Reviewerly/exHarmony huggingface dataset:
Exploiting mixing regularization for truly unsupervised font synthesis

Creating a novel font set requires domain expertise and is a laborious and time-consuming process, particularly for languages with a large number of characters and complicated structures. Existing deep learning based methods consider font generation (FG) as an image-to-image translation problem, mostly in a supervised setting, either in the form of pair images (paired data) or font labels (character or style labels), which requires extensive effort and is expensive to collect. Additionally, these supervised counter parts lack generalization for extending to other text image-related tasks, such as word image generation and font attribute control at inference time. We found that these drawbacks are mainly due to the supervised setting adopted by these existing methods for font generation. In this paper, we tackle the FG problem in a truly unsupervised fashion, where a complete font set can be generated by training the generator such that adjacent styles are not correlated and projecting the input glyph image into its corresponding font style latent space. To accomplish this, we propose the Font Mixing Generative Adversarial Network (FM-GAN), which employs mixing regularization to supervise the generator to localize the font styles, and a projection encoder to project an arbitrary glyph image into its corresponding semantic space that is compatible with the generator. In the experiments, we demonstrated that our unsupervised model synthesizes font images that are comparable to supervised state-of-the-art FG baselines. Furthermore, FM-GAN can be directly applied to other text image related tasks, such as multi-lingual font style transfer, word image generation, and font attribute control.
MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning

Few-shot object detection (FSOD) is proposed to solve the application problem of traditional detectors in scenarios lacking training samples. The meta-learning methods have attracted the researchers' attention for their excellent generalization performance. They usually select the same class of support features according to the query labels to weight the query features. However, the model cannot possess the ability of active identification only by using the same category support features, and feature selection causes difficulties in the testing process without labels. The single-scale feature of the model also leads to poor performance in small object detection. In addition, the hard samples in the support branch impact the backbone's representation of the support features, thus impacting the feature weighting process. To overcome these problems, we propose a multi-scale feature fusion and attentive learning (MSFFAL) framework for few-shot object detection. We first design the backbone with multi-scale feature fusion and channel attention mechanism to improve the model's detection accuracy on small objects and the representation of hard support samples. Based on this, we propose an attention loss to replace the feature weighting module. The loss allows the model to consistently represent the objects of the same category in the two branches and realizes the active recognition of the model. The model no longer depends on query labels to select features when testing, optimizing the model testing process. The experiments show that MSFFAL outperforms the state-of-the-art (SOTA) by 0.7-7.8% on the Pascal VOC and exhibits 1.61 times the result of the baseline model in MS COCO's small objects detection.
Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study

Mining of electronic health record (EHRs) data is increasingly being implemented all over the world but mainly focuses on structured data. The capabilities of artificial intelligence (AI) could reverse the underusage of unstructured EHR data and enhance the quality of medical research and clinical care. This study aims to develop an AI-based model to transform unstructured EHR data into an organised, interpretable dataset and form a national dataset of cardiac patients.CardioMining is a retrospective, multicentre study based on large, longitudinal data obtained from unstructured EHRs of the largest tertiary hospitals in Greece. Demographics, hospital administrative data, medical history, medications, laboratory examinations, imaging reports, therapeutic interventions, in-hospital management and postdischarge instructions will be collected, coupled with structured prognostic data from the National Institute of Health. The target number of included patients is 100 000. Natural language processing techniques will facilitate data mining from the unstructured EHRs. The accuracy of the automated model will be compared with the manual data extraction by study investigators. Machine learning tools will provide data analytics. CardioMining aims to cultivate the digital transformation of the national cardiovascular system and fill the gap in medical recording and big data analysis using validated AI techniques.This study will be conducted in keeping with the International Conference on Harmonisation Good Clinical Practice guidelines, the Declaration of Helsinki, the Data Protection Code of the European Data Protection Authority and the European General Data Protection Regulation. The Research Ethics Committee of the Aristotle University of Thessaloniki and Scientific and Ethics Council of the AHEPA University Hospital have approved this study. Study findings will be disseminated through peer-reviewed medical journals and international conferences. International collaborations with other cardiovascular registries will be attempted.NCT05176769.
Biomedical extractive question answering based on dynamic routing and answer voting

