Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine This version is available at http://eprints.hud.ac.uk/16585/ The University Repository is a digital collection of the research output of the University, available on Open Access. Copyright and Moral Rights for the items on this site are retained by the individual author and/or other copyright owners. Users may access full items free of charge; copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational or notforprofit purposes without prior permission or charge, provided:• The authors, title and full bibliographic details is credited in any copy;• A hyperlink and/or URL is included for the original metadata page; and • The content is not changed in any way.For more information, including our policy and submission procedure, please contact the Repository Machine performance degradation assessment and remaining useful life (RUL) prediction are of crucial importance in condition-based maintenance to reduce the maintenance cost and improve the reliability. They provide a potent tool for operators in decision-making by specifying the present machine state and estimating the remaining time. For this ultimate purpose, a threestage method for assessing the machine health degradation and forecasting the RUL is proposed.In the first stage, only the normal operating condition of machine is used to create identification model for recognizing the dynamic system behavior. Degradation index which is used for indicating the machine degradation is subsequently created based on the root mean square of residual errors. These errors are the difference between identification model and behavior of system. In the second stage, the Cox's proportional hazard model is generated to estimate the survival function of the system. In the last stage, support vector machine, which is one of the remarkable machine learning techniques, in association with time-series techniques is utilized to forecast the RUL. The data of low methane compressor acquired from condition monitoring routine is used for validating the proposed method. The result shows that the proposed method could be used as a reliable tool to machine prognostics. Machinery operation generally consists of a series of states which are transient, normal operating, and degrading before fault occurrences. Among these, even degrading state plays an important role in maintenance activity, it is not a root cause of the machine breakdown.. However, it does decline the performance reliability and increase the potential for faults and failures. Once the failure degradation has been detected, the remaining lifetime of machinery needs to be predicted in order that system operators implement the timely maintenance actions to avoid the catastrophic failures.. In order to estimate the remaining useful life (RUL), condition-based maintenance (CBM), one of the efficient maintenance strategies, has been recently received considerable attention in modern industries. In CBM, prognostics is of necessity and gradually becomes a key component due to its capability to foretell the remaining operation life, future machine state, or probability of reliable machine operation based on the data acquired from condition monitoring process [1] .. Additionally, more advanced prognostics are being focused on performance degradation monitoring and assessment so that failure can be predicted and prevented. According to [2] , three crucial steps are necessary to fulfill the goal of prognostics. Firstly, the defect or abnormality should be able to be detected at an early stage. Secondly, the machine performance degradation should be assessed robustly and tracked continuously. Finally, the RUL and possible failure mode of the machine should be effectively predicted. Therefore, the key challenges of implementing machine prognostics are machine performance degradation assessment and RUL prediction in which the former is a critical procedure to the latter.. Lately, several considerable efforts which range from data-driven based techniques to modelbased techniques have been implemented to develop methods and tools for these challenges.. Qiu et al. [2] developed a robust performance degradation method for rolling element bearing.. This method based on a combination of wavelet filter and self-organizing map (SOM) in which the former was used for enhancing weak signal for fault identification whilst the latter was applied for performance degradation assessment. Lee et al. [3, 4] applied a pattern discrimination model based on a cerebellar model articulation controller (CMAC) neural network to monitor and assess performance degradation in a robot. Lin et al. [5] combined traditional reliability modeling methods with vibration-based monitoring techniques and CMAC neural network in an integrated system to perform the machine reliability and determine the health status of machine. The results of CMAC were then verified by Weibull proportional hazard model. Xu et al. [6] proposed a fuzzy-based extension of CMAC neural network to analyze two types of machine degradation severities: the network was trained by signals from different levels of machine degradation severity, and the network was only trained by signals of machine normal state. Other neural network-based approaches to assess bearing performance degradation and forecast the RUL were presented by Gebraeel et al. [7] and Huang et al. [8] .. Liao and Lee [9] proposed a method which combined wavelet packet analysis, principal component analysis, and Gaussian mixture model to define performance index. This index was determined based on the overlap between the distribution of the baseline feature space and that of the testing feature space. An extension of support vector machine (SVM), namely least squares SVM, was used for assessment of machine performance degradation proposed in reference [10] . Using fuzzy c-means (FCM) clustering method is a trendy approach to assess the machine performance degradation. Huang et al. [11] proposed a combined model in which wavelet packet transform was used to process raw signals, the Fisher criterion was applied for feature selection, and FCM was utilized to assess and classify the performance of machine. Pan et al. [12] proposed an FCM-based method for bearing performance assessment. This method comprised lifting wavelet packet decomposition for feature extraction and FCM for bearing performance degradation assessment. In case of model-based techniques, proportional hazard model (PHM) and logistic regression model (LRM) which are related to multiple degradation features were implemented to perform machine reliability indices and enable machine RUL forecast [13] . Other applications of LRM to realize machine performance assessment and RUL prediction could be found in references [14] [15] [16] .. Generally, these above approaches can indicate the performance degradation of machine as well as assist in forecasting the machine RUL. However, some existing disadvantages lead to difficulty of applying these approaches to real industrial equipment. FCM, SVM, and back propagation neural network need to be trained by historical data including both normal and failure operation to generate the assessment models, which is costly or inapplicable in industrial area. SOM and CMAC neural network assessment models could be created by using only normal operation data which is more beneficial. Nevertheless, they are either not intuitive enough to reflect degradation degree or seriously influenced by some parameters defined by user, which makes it unpractical [12] . Moreover, these approaches only focused on the machine performance degradation assessment without RUL prediction which is the ultimate goal of prognostics. Performance degradation assessment and RUL prediction methods based on PHM and LRM also need data of whole machine life. This is impossible in case of newly installed machine operating in normal condition. Yan et al. [15] proposed a solution for this problem by using technician's experience for acceptable level or unacceptable level to predefine the probability thresholds. However, these levels are non-scientific and human intuitive.. In this study, a three-stage method for both targets involving machine performance degradation assessment and RUL prediction is proposed. In the first stage, autoregressive moving average (ARMA) model [17] , which is one of the system identification techniques, is generated by using only normal operating data to identify the behavior of the complex system. Degradation index defined as the root mean square of residual errors is then used to indicate the machine degradation. The residual errors are the different outputs between identification model and behavior of system. By this degradation index, operators or maintainers could define the failure threshold for the system. In the second stage, the Cox's PHM is established to estimate the survival function of system. Finally, support vector machine, which is one of the remarkable machine learning techniques, in association with multi-step ahead direct prediction method of time-series forecasting techniques is utilized to forecast the RUL in the last stage. In this study, the method is proposed for assessing the health degradation and forecasting the RUL based on only the normal operating condition of machine. This method consists of three stages in which ARMA identification model, Cox's PHM, and SVM are combined in an integrated tool. In the first stage, only the normal operating condition of machine is used to create ARMA model for recognizing the dynamic system behavior. Base on the difference between identification model and behavior of system, degradation index is generated to indicate the machine status and determine the failure threshold. In the second stage, the Cox's proportional hazard model is built to estimate the survival function of the system once the degradation index is higher than the failure threshold. In the last stage, support vector machine association with time-series techniques is utilized to forecast the RUL. Furthermore, the feasible method is also proposed for further improvement of predicting accuracy. The data of low methane compressor acquired from condition monitoring routine is used for appraising the proposed method. The high accuracy of predicted results indicates that the proposed could be used as a reliable tool to machine prognostics.

