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Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

This paper presents an enhanced approach for classifying Bengali songs into different genres by leveraging feature importance analysis and deep learning techniques. The research addresses the challenge of limited data points in the Bengali Song Dataset by employing strategies, including audio segmentation and feature importance analysis, to enhance model performance. Multiple machine learning and deep learning architectures are evaluated to identify the most effective models for Bengali song classification. Additionally, this research conducts feature importance analysis to identify significant audio features contributing to classification accuracy. The best-performing deep learning model achieves an impressive validation accuracy of 94.17%, showcasing the project efficacy of the proposed methodology. Our findings highlight the effectiveness of our proposed methodology, demonstrating significant improvements in classification accuracy and contributing to advancements in Bengali music classification research. [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]

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]

Spasticity management should be provided within the context of a comprehensive person-centered rehabilitation program. Furthermore, active goal setting for specific spasticity interventions is also important, with a well-established "more is better" approach. It is critical to consider adjunctive therapy and multimodal approaches if patients are not attaining their treatment goals. Often used interchangeably, there may be confusion between the terms adjunctive and multimodal therapy. Yet it is imperative to understand the differences between these approaches to achieve treatment goals in spasticity management. Addition of a secondary pharmacologic or non-pharmacologic treatment to optimize the efficacy of the initial modality, such as adding electrical stimulation or casting to BoNT-A, is considered an adjunctive therapy. Adjunctive therapy is time-specific and requires the added therapy be initiated within a specific period to enhance the primary treatment; usually within 2 weeks. Multimodal therapy is an integrated, patient-centric program of pharmacologic and nonpharmacologic strategies utilized in a concurrent/integrated or sequential manner to enhance the overall treatment effect across a variety of spasticityassociated impairments (e.g., neural and non-neural components). Moreover, within a multimodal approach, adjunctive therapy can be used to help enhance the treatment effect of one specific modality. The objectives of this paper are to clarify the differences between adjunctive and multimodal therapies, provide a brief evidence-based review of such approaches, and highlight clinical insights on selecting multimodal and adjunctive therapies in spasticity management. [ABSTRACT FROM AUTHOR]

Significant progress has been made recently with the contribution of technological advances in studies on brain cancer. Regarding this, identifying and correctly classifying tumors is a crucial task in the field of medical imaging. The disease-related tumor classification problem, on which deep learning technologies have also become a focus, is very important in the diagnosis and treatment of the disease. The use of deep learning models has shown promising results in recent years. However, the sparsity of ground truth data in medical imaging or inconsistent data sources poses a significant challenge for training these models. The utilization of StyleGANv2-ADA is proposed in this paper for augmenting brain MRI slices to enhance the performance of deep learning models. Specifically, augmentation is applied solely to the training data to prevent any potential leakage. The StyleGanv2-ADA model is trained with the Gazi Brains 2020, BRaTS 2021, and Br35h datasets using the researchers' default settings. The effectiveness of the proposed method is demonstrated on datasets for brain tumor classification, resulting in a notable improvement in the overall accuracy of the model for brain tumor classification on all the Gazi Brains 2020, BraTS 2021, and Br35h datasets. Importantly, the utilization of StyleGANv2-ADA on the Gazi Brains 2020 Dataset represents a novel experiment in the literature. The results show that the augmentation with StyleGAN can help overcome the challenges of working with medical data and the sparsity of ground truth data. Data augmentation employing the StyleGANv2-ADA GAN model yielded the highest overall accuracy for brain tumor classification on the BraTS 2021 and Gazi Brains 2020 datasets, together with the BR35H dataset, achieving 75.18%, 99.36%, and 98.99% on the EfficientNetV2S models, respectively. This study emphasizes the potency of GANs for augmenting medical imaging datasets, particularly in brain tumor classification, showcasing a notable increase in overall accuracy through the integration of synthetic GAN data on the used datasets. [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]

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]

Skin cancer is one of the most prevalent forms of cancer around the world. Initial diagnosis relies on visual assessment of the affected area, followed by detailed dermoscopic analysis. The development of an automated system for classifying skin lesions poses a considerable challenge due to inherent noise and subtle variability in lesion images. Deep convolutional neural networks have demonstrated exceptional proficiency in image classification tasks spanning diverse domains. This paper demonstrates an end-to-end classification architecture built on top of transfer learning with data augmentation. The proposed model capitalizes on pre-trained architectures such as DenseNet, VGG-16, and Inception for feature extraction, and employs fully connected dense layers for categorizing seven distinct types of lesions. Several data augmentation techniques are also used to handle the class imbalance problem. Extensive experimentation encompassing various hyperparameters, imbalanced data scenarios, and balanced datasets was conducted to refine the automated skin lesion system. The proposed approach achieved a notable accuracy of 98% on the HAM10000 dataset and 94% on the ISIC-2019 dataset. Importantly, the experimental findings surpassed the performance of current state-of-the-art models for lesion classification. Furthermore, this paper examines the impact of class imbalance and data augmentation on the model's accuracy. [ABSTRACT FROM AUTHOR]

