Informatica

Following the White Paper HCE that Google released in August 2014, it was expected that NFC mobile phone payment would cause a wave of security discussion. After all, Android HCE will allow anyone to develop his own payment service on the Android platform to get rid of restriction from telecommunications, financial industries, or third party trust centers. On this security mechanism observation period, we propose a lightweight authentication protocol on NFC mobile sensors payment. Through introducing this security agreement, it not only allows individuals to have privacy protected, but also can prevent malicious attackers from the track, which will make legitimate Tags verified, while effectively preventing an illegal Tag from being forged as an authenticated Tag. Therefore, constructing full security mechanisms will benefit to the development of mobile NFC payment.

Our research is devoted to development of information infrastructure for e-service semi-automatic provision by using distributed data warehouses (DWs) of water protection domain. Development of software for semi-automatic service provision is based on artificial planner and structure of goals adapted for specialized needs of end users. Such e-service preparation mechanism can work under the unified coherent framework for solving the environment protection problems by evaluation of the processes of water consumption and contamination. The possibilities of integration of distributed DWs of water management sector into web portal meeting the requirements of conceptual interoperability are presented. Design approach is based on development of decision support system (DSS) that is designed as multilayered system with multi–componential, interoperable structure of databases (DBs), which are under responsibility of different public administration institutions such as European Environment Information and Observation Network (EIONET) and national environment protection agencies. The infrastructure of EIONET is used for supporting and improving data and information flows. The Water resource management information system (WRMIS) became the kernel component of DSS. WRMIS prototype facilitates data flows between the institutions and gives access to data for relevant institutions and the public providing e-services using proposed DSS. The research investigations are made according to the requirements of European Union Water Framework Directive, Sustainable Development Strategy and ReportNet as the EIONET infrastructure for supporting and improving data and information flows. Additional means are integrated in the structures of DSS as knowledge representation techniques based on conceptual schemas, data flow diagrams, and decision-making rules. The on-line management techniques are based on assurance of interoperability by using Open Web Platform W3C standards for web service development, such as XML, SOAP, HTTP, etc.

In this paper, we focus on group decision making problems with uncertain preference ordinals, in which the weight information of decision makers is completely unknown or partly unknown. First of all, the consistency and deviation measures between two uncertain preference ordinals are defined. Based on the two measures, a multi-objective optimization model which aims to maximize the deviation of each decision maker’s judgements and the consistency among different decision makers’ judgements is established to obtain the weights of decision makers. The compromise solution method, i.e. the VIKOR method is then extended to derive the compromise solution of alternatives for group decision making problems with uncertain preference ordinals. Finally, three examples are utilized to illustrate the feasibility and effectiveness of the proposed approach.

This paper presents the corpus-driven approach in building the computational model of fundamental frequency, or ${\mathit{F}}_0$, for Lithuanian language. The model was obtained by training the HMM-based speech synthesis system HTS on six hours of speech coming from multiple speakers. Several gender specific models, using different parameters and different contextual factors, were investigated. The models were evaluated by synthesizing ${\mathit{F}}_0$ contours and by comparing them to the original ${\mathit{F}}_0$ contours using criteria of root mean square error (RMSE) and voicing classification error. The HMM-based models showed an improvement of the RMSE over the mean-based model that predicted ${\mathit{F}}_0$ of the vowel on the basis of its average normalized pitch.

The aim of this paper is twofold. Firstly, to discuss a clustering of a given set of the European banks into groups based on their performance during 1999–2013. Secondly, to compare different dissimilarity measures and to determine which of them suits best for clustering banking ratios. Six ratios that reveal profitability, efficiency, stability and loan portfolio quality of the banks were used. The similarity/dissimilarity between banks was estimated using measures that are based on time series or functional data properties. Two dissimilarity measures that are not commonly used in the literature are proposed and two measures are extended from univariate into multivariate case. The results of our study show that there is no dissimilarity measure which would provide the best clustering results for all ratios. However, dissimilarity measures based on functional data properties in many cases outperfomed measures based on time series properties. The choice of the number of clusters is not that clear. According to different banking ratios, it is found that banks could be grouped into 6–12 clusters.

Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favour of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on energy planning decisions.

The paper presents analytic and stochastic methods of structure parameters estimation for a model selection problem. Structure parameters are covariance matrices of parameters of linear and non-linear regression models. To optimize model parameters and structure parameters we maximize a model evidence, a convolution of a data likelihood with a prior distribution of model parameters. The analytic methods are based on the derivatives computation of the approximated model evidence. The stochastic methods are based on the model parameters sampling and data cross-validation. The proposed methods are tested and compared on the synthetic and real data.

An optimization problem is formulated in the tropical mathematics setting to maximize a nonlinear objective function defined by conjugate transposition on vectors in a semimodule over a general idempotent semifield. The study is motivated by problems from project scheduling, where the deviation between completion times of activities is to be maximized subject to precedence constraints. To solve the unconstrained problem, we establish an upper bound for the function, and then obtain a complete solution to a system of vector equations to find all vectors that yield the bound. An extension of the solution to handle constrained problems is discussed. The results are applied to give direct solutions to the motivational problems, and illustrated with numerical examples.

Phoneme duration modelling is one of the stages in prosody modelling for text-to-speech systems. The rule-based phoneme duration model proposed by Klatt (1979) is still quite a popular method. One of the main shortcomings of this method is that the values of the parameters are selected in an experimental way. This work proposes a new iterative algorithm for the automatic estimation of the factors for the Klatt model using the corpus of an annotated audio record of the speaker. The phoneme duration models were built for three different Lithuanian speakers. The quality of the estimation of phonemes durations was evaluated by the root mean square error, the mean absolute error and the correlation coefficient.

Certificateless short signature (CLSS) possesses the advantages of both certificateless signature and short signature. CLSS eliminates the certificate management in conventional signatures and solves the key escrow problem in ID-based signatures. In the meantime, due to its short signature length, CLSS reduces the bandwidth for communication so that it is suitable for some specific authentication applications requiring bandwidth-constrained communication environments. However, up to now, there is no work on studying the revocation problem in existing CLSS schemes. In this article, we address the revocation problem and propose the first revocable certificateless short signature (RCLSS) scheme. Based on the computational Diffie–Hellman (CDH) assumption, we demonstrate that our RCLSS scheme possesses strong unforgeability against adaptive chosen-message attacks under an accredited security model. It turns out that our scheme has the shortest signature length while retaining computational efficiency. Thus, the proposed RCLSS scheme is well suited for low-bandwidth communication environments. Finally, we combine the proposed RCLSS scheme with cloud revocation authority (CRA) to present a CRA-aided authentication scheme with period-limited privileges for mobile multi-server environment.