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


Schemas for Unordered XML on a DIME We investigate schema languages for unordered XML having no relative order among siblings. First, we propose unordered regular expressions (UREs), essentially regular expressions with unordered concatenation instead of standard concatenation, that define languages of unordered words to model the allowed content of a node (i.e., collections of the labels of children). However, unrestricted UREs are computationally too expensive as we show the intractability of two fundamental decision problems for UREs: membership of an unordered word to the language of a URE and containment of two UREs. Consequently, we propose a practical and tractable restriction of UREs, disjunctive interval multiplicity expressions (DIMEs). Next, we employ DIMEs to define languages of unordered trees and propose two schema languages: disjunctive interval multiplicity schema (DIMS), and its restriction, disjunction-free interval multiplicity schema (IMS). We study the complexity of the following static analysis problems: schema satisfiability, membership of a tree to the language of a schema, schema containment, as well as twig query satisfiability, implication, and containment in the presence of schema. Finally, we study the expressive power of the proposed schema languages and compare them Theory Comput Syst (2015) 57:337-376 with yardstick languages of unordered trees (FO, MSO, and Presburger constraints) and DTDs under commutative closure. Our results show that the proposed schema languages are capable of expressing many practical languages of unordered trees and enjoy desirable computational properties. When XML is used for document-centric applications, the relative order among the elements is typically important e.g., the relative order of paragraphs and chapters in a book. On the other hand, in case of data-centric XML applications, the order among the elements may be unimportant [1] . In this paper we focus on the latter use case. As an example, take a trivialized fragment of an XML document containing the DBLP repository in Fig. 1 . While the order of the elements title, author, and year may differ from one publication to another, it has no impact on the semantics of the data stored in this semi-structured database.. Typically, a schema for XML defines for every node its content model i.e., the children nodes it must, may, and cannot contain. For instance, in the DBLP example, one would require every article to have exactly one title, one year, and one or more authors. A book may additionally contain one publisher and may also have one or more editors instead of authors. A schema has numerous important uses. For instance, it allows to validate a document against a schema and identify potential errors. A schema also serves as a reference for a user who does not know yet the structure of the XML document and attempts to query or modify its content.. The Document Type Definition (DTD), the most widespread XML schema formalism for (ordered) XML [8, 23] , is essentially a set of rules associating with each label a regular expression that defines the admissible sequences of children. The DTDs are best fitted for ordered content because they use regular expressions, a formalism that defines sequences of labels. However, when unordered content model needs to be defined, there is a tendency to use over-permissive regular expressions. This DTD allows an article to contain any number of titles, years, and authors. A book may also have any number of titles, years, authors, editors, and publishers. These regular expressions are clearly over-permissive because they allow XML documents that do not follow the intuitive guidelines set out earlier e.g., an XML document containing an article with two titles and no should not be valid.. While it is possible to capture unordered content models with regular expressions, a simple pumping argument shows that their size may need to be exponential in the number of possible labels of the children. In case of the DBLP repository, this number reaches values up to 12, which basically precludes any practical use of such regular expressions. This suggests that over-permissive regular expressions may be employed for the reasons of conciseness and readability, a consideration of great practical importance.. The use of over-permissive regular expressions, apart from allowing documents that do not follow the guidelines, has other negative consequences e.g., in static analysis tasks that involve the schema. Take for example the following two twig queries [3, 47] : The first query selects the elements labeled book, children of dblp and having an author containing the text "C. Papadimitriou." The second query additionally requires that book has a title. Naturally, these two queries should be equivalent because every book should have a title. However, the DTD above does not capture properly this requirement, and consequently the two queries are not equivalent w.r.t. this DTD.. In this paper, we investigate schema languages for unordered XML. First, we study languages of unordered words, where an unordered word can be seen as a multiset of symbols. We consider unordered regular expressions (UREs), which are essentially regular expressions with unordered concatenation "||" instead of standard concatenation. The unordered concatenation can be seen as union of multisets, and consequently, the star " * " can be seen as the Kleene closure of unordered languages.. Similarly to a DTD which associates to each label a regular expression to define its (ordered) content model, an unordered schema uses UREs to define for each label its unordered content model. For instance, take the following schema (satisfied by the tree in Fig. 1 The above schema uses UREs and captures the intuitive requirements for the DBLP repository. In particular, an article must have exactly one title, exactly one year, and at least one author. A book may additionally have a publisher and may have one or more editors instead of authors. Note that, unlike the DTD defined earlier, this schema does not allow documents having an article with several titles or without any author. Using UREs is equivalent to using DTDs with regular expressions interpreted under the commutative closure [4, 34] : essentially, a word matches the commutative closure of a regular expression if there exists a permutation of the word that matches the regular expression in the standard way. Deciding this problem is known to be NPcomplete [26] for arbitrary regular expressions. We show that the problem of testing the membership of an unordered word to the language of a URE is NP-complete even for a restricted subclass of UREs that allows unordered concatenation and the option operator "?" only. Not surprisingly, testing the containment of two UREs is also intractable. These results are of particular interest because they are novel and do not follow from complexity results for regular expressions, where the order plays typically an essential role [31, 46] . Consequently, we focus on finding restrictions rendering UREs tractable and capable of capturing practical languages in a simple and concise manner.. The first restriction is to disallow repetitions of a symbol in a URE, thus banning expressions of the form a||a ? because the symbol a is used twice. Instead we add general interval multiplicities a [1, 2] which offer a way to specify a range of occurrences of a symbol in an unordered word without repeating a symbol in the URE. While the complexity of the membership of an unordered word to the language of a URE with interval multiplicities and without symbol repetitions has recently been shown to be in PTIME [11] , testing containment of two such UREs remains intractable. We, therefore, add limitations on the nesting of the disjunction and the unordered concatenation operators and the use of intervals, which yields the proposed class of disjunctive interval multiplicity expressions (DIMEs). DIMEs enjoy good computational properties: both the membership and the containment problems become tractable. Also, we believe that despite the imposed restriction DIMEs remain a practical class of UREs. For instance, all UREs used in the schema for the DBLP repository above are DIMEs.. Next, we employ DIMEs to define languages of unordered trees and propose two schema languages: disjunctive interval multiplicity schema (DIMS), and its restriction, disjunction-free interval multiplicity schema (IMS). Naturally, the above schema for the DBLP repository is a DIMS. We study the complexity of several basic decision