Reviewerly/exHarmony huggingface dataset:
Design of Chained Document HTML Generation Technique Based on Blockchain for Trusted Document Communication

Digital document communication between an enterprise and a customer is becoming a primary form of communication rather than the traditional physical document communication. A PDF document, the most popular document format, provides an identical document layout regardless of OS or device and has a content integrity verification feature with a digital signature. However, it has a bad user experience, such as low readability on a mobile device. On the other hand, an HTML document has a weakness in verifying the content integrity even though it is the primary document format and provides a good user experience on mobile devices. There are certified document services using blockchain technology, but it is still vulnerable to verifying content integrity. Furthermore, research on the document HTML has proposed the trusted document generation technique by HTML conformance and digital signature; however, this research does not provide content delivery verification, and there is a file size overhead. In this paper, we have developed the chained document HTML by defining HTML conformance, digital signature, and blockchain technology. First, the chained document HTML has to embed all resources and does not allow loading content on-demand. Second, the file is signed by a digital signature, and the signature value is added in the file header. Lastly, the metadata to verify the content integrity is inserted in a blockchain node. We have created the chained document HTML generation and verification experiment environment by Ethereum and Python. We have confirmed that the chained document HTML provides content and delivery integrity verification in the research. We expect the chained document HTML will be widely used in document communication between an enterprise and a customer, especially if the document has sensitive personal information that might have a legal dispute.
Generative Adversarial Network for an Improved Arabic Handwritten Characters Recognition

Abstract Currently, Arabic character recognition remains one of the most complicated challenges in image processing and character identification. Many algorithms exist in neural networks, and one of the most interesting algorithms is called generative adversarial networks (GANs), where 2 neural networks fight against one another. A generative adversarial network has been successfully implemented in unsupervised learning and it led to outstanding achievements. Furthermore, this discriminator is used as a classifier in most generative adversarial networks by employing the binary sigmoid cross-entropy loss function. This research proposes employing sigmoid cross-entropy to recognize Arabic handwritten characters using multi-class GANs training algorithms. The proposed approach is evaluated on a dataset of 16800 Arabic handwritten characters. When compared to other approaches, the experimental results indicate that the multi-class GANs approach performed well in terms of recognizing Arabic handwritten characters as it is 99.7% accurate. Keywords: Generative Adversarial Networks (GANs), Arabic Characters, Optical Character Recognition, Convolutional Neural Networks (CNNs).
Adaptive Coverage Path Planning Policy for a Cleaning Robot with Deep Reinforcement Learning

This paper presents an adaptive policy for coverage path planning for a cleaning robot in 2D environments based on reinforcement learning. We applied an actor-critic model and a simulator to make a robot learn a path planning policy. In the view of consumer electronics, our objective function is designed to generate the minimum energy path. We used a real cleaning robot called R9 made by LG to evaluate our algorithm. Compared with a rule-based algorithm and other learning-based algorithms, our algorithm is probably more efficient in the view of energy saving.
Dynamical Pseudo-Random Number Generator Using Reinforcement Learning

Pseudo-random number generators (PRNGs) are based on the algorithm that generates a sequence of numbers arranged randomly. Recently, random numbers have been generated through a reinforcement learning mechanism. This method generates random numbers based on reinforcement learning characteristics that select the optimal behavior considering every possible status up to the point of episode closing to secure the randomness of such random numbers. The LSTM method is used for the long-term memory of previous patterns and selection of new patterns in consideration of such previous patterns. In addition, feature vectors extracted from the LSTM are accumulated, and their images are generated to overcome the limitation of LSTM long-term memory. From these generated images, features are extracted using CNN. This dynamical pseudo-random number generator secures the randomness of random numbers.
Virtual Reality Primary School Mathematics Teaching System Based on GIS Data Fusion