The digital transformation of banks has led to a paradigm shift, promoting the open sharing of data and services with third-party providers through APIs, SDKs, and other technological means. While data sharing brings personalized, convenient, and enriched services to users, it also introduces security risks, including sensitive data leakage and misuse, highlighting the importance of data classification and grading as the foundational pillar of security. This paper presents a cloud-edge collaborative banking data open application scenario, focusing on the critical need for an accurate and automated sensitive data classification and categorization method. The regulatory outpost module addresses this requirement, aiming to enhance the precision and efficiency of data classification. Firstly, regulatory policies impose strict requirements concerning data protection. Secondly, the sheer volume of business and the complexity of the work situation make it impractical to rely on manual experts, as they incur high labor costs and are unable to guarantee significant accuracy. Therefore, we propose a scheme UP-SDCG for automatically classifying and grading financially sensitive structured data. We developed a financial data hierarchical classification library. Additionally, we employed library augmentation technology and implemented a synonym discrimination model. We conducted an experimental analysis using simulation datasets, where UP-SDCG achieved precision surpassing 95%, outperforming the other three comparison models. Moreover, we performed real-world testing in financial institutions, achieving good detection results in customer data, supervision, and additional in personally sensitive information, aligning with application goals. Our ongoing work will extend the model's capabilities to encompass unstructured data classification and grading, broadening the scope of application. [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]

This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, such as offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams, etc. Our focus is on the use of machine-learning-based approaches. First, a detailed literature review of the state of the current methods for underwater surface crack detection is presented, highlighting challenges and opportunities. An overview of the image augmentation approach for the creation of underwater optical effects is also presented. Experimental results using a standard network-based machine learning approach, which is used for surface crack detection in onshore environments, are presented. A series of test cases is presented in which existing networks' performance is improved using augmented images for underwater conditions. The effectiveness and accuracy of the proposed approach in detecting cracks in underwater concrete structures are demonstrated. The proposed approach has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures. [ABSTRACT FROM AUTHOR]

This work develops a new method for vector data augmentation. The proposed method applies principal component analysis (PCA), determines the eigenvectors of a set of training vectors for a machine learning (ML) method and uses them to generate the distilled vectors. The training and PCA-distilled vectors have the same dimension. The user chooses the number of vectors to be distilled and augmented to the set of training vectors. A statistical approach determines the lowest number of vectors to be distilled such that when augmented to the original vectors, the extended set trains an ML classifier to achieve a required accuracy. Hence, the novelty of this study is the distillation of vectors with the PCA method and their use to augment the original set of vectors. The advantage that comes from the novelty is that it increases the statistics of ML classifiers. To validate the advantage, we conducted experiments with four public databases and applied four classifiers: a neural network, logistic regression and support vector machine with linear and polynomial kernels. For the purpose of augmentation, we conducted several distillations, including nested distillation (double distillation). The latter notion means that new vectors were distilled from already distilled vectors. We trained the classifiers with three sets of vectors: the original vectors, original vectors augmented with vectors distilled by PCA and original vectors augmented with distilled PCA vectors and double distilled by PCA vectors. The experimental results are presented in the paper, and they confirm the advantage of the PCA-distilled vectors increasing the classification statistics of ML methods if the distilled vectors augment the original training vectors. [ABSTRACT FROM AUTHOR]

The last two years are considered the most crucial and critical period of the COVID-19 pandemic affecting most life aspects worldwide. This virus spreads quickly within a short period, increasing the fatality rate associated with the virus. From a clinical perspective, several diagnosis methods are carried out for early detection to avoid virus propagation. However, the capabilities of these methods are limited and have various associated challenges. Consequently, many studies have been performed for COVID-19 automated detection without involving manual intervention and allowing an accurate and fast decision. As is the case with other diseases and medical issues, Artificial Intelligence (AI) provides the medical community with potential technical solutions that help doctors and radiologists diagnose based on chest images. In this paper, a comprehensive review of the mentioned AI-based detection solution proposals is conducted. More than 200 papers are reviewed and analyzed, and 145 articles have been extensively examined to specify the proposed AI mechanisms with chest medical images. A comprehensive examination of the associated advantages and shortcomings is illustrated and summarized. Several findings are concluded as a result of a deep analysis of all the previous works using machine learning for COVID-19 detection, segmentation, and classification. [ABSTRACT FROM AUTHOR]