problems: schema satisfiability, membership of a tree to the language of a schema, containment of two schemas, twig query satisfiability, implication, and containment in the presence of schema. We present in Table 1 a summary of the complexity results and we observe that DIMSs and IMSs enjoy the same computational properties as general DTDs and disjunction-free DTDs, respectively. The lower bounds for the decision problems for DIMSs and IMSs are generally obtained with easy adaptations of their counterparts for general DTDs and disjunction-free DTDs. To obtain the upper bounds we develop several new tools. We propose to represent DIMEs with characterizing tuples that can be efficiently computed and allow deciding in polynomial time the membership of a tree to the language of a DIMS and the containment of two DIMSs. Also, we develop dependency graphs for IMSs and a generalized definition of an embedding of a query. These two tools help us to reason about query satisfiability, query implication, and query containment in the presence of IMSs. Our constructions and results for IMSs allow also to characterize the complexity of query implication and query containment in the presence of disjunction-free DTDs, which, to the best of our knowledge, have not been previously studied.. Finally, we compare the expressive power of the proposed schema languages with yardstick languages of unordered trees (FO, MSO, and Presburger constraints) and DTDs under commutative closure. We show that the proposed schema languages are capable of expressing many practical languages of unordered trees.. It is important to mention that this paper is a substantially extended version of a preliminary work presented in [10] . More precisely, in this paper we show novel intractability results for some subclasses of unordered regular expressions and we extend the expressibility of the tractable subclasses. While in [10] we have considered only simple multiplicities ( * , +, ?), in this paper we deal with arbitrary interval multiplicities of the form [n, m].. Organization In Section 2 we introduce some preliminary notions. In Section 3 we study the reasons of intractability of unordered regular expressions while in Section 4 we present the tractable subclass of disjunctive interval multiplicity expressions (DIMEs). In Section 5 we define two schema languages: the disjunctive interval multiplicity schemas (DIMSs) and its restriction, the disjunction-free interval multiplicity schemas (IMSs), and the related problems of interest. In Sections 6 and 7 we analyze the complexity of the problems of interest for DIMSs and IMSs, respectively.. In Section 8 we discuss the expressiveness of the proposed formalisms. In Section 9 we present related work. In Section 10 we summarize our results and outline further directions. We have studied schema languages for unordered XML. First, we have investigated languages of unordered words and we have proposed disjunctive interval multiplicity expressions (DIMEs), a subclass of unordered regular expressions for which two fundamental decision problems, membership of an unordered word to the language of a DIME and containment of two DIMEs, are tractable. Next, we have employed DIMEs to define languages of unordered trees and have proposed disjunctive interval multiplicity schema (DIMS) and its restriction, disjunction-free interval multiplicity schema (IMS). DIMSs and IMSs can be seen as DTDs using restricted classes of regular expressions and interpreted under commutative closure to define unordered content models. These restrictions allow to maintain a relatively low computational complexity of basic static analysis problems while allowing to capture a significant part of the expressive power of practical DTDs.. As future work, we want to study whether the restrictions imposed by the grammar of DIMEs can be relaxed while maintaining the tractability of the problems of interest. Moreover, we would like to investigate learning algorithms for the unordered schema languages proposed in this paper. We have already proposed learning algorithms for restrictions of DIMSs and IMSs [16] and we want to extend them to take into account all the expressive power. We also aim to apply the unordered schemas to query minimization [3] i.e., given a query and a schema, find a smaller yet equivalent query in the presence of the schema. Furthermore, we want to use unordered schemas and optimization techniques to boost the learning algorithms for twig queries [45] . Languages of unordered trees can be expressed by logic formalisms or by tree automata. Boneva et al. [12, 13] make a survey on such formalisms and compare their expressiveness. The fundamental difference resides in the kind of constraints that can be expressed for the allowed collections of children for some node. We mention here only formalisms introduced in the context of XML. Presburger automata [43] , sheaves automata [20] , and the TQL logic [15] allow to express Presburger constraints on the numbers of occurrences of the different symbols among the children of some node. Suitable restrictions allow to obtain the same expressiveness as the Presburger MSO logic on unordered trees [12, 13] , strictly more expressive than DIMSs. Additionally, we believe that DIMSs are more appropriate to be used as schema languages, as they were designed as such, in particular regarding the more user-friendly DTD-like syntax.. Languages of unordered trees can be also expressed by considering DTDs under commutative closure [4, 34] . We assume DTDs using arbitrary regular expressions, not necessarily one-unambiguous [14] as required by the W3C. We also point out that it has been recently shown that it is PSPACE-complete to decide whether a given regular expression can be rewritten as an equivalent one-unambiguous one [19] . Given a DTD using arbitrary regular expressions under commutative closure, we say that an (ordered) tree matches such a DTD iff every tree obtained by reordering of sibling nodes also matches the DTD. However, it is PSPACE-complete to test whether a DTD defines a commutatively-closed set of trees [34] and, moreover, such a DTD may be of exponential size w.r.t. the size of the alphabet, which makes such DTDs unfeasible. Another consequence of the high expressive power of DTDs under commutative closure is that the membership problem is NP-complete [26] . Therefore, these formalisms were not extensively used in practice. From a different point of view, Martens et al. [27, 28] investigate DTDs equipped with formulas from the SL logic that specifies unordered languages and obtain complexity improvements for typechecking XML transformations.. The unordered concatenation operator "||" should not be confused with the shuffle (interleaving) operator "&" used in a restricted form in XML Schema and RELAX NG to define order-oblivious, yet still ordered, content. On the one hand, a * &b defines all ordered words with an arbitrary number of a's and exactly one occurrence of b, and analogously, a * || b defines all unordered words with exactly the same characteristic. On the other hand, (a&b) * defines ordered words of the form w 1 · . . . · w n , where the factors w 1 , . . . , w n are either ab or ba, while (a || b) * defines unordered words having the same number of a's and b's. For instance, (a&b) * does not accept the ordered word aabb while it has the same number of a's and b's. Adding the shuffle and interval multiplicities to the regular expressions increases the computational complexity of fundamental decision problems such as: membership [6, 25] , inclusion, equivalence, and intersection [21] . Colazzo et al. [17, 18, 22] propose efficient algorithms for membership and inclusion of conflict-free types, a class of regular expressions with shuffle and numerical constraints using intervals. Their approach is based on capturing a language with a set of constraints, similar to our characterizing tuples for DIMEs. While conflict-free types and DIMEs both forbid repetitions of symbols, they differ on the restrictions imposed on the use of the operators and the interval multiplicities. Consequently, they are incomparable.. We finally point out that the static analysis problems involving twig queries i.e., twig query satisfiability [5] , implication [9, 24] , and containment [35] in the presence of schema have been extensively studied in the context of DTDs. However, to the best of our knowledge, these problems have not been previously studied neither for the mentioned unordered schema languages, nor for DTDs using classes of regular expressions extended with counting and interleaving.