Many existing biomedical extractive question answering methods are based on pre-trained models, which do not take full advantage of the hidden layer knowledge of pretrained models and do not consider span overlap between answers when predicting. To address these issues, we propose a new question answering model, called ALBERT with Dynamic Routing and Answer Voting (ADRAV). The ADRAV can reasonably utilize hidden layer knowledge through dynamic routing, and consider span similarity between answers through answer voting. To improve the performance of the model, we also carry out pre-fine-tuning, and add a dynamic parameter adjustment mechanism in the process of pre-fine-tuning. Experimental results show that our model achieves significant performance improvement with fewer parameters on BioASQ 4b, 5b, 6b, 9b, and outperforms SOTA baselines on BioASQ 4b, 6b.
Grey-level intensity measurements processing by means of Volterra equations and Least Squares Method for Video restoration

Abstract In this paper a video sequence is considered as a two dimensional time evolving process. Under this assumption two Volterra equations based mathematical models are introduced for video restoration purposes. The first one is based on geometric features related to the spatial–time structure of the video sequence and gives rise to a nonlinear Volterra equation. This model arises from the Mean Curvature Flow linked to evolving surfaces. The second one is based on analytic features and leads to the formulation of a linear Volterra model. The second procedure relies on the assumption of local time coherence in the sequence of frames, at least short back in time. In both cases Volterra equations based approach introduces a memory effect in the process which in the present terminology means that several frames back may be taken into account for the better reconstruction of the current frame throughout a convenient choice of the convolution kernel. On the other hand the role played by Least Squares Method focuses on the practical computation of that convolution kernel just at discrete level. The performance of both approaches is shown through a list of suitable experiments, and the better performance of the second approach is illustrated with remarkable improvements in critical cases.
Deepfake Detection using Inception-ResNet-V2 Network

Deepfakes entails obscene videos in which a face can be changed with someone else's utilizing neural networks. Deepfakes are a public problem, thus developing methods to detect them is critical. With deepfake and human-level control, deep learning has already utilized to address complicated issues. Deepfake is a new branch of AI technology, which in that one's face is superimposed on another's face, which is popular on social media. Deep learning techniques were also employed in the development of software that affects national security, democracy and privacy. One of the most current applications that use deep learning is deepfakes. Algorithms for deepfake may generate fake photos and movies which are indistinguishable from real ones. As a result, the development of technology capable of automatically detecting and assessing the reliability of digital video is critical. This research describes algorithms for detecting deepfakes. By reviewing the influence of deepfakes and deepfake recognition systems, this work enables the creation of new and so many effective methodologies to cope with increasingly complex deepfakes. InceptionResNetV2 architecture in Convolutional Neural Networks (CNN) is utilized in this comparative study to distinguish real and deepfake images.
Natural Language Processing using Convolutional Neural Network

Convolutional neural networks (CNN) are multi-layer neural networks that are used to learn hierarchical data properties. In recent times, CNN has achieved remarkable advances in the architecture and computation of Natural Language Processing (NLP). The Word2vec technique is considered to introduce Word embeddings, which are used to improve the performance of a variety of Natural Language Processing (NLP) applications. It is a well-known technique for learning word embeddings, which are dense representations of words in a lower-dimensional vector space. Two prominent approaches are used for learning word embeddings, which are dense representations of words in a lower-dimensional vector space, are Continuous Bag-of-Words (CBOW) and Skip-Gram.
A Study on Metaverse in Education

Most nations have used online meeting software platforms for distant education in some capacity during the COVID-19 epidemic. These software applications do, however, have substantial drawbacks that hinder engagement and fall short of simulating the classroom environment. Many of these restrictions are resolved by the newly forming Metaverse. In education in Metaverse, learners have the opportunity to engage with digital content and each other in a more interactive and immersive way. For example, learners can participate in virtual simulations, role-playing activities, and collaborative projects with other learners from around the world. They can also access a wide range of digital resources, such as virtual textbooks, lectures, and assessments, all within the same platform. This paper reviews different Metaverse models, frameworks for applying Metaverse in the field of education.
Privacy and Security of Healthcare Data in Cloud based on the Blockchain Technology