One of the most prevalent diseases affecting women in recent years is breast cancer. Early breast cancer detection can help in the treatment, lower the infection risk, and worsen the results. This paper presents a hybrid approach for augmentation and segmenting breast cancer. The framework contains two main stages: augmentation and segmentation of ultrasound images. The augmentation of the ultrasounds is applied using generative adversarial networks (GAN) with nonlinear identity block, label smoothing, and a new loss function. The segmentation of the ultrasounds applied a modified U-Net 3+. The hybrid approach achieves efficient results in the segmentation and augmentation steps compared with the other available methods for the same task. The modified version of the GAN with the nonlinear identity block overcomes different types of modified GAN in the ultrasound augmentation process, such as speckle GAN, UltraGAN, and deep convolutional GAN. The modified U-Net 3+ also overcomes the different architectures of U-Nets in the segmentation process. The GAN with nonlinear identity blocks achieved an inception score of 14.32 and a Fréchet inception distance of 41.86 in the augmenting process. The GAN with identity achieves a smaller value in Fréchet inception distance (FID) and a bigger value in inception score; these results prove the model's efficiency compared with other versions of GAN in the augmentation process. The modified U-Net 3+ architecture achieved a Dice Score of 95.49% and an Accuracy of 95.67%. [ABSTRACT FROM AUTHOR]

Morphea en coup de sabre and progressive hemifacial atrophy are extremely rare connective tissue disorders causing facial deformity. In extreme cases, morphological disorders are accompanied by symptoms of a clear impairment of the stomatognathic system. The aetiology of the above-mentioned diseases is still unknown. Properly planned therapy in the field of maxillofacial orthopaedics makes it possible to correct the asymmetric pattern of hard tissue growth and thus enable rehabilitation. The task of augmentation techniques is the volumetric supplementation of tissue defects resulting from atrophic processes. The degree of destruction and the extent of changes determine the method of correction. Mild and moderate defects are treated mainly with biomaterials and autologous adipose tissue. The severe course of hemifacial atrophy and morphea en coup de sabre and the associated significant tissue atrophy necessitate the search for more complex methods of treatment. In this paper, we summarize the disturbances of the stomatognathic system in patients with craniofacial morphea, together with an analysis of current treatment options. [ABSTRACT FROM AUTHOR]

Understanding human sentiment from their expressions is very important in human-robot interaction. But deep learning models are hard to represent grammatical changes for natural language processing (NLP), especially for sentimental analysis, which influence the robot's judgment of sentiment. This paper proposed a novel sentimental analysis model named MoLeSy, which is an augmentation of neural networks incorporating morphological, lexical, and syntactic knowledge. This model is constructed from three concurrently processed classical neural networks, in which output vectors are concatenated and reduced with a single dense neural network layer. The models used in the three grammatical channels are convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and fully connected dense neural networks. The corresponding output in the three channels is morphological, lexical, and syntactic results, respectively. Experiments are conducted on four different sentimental analysis corpuses, namely, hotel, NLPCC2014, Douban movie reviews dataset, and Weibo. MoLeSy can achieve the best performance over previous state-of-artmodels. It indicated that morphological, lexical, and syntactic grammar can augment the neural networks for sentimental analysis. [ABSTRACT FROM AUTHOR]

Featured Application: The results of the work can be used in computer vision systems for medical problems, or other applications where the training data is highly imbalanced. One of the critical problems in multiclass classification tasks is the imbalance of the dataset. This is especially true when using contemporary pre-trained neural networks, where the last layers of the neural network are retrained. Therefore, large datasets with highly unbalanced classes are not good for models' training since the use of such a dataset leads to overfitting and, accordingly, poor metrics on test and validation datasets. In this paper, the sensitivity to a dataset imbalance of Xception, ViT-384, ViT-224, VGG19, ResNet34, ResNet50, ResNet101, Inception_v3, DenseNet201, DenseNet161, DeIT was studied using a highly imbalanced dataset of 20,971 images sorted into 7 classes. It is shown that the best metrics were obtained when using a cropped dataset with augmentation of missing images in classes up to 15% of the initial number. So, the metrics can be increased by 2–6% compared to the metrics of the models on the initial unbalanced data set. Moreover, the metrics of the rare classes' classification also improved significantly–the True Positive value can be increased by 0.3 or more. As a result, the best approach to train considered networks on an initially unbalanced dataset was formulated. [ABSTRACT FROM AUTHOR]

Detection of targets on sea surfaces is an important area of application that can bring great benefits to the management and control systems in marine environments. However, there are few open-source datasets accessible for the purpose of object detection on seas and rivers. In this paper, a study is conducted on the improved detection algorithms based on the YOLOv5 model. The dataset for the tests contains ten categories of typical objects that are commonly seen in the contexts of seas, including ships, devices, and structures. Multiple augmentation methods are employed in the pre-processing of the input data, which are verified to be effective in enhancing the generalization ability of the algorithm. Moreover, a new form of the loss function is proposed that highlights the effects of the high-quality boxes during training. The results demonstrate that the adapted loss function contributes to a boost in the model performance. According to the ablation studies, the synthesized methods raise the inference accuracy by making up for several shortcomings of the baseline model for the detection tasks of single or multiple targets from varying backgrounds. [ABSTRACT FROM AUTHOR]

