Reviewerly/exHarmony huggingface dataset:
Rough Entropy-Based Fused Granular Features in 2-D Locality Preserving Projections for High-Dimensional Vision Sensor Data

Locality preserving projection (LPP) is a manifold learning-based nonlinear dimensionality reduction (DR) technique, which has seen successful implementations in pattern recognition problems. In the case of 2-D images, if they are vectorized in 1-D shapes before applying LPP, significant spatial neighborhood information can be lost. Two-dimensional LPP (2DLPP) can overcome this problem but suffers when data are susceptible to noise, outliers, and intensity variation. To address these issues, we propose a novel rough entropy-based granular fusion (REGF) scheme to capture the intensity variation in the data in the form of feature information and propose hybridization of REGF with 2DLPP for feature extraction. The fusion technique simultaneously imbibes good features of crisp granulation (CG) and quad-tree decomposition (QTD), and considers the uncertainties caused by these homogenous and non-homogeneous granulation techniques in defining the indiscernible image regions. Moreover, it works in the RGB color space to alleviate the loss of information encountered by the conventional granulation techniques in the gray space. Extensive experimental studies in a real-world vision sensor-based human–robot interaction (HRI) framework have been conducted to demonstrate the effectiveness of the proposed granular computing (GrC)-based technique, especially in rugged environments.
A High-Precision Remote Sensing Identification Method on Saline-Alkaline Areas Using Multi-Sources Data

Soil salinization is a widespread and important environmental problem. We propose a high-precision remote sensing identification method for saline-alkaline areas using multi-source data, a method which is of some significance for improving ecological and environmental problems on a global scale which have been caused by soil salinization. Its principle is to identify saline-alkaline areas from remote sensing imagery by a decision tree model combining four spectral indices named NDSI34 (Normalized Difference Spectral Index of Band 3 and Band 4), NDSI25 (Normalized Difference Spectral Index of Band 2 and Band 5), NDSI237 (Normalized Difference Spectral Index of Band 3 and Band 4) and NDSInew (New Normalized Difference Salt Index) that can distinguish saline-alkaline areas from other features. In this method, the complementary information within the multi-source data is used to improve classification accuracy. The main steps of the method include multi-source data acquisition, adaptive feature fusion of multi-source data, feature identification and integrated expression of the saline-alkaline area from multi-source data, fine classification of the saline-alkaline area, and accuracy verification. Taking Minqin County, Gansu Province, China as the study area, we use the method to identify saline-alkaline areas based on GF-2, GF-6/WFV and DEM data. The results show that the overall accuracy of the method is 88.11%, which is 7.69% higher than that of the traditional methods, indicating that it could effectively identify the distribution of saline-alkaline areas, and thus provide a scientific technique for the quick identification of saline-alkaline areas in large regions.
Image Dehazing Based on Improved Color Channel Transfer and Multiexposure Fusion

Image dehazing is one of the problems that need to be solved urgently in the field of computer vision. In recent years, more and more algorithms have been applied to image dehazing and achieved good results. However, the image after dehazing still has color distortion, contrast and saturation disorder, and other challenges; in order to solve these problems, in this paper, an effective image dehazing method is proposed, which is based on improved color channel transfer and multiexposure image fusion to achieve image dehazing. First, the image is preprocessed using a color channel transfer method based on k-means. Second, gamma correction is introduced on the basis of guided filtering to obtain a series of multiexposure images, and the obtained multiexposure images are fused into a dehazed image through a Laplacian pyramid fusion scheme based on local similarity of adaptive weights. Finally, contrast and saturation corrections are performed on the dehazed image. Experimental verification is carried out on synthetic dehazed images and natural dehazed images, and it is verified that the method proposed is superior to existing dehazed algorithms from both subjective and objective aspects.
Heatmap-Guided Selective Feature Attention for Robust Cascaded Face Alignment