O O B I O O O O O O O O B I O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O 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 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O B I O O O O 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B 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 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Bad Luck When Joining the Shortest Queue A frequent observation in service systems with queues in parallel is that customers in other queues tend to be served faster than those in one's own queue. This paper quantifies the probability that one's service would have started earlier if one had joined another queue than the queue that was actually chosen, for exponential multiserver systems with queues in parallel in which customers join one of the shortest queues upon arrival and in which jockeying is not possible.Jel codes: C44, C60 Consider a service system with c ≥ 2 parallel servers. Separate queues are formed in front of each server. Throughout, queues are defined as including the customer in service, if there is one. Each queue is served in a FIFO order. Customers arrive according to a Poisson process at rate λ. They join one of the shortest queues upon arrival and stay in the queue of their choice until they have been served. Then, they leave the system. This means that jockeying (see Zhao and Grassmann, 1990 ) is not considered. An example of a parallel service system in which jockeying is hardly possible is a toll booth at an autostrada (see Conolly, 1984) . Services performed by server j have an exponentially distributed duration with a mean of 1/µ j , j = 1, . . . , c. Customers in such systems often notice that customers in other queues are being served faster than those in their own queue, and that they are overtaken by customers that arrived later. Of course, this phenomenon may be due to different skills, and hence different service rates, among the servers. If customers are aware of such differences, joining the shortest queue may not be the optimal decision. But even if the service rates of all servers are equal, this phenomenon frequently occurs. A simple explanation is found by considering the situation that a customer meets an equal number of customers n ≥ 1 in each of the queues upon arrival. Then, by the lack of memory of the exponential service time distributions and the symmetry of the system, each queue has the same probability of becoming the queue that is soonest exempted of its n customers. Hence, the arriving customer has in this situation a probability of (c − 1)/c of bad luck, in the sense that he does not join the queue in which his service would have started earliest. The aim of the present paper is to quantify the probability of bad luck for systems in which customers join one of the shortest queues upon arrival. For the computations reported in this paper we have used the power-series algorithm to compute the stationary queue length distribution as described in Blanc (1987a Blanc ( , 1987b Blanc ( , 1992 for the shortest-queue system. The efficiency of the algorithm is further enhanced in Blanc (1993) . Other approaches to shortest-queue systems can be found, among others, in Haight (1958) , Flatto and McKean (1977) , Halfin (1985) , Rao and Posner (1987) , Hanqin and Rongxin (1989) , Adan, Wessels and Zijm (1990) , Adan, Van Houtum and Van der Wal (1994) and Wu and Posner (1997) . Winston (1977) , Johri (1989) and Hordijk and Koole (1990) consider the optimality of the shortest queue discipline. The organization of the rest of this paper is as follows. Section 2 considers the probability of bad luck for symmetric shortest-queue systems. Section 3 contains a discussion of this probability for asymmetric shortest-queue systems with different service rates among the servers. Section 4 is devoted to systems with both customers who join a shortest queue and customers who are dedicated to specific servers. A conclusion can be found in Section 5. This paper has studied what we have called the probability of bad luck for shortest-queue systems. A customer is said to experience bad luck if he joined one of the shortest queues upon arrival, but his service would have started earlier if he had joined one of the other queues. In symmetric systems, the probability of bad luck may well exceed 1 2 when there are three or more servers, but this only occurs if the load of the system is very close to 1. The approach of this probability to its heavy traffic limit is very steep, so that this limit, which is easily computable, will not be a good approximation for most values of the load. Asymmetry in the service rates tends to increase this probability in light traffic, but to decrease it in moderate to heavy traffic. Dedicated background traffic decreases this probability.

