Data-Mining-Driven Neighborhood Search Metaheuristic approaches based on neighborhood search escape local optimality by applying predefined rules and constraints, such as tabu restrictions (in tabu search), acceptance criteria (in simulated annealing), and shaking (in VNS). We propose a general approach that attempts to learn (offline) the guiding constraints that, when applied online, will result in effective escape directions from local optima. Given a class of problems, the learning process is performed offline and the results are applied to constrained neighborhood searches to guide the solution process out of local optimality. Computational results on the Constrained Task Allocation Problem (CTAP) show that adding these guiding constraints to a simple tabu search improves the quality of the solutions found, making the overall method competitive with state-of-the-art methods for this class of problems. We also present a second set of tests on the matrix bandwidth minimization problem. Heuristic and metaheuristic techniques (Glover and Kochenberger 2003) are widely used for finding high quality solutions to large combinatorial optimization problems, for which identifying the optimal solution through an exact procedure would be computationally impractical. Some metaheuristic approaches are based on maintaining a set of solutions and creating new ones by way of executing combination procedures. Others, however, use a neighborhood search (NS) to move from one point to another in the solution space. The neighborhood of a solution is defined by the move mechanisms that are applied to transform one solution into another. Local search is the simplest form of a NS procedure, which limits the moves to those that direct the search from the current solution to a neighboring solution with a better objective function value. In other words, a local search accepts only improving moves to transform the current solution into one that is in its neighborhood. The process stops when no improving move is available and the resulting solution is called a local optimum (with respect to the defined neighborhood).. Metaheuristic procedures include strategies that aim at escaping from local optima with the goal of improving the best solution found during the search. In tabu search, escaping local optima is achieved by imposing tabu restrictions (Glover and Laguna 1997) or executing moves that are guided by proxy objective functions, as in path relinking (Glover, Laguna, and Marti 2003) . Simulated annealing relies on randomization to allow non-improving moves to be executed in order to escape local optimal points. Greedy Randomized Adaptive Search Procedures (GRASP) restart the search after reaching a local optimum. A common aspect to these strategies is that escape directions are determined by rules that are defined a-priori. For instance, tabu activation rules forbid certain moves by restricting a subset of attributes from assuming certain values for a predetermined number of iterations. In path relinking, only moves that take the current solution "closer" to a guiding solution are considered. In general, escaping from local optima in a NS-based approach requires the execution of a non-improving move and the reduction of the neighborhood. Often, the neighborhood reduction is achieved by enforcing guiding constraints. These guiding constraints are the result of design decisions that may or may not be customized to a particular class of problems. For instance, a tabu search approach for permutation problems may use swaps to explore the neighborhood of a given solution. After a swap of two elements, a tabu restriction could be imposed to forbid the exchange of positions of these two elements for a pre-specified number of iterations. This tabu-activation rule effectively restricts the available moves and hence shrinks the neighborhood of the current solution. In Variable Neighborhood Search (VNS), a metaheuristic methodology suggested by Mladenovic and Hansen (1997) , escaping from local optima requires the systematic change from one neighborhood to another. This is achieved by embedding a set of move mechanisms of various degrees of complexity. Simple moves are tried first, followed by more complex moves as the search becomes trapped in local optima corresponding to the neighborhood defined by the moves currently under consideration. Both the set of moves (and therefore neighborhoods) to be explored and the order in which they will be considered are specified apriori, a task that often requires a considerable effort in the development phase of the solution procedure.. Our goal is to define a general learning model, valid for any class of problems, whose output is the definition of the best set of guiding constraints and the conditions for their applications. The learning is performed offline by analyzing pairs of local optima drawn from a training set of problem instances belonging to a class under consideration. The learning process focuses on formulating constraints that guide the search in effective directions to escape local optimality. Effectiveness is measured by the ability to improve the current position after executing non-improving moves that are necessary to leave the local optimal point. We show how the rules learned during the offline process can be embedded in search procedures to be used online for tackling instances of the same class of problems. The process requires the following four elements:. 1. A procedure to randomly generate feasible solutions for a given problem instance 2. A set of basic moves that define the neighborhood search 3. An attribute representation of a solution 4. An attribute representation of a pair of solutions where the "attribute representation" of an object is a numerical vector whose components characterize the object.. Some work has already been done to couple machine learning and data mining (DM) techniques in meta-heuristic searches, as reported by Boyan, Buntine and Jagota (2000) and Jourdan, Dhaenens and Talbi (2006) . In particular, the existing learning methods can be classified into two categories: online and offline methods. Both of these approaches are learning procedures that -after a training phase -are used within a search process in an attempt to improve its effectiveness. The main difference between the two approaches lies on the training: online methods are trained using information collected during the current execution of the search procedure, while offline methods learn from a training set of instances of the same class of problems. Online procedures are used, for instance, to select a branch in a branch-and-bound algorithm given information gathered during the search itself, as in (Glover, Klingman, and Phillips 1989) , or to find patterns useful to enhance the construction phase of GRASP, as proposed by Santos et al. (2008) . On the other hand, offline procedures attempt to identify structures and patterns shared by the entire class of problems. In the literature, offline approaches have been further divided into two categories, depending on their goal: 1) algorithm-selection approaches and 2) parameter-tuning approaches. Algorithm-selection approaches use historical performance data with the goal of selecting the most promising procedure from a portfolio of available ones. Parameter-tuning approaches have the goal of finding the set of parameter values that will make a particular procedure perform at the highest level. Our work is an offline approach that cannot be directly cast as an algorithm-selection or parameter-tuning approach. We argue that our approach defines a new category of offline methods that may be referred to as data mining driven neighborhood search (DMDNS). This category refers to search procedures that modify neighborhoods with constraints that have been learned offline by mining data. Padmanabhan and Tuzhilin (2003) point out that this area has been largely neglected in the literature. Broadly speaking, DMDNS consists of two components: 1) an offline learning procedure that learns the guiding rules for escaping local optimality, and 2) a procedure that applies the guiding rules online. The offline learning procedure may be viewed as a process that is in some ways similar to fitness landscape analysis (Höhn and Reeves 1996) . Both approaches attempt to characterize the search space. However, while the results of fitness landscape analysis are interpreted by the analysis in order to design global search strategies (e.g., effective neighborhood functions), the results of a learning procedure such as the one we propose are directly embedded in the neighborhood search in the form of constraints that are activated upon reaching local optima. In this work, we propose a data mining driven tabu search ( ) and we show that our approach leads to improved escape directions when compared to those resulting from enforcing customary tabu search rules. We intend to establish a new research avenue in the development of learning procedures whose purpose is to capture aspects of the structure of a given class of problems by considering a "training set" of instances. The approach focuses on learning "off line" and only once for a given class of problems.. We will be using the Constrained Task Allocation Problem (CTAP) as an example throughout the paper in order to enhance the presentation of our concepts. We then apply the method to the Matrix Bandwidth Minimization Problem (MBMP), in an attempt to show its general application. We realize, however, that to claim success in a variety of problem settings, additional applications are required. We hope that our work inspires others to follow this line of research and expand the applicability of the concepts that we introduce. Existing approaches based on neighborhood search may be viewed as consisting of a local search and a set of predefined rules (e.g., tabu activation) that impose constraints (e.g., tabu restrictions) that are enforced during the search in order to allow the process to escape local optimality. We propose a method that learns the rules (learning procedure) from a training set of instances of a problem class and applies them (escape procedure) to new instances. Our method is general and we show its application to the constrained task allocation problem. The escape procedure is also one instance of a set of possible ways in which the rules that result from the learning procedure may be used to constrain a neighborhood search. The results that we obtained for the CTAP are encouraging. First, we showed that the "enhanced" tabu search outperforms the version that does not take advantage of the learning process. Then we showed that it also outperforms a tabu search that is hybrid in nature (Chen and Lin, 2000) , where the hybridization is the result of a predefined noising strategy that modifies the search directions by perturbing key problem data. This is equivalent to adding "soft" constraints with the purpose of finding new search directions out of local optimal points. The difference is that the directions are not the result of rules that were learned but of design choices (e.g., the parameters to perturb and the amount of the perturbation). Although our method requires the design of problem-specific attributes, the tradeoff is that the effort required to implement the search procedure is minimized. In other words, our framework focuses on the characterization of the problem (e.g., by the definition of attributes) rather than on the design of specialized solution strategies. While what we propose may not always be the faster approach, our experiments show that it can achieve better results. Furthermore, our approach may be overlaid on existing procedures based on neighborhood search in order to improve performance, as we did for the MBMP.. Our proposal involves some offline effort because the learning procedure is based on a mathematical formulation that is solved exactly with commercial software. This opens the door to interesting opportunities for future research, for instance, one consisting of the development of efficient exact or heuristic procedures to accelerate the learning process. The need for such a method depends on the context because typically the learning process would be performed once for a given class of problems. A more challenging problem perhaps will be to design on-line learning procedures that use a portion of the computational budget to learn rules and apply them to the problem instance being solved. Furthermore, computational studies on the impact obtained by varying the training set or the effort spent on the learning procedure would certainly be of interest.. Other research directions point to the application of our concepts to more sophisticated neighborhood searches, such as those based on VNS, or to search techniques that are based on evolving a population of solutions via combination mechanisms. In the former, the learning must consider a paired-data set originated from points that are local optima relative to all VNS moves. In the latter, the rules could identify situations for applying certain combination methods over others. We envision an alternative paradigm for the design of metaheuristic procedures, where data mining techniques are used not as a second level enhancement (e.g. for parameter tuning), but as a main tool to design the rules used to search the solution space.