Blockchain is basically a conveyed database that contains records and public record for all exchanges or computerized occasions that have been performed and shared among members. Every transaction in the public record is checked by agreement of most individuals in the system. When the data is placed, the data won't ever vanish. Block chain contains a conclusive and undeniable record of each and every transaction ever. The primary rationale of this of this proposed work is safely store and keeps up with the patient records in cloud database. Healthcare is a data serious space where a lot of data is created, dispersed, put away, and got to day to day. The Blockchain innovation is utilized to safeguard the healthcare data facilitated inside the cloud. The blockchain that contains the clinical data and Distributed computing will associate different healthcare suppliers. It permits healthcare supplier to get to the patient subtleties all the more safely from anyplace. It secures the data from attackers. The data is encrypted before moving to the cloud. The healthcare supplier needs to decrypt the data before downloading the information. The information is finally obtained by involving cryptography in the encryption stage and can be simply acquired by the client and server. In this proposed study, a healthcare record-based protection and security of healthcare-related information in the cloud has been performed on java platform.
IOT Based Smart Wheelchair for Elderly Healthcare Monitoring

With every field of technology witnessing advancement and innovation, application of such innovations with refined expertise has eased human welfare. In present times also, Internet of Things (IOT) is proving to be panacea of all hiccups for handicapped patients. Patients enduring physical disabilities cannot take care of themselves sans help of other individuals, and therefore need artificial tools to move around like wheelchair. Under a promising technology, smart/capable wheelchairs are supposed to be developed which are laced with pro-active sensory system. The technology incorporated is Internet of Things (IOT). The disabled patients using these rare wheelchairs, no longer need to depend on others rather sensors fitted with the wheelchairs, monitor the vital bio indices as that of blood, heart, lungs and neural system. In case of any irregularity, signal transaction using embedded systems, onto cloud takes place. This results in flagging a signal which can be accessed by doctors available in online mode. Hence the redressel of medical problems would become much more effective, easier and in less time. IOT is certainly a promising technology for medical care also.
An English pronunciation and intonation evaluation method based on the DTW algorithm

The development of English as the global language has an impact on pronunciation instruction, where comfortable intelligibility should be the objective rather than pronunciation that sounds like a native speaker. Successful communication between speakers depends on accurate English pronunciation. When learning how to pronounce, it is critical to consider how the learner’s native language may affect their ability to pronounce specific sounds in the second language. Oral pronunciation is an important embodiment of English ability. The accuracy of pronunciation determines the smoothness of communication. Incorrect oral pronunciation makes it difficult for the speaker to misunderstand. In order to solve the problems of single feature dimension and low accuracy in traditional oral English evaluation, this study used the support vector regression algorithm to achieve the effective integration of different dimension evaluation features. The study comprehensively evaluates the overall reading and pronunciation quality of students. This paper designs an evaluation model of spoken English pronunciation quality based on a dynamic time warping (DTW) algorithm and introduces the evaluation algorithms of pronunciation standard, fluency, and intonation, respectively. A support vector regression (SVR) algorithm is used to realize the effective fusion of different dimension evaluation features. In addition, the correlation between machine rating and manual rating was used as an evaluation index to evaluate students’ oral pronunciation quality from different dimensions. The model shows superior performance in the pronunciation of both monosyllabic and polysyllabic words, which is beneficial to improving students’ oral English learning effect.
Low-light Image Enhancement via a Frequency-based Model with Structure and Texture Decomposition

This article proposes a frequency-based structure and texture decomposition model in a Retinex-based framework for low-light image enhancement and noise suppression. First, we utilize the total variation-based noise estimation to decompose the observed image into low-frequency and high-frequency components. Second, we use a Gaussian kernel for noise suppression in the high-frequency layer. Third, we propose a frequency-based structure and texture decomposition method to achieve low-light enhancement. We extract texture and structure priors by using the high-frequency layer and a low-frequency layer, respectively. We present an optimization problem and solve it with the augmented Lagrange multiplier to generate a balance between structure and texture in the reflectance map. Our experimental results reveal that the proposed method can achieve superior performance in naturalness preservation and detail retention compared with state-of-the-art algorithms for low-light image enhancement. Our code is available on the following website. 1
Evaluation of Camera Pose Estimation Using Human Head Pose Estimation

Abstract We introduce and evaluate a novel camera pose estimation framework that uses the human head as a calibration object. The proposed method facilitates extrinsic calibration from 2D input images (NIR and/or RGB), while merely relying on the detected human head, without the need for depth information. The approach is applicable to single cameras or multi-camera networks. Our implementation uses a fine-tuned deep learning-based 2D human facial landmark detector to estimate the 3D human head pose by fitting a 3D head model to the detected 2D facial landmarks. Our work focuses on an evaluation of the proposed approach on real multi-camera recordings and synthetic renderings to determine the accuracy of the pose estimation results and their applicability. We assess the robustness of our method against different input parameters, such as varying relative camera positions, variations of head models, face occlusions (by masks, sun glasses, etc.), potential biases and variance among humans. Based on the experimental results, we expect our approach to be effective for numerous use cases including automotive attention monitoring, robotics, VR/AR and other scenarios where ease of handling outweighs accuracy.
A Robust Fuzzy Equilibrium Optimization-Based ROI Selection and DWT-Based Multi-Watermarking Model for Medical Images