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


Application of classification trees to determine biological and chemical indicators for river assessment: case study in the Chaguana watershed (Ecuador) Benthic macroinvertebrates were sampled in the Chaguana river basin in SW Ecuador in March (wet season) and September (dry season) of 2005 and 2006. Aquatic insects dominated the macrobenthos, with Trichoptera, Diptera, Ephemeroptera, Hemiptera and Odonata being the orders with the highest diversity and Ephemeroptera and Diptera being most abundant. No systematic differences in richness and abundance were observed between dry and wet seasons, which is in agreement with the literature. It is concluded that, in the neotropics, macroinvertebrates can probably be sampled for water quality assessments during the whole year: however, sampling soon after spates should be avoided. Using multivariate analysis, stations could be clustered into three groups based on their macroinvertebrate community composition: sites with low, intermediate and high human impact. Classification trees indicated that stations with low human impact had low conductivities, while stations with high conductivities were characterised as highly impacted if the dissolved oxygen concentration was low and intermediately impacted if the dissolved oxygen concentration was high. Classification trees also indicated that Leptophlebiidae (Ephemeroptera) were characteristic for sites with low impact; in sites with intermediate impact, this family was absent but Hydropsychidae (Trichoptera) were present; when both families were absent, impact was high.Key words 9 9 9 9 aquatic insects, community analysis, river basin, water quality assessment Seasonality is known to play an important role in structuring the macroinvertebrate community in temperate watercourses and can influence the outcome of bioassessment methods (Linke et al. 1999; Clarke et al. 2002) . Although several studies have already been performed in order to increase the knowledge of seasonal patterns of benthic macroinvertebrates in the neotropics (Flecker & Feifarek 1994; Jacobsen 1998; Jacobsen & Encalada 1998; Melo & Froehlich 2001; Carrera Burneo & Gunkel 2003 , Buss et al. 2004 , further studies exploring the influence of seasonal variation on existing and forthcoming bioassessment methods are required.. From a spatial viewpoint, latitude and altitude can both influence aquatic invertebrate communities in the tropics.. The majority of the reviewed assessment methods for neotropical areas are generally based on a biotic approach, in which the confidence relies on its application within a delimitated geographical area. Therefore, regions should be defined in which the method could be confidently applied to similar aquatic macroinvertebrate communities. These geographical areas, called ecoregions, provide the basis for the implementation of biological methods with score systems related to reference conditions (Gerritsen et al. 2000) .. Artificial intelligence has played a crucial role in modelling the distribution of organisms as a function of the abiotic environment, often called habitat suitability modelling. A range of modelling techniques have been applied for the assessment of running waters based on the distribution of macroinvertebrates. Artificial neural networks , fuzzy logic (Adriaenssens et al. 2004a; Mouton et al. 2009 ), classification trees (Dzeroski et al. 2000; Dakou et al. 2007) , Bayesian belief networks (Adriaenssens et al. 2004b ) and support vector machines (Hoang et al. 2010 ) have proven to have a high potential in macroinvertebrate habitat suitability modelling. Other promising techniques are, for example, evolutionary polynomial regression (Elshorbagy & El-Baroudy 2009; Savic et al. 2009) and cellular automata (Chen et al. 2009 ). However, in the present study, classification trees were used because this technique is applicable for relatively small datasets and because their transparency makes them suitable as a practical tool for decision support by water quality managers.. While Walley & Dzeroski (1995) already applied data mining techniques for biological river quality assessment, Dzeroski et al. (1997) were among the first to describe applications of classification trees in river community analysis. These included the biological classification of British rivers based on biological data, the analysis of the influence of physical and chemical parameters on selected bioindicator organisms in Slovenian rivers and the biological classification of Slovenian rivers based on physical and chemical parameters as well as bioindicator data. Blockeel et al. (1999) made simultaneous predictions of multiple physical-chemical properties of the water from its biological properties using a single decision tree and also predictions of past physicalchemical properties of the river water from its current biological properties. Dzeroski et al. (2000) predicted physical-chemical variables on the basis of biological data; taxa that occurred in many trees were considered as useful indicator taxa. Dakou et al. (2007) induced decision trees to predict the habitat suitability of six macroinvertebrate taxa in the river Axios (Northern Greece). predicted the presence of several macroinvertebrate taxa in Flanders based on a selection of environmental variables. Turak & Koop (2008) defined river types for abiotic features, riffle zones, fish assemblages and macroinvertebrate assemblages from river edges on the basis of reference sites in New South Wales (Australia).. Thus far, most papers dealing with classification trees were mainly focused on the optimisation of model performance, while in the present paper, classification trees were used as a practical tool: indicator taxa and key environmental parameters were derived that can be used for water quality assessment. In our case study in the neotropical Chaguana basin, the water quality was predicted on the basis of the occurring macroinvertebrates and the measured environmental parameters, taking spatial and temporal variability into account. In 2005 (Table 1 ). An unpruned classification resulted in a more complicated tree with 24 leaves: however, such a tree did not perform better (CCI ¼ 61%, K ¼ 0.38).. Another classification tree was developed for predicting the water quality based on the environmental parameters (CCI ¼ 71%, K ¼ 0.52) ( Figure 5 ). Sites with a low impact had a low conductivity, while sites with a high conductivity were highly impacted when the oxygen concentration was low and intermediately impacted when the oxygen concentration was high. The confusion matrix indicated that this tree tended to overpredict the water quality: only on two occasions was the water quality underpredicted, while the water quality was overpredicted on 23 occasions (Table 1) patterns. An inverse relationship is frequently observed between the altitude and the intensity of human activities, such as agriculture, human settlements and cattle grazing, and its related environmental degradation (e.g. organic, chemical and morphological) (Lang & Reymond 1993) . Also in the Chaguana basin, major human activities are carried out in the lowlands, mainly banana farming, which is the dominant crop cultivated in the basin (Matamoros et al. 2005) . The results of the present study confirmed that the higher reach of the basin is only slightly affected by human settlements, leaving streams and its macroinvertebrates in that area in a nearly pristine state (Figure 1 ). However, based on the macroinvertebrate communities, the water quality in the downstream sites was evaluated as poor due to the high human impact. As the highest sampling point in the present study was only 1600 m above sea level, altitude was of lesser importance as a factor limiting oxygen availability by reduced atmospheric pressure.. In lotic ecosystems, the physical environment exerts substantial control over the population abundances and hence the community composition of insects. Some of the physical factors that are of particular importance to aquatic insects include dissolved oxygen concentrations, water temperature and hydrodynamics, and all these factors are characterised by seasonal fluctuations. In contrast to temperate areas, tropical regions are not characterised by strong thermal changes over time. Most tropical regions experience, rather, some degree of seasonality related to rainfall. Also in the Chaguana basin, the annual rainfall is highly seasonal, with major peaks during the months of April and November and significant differences between seasons were detected for parameters such as water temperature, dissolved oxygen and current velocity.. In a coastal Atlantic Forest river in SE Brazil, no differences were found between macroinvertebrate communities at Leptophlebiidae Good water quality Hydropsychidae. Moderate water quality present absent present absent

O O O 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 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 B I B I B I 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 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 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 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


Fast generation of 2-D node distributions for mesh-free PDE discretizations Many applications require that nodes be scattered locally quasi-uniformly within 2-D regions or on curved surfaces in 3-D space, while obeying some prescribed spatially varying density function. This study focuses on creating variable density node layouts that are suitable for meshfree discretizations of PDEs. Another application is 'dithering' to simulate half-tone images. The method is an 'advancing-front type' scheme, inspired by the physical process of dropping of variable sized grains into a bucket. Discretizations of PDEs have traditionally relied on structured meshes. Requirements for geometric flexibility, both to conform to irregular geometries and to achieve local refinement in critical areas, have led to an increased use of unstructured meshes, often in the form of triangular/tehrahedral elements. In contrast, RBF-generated finite differences (RBF-FD) is a recent and altogether meshfree approach. It makes it easy to combine geometric flexibility with high levels of accuracy and computational efficiency [6, 7, 22, 23, 25, 28] . It uses scattered nodes with spatially varying density, without forming any associated 'elements' or connectivites between nodes.. A key to successful implementation of RBF-FD methods is being able to simply and very rapidly generate node layouts, as needed for applications such as real-time weather forecasting, tsunami modeling, and seismic imaging. Current methods in the literature for node distribution can be classified into two categories: 1) iterative methods, where for a given number of nodes the quality of the distribution is dependent on how soon the iteration is stopped, and 2) advancing front methods that start at a boundary and advance forward until the domain is filled.. Examples of iterative-type schemes include minimal energy (ME) distributions [10, 15, 27] (simulating equilibria for freely moving electrons), where the repelling force can be made spatially variable in order to concentrate nodes in areas where increased resolution is needed [5] . It was noted in [11, 24] that the energy minimization can be formulated in such a way that it can be applied to the task of image dithering, in which there is a rich literature. In this context, there may be additional requirements motivated by the characteristics of certain printers. For example, dot sizes may be allowed to be variable, and/or the dot locations may be confined to a Cartesian or hexagonal lattice; see [20] and references therein. Additional iterative strategies include Lloyd's algorithm [1, 16] , also known as Voronoi relaxation, and the approach presented in [21] , in which Delaunay triangulations are used iteratively to adjust a partly random initial distribution. A Matlab implementation distmesh2d of the latter algorithm is available online at http://persson.berkeley.edu/distmesh/, aimed towards finite element diretizations of PDEs.. The algorithms from the literature that are conceptually closest to the present one are known as advancing front methods [4, 14, 26] . These differ however significantly from the present algorithm. In most 2-D cases, triangular mesh generation typically proceeds inwards from boundaries. In some cases, generalizations are available to 3-D. These methods often require background grids, causing effectiveness issues in cases of strong node density variations. Several strategies and implementations are compared in [3] . In cases where no spatial node density variations are needed, the method in [2] is effective in 2-D.. The primary advantages of the present algorithm are computational speed, algorithmic simplicity, and the quality of the generated node distributions. Since it is not iterative, we refer to it as 'node placing'. The user does not need to decide on trade-offs between computer time and distribution quality. Our focus will be on how to implement it simply and effectively, and not on mathematical analysis of either the algorithm itself or of the generated node distributions. For certain aspects (e.g. internal parameter choices) that only minimally influence the quality of the obtained node distributions, our preference has been to make robust ad hoc choices rather than to pursue theoretical analysis of possible variations. Figure 1 illustrates in a very small case the node sets the present procedure will produce very rapidly -in Matlab at a rate of roughly 10,000 nodes per second.. There is a rapidly growing literature on applying RBF-FD to solve many different types of PDEs. This includes a wide range of systems arising in elasticity, geophysics, seismic exploration, electromagnetics, etc., mostly of time dependent character, but also elliptic-type equilibrium equations. The present paper will not repeat this literature but instead focus on associated node generation issues. Although the RBF-FD literature by now is quite extensive, there are as of yet few survey type articles in the field. The first one, with focus on geosciences, appeared recently [7] , with an a more general survey article [9] as well as a monograph [8] scheduled for publication in 2015.. The outline of this paper is as follows: Section 2 presents the basic node placing algorithm, with the Matlab code given in the Appendix, as well as a common test case used in image rendering. The paper starts with this test case as it provides a much more challenging example than normally would be found in the PDE discretizations for creating layouts due to its extreme variations in node density. Section 3 discusses the use of a local node repel algorithm for making minor adjustments near non-rectangular boundaries, as required in RBF-FD discretizations. Section 4 describes how to generalize the domain from a rectangle to 2-D surfaces embedded in 3-D space, such as spheres and tori. This is followed by some conclusions.. 2 The basic algorithm: Creating node sets of variable density in a rectangular domain Numerous applications require node distributions of greatly variable density while still locally maintaining a quasi-uniform structure. The proposed scheme is very simple to implement (cf. the Matlab code in the Appendix). It provides a computationally fast alternative to algorithms based on energy minimization and Voronoi tessellations, with the additional benefit of both algorithmic and mathematical simplicity. The high computational speed is especially significant for time dependent problems that require the distribution to be updated frequently.. For irregularly shaped 2-D regions, node distributions can be specified as desired along the boundaries. This provides an approach for creating variable density node sets not only for planar regions, but also for curved surfaces. In some applications, such as creating node sets for seismic modeling, the presence of many separate regions allows straightforward parallelization on distributed memory computers.