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


Eminence of scientists in the light of the h-index and other scientometric indicators Scientometrics cannot offer a simple consistent method for measuring the scientific eminence of individuals. The h-index method introduced by Hirsch was found applicable for evaluating publications of senior scientists with similar publishing features, only. Some simple methods -using the number of citations and journal papers, and the number of citations obtained by the most frequently cited papers -are suggested and tested to demonstrate the advantages and disadvantages of such indexes. The results indicate that calculating scientometric indexes for individuals, self-citations should be excluded and the effect of the different bibliometric features of the field should be taken into account. The correctness of the indexes used for evaluating journal papers of individuals should be investigated also on the individual level. Verification of scientometric indicators may be performed by peer review. Verification of peer review may be performed by scientometric indicators. Consequently, if the results obtained by both methods lead to the same conclusion, we may regard the methods to be correct. This process corresponds to the convergent indicators principle of Martin and Irvine [1] .. The scientometric assessment of publications of individuals is a more complicated endeavour than that of teams, journals or countries. The reason for this is not only the smaller number of papers to be assessed but the increasing importance of unified publication features. Some characteristic publication features of individuals may be, for example, preference of special types of publications (i.e. journal papers vs conference proceedings or book chapters; letters, short communications vs articles or reviews etc.); high or low self-citation rate; or working with a high or low number of co-workers, cooperating partners and co-authors. These factors may strongly influence the scientometric indicators selected. Therefore, because of the specificities of methods applied to By this classification scheme high impact (manifested by citations) may be attained either by many papers or by few papers.. Eminence of scientists and research teams should be assessed primarily by their impact on science and not by the amount of information produced. From the definition of the h-index, it follows, however, that the value of the index may be equal to or less than the number of papers published (h ≤ P). The h-index cannot exceed the number of papers. Scientists having published for example 40 papers cannot attain an index higher than 40.. The measure of the index is strongly influenced by the distribution of citations over papers, as well. Scientific eminence may be related to the eminence of scientific publications. Two methods may be applied to obtain criteria of publication eminence. We can select elite groups of individuals, teams or countries by peer assessment and calculate the respective averages of their publication and citation indicators. These measures may serve as standards or norms of eminence. In this way we can calculate the number of journal papers published, citations obtained or citations by paper of Nobel-prize winners [6] , members of national academies, persons decorated by national or international awards [5] , elite university teams [7] etc.. Another method of determining publication and citation standards of eminence is the selection of a share within the total which may be assumed as an elite group. Essential Science Indicators, for example, apply 0.1 or 1.0 or 10% of total items as an elite. The percentage criteria may be valid only for whole scientometric systems. Partial systems, namely, may not show all characteristics of the respective whole scientometric system and the distribution of respective indexes may also differ.. The concept behind eminence thresholds dates back to Galton, cited in [8] , who found that the square root of the population of a country or profession or other category may represent the number of truly eminent individuals.. indicators for greater sets of papers (e.g. those of countries). However, low aggregation levels (publications of teams or persons) may cause great discrepancies. Especially individuals with many cooperating partners may receive many self-citations.. • Application of the Hirsch method [2] strongly handicaps those who work alone or with small teams as the highest number of journal papers published represents the highest limit of the h-index.. • The Hirsch index does not take into account the various bibliometric features of different fields and subfields.. In Table 1 some examples are given to demonstrate some shortcomings of the Hirsch index in the case of a low number of papers and special distribution of citations over papers. The comments made may throw a light on some undesirable tendencies in applying the h-index method where an increasing number of citations or enhanced citedness is often not acknowledged. Table 1 A: Scientific research is multifaceted, consequently it is very dangerous to rely on a single indicator. Instead, the application of composite indicators can be recommended with several partial indicators weighted by the purpose of the assessment [9] . The Hirsch index seems to be a composite index as well. Its value depends not only on the number and distribution of citations over journal papers but on the number of papers as well. The relationships between the factors mentioned, however, have not been clarified yet.. In summary, it may be concluded that:. • the Hirsch index [2] alone is not appropriate for analysing the publication performance of scientists with a relatively low number of papers;. • calculating with self(dependent)-citations included may strongly distort publication impact indexes. Therefore, the application of such indexes for evaluating individuals seems to be dangerous.. Self-citations may artificially enhance the value of the indexes, which may harmfully influence the consequences of assessments [14] ;. • the h-index strongly handicaps scientists with only some papers outstandingly cited; at the same time preference is given to scientists with many frequently cited papers (among these there may or may not be highly cited papers);. • the h-index and the PI type indexes strongly depend on fields and subfields;. • the h-index may be used together with relative impact indexes to approximate the publication performance of senior scientists with similar publication characteristics and working in fields with similar bibliometric features. The h-values of outstanding scientists may serve as examples for the highest possible level of publication activity in different fields;. • the means of different scientometric indexes obtained from data of greater sets of publications may yield similar conclusions; this is, however, not sufficient in each case for verifying the correctness of the individual indexes. Because of the specificities of the evaluation methods some individuals may suffer disadvantages while others may enjoy undeserved advantages. Correctness of the indexes should be investigated also on an individual level; and. • when assessing the publications of individuals the application of relative impact indicators [15, 17] seems to be inevitable.

