, process, and/or service (Stamatis 2003) . A failure mode is defined as the manner in which a component, subsystem, system, or process can potentially fail to meet the designed intent (Liu et al., 2010) . A successful FMEA implementation helps the manufacturing team to identify potential failure modes based on their past experience with similar products or processes; hence enabling the team to eliminate or reduce system failures with the minimum effort and resource expenditure. From the literature, the use of FMEA in the food industry is not new. Scipioni et al. (2002 Scipioni et al. ( , 2005 demonstrated an FMEA which was integrated with the hazard analysis and critical control points approach in a food company. It was used as a tool to assure product quality and as a means to improve the operational performance of the production cycle. Besides, FMEA was employed as a risk assessment tool in salmon manufacturing (Arvanitoyannis and Varzakas, 2008) and red pepper spice production processes (Ozilgen et al., in press ). However, the use of FMEA in EBN production processes is new.. Recently, a number of enhancements to FMEA using soft computing modeling techniques have been proposed, e.g., the use of a Fuzzy Inference System (FIS) to replace the conventional Risk Priority Number (RPN) model in FMEA (Liu et al., 2010; Yang et al., 2008; Guimares and Lapa, 2004; Tay and Lim, 2006) . The conventional RPN score is obtained by multiplying three risk factors, i.e., Severity (S), Occurrence (O), and Detect (D), RPN = S Â O Â D). As an alternative, the FIS-based RPN model uses an FIS model to aggregate these three risk factors, and produces a fuzzy RPN (FRPN) score, i.e., FRPN ¼ f RPN ðS; O; DÞ. The FIS-based RPN model has been successfully applied to a variety of domains, e.g. maritime (Yang et al., 2008) , nuclear power plant (Guimares and Lapa, 2004) , and semiconductor manufacturing (Tay and Lim, 2006) . The FIS-based model has several advantages. These include (i) the FIS-based model allows the modeling of nonlinear relationships between the RPN score and the three risk factors (Bowles and Peláez, 1995) ; (ii) it is robust against uncertainty and vagueness (Yang et al., 2008) ; and (iii) the scales of the attribute(s) can be qualitative, instead of quantitative (Bowles and Peláez, 1995) .. To the best of our knowledge, the use of fuzzy FMEA in EBN production has never been reported before. Besides, it is worth mentioning that the use of fuzzy logic related techniques in agriculture is a new and popular research direction. Examples include a fuzzy decision support system for nitrogen fertilization (Papadopoulos et al., 2011) , a fuzzy controller for decreasing tomato cracking in greenhouses (Hahn, 2011) , as well as a fuzzy logicbased disease diagnosis system for crops (Kolhe et al., 2011) . The aim of this paper is to analyze EBN production processes with the fuzzy FMEA methodology and to improve EBN food processing control and management. Potential failure modes and their S, O, and D ratings are firstly determined. The FIS-based RPN model is constructed with data/information gathered from domain experts. It is essentially a computerized risk assessment and failure analysis tool that mimics human reasoning. The tool is implemented as a computer software, which can be used to compute analyze fuzzy RPN scores of failure modes, and subsequently prioritize the failure modes for appropriate remedial actions. This study is important Fig. 1 . Geographical locations of two swiftlets farms and two EBN production plants in Sarawak, Malaysia. because it contributes towards improving the quality and safety issues of EBN related products. Besides, this study establishes an effective management for swiftlets cultivation and EBN food processing. Real data/information gathered from two swiftlets farms located at Sarikei and Asajaya and two EBN processing plants located at Batu Kawah and Baki (all in Sarawak) are used in this study.. This paper is organized as follows. In Section 2, the geographical locations of the EBN farms and production plants used in this study are described. Besides, the EBN production procedure is explained. In Section 3, the use of the fuzzy FMEA methodology in EBN production is detailed. In Section 4, the evaluation results with fuzzy FMEA are presented. Finally, concluding remarks and suggestions for further work are given in Section 5. In this paper, the use of the fuzzy FMEA methodology to analyze the EBN production processes in Sarawak, Malaysia, has been studied. The potential failure modes have been identified and analyzed based on their effects, root causes, and detection methods. The FMEA tables have been formulated, and the S, O, and D scores have been obtained. The RPN and FRPN scores have also been computed, with the results analyzed and discussed. The contributions of this paper are two folds. On one hand, this study addresses the issues of control and management of EBN processing with a formal and effective quality and risk assessment tool. On the other hand, it contributes towards a new application of the fuzzy FMEA methodology to the agriculture and food production domain.. For further studies, investigations on the use of fuzzy FMEA in other valuable food processing and agricultural products can be conducted. Examples include risk assessment in palm oil plantation and Tor Duoronensis fish farming. These are some of the important industries that require proper quality and risk assessment tools for their production in Sarawak. P.1. P.2. P.3. P.4. P.5. Clean the mold before using it Analysis of the FMEA results is as follows. In this section, the geographical locations of the swiftlets farms and EBN productions plants are firstly described. The EBN production processes are then explained in details. Fig. 1

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