An efficient algorithm for application-layering anycasting

Anycasting communication is proposed in IPv6, and it is designed to support server replication by allowing applications to select and communicate with the “best” server, according to some performance or policy criteria, among the replicated servers. Originally any-cast researchers focus on network layer. In this paper we pay more attention to application-layer anycasting, because at application layer we can obtain more flexibility and scalability. First of all, we describe the application-layer anycast model, and then summarize the previous work in application-layer anycasting, especially the periodical probing algorithms for updating the database of anycast resolver. After that, we present our algorithm, the requirement-based probing algorithm, an efficient and practical algorithm. In the end, we analyse the algorithms using the queuing theory and the statistics characteristics of Internet traffic. The results show that the requirement-base probing algorithm has better performance not only in the average waiting time for all anycast queries, but also in the average time used for an anycast query.

Efficient algorithm for mobile multicast using anycast group

The authors present a novel and efficient multicast algorithm that aims to reduce delay and communication cost for the registration between mobile nodes and mobility agents and solicitation for foreign agent services based on the mobile IP. The protocol applies anycast group technology to support multicast transmissions for both mobile nodes and home/foreign agents. Mobile hosts use anycast tunnelling to connect to the nearest available home/foreign agent where an agent is able to forward the multicast messages by selecting an anycast route to a multicast router so as to reduce the end-to-end delay. The performance analysis and experiments demonstrated that the proposed algorithm is able to enhance the performance over existing remote subscription and bidirectional tunnelling approaches regardless of the locations of mobile nodes/hosts

An efficient algorithm for partitioning and authenticating problem-solutions of elearning contents

Content authenticity and correctness is one of the important challenges in eLearning as there can be many solutions to one specific problem in cyber space. Therefore, the authors feel it is necessary to map problems to solutions using graph partition and weighted bipartite matching. This article proposes an efficient algorithm to partition question-answer (QA) space and explores the best possible solution to a particular problem. The approach described can be efficiently applied to social eLearning space where there are one-to-many and many-to-many relationships with a level of bonding. The main advantage of this approach is that it uses QA ranking by adjusted edge weights provided by subject-matter experts or the authors' expert database.

Efficient resource selection algorithm for enterprise grid systems

This paper addresses a resource selection problem for applications that update data in enterprise grid systems. The problem is insufficiently addressed as most of the existing resource selection approaches in grid environments primarily deal with read-only job. We propose a simple yet efficient algorithm that deals with the complexity of resource selection problem in enterprise grid systems. The problem is formulated as a Multi Criteria Decision Making (MCDM) problem. Our proposed algorithm hides the complexity of resource selection process without neglecting important components that affect job response time. The difficulty on estimating job response time is captured by representing them in terms of different QoS criteria levels at each resource. Our experiments show that the proposed algorithm achieves very good results with good system performance as compared to existing algorithms.

Further pruning for efficient association rule discovery

The Apriori algorithm’s frequent itemset approach has become the standard approach to discovering association rules. However, the computation requirements of the frequent itemset approach are infeasible for dense data and the approach is unable to discover infrequent associations. OPUS AR is an efficient algorithm for association rule discovery that does not utilize frequent itemsets and hence avoids these problems. It can reduce search time by using additional constraints on the search space as well as constraints on itemset frequency. However, the effectiveness of the pruning rules used during search will determine the efficiency of its search. This paper presents and analyses pruning rules for use with OPUS AR. We demonstrate that application of OPUS AR is feasible for a number of datasets for which application of the frequent itemset approach is infeasible and that the new pruning rules can reduce compute time by more than 40%.

Candidate working set strategy based SMO algorithm in support vector machine

Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vector machine. The most important step of this algorithm is the selection of the working set, which greatly affects the training speed. The feasible direction strategy for the working set selection can decrease the objective function, however, may augment to the total calculation for selecting the working set in each of the iteration. In this paper, a new candidate working set (CWS) Strategy is presented considering the cost on the working set selection and cache performance. This new strategy can select several greatest violating samples from Cache as the iterative working sets for the next several optimizing steps, which can improve the efficiency of the kernel cache usage and reduce the computational cost related to the working set selection. The results of the theory analysis and experiments demonstrate that the proposed method can reduce the training time, especially on the large-scale datasets.

Interpolation of Lipschitz functions

This paper describes a new computational approach to multivariate scattered data interpolation. It is assumed that the data is generated by a Lipschitz continuous function f. The proposed approach uses the central interpolation scheme, which produces an optimal interpolant in the worst case scenario. It provides best uniform error bounds on f, and thus translates into reliable learning of f. This paper develops a computationally efficient algorithm for evaluating the interpolant in the multivariate case. We compare the proposed method with the radial basis functions and natural neighbor interpolation, provide the details of the algorithm and illustrate it on numerical experiments. The efficiency of this method surpasses alternative interpolation methods for scattered data.