Virtual reality technology is becoming more and more well-known by more and more people. Informatization of education is vigorously promoted in the modernization of the country. As a new type of digital information processing platform, virtual reality technology has a huge advantage in elementary school mathematics teaching. It is based on three-dimensional spatial data for modeling, analysis, and display. In addition, as a data synthesis and processing technology, data fusion is actually the integration and application of many traditional disciplines and new technologies. Geographic information system (GIS) is a specific and very important spatial information system. This article mainly uses the method of case analysis, giving examples of virtual experimental teaching system, intelligent auxiliary teaching system, and virtual classroom teaching system to analyze the related technologies and then apply the investigation method and experimental method to test the system proposed in this article and organize students’ opinions systematic attitude. Experimental data and survey results show that 78 students believe that the virtual mathematics education system is most suitable for simulating operation experience and understanding principles. And more than 50% of students believe that the virtual experimental education system has significantly increased their interest in learning. For this reason, the research on the virtual reality elementary school mathematics teaching system based on GIS data fusion is of great significance.
An Improved Image Enhancement Method for Traffic Sign Detection

Traffic sign detection (TRD) is an essential component of advanced driver-assistance systems and an important part of autonomous vehicles, where the goal is to localize image regions that contain traffic signs. Over the last decade, the amount of research on traffic sign detection and recognition has significantly increased. Although TRD is a built-in feature in modern cars and several methods have been proposed, it is a challenging problem due to the high computational demand, the large number of traffic signs, complex traffic scenes, and occlusions. In addition, it is not clear how can we perform real-time traffic sign detection in embedded systems. In this paper, we focus on image enhancement, which is the first step of many object detection methods. We propose an improved probability-model-based image enhancement method for traffic sign detection. To demonstrate the efficiency of the proposed method, we compared it with other widely used image enhancement approaches in traffic sign detection. The experimental results show that our method increases the performance of object detection. In combination with the Selective Search object proposal algorithm, the average detection accuracies were 98.64% and 99.1% on the GTSDB and Swedish Traffic Signs datasets. In addition, its relatively low computational cost allows for its usage in embedded systems.
Homography estimation from a single-point correspondence using template matching and particle swarm optimization

Existing feature-based methods for homography estimation require several point correspondences in two images of a planar scene captured from different perspectives. These methods are sensitive to outliers, and their effectiveness depends strongly on the number and accuracy of the specified points. This work presents an iterative method for homography estimation that requires only a single-point correspondence. The homography parameters are estimated by solving a search problem using particle swarm optimization, by maximizing a match score between a projective transformed fragment of the input image using the estimated homography and a matched filter constructed from the reference image, while minimizing the reprojection error. The proposed method can estimate accurately a homography from a single-point correspondence, in contrast to existing methods, which require at least four points. The effectiveness of the proposed method is tested and discussed in terms of objective measures by processing several synthetic and experimental projective transformed images.
Analysis of the Application of Virtual Reality Technology in Football Training

As computer science and information technology develop rapidly, virtual reality (VR) technology has evolved from theory to application. As a key technology in modern society, VR technology is increasingly influencing more and more aspects of people’s daily lives, including sports training. VR technology can be seen as an assistive technology that provides specific support for athletes’ sports training through various means such as simulating training scenarios and conducting data analysis. This paper focuses on exploring the application of VR technology in football training and the combination of sports training and VR technology. In addition, a feasibility analysis of the application of VR technology in football is carried out, and software such as Poser 8.0, 3ds MAX, and EON Studio in the virtual football training system are introduced. The aim is to further provide theoretical support for the development and research of virtual football sports system software and to study virtual systems that are not disturbed by external natural conditions. It will break through the limitations of sports training due to factors such as weather, player injuries, lack of training space, and funding. This is conducive to improving teaching and training methods, promoting the mastery of technical movement essentials and the improvement of football skills.
Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-Supervision

