Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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SMoT+: Extending the SMoT Algorithm for Discovering Stops in Nested Sites
Get PDFSeveral methods have been proposed to analyse trajectory data. However, a few of these methods consider trajectory relations with relevant features of the geographic space. One of the best-known methods that take into account the geographical regions crossed by a trajectory is the SMoT algorithm. Nevertheless, SMoT considers only disjoint geographic regions that a trajectory may traverse, while many regions of interest are contained in other regions. In this article, we extend the SMoT algorithm for discovering stops in nested regions. The proposed algorithm, called SMoT+, takes advantage of information about the hierarchy of nested regions to efficiently discover the stops in regions at different levels of this hierarchy. Experiments with real data show that SMoT+ detects stops in nested regions, which are not detected by the original SMoT algorithm, with minor growth of processing time
Pay-As-You-Go Software Artifacts Managemen
Get PDFOne of the major challenges in software engineering research is to manage software artifacts effectively. However, software artifacts are often changed during software development, the full, one-time integration technique is not feasible to manage such heterogeneity and evolving data. In this paper, we concern about the application of dataspace techniques, which emphasize the idea of pay-as-you-go data management, to software artifacts management. To this end, we present a loosely structured data model based on the current dataspace models to describe software artifacts, and a strategy to query this model. We also present how to gradually add semantics to query processing for improving the precision and recall of query results. Furthermore, the validity of our work is proved by experiment. Finally, the differences between our work and traditional work are discussed
Fuzzy Modeling of Thermoplastic Composites' Melt Volume Rate
Get PDFMelt volume-flow rate (MVR) is one of the most important quality indicators of composite materials, which depends on the proportion of the component materials. This paper reports the development of a low complexity fuzzy model that describes the relation between percentage amount of multiwall carbon nanotube (MWCNT), acrylonitrile-butadiene-styrene (ABS), polycarbonate (PC) and MVR of the resulting composite. The rule base was generated from a sample data set obtained from experiments by the rule base extension using default set shapes (RBE-DSS) method, and the applied fuzzy inference technique was the least squares method based fuzzy rule interpolation (LESFRI). The resulting model was validated against a separate test data set as well, and it was compared to a fuzzy model generated by a traditional commercial software tool
Towards an Unsupervised Method for Network Anomaly Detection in Large Datasets
Get PDFIn this paper, we present an effective tree based subspace clustering technique (TreeCLUSS) for finding clusters in network intrusion data and for detecting known as well as unknown attacks without using any labelled traffic or signatures or training. To establish its effectiveness in finding the appropriate number of clusters, we perform a cluster stability analysis. We also introduce an effective cluster labelling technique (CLUSSLab) to label each cluster based on the stable cluster set obtained from TreeCLUSS. CLUSSLab is a multi-objective technique that employs an ensemble approach for labelling each stable cluster generated by TreeCLUSS to achieve high detection rate. We also introduce an effective unsupervised feature clustering technique to identify the dominating feature set from each cluster. We evaluate the performance of both TreeCLUSS and CLUSSLab using several real world intrusion datasets to identify known as well as unknown attacks and find that results are excellent
A MapReduce Based Distributed LSI for Scalable Information Retrieval
Get PDFLatent Semantic Indexing (LSI) has been widely used in information retrieval due to its efficiency in solving the problems of polysemy and synonymy. However, LSI is notably a computationally intensive process because of the computing complexities of singular value decomposition and filtering operations involved in the process. This paper presents MR-LSI, a MapReduce based distributed LSI algorithm for scalable information retrieval. The performance of MR-LSI is first evaluated in a small scale experimental cluster environment, and subsequently evaluated in large scale simulation environments. By partitioning the dataset into smaller subsets and optimizing the partitioned subsets across a cluster of computing nodes, the overhead of the MR-LSI algorithm is reduced significantly while maintaining a high level of accuracy in retrieving documents of user interest. A genetic algorithm based load balancing scheme is designed to optimize the performance of MR-LSI in heterogeneous computing environments in which the computing nodes have varied resources
Experimental Analysis of the Prediction Model Based on String Invariants
Get PDFA new approach of the string theory called the Prediction Model Based on String Invariants (PMBSI) was applied here to time-series forecast. We used 2-end-point open string that satisfies the Dirichlet and Neumann boundary conditions. The initial motivation was to transfer modern physical ideas into the neighboring field called econophysics. The physical statistical viewpoint has proved to be fruitful, namely in the description of systems where many-body effects dominate. However, PMBSI is not limited to financial forecast. The main advantage of PMBSI include absence of the learning phase when large number of parameters must be set. Comparative experimental analysis of PMBSI vs. SVM was performed and the results on artificial and real-world data are presented. PMBSI performance was in a close match with SVM