Face alignment methods have been actively studied using coordinate and heatmap regression tasks. Although these regression tasks have the same objective for facial landmark detection, each task requires different valid feature maps. Therefore, it is not easy to simultaneously train two kinds of tasks with a multi-task learning network structure. Some studies have proposed multi-task learning networks with two kinds of tasks, but they do not suggest an efficient network that can train them simultaneously because of the shared noisy feature maps. In this paper, we propose a heatmap-guided selective feature attention for robust cascaded face alignment based on multi-task learning, which improves the performance of face alignment by efficiently training coordinate regression and heatmap regression. The proposed network improves the performance of face alignment by selecting valid feature maps for heatmap and coordinate regression and using the background propagation connection for tasks. This study also uses a refinement strategy that detects global landmarks through a heatmap regression task and then localizes landmarks through cascaded coordinate regression tasks. To evaluate the proposed network, we tested it on the 300W, AFLW, COFW, and WFLW datasets and obtained results that outperformed other state-of-the-art networks.
Efficient large invisible color watermark embedding using conditional deep autoencoder model for medical applications

The widespread availability of the internet, in conjunction with very low-cost digital recording and storage devices, has ushered in an age in which the reproduction, illegal use, and malicious dissemination of digital information have become much more straightforward. Authentication of multimedia materials has garnered a lot of interest in recent days as a means of preventing illegal usage, theft, and misrepresentation of the content. Invisible watermarking tries to hide information in a medium in order to demonstrate ownership, integrity, or to hide a secret message. The goal of invisible watermarking is to hide the watermark and extract it without making it evident that the cover image is watermarked. This paper presents an invisible watermarking technique that can hide large color water marks in the cover media. The proposed model uses a Conditional Variational Autoencoder (CVAE) to encode the watermark into the cover image. The watermarked image is then decoded at the receiver to extract the watermark. Unlike the conventional watermarking techniques which hide a simple, small black and white image as watermark, the proposed model can embed large full color images as watermarks into the cover images. This makes the proposed model superior to the existing models by hiding large watermarks into the color images. The stego images produced a high PSNR greater than 40 for different watermarks and the images are visually indistinct from the original cover images.
Optical Convolutional Neural Networks: Methodology and Advances (Invited)

As a leading branch of deep learning, the convolutional neural network (CNN) is inspired by the natural visual perceptron mechanism of living things, showing great application in image recognition, language processing, and other fields. Photonics technology provides a new route for intelligent signal processing with the dramatic potential of its ultralarge bandwidth and ultralow power consumption, which automatically completes the computing process after the signal propagates through the processor with an analog computing architecture. In this paper, we focus on the key enabling technology of optical CNN, including reviewing the recent advances in the research hotspots, overviewing the current challenges and limitations that need to be further overcome, and discussing its potential application.
Recommender System for Arabic Content Using Sentiment Analysis of User Reviews

Recommender systems are used as effective information-filtering techniques to automatically predict and identify sets of interesting items for users based on their preferences. Recently, there have been increasing efforts to use sentiment analysis of user reviews to improve the recommendations of recommender systems. Previous studies show the advantage of integrating sentiment analysis with recommender systems to enhance the quality of recommendations and user experience. However, limited research has been focused on recommender systems for Arabic content. This study, therefore, sets out to improve Arabic recommendation systems and investigate the impact of using sentiment analysis of user reviews on the quality of recommendations. We propose two collaborative filtering recommender systems for Arabic content: the first depends on users’ ratings, and the second uses sentiment analysis of users’ reviews to enhance the recommendations. These proposed models were tested using the Large-Scale Arabic Book Reviews dataset. Our results show that, when the user review sentiment analysis is combined with recommender systems, the quality of the recommendations is improved. The best model was the singular value decomposition (SVD) with the Arabic BERT–mini model, which yielded minimum errors in terms of RMSE and MAE values and outperformed the performance of other previous studies in the literature.
THE POTENTIAL OF THE ACADEMIC DISCIPLINE "HISTORY OF AVIATION" IN THE FORMATION OF SOFT SKILLS OF STUDENTS OF HIGHER EDUCATION