“Crossmodal correspondences” are the consistent mappings between perceptual dimensions or stimuli from different sensory domains, which have been widely observed in the general population and investigated by experimental psychologists in recent years. At the same time, the emerging field of human movement augmentation (i.e., the enhancement of an individual’s motor abilities by means of artificial devices) has been struggling with the question of how to relay supplementary information concerning the state of the artificial device and its interaction with the environment to the user, which may help the latter to control the device more effectively. To date, this challenge has not been explicitly addressed by capitalizing on our emerging knowledge concerning crossmodal correspondences, despite these being tightly related to multisensory integration. In this perspective paper, we introduce some of the latest research findings on the crossmodal correspondences and their potential role in human augmentation. We then consider three ways in which the former might impact the latter, and the feasibility of this process. First, crossmodal correspondences, given the documented effect on attentional processing, might facilitate the integration of device status information (e.g., concerning position) coming from different sensory modalities (e.g., haptic and visual), thus increasing their usefulness for motor control and embodiment. Second, by capitalizing on their widespread and seemingly spontaneous nature, crossmodal correspondences might be exploited to reduce the cognitive burden caused by additional sensory inputs and the time required for the human brain to adapt the representation of the body to the presence of the artificial device. Third, to accomplish the first two points, the benefits of crossmodal correspondences should be maintained even after sensory substitution, a strategy commonly used when implementing supplementary feedback. [ABSTRACT FROM AUTHOR]

This paper evaluates the Achilles exoskeleton. The exoskeleton is intended to provide push-off assistance for healthy subjects during walking. The assistance is provided by a series elastic actuator that has been optimized to provide maximal push-off power. The paper presents the control method of the exoskeleton and the evaluation of the exoskeleton. [ABSTRACT FROM AUTHOR]

Ovarian cancer ranks as the fifth leading cause of cancer-related mortality in women. Late-stage diagnosis (stages III and IV) is a major challenge due to the often vague and inconsistent initial symptoms. Current diagnostic methods, such as biomarkers, biopsy, and imaging tests, face limitations, including subjectivity, inter-observer variability, and extended testing times. This study proposes a novel convolutional neural network (CNN) algorithm for predicting and diagnosing ovarian cancer, addressing these limitations. In this paper, CNN was trained on a histopathological image dataset, divided into training and validation subsets and augmented before training. The model achieved a remarkable accuracy of 94%, with 95.12% of cancerous cases correctly identified and 93.02% of healthy cells accurately classified. The significance of this study lies in overcoming the challenges associated with the human expert examination, such as higher misclassification rates, inter-observer variability, and extended analysis times. This study presents a more accurate, efficient, and reliable approach to predicting and diagnosing ovarian cancer. Future research should explore recent advances in this field to enhance the effectiveness of the proposed method further. [ABSTRACT FROM AUTHOR]

In recent years, crowdsourcing approaches have been proposed to record the WiFi signals annotated with the location of the reference points (RPs) extracted from the trajectories of common users to reduce the burden of constructing a fingerprint (FP) database for indoor positioning. However, crowdsourced data is usually sensitive to crowd density. The positioning accuracy degrades in some areas due to a lack of FPs or visitors. To improve the positioning performance, this paper proposes a scalable WiFi FP augmentation method with two major modules: virtual reference point generation (VRPG) and spatial WiFi signal modeling (SWSM). A globally self-adaptive (GS) and a locally self-adaptive (LS) approach are proposed in VRPG to determine the potential unsurveyed RPs. A multivariate Gaussian process regression (MGPR) model is designed to estimate the joint distribution of all WiFi signals and predicts the signals on unsurveyed RPs to generate more FPs. Evaluations are conducted on an open-source crowdsourced WiFi FP dataset based on a multi-floor building. The results show that combining GS and MGPR can improve the positioning accuracy by 5% to 20% from the benchmark, but with halved computation complexity compared to the conventional augmentation approach. Moreover, combining LS and MGPR can sharply reduce 90% of the computation complexity against the conventional approach while still providing moderate improvement in positioning accuracy from the benchmark. [ABSTRACT FROM AUTHOR]

Copyright of Suvremena Lingvistika is the property of Suvremena Lingvistika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper provides a complete matrix analysis on equivalence problems of estimation and inference results under a true multivariate linear model Y = X Θ + Ψ and its misspecified form Y = X Θ + Z Γ + Ψ with an augmentation part Z Γ through the cogent use of various algebraic formulas and facts in matrix analysis. The coverage of this study includes the matrix derivations of the best linear unbiased estimators under the true and misspecified models, and the establishment of necessary and sufficient conditions for the different estimators to be equivalent under the model assumptions. [ABSTRACT FROM AUTHOR]

