Reviewerly/exHarmony huggingface dataset:
Unified Representation for XR Content and its Rendering Method

Virtual Reality (VR) and Augmented Reality (AR) have become familiar technologies with related markets growing rapidly every year. Moreover, the idea of considering VR and AR as one eXtended reality (XR) has broken the border between virtual space and real space. However, there is no formal way to create such XR content except through existing VR or AR content development platforms. These platforms require the content author to perform additional tasks such as duplicating content for a specific user interaction environment (VR or AR) and associating them as one. Also, describing the content in an existing markup language (e.g., X3D, X3DOM, A-frame) has limitations of that the content author should predefine the user interaction environment (i.e., either of VR and AR). In this study, a unified XR representation is defined for describing XR content, and the method to render it has been proposed. The unified XR representation extends the HTML so that content authored with this representation can be harmoniously incorporated into existing web documents and can exploit resources on the World Wide Web. The XR renderer, which draws XR content on the screen, follows different procedures for both VR and AR situations. Consequently, the XR content works in both user interaction environment (VR and AR). Hence, this study provides a straightforward XR content authoring method that users access anywhere through a web browser regardless of their situational contexts, such as VR or AR. It facilitates XR collaboration with real objects by providing both VR and AR users with accessing an identical content.
Towards Customizable Chart Visualizations of Tabular Data Using Knowledge Graphs

Scientific articles are typically published as PDF documents, thus rendering the extraction and analysis of results a cumbersome, error-prone, and often manual effort. New initiatives, such as ORKG, focus on transforming the content and results of scientific articles into structured, machine-readable representations using Semantic Web technologies. In this article, we focus on tabular data of scientific articles, which provide an organized and compressed representation of information. However, chart visualizations can additionally facilitate their comprehension. We present an approach that employs a human-in-the-loop paradigm during the data acquisition phase to define additional semantics for tabular data. The additional semantics guide the creation of chart visualizations for meaningful representations of tabular data. Our approach organizes tabular data into different information groups which are analyzed for the selection of suitable visualizations. The set of suitable visualizations serves as a user-driven selection of visual representations. Additionally, customization for visual representations provides the means for facilitating the understanding and sense-making of information.
The COVID-19 pandemic: when e-learning becomes mandatory not complementary

Many countries have implemented extraordinary measures in response to the COVID-19 outbreak, including closing schools and universities and moving to remote teaching. E-learning was previously a complementary methodology - or a luxury - but the COVID-19 outbreak has made e-learning mandatory. It is the only way to continue schools' and universities' educational programs. Assessing the value of e-learning is more important than ever. Students are the focal point of the educational process; continuous and conscious monitoring of their satisfaction is crucial for the feasibility of e-learning. This paper aims to study the impact of the sudden move to remote teaching (e-learning educational process) on students at four universities in two countries (the Kingdom of Saudi Arabia and the Hashemite Kingdom of Jordan). Participating students were surveyed to measure their perceptions in terms of current e-learning processes and systems, and their experience with the e-learning educational process.
Dense Dilated Inception Network for Medical Image Segmentation

In recent years, various encoder-decoder-based U-Net architecture has shown remarkable performance in medical image segmentation.However, these encoder-decoder U-Net has a drawback in learning multi-scale features in complex segmentation tasks and weak ability to generalize to other tasks.This paper proposed a generalize encoder-decoder model called dense dilated inception network (DDI-Net) for medical image segmentation by modifying U-Net architecture.We utilize three steps; firstly, we propose a dense path to replace the skip connection in the middle of the encoder and decoder to make the model deeper.Secondly, we replace the U-Net's basic convolution blocks with a modified inception module called multi-scale dilated inception module (MDI) to make the model wider without gradient vanish and with fewer parameters.Thirdly, data augmentation and normalization are applied to the training data to improve the model generalization.We evaluated the proposed model on three subtasks of the medical segmentation decathlon challenge.The experiment results prove that DDI-Net achieves superior performance than the compared methods with a Dice score of 0.82, 0.68, and 0.79 in brain tumor segmentation for edema, non-enhancing, and enhancing tumor.For the hippocampus segmentation, the result achieves 0.92 and 0.90 for anterior and posterior, respectively.For the heart segmentation, the method achieves 0.95 for the left atrial.
An Intelligent Phishing Detection Scheme Using Machine Learning