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


User-assisted similarity estimation for searching related web pages To utilize the similarity information hidden in the Web graph, we investigate the problem of adaptively retrieving related Web pages with user assistance. Given a definition of similarities between pages, it is intuitive to estimate that any similarity will propagate from page to page, inducing an implicit topical relatedness between pages. In this paper, we extract connected subgraphs from the whole graph that consists of all pairs of pages whose similarity scores are above a given threshold, and then sort the candidates of related pages by a novel rank measure which is based on the combination distances of a flexible hierarchical clustering. Moreover, due to the subjectivity of similarity values, we dynamically supply the ordering list of related pages according to a parameter adjusted by users. We show our approach effectively handles a set of pages originating from three related categories of Web hierarchies, such as Google Directory. The experiments with three similarity measures demonstrate that using in-link information is favorable while using a combination measure of in-links and out-links lowers the precision of identifying similar pages. Modern search engines store the link structure of a large part of the Web to serve users in two main ways, namely Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. through "keyword" and "related pages" searches [1] . Both broad-topic and short queries will retrieve thousands of "hits" which may prove difficult for users to locate pages from. Therefore, query expansion and query recommendation techniques have been studied to help users formulate better queries [3, 9] . On the other hand, it often happens that a user is already familiar with some Web pages and needs to find more related ones. Thus, the task of identifying similar pages on the Web has also become an important issue.. Recently the link-based similarity search has gained much attention. Co-citation [26] and bibliographic coupling [19] are two of the more fundamental measures used to evaluate the similarities between two scientific papers. When applied in the Web, these bibliometrics measures can be thought of as local in nature because they typically consider only the local link properties between two pages which exist inside a narrow area of the Web graph. Kumar et al. [21] used bipartite subgraphs to recognize the cores of the communities and empirically concluded that a large fraction is in fact topically coherent. These approaches are not good at finding large related subsets of the Web graph due to their localized structures. PageRank [4] , HITS [20] , SimRank [18] , Random Walk with Restart [27] are more global since they work by iteratively transmitting weights through the Web graph. The weights reflect the similarity approximation between pages and, therefore, can search large collections of related and valuable Web pages. However, if the Web page collection comes within several topics, they may only cover the most prevalent topics and leave out the less prevalent ones. In a solution for HITS, one has to compute multiple eigenvectors in order to extract the smaller community that one is interested in. Calado et al. [5] compared the bibliometrics and HITS-based similarity measures, from which the co-citation measure presented the best performance in determining if two pages are related.. Alternatively, similarity metrics such as co-citation and bibliographic coupling can be used along with clustering techniques, thus identifying related pages in a cluster that is a collection of pages which are similar between them and are dissimilar to the pages belonging to other clusters. The fundamental problem in attempting to separate the Web into clusters based upon its link structure and any kind of normative definition of a cluster is that the size of the Web means that any standard approach based upon the whole Web graph would be computationally infeasible. Flake et al. [12] figured out this problem in another way by developing a heuristic-based algorithm that iteratively extends any seed set of pages into a larger interconnected one.. However, the Web graph is dynamic and massive in nature, which brings about three limitations during computing similarities: 1) links of pages have not yet been crawled; 2) loading the whole Web graph to memory is unavailable for the present; 3) some pages have good content but have not been linked by many authors, which has already been addressed by Dean et al. [10] . They also pointed that their algorithm does not claim to find all relevant pages. Due to the limited information is available, we consider how to estimate relatedness between pages Our idea is that the propagation of localized bibliometrics similarity on a globalized neighborhood graph of similarity that is explained in Section 5.1 will alleviate the problem. For example, using co-citation as a similarity measure, if a page A is with the score of 0.5, and similar to a page B that is similar to a page C with the score of 0.4, it is intuitive for us to infer that A is also similar to C with some probability (e.g., 0.5 * 0.4=0.2), though the co-citation between A and C is 0. The score of 0 does NOT mean the two pages are definitely dissimilar. They may only be not popular enough to be linked by many other pages, or their link information may still be un-crawled or unloaded. We think that the similarity scores will propagate from page to page to compensate for these limitations of missing information. One more observation is that if there is a page D similar to C with the score of 0.01, our inference is that A is more similar to C than D. However, because the link information in the Web is noisy, the score between C and D (i.e., 0.01) is too small to be credible.. In addition, we should not overlook the subjectivity of similarity. SimRank [18] and Random Walk with Restart [27] , as link-based methods, computed the static similarity scores which are sensitive to specific choices made by the authors of Web pages. Different users, however, have different standards to measure relatedness. For example, given a page about knowledge discovery, some people point to database technologies for useful information, while others prefer the topic of Artificial Intelligence. Sometimes they cannot precisely judge whether a page is related or not by themselves. Therefore, page-to-page similarity is not a fixed value, especially for cross-topic pages. If the ordering list of related pages may be able to be changed under some constraints, then the users will be supplied with more candidates of related pages.. Based on the above facts, we propose a user-assisted estimation approach for similarity search in this paper. Our approach uses three bibliometrics measures, and has three major steps: computing similarity scores of pairs of pages, extracting connected subgraphs from the neighborhood graph of similarity, and applying sequential, agglomerative, hierarchical, nonoverlaping (SAHN) clustering on these subgraphs for ranking related pages. The flexibility in the SAHN clustering makes it possible for users to take part in the process of searching related pages. The main contributions of this paper are as follows.. (1) We put forward the problem of similarity estimation for searching related pages.. (2) A novel rank mechanism is proposed to put in order the related pages based on the monotonicity of the successive combination distances in the SAHN clustering.. (3) Through our approach, users can become active participants during the search instead of just being passive acceptors of results.. The rest of this paper begins with a review of the related work. We then describe three bibliometrics measures in Section 3. In Section 4, concerns regarding the SAHN clustering method are addressed. The details of our approach are given in Section 5. Finally, we report on the experimental results and follow them up with our conclusions and directions towards future works. In this paper, we put forward the problem of the similarity estimation for searching related pages. To solve this problem, our proposed approach efficiently computed the similarity graph above a specified threshold and used the combination distances of the SAHN clustering method to adaptively rank the related pages according to a parameter adjusted by users. Our experimental results show that the co-citation measure, an in-link based method, generally outperformed the other two measures in precision. Exploring larger out-links on the web can be futile and dangerous. The results may be offset by incorrect information collected in the search. In addition, the flexible strategy of the SAHN clustering makes users pick up different related pages in terms of their requirements. Finally, we described the potential of similarity scores used in a novel way to identify cross-topic pages. We are now crawling much larger amount of web page information to further test our approach. In the future, scalable evaluation strategies should be studied, especially when tuning parameters is needed. Figure 3 ; "X": non-related pages; other numbers are positions in the ordering list. A considerable amount of research has focused on examining collections of hyper-linked pages and structures. These works have been very cross-disciplinary, touching the hypermedia, the World Wide Web, Sociology, bibliometrics, and even culture and the communication fields. We summarize a small part of these works to put our own study within the proper context. The notion of "similarity" is subjective and difficult to measure. To make experimental results more objective, we used the Google Directory as a form of "ground truth" for relatedness to evaluate three similarity measures discussed in Section 3.

