Informatica

Machine Translation has become an important tool in overcoming the language barrier. The quality of translations depends on the languages and used methods. The research presented in this paper is based on well-known standard methods for Statistical Machine Translation that are advanced by a newly proposed approach for optimizing the weights of translation system components. Better weights of system components improve the translation quality. In most cases, machine translation systems translate to/from English and, in our research, English is paired with a Slavic language, Slovenian. In our experiment, we built two Statistical Machine Translation systems for the Slovenian-English language pair of the Acquis Communautaire corpus. Both systems were optimized using self-adaptive Differential Evolution and compared to the other related optimization methods. The results show improvement in the translation quality, and are comparable to the other related methods.

A major challenge in face recognition is handling large pose variations. Here, we proposed to tackle this challenge by a three step sparse representation based method: estimating the pose of an unseen non-frontal face image, generating its virtual frontal view using learned view-dependent dictionaries, and classifying the generated frontal view. It is assumed that for a specific identity, the representation coefficients based on the view dictionary are invariant to pose and view-dependent frontal view generation transformations are learned based on pair-wise supervised dictionary learning. Experiments conducted on FERET and CMU-PIE face databases depict the efficacy of the proposed method.

The present research shows the implementation of a virtual sensor for fault detection with the feature of recovering data. The proposal was implemented over a bicomponent mixing machine used for the wind generator blades manufacture based on carbon fiber. The virtual sensor is necessary due to permanent problems with wrong sensor measurements. The solution proposed uses an intelligent model able to predict the sensor measurements, which are compared with the measured value. If this value belongs to a specified range, it is valid. Otherwise, the prediction replaces the read value. The process fault detection feature has been added to the proposal, based on consecutive erroneous readings, obtaining satisfactory results.

Construction site selection is a complex problem involving many alternatives and conflicting criteria with vague and imprecise evaluations. Fuzzy multi-criteria decision-making methods are the most effective tools to obtain optimum solutions under possibilistic uncertainty. In this paper, a novel interval hesitant fuzzy CODAS method is proposed and applied to a residential construction site selection problem. A comparative analysis with ordinary fuzzy CODAS method is applied for validating the proposed method. Also, a sensitivity analysis is conducted for the stability of the ranking results of the interval hesitant fuzzy CODAS method. The results of the analyses demonstrate the effectiveness of our proposed method.

The primitive of certificateless signature, since its invention, has become a widely studied paradigm due to the lack of key escrow problem and certificate management problem. However, this primitive cannot resist catastrophic damage caused by key exposure. Therefore, it is necessary to integrate revocation mechanism into certificateless signature. In this paper, we propose a new certificateless signature scheme with revocation (RCLS) and prove its security under the standard model. In the meanwhile, our scheme can resist malicious-but-passive Key Generation Center (KGC) attacks that were not possible in previous solutions. The theoretical analysis shows our scheme has high efficiency and practicality.

In this paper, we present the progress of blockchain technology from the advent of the original publication titled “Bitcoin: A Peer-to-Peer Electronic Cash System,” written by the mysterious Satoshi Nakamoto, until the current days. Historical background and a comprehensive overview of the blockchain technology are given. We provide an up-to-date comparison of the most popular blockchain platforms with particular emphasis given to consensus protocols. Additionally, we introduce a BlockLib, an extensively growing online library on blockchain platforms collected from the various sources and designed to enable contributions from the blockchain community. Main directions of the current blockchain research, facing challenges as well as the main fields of applications, are summarized. We also layout the possible future lines in the blockchain technology development.

Despite the mass of empirical data in neuroscience and plenty of interdisciplinary approaches in cognitive science, there are relatively few applicable theories of how the brain as a coherent system functions in terms of energy and entropy processes. Recently, a free energy principle has been portrayed as a possible way towards a unified brain theory. However, its capacity, using free energy and entropy, to unify different perspectives on brain function dynamics is yet to be established. This multidisciplinary study attempts to make sense of the free energy and entropy not only from the perspective of Helmholtz thermodynamic basic principles but also from the information theory framework. Based on the proposed conceptual framework, we constructed (i) four basic brain states (deep sleep, resting, active wakeful and thinking) as dynamic entropy and free energy processes and (ii) stylized a self-organizing mechanism of transitions between the basic brain states during a day period. Adaptive transitions between brain states represent homeostatic rhythms, which produce complex daily brain states dynamics. As a result, the proposed simulation model produces different self-organized circadian dynamics of brain states for different types of chronotypes, which corresponds with the empirical observations.

Brain Computer Interfaces (BCI) are devices that use brain signals for control or communication. Since they don’t require movement of any part of the body, BCI are the natural choice for assisted communication when a person is unable to move.

In this paper, we present the 2-tuple linguistic neutrosophic MABAC model based on the traditional MABAC (multi-attributive border approximation area comparison) model and some fundamental theories of 2-tuple linguistic neutrosophic information. Firstly, we briefly review the definition of 2-tuple linguistic neutrosophic sets (2TLNNSs) and introduce the score function, accuracy function, operation laws and some aggregation operators of 2TLNNs. Then, the calculation steps of traditional MABAC model are briefly presented. Furthermore, combine the traditional MABAC model with 2TLNNs information, the 2-tuple linguistic neutrosophic MABAC model is established for multiple attribute group decision making (MAGDM) and the computing steps are simply depicted. In our presented model; it’s more accuracy and effective for computing the distance between each alternatives and the border approximation area (BAA). Finally, a numerical example for safety assessment of construction project has been given to illustrate this new model and some comparisons between 2TLNNs MABAC model and two 2TLNNs aggregation operators are also conducted to further illustrate advantages of the new method.

This paper introduces a new method for multi-criteria analyses where the failure to meet the dominant criterion of an alternative causes low values for the entire alternative. In this method, the introduction of new alternatives into the multi-criteria model does not affect the existing alternatives in the model. The new method was applied for the rating of ten websites of dental clinics in Serbia, which provide prosthetic services to tourists. The dominant criterion was the amount of information provided by the site.