In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. However, GCNs may be vulnerable to adversarial attacks on label-scarce dynamic graphs. Many existing works aim to strengthen the robustness of GCNs; for instance, adversarial training is used to shield GCNs against malicious perturbations. However, these works fail on dynamic graphs for which label scarcity is a pressing issue. To overcome label scarcity, self-training attempts to iteratively assign pseudo-labels to highly confident unlabeled nodes but such attempts may suffer serious degradation under dynamic graph perturbations. In this paper, we generalize noisy supervision as a kind of self-supervised learning method and then propose a novel Bayesian self-supervision model, namely GraphSS, to address the issue. Extensive experiments demonstrate that GraphSS can not only affirmatively alert the perturbations on dynamic graphs but also effectively recover the prediction of a node classifier when the graph is under such perturbations. These two advantages prove to be generalized over three classic GCNs across five public graph datasets.
iCatcher: A neural network approach for automated coding of young children's eye movements

Abstract Infants' looking behaviors are often used for measuring attention, real‐time processing, and learning—often using low‐resolution videos. Despite the ubiquity of gaze‐related methods in developmental science, current analysis techniques usually involve laborious post hoc coding, imprecise real‐time coding, or expensive eye trackers that may increase data loss and require a calibration phase. As an alternative, we propose using computer vision methods to perform automatic gaze estimation from low‐resolution videos. At the core of our approach is a neural network that classifies gaze directions in real time. We compared our method, called iCatcher, to manually annotated videos from a prior study in which infants looked at one of two pictures on a screen. We demonstrated that the accuracy of iCatcher approximates that of human annotators and that it replicates the prior study's results. Our method is publicly available as an open‐source repository at https://github.com/yoterel/iCatcher .
DeepLumina: A Method Based on Deep Features and Luminance Information for Color Texture Classification

Color texture classification is a significant computer vision task to identify and categorize textures that we often observe in natural visual scenes in the real world. Without color and texture, it remains a tedious task to identify and recognize objects in nature. Deep architectures proved to be a better method for recognizing the challenging patterns from texture images. This paper proposes a method, DeepLumina, that uses features from the deep architectures and luminance information with RGB color space for efficient color texture classification. This technique captures convolutional neural network features from the ResNet101 pretrained models and uses luminance information from the luminance (Y) channel of the YIQ color model and performs classification with a support vector machine (SVM). This approach works in the RGB-luminance color domain, exploring the effectiveness of applying luminance information along with the RGB color space. Experimental investigation and analysis during the study show that the proposed method, DeepLumina, got an accuracy of 90.15% for the Flickr Material Dataset (FMD) and 73.63% for the Describable Textures dataset (DTD), which is highly promising. Comparative analysis with other color spaces and pretrained CNN-FC models are also conducted, which throws light into the significance of the work. The method also proved the computational simplicity and obtained results in lesser computation time.
Fully Deformable Convolutional Network for Ship Detection in Remote Sensing Imagery

In high spatial resolution remote sensing imagery (HRSI), ship detection plays a fundamental role in a wide variety of applications. Despite the remarkable progress made by many methods, ship detection remains challenging due to the dense distribution, the complex background, and the huge differences in scale and orientation of ships. To address the above problems, a novel, fully deformable convolutional network (FD-Net) is proposed for dense and multiple-scale ship detection in HRSI, which could effectively extract features at variable scales, orientations and aspect ratios by integrating deformable convolution into the entire network structure. In order to boost more accurate spatial and semantic information flow in the network, an enhanced feature pyramid network (EFPN) is designed based on deformable convolution constructing bottom-up feature maps. Additionally, in considering of the feature level imbalance in feature fusion, an adaptive balanced feature integrated (ABFI) module is connected after EFPN to model the scale-sensitive dependence among feature maps and highlight the valuable features. To further enhance the generalization ability of FD-Net, extra data augmentation and training methods are jointly designed for model training. Extensive experiments are conducted on two public remote sensing datasets, DIOR and DOTA, which then strongly prove the effectiveness of our method in remote sensing field.
Model-Based Underwater Image Simulation and Learning-Based Underwater Image Enhancement Method