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


A Variational Multiscale Method for Turbulent Flow Simulation with Adaptive Large Scale Space In turbulent flows it is only feasible to simulate large flow structures. Variational multiscale (VMS) methods define these flow structures by projections into appropriate function spaces. This paper presents a finite element VMS method which defines the large scale projection space adaptively. The adaption controls the influence of an eddy viscosity model and it is based on the size of the so-called resolved small scales. The adaptive procedure is described in detail. Numerical studies at a turbulent channel flow and a turbulent flow around a cylinder are presented. It is shown that the method selects the large scale space in a correct way and that appropriately chosen parameters improve the results compared to the basic method, which uses the same local large scale space in the whole domain and for all times. Incompressible flows are governed by the incompressible Navier-Stokes equations which, in dimensionless form, read. (1). Here, Ω ⊂ R 3 is a bounded, connected domain with polygonal boundary ∂Ω, [0, T ] is a finite time interval, u is the fluid velocity, p is the pressure, f is an external force, ν is the kinematic viscosity, u 0 is the initial velocity field, and D(u) = (∇u + (∇u) T )/2 is the velocity deformation tensor (symmetric part of the gradient). Turbulent flows are characterized by a multitude of different sizes for the flow scales, fact which makes the simulation of such flows by direct discretization generally infeasible with the currently available computer hardware. As the resolution of all flow scales is not possible, and the unresolved scales are important for the turbulent character of the flow, their influence onto the resolved scales needs to be taken into account via a turbulence model. Popular methods for simulating turbulent flows include k − ε models [27] and Large Eddy Simulation (LES) [30, 2] . In LES, the flow field is decomposed through spatial filtering and this method aims at an accurate simulation of only the so-called resolved scales. Widely used traditional LES models are Smagorinsky-type models [31, 6, 26] . As an alternative, Variational Multiscale (VMS) methods are a rather new approach for simulating turbulent flows. The basic idea of VMS methods, in contrast to traditional LES, is the use of variational projections instead of filtering for the scale decomposition, thus eliminating several difficulties of the traditional LES, e.g. commutation errors [5, 15, 3, 1] . VMS methods for turbulent flow simulations were derived from general principles for treating multiscale phenomena [11, 10] . For an introduction as well as a review to VMS methods and their relation and differences to traditional LES methods, we refer to [12, 13, 17] . There are meanwhile several realizations of VMS methods, see the reviews in [25, 21] . The present paper considers a three-scale VMS method, i.e. the flow is decomposed into three scales: large (resolved) scales, resolved small scales, and (small) unresolved scales. Assuming that the direct influence of the unresolved scales onto the large scales is negligible, and thus the direct influence of the unresolved scales is confined to the resolved small scales, the influence of the unresolved scales onto the resolved small scales is modeled with a turbulence model of eddy viscosity type. There is no direct turbulence modeling for the large scales, however the large scales are still influenced indirectly by the small unresolved scales due to the coupling of all three scales. The considered VMS method uses finite elements as underlying spatial discretization, therefore it is called finite element VMS (FEVMS) method. The projection for the definition of the scales is contained explicitly in the set of equations. This projection-based FEVMS method was proposed in [18] . Its parameters are the finite element spaces used to define the scale decomposition and the turbulence model acting directly only on the resolved small scales. Regarding the turbulence model, the parameter in the additional viscous term added to the momentum equation is generally chosen to be an eddy viscosity model of Smagorinsky type [25, 20, 21] . Regarding the spaces, standard finite element spaces for the incompressible Navier-Stokes equations are used for all resolved scales and the separation of the large and the resolved small scales is achieved through an additional tensor-valued large scale space. Available numerical studies [25, 20, 21] show that the choice of the additional large scale space has more influence on the results than changing the parameter in the eddy viscosity turbulence model. All these studies used globally uniform large scale spaces, i.e. the polynomial degree of the finite element tensors was the same for all mesh cells. This polynomial degree was chosen before starting the computations and it remained fixed during the simulations of the flows. This paper will present an extension of the projection-based FEVMS method which computes the large scale space a posteriorily and adaptively. Firstly, it is no longer necessary to choose a large scale space for the whole simulation, the new method computes an appropriate large scale space during the simulation. Secondly, the large scale space may change during the simulation. This property is of importance if main features of the flow field change during the simulation. And thirdly, the large scale space is no longer uniform, different mesh cells may have finite element tensors with different polynomial degrees. This feature of the new method takes into account that in general the flow is not equally turbulent in the whole domain. There are subregions, e.g. at walls, with a strong turbulent character and in other subregions the flow behaves more or less laminar. With respect to the large scale space in the projection-based FEVMS method, the first situation corresponds to the necessity of using a locally small large scale space which allows a stronger influence of the eddy viscosity turbulence model. The second situation is vice versa.. The remainder of the paper is organized as follows: Section 2 introduces the considered projection-based FEVMS method. The adaptive algorithm for choosing the large scale space is described in detail in Section 3. A turbulent channel flow and a turbulent flow around a cylinder are studied in Section 4. Finally, Section 5 contains a summary of this paper. The paper presented a three-level finite element variational multiscale method for turbulent flow simulations with an adaptive choice of the large scale space. The adaption is based on the size of the computed resolved small scales. Simulations were performed for a turbulent channel flow and a turbulent flow around a cylinder. It was demonstrated that the method chooses the large scale space in an appropriate way, i.e. the effect of the turbulence model is controlled by the size of the local turbulence intensity. Compared with choosing the same large scale space in all mesh cells and for all times, it was shown that appropriate choices of parameters in the adaptive method lead to improvements of the results. The presented numerical studies give first guidelines on the importance of the parameters and their appropriate choice. Further studies, also at different flows, have to refine these guidelines. Acknowledgment. The research of A. Kindl was supported by the Deutsche Forschungsgemeinschaft (DFG) by grant No. Jo 329/7-2.

