Spatio-temporal data analysis has recently gained considerable attention from both the research and practitioner communities because of the increasing availability of datasets with prominent spatial and temporal data ...
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Spatio-temporal data analysis has recently gained considerable attention from both the research and practitioner communities because of the increasing availability of datasets with prominent spatial and temporal data elements. In this paper, we develop a new spatio-temporal data analysis approach aimed at discovering abnormal spatio-temporal clustering patterns. We also propose a quantitative evaluation framework and compare our approach against a widely used space-time scan statistic-based method under this framework. Our approach is based on a robust clustering engine using support vector machines and incorporates ideas from existing online surveillance methods to track incremental changes over time. Initial experimental results using both simulated and real-world datasets indicate that Our approach is able to detect abnormal areas with irregular shapes more accurately than the space-time scan statistic-based method. (C) 2007 Elsevier B.V. All rights reserved.
The Matlab implementation of a trust-region Gauss-Newton method for bound-constrained nonlinear least-squares problems is presented. The solver, called TRESNEI, is adequate for zero and small-residual problems and han...
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The Matlab implementation of a trust-region Gauss-Newton method for bound-constrained nonlinear least-squares problems is presented. The solver, called TRESNEI, is adequate for zero and small-residual problems and handles the solution of nonlinear systems of equalities and inequalities. The structure and the usage of the solver are described and an extensive numerical comparison with functions from the Matlab Optimization Toolbox is carried out.
In this paper, we study the influence of the number of objectives of a continuous multiobjective optimization problem on its hardness for evolution strategies which is of particular interest for many-objective optimiz...
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In this paper, we study the influence of the number of objectives of a continuous multiobjective optimization problem on its hardness for evolution strategies which is of particular interest for many-objective optimization problems. To be more precise, we measure the hardness in terms of the evolution (or convergence) of the population toward the set of interest, the Pareto set. Previous related studies consider mainly the number of nondominated individuals within a population which greatly improved the understanding of the problem and has led to possible remedies. However, in certain cases this ansatz is not sophisticated enough to understand all phenomena, and can even be misleading. In this paper, we suggest alternatively to consider the probability to improve the situation of the population which can, to a certain extent, be measured by the sizes of the descent cones. As an example, we make some qualitative considerations on a general class of uni-modal test problems and conjecture that these problems get harder by adding an objective, but that this difference is practically not significant, and we support this by some empirical studies. Further, we address the scalability in the number of objectives observed in the literature. That is, we try to extract the challenges for the treatment of many-objective problems for evolution strategies based on our observations and use them to explain recent advances in this field.
Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social...
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Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects: (1) a review of well-accepted diffusion models that capture the information diffusion process and build the foundation of the IM problem, (2) a fine-grained taxonomy to classify existing IM algorithms based on their design objectives, (3) a rigorous theoretical comparison of existing IM algorithms, and (4) a comprehensive study on the applications of IM techniques in combining with novel context features of social networks such as topic, location, and time. Based on this analysis, we then outline the key challenges and research directions to expand the boundary of IM research.
In the contemporary literature on deterministic machine scheduling, problems are formulated from three different, but equivalent, perspectives. Algebraic models provide a rigorous problem statement in the language of ...
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In the contemporary literature on deterministic machine scheduling, problems are formulated from three different, but equivalent, perspectives. Algebraic models provide a rigorous problem statement in the language of set theory and are typical of the more abstract development of scheduling theory in mathematics and computer science. Mathematical programming models rely on familiar concepts of nonlinear optimization and are generally the most accessible. Network models (disjunctive graphs) are best suited to the development of solution approaches and figure prominently in discussions of algorithm design and analysis. In this tutorial, it is shown how the minimum-makespan job-shop problem (n/m/G/C(max)) is realized in each of these three model forms. A common notation is developed and how the underlying structure and fundamental difficulty of the problem are expressed in each model is demonstrated.
A new algorithm, word-based dynamic Lempel-Ziv (WDLZW) for universal (lossless) data compression, is introduced. The novel feature is that the algorithm is optimised for the compression of natural language data, in wh...