Due to the absorption and scattering effects of light in water bodies and the non-uniformity and insufficiency of artificial illumination, underwater images often present various degradation problems, impacting their utility in underwater applications. In this paper, we propose a model-based underwater image simulation and learning-based underwater image enhancement method for coping with various degradation problems in underwater images. We first derive a simplified model for describing various degradation problems in underwater images, then propose a model-based image simulation method that can generate images with a wide range of parameter values. The proposed image simulation method also comes with an image-selection part, which helps to prune the simulation dataset so that it can serve as a training set for learning to enhance the targeted underwater images. Afterwards, we propose a convolutional neural network based on the encoder-decoder backbone to learn to enhance various underwater images from the simulated images. Experiments on simulated and real underwater images with different degradation problems demonstrate the effectiveness of the proposed underwater image simulation and enhancement method, and reveal the advantages of the proposed method in comparison with many state-of-the-art methods.
Automobile insurance fraud detection in the age of big data – a systematic and comprehensive literature review

Purpose The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection. Design/methodology/approach Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics. Findings This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research. Practical implications This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets. Originality/value This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.
The recognition of emotions conveyed by emoticons and emojis: A systematic literature review.

The growing interest for emoticons and emojis has recently led to research examining their use and impact on various behaviors.As emoticons and emojis may lead to misinterpretations and misunderstandings between senders and recipients in online communication, it is necessary to examine whether emotions conveyed by these symbols are well recognized by individuals.In this perspective, a systematic review from 2001 to 2021 using the Preferred Reporting of Items for Systematic reviews and Meta-Analyses (PRISMA) method was conducted to determine which emoticons and emojis can help individuals to recognize emotions, and how the recognition of emotions based on emoticons and emojis is studied.A total of 501 articles were screened from three major databases in psychology, and 23 articles met the predefined inclusion criteria.The results suggest that the recognition of emotions should be examined before using emoticons in larger studies.They also revealed that the recognition varied according to the methods used to assess the valence of emoticons or to attribute a specific emotion to them (self-report, free expression, or categorization).Finally, a summary Table of the emotions conveyed by emoticons and emojis is proposed in this review.
YouTube Videos as a Resource for Self-Regulated Pronunciation Practice in EFL Distance Learning Environments

Many EFL students have limited opportunities to listen to native speakers, practice English out of class, have problems in listening comprehension, oral expression and lack oral fluency. This article proposes using YouTube videos to improve EFL students' pronunciation. It aims to show the following: (i) How YouTube videos can be integrated in EFL instruction to teach pronunciation; (ii) the advantages of integrating YouTube videos: They are free, provide variety of topics, speakers, difficulty level, can be easily downloaded to the laptop or mobile phone and can be viewed anywhere and anytime; (iii) show how examples YouTube videos that target a specific pronunciation skill can be located; (iv) criteria for selecting online videos such as the video length in minutes, topic familiarity, difficulty level, speed of the speakers, students’ proficiency level, and students’ interests; (v) pronunciation subskills that can be developed through supplementary YouTube videos; (vi) teaching and learning with YouTube videos before watching a video, while watching the video and after watching the video. Further recommendations for developing students’ pronunciation are given.
Uncertainty-Aware Representation Learning for Action Segmentation

In this paper, we propose an uncertainty-aware representation Learning (UARL) method for action segmentation. Most existing action segmentation methods exploit continuity information of the action period to predict frame-level labels, which ignores the temporal ambiguity of the transition region between two actions. Moreover, similar periods of different actions, e.g., the beginning of some actions, will confuse the network if they are annotated with different labels, which causes spatial ambiguity. To address this, we design the UARL to exploit the transitional expression between two action periods by uncertainty learning. Specially, we model every frame of actions with an active distribution that represents the probabilities of different actions, which captures the uncertainty of the action and exploits the tendency during the action. We evaluate our method on three popular action prediction datasets: Breakfast, Georgia Tech Egocentric Activities (GTEA), and 50Salads. The experimental results demonstrate that our method achieves the performance with state-of-the-art.
A twinning bare bones particle swarm optimization algorithm

A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.