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


Value-based argumentation frameworks as neural-symbolic learning systems While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments. The study of formal models of argumentation has long been a subject of intensive investigation in several areas, notably in logic, philosophy, decision making, artificial intelligence, and law [5, 6, 8, 9, 15, 17, 22, 27, 28, 34] . In artificial intelligence, models of argumentation have been one of the approaches used in the representation of commonsense, nonmonotonic reasoning. They have been particularly successful when modelling chains of defeasible arguments so as to reach a conclusion [25, 26] . Although logic-based models have been the standard for the representation of argumentative reasoning [6, 16] , such models are intrinsically related to artificial neural networks, as we will show in this paper.. Neural networks have been successfully used in a number of computational learning applications [20, 24] . By establishing a relationship between neural networks and argumentation networks, we aim to provide a model in which the learning of arguments can be combined with reasoning capabilities within the same framework. In more general terms, the integration of reasoning and learning In this paper, we have presented a new hybrid model of computation that allows for the deduction and learning of argumentative reasoning. The model combines value-based argumentation frameworks and neural-symbolic learning systems by providing a translation from argumentation networks to neural networks. A theorem then shows that such a translation is correct. We have shown that the model works not only for acyclic argumentation networks, but also for circular networks, and it enables cumulative argumentation through learning. Larger-scale experiments on learning argumentation over time are currently being conducted. Complexity issues regarding the parallel computation of argumentation neural networks in contrast with standard value-based argumentation frameworks are also being investigated. We believe that a neural implementation of this reasoning process may, in fact, be advantageous from a purely computational point of view due to the massive parallelism of neural networks.. As future work, we shall consider fibring neural networks [12] and their relation to the general argumentation framework of Williamsom and Gabbay [35] . This framework incorporates, in addition to the notions of attack and support, the idea of recursive causality, according to which causal relations may take causal relations as input values; for example, the fact that smoking causes cancer may cause the government to restrict smoking advertising [35] . This corresponds to fibring neural networks, allowing nodes to behave as networks in a recursive way, and weights to be defined as functions of the values of other networks. Finally, the model presented here could be extended so as to consider probabilistic weights in argumentation frameworks, in the style of [19] . This would allow for a quantitative approach to argumentation in an integrated model of reasoning under uncertainty and inductive learning. In this section, we present the basic concepts of value-based argumentation frameworks used throughout this paper. We also define neural networks and introduce neural-symbolic learning systems.