Learning good data representations for medical imaging tasks ensures the preservation of relevant information and the removal of irrelevant information from the data to improve the interpretability of the learned features. In this paper, we propose a semi-supervised model—namely, combine-all in semi-supervised learning (CqSL)—to demonstrate the power of a simple combination of a disentanglement block, variational autoencoder (VAE), generative adversarial network (GAN), and a conditioning layer-based reconstructor for performing two important tasks in medical imaging: segmentation and reconstruction. Our work is motivated by the recent progress in image segmentation using semi-supervised learning (SSL), which has shown good results with limited labeled data and large amounts of unlabeled data. A disentanglement block decomposes an input image into a domain-invariant spatial factor and a domain-specific non-spatial factor. We assume that medical images acquired using multiple scanners (different domain information) share a common spatial space but differ in non-spatial space (intensities, contrast, etc.). Hence, we utilize our spatial information to generate segmentation masks from unlabeled datasets using a generative adversarial network (GAN). Finally, to reconstruct the original image, our conditioning layer-based reconstruction block recombines spatial information with random non-spatial information sampled from the generative models. Our ablation study demonstrates the benefits of disentanglement in holding domain-invariant (spatial) as well as domain-specific (non-spatial) information with high accuracy. We further apply a structured L 2 similarity (S L 2 SIM) loss along with a mutual information minimizer (MIM) to improve the adversarially trained generative models for better reconstruction. Experimental results achieved on the STACOM 2017 ACDC cine cardiac magnetic resonance (MR) dataset suggest that our proposed (CqSL) model outperforms fully supervised and semi-supervised models, achieving an 83.2% performance accuracy even when using only 1% labeled data. We hypothesize that our proposed model has the potential to become an efficient semantic segmentation tool that may be used for domain adaptation in data-limited medical imaging scenarios, where annotations are expensive. Code, and experimental configurations will be made available publicly. [ABSTRACT FROM AUTHOR]

Extraction of chemical formulas from images was not in the top priority of Computer Vision tasks for a while. The complexity both on the input and prediction sides has made this task challenging for the conventional Artificial Intelligence and Machine Learning problems. A binary input image which might seem trivial for convolutional analysis was not easy to classify, since the provided sample was not representative of the given molecule: to describe the same formula, a variety of graphical representations which do not resemble each other can be used. Considering the variety of molecules, the problem shifted from classification to that of formula generation, which makes Natural Language Processing (NLP) a good candidate for an effective solution. This paper describes the evolution of approaches from rule-based structure analyses to complex statistical models, and compares the efficiency of models and methodologies used in the recent years. Although the latest achievements deliver ideal results on particular datasets, the authors mention possible problems for various scenarios and provide suggestions for further development. [ABSTRACT FROM AUTHOR]

Lumbar support exoskeletons with active and passive actuators are currently the cutting-edge technology for preventing back injuries in workers while lifting heavy objects. However, many challenges still exist in both types of exoskeletons, including rigid actuators, risks of human–robot interaction, high battery consumption, bulky design, and limited assistance. In this paper, the design of a compact, lightweight energy storage device combined with a rotary series elastic actuator (ES-RSEA) is proposed for use in a lumbar support exoskeleton to increase the level of assistance and exploit the human bioenergy during the two stages of the lifting task. The energy storage device takes the responsibility to store and release passive mechanical energy while RSEA provides excellent compliance and prevents injury from the human body's undesired movement. The experimental tests on the spiral spring show excellent linear characteristics (above 99%) with an actual spring stiffness of 9.96 Nm/rad. The results demonstrate that ES-RSEA can provide maximum torque assistance in the ascent phase with 66.6 Nm while generating nearly 21 Nm of spring torque during descent without turning on the DC motor. Ultimately, the proposed design can maximize the energy storage of human energy, exploit the biomechanics of lifting tasks, and reduce the burden on human effort to perform lifting tasks. [ABSTRACT FROM AUTHOR]

In this paper, we report an implementation of a computer-generated holographic projection technique to display a holographic scene like a measuring graticule around the magnified sample image in a reflected bright-field microscopy. The implemented system acts as a gauging tool for lateral and longitudinal measurements of a sample that is being observed under a microscope through the assistance of a holographic measuring graticule. Numerical and experimental verifications have been performed, demonstrating the successful augmentation of a holographic projection system as a measuring tool with a conventional bright-field microscopic system. [ABSTRACT FROM AUTHOR]

