Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Propagation-Based Biclustering Algorithm for Extracting Inclusion-Maximal Motifs
Get PDFBiclustering, which is simultaneous clustering of columns and rows in data matrix, became an issue when classical clustering algorithms proved not to be good enough to detect similar expressions of genes under subset of conditions. Biclustering algorithms may be also applied to different datasets, such as medical, economical, social networks etc. In this article we explain the concept beneath hybrid biclustering algorithms and present details of propagation-based biclustering, a novel approach for extracting inclusion-maximal gene expression motifs conserved in gene microarray data. We prove that this approach may successfully compete with other well-recognized biclustering algorithms
Test Suite Reduction Using HGS Based Heuristic Approach
Get PDFRegression testing is performed throughout the software lifecycle to uncover the faults as early as possible and to ensure that changes do not have any adverse effect in the software that is operational. Test suites once developed are reused and updated frequently. As the software evolves, test cases in the test suite may become redundant. The reason behind this is that the requirements covered by newly added test cases may also be covered by the existing test cases. This redundant nature of test suite increases the cost of executing the same. Further, resource and time constraints impose the necessity to develop techniques to minimize test suites by removing redundant test cases. Few heuristic approaches have been used to solve the test suite minimization problem. Even though solutions exist, still the redundancy of test case remains. In order to solve this problem, this paper proposes two Harrold-Gupta-Soffa (HGS) based heuristic algorithms namely, Non Redundant HGS and Enhanced HGS. The former utilizes the redundant strategy available with Greedy, Redundant, Essential (GRE) to get rid of redundancy, whereas the latter selects a test case for higher cardinalities based on overall coverage of unmarked associated testing sets and thus arrives at reduced, non-redundant test suite. The experiments show that the proposed algorithms always select smaller size of test suite, compared to the existing HGS heuristics
Use Case Specification Using the SILABREQ Domain Specific Language
Get PDFThe software requirements engineering process is a part of a software development process and one of the key processes in software development. The elicitation, analysis, specification and validation of software requirements occur during the requirements engineering process. Use cases are used as a technique for functional system specification. Different notations can be used for a use case specification. In this paper, we present SilabReq Domain Specific Language (SilabReq DSL) for use case specification. On the one hand, we develop this language to describe the use cases in clear and precise way through the meta-model, and on the other hand to specify the use cases to be readable and understandable for all stakeholders in the software development project. This allows us to develop different transformations to get the structure and the behavior of the system from defined use cases. In this paper apart from the SilabReq DSL, we present some of these transformations
Analysis of Range Images Used in 3D Facial Expression Recognition Systems
Get PDFWith the creation of BU-3DFE database the research on 3D facial expression recognition has been fostered; however, it is limited by the development of 3D algorithms. Range image is the strategy for solving the problems of 3D recognition based on 2D algorithms. Recently, there are some methods to capture range images, but they are always combined with the preprocess, registration, etc. stages, so it is hard to tell which of these generated range images is of higher quality. This paper introduces two kinds of range images and selects different kinds of features based on different levels of expressions to validate the performances of proposed range images; two other kinds of range images based on previously used nose tip detection methods are applied to compare the quality of generated range images; and finally some recently published works on 3D facial expression recognition are listed for comparison. With the experimental results, we can see that the performances of two proposed range images with different kinds of features are all higher than 88 % which is remarkable compared with the most recently published methods for 3D facial expression recognition; the analysis of the different kinds of facial expressions shows that the proposed range images do not lose primary discriminative information for recognition; the performances of range images using different kinds of nose tip detection methods are almost the same what means that the nose tip detection is not decisive to the quality of range images; moreover, the proposed range images can be captured without any manual intervention what is eagerly required in safety systems
Simple Multi-Authority Attribute-Based Encryption for Short Messages
Get PDFCentral authority free multi-authority attribute based encryption scheme for short messages will be presented. Several multi-authority attribute based encryption schemes were recently proposed. We can divide these schemes into two groups, one of them are the ciphertext-policy attribute based encryption schemes (CP-ABE), the another one are the key-policy attribute based encryption schemes (KP-ABE). In our new multi-authority attribute based encryption scheme we combine them: the access structure will be given by authorities and the encryptor in conjunction. The authorities will be able to decide who is able to decrypt a ciphertext under their names, but the encryptor will choose the authorities whom he would involve in the encryption. In addition, our scheme is free of any central authority. The security of our new scheme relies on the decisional 3-party Diffie-Hellman assumption