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


Detection

of

temporal

lobe

epilepsy

using

support

vector

machines

in

multi-parametric

quantitative

MR

imaging

Abstract

The

detection

of

MRI

abnormalities

that

can

be

associated

to

seizures

in

the

study

of

temporal

lobe

epilepsy

(TLE)

is

a

challenging

task.

In

many

cases,

patients

with

a

record

of

epileptic

activity

do

not

present

any

discernible

MRI

findings.

In

this

domain,

we

propose

a

method

that

combines

quantitative

relaxometry

and

diffusion

tensor

imaging

(DTI)

with

support

vector

machines

(SVM)

aiming

to

improve

TLE

detection.

The

main

contribution

of

this

work

is

two-fold:

on

one

hand,

the

feature

selection

process,

principal

component

analysis

(PCA)

transformations

of

the

feature

space,

and

SVM

parameterization

are

analyzed

as

factors

constituting

a

classification

model

and

influencing

its

quality.

On

the

other

hand,

several

of

these

classification

models

are

studied

to

determine

the

optimal

strategy

for

the

identification

of

TLE

patients

using

data

collected

from

multi-parametric

quantitative

MRI.

A

total

of

TLE

patients

and

control

volunteers

were

analyzed.

Four

images

were

considered

for

each

subject

map,

map,

fractional

anisotropy,

and

mean

diffusivity)

generating

regions

of

interest

per

subject,

then

different

classification

models

were

studied,

each

one

comprised

by

a

distinct

set

of

factors.

Subjects

were

correctly

classified

with

an

accuracy

of

Further

analysis

revealed

that

the

heterogeneous

nature

of

the

disease

impeded

an

optimal

outcome.

After

dividing

patients

into

cohesive

groups

left-sided

seizure

onset,

right-sided

seizure

onset)

perfect

classification

for

the

left

group

was

achieved

accuracy)

whereas

the

accuracy

for

the

right

group

remained

the

same

We

conclude

that

a

linear

SVM

combined

with

an

ANOVA-based

feature

selection+PCA

method

is

a

good

alternative

in

scenarios

like

ours

where

feature

spaces

are

high

dimensional,

and

the

sample

size

is

limited.

The

good

accuracy

results

and

the

localization

of

the

respective

features

in

the

temporal

lobe

suggest

that

a

multi-parametric

quantitative

MRI,

ROI-based,

SVM

classification

could

be

used

for

the

identification

of

TLE

patients.

This

method

has

the

potential

to

improve

the

diagnostic

assessment,

especially

for

patients

who

do

not

have

any

obvious

lesions

in

standard

radiological

examinations.

Magnetic

resonance

imaging

(MRI)

is

a

powerful

tool

for

the

evaluation

of

patients

with

brain

disorders

and

neurological

diseases.

For

those

with

temporal

lobe

epilepsy

(TLE),

the

most

common

type

of

epilepsy

in

adults

it

is

the

entry

point

to

a

clinical

workflow

that

may

conclude

with

temporal

lobe

surgery

and

an

improved

quality

of

life.

Finding

evidence

of

seizures

on

MRI

is

a

clear

diagnostic

element

for

TLE,

however

this

task

is

not

easy

given

that

epileptogenic

lesions

are

often

small

and

can

be

missed,

they

can

be

uncertain

due

to

subtle

intensity

changes,

or

only

perceptible

after

image

post-processing.

Furthermore,

due

to

the

multi-factorial

nature

of

the

disease,

the

localization

and

type

of

the

lesions

can

vary

from

patient

to

patient..

While

the

visual

inspection

is

a

common

radiological

procedure

for

the

diagnosis

of

TLE,

it

has

been.

shown

that

the

detection

of

brain

pathologies

associated

with

TLE

can

be

improved

with

computer-assisted,

automatic

multi-parametric

MRI

analysis.

For

example,

the

detection

of

changes

in

the

shape,

volume

and

intensity

of

the

hippocampus

has

been

studied

using

structural

images

White

matter

abnormalities

have

been

detected

with

diffusion

tensor

imaging

(DTI)

in

TLE

patients

and

DTI

has

been

employed

concurrently

with

functional

MRI

(fMRI)

to

perform

language

lateralization

of

TLE

patients

A

support

vector

machine

(SVM)

is

a

classifier

that

uses

a

priori

knowledge

in

the

form

of

group

labels

(supervised

learning)

and

produces

a

decision

boundary

that

can

be

used

to

determine

the

label

of

new

examples

Recent

studies

have

examined

the

possibility

of

improving

TLE

detection

using

SVMs

on

MRI

data.

For

example

Focke

et

al.

show

correct

patient

lateralization

(left

vs.

right

seizure

onset)

using.

SVMs

on

and

DTI

data.

In

addition

to

lateralization,

Keihaninejad

et

al.

demonstrate

the

identification

of

TLE

cases

with

hippocampal

atrophy

from

cases

without

it

using

SVM

on

regional

volumes

obtained

from

MRI..

