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Speech recognition applications enable the recognition and translation of spoken languages into text by computers. Due to the fact that the traditional approaches are costly and time consuming, the worldwide industry adopted speech recognition systems.
Andrei Scutelnicu, Anca Diana Bibiri, Mihaela Onofrei, and Mircea Hulea will present in their paper “A speech to text transcription approach based on Romanian corpus” a simple and efficient method for speech to text recognition, based on a machine learning approach, using a Romanian speech corpus.
Internet reviews can be seen as an efficient communication form, adapted to the digital world of today. However, researchers are, for the most part, oriented towards English based ones. The Romanian language reviews exhibit specific grammar rules and challenges that need customized methods to be dealt with. In their paper, Versavia-Maria Ancusa, Olimpia Ban, and Marian Cornea offer a method for aggregating heterogeneous Romanian language reviews into a homogenous corpus, fit for further analyse.
Basically, their aim is to buil a eWOM data cleaning algorithm and apply it on the Romanian language. The algorithm will be based on a three-phase process. The first stage consists in data collection, it continues with the basic processing, and focuses, in the last stage, on analyzing.
BRAIN: Validation of Enhanced Emotion Enabled Cognitive Agent Using Virtual Overlay Multi-Agent System Approach
In order to avoid car accidents and ensure safer roads, Autonomous vehicles (AVs) have been created. These vehicles are capable of sensing its environment and navigating without human input, which is indeed a great step towards more secure experiences for drivers. As a method of improvement for the AVs, agent-based collision avoidance components that represent human cognition and emotions have been designed. However, agent-based solutions have not been validated using any key validation technique.
Considering this lack of validation practices, the authors F. Riaz and M. A. Niazi present in their upcoming paper state-of-the-art Emotion Enabled Cognitive Agent (EEC_Agent), which was proposed to avoid lateral collisions between semi-AVs. It is stated in the paper that the main drawback of EEC_Agent is that it is claiming utilization of emotions in making collision avoidance decisions, but no proper emotion generation mechanism has been proposed, which helps the cognitive agent to feel emotion according to the changing in the dynamic environment. In order to overcome this problem, they have redesigned the architecture of EEC_Agent using EABM (Exploratory Agent Based Modeling) and explored the role of OCC (Ortony, Clore & Collins) model in the fear generation of EEC_Agent.
A pseudo-holographic display is a display that creates a virtual three-dimensional image of an object, producing viewing experiences that are virtually indistinguishable from viewing a true hologram.
In the upcoming issue of BRAIN Journal the researchers Monica Ciobanu, Antoanela Naaji, Ioan Dascal, and Ioan Virag will present in their paper “Pseudo-holographic Displays as Teaching Tools in Mathematics” an innovative approach for the education system. This team of researchers came up with the idea of creating and implementing a set of interactive teaching tool packages (ITTPs) to help students grasp abstract mathematical notions by linking them to a specific physical representation.
Because of their ability to reproduce the biological neural networks, ANNs (Artificial neural networks) have found uncountable applications to a wide range of disciplines. Simina Maris, Titus Slavici, Petre Nenu and Liliana Baciu will present in the latest volume of BRAIN journal an article about the usage of artificial intelligence, especially artificial neural networks (ANNs), in the development of an efficient research plan for studying the quality of finite products, in particular wood briquettes obtained from various biomass mixtures.
Their work is a response to the needs expressed by a private company (SC Andrei Slavici SRL) searching for innovation in the production of wood pellets and briquettes on the market of nonconventional bio-fuels.
In this era of technology, it cannot come as a surprise that E-Commerce sites have become a significant part of the user’s online activity. In order for these websites to remain relevant to the visitors over long periods of time, attention should be brought to their efficiency and effectiveness when it comes to their interactivity dimensions. The study The Analysis of E-Commerce Sites with Eye-Tracking Technologies – written by O. Dospinescu and A. E. Percă-Robu – is examining the effects of the interactivity dimensions on users’ content comprehension and their attitudes towards e-commerce websites by using eye-tracking technologies.
Various fields use the eye tracking technology to assess the visual attention, but when considering the decision-making process, the specialized literature acknowledge that the eye movements are directly linked to peoples’ cognitive goals. By investigating the website’s interactivity dimensions, the authors explore the visual process and drawing the time spent on the site or on various regions of it. Dospinescu and Percă-Robu assume that a high level of interactivity leads to a favourable attitude towards the website, therefore it is important for the content creators to pay attention to the ease of use and the visual elements of their E-Commerce bussiness.
In the field of computer vision and pattern recognition, face orientation recognition stands as a significant topic. In the paper A Robust Approach of Facial Orientation Recognition from Facial Features, the authors Stefan Andrei, Kishor Datta Gupta, Md Manjurul Ahsan and Kazi Md. Rokibul Alam introduce us to an image mapping technique for face analysis.
The methodology of the study consists of two main phases: Face Feature Extraction and creating the graph image, and Matching Graph image with stored images. The Face Feature Extraction presents four steps that include Face Detection, Feature Extraction, Obtaining Feature Data and Creating image with these data.
The first step in facial feature detection is detecting the face. This requires analyzing the entire image. The second step is using the isolated face(s) to detect each feature. The technique of this study relies on the four main features of the face: left eye, right eye, nose, and mouth. It is mandatory to obtain the positions, size, height, width, and angle of these features respective to each face. By acquiring the data from the features, a new picture including the model and shape of the face is created.
BRAIN: Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics
In order to ensure optimal teaching conditions for university students, attention should be brought to their personality traits and academic performance. In the paper Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics, the authors Vladimir Ivančević, Marko Knežević, and Ivan Luković present the foundation for such an adaptation of the teaching process, supported by an analytical software solution (in its initial version).
The software solution presents two main components: a data warehouse for storing collected data and an analytical software tool (built using the Shiny framework). The data warehouse contains collected data about student academic performance and personality traits, while the analytical tool is a web application that retrieves data from the data warehouse or external CSV files matching the required structure and allows analysts to perform exploration and analysis of data concerning student performance and personality.
In neurology and neuroscience research, Steady-State Visually Evoked Potential (SSVEP) are brain signals which occur in response to visual stimulation. The paper Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed – written by Abdullah Talha Sözer and Can Bülent Fidan – aims to introduce novel detection features for the SSVEP based brain computer interfaces. Brain-computer interface (BCI) is a collaboration between a brain and a device that enables signals from the brain to direct some external activity, such as control of a cursor or a prosthetic limb. The interface enables direct communication between the brain and the object to be controlled.