A Hybrid Evolutionary Algorithm for Efficient Exploration of Online Social Networks
Get PDFOnline social networks provide large amount of valuable data and may serve as research platforms for various social network analysis tools. In this study, we propose a mathematical model for efficient exploration of an online social network. The goal is to spend minimal amount of time searching for characteristics which define a sub-network of users sharing the same interest or having certain common property. We further develop an efficient hybrid method (HEA), based on the combination of an Evolutionary Algorithm (EA) with Local Search procedure (LS). The proposed mathematical model and hybrid method are benchmarked on real-size data set with up to 10 000 users in a considered social network. We provide optimal solutions obtained by CPLEX solver on problem instances with up to 100 users, while larger instances that were out of reach of the CPLEX were efficiently solved by the proposed hybrid method. Presented computational results show that the HEA approach quickly reaches all optimal solutions obtained by CPLEX solver and gives solutions for the largest considered instance in very short CPU time
Word Combination Kernel for Text Classification with Support Vector Machines
Get PDFIn this paper we propose a novel kernel for text categorization. This kernel is an inner product defined in the feature space generated by all word combinations of specified length. A word combination is a collection of unique words co-occurring in the same sentence. The word combination of length k is weighted by the k rm th root of the product of the inverse document frequencies (IDF) of its words. By discarding word order, the word combination features are more compatible with the flexibility of natural language and the feature dimensions of documents can be reduced significantly to improve the sparseness of feature representations. By restricting the words to the same sentence and considering multi-word combinations, the word combination features can capture similarity at a more specific level than single words. A computationally simple and efficient algorithm was proposed to calculate this kernel. We conducted a series of experiments on the Reuters-21578 and 20 Newsgroups datasets. This kernel achieves better performance than the word kernel and word-sequence kernel. We also evaluated the computing efficiency of this kernel and observed the impact of the word combination length on performance
Agent Strategy Generation by Rule Induction
No full textThis paper presents a study on a rule induction application for generating an agent strategy. It is a new approach in multi-agent systems, where reinforcement learning and evolutionary computation is broadly used for this purpose. Experimental results show that rule induction improves agent performance very quickly. What is more, rule-based knowledge representation has many advantages. It is comprehensive and clear. It allows for the examination of the learned knowledge by humans. Because of modularity of the knowledge, it also allows for the implementation of the knowledge exchange in a natural way -- only necessary set of rules can be sent. Rule induction is tested in two domains: Fish Banks game, in which agents run fishing companies and learn how to allocate ships, and Predator-Prey domain, in which predator agents learn how to capture preys. The proposed learning mechanism should be beneficial in all domains, in which agents can determine the results of their actions
On Acceleration of Evolutionary Algorithms Taking Advantage of A Posteriori Error Analysis
Get PDFA variety of important engineering and scientific tasks may be formulated as non-linear, constrained optimization problems. Their solution often demands high computational power. It may be reached by means of appropriate hardware, software or algorithm improvements. The Evolutionary Algorithms (EA) approach to solution of such problems is considered here. The EA are rather slow methods; however, the main advantage of their application is observed in the case of non-convex problems. Particularly high efficiency is demanded in the case of solving large optimization problems. Examples of such problems in engineering include analysis of residual stresses in railroad rails and vehicle wheels, as well as the Physically Based Approximation (PBA) approach to smoothing experimental and/or numerical data. Having in mind such analysis in the future, we focus our current research on the significant EA efficiency increase. Acceleration of the EA is understood here, first of all, as decreasing the total computational time required to solve an optimization problem. Such acceleration may be obtained in various ways. There are at least two gains from the EA acceleration, namely i) saving computational time, and ii) opening a possibility of solving larger optimization problems, than it would be possible with the standard EA. In our recent research we have preliminarily proposed several new speed-up techniques based on simple concepts. In this paper we mainly develop acceleration techniques based on simultaneous solutions averaging well supported by a non-standard application of parallel calculations, and a posteriori solution error analysis. The knowledge about the solution error is used to EA acceleration by means of appropriately modified standard evolutionary operators like selection, crossover, and mutation. Efficiency of the proposed techniques is evaluated using several benchmark tests. These tests indicate significant speed-up of the involved optimization process. Further concepts and improvements are also currently being developed and tested