Word Sense Disambiguation: A Structured Learning Perspective
Get PDFThis paper explores the application of structured learning methods (SLMs) to word sense disambiguation (WSD). On one hand, the semantic dependencies between polysemous words in the sentence can be encoded in SLMs. On the other hand, SLMs obtained significant achievements in natural language processing, and so it is a natural idea to apply them to WSD. However, there are many theoretical and practical problems when SLMs are applied to WSD, due to characteristics of WSD. Beginning with the method based on hidden Markov model, this paper proposes for the first time a comprehensive and unified solution for WSD based on maximum entropy Markov model, conditional random field and tree-structured conditional random field, and reduces the time complexity and running time of the proposed methods to a reasonable level by beam search, approximate training, and parallel training. The update of models brings performance improvement, the introduction of one step dependency improves performance by 1--5 percent, the adoption of non-independent features improves performance by 2--3 percent, and the extension of underlying structure to dependency parsing tree improves performance by about 1 percent. On the English all-words WSD dataset of Senseval-2004, the method based on tree-structured conditional random field outperforms the best attendee system significantly. Nevertheless, almost all machine learning methods suffer from data sparseness due to the scarcity of sense tagged data, and so do SLMs. Besides improving structured learning methods according to the characteristics of WSD, another approach to improve disambiguation performance is to mine disambiguation knowledge from all kinds of sources, such as Wikipedia, parallel corpus, and to alleviate knowledge acquisition bottleneck of WSD
Optimal Performance: Underlying Octahedron Graph of Evolutionary Processors
Get PDFNetworks of evolutionary processors with an underlying octahedron graph consist of 7 language processors which are linked to the vertices of the octahedron graph. Notice that they are located in the 6 facets and the core of a cube graph. Also note that the nodes are only able to perform a type of mutation based on the words found in that node. Each node is associated with an input filter and an output filter, defined by some regular language. Rules are applied to all the words existing in every node. The words, able to pass the output filter of the respective node, are sent out and they navigate through the graph. Such words will enter those nodes provided their input filters are satisfied. The computational power of the network is comparable to Turing machines when the filters are regular languages. We introduce several variants of octahedron networks, depending on rule types and the way of computation plus their computational power. Some known problems are addressed at the end
A Novel Way of Using Simulations to Support Urban Security Operations
Get PDFThe growing importance of security operations in urban terrain has triggered many attempts to address the perceived gaps in the readiness of security forces for this type of combat. One way to tackle the problem is to employ simulation techniques. Simulations are widely used to support both mission rehearsal and mission analysis, but these two applications tend to be seen as distinctly separate. We argue that integrating them in a unified framework can bring significant benefits for end-users. We perform a structured walk-through of such a unified system, in which a novel approach to integration through the behaviour cloning enabled the system to capture the operational knowledge of security experts, which is often difficult to express verbally. This capability emerged as essential for the operation of the integrated system. We also illustrate how the interplay between the system components for the mission analysis and mission rehearsal is realized
Differential Evolution Based Multiple Vector Prototype Classifier
Get PDFIn this article we introduce differential evolution based multiple vector prototype classifier (shortly MVDE). In this method we extend the previous DE classifier so that it can handle several class vectors in one class. Classification problems which are so complex that they are simply not separable by using distance based algorithms e.g. differential evolution (DE) classifier or support vector machine (SVM) classifier have troubled researchers for years. In this article, we propose a solution for one area of this problem type in which we extend DE classifier in a way that we allow several class vectors to exist for optimizing one class. This way a part of such complex data can be handled by one vector and other part can be handled by another vector. Differential evolution algorithm is a clear choice for handling such a multiple vector classification tasks because of its remarkable optimization capabilities. MVDE classifier is tested with several different benchmark classification problems to show its capabilities and its performance is compared to DE classifier, SVM and backpropagation neural network classifier. MVDE classifier managed to get best classification performance of these classifiers and clearly indicates it has a potential in this type of classification problems
IPO: An Inclined Planes System Optimization Algorithm
Get PDFIn the last decades, heuristic algorithms are widely used in solving problems in different fields of science and engineering. Most of these methods are inspired by natural phenomena, such as biological behaviours or physical principles. In this paper, a new optimization method based on the dynamic of sliding motion along a frictionless inclined plane is introduced. In the proposed algorithm, a collection of agents cooperate with each other and move toward better positions in the search space by employing Newton's second law and equations of motion. Our method is compared with other popular optimization algorithms and the results on 23 standard benchmark functions show its effectiveness in most cases