The article is on the issue of the formation of universal soft skills of higher education students in aviation specialties in the process of teaching the academic discipline ″History of aviation″ at the Flight Academy of NAU. It is emphasized that soft skills are not only key predictors of professional development, but also ensure the formation of an active, creative position in relation to one's activities and one's own life. The article pays special attention to the substantiation to the possibilities of interactive teaching methods used in classes – ″case study″ and ″debates″ – in the context of the process of developing flexible skills of students related to social, intellectual, volitional and leadership competencies. The purpose of the article is to clarify the notion ″soft skills″, to analyze the component composition of universal skills of a modern aviation specialist, as well as to study methods and technologies of teaching ″History of aviation″ for the purpose of soft skills formation of students of higher education. It is essential to single out three areas as for the formation of ″soft skills″: cognitive, active and personal. The following technologies ensure the highest effectiveness of the educational process: problem and exploring technologies (partial exploring training, problem and research training, training based on ″brainstorming″ and others); modelling training technologies, or edutainment (situation playing, story playing, simulation, etc.); communicative and dialogic technologies (problem discussion, intergroup dialogue, discussion with playing modelling and some others). The training process at aviation higher school has to be targeted simultaneously at professional competences formation and transfer of fundamental knowledge in aviation field, as well as development of soft skills that will permit future graduate to ensure the functioning of the main production processes; to manage the situation optimally, to build team interaction, to have flexible approach in decision-making. History disciplines have great educational potential, promoting the development of personal qualities of students, make substantial contribution to improving quality of professional education and can be considered not only as academic discipline, but also as a means of obtaining valuable information regarding intellectual development and professional growth, improving students' mental skills. In our opinion, academic discipline ″History of aviation″ is an effective means of soft skills students' development. It was concluded that the introduction of interactive technologies will contribute to the intensification of the educational process, the formation of skills and habits that ensure the success of students' self-realization. Key words: ″soft skills″, students of higher education of aviation specialties, academic discipline ″History of aviation″, interactive teaching methods, innovative educational technology, case-study, ″debate″ technology.
Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling

Chaotic time series are widely present in practice, but due to their characteristics-such as internal randomness, nonlinearity, and long-term unpredictability-it is difficult to achieve high-precision intermediate or long-term predictions. Multi-layer perceptron (MLP) networks are an effective tool for chaotic time series modeling. Focusing on chaotic time series modeling, this paper presents a generalized degree of freedom approximation method of MLP. We then obtain its Akachi information criterion, which is designed as the loss function for training, hence developing an overall framework for chaotic time series analysis, including phase space reconstruction, model training, and model selection. To verify the effectiveness of the proposed method, it is applied to two artificial chaotic time series and two real-world chaotic time series. The numerical results show that the proposed optimized method is effective to obtain the best model from a group of candidates. Moreover, the optimized models perform very well in multi-step prediction tasks.
Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Gesture recognition, as a core technology of human–computer interaction, has broad application prospects and brings new technical possibilities for smart homes, medical care, sports training, and other fields. Compared with the traditional human–computer interaction models based on PC use with keyboards and mice, gesture recognition-based human–computer interaction modes can transmit information more naturally, flexibly, and intuitively, which has become a research hotspot in the field of human–computer interaction in recent years. This paper described the current status of gesture recognition technology, summarized the principles and development history of electromagnetic wave sensor recognition, stress sensor recognition, electromyographic sensor recognition, and visual sensor recognition, and summarized the improvement of this technology by researchers in recent years through the direction of sensor structure, selection of characteristic signals, the algorithm of signal processing, etc. By sorting out and comparing the typical cases of the four implementations, the advantages and disadvantages of each implementation and the application scenarios were discussed from the two aspects of dataset size and accuracy. Based on the abovementioned discussion, the problems and challenges of current gesture recognition technology were discussed in terms of the biocompatibility of sensor structures, wearability and adaptability, stability, robustness, and crossover of signal acquisition and analysis algorithms, and the future development directions in this field were proposed.
Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals.
A Novel Color Visual Cryptography Approach Based on Harris Hawks Optimization Algorithm

