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"content": "Interconnected systems, such as Web servers, database servers, cloud computing servers and so on, are now under threads from network attackers. As one of most common and aggressive means, denial-of-service (DoS) attacks cause serious impact on these computing systems. In this paper, we present a DoS attack detection system that uses multivariate correlation analysis (MCA) for accurate network traffic characterization by extracting the geometrical correlations between network traffic features. Our MCA-based DoS attack detection system employs the principle of anomaly based detection in attack recognition. This makes our solution capable of detecting known and unknown DoS attacks effectively by learning the patterns of legitimate network traffic only. Furthermore, a triangle-area-based technique is proposed to enhance and to speed up the process of MCA. The effectiveness of our proposed detection system is evaluated using KDD Cup 99 data set, and the influences of both non-normalized data and normalized data on the performance of the proposed detection system are examined. The results show that our system outperforms two other previously developed state-of-the-art approaches in terms of detection accuracy.",
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"abstract": "The well known divide-and-conquer paradigm has proved to be useful for deriving efficient algorithms for many problems. Several researchers have pointed out its usefulness for parallel processing; however, the problem of analyzing such parallel algorithms in a realistic setting has been largely overlooked. In this paper a realistic model for divide-and-conquer based algorithms is postulated; the efficiency of some algorithms is then analyzed, taking into account all relevant parameters of the model (time, data movement and number of processors.)",
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"abstract": "In the tree reconciliation approach to infer the duplication history of a gene family, the gene (family) tree is compared to the corresponding species tree. Incomplete lineage sorting (ILS) gives rise to stochastic variation in the topology of a gene tree and hence likely introduces false duplication events when a tree reconciliation method is used. We quantify the effect of ILS on gene duplication inference in a species tree in terms of the expected number of false duplication events inferred from reconciling a random gene tree, which occurs with a probability predicted in coalescent theory, and the species tree. We computationally examine the relationship between the effect of ILS on duplication inference in a species tree and its topological parameters. Our findings suggest that ILS may cause non-negligible bias on duplication inference, particularly on an asymmetric species tree. Hence, when gene duplication is inferred via tree reconciliation or any other approach that takes gene tree topology into account, the ILS-induced bias should be examined cautiously.",
"abstracts": [
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"content": "In the tree reconciliation approach to infer the duplication history of a gene family, the gene (family) tree is compared to the corresponding species tree. Incomplete lineage sorting (ILS) gives rise to stochastic variation in the topology of a gene tree and hence likely introduces false duplication events when a tree reconciliation method is used. We quantify the effect of ILS on gene duplication inference in a species tree in terms of the expected number of false duplication events inferred from reconciling a random gene tree, which occurs with a probability predicted in coalescent theory, and the species tree. We computationally examine the relationship between the effect of ILS on duplication inference in a species tree and its topological parameters. Our findings suggest that ILS may cause non-negligible bias on duplication inference, particularly on an asymmetric species tree. Hence, when gene duplication is inferred via tree reconciliation or any other approach that takes gene tree topology into account, the ILS-induced bias should be examined cautiously.",
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"title": "Effect of Incomplete Lineage Sorting On Tree-Reconciliation-Based Inference of Gene Duplication",
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"title": "On the Complexity of Duplication-Transfer-Loss Reconciliation with Non-Binary Gene Trees",
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"abstract": "Coalescent histories provide lists of species tree branches on which gene tree coalescences can take place, and their enumerative properties assist in understanding the computational complexity of calculations central in the study of gene trees and species trees. Here, we solve an enumerative problem left open by Rosenberg (IEEE/ACM Transactions on Computational Biology and Bioinformatics 10: 1253-1262, 2013) concerning the number of coalescent histories for gene trees and species trees with a matching labeled topology that belongs to a generic caterpillar-like family. By bringing a generating function approach to the study of coalescent histories, we prove that for any caterpillar-like family with seed tree Z_$t$_Z , the sequence Z_$(h_n)_{n\\ge 0}$_Z describing the number of matching coalescent histories of the Z_$n$_Z th tree of the family grows asymptotically as a constant multiple of the Catalan numbers. Thus, Z_$h_n \\sim \\beta _t c_n$_Z , where the asymptotic constant Z_$\\beta _t > 0$_Z depends on the shape of the seed tree Z_$t$_Z . The result extends a claim demonstrated only for seed trees with at most eight taxa to arbitrary seed trees, expanding the set of cases for which detailed enumerative properties of coalescent histories can be determined. We introduce a procedure that computes from Z_$t$_Z the constant Z_$\\beta _t$_Z as well as the algebraic expression for the generating function of the sequence Z_$(h_n)_{n\\ge 0}$_Z .",
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"content": "Coalescent histories provide lists of species tree branches on which gene tree coalescences can take place, and their enumerative properties assist in understanding the computational complexity of calculations central in the study of gene trees and species trees. Here, we solve an enumerative problem left open by Rosenberg (IEEE/ACM Transactions on Computational Biology and Bioinformatics 10: 1253-1262, 2013) concerning the number of coalescent histories for gene trees and species trees with a matching labeled topology that belongs to a generic caterpillar-like family. By bringing a generating function approach to the study of coalescent histories, we prove that for any caterpillar-like family with seed tree $t$ , the sequence $(h_n)_{n\\ge 0}$ describing the number of matching coalescent histories of the $n$ th tree of the family grows asymptotically as a constant multiple of the Catalan numbers. Thus, $h_n \\sim \\beta _t c_n$ , where the asymptotic constant $\\beta _t > 0$ depends on the shape of the seed tree $t$ . The result extends a claim demonstrated only for seed trees with at most eight taxa to arbitrary seed trees, expanding the set of cases for which detailed enumerative properties of coalescent histories can be determined. We introduce a procedure that computes from $t$ the constant $\\beta _t$ as well as the algebraic expression for the generating function of the sequence $(h_n)_{n\\ge 0}$ .",
