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The integration of emotional intelligence in machines is an important step in advancing human–computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems. • Overview of the use of generative models in augmenting affective neurophysiological data. • Summarized the common strategies in synthesizing EEG and fNIRS signals for affective computing. • Identified gaps in developing generative framework for emotion recognition system. [ABSTRACT FROM AUTHOR]

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The aim of this paper is to investigate the impact of selected data augmentation techniques on the learning performance of neural networks for dynamic signature verification. The paper investigates selected data augmentation techniques in deep learning for verification purpose of dynamic signature. Two neural networks were used as classifiers: MLP and LSTM-FCN. Investigation of five selected augmentation methods and experiments were performed on the open source signature database SVC2004. The authors tested both classifiers without augmentation and then with data augmentation for three extensions of the learning set and three sizes of the user database. They presented the results of the experiments in tabular form for each augmentation method. The results were compared with the existing dynamic signature verification methods and given in the paper. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present a repository of image databases whose features are augmented with embedded vector field (VF) features. The repository is designed to provide the user with image databases that enhance machine learning (ML) classification. Also, six VFs are provided, and the user can embed them into her/his own image database with the help of software named ELPAC. Three of the VFs generate real-shaped singular points (SPs): springing, sinking, and saddle. The other three VFs generate seven kinds of SPs, which include the real-shaped SPs and four complex-shaped SPs: repelling and attracting (out and in) spirals and clockwise and counterclockwise orbits (centers). Using the repository, this work defines the locations of the SPs according to the image objects and the mappings between the SPs' shapes if separate VFs are embedded into the same image. Next, this paper produces recommendations for the user on how to select the most appropriate VF to be embedded in an image database so that the augmented SP shapes enhance ML classification. Examples of images with embedded VFs are shown in the text to illustrate, support, and validate the theoretical conclusions. Thus, the contributions of this paper are the derivation of the SP locations in an image; mappings between the SPs of different VFs; and the definition of an imprint of an image and an image database in a VF. The advantage of classifying an image database with an embedded VF is that the new database enhances and improves the ML classification statistics, which motivates the design of the repository so that it contains image features augmented with VF features. [ABSTRACT FROM AUTHOR]

Trust in highly automated driving increased by intuitive dialogue between the driver and driver assistance systems. Predestined are augmented reality head‐up displays (AR HUD) able to display virtual images in large projection distances in front of the driver in context to the environment. A volume optimized mirror‐less AR HUD solution, based on a disruptive holographic waveguide technology with first results from a first real demonstrator. [ABSTRACT FROM AUTHOR]

Named entity recognition (NER) is a crucial Natural language processing (NLP) task used in applications like voice assistants, search engines, customer support, etc. A lack of entities relevant to the use case makes the available datasets insufficient for training. Data augmentation is a method in which synthetic data is fabricated from existing data to enhance the existing dataset. The existing data augmentation methods do not consider the grammatical and logical correctness of the fabricated sentences, resulting in a decrease in the performance of transformer-based NER models. This paper proposes a novel data augmentation method with a sanity-checker that checks the correctness of the augmented sentences and produces augmented data that improves the performance of transformer-based NER models. When the proposed augmentation algorithm was tested with the CoNLL-2003 dataset, a significant increase in the F1 score of BERT based NER from 94.73 to 95.37% and RoBERTa based NER from 94.13 to 95.14% was observed. [ABSTRACT FROM AUTHOR]

The paper presents a review of modern generative adversarial models for data augmentation, focusing on research, aimed at creating images and their corresponding segmentation masks. This task is particularly useful in cases where data is insufficient, hard to access, has confidential nature, or where labeling requires significant resources. The paper is aimed at the task of augmenting the minority class by transforming an image from another class and creating a segmentation mask. New approach is proposed for the simultaneous generation of the image and segmentation mask, using a generative adversarial network with U-Net generator. This generator takes an image of one class and noise, which is fed as an additional image channel. The generator tries to create an image of another class, minimizing changes in the original image and adding features of another along with the segmentation mask of the new class. The discriminator then determines whether the picture-mask pair is real or generated. The algorithm that applies only those changes of the generated image that are indicated by the created segmentation mask used to preserve the original appearance of the input image with minimal changes. This technique allows to obtain an image with features of the new class with minimal changes. The practical implementation of the proposed approach was conducted on a dataset of panoramic dental X-rays, based on which a set of individual teeth was created, some with fillings and some without. The experimental data set included 128 teeth without fillings and 128 with fillings. The GAN is trained to transform images without fillings into similar ones with fillings using all input images. Two experiments of 50 simulations each with different random states were conducted for training the segmentation model U-Net with ResNet-34 backbone to check the effectiveness of this augmentation. The first experiment used only real data for training, while the second included 64 additional images and masks created by the generator based on existing zero-class images. The average Jaccard score among all simulations for the first and second experiments were respectively 94.2 and 96.1. This result indicates that data generated using the proposed augmentation helps improve the quality of segmentation models and this approach can be combined with other augmentation techniques. [ABSTRACT FROM AUTHOR]