In

this

context,

the

goal

of

the

current

study

is

to

explore

TLE

detection

using

multi-parametric

quantitative

MRI

and

support

vector

machines.

For

that

purpose,

quantitative

maps

of

as

well

as

fractional

anisotropy

(FA),

and

mean

diffusivity

(MD)

are

estimated

for

every

participating

subject.

Quantitative

MRI

measures

biophysical

tissue

properties

and

it

has

the

potential

to

be

more

sensitive

to

TLE

detection

than

images.

Additionally,

quantitative

measurements

are

independent

of

experimental

settings

and

thus

comparable

between

different

scanners,

institutions

and

over

different

points

in

time

Methods

All

subjects

in

this

study

underwent

an

imaging

protocol

approved

by

the

Office

of

Research

Ethics

of

Western

University

(Canada).

The

imaging

protocol

comprised

and

DTI

sequences,

resulting

image

data

being

processed

to

obtain

four

different

quantitative

maps:

fractional

anisotropy

(FA)

and

mean

diffusivity

(MD).

Anatomical

atlas-based

labeling

was

used

to

define

ROIs

on

each

one

of

the

quantitative

maps

and

subsequently

to

measure

and

extract

regional

features..

Given

that

the

number

of

features

exceeded

the

number

of

subjects

features,

subjects),

two

different

feature

selection

methods

were

explored

to

discard

irrelevant

or

uninformative

features.

Then,

the

feature

space

was

further

reduced

using

principal

component

analysis

(PCA)..

An

SVM

was

trained/tested

on

the

filtered

feature

space

using

a

leave-one-out

cross-validation

strategy

(LOOCV),

where

the

SVM

was

tasked

with

predicting

the

label

(patient

or

control)

for

the

omitted

subject

Figure

Once

all

subjects

were

evaluated,

sensitivity,

specificity

and

classification

accuracy

were

measured

and

reported.

This

procedure

was

repeated

for

each

classification

model

obtained

by

the

combination

of

the

following

elements:.

the

image

that

originates

the

features

FA,

MD,

or

all

combined).

the

method

to

select

features.

the

cardinality

of

the

requested

feature

set

[K]

the

use

of

PCA

to

reduce

the

feature

space.

the

type

of

SVM

A

detailed

comparison

among

classification

models

is

reported

in

the

results

section

along

the

best

classification

scenarios.

Specific

recommendations

regarding

the

optimal

model

are

given

in

the

discussion

section.

In

addition,

an

analysis

of

the

elements

constituting

a

classification

model

and

their

influence

in

the

classifiers

performance

is

also

discussed.

Finally,

the

features

relevant

for

classification

are

analyzed

and

their

clinical

significance

is

considered.

This

paper

describes

a

novel

approach

for

the

detection

of

TLE

patients

using

feature

selection

and

support

vector

machine

methods.

The

novelty

of

this

work

consists

of

the

use

of

multi-parametric

quantitative

imaging,

the

definition

of

a

measurement

of

regional

asymmetry

using

a

non-parametric

statistical

test

and

the

evaluation

and

validation

of

the

optimal

cardinality

of

the

problem.

The

main

contribution

of

this

paper

comprises

the

evaluation

of

key

factors

influencing

classification

performance,

namely

the

feature

selection

method,

the

possibility

of

a

further

dimensionality

reduction

step

with

the

use

of

PCA,

and

the

type

of

support

vector

machine

for

this

type

of

data.

Then,

relevant

features

are

identified

and

their

clinical

significance

is

addressed..

Our

results

demonstrate

that

the

identification

of

TLE

subjects

based

on

quantitative

MR

images

is

possible.

In

particular,

DTI

derived

features

seemed

to

be

more

effective

than

features,

which

in

general

performed

sub-optimally.

In

all

experiments,

good

accuracies

were

attainable.

However,

the

identification

of

R-TLE

patients

proved

to

be

a

difficult

problem

and

in

this

case,

the

multi-parametric

approach

proved

to

be

slightly

better

accuracy)

than

the

classification

on

individual

feature

spaces.

The

subsequent

feature

analysis

confirmed

that

the

key

ROIs

for

patient

identification

do

indeed

belong

to

the

temporal

lobe.

Their

relevance

in

TLE

has

been

indicated

by

clinical

findings

and

similar

research

studies.

These

results

reflect

the

sensitivity

of

quantitative

imaging

and

the

utility

of

the

presented

method

towards

the

detection

of

TLE..

Classification

models

including

PCA

transformation

after

feature

selection

in

general

provided

better

results

than

the

non-PCA

models.

Among

the

classification

models

evaluated,

the

anova-pca-linear

model

demonstrated

the

best

balance

between

classification

performance

and

computation

time,

both

key

elements

in

the

analysis

large

datasets.

Although

SVM

behaves

coherently

in

small

sample

scenarios,

a

larger

patient

sample

would

allow

the

amount

of

internal

cross-validation

and

sub-sampling

to

be

reduced.

In

machine

learning

terms,

this

could

lead

to

a

better

balance

between

bias

and

variance.

in

diffusion-weighted

images

using

large-deformation

diffeomorphic

metric

mapping,

Magnetic

Resonance

Imaging

A.

Figures

Figure

Classification

accuracy

versus

number

of

features

(K

parameter).

As

the

requested

cardinality

increases,

the

classification

accuracy

drops.

This

is

expected

given

that

important

features

are

picked

first

and

adding

irrelevant

features

will

not

improve

accuracy

but

rather

will

add

noise

and

increase

the

dimensionality

of

the

feature

space

beyond

the

intrinsic

dimension

of

the

classification

problem.

B.

Tables

Highlights

Measuring

regional

asymmetry

is

fundamental

for

optimal

classification

results.

DTI

derived

measures

seem

to

be

more

informative

than

maps

for

classification

of

TLE

patients.

Each

of

the

eight

classification

models

was

evaluated

on

the

full

range

of

possible

K

values

(from

to

features

for

the

universal

feature

space

and

from

to

for

individual

image

subspaces).