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"title": "Species Tree Inference from Gene Splits by Unrooted STAR Methods",
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"abstract": "In the minimizing-deep-coalescences (MDC) approach for species tree inference, a tree that has the minimal deep coalescence cost for reconciling a collection of gene trees is taken as an estimate of the species tree topology. The MDC method possesses the desirable Pareto property, and in practice it is quite accurate and computationally efficient. Here, in order to better understand the MDC method, we investigate some properties of the deep coalescence cost. We prove that the unit neighborhood of either a rooted species tree or a rooted gene tree under the deep coalescence cost is exactly the same as the tree's unit neighborhood under the rooted nearest-neighbor interchange (NNI) distance. Next, for a fixed species tree, we obtain the maximum deep coalescence cost across all gene trees as well as the number of gene trees that achieve the maximum cost. We also study corresponding problems for a fixed gene tree.",
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"abstract": "Parallel coordinates is a popular and well-known multivariate data visualization technique. However, one of their inherent limitations has to do with the rendering of very large data sets. This often causes an overplotting problem and the goal of the visual information seeking mantra is hampered because of a cluttered overview and non-interactive update rates. In this paper, we propose two novel solutions, namely, angular histograms and attribute curves. These techniques are frequency-based approaches to large, high-dimensional data visualization. They are able to convey both the density of underlying polylines and their slopes. Angular histogram and attribute curves offer an intuitive way for the user to explore the clustering, linear correlations and outliers in large data sets without the over-plotting and clutter problems associated with traditional parallel coordinates. We demonstrate the results on a wide variety of data sets including real-world, high-dimensional biological data. Finally, we compare our methods with the other popular frequency-based algorithms.",
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"content": "Parallel coordinates is a popular and well-known multivariate data visualization technique. However, one of their inherent limitations has to do with the rendering of very large data sets. This often causes an overplotting problem and the goal of the visual information seeking mantra is hampered because of a cluttered overview and non-interactive update rates. In this paper, we propose two novel solutions, namely, angular histograms and attribute curves. These techniques are frequency-based approaches to large, high-dimensional data visualization. They are able to convey both the density of underlying polylines and their slopes. Angular histogram and attribute curves offer an intuitive way for the user to explore the clustering, linear correlations and outliers in large data sets without the over-plotting and clutter problems associated with traditional parallel coordinates. We demonstrate the results on a wide variety of data sets including real-world, high-dimensional biological data. Finally, we compare our methods with the other popular frequency-based algorithms.",
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"abstract": "SimRank is a powerful model for assessing vertex-pair similarities in a graph. It follows the concept that two vertices are similar if they are referenced by similar vertices. The prior work [18] exploits partial sums memoization to compute SimRank in Z_$O(Kmn)$_Z time on a graph of Z_$n$_Z vertices and Z_$m$_Z edges, for Z_$K$_Z iterations. However, computations among different partial sums may have redundancy. Besides, to guarantee a given accuracy Z_$\\epsilon$_Z , the existing SimRank needs Z_$K=\\lceil \\log _C \\,\\epsilon \\rceil$_Z iterations, where Z_$C$_Z is a damping factor, but the geometric rate of convergence is slow if a high accuracy is expected. In this paper, (1) a novel clustering strategy is proposed to eliminate duplicate computations occurring in partial sums, and an efficient algorithm is then devised to accelerate SimRank computation to Z_$O(K d^{\\prime } n^2)$_Z time, where Z_$d^{\\prime }$_Z is typically much smaller than Z_$\\frac{m}{n}$_Z . (2) A new differential SimRank equation is proposed, which can represent the SimRank matrix as an exponential sum of transition matrices, as opposed to the geometric sum of the conventional counterpart. This leads to a further speedup in the convergence rate of SimRank iterations. (3) In bipartite domains, a novel finer-grained partial max clustering method is developed to speed up the computation of the Minimax SimRank variation from Z_$O(Kmn)$_Z to Z_$O(Km^{\\prime }n)$_Z time, where Z_$m^{\\prime } \\ ({\\le} m)$_Z is the number of edges in a reduced graph after edge clustering, which can be typically much smaller than Z_$m$_Z . Using real and synthetic data, we empirically verify that (1) our approach of partial sums sharing outperforms the best known algorithm by up to one order of magnitude; (2) the revised notion of SimRank further achieves a 5X speedup on large graphs while also fairly preserving the relative order of original SimRank scores; (3) our finer-grained partial max memoization for the Minimax SimRank variation in bipartite domains is 5X-12X faster than the baselines.",
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"content": "Visualizing the intellectual structure of scientific domains using co-cited units such as references or authors has become a routine for domain analysis. In previous studies, paper-reference matrices are usually transformed into reference-reference matrices to obtain co-citation relationships, which are then visualized in different representations, typically as node-link networks, to represent the intellectual structures of scientific domains. Such network visualizations sometimes contain tightly knit components, which make visual analysis of the intellectual structure a challenging task. In this study, we propose a new approach to reveal co-citation relationships. Instead of using a reference-reference matrix, we directly use the original paper-reference matrix as the information source, and transform the paper-reference matrix into an FP-tree and visualize it in a Java-based prototype system. We demonstrate the usefulness of our approach through visual analyses of the intellectual structure of two domains: Information Visualization and Sloan Digital Sky Survey (SDSS). The results show that our visualization not only retains the major information of co-citation relationships, but also reveals more detailed sub-structures of tightly knit clusters than a conventional node-link network visualization.",
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