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer's disease (AD). Mild cognitive impairment (MCI) is a condition that falls between the spectrum of normal cognitive function and AD. However, previous studies have mainly used handcrafted features to classify MCI, AD, and normal control (NC) individuals. This paper focuses on using gray matter (GM) scans obtained through magnetic resonance imaging (MRI) for the diagnosis of individuals with MCI, AD, and NC. To improve classification performance, we developed two transfer learning strategies with data augmentation (i.e. shear range, rotation, zoom range, channel shift). The first approach is a deep Siamese network (DSN), and the second approach involves using a cross-domain strategy with customized VGG-16. We performed experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to evaluate the performance of our proposed models. Our experimental results demonstrate superior performance in classifying the three binary classification tasks: NC vs. AD, NC vs. MCI, and MCI vs. AD. Specifically, we achieved a classification accuracy of 97.68%, 94.25%, and 92.18% for the three cases, respectively. Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI, AD, and normal control individuals using GM scans. Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD. [ABSTRACT FROM AUTHOR]

The acquisition of word formation processes is considered to be the necessary prerequisite for the mastery of the morphology of the mother language as well as vocabulary development and vocabulary learning and teaching (Nagy et al., 2006; Nagy & Herman, 1987; Templeton, 1989). In addition, the acquisition of the morphological component of a language makes predictions regarding the acquisition of other linguistic components, such as the syntax and/or the semantics. The aim of this paper is to describe the main axes of a program of teaching the morphology of Greek through children's stories and the results of its implementation in class. The core of the program is a story accompanied by consolidation exercises. Aki-aros-itsa, the teaching program, was implemented to a) a group of 94 monolingual preschool children (age range: 5-6 years) who served as the experimental group and b) a group of 54 adults (age range 18-50 years) who served as the control group. The results of the implementation of the program underlined the fact that the experimental and control groups' scores improved with respect to the assimilation of derivational rules and principles after the teaching intervention. This entails that focused children's stories provide an effective and fast way of teaching the morphology of Greek L1. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of data scarcity for training automated intelligent systems in various spe)cific fields such as medicine, satellite image analysis, agriculture, ecology, and language. It describes modern methods for solving this problem, including augmentation, generative adversarial networks, diffusion models, and inpainting. The focus is on the task of segmentation, where it is necessary to create masks for new objects in addition to the image. The subjective and manual process of selecting the best epoch during model training is also noted, and alternatives that can help solve this problem, such as inception score and frechet inception distance, are outlined. An improved model of partial transformation of the target class of segmentation is proposed, which in)cludes new self-validation components, such as an additional loss function that controls the similarity of the output image to the input one, a pretrained segmentation model, and a metric for assessing the quality of the generated masks with segmentation masks of generated images. These improvements allow the system to more effectively transform the background or zero class into the target one, create more accurate segmentation masks, and automatically select the best epochs during training. Experiments on a dataset of panoramic tooth images showed that the use of this technology allowed increasing the accuracy of filling segmentation by 0.9 %, raising the Jaccard coefficient value from 90.5 % to 91.4 %. The generative adversarial network model was trained for 150 epochs with automatic selection of the best epoch, which was the 135th epoch, and the quality of the generated images of this epoch was confirmed by expert evaluation. On satellite images of ships, the use of the model showed an improvement in segmentation accuracy from 63.4% to 65.2 %. Despite the complexity of the data, the model was able to adequately transform the input data of the empty sea into ship objects. The best results were achieved at the 82nd epoch, which also coincided with the expert's choice of the best epoch, demonstrating the importance of automatic selection of the optimal epoch during training to eliminate additional subjective factors from this process and speed up model preparation. These results confirm the effectiveness of the proposed approach, showing metrics improvements and better automation of the basic approach. The proposed methods and approaches have the potential for wide application in various fields, contributing to the development of new intelligent systems and increas)ing their accuracy [ABSTRACT FROM AUTHOR]

This paper aims to systematically assess the local radial basis function collocation method, structured with multiquadrics (MQs) and polyharmonic splines (PHSs), for solving steady and transient diffusion problems. The boundary value test involves a rectangle with Dirichlet, Neuman, and Robin boundary conditions, and the initial value test is associated with the Dirichlet jump problem on a square. The spectra of the free parameters of the method, i.e., node density, timestep, shape parameter, etc., are analyzed in terms of the average error. It is found that the use of MQs is less stable compared to PHSs for irregular node arrangements. For MQs, the most suitable shape parameter is determined for multiple cases. The relationship of the shape parameter with the total number of nodes, average error, node scattering factor, and the number of nodes in the local subdomain is also provided. For regular node arrangements, MQs produce slightly more accurate results, while for irregular node arrangements, PHSs provide higher accuracy than MQs. PHSs are recommended for use in diffusion problems that require irregular node spacing. [ABSTRACT FROM AUTHOR]

Digital Human Resource Management transformation represent a reality that each organisation is dealing with these days, a situation that was accelerated by COVID-19 pandemic period. Organisations that are taking this step are determined by the benefits that the transformation could have for both employees and organisation's performance. However, research in the field failed in offering the evidence on these benefits in relation to all type of performance. At the same time, despite the advantages brought, the process could still be a challenge for HR professionals to implement, as blending digital processes and human touch is not so easy to be done. Research on the way in which the digital HRM transformation should take place, focusing on factors that facilitate the transformation as well as on HR professionals competencies needed is recommended. Thereby, the purpose of our paper is to analyse the main concepts of augmenting HRM and to propose a future research methodology for this topic. [ABSTRACT FROM AUTHOR]