Informatica

Ontology Driven Software Development (ODSD) combines traditional Model Based Software Development (MBSD) techniques with ontology technology in order to provide extensions to and advantages over MBSD. The goal of the paper is to identify current ODSD approaches and to provide qualitative and comparative analysis of the collection of identified approaches. Main research questions of the paper concern the ways of how ontologies are integrated to MBSD process and how their usage advances MBSD. Benefits and challenges of each of the discussed approaches are presented. The analysis is based on literature and projects reviews in the fields of ontology engineering, MBSD and ODSD. The result of the analysis provides understanding of what is the role of ontologies in ODSD and shows whether application of ontology technologies to the MBSD process gives rise to a new paradigm called consistency preserving software development or not.

We attempted to determine the most common localizations of epileptogenic foci by using common functional (EEG and PET/CT) and structural (MRI) imaging methods. Also, we compared the number of epileptogenic foci detected with all diagnostic methods and determined the success rate of surgery in the operated patients when the epileptogenic foci coincided on all three imaging methods. 35 patients (including children) with clinically proven refractory epilepsy were included into the study. All patients underwent an MRI scan with epilepsy protocol, Fluorodeoxyglucose-18-PET/CT scan, and an EEG prior to a PET study. 14 patients underwent neurosurgery for removal of epileptogenic foci. We found a statistically significant difference between the number of epileptogenic foci which were found in PET/CT and EEG studies but there was no significant difference between MRI and PET/CT lesion numbers. The most common localization of epileptogenic activity on EEG was right temporal lobe (54.3%); the most common lobe with structural changes on MRI was right temporal lobe (42.9%); the most common hypometabolism zone on PET/CT was in right temporal lobe (45.7%). 10 out of 14 patients who underwent surgery demonstrated excellent postsurgical outcomes, with no epileptic seizures one year or more after the operation; 3/14 patients had 1–2 seizures after surgery and one patient had the same count or more epileptic seizures in duration of one year or more. The measure of Agreement Kappa between PET/CT and EEG value was 0.613 $(p<0.05)$. Between PET/CT and MRI the value was 0.035 $(p>0.05)$. Surgical treatment may offer hope for patients with intractable epileptic seizures. PET/CT was an extremely useful imaging method to assist in the localization of epileptogenic zones. The dynamic functional information that brain PET/CT provides is complementary to anatomical imaging of MRI and functional information of EEG.

This paper introduces a new similarity measure derived from the Common Submatrix-based measures for comparing square matrices. The novelty is that the similarity between two matrices is computed as the average area of the largest sub-matrices exactly matching and being located at the same position in the two matrices. By contrast, in the original similarity measures, the largest sub-matrices can exactly or approximately match and be located at different positions. An experiment conducted on a subset of the MNIST and NIST datasets shows that the new similarity measure is very promising in retrieving relevant handwritten character images.

Intuitionistic uncertain linguistic variables (IULVs) are useful to express the qualitative and quantitative recognitions of decision makers. However, after reviewing the previous operational laws on IULVs, we find there are some limitations. To address these issues, we define several new operations on IULVs and give a new ranking method. To improve the utilization of IULVs, this paper defines two Choquet operators: the intuitionistic uncertain linguistic symmetrical Choquet averaging (IULSCA) operator and the intuitionistic uncertain linguistic symmetrical Choquet geometric mean (IULSCGM) operator, which can address the internal correlations among elements. To globally reflect the interactive characteristics of the importance of elements, two generalized Shapley intuitionistic uncertain linguistic symmetrical Choquet operators are presented. Subsequently, a new distance measure is defined, which is then used to build models to ascertain fuzzy measures on decision maker and criteria sets to address the case where the weighting information is partly known. After that, a new procedure to intuitionistic uncertain linguistic group decision making is developed. Finally, a specific example is offered to illustrate the practicality of the new procedure, and the comparison analysis is also made.

In this paper an exploratory classification, so called open set problem, is investigated. Open set recognition assumes there is incomplete knowledge of the world at training time, and unknown classes can be submitted to an algorithm during testing. For this problem we elaborated a theoretical model, Double Probability Model (DPM), based on likelihoods of a classifier. We developed it with double smoothing solution in order to solve technical difficulties avoiding zero values in the predictions. We applied the GMM based Fisher vector for the mathematical representation of the images and the C-SVC with RBF kernel for the classification. The last contributions of the paper are new goodness indicators for classification in open set problem, the new type of accuracies. The experimental results present that our Double Probability Model helps with classification, the accuracy increases by using our proposed model. We compared our method to a state-of-the-art open set recognition solution and the results showed that DPM outperforms existing techniques.

