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We study efficient computation of Minimax distances measures, which enable to capture the correct structures via taking the transitive relations into account. We analyze in detail two settings, the dense graphs and the sparse graphs. In particular, we show that an adapted variant of the Kruskal’s algorithm is the most efficient approach for computing pairwise Minimax distances. However, for dense...
The closest pair problem (CPP) is an important problem that has numerous applications in clustering, graph partitioning, image processing, patterns identification, intrusion detection, etc. Numerous algorithms have been presented for solving the CPP. For instance, on n points there exists an O(n log n) time algorithm for CPP (when the dimension is a constant). There also exist randomized algorithms...
Change point analysis is a statistical tool to identify homogeneity within time series data. We propose a pruning approach for approximate nonparametric estimation of multiple change points. This general purpose change point detection procedure 'cp3o' applies a pruning routine within a dynamic program to greatly reduce the search space and computational costs. Existing goodness-of-fit change point...
Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on...
In recent years, there has been growing interest in learning to rank. We considered the current state of learning to rank in information retrieval systems. We proposed an approach for learning to rank problem based on multi-criteria optimization using the method of Pareto optimization and Genetic Algorithms. The performance of the method has been investigated on test data collections, also a comparison...
The solution of difficult problems can be realized in shorter time with heuristic algorithms. There are many heuristic algorithms. In this study, artificial bee colony (ABC), biogeography based optimization (BBO), cuckoo bird search algorithm (CSO), differential evolution (DE), imperialist competitive algorithm (ICA) and particle swarm algorithm (PSO) have been chosen due to reasons such as the widespread...
In this study, crabs mating optimization (CRAB) algorithm that is one of the heuristic algorithms, has been developed and a monogamous crab mating optimization (MCO) algorithm has been proposed. In development, the main goal is to develop an algorithm that runs faster than the CRAB algorithm, to ensure obtaining good results like the CRAB algorithm. The developed MCO algorithm is compared with the...
Identifying families of malware is today considered a fundamental problem in the context of computer security. The correct mapping of a malicious sample to a known family simplifies its analysis and allows experts to focus their efforts only on those samples presenting unknown characteristics or behaviours, thus improving the efficiency of the malware analysis process. Grouping malware in families...
Inferring fine-grained link metrics by using aggregated path measurements, known as network tomography, is essential for various network operations, such as network monitoring, load balancing, and failure diagnosis. Given a set of interesting links and the changing topologies of a dynamic network, we study the problem of calculating the link metrics of these links by end-to-end cycle-free path measurements...
Fragmentation in Elastic Optical Networks is an issue caused by isolated, non-aligned, and non-contiguous frequency slots that can not be used to allocate new connection request to the network, due to the optical layer restrictions imposed to the Routing and Spectrum Assignment (RSA) algorithms. To deal with this issue, several studies about Spectrum Defragmentation have been presented. In this work,...
The energy consumption of data centers has been increasing continuously during the last years due to the rising demands of computational power especially in current Grid- and Cloud Computing systems, which directly influence the increment in operational costs as well as carbon dioxide (CO2) emission. To reduce energy consumption within the cloud data center, it required energy-aware virtual machines...
The present paper deals with online convex optimization involving adversarial loss functions and adversarial constraints, where the constraints are revealed after making decisions, and can be tolerable to instantaneous violations but must be satisfied in the long term. Performance of an online algorithm in this setting is assessed by: i) the difference of its losses relative to the best dynamic solution...
Software-defined networking (SDN) is a promising technique that has reshaped the landscape of network management. By providing simplified, cost-effective management, SDN has been envisioned as the next-generation network paradigm. However, due to economic, organizational, and technical challenges, replacing all conventional switches in current operational networks by SDN-enabled switches is impractical...
The focus of this paper is on detecting overlapping communities for the directed graphs by implementing a new algorithm and analyzing it with various performance metrics. The algorithm aims at finding core nodes for the directed graph which are subset of communities and have higher contact frequency. These are then extended to find communities using compactness measurement (CM). The compactness of...
We design a metric to measure the occupied holding time difference of frequency slots. With the metric, a routing and spectrum allocation algorithm in dynamic scenario is proposed to improve the network spectral utilization. Evaluation results show the proposed algorithm outperforms obviously in blocking probability.
Fuzzy rough technique is a mathematical tool to deal with fuzzy and rough knowledge, which could reduce the redundant objects and attributes by keeping the information invariant. In the existing researches on fuzzy rough sets, all the attributes are assumed to have the same weights for the decision. Actually different attributes may play different roles on the decision. As a result, we introduce weights...
Parallel applications are highly irregular and high performance computing (HPC) infrastructures are very complex. The HPC applications of interest herein are timestepping scientific applications (TSSA). Often, TSSA involve the repeated execution of multiple parallel loops with thousands of iterations and irregular behavior. Dynamic loop scheduling (DLS) techniques were developed over time and have...
Most of the existing population behavior studies are about the analysis of the population dynamic behavior of genetic algorithm, while there is little analysis of the population dynamic behavior of particle swarm optimization (PSO). Therefore, there is an urgent need for a new method to characterize the population dynamic behavior of PSO in the search process. In this paper, we propose some metrics...
Recently multi-agent patrolling became more and more crucial in security, monitoring, etc. applications. It can be used, for example, to monitor points of interest in space, such as measurement points or entrances to a guarded area. A good patrolling strategy would ensure frequent visits to all points of interest defined by a user. A variety of centralized and distributed approaches exist already...
This paper proposes a modified IMWBF decoding algorithm for low-density parity-check (LDPC) codes. In the proposed modification, the magnitude of the received signal is updated when the number of the unsatisfied parity-check equations participated by this received signal is larger than a predetermined threshold. Through this modification, the outlier, defined as the undesired large-magnitude received...
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