Hundreds of millions of people worldwide use computing devices and services, including smartphones, laptops, and messaging apps. Visual cryptography (VC) is one of the most secure encryption methods for image encryption in many applications, such as voting security, online transaction security, and privacy protection. An essential step in VC is encrypting secret images into multiple digital shares to hide them with the intention of successfully reverting them to their original form. Hence, a single share cannot reveal information about the secret image. Issues including pixel enlargement, high processing costs, and low decryption quality influence the current state of VC. We address these issues by introducing a novel technique based on (2, 2) secret sharing and the algorithm of Harris hawks optimization (HHO) for color photos. For the encryption process, the appropriate color levels are determined using the HHO algorithm. Consequently, images are decrypted with improved quality and a small impact on the overall processing complexity. The suggested scheme is also non-expandable due to the equal size of the initial secret image and the shared images. This results in lower memory requirements and improved image quality. The approach is applied to a set of well-known benchmark images. Moreover, a set of standard metrics is used to assess the robustness of the proposed scheme, including its capability in defending against cryptanalytic attacks, a correlation, a histogram, and the quality of encryption. According to the findings, the proposed solution provides better reconstructed image quality, time-efficient encryption, and nearly optimal statistical properties compared to previous approaches.
An intelligent system control method based on visual sensor

In order to solve the complexity problem caused by the uncertainty of control system models, this paper utilizes visual sensors and intelligent control technology, and uses data-driven machine learning algorithms to extract representations from the original video for video representation learning, providing crucial semantic features for related tasks. Convolutional Neural Network (CNN) greatly improves the utilization efficiency of visual data and model performance, and realizes the recognition of complex application scenarios. In this paper, an intelligent system control method of time-domain vision sensor is proposed. The proposed method locate, track and measure the speed of moving objects based on CNN and image acquisition device, Asynchronous Temporal Vision Sensor (ATVS). The experimental results show that our proposed algorithm has improved its overall performance through video feature learning and clustering. It not only pays more attention to video spatial information to enhance the discrimination ability of learned video representations, such as scenes and objects, but also improves the tracking performance of visual sensors under various interference attributes.
Web-Based 3D Virtual Environments Utilization in Primary and Secondary Education of Children with Multiple Impairments

The technological advances we are witnessing today have stimulated the creation of many 3D virtual environments for various purposes, from entertainment to industry to education. While the majority of these environments are perfectly suited for the healthy population, we should not forget about impaired people living among us. Regarding children’s education, one may wonder how impaired children handle them. Do they find them usable and attractive? How well do they handle basic activities in 3D environments, including orientation and interaction with objects? The experiment presented in this article provides answers to these questions within a specific setup. The experiment used a custom web application with several 3D virtual environments in a desktop virtual reality setting. The participants were 12 children, aged 8–14, with multiple impairments, predominantly hearing impairment, borderline and mild degree of mental retardation, and inferior communication skills. The answers can be regarded as positive and are based on results gathered in the form of completion times and the System Usability Scale questionnaire scores. The article also reports on a significant relation found between completion times and questionnaire scores. Future research directions, including those related to the Metaverse concept, are discussed, too.
Critical Controlling for the Network Security and Privacy Based on Blockchain Technology: A Fuzzy DEMATEL Approach

The Internet of Things (IoT) has been considered in various fields in the last decade. With the increasing number of IoT devices in the community, secure, accessible, and reliable infrastructure for processing and storing computed data has become necessary. Since traditional security protocols are unsuitable for IoT devices, IoT implementation is fraught with privacy and security challenges. Thus, blockchain technology has become an effective solution to the problems of IoT security. Blockchain is an empirical data distribution and storage model involving point-to-point transmission, consensus mechanism, asymmetric encryption, smart contract, and other computer technologies. Security and privacy are becoming increasingly important in using the IoT. Therefore, this study provides a comprehensive framework for classifying security criteria based on blockchain technology. Another goal of the present study is to identify causal relationship factors for the security issue using the Fuzzy Decision-Making Trial-and-Evaluation Laboratory (FDEMATEL) approach. In order to deal with uncertainty in human judgment, fuzzy logic is considered an effective tool. The present study’s results show the proposed approach’s efficiency. Authentication (CR6), intrusion detection (CR4), and availability (CR5) were also introduced as the most effective and essential criteria, respectively.