The rapid increase of technologically enhanced listening platforms gives listeners access to music with ever-increasing ease and ubiquity, giving rise to the suggestion that we should now conceptualize music as a resource similar to water; something that is utilized to achieve everyday goals. This paper proposes that music is a utilitarian resource employed by listeners to augment cognitive, emotional, behavioral, and physiological aspects of the self. To better explore these notions this paper examines the potential role of the "functions of music," first espoused by Alan P. Merriam in 1964. Merriam suggested music has a situational use and an underlying function (music's ability to alter the self through listening). The research presented here asserts that listeners interact with specific musical materials to achieve or orientate themselves towards contextually-rooted goals. Reinforcing Tia DeNora's suggestion that music is a "technology of the self" this research presents the results of a 41 publication meta-analysis exploring the possible functions of music. The resultant Aggregate Thematic Functions Framework (ATF framework) identifies 45 possible utilitarian functions of music, spread across five domains of action. The framework also proposes a meta-domain and an emotional sub-domain. [ABSTRACT FROM AUTHOR]

The paper analyzes the possibility of obtaining polyhedral shapes formed by combining polyhedral surfaces based on the segment surface of elongated concave pyramids of the second sort (CeP II-10, type A and type B). In previous research, CP II type A and CP II type B were elaborated in detail. In this paper we discuss further potential of these polyhedral surfaces, on the example of combining them with Archimedean solid - Truncated dodecahedron (U26). The faces of this solid consist of 12 decagons and 20 triangles. On the decagonal faces, decagonal polygons of the CeP II segments (CP II-10) can be added, which provides the new polyhedral composite forms that are, furthermore, concave deltahedra. There are considered possibilities of obtaining polyhedral shapes by combining sheet segments CP II-10-A, as well as of CP II-10-B with U26. Finally, a couple of new shape suggestions are given: compound polyhedra, obtained by intersection of paired composite concave polyhedra originated in the described manner. [ABSTRACT FROM AUTHOR]

Occurrence and intensity of systemic invasive fungal infections have significantly risen in recent decades with large amount of mortality and morbidity rates at global level. Treatment therapy lies on the current antifungal interventions and are often limited due to the emergence of resistance to antifungal agents. Chemosensitization of fungal strains to the conventional antimycotic drugs are of growing concern. Current antifungal drugs often have been reported with poor activity and side effects to the host and have a few number of targets to manifest their efficacy on the pathogens. Indiscriminately, the aforementioned issues have been easily resolved by the development of new intervention strategies. One such approach is to employ combinational therapy that has exhibited a great level of inhibitions than that of a single compound. Chemosensitization of pathogenic mycoses to commercial antifungal drugs could be drastically enhanced by co-application of chemosensitizers along with the conventional drugs. Chemosensitizers could address the resistance mechanisms evolved in the pathogenic fungi and targeting the system to make the organism susceptible to commercially and clinically proven antifungal drugs. However, this strategy has not been overreached to the greater level, but it needs much attention to fight against not only with the pathogen but combat the resistance mechanisms of pathogens to drugs. Natural compounds including plant compounds and microbial proteins act as potential chemosensitizers to break the resistance in mycoses. Aspergillus giganteus , a filamentous fungus, is known to produce a cysteine rich extracellular protein called as antifungal protein (AFP). AFP has shown enhanced efficacy against several filamentous and non-filamentous fungal pathogens. On the basis of the reported studies on its targeted potential against pathogenic mycoses, AFP would be fabricated as a good chemosensitizer to augment the fungicidal efficacy of commercial antimycotic drugs. This paper reviews on breakthrough in the discovery of antifungal drugs along with the resistance patterns of mycoses to commercial drugs followed by the current intervention strategies applied to augment the fungicidal potential of drugs. [ABSTRACT FROM AUTHOR]

Surface quality measures such as roughness, and especially its uncertain character, affect most magnetic non-destructive testing methods and limits their performance in terms of an achievable signal-to-noise ratio and reliability. This paper is primarily focused on an experimental study targeting nuclear reactor materials manufactured from the milling process with various machining parameters to produce varying surface quality conditions to mimic the varying material surface qualities of in-field conditions. From energising a local area electromagnetically, a receiver coil is used to obtain the emitted Barkhausen noise, from which the condition of the material surface can be inspected. Investigations were carried out with the support of machine-learning algorithms, such as Neural Networks (NN) and Classification and Regression Trees (CART), to identify the differences in surface quality. Another challenge often faced is undertaking an analysis with limited experimental data. Other non-destructive methods such as Magnetic Adaptive Testing (MAT) were used to provide data imputation for missing data using other intelligent algorithms. For data reinforcement, data augmentation was used. With more data the problem of 'the curse of data dimensionality' is addressed. It demonstrated how both data imputation and augmentation can improve measurement datasets. [ABSTRACT FROM AUTHOR]