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A new algorithm, word-based dynamic Lempel-Ziv (WDLZW) for universal (lossless) data compression, is introduced. The novel feature is that the algorithm is optimised for the compression of natural language data, in which all the spaces between words are deleted whenever copy codes or literal codes are sent out. Therefore better compression rates can be achieved. The algorithm can still compress alternative forms of data. The structure, operation and implementation of the WDLZW is described. A comparison with other algorithms when compressing a wide range of data forms is reported. For text-based information WDLZW offers attractive performance. For other forms of data, WDLZW provides compression rates similar to those of dynamic Lempel Ziv systems.
A randomized on-line algorithm is given for competitiveness less than 1901 against the previously best known competitiveness of IN uses a new approach and defines a potential in the 2-server problem on the line, with ...
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A randomized on-line algorithm is given for competitiveness less than 1901 against the previously best known competitiveness of IN uses a new approach and defines a potential in the 2-server problem on the line, with oblivious adversary. This improves the 155/78 approximate to 1.987 for the problem. The algorithm terms of isolation indices from T-theory. (C) 2015 Elsevier B.V. All rights reserved.
Symmetry is an obvious phenomenon in two-way communications. in this paper, we present an adaptive nonparametric method that can be used for anomaly detection in symmetric network traffic. Two important features are e...
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Symmetry is an obvious phenomenon in two-way communications. in this paper, we present an adaptive nonparametric method that can be used for anomaly detection in symmetric network traffic. Two important features are emphasized in this method: (i) automatic adjustment of the detection threshold according to the traffic conditions;and (ii) timely detection of the end of an anomalous event. Source-end defense against SYN flooding attacks is used to illustrate the efficacy of this method. Experiments on real traffic traces show that this method has high detection accuracy and low detection delays, and excels at detecting low intensity attacks. (c) 2007 Elsevier Ltd. All rights reserved.
An improved algorithm design methodology of vehicle airbag deployment decision is proposed in this paper. Firstly, the vehicle impact severity is analyzed to get four characteristic factors utilized as fuzzy inputs. F...
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An improved algorithm design methodology of vehicle airbag deployment decision is proposed in this paper. Firstly, the vehicle impact severity is analyzed to get four characteristic factors utilized as fuzzy inputs. From these four characteristics factors, the 'two stage fuzzy algorithm' is developed and used as the airbag deployment algorithm for identifying the vehicle impact severity. Finally, the adaptive-network-based fuzzy inference system (ANFIS) is used to train the suitable fuzzy membership functions and fuzzy rules based on crash data for improving the performance of the 'two stage fuzzy algorithm'. Simulation results for different vehicle crash data demonstrate the validity and effectiveness of the proposed design methodology. (C) 2007 Elsevier B.V. All rights reserved.
Today's laaS clouds allow dynamic scaling of VMs allocated to a user, according to real-time demand of the user. There are two types of scaling: horizontal scaling (scale-out) by allocating more VM instances to th...
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Today's laaS clouds allow dynamic scaling of VMs allocated to a user, according to real-time demand of the user. There are two types of scaling: horizontal scaling (scale-out) by allocating more VM instances to the user, and vertical scaling (scale-up) by boosting resources of VMs owned by the user. It has been a daunting issue how to efficiently allocate the resources on physical servers to meet the scaling demand of users on the go, which achieves the best server utilization and user utility. An accompanying critical challenge is how to effectively charge the incremental resources, such that the economic benefits of both the cloud provider and cloud users are guaranteed. There has been online auction design dealing with dynamic VM provisioning, where the resource bids are not related to each other, failing to handle VM scaling where later bids may rely on earlier bids of the same user. As the first in the literature, this paper designs an efficient, truthful online auction for resource provisioning and pricing in the practical cases of dynamic VM scaling, where: (i) users bid for customized VMs to use in future durations, and can bid again in the following time to increase resources, indicating both scale-up and scale-out options;(ii) the cloud provider packs the demanded VMs on heterogeneous servers for energy cost minimization on the go. We carefully design resource prices maintained for each type of resource on each server to achieve threshold-based online allocation and charging, as well as a novel competitive analysis technique based on submodularity of the offline objective, to show a good competitive ratio is achieved. The efficacy of the online auction is validated through solid theoretical analysis and trace-driven simulations.
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