Image watermarking is the process of securely embedding a higher amount of information in the host object. These processes ensure authentication, image integration, and content verification. Several existing methods face complicated problems, such as security issues, robustness, and data leakage. Therefore, researchers developed specific methods for different applications. However, the performance of the currently obtained method was lower due to their low resistances. Therefore, to overcome this issue, we employed a novel technique, a fuzzy equilibrium optimization (FEO) approach, for embedding water image encryption. Initially, the raw image undergoes fuzzification to determine the critical point; thus, the intensity of the radial line selects a region of interest (ROI). Finally, the watermarking images are converted into a time-frequency domain via discrete wavelet transform (DWT), where the sub-band is converted based on value of magnitude. The proposed technique is analyzed using three medical image datasets, namely magnetic resonance imaging (MRI), ultrasound (US), and computed tomography (CT) datasets. However, all pixels in each sub-band are replaced to form a fully encrypted image, guaranteeing a watermarked reliable, secure, non-breakable format. Singular values are obtained for the encrypted watermarking image to provide high robustness to the watermarked image. After validation, the proposed fuzzy equilibrium optimization technique achieved higher robustness and security against different types of attacks. Moreover, the proposed FEO technique achieved a value of peak signal to noise ratio (PSNR) about 42.5 dB higher than other compared techniques.
BoT2L-Net: Appearance-Based Gaze Estimation Using Bottleneck Transformer Block and Two Identical Losses in Unconstrained Environments

As a nonverbal cue, gaze plays a critical role in communication, expressing emotions and reflecting mental activity. It has widespread applications in various fields. Recently, the appearance-based gaze estimation method, which utilizes CNN (convolutional neural networks), has rapidly improved the accuracy and robustness of gaze estimation algorithms. Due to their insufficient ability to capture global relationships, the present accuracy of gaze estimation methods in unconstrained environments, has the potential for improvement. To address this challenge, the focus of this paper is to enhance the accuracy of gaze estimation, which is typically measured by mean angular error. In light of Transformer’s breakthrough in image classification and target detection tasks, and the need for an efficient network, the Transformer-enhanced-CNN method is a suitable choice. This paper proposed a novel model for 3D gaze estimation in unconstrained environments, based on the Bottleneck Transformer block and multi-loss methods. Our designed network (BoT2L-Net), incorporates self-attention through the BoT block, utilizing two identical loss functions to predict the two gaze angles. Additionally, the back-propagation network was combined with classification and regression losses, to improve the network’s accuracy and robustness. Our model was evaluated on two commonly used gaze datasets: Gaze360 and MPIIGaze, achieving mean angular errors of 11.53° and 9.59° for front 180° and front-facing gaze angles, respectively, on the Gaze360 testing set, and a mean angular error of 3.97° on the MPIIGaze testing set, outperforming the CNN-based gaze estimation method. The BoT2L-Net model proposed in this paper performs well on two publicly available datasets, demonstrating the effectiveness of our approach.
Which is the Digital Competence of Each Member of Educational Community to Use the Computer? Which Predictors Have a Greater Influence?

Abstract Since the COVID-19 pandemic, the use of digital resources and virtual platforms is even more essential to continue the educational process, either in person or online, affecting all the members involved in the teaching–learning process of the students. Therefore, this study is aimed: (1) to know and compare the digital competence of the agents that are the main integrators of the educational community (parents, teachers, students) about the use of the computers, according to gender and educational stage (Pre-school, Primary and Secondary Education stages); and (2) to identify significant predictors that affect the acquisition of this competence. An ex-post-facto design was used with a sample of 786 participants from Andalusia (Spain). Research methods such as contrasts of means and multiple linear regression analysis were used. The results showed high average levels of basic digital competences for all the agents involved. A gender gap was found between mothers and fathers of students, with higher scores for the latter group. In addition, the use of videogames, the parents' academic background and the use of digital tablets or Google+ are the most significant predictors that affect the acquisition of this competence.