The retina is the most important part of the eye. By proper feature extraction, it can be the first step to detect a disease. Morphology of retina blood vessels can be used to identify and classify a disease. A step, such as segmentation and analysis of retinal blood vessels, can assist medical personnel in detecting the severity of a disease. In this paper, vascular segmentation using U-net architecture in the Convolutional Neural Network (CNN) method is proposed to train a sematic segmentation model in retinal blood vessel. In addition, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used to increase the contrast of the grayscale and Median Filter is used to obtain better image quality. Data augmentation is also used to maximize the number of datasets owned to make more. The proposed method allows for easier implementation. In this study, the dataset used was STARE with the result of accuracy, sensitivity, specificity, precision, and F1-score that reached 97.64%, 78.18%, 99.20%, 88.77%, and 82.91%. [ABSTRACT FROM AUTHOR]

With the development of the Low Earth Orbit (LEO) communication constellations, it has become a hot area of research to provide additional navigation augmentation services. Limited by volume, weight, power consumption, and running time, the in-flight performance of navigation augmentation payload remains to be investigated. In this paper, we analyze the data quality of on-board GNSS observation and evaluate the precision of short-arc dynamic Precise Orbit Determination (POD) performance based on the WangTong-01 (WT01) mission. Furthermore, the downlink navigation measurement data of WT01 satellites are analyzed and compared with the GNSS observations. The results show that the average multipath errors of the WT01 on-board GPS L1, L2 and BeiDou Satellite Navigation System (BDS) B1, B2 code observation are 0.54, 0.74, 0.65, and 0.58 m, respectively. The short-arc dynamic POD three-dimensional (3D) overlapping accuracy is 7.1 cm. The average multipath errors of downlink navigation signal Z1 and Z2 are 0.81 and 0.80 m, respectively, which at the same order of magnitude as GNSS signals. The maximum Carrier-to-Noise Ratio (C/N0) value of WT01 downlink measurement data can reach 60 dB Hz, which is much stronger than GNSS and indicates the navigation signals of LEO satellites can meet the basic requirement of navigation augmentation. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the concept and an implementation of geospatial 3D image spaces as new type of native urban models. 3D image spaces are based on collections of georeferenced RGB-D imagery. This imagery is typically acquired using multi-view stereo mobile mapping systems capturing dense sequences of street level imagery. Ideally, image depth information is derived using dense image matching. This delivers a very dense depth representation and ensures the spatial and temporal coherence of radiometric and depth data. This results in a high-definition WYSIWYG ("what you see is what you get") urban model, which is intuitive to interpret and easy to interact with, and which provides powerful augmentation and 3D measuring capabilities. Furthermore, we present a scalable cloud-based framework for generating 3D image spaces of entire cities or states and a client architecture for their web-based exploitation. The model and the framework strongly support the smart city notion of efficiently connecting the urban environment and its processes with experts and citizens alike. In the paper we particularly investigate quality aspects of the urban model, namely the obtainable georeferencing accuracy and the quality of the depth map extraction. We show that our image-based georeferencing approach is capable of improving the original direct georeferencing accuracy by an order of magnitude and that the presented new multi-image matching approach is capable of providing high accuracies along with a significantly improved completeness of the depth maps. [ABSTRACT FROM AUTHOR]

This document provides a comprehensive summary of a study that explores the use of machine learning algorithms for the detection of pemphigus, an autoimmune skin disease. The study compares the performance of different algorithms and finds that the Convolutional Neural Network (CNN) algorithm achieved the highest accuracy. The document also references existing literature on machine learning in dermatology and highlights the potential of this technology in the field. [Extracted from the article]

Production of crops is increasing day by day in agriculture sectors. The insecurity of food is a main reason of plant disease and is a main global issue that humans face these days. With the design of contemporary environmental agriculture, more focus is devised for yielding the crop and elevating its quality. The occurrence of crops has elevated in years and the kind of disease has become more and more complex. The disease in plants and the pernicious insects are the major risks in agriculture field. Thus, earlier discovery and treatment of this disease are imperative. The major design of Deep Learning (DL) model helped in detecting the plant disease and grants a dynamic tool with accurate results. This paper presents DL-assisted technique for detecting and classifying the tomato disease and used deep batch-normalized eLu Alex Net (DbneAlexnet) for classifying the tomato plant leaves. Initially, tomato plant leaf images are taken as an input from specific dataset represented and it is subjected to preprocessing phase to eliminate unwanted distortions using anisotropic filtering. Then, the segmentation is carried out using U-net, which is trained by Gradient-Golden search optimization (Gradient-GSO) Algorithm and it is incorporation of both Golden search optimization (GSO) and Gradient concept. Thereafter the segmented image is given to image augmentation process, where position augmentation and color augmentation are considered. Finally, the multiclass plant leaf disease is classified using DbneAlexnet and is trained using proposed Gradient Jaya- Golden search optimization (GJ-GSO). Here, the GJ-GSO is devised with the integration of Gradient concept, Jaya algorithm, and GSO algorithm. The proposed GJ-GSO-based DbneAlexnet outperformed highest accuracy of 92.4%, True positive rate (TPR) of 91.9%, True negative rate (TNR) of 92.2% and smallest False Positive Rate (FPR) of 0.078. Hence, the technique with unified segmentation and classification is effectual for identifying the plant disease and the empirical research verifies the benefits of the developed model. • DL-assisted technique is provided for classifying tomato plant leaf disease. • Pre-processing is done with anisotropic filter. • U-net with Gradient-Golden search optimization is used for segmentation. • Accuracy, TPR, and TNR of 92.4%, 91.9%, 92.2% and FPR of 0.078 by proposed model. • Provided effective performance with certain metrics. [ABSTRACT FROM AUTHOR]

