Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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1506 research outputs found
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Data Mining for Fog Prediction and Low Clouds Detection
Get PDFhis paper describes our contribution to the research of parametrized models and methods for detection and prediction of significant meteorological phenomena, especially fog and low cloud cover. The project covered methods for integration of distributed meteorological data necessary for running the prediction models, training models and then mining the data in order to be able to efficiently and quickly predict even sparsely occurring phenomena. The detection and prediction methods are based on knowledge discovery -- data mining of meteorological data using neural networks and decision trees. The mined data were mainly METAR aerodrome messages, meteorological data from specialized stations and cloud data from special airport sensors -- laser ceilometers
NOA: An Information Retrieval Based Malware Detection System
Get PDFMalware refers to any type of code written with the intention of harming a computer or network. The quantity of malware being produced is increasing every year and poses a serious global security threat. Hence, malware detection is a critical topic in computer security. Signature-based detection is the most widespread method used in commercial antivirus solutions. However, signature-based detection can detect malware only once the malicious executable has caused damage and has been conveniently registered and documented. Therefore, the signature-based method fails to detect obfuscated malware variants. In this paper, a new malware detection system is proposed based on information retrieval. For the representation of executables, the frequency of the appearance of opcode sequences is used. Through this architecture a malware detection system prototype is developed and evaluated in terms of performance, malware variant recall (false negative ratio) and false positives
Text Segmentation Using Roget-Based Weighted Lexical Chains
Get PDFIn this article we present a new method for text segmentation. The method relies on the number of lexical chains (LCs) which end in a sentence, which begin in the following sentence and which traverse the two successive sentences. The lexical chains are based on Roget's thesaurus (the 1987 and the 1911 version). We evaluate the method on ten texts from the DUC 2002 conference and on twenty texts from the CAST project corpus, using a manual segmentation as gold standard
Complexity and Approximation Results for the Min-Sum and Min-Max Disjoint Paths Problems
Get PDFGiven a graph G=(V, E) and k source-sink pairs (s1, t1), …, (sk, tk) with each si, ti V, the Min-Sum Disjoint Paths problem asks to find k disjoint paths connecting all the source-sink pairs with minimized total length, while the Min-Max Disjoint Paths problem asks for k disjoint paths connecting all the source-sink pairs with minimized length of the longest path. We show that the weighted Min-Sum Disjoint Paths problem is FPNP-complete in general graphs, and the unweighted Min-Sum Disjoint Paths problem and the unweighted Min-Max Disjoint Paths problem cannot be approximated within m(m1-1) for any constant > 0 even in planar graphs, assuming P P NP, where m is the number of edges in G. We give for the first time a simple bicriteria approximation algorithm for the unweighted Min-Max Edge-Disjoint Paths problem and the weighted Min-Sum Edge-Disjoint Paths problem, w
Montera: A Framework for Efficient Execution of Monte Carlo Codes on Grid Infrastructures
Get PDFhe objective of this work is to improve the performance of Monte Carlo codes on Grid production infrastructures. To do so, the codes and the grid sites are characterized with simple parameters to model their behaviors. Then, a new performance model for grid infrastructures is proposed, and an algorithm that employs this information is described. This algorithm dynamically calculates the number and size of tasks to execute on each site to maximize the performance and reduce makespan. Finally, a newly developed framework called Montera is presented. Montera deals with the execution of Monte Carlo codes in an unattended way, isolating the complexity of the problem from the final user. By employing two fusion Monte Carlo codes as example cases, along with the described characterizations and scheduling algorithm, a performance improvement up to 650 % over current best results is obtained on a real production infrastructure, together with enhanced stability and robustness
Reducing the Gap Between Business and Information Systems Through Complex Event Processing
Get PDFAccording to the Object Management Group, a rule is a proposition that is a claim of obligation or of necessity. The concept of rule is usually employed in the context of business process to manage companies operations. While a workflow is an explicit specification of tasks' execution flow, business rules only impose restrictions on the tasks' execution. This provides a great deal of flexibility for the process execution, since the stakeholders are free to choose an execution flow which does not violate the rules. The execution of a task in a process can be seen as the occurrence of an event, which may enable/disable the execution of some other tasks in the process. Event-driven programming is a paradigm in which the program control-flow is determined by the occurrence of events. The capacity to handle processes that are unpredictably non-linear and dynamic makes the event-driven paradigm an effective solution for the implementation of business rules. However, the connection between the business rules and their implementation through event-driven programming has been made in an ad-hoc and unstructured manner. This paper proposes a methodology to tackle such a problem by systematically moving from business rules described in natural language toward a concrete implementation of a business process. We use complex event processing (CEP) to implement the process. CEP relies on the event driven paradigm for monitoring and processing events. The methodology allows for the active participation of business people at all stages of the refinement process. Throughout the paper, we show how our methodology was employed to implement the operations of the World Bank
Recognition and Combinatorial Optimization Algorithms for Bipartite Chain Graphs
Get PDFIn this paper we give a recognition algorithm in O(n(n+m)) time for bipartite chain graphs, and directly calculate the density of such graphs. For their stability number and domination number, we give algorithms comparable to the existing ones. We point out some applications of bipartite chain graphs in chemistry and approach the Minimum Chain Completion problem
Generalization of One-Sided Concept Lattices
Get PDFWe provide a generalization of one-sided (crisp-fuzzy) concept lattices, based on Galois connections. Our approach allows analysis of object-attribute models with different structures for truth values of attributes. Moreover, we prove that this method of creating one-sided concept lattices is the most general one, i.e., with respect to the set of admissible formal contexts, it produces all Galois connections between power sets and the products of complete lattices. Some possible applications of this approach are also included
A Double Scoring Method for XML Element Retrieval
Get PDFEfficient retrieval of XML elements and documents is essential in the effective application of the XML format. The ranking function BM25F is composed of several document fields with potentially different degrees of importance; these fields are known as selected fields that give substantial improvements over the baseline BM25. The BM25F function has performed well in past evaluations; however, there are issues that require additional attention. In the first instance, which elements should be treated as fields? Secondly, what is an appropriate weight for each field? Previously, document fields were selected manually, and the weight for each chosen field was tuned before being assigned. Two automatic methods are introduced in this paper that enable the extraction of fields in document-centric XML documents and the assignment weights to the selected fields. Our experiments show an improvement of up to 28 % over BM25, and up to 15 % over BM25F at iP[0.01] based on INEX evaluations
Investigations into Lamarckism, Baldwinism and Local Search in Grammatical Evolution Guided by Reinforcement
Get PDFGrammatical Evolution Guided by Reinforcement is an extension of Grammatical Evolution that tries to improve the evolutionary process adding break a learning process for all the individuals in the population. With this aim, each individual is given a chance to learn through a reinforcement learning mechanism during its lifetime. The learning process is completed with a Lamarckian mechanism in which an original genotype is replaced by the best learnt genotype for the individual. In a way, Grammatical Evolution Guided by Reinforcement shares an important feature with other hybrid algorithms, i.e. global search in the evolutionary process combined with local search in the learning process. In this paper the role of the Lamarck Hypothesis is reviewed and a solution inspired only in the Baldwin effect is included as well. Besides, different techniques about the trade-off between exploitation and exploration in the reinforcement learning step followed by Grammatical Evolution Guided by Reinforcement are studied. In order to evaluate the results, the system is applied on two different domains: a simple autonomous navigation problem in a simulated Kephera robot and a typical Boolean function problem