This paper aims to propose a new distance measure, the interval-valued 2-tuple linguistic induced continuous ordered weighted distance (IT-ICOWD) measure, which consists of the interval-valued 2-tuple linguistic induced continuous ordered weighted averaging (IT-ICOWA) operator and the ordered weighted distance (OWD) measure. In these operators, we consider the risk attitude of decision maker. Furthermore, we discuss some desired properties and various special cases of the IT-ICOWD measure. Additionally, a method of multiple attribute group decision making (MAGDM) in interval-valued 2-tuple linguistic environment is developed on the basis of the IT-ICOWD measure. Through this method, we obtain three simple and exact formulae to determine the order-inducing variables of the IT-ICOWD measure, the weighting vector of decision makers and the weighting vector of attributes, respectively. At last, a numerical example is presented to illustrate the practicability and feasibility of proposed method.

The probabilistic linguistic terms set (PLTS) can reflect different importance degrees or weights of all possible linguistic terms (LTs) given by the experts for a specific object. The PROMETHEE II method is an important ranking method which can comprise preferences as well as indifferences, and it has a unique characteristic that can provide different types of preference functions. Based on the advantages of the PLTS and the PROMETHEE II method, in this paper, we extend the PROMETHEE II method to process the probabilistic linguistic information (PLI), and propose the PL-PROMETHEE II method with an improved possibility degree formula which can avoid the weaknesses from the original formula. Then concerning the multi-attribute decision making (MADM) problems with totally unknown weight information, the maximum deviation method is used to get the objective weight vector of the attributes, and net flows of the alternatives from the PROMETHEE II method are used to rank the alternatives. Finally, a numerical example is given to illustrate the feasibility of the proposed method.

This paper studies a set of novel integrated scheduling problems by taking into account the combinatorial features of various groups, parallel-batching, deteriorating jobs, and time-dependent setup time simultaneously under the settings of both single-machine and parallel-machine, and the objective of the studied problems is to minimize the makespan. In order to solve the single-machine scheduling problem, we first investigate the structural properties on jobs sequencing, jobs batching, and batches sequencing for the optimal solution, and then develop a scheduling rule. Moreover, for solving the parallel-machine scheduling problem, we exploit the optimal structural properties and batching rule, and propose a novel hybrid AIS-VNS algorithm incorporating Artificial Immune System algorithm (AIS) and Variable Neighbourhood Search (VNS). Extensive computational experiments are conducted to evaluate the performance of the proposed AIS-VNS algorithm, and comparison results show that the proposed algorithm performs quite well in terms of both efficiency and solution quality.

In the discrete form of multi-criteria decision-making (MCDM) problems, we are usually confronted with a decision-matrix formed from the information of some alternatives on some criteria. In this study, a new method is proposed for simultaneous evaluation of criteria and alternatives (SECA) in an MCDM problem. For making this type of evaluation, a multi-objective non-linear programming model is formulated. The model is based on maximization of the overall performance of alternatives with consideration of the variation information of decision-matrix within and between criteria. The standard deviation is used to measure the within-criterion, and the correlation is utilized to consider the between-criterion variation information. By solving the multi-objective model, we can determine the overall performance scores of alternatives and the objective weights of criteria simultaneously. To validate the proposed method, a numerical example is used, and three analyses are made. Firstly, we analyse the objective weights determined by the method, secondly, the stability of the performance scores and ranking results are examined, and finally, the ranking results of the proposed method are compared with those of some existing MCDM methods. The results of the analyses show that the proposed method is efficient to deal with MCDM problems.

This paper introduces how predictor-based control principles are applied to the control of human excitement signal as a response to a 3D face virtual stimuli. A dynamic human 3D face is observed in a virtual reality. We use changing distance-between-eyes in a 3D face as a stimulus – control signal. Human responses to the stimuli are observed using EEG-based signal that characterizes excitement. A parameter identification method for predictive and stable model building with the smallest output prediction error is proposed. A predictor-based control law is synthesized by minimizing a generalized minimum variance control criterion in an admissible domain. An admissible domain is composed of control signal boundaries. Relatively high prediction and control quality of excitement signals is demonstrated by modelling results.