Informatica

Manifold learning algorithms do not extract the structure of datasets in an abstract form or they do not have high performance for complex data.In this paper, a method for Learning an Inductive Riemannian Manifold in Abstract form (LIRMA) is presented in which the structure of patterns is determined by solving the embedded dynamical system of the patterns. In order to model corresponding system, the true sequence of patterns is estimated using a topology preserving method. LIRMA has the advantage of being an inductive method with low complexity. Additionally, it is a topology preserving method with respect to quantitative measures.

Background: In the area of artificial learners, not much research on the question of an appropriate description of artificial learner's (empirical) performance has been conducted. The optimal solution of describing a learning problem would be a functional dependency between the data, the learning algorithm's internal specifics and its performance. Unfortunately, a general, restrictions-free theory on performance of arbitrary artificial learners has not been developed yet.

We propose an adaptive inverse control scheme, which employs a neural network for the system identification phase and updates its weights in online mode. The theoretical basis of the method is given and its performance is illustrated by means of its application to different control problems showing that our proposal is able to overcome the problems generated by dynamic nature of the process or by physical changes of the system which originate important modifications in the process. A comparative experimental study is presented in order to show the more stable behavior of the proposed method in several working ranks.

Tablet computers and other mobile devices are widely used in education and other life activities. However, touch screen keyboards are not adapted to languages using alphabets with more letters than the English alphabet has. In this paper, we analyze the existing keyboards on such devices for various languages, influence of keyboard usability on the number of typing errors, and propose keyboard design guidelines for non-English languages. As an example, the layout for the Lithuanian language keyboard (32 native letters and 3 foreign letters) has been presented here. The described solution is not strictly related to the peculiarities of the Lithuanian language alphabet (apart from the number of letters), therefore it is suitable for other languages using a similar number of letters.

This paper introduces a comparison of one linear and two nonlinear one-step-ahead predictive models that were used to describe the relationship between human emotional signals (excitement, frustration, and engagement/boredom) and virtual dynamic stimulus (virtual 3D face with changing distance-between-eyes). An input–output model building method is proposed that allows building a stable model with the smallest output prediction error. Validation was performed using the recorded signals of four volunteers. Validation results of the models showed that all three models predict emotional signals in relatively high prediction accuracy.

We consider the Multilevel Uncapacitated Facility Location Problem (MLUFLP) and propose a new efficient integer programming formulation of the problem that provides optimal solutions for the MLUFLP test instances unsolved to optimality up to now. Further, we design a parallel Memetic Algorithm (MA) with a new strategy for applying the local search improvement within the MA frame. The conducted computational experiments show that the proposed MA quickly reaches all known optimal and best known solutions from the literature and additionally improves several solutions for large-scale MLUFLP test problems.

This article discusses about ways of organizing and using the system for typical user behavior model construction within a distributed information system using Python servers. Much attention is paid to analysis of the issue of effective organization of the research process using the Python language in all its phases.

Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others.

Up to date, a large number of ID-based signature (IBS) schemes based on bilinear pairings have been proposed. Most of these IBS schemes possess existential unforgeability under adaptive chosen-message attacks, among which some offer strong unforgeability. An IBS scheme is said to be strongly unforgeable if it possesses existential unforgeability and an adversary who is given signatures of the IBS scheme on some message m is unable to generate a new signature on m. Strong unforgeable IBS schemes can be used to construct many important ID-based cryptographic schemes. However, the existing strongly unforgeable IBS schemes lack efficiency for the signature size and the computation cost of verification phase. In this paper, we propose an efficient strongly unforgeable IBS scheme without random oracles. Under the computational Diffie–Hellman and collision resistant hash assumptions, we demonstrate that the proposed IBS scheme possesses strong unforgeability against adaptive chosen-message attacks. When compared with previously proposed strongly unforgeable IBS schemes, our scheme has better performance in terms of signature size and computation cost.