Birds are excellent bioindicators, playing a vital role in maintaining the delicate balance of ecosystems. Identifying species from bird vocalization is arduous but has high research gain. The paper focuses on the detection of multiple bird vocalizations from recordings. The proposed work uses a deep convolutional neural network (DCNN) and a recurrent neural network (RNN) architecture to learn the bird's vocalization from mel-spectrogram and mel-frequency cepstral coefficient (MFCC), respectively. We adopted a sequential aggregation strategy to make a decision on an audio file. We normalized the aggregated sigmoid probabilities and considered the nodes with the highest scores to be the target species. We evaluated the proposed methods on the Xeno-canto bird sound database, which comprises ten species. We compared the performance of our approach to that of transfer learning and Vanilla-DNN methods. Notably, the proposed DCNN and VGG-16 models achieved average F1 metrics of 0.75 and 0.65, respectively, outperforming the acoustic cue-based Vanilla-DNN approach. [ABSTRACT FROM AUTHOR]

The multi criterion decision making (MCDM) method and experimental investigation on free convective heat transfer performance of oxide-based water nanofluids along a vertical cylinder are the two methods used to compare the performance in this paper. Al2O3, CuO, TiO2, SiO2, Fe3O4, and ZnO were the metal oxide nanoparticles used in the study to make water-based metal oxide nanofluids with volume fractions ranging from 0% to 1%. Two step method was used to create nanofluids. Thermo-physical properties like density, specific heat, viscosity, and thermal conductivity were measured after the various nanofluids were synthesized. Then, the performance of each nanofluid was evaluated based on various attributes using the weighted sum model (WSM) method, and the ranking of nanofluids was given. To begin, water served as the medium for free convection heat transfer experiments to validate the experimental setup. Free convection heat transfer experiments were carried out using metal oxidebased water nanofluids as mediums at volume fractions ranging from 0% to 1% for various heat inputs in the range of 30 W and 50 W. The heat transfer coefficient augments with percentage volume concentration up to 0.1 % for all types of nanofluids and then decreases until it reaches 0.6% volume fraction. Al2O3-water nanofluid performs better than other metal oxide nanofluids in both WSM and experimental methods. [ABSTRACT FROM AUTHOR]

Background: Oxytocin is widely used for induction and augmentation of labour, particularly in low- and middle-income countries (LMICs). In this systematic review and meta-analysis, we examined the effect of intra-partum Oxytocin use on neonatal encephalopathy.Methods: The protocol for this study was registered with PROSPERO (ID: CRD42020165049). We searched Medline, Embase and Web of Science Core Collection databases for papers published between January 1970 and May 2021. We considered all studies involving term and near-term (≥36 weeks' gestation) primigravidae and multiparous women. We included all randomised, quasi-randomised clinical trials, retrospective studies and non-randomised prospective studies reporting intra-partum Oxytocin administration for induction and/or augmentation of labour. Our primary outcome was neonatal encephalopathy. Risk of bias was assessed in non-randomised studies using the Risk Of Bias In Non-randomised Studies of Interventions (ROBINS-I) tool. The RoB 2.0 tool was used for randomised studies. A Mantel-Haenszel statistical method and random effects analysis model were used for meta-analysis. Odds ratios were used to determine effect measure and reported with 95% confidence intervals.Results: We included data from seven studies (6 Case-control studies, 1 cluster-randomised trial) of which 3 took place in high-income countries (HICs) and 4 in LMICs. The pooled data included a total of 24,208 women giving birth at or after 36 weeks; 7642 had intra-partum Oxytocin for induction and/or augmentation of labour, and 16,566 did not receive intra-partum Oxytocin. Oxytocin use was associated with an increased prevalence of neonatal encephalopathy (Odds Ratio 2.19, 95% CI 1.58 to 3.04; p < 0.00001).Conclusions: Intra-partum Oxytocin may increase the risk of neonatal encephalopathy. Future clinical trials of uterotonics should include neonatal encephalopathy as a key outcome. [ABSTRACT FROM AUTHOR]