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


Event-based approach to money laundering data analysis and visualization Crime specific event patterns are crucial in detecting potential relationships among suspects in criminal networks. However, current link analysis tools commonly used in detection do not utilize such patterns for detecting various types of crimes. These analysis tools usually provide generic functions for all types of crimes and heavily rely on the user's expertise on the domain knowledge of the crime for successful detection. As a result, they are less effective in detecting patterns in certain crimes. In addition, substantial effort is also required for analyzing vast amount of crime data and visualizing the structural views of the entire criminal network. In order to alleviate these problems, an event-based approach to money laundering data analysis and visualization is proposed in this paper. The effectiveness of the proposed method is demonstrated on a money laundering case from Taiwan. In criminal networks, offenders play different roles for various illegal activities [19] . These offenders are usually connected via various relationships such as co-workers, friends, business partner and kinship [12] . Criminal networks usually involve in organized crimes such as armed robbery, drug trafficking and money laundering, etc. Link analysis tools [11] are often used in investigation to uncover the information among criminals or criminal networks. Link analysis [28] may uncover the offenders who belong to the same supply chain in illicit drug supply networks. The main function of link analysis is to retrieve information from raw data which is related to entities such as bank accounts, telephone records, and criminal reports. The data is then processed and presented in a structured format. Link analysis further analyzes the information and identifies the relationship between the entities.. In recent years, a number of researches on link analysis systems have been undertaken. These system includes NETMAP [21] , Analyst's Notebook [1] , COPLINK [7] [13] [14] , etc. Most of these systems are originated from academic researches. Some of them have been extended into commercial products and are being used by various investigation departments. Majority of these systems use proprietary data formats for analysis.. Link analysis can be time-consuming and requires substantial amount of manual work. This includes ad-hoc tasks such as searching heterogeneous databases, analyzing reports on crime, and reviewing clues from criminal networks. Although some commercial software are labeled as link analysis tools, these software only provide functions for visualizing manually constructed criminal networks. In these systems, associations between entities are often required to be input by the user manually.. In addition, association analysis still encounters many challenging issues. For instance, it is extremely time consuming in analyzing vast amount of available crime data and visualizing structural views of the entire criminal network. As a result, these tools are less effective in detecting patterns in certain crimes. In order to alleviate these problems, an event-based approach to criminal data analysis and visualization is proposed in this paper. The proposed approach specifically targets in detecting money laundering cases.. The overall system utilizes a structured event-based database to store crime related records. The system also includes modules for detection of clusters, modules for calculation of degree of suspicion and degree of association, and a module for criminal networks visualization. A prototype system based on the proposed techniques is being designed and implemented in Microsoft Visual Studio [23] .. The paper is structured as follows. In Section 2, we review related work on link analysis, visualization tools, and event-based techniques. In Section 3, different corruption and money laundering methods and trends are introduced. In Section 4, we briefly describe the overall system design. In Section 5, a brief introduction on heuristics for detecting money laundering is discussed. In section 6, algorithms for proposed crime detection system is detailed. In Section 7, a case study on a money laundering case from Taiwan is given. In Section 8, we conclude the paper with future work. In this paper, we propose an event-based approach to criminal data analysis and visualization. The overall system utilizes a structured event-based database to store criminal records. In this system, several algorithms are developed for identification of clusters (family and colleagues) and calculation of suspicious degree and association degree. The result of the calculation is then visualized as connected graphs. The effectiveness of the proposed method is demonstrated on a money laundering case from Taiwan.. The main contributions of our research work are two-fold; from the theoretical standpoint, we contribute to the analysis, design, and development of an event-based approach for money laundering data analysis and visualization. The proposed algorithms can (a) iteratively identify clusters, (b) calculate degree of suspicion and association based on the event records, and (c) visualize the analysis outcomes. Such automated processes presented in this paper can be extremely helpful for investigators since tedious manual network construction process is no longer required. From the practical standpoint, our research opens the door to the further development of mechanisms for detecting different types of crime. For instance, the heuristics described in section 5 can be extended for crimes such as drug trafficking and homicide.. As for the future work, we are extending the visualization module. We are also planning to extend the knowledge base for detecting other crimes.

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Lightweight computational steering of very large scale molecular dynamics simulations We present a computational steering approach for controlling, analyzing, and visualizing very large scale molecular dynamics simulations involving tens to hundreds of millions of atoms. Our approach relies on extensible scripting languages and an easy to use tool for building extensions and modules. The system is easy to modify, works with existing C code, is memory efficient, and can be used from inexpensive workstations over standard Internet connections. We demonstrate how we have been able to explore data from production MD simulations involving as many as 104 million atoms running on the CM-5 and Cray T3D. We also show how this approach can be used to integrate common scripting languages (including Python, Tcl/Tk, and Perl), simulation code, user extensions, and commercial data analysis packages. With the development of massively parallel supercomputers, the materials science community has experienced an unprecedented explosion in both the size and complexity of short-range molecular dynamics simulations [1, 2, 3, 4, 5] . The method of molecular dynamics (MD) has been used since the 1950's for a variety of computational problems in physics, chemistry, and materials science [6] . The idea is really quite simple--given a collection of atoms, we solve Newton's equation of motion F=ma and track the atoms' trajectories. The physics of the simulation is incorporated into the force law in the form of a potential energy function. This function can range from a simple pair-potential to complicated many-body potentials [7] .. Prior to 1992, MD simulations were usually performed on relatively small systems (often in 2D) involving fewer than 1 million atoms [8] . However, in a span of only 3 years, MD simulation sizes grew to as many as 1 billion atoms in 3D [2, 4, 5] . Today, production simulations involving tens to hundreds of millions of atoms are possible, but analyzing the resulting data has proven to be extraordinarily difficult. As a result, most MD simulations remain small even though many researchers agree that large-scale simulations are useful for studying certain types of material properties.. In this paper, we describe our efforts in addressing the practical problems of working with very large molecular dynamics simulations. We have taken a computational steering approach in which simulation, data analysis, and visualization are combined into a single package [9, 10, 11, 12] . However, we have adopted an approach that is extremely lightweight--that is, it is memory efficient, simple to use, portable, easily extensible, and not dependent on expensive special purpose hardware. (ie. graphics workstations or high-speed networking). We have presented a simple steering approach based on scripting languages and an automated interface building tool, SWIG, that we have developed for building extensions and modules. This approach has been used with the SPaSM code for about one year. We feel that this has been a step in the right direction, but want to emphasize the differences between our approach and others. First, rather than trying to build a sophisticated computational steering "environment" and integrating simulation codes into it, we have developed an extremely lightweight tool that works with our existing codes, but tries to stay out of the way. Secondly, our system has been designed primarily to support very-large scale simulation. While we can run interactively, the system works equally well in a batch processing mode for long-running simulations. The memory efficiency of the approach has allowed us to continue running very large simulations, even when performing analysis and visualization. Our approach also works well on inexpensive workstations and slow networks--making it useful to users who only have remote access to a large parallel supercomputer. Finally, we recognize that most scientists who would be using SPaSM are qualified professionals who demand both simplicity and functionality. Our approach works with existing C functions and is easy to extend. At the same, we impose no restrictions on the scripting language or types of C code that can be glued together. Thus, a scientist can easily build a simple implementation or one of almost arbitrary complexity.. Currently, we are working on the development of new graphics and data analysis modules for SPaSM. We are also interested in extending our work with the Python scripting language and exploring extensions such as Numerical Python which provide high-level mathematical operations on arrays and matrices [22] . We have no plans to build a sophisticated graphical user interface at this time. If this is desired, we feel that we could probably use any number of existing systems such as the SCIRun steering software developed at the University of Utah [9] . Finally, as data analysis and visualization become commonplace, we feel that data management and organization of results will be critical. Therefore we are quite interested in extending some of our work to scientific databases and data management systems. We feel that this management of data, run parameters, and output, will be more critical than simply providing more interactivity.

