Informatica

It is well known that in situations involving the study of large datasets where influential observations or outliers maybe present, regression models based on the Maximum Likelihood criterion are likely to be unstable. In this paper we investigate the use of the Minimum Density Power Divergence criterion as a practical tool for parametric regression models building. More precisely, we suggest a procedure relying on an index of similarity between estimated regression models and on a Monte Carlo Significance test of hypothesis that allows to check the existence of outliers in the data and therefore to choose the best tuning constant for the Minimum Density Power Divergence estimators. Theory is outlined, numerical examples featuring several experimental scenarios are provided and main results of a simulation study aiming to verify the goodness of the procedure are supplied.

This paper offers an analysis of HIV/AIDS dynamics, defined by CD4 levels and Viral load, carried out from a macroscopic point of view by means of a general stochastic model. The model focuses on the patient's age as a relevant factor to forecast the transitions among the different levels of seriousness of the disease and simultaneously on the chronological time. The third model considers the two previous features simultaneously. In this way it is possible to quantify the medical scientific progresses due to the advances in the treatment of the HIV. The analyses have been performed through non-homogeneous semi-Markov processes. These models have been implemented by using real data provided by ISS (Istituto Superiore di Sanità, Rome, Italy). They refer to 2159 subjects enrolled in Italian public structures from September 1983 to January 2006. The relevant results include also the survival analysis of the infected patients. The computed conditional probabilities show the different responses of the subjects depending on their ages and the elapsing of time.

For the purpose of exploring and modelling the relationships between a dataset and several datasets, multiblock Partial Least Squares is a widely-used regression technique. It is designed as an extension of PLS which aims at linking two datasets. In the same vein, we propose an extension of Redundancy Analysis to the multiblock setting. We show that PLS and multiblock Redundancy Analysis aim at maximizing the same criterion but the constraints are different. From the solutions of both these approaches, it turns out that they are the two end points of a continuum approach that we propose to investigate.

Estimation and modelling problems as they arise in many data analysis areas often turn out to be unstable and/or intractable by standard numerical methods. Such problems frequently occur in fitting of large data sets to a certain model and in predictive learning. Heuristics are general recommendations based on practical statistical evidence, in contrast to a fixed set of rules that cannot vary, although guarantee to give the correct answer. Although the use of these methods became more standard in several fields of sciences, their use for estimation and modelling in statistics appears to be still limited. This paper surveys a set of problem-solving strategies, guided by heuristic information, that are expected to be used more frequently. The use of recent advances in different fields of large-scale data analysis is promoted focusing on applications in medicine, biology and technology.

This paper presents an entire chaos-based biometric remote user authentication scheme on tokens without using passwords. The proposed scheme is based on the chaotic hash function and chaotic pseudo-random number generator to provide secure mutual authentication over an insecure channel between the user and remote server. Compared with the related biometric authentication schemes, the proposed scheme does not require the user password to provide convenience to users. It also does not require time synchronization or delay-time limitations between the user and remote server to resolve time synchronization problems.

Combinatorial problems serve as an important resource for developing practical public key cryptosystems and several combinatorial cryptosystems have been proposed in the cryptographic community. In this paper, a combinatorial public key cryptosystem is proposed. The security of the proposed cryptosystem is dependent on a combinatorial problem involving matrices. The system features fast encryption and decryption. However, the system also suffers from some drawbacks. The ciphertext expansion is relatively large and the key sizes are somewhat larger than that of RSA. The security of the system is carefully examined by illustrating the computational infeasibilities of some attacks on the system.

The paper presents the process of the selection of a potential supplier, which have to be the most appropriate to stakeholders. The selection is based on a set of criteria: Delivery Price, Financial Position, Production Specifications, Standards and Relevant Certificates, Commercial Strength, and the Performance of supplier, etc. The criteria for evaluation and their importance are selected by taking into consideration the interests and goals of the stakeholders. The solution of problem was made by applying a new Additive Ratio ASsessment (ARAS) method with the grey criteria scores – ARAS-G method. The proposed technique could be applied to substantiate the selection of effective alternative of sustainable development, impact on environment, structures, technologies, investments, etc.

This paper presents GSM speech coder indirect identification algorithm based on sending novel identification pilot signals through the GSM speech channel. Each GSM subsystem disturbs identification pilot, while speech coder uniquely changes the tempo-spectral characteristics of the proposed pilot signal. Speech coder identification algorithm identifies speech coder with the usage of robust linear frequency cepstral coefficient (LFCC) feature extraction procedure and fast artificial neural networks. First step of speech coder identification algorithm is the exact position detection of the identification pilot signal using normalized cross correlation approach. Next stage is time-domain windowing of the input signal to convolve each frame of the input speech signal and window spectrum. Consecutive step is a short-time Fast Fourier Transformation to produce the magnitude spectrum of each windowed frame. Further, a noise reduction with spectral subtraction based on spectral smoothing is carried out. In last steps we perform the frequency filtering and Discrete Cosine Transformation to receive 24 uncorrelated cepstral coefficients per frame as a result. Speech coder identification is completed with fast artificial neural network classification using the input feature vector of 24 LFCC coefficients, giving a result of identified speech coder. For GSM speech coder indirect identification evaluation, the standardized GSM ETSI bit-exact implementations were used. Furthermore, a set of custom tools was build. These tools were used to simulate and control various conditions in the GSM system. Final results show that proposed algorithm identifies the GSM-EFR speech coder with the accuracy of 98.85%, the GSM-FR speech coder with 98.71%, and the GSM-HR coder with 98.61%. These scores were achieved at various types of surrounding noises and even at very low SNR conditions.

We present a novel dynamic network interdiction model that accounts for interactions between an interdictor deploying resources on arcs in a digraph and an evader traversing the network from a designated source to a known terminus, wherein the agents may modify strategies in selected subsequent periods according to respective decision and implementation cycles. For the resulting minimax model, we develop a reformulation that facilitates a direct solution procedure using commercial software or via a proposed alternating heuristic. We examine certain related stability and convergence issues, demonstrate special convergence cases, and provide insights into the computational performance of the solution procedures.

In this paper we propose facilitating ontology development by constant evaluation of steps in the process of ontology development. Existing methodologies for ontology development are complex and they require technical knowledge that business users and developers don't poses. By introducing ontology completeness indicator developer is guided throughout the development process and constantly aided by recommendations to progress to next step and improve the quality of ontology. In evaluating the ontology, several aspects are considered; from description, partition, consistency, redundancy and to anomaly. The applicability of the approach was demonstrated on Financial Instruments and Trading Strategies (FITS) ontology with comparison to other approaches.