Informatica

We propose a fast MATLAB implementation of the mini-element (i.e. $P1$-Bubble/$P1$) for the finite element approximation of the generalized Stokes equation in 2D and 3D. We use cell arrays to derive vectorized assembling functions. We also propose a Uzawa conjugate gradient method as an iterative solver for the global Stokes system. Numerical experiments show that our implementation has an (almost) optimal time-scaling. For 3D problems, the proposed Uzawa conjugate gradient algorithm outperforms MATLAB built-in linear solvers.

Neutrosophic hesitant fuzzy set (NHFS) is a convincing tool that deals with uncertain information. In this paper, we propose an NH-MADM strategy for solving MADM with NHFSs based on extended GRA. We assume that the information of attributes is partially known or completely unknown. We develop two models to determine the weights of attributes. Then we rank the alternatives based on the strategy. Further, we extend the strategy into MADM in interval neutrosophic hesitant fuzzy set environment which we call INH-MADM strategy. Finally, we provide two illustrative examples to show the validity and effectiveness of the proposed strategies.

The risk analysis has always been one of the essential procedures for any areas. The majority of security incidents occur because of ignoring risks or their inaccurate assessment. It is especially dangerous for critical infrastructures. Thus, the article is devoted to the description of the developed model of risk assessment for the essential infrastructures. The goal of the model is to provide a reliable method for multifaceted risk assessment of information infrastructure. The purpose of the article is to present a developed model based on integrated MCDM approaches that allow to correctly assess the risks of the critical information infrastructures.

In this paper, we extend MM operator and dual MM (DMM) operator to process the interval-valued Pythagorean fuzzy numbers (IVPFNs) and then to solve the MADM problems. Firstly, we develop some interval-valued Pythagorean fuzzy Muirhead mean operators by extending MM and DMM operators to IVPFNs. Then, we prove some properties and discuss some special cases with respect to the parameter vector. Moreover, we present some new methods to deal with MADM problems with the IVPFNs based on the proposed MM and DMM operators. Finally, we verify the validity and reliability of our methods by using an application example for green supplier selections, and analyse the advantages of our methods by comparing it with other existing methods.

The aim of this paper is to make a proposal for a new extension of the MULTIMOORA method extended to deal with bipolar fuzzy sets. Bipolar fuzzy sets are proposed as an extension of classical fuzzy sets in order to enable solving a particular class of decision-making problems. Unlike other extensions of the fuzzy set of theory, bipolar fuzzy sets introduce a positive membership function, which denotes the satisfaction degree of the element x to the property corresponding to the bipolar-valued fuzzy set, and the negative membership function, which denotes the degree of the satisfaction of the element x to some implicit counter-property corresponding to the bipolar-valued fuzzy set. By using single-valued bipolar fuzzy numbers, the MULTIMOORA method can be more efficient for solving some specific problems whose solving requires assessment and prediction. The suitability of the proposed approach is presented through an example.

This paper introduces two novel algorithms for the 2-bit adaptive delta modulation, namely 2-bit hybrid adaptive delta modulation and 2-bit optimal adaptive delta modulation. In 2-bit hybrid adaptive delta modulation, the adaptation is performed both at the frame level and the sample level, where the estimated variance is used to determine the initial quantization step size. In the latter algorithm, the estimated variance is used to scale the quantizer codebook optimally designed assuming Laplace distribution of the input signal. The algorithms are tested using speech signal and compared to constant factor delta modulation, continuously variable slope delta modulation and instantaneously adaptive 2-bit delta modulation, showing that the proposed algorithms offer higher performance and significantly wider dynamic range.

Integration of algorithms of investment theory and artificial intelligence allows one to create a support system for investors in exchange markets based on the ensemble of long-short-term-memory (LSTM) based recurrent neural networks (RNN). The proposed support system contains five stages: preparation of historical data, prediction by an ensemble of LSTM RNNs, assessment of prediction distributions, investment portfolio formation and verification. The prediction process outputs a multi-modal distribution, which provides useful information for investors. The research compares four different strategies based on a combination of distribution forecasting models. The high-low strategy helps decision-makers in exchange markets to recognize signals of transactions and fix limits for expectations. A combination of high-low-daily-weekly predictions helps investors to make daily transactions with knowing distribution of exchange rates during the week. The shift in time of five hours between London and New York inspired us to create a UK-NY strategy, which allows investors to recognize the signals of the market in a very short time. The joined high-low-UK-NY strategy increases the possibility of recognizing the signals of transactions in a very short time and of fixing the limits for day trading. So, this support system for investors is verified as a profitable tool for speculators in the relatively risky currency market.

Saliency detection has been deeply studied in the last few years and the number of the designed computational models is increasing. Starting from the assumption that spatial and temporal information of an input video frame can provide better saliency results than using each information alone, we propose a spatio-temporal saliency model for detecting salient objects in videos. First, spatial saliency is measured at patch-level by fusing local contrasts with spatial priors to label each patch as a foreground or a background one. Then, the newly proposed motion distinctiveness feature and gradient flow field measure are used to obtain the temporal saliency maps. Finally, spatial and temporal saliency maps are fused together into one final saliency map.

The raw trajectories contain large amounts of redundant data that bring challenges to storage, transmission and processing. Trajectory compression algorithms can reduce the number of positioning points while minimizing the loss of information. This paper proposes a heading maintaining oriented trajectory compression algorithm, which takes into account both position information and direction information. By setting an angle threshold, the algorithm can achieve a more accurate approximation of trajectories than traditional position-preserving trajectory compression algorithms. The experimental results show that the algorithm can ensure certain effect on the direction information and is more flexible.

In Computer Vision and Pattern Recognition, surveillance-video crowded scenes have been analysed according to their structure, where the detection of distinguishable people groups is an essential step. In this paper, we are interested in detecting F-Formations (i.e. free standing conversational groups) on video, which are formed by people social relations. We proposed a new method based on fuzzy relations, where a new social representation for computing relation between individuals, fusion for search consensus in multiple frame and clustering are introduced. Finally, our proposal was tested in a real-world dataset, improving the already reported scores from literature.