Research on network anycast

Anycast is defined as a service in IPv6, which provides stateless best effort delivery of an anycast datagram to at least one, and preferably only one host. It is a topic of increasing interest. This paper is an attempt to gather and report on the work done on anycast. There are two main categories at present: network-layer anycast and application-layer anycast. Both involve anycast architectures, routing algorithms, metrics, applications, etc. We also present an efficient algorithm for application-layer anycast, and point out possible research directions based on our research.

Smoothing Lipschitz functions

This paper describes a new approach to multivariate scattered data smoothing. It is assumed that the data are generated by a Lipschitz continuous function f, and include random noise to be filtered out. The proposed approach uses known, or estimated value of the Lipschitz constant of f, and forces the data to be consistent with the Lipschitz properties of f. Depending on the assumptions about the distribution of the random noise, smoothing is reduced to a standard quadratic or a linear programming problem. We discuss an efficient algorithm which eliminates the redundant inequality constraints. Numerical experiments illustrate applicability and efficiency of the method. This approach provides an efficient new tool of multivariate scattered data approximation.

Extended cutting angle method of global optimization

Methods of Lipschitz optimization allow one to find and confirm the global minimum of multivariate Lipschitz functions using a finite number of function evaluations. This paper extends the Cutting Angle method, in which the optimization problem is solved by building a sequence of piecewise linear underestimates of the objective function. We use a more flexible set of support functions, which yields a better underestimate of a Lipschitz objective function. An efficient algorithm for enumeration of all local minima of the underestimate is presented, along with the results of numerical experiments. One dimensional Pijavski-Shubert method arises as a special case of the proposed approach.

Dynamic route construction for mobile collectors in wireless sensor networks

Wireless sensor networks with mobile data collectors have been recently proposed for extending the sensor network lifetime. Powerful mobile collectors are deployed to patrol the network and approach the static sensors for collecting their data buffers using single hop communication. The route followed by the mobile collector is very crucial for the data collection operation performed in the network and highly impacts the data collection time. This paper presents a practically efficient algorithm for constructing the mobile collector route. The route is constructed dynamically during the network operational time regardless of the sensors data generation rates. The algorithm acts on minimizing the sleeping time and the number of sensors waiting for the arrival of the mobile collector. Simulation results demonstrate that the presented algorithm can effectively reduce the overall data collection time.

Laguerre Runge-Kutta-Fehlberg method for simulating laser pulse propagation in biological tissue

An efficient algorithm for solving the transient radiative transfer equation for laser pulse propagation in biological tissue is presented. A Laguerre expansion is used to represent the time dependency of the incident short pulse. The Runge–Kutta– Fehlberg method is used to solve the intensity. The discrete ordinates method is used to discretize with respect to azimuthal and zenith angles. This method offers the advantages of representing the intensity with a high accuracy using only a few Laguerre polynomials, and straightforward extension to inhomogeneous media. Also, this formulation can be easily extended for solving the 2-D and 3-D transient radiative transfer equations.

Nonnegative shared subspace learning and its application to social media retrieval

Although tagging has become increasingly popular in online image and video sharing systems, tags are known to be noisy, ambiguous, incomplete and subjective. These factors can seriously affect the precision of a social tag-based web retrieval system. Therefore improving the precision performance of these social tag-based web retrieval systems has become an increasingly important research topic. To this end, we propose a shared subspace learning framework to leverage a secondary source to improve retrieval performance from a primary dataset. This is achieved by learning a shared subspace between the two sources under a joint Nonnegative Matrix Factorization in which the level of subspace sharing can be explicitly controlled. We derive an efficient algorithm for learning the factorization, analyze its complexity, and provide proof of convergence. We validate the framework on image and video retrieval tasks in which tags from the LabelMe dataset are used to improve image retrieval performance from a Flickr dataset and video retrieval performance from a YouTube dataset. This has implications for how to exploit and transfer knowledge from readily available auxiliary tagging resources to improve another social web retrieval system. Our shared subspace learning framework is applicable to a range of problems where one needs to exploit the strengths existing among multiple and heterogeneous datasets.

Finding the optimal temporal partitioning of video sequences

The existing techniques for shot partitioning either process each shot boundary independently or proceed sequentially. The sequential process assumes the last shot boundary is correctly detected and utilizes the shot length distribution to adapt the threshold for detecting the next boundary. These techniques are only locally optimal and suffer from the strong assumption about the correct detection of the last boundary. Addressing these fundamental issues, in this paper, we aim to find the global optimal shot partitioning by utilizing Bayesian principles to model the probability of a particular video partition being the shot partition. A computationally efficient algorithm based on Dynamic Programming is then formulated. The experimental results on a large movie set show that our algorithm performs consistently better than the best adaptive-thresholding technique commonly used for the task.