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This paper deals with designing a multi-objective algorithm based on recently proposed Whale Optimization Algorithm (WOA), termed as MOWOA. The original WOA algorithm is popular among the researchers due to the encircling moments of agents (Humpback whales) in the search space which provides proper balance among the exploration and exploitation, faster convergence and lessor number of parameters....
Inverse classification is the process of manipulating an instance such that it is more likely to conform to a specific class. Past methods that address such a problem have shortcomings. Greedy methods make changes that are overly radical, often relying on data that is strictly discrete. Other methods rely on certain data points, the presence of which cannot be guaranteed. In this paper we propose...
Intrusion detection systems are a central component of cyber security architecture, and their accuracy is a critical performance metric for any security deployment. Most of the current performance analysis of intrusion detection systems relies on empirical profiling of a given algorithm or implementation against a benchmark dataset. Whilst effective to a point, this traditional evaluation methodology...
Bayesian optimization (BO) has recently emerged as a powerful and flexible tool for hyper-parameter tuning and more generally for the efficient global optimization of expensive black-box functions. Systems implementing BO has successfully solved difficult problems in automatic design choices and machine learning hyper-parameters tunings. Many recent advances in the methodologies and theories underlying...
With the ever increasing volume of video content, efficient and effective video summarization (VS) techniques are urgently demanded to manage the large amount of video data. Recent developments on sparse representation based approaches have demonstrated promising results for VS. While most existing approaches treat each frame independently, in this paper, the block-sparsity, which means the keyframes...
Clustering is an important unsupervised data analysis technique, which divides data objects into clusters based on similarity. Clustering has been studied and applied in many different fields, including pattern recognition, data mining, decision science and statistics. Clustering algorithms can be mainly classified as hierarchical and partitional clustering approaches. Partitioning around medoids...
To reduce the false positives of static analysis, many tools collect path constraints and integrate SMT solvers to filter unreachable execution paths. However, the accumulated calling and computing of SMT solvers are time and resource consuming. This paper presents TsmartLW, an alternate static analysis tool in which we implement a path constraint solving engine to speed up reachability determination...
The set-based concept approach has been suggested as a means to simultaneously explore different design concepts, which are meaningful sub-sets of the entire set of solutions. Previous efforts concerning the suggested approach focused on either revealing the global front (s-Pareto front), of all the concepts, or on finding the concepts' fronts, within a relaxation zone. In contrast, here the aim is...
Quantum-behaved particle swarm optimization (QPSO) is a novel variant of particle swarm optimization (PSO), inspired by quantum mechanics. Compared with traditional PSO, the QPSO algorithm guarantees global convergence and has less number of controlling parameters. However, QPSO is likely to get trapped into a local optimum because of using a single search strategy. This paper proposes a cooperative...
We propose a new method to analyze the impact of errors in algorithms for multi-instance pose estimation and a principled benchmark that can be used to compare them. We define and characterize three classes of errors - localization, scoring, and background - study how they are influenced by instance attributes and their impact on an algorithm’s performance. Our technique is applied to compare the...
Dividing a dataset into disjoint groups of homogeneous structure, known as data clustering, constitutes an important problem of data analysis. It can be solved with broad range of methods employing statistical approaches or heuristic procedures. The latter often include mechanisms known from nature as they are known to serve as useful components of effective optimizers. The paper investigates the...
The rapidly growing number of large network analysis problems has led to the emergence of many parallel and distributed graph processing systems—one survey in 2014 identified over 80. Determining the best approach for a given problem is infeasible for most developers. We present an approach and associated software for analyzing the performance and scalability of parallel, open-source graph libraries...
The k-truss of a graph is a subgraph such that each edge is tightly connected to the remaining elements in the k-truss. The k-truss of a graph can also represent an important community in the graph. Finding the k-truss of a graph can be done in a polynomial amount of time, in contrast finding other subgraphs such as cliques. While there are numerous formulations and algorithms for finding the maximal...
The capacitated vehicle routing problem (CVRP) is one of the most challenging problems in the optimization of distribution. Most approaches can solve case studies involving less than 100 nodes to optimality, but time-consuming. To overcome the limitation, this paper presents a novel two-phase heuristic approach for the capacitated vehicle routing problem. Phase I aims to identifying sets of cost-effective...
The increased interest in autonomic system requires that control algorithms must facilitate implementation of some kind of decision-making process. Such a process may be to a different extent supported by a human operator who will analyse the system behaviour and, if needed, will make amendments to a control strategy. However, a human operator can only process a limited amount of information at a...
In optimization problems, nature inspired algorithms are able to generate near optimal solutions faster than other optimization algorithms. Based on nature intelligence, these algorithms are preferable especially when the function to be optimized is computationally intensive. In this paper it is proposed an image registration procedure based on the Artificial Bee Colony algorithm. First, its performances...
The main memory in today's systems is based on DRAMs, which may offer low cost and high density storage for large amounts of data but it comes with a main drawback; DRAM cells need to be refreshed frequently for retaining the stored data. The refresh rate in modern DRAMs is set based on the worst-case retention time without considering access statistics, thereby resulting in very frequent refresh...
Glowworm Swarm Optimization Algorithm (GSO) is one of new swarm intelligence optimization algorithms in recent years. Its main idea comes from the cooperative behavior source among individuals during the process of courtship and foraging. In this paper, in order to improve convergence speed in the late iteration, avoid the algorithm falling into local optimum, and reduce isolated nodes, the Adaptive...
We introduce a novel approach to jointly estimate consistent depth and normal maps from 4D light fields, with two main contributions. First, we build a cost volume from focal stack symmetry. However, in contrast to previous approaches, we introduce partial focal stacks in order to be able to robustly deal with occlusions. This idea already yields significanly better disparity maps. Second, even recent...
Previous research on dimensionality reduction has shown that global and local information is both important for capturing the crucial features of data sets. In this paper, we develop a new approach that can effectively retain both global and local features of a dataset for supervised dimensionality reduction. A new quadratic measure is developed to accurately describe the local features of a dataset...
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