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Intelligent distributed and supervised flow control methodology for production systems This paper deals with the development of an intelligent distributed and supervised control approach for highvolume production systems in which the flow of parts can be approximated by a continuous (fluid) model. The proposed approach is based on the decomposition of the production system into elementary modules in order to reduce the control design computational complexity. In this context, a two levels control structure is proposed. At the local level, a surplus-based principle is adopted to regulate the production flow for each module according to the distributed structure. The proposed control methodology decides how to adjust the production rate in order to avoid system overloading and eliminate machine starvation or blocking. In this context, the local control law is synthesized by using the Takagi-Sugeno fuzzy systems. At the high level, a supervisory controller is designed to improve the overall system performances. A supervisor provides an additive component for each local controller when the overall system performances deviate from their acceptable domains (degraded mode). This is done by combining both local and global information into a unified formalism by using aggregation operators and according to fuzzy interval representation of the desired objectives. Finally, the feasibility of the proposed methodology is validated with simulation examples. In this article, intelligent distributed and supervised control architecture for continuous-flow production systems has been presented. The hierarchical structure consists of a lower level of distributed fuzzy controllers, which is supervised by a higher level of decision-making. The lower level regulates the production flow by adjusting the machine processing rates. It uses a fuzzy controller based on the Takagi-Sugeno fuzzy system. The higher level of supervision monitors the system by using global performance indicators. The supervisory mechanism is based on aggregation operator mechanisms which provide an additive component to the local controller when degraded operating modes are detected. The supervisor is built according to the satisfaction degree of the different and possibility conflicting objectives quantified by fuzzy intervals. For the studied cases, the obtained results show a promise improvement of performances when compared with the unsupervised distributed control. To summarize, the main advantages of the proposed control approach are:.  It takes into account the different degrees of importance for each control objective,  It facilitates the implementation phase due to the modularity and the distributivity of the control architecture. The robustness of the proposed approach is evaluated regardless to the demand variation (random variation) in [44] . The obtained results show acceptable improvement of performances providing that the demand changes don't exceed the system's capacity. An attractive extension is to study the structured methods traditionally used in the multiple criteria decision problems in order to quantify the fuzzy intervals of the desired objectives.

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The sum of d small-bias generators fools polynomials of degree d We prove that the sum of d small-bias generators L : F s → F n fools degree-d polynomials in n variables over a prime field F, for any fixed degree d and field F, including F = F 2 = {0, 1}. Our result builds on, simplifies, and improves on both the work by Bogdanov and Viola (FOCS '07) and the follow-up by Lovett (STOC '08). The first relies on a conjecture that turned out to be true only for some degrees and fields, while the latter considers the sum of 2 d small-bias generators (as opposed to d in our result). A pseudorandom generator G : F s → F n for polynomials of degree d over a prime field F is an efficient procedure that stretches s field elements into n s field elements and fools any polynomial of degree d in n variables over F: For every such polynomial p, the statistical distance between p(U ), for uniform U ∈ F n , and p(G(S)), for uniform S ∈ F s , is at most a small . The fundamental case of linear, i.e. degree-1, polynomials is first studied by Naor and Naor [NN] who give a generator with seed length s = O(log |F| n) (for error = 1/n), which is optimal up to constant factors (cf. [AGHP] ).. 1 This generator is known as small-bias generator, and is one of the most celebrated results in pseudorandomness, with a myriad of applications (see, e.g., the references in [BV] ).. The case of higher degree is first addressed by Luby, Veličković, and Wigderson [LVW] , and a decade later by Bogdanov [Bog] . However, the generators in [LVW, Bog] have poor seed length or only work over large fields.. Recently, Bogdanov and the author [BV] introduce a new approach to attack this problem over small fields, which we now describe. The work considers the generator G k : F s → F n that is obtained by summing k copies of a small-bias generator L : F s → F n by Naor and Naor [NN] , which fools linear (i.e., degree-1) polynomials:. where the sum is element-wise. [BV] shows that such a generator can be analyzed using the so-called Gowers norms. It unconditionally shows that G d fools polynomials of degree d for d ≤ 3. For larger d > 3, the work proves a conditional result. Specifically, it introduces a special case of a conjecture known as the Inverse Conjecture for the Gowers norm [GT2, Sam] . This special case is called the "d vs. d − 1 Inverse Conjecture for the Gowers norm" and we subsequently refer to it as "d-ICG." Under d-ICG, [BV] [BV] . Recently, Green and Tao [GT1] prove that d-ICG is true when the field size |F| is bigger than the degree d of the polynomial. Thus, in this case, the approach in [BV] works and in particular one has that G d fools polynomials of degree d. On the negative side, Green and Tao [GT1] , and independently Lovett, Meshulam, and Samorodnitsky [LMS] , show that d-ICG is false when the field size is much smaller than the degree of the polynomial (which in particular falsifies the more general Inverse Conjecture for the Gowers norm [GT2, Sam] ). This falsity prevents the analysis in [BV] from going through for small fields, notably over F 2 = {0, 1}. Still, it was left open to understand whether, regardless of inverse conjectures, the generator G d in [BV] fools polynomials of degree d over small fields such as F 2 . In this work we answer this question in the affirmative.