Given

that

K

is

the

only

parameter

that

needs

to

be

set

by

the

user,

an

adequate

range

for

the

choice

of

K

is

addressed

in

the

discussion

section

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


User Profiling in the Chronobot/Virtual Classroom System The Chronobot/Virtual Classroom (CVC) system is a novel time knowledge exchange platform where any pair of users can exchange their time and knowledge. User profile that contains user attributes, preferences, and learning patterns serves as a primary basis to identify exchange partners and determine exchange rates. In this paper, we described the methodology to acquire knowledge about users i.e. user profile from their activities. The association between user profile and user behaviors (e.g. online reading, chatting and time/knowledge exchanging) is identified by several feedback indicators extracted from browsing history, chatting session and exchange transaction. A linear learning model is constructed to fuse multiple feedback indicators to infer user preference. The methods utilizing user profile to identify the exchange partners and determine the exchange rate are also described in detail. Comparing with the traditional face-to-face style teaching and learning, e-Learning is indeed a revolutionary way to provide education in life long term. Nowadays more and more people have benefited from various e-Learning programs. However, present-day e-Learning systems are still too rigid and do not lend themselves to the peer-to-peer learning in which any users can exchange their knowledge with any others. Chronobot/Virtual Classroom (CVC) [3] is a novel time knowledge exchange platform where any pair of users can exchange their time and knowledge. The chronobot is a time manage tool for storing and borrowing time. Using chronobot one can borrow time from some one and return time to the same person or someone else. The virtual classroom is a versatile communication tool that combines the functions of web browser, chatting room, white board, and multimedia display. The CVC system is an integration of chronobot and virtual classroom that allows users freely to switch between these two applications and get maximum benefits from both.. For example, illustrated in figure 1, George, Bill, and Suzie are all students who are doing a group project together in a graphics design course in which they use the CVC system to collaborate with each other. The whole project is divided into several tasks, each of which is mainly assigned to one person. George meets a problem in his task and can not solve it by himself. So he interacts with Bill and Suzie in the virtual classroom, and eventually they help him out. However, in order to keep workload even among teammates, George has to put in efforts either in the past or in the future to help Bill and Suzie. The chronobot serves as a platform for them to do such time and knowledge exchanges which could have significant value for many applications. Many more interesting scenarios can be found in [3] . Generally speaking, a transaction of the time/knowledge exchange in the CVC system includes the following steps:. 1. Identify a slice of time or knowledge for exchange; 2. Search for exchange partner or partners; 3. Perform time or knowledge exchange through bidding and negotiation; 4. Manage the exchange slice of time or knowledge; 5. Provide feedback on the results. In these processes, user profile that contains user attributes, preferences and learning patterns plays a vital role because:. It provides fundamental information to identify time or knowledge for exchange and exchange partners. It serves as a primary basis to determine time/knowledge exchange rate.. In this paper, we describe an approach to acquire knowledge about users represented by user profile based upon user activities such as web browsing, chatting and time/knowledge exchanging in the CVC system. We believe that our work has significant value, because the acquisition of knowledge about users is a key process not only in our system but also in many other applications. For example, the recommendation systems [1, 4, 6, 9] mainly depend on user profiles in terms of similarity and differences to provide particular suggestions. The personalized web search engine [10] can construct user profiles from browsing history and consequently provide personalized results to match the information needs of individuals. Compared to these applications, an effective user profiling is much more feasible in our system because of the following two reasons:. 1. Time/knowledge exchange (i.e. peer-to-peer learning) is a much more continuous process than the activities (e.g. online news reading and web searching) in many other applications. 2. Multiple data sources can be employed to assess user profile in our system. For example, in addition to browsing history, chatting session and knowledge/time exchange transaction can also serve as important input sources for the profiling process.. User preference is key information in user profile. As far as we studied, the majority of user profiling approaches mainly depends on the user feedbacks to retrieve user preference. The feedback can be assessed explicitly by rating, or implicitly by the user behaviors such as print and save. In this paper, we are not advocating either of these two approaches because both of them have significant advantage and disadvantages [10] . Instead we propose a methodology which can combine multiple feedback measures to get more complete and accurate assessment. User preference can be inferred on the basis of data from three sources, i.e. browsing history, chatting session, and knowledge/time exchange transaction. A linear learning model is constructed to fuse all the related data for the inference of user preference. Five feedback indicators -reading time, scroll number and print/save from browsing history, relational index from chatting session, and the exchange index from knowledge/time exchange transaction serve as input variables of the model. Demonstrated by the experiments in the prototype system, the proposed model can infer user preference much more accurately than any single one of these indicators. The applications of user profile -to identify the exchange partners and determine the exchange rate are also described in detail. A preliminary report about this work has been published in [7] .. The rest of this paper is organized as follows: two basic concepts user profile and ontology knowledge base in our system are described in section 2 and 3 respectively. Section 4 discusses the measures to identify the association between user activities and preferences, where five implicit feedback indicators are defined. The learning model to fuse these indicators for a final assessment of user preference is described in Section 5. The application of user profile is described in Section 6. In Section 7, our prototype system and experiments in it are described. The related research is discussed in Section 8, followed by brief conclusions in Section 9. Figure 2 User Profile in the CVC System The user profile is the physical realization of the user model, which is an abstraction of the user preferences and characteristics. Shown in figure 2, in the CVC system a user profile upf is organized as a 6-tuple: upf = < id, user-attributes, browsing-history, chatting-session, exchange-transaction, preference> id is a unique identification number. user-attributes is a vector A(u) = (a 1 (u), a 2 (u), …., a n (u)) = (x 1 , x 2 , …., x n ), where a i (u) = x i is the i th attribute for the user u -it could be user's name, expertise level, schedule and so on. browsing-history is a set of the pages which user has visited. For the purpose of user profiling, the corresponding behaviors are recorded for each page, e.g. reading time, number of scrolls and print/save. chatting-session is a set of conversations which user participated in the virtual classroom. All the contents are recorded in natural language. exchange-transaction is a set of time/knowledge exchange transactions which user performed in the chronobot. preference is a vector P(u) = (p 1 (u), p 2 (u), …., p n (u)) = (y 1 , y 2 , …., y n ), where p i (u) = y i is the preference of user on the i th topic. The topics are defined by the ontology knowledge base (which will be discussed in the next section). The CVC system is a novel time knowledge exchange platform that supports peer-to-peer learning. In this paper, we describe an approach to acquire and utilize the knowledge about users i.e. user profile in the CVC system. User preference -the most important information in user profile, is inferred from multiple data sources i.e. browsing history, chatting session, and time knowledge exchange transaction. The methods utilizing user profile to identify the exchange partners and determine the exchange rate are also described in detail.

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Alignment-Free Sequence Comparison Based on Next-Generation Sequencing Reads Next-generation sequencing (NGS) technologies have generated enormous amounts of shotgun read data, and assembly of the reads can be challenging, especially for organisms without template sequences. We study the power of genome comparison based on shotgun read data without assembly using three alignment-free sequence comparison statistics, , respectively, are used to first cluster five mammalian species with known phylogenetic relationships, and then cluster 13 tree species whose complete genome sequences are not available using NGS shotgun reads. The clustering results using d S 2 are consistent with biological knowledge for the 5 mammalian and 13 tree species, respectively. Thus, the statistic d S 2 provides a powerful alignment-free comparison tool to study the relationships among different organisms based on NGS read data without assembly. In this section, we first study the power of D 2 , D Ã 2 , and D S 2 for detecting the relationships between two sequences related through the common motif model using NGS data. In the simulation study, we consider both homogeneous sampling and heterogeneous sampling of the reads across the genome. Then we compare the simulated power and the theoretical power using the formulas given in Theorem 2.5. Finally, we use the d 2 , d Ã 2 , and d S 2 dissimilarity measures to first cluster human, rabbit, mouse, opossum, and chicken, and then cluster 13 tree species using NGS read data.

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