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Correlation Clustering in General Weighted Graphs We consider the following general correlation-clustering problem [1] : given a graph with real nonnegative edge weights and a + / − edge labeling, partition the vertices into clusters to minimize the total weight of cut + edges and uncut − edges. Thus, + edges with large weights (representing strong correlations between endpoints) encourage those endpoints to belong to a common cluster while − edges with large weights encourage the endpoints to belong to different clusters. In contrast to most clustering problems, correlation clustering specifies neither the desired number of clusters nor a distance threshold for clustering; both of these parameters are effectively chosen to be the best possible by the problem definition.Correlation clustering was introduced by Bansal, Blum, and Chawla [1], motivated by both document clustering and agnostic learning. They proved NP-hardness and gave constant-factor approximation algorithms for the special case in which the graph is complete (full information) and every edge has the same weight. We give an O(log n)-approximation algorithm for the general case based on a linear-programming rounding and the "region-growing" technique. We also prove that this linear program has a gap of Ω(log n), and therefore our approximation is tight under this approach. We also give an O(r 3 )-approximation algorithm for K r,r -minor-free graphs. On the other hand, we show that the problem is equivalent to minimum multicut, and therefore APX-hard and difficult to approximate better than Θ(log n). Clustering objects into groups is a common task that arises in many applications such as data mining, web analysis, computational biology, facility location, data compression, marketing, machine learning, pattern recognition, and computer vision. Clustering algorithms for these and other objectives have been heavily investigated in the literature. For partial surveys, see e.g. [8, 14, 19, 20, 21, 25] .. In a theoretical setting, the objects are usually viewed as points in either a metric space (typically finite) or a general distance matrix, or as vertices in a graph. Typical objectives include minimizing the maximum diameter of a cluster (k-clustering) [11] , minimizing the average distance between pairs of clustered points (k-clustering sum) [24] , minimizing the maximum distance to a "centroid object" chosen for each cluster (k-center) [11] , minimizing the average distance to such a centroid object (k-median) [13] , minimizing the average squared distance to an arbitrary centroid point (k-means) [14] , and maximizing the sum of distances between pairs of objects in different clusters (maximum k-cut) [17] . These objectives interpret the distance between points as a measure of their dissimilarity: the larger the distance, the more dissimilar the objects. Another line of clustering algorithms intreprets the distance or weights between pairs of points as a measure of their similarity: the larger the weight, the more similar the objects. In this case, the typical objective is to find a k-clustering that minimizes the sum of weights between pairs of objects in different clusters (minimum k-cut) [17] . All of these clustering problems are parameterized by the desired number k of clusters. Without such a restriction, these clustering objective functions would be optimized when k = n (every object is in a separate cluster) or when k = 1 (all objects belong to a single cluster).. In the correlation-clustering problem introduced by Bansal, Blum, and Chawla [1] , the underlying model is that objects can be truly categorized, and we are given probabilities about pairs of objects belonging to common categories. For example, the multiset of objects might consist of all authors of English literature, and two authors belong to the same category if they correspond to the same real person. This task would be easy if authors published papers consistently under the same name. However, some authors might publish under several different names such as William Shakespeare, W. Shakespeare, Bill Shakespeare, Sir Francis Bacon, Edward de Vere, and Queen Elizabeth I 1 . Given some confidence about the similarity and dissimilarity of the names, our goal is to cluster the objects to maximize the probability of correctness. There are two main objective functions for this problem, that of minimizing disagreements and that of maximizing agreements between the input estimates and the output clustering. The decision versions of these two optimization problems are identical, and known to be NP-complete [1] . However, they differ in their approximability.. As we consider both similarity and dissimilarity measures in our problem, it is in a sense a generalization of the typical clustering objectives mentioned above. In fact, an appropriate interpretation of our problem instance suggests that our objective is a combination of the minimum k-clustering sum and minimum k-cut clustering objectives. An interesting property of our problem is that the number k of clusters is no longer a parameter of the input; there is some "ideal" k which the algorithm must find. Another clustering problem with this property is location area design, a problem arising in cell phone network design. As formulated by Bejerano et al. [2] , this problem attempts to minimize the sum of the squared sizes of the clusters plus the weight of the cut induced by the clustering. The similarities between these two problems allow us to apply many of the same techniques. In this paper, we have investigated the problem of minimizing disagreements in the correlationclustering problem. We gave an O(log n) approximation for general graphs, an O(r 3 ) approximation for K r,r -minor-free graphs, and showed that the natural linear-programming formulation upon which these algorithms are based has a gap of Ω(log n) in general graphs. We also showed that this problem is equivalent minimum multicut, yielding another implicit O(log n) approximation as well as an APX-hardness result.. A natural extension of this work would be to improve the approximation factor for minimizing disagreements. Given our hardness result and the history of the minimum-multicut problem, this goal is probably very difficult. Another option is to improve the lower bound, but for the same reason, this goal is probably very difficult. On the other hand, one might try to design an alternate O(log n)-approximation algorithm that is combinatorial, perhaps by counting erroneous cycles in a cycle cover of the graph.. Another interesting direction is to explore other objective functions of the correlation-clustering problem. Bansal et al. [1] give a polynomial-time approximation scheme (PTAS) for maximizing agreements in unweighted complete graphs. For weighted general graphs, one of two trivial clusterings (every vertex in a distinct cluster or all vertices in a single cluster) is a 1/2 approximation. Recently, Swamy [26] gave a 0.7666-approximation algorithm for the unweighted general graphs based on semidefinite programming. It would be interesting to obtain better approximations, in particular a PTAS, for general graphs. Bansal et al. [1] also mention the objective of maximizing agreements minus disagreements. This objective is of particular practical interest. However, there are no known approximation algorithms for this objective, even for complete graphs.. Finally, it would be interesting to apply the techniques presented here to other problems. The region-growing technique and Klein et al. [16] rounding technique both provide a radius guarantee on the output clusters. Many papers have used this radius guarantee to demonstrate that the solution is feasible, i.e., satisfies the constraints. Inspired by Bejerano et al. [2] , we also use the radius guarantee to bound the approximation factor. This idea might be applicable to other problems. The only previous work on the correlation-clustering problem is that of Bansal et al. [1] . Their paper considers correlation clustering in an unweighted complete graph, i.e., every pair of objects has an estimate of either "similar" or "dissimilar". For minimizing disagreements, they give a constant-factor approximation via a combinatorial algorithm. For maximizing agreements, they give a polynomial-time approximation scheme (PTAS). The limitation of these two algorithms is that they assume the input graph is complete, while in many applications, estimate information is incomplete. Table 2 summarizes the results of their paper.

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