In today’s era, theMakkar, Aaisha worldKumar, Neeraj isSama, Lakshit movingMishra, Satyam towardSamdani, Yash automation, in which objects connected to Internet can take independent decisions. In such an environment, machine learning which is an integral part of artificial intelligence, is used widely to design algorithms based on the data trends and historical relationships between data. However, security and privacy preservation in such an environment are key challenges in front of the research communities to handle. Hence, in this paper we present a framework which detects the phishing websites using machine learning. The proposed framework implements ten machine learning models and the best three models are ensembled followed by ten rounds of cross-validation. The overall performance of the proposed framework resulted an accuracy of 97.27% which is better than the existing proposals in the literature.
Data Visualization with Python Libraries

n the last chapter, we read about Pandas, the library with various functions for preparing data in order to make it ready for analysis and visualization. Visualization is a means to understand patterns in your data, identify outliers and other points of interest, and present our findings to an outside audience, without having to sift through data manually. Visualization also helps us to glean information from raw data and gain insights that would otherwise be difficult to draw.
Improved Dynamic Load Balancing Approach in Cloud Computing

Cloud computing is one of the important technologies in the field of Information Technology. Many services are available and provided by different service providers using different cloud technology nowadays. The main problem of cloud computing-based technology is load balancing in different cloud servers. It is one of the important issues in the necessary growth of cloud computation. Demand for the new cloud services with a high-speed service is important issue in this current era. There are various algorithms of load balancing which have been already discussed for an efficient allocation of requests through a proper selection of virtual machines in a cloud environment. In this paper, a new distribution technique of the entire incoming requests among the virtual machines has been proposed with an improved dynamic load balancing approach (IDLBA) in the cloud environment. Thus, its simulation is performed using the CloudAnalyst simulator three times with different numbers of tasks of different length. The simulation result is compared with some previously designed load balancing algorithms [1][2] in the cloud environment. Comparative analysis of simulation results establishes the fact that the incoming tasks are distributed dynamically among different available virtual machines which are of different configurations in a different data center in such a way that comparatively better response time and makespan time are achieved.
Facial Expression Recognition using Distance Importance Scores Between Facial Landmarks

In this paper, we present a feature extraction approach for facial expressions recognition based on distance importance scores between the coordinates of facial landmarks. Two audio-visual speech databases (CREMA-D and RAVDESS) were used in the research. We conducted experiments using the Long Short-Term Memory Recurrent Neural Network model in a single corpus and cross-corpus setup with different length sequences. Experiments were carried out using different sets and types of visual features. An accuracy of facial expression recognition was 79.1% and 98.9% for the CREMA-D and RAVDESS databases, respectively. The extracted features provide a better recognition result compared to other methods based on the analysis of facial graphical regions.
How Positive Are You: Text Style Transfer using Adaptive Style Embedding

The prevalent approach for unsupervised text style transfer is disentanglement between content and style.However, it is difficult to completely separate style information from the content.Other approaches allow the latent text representation to contain style and the target style to affect the generated output more than the latent representation does.In both approaches, however, it is impossible to adjust the strength of the style in the generated output.Moreover, those previous approaches typically perform both the sentence reconstruction and style control tasks in a single model, which complicates the overall architecture.In this paper, we address these issues by separating the model into a sentence reconstruction module and a style module.We use the Transformer-based autoencoder model for sentence reconstruction and the adaptive style embedding is learned directly in the style module.Because of this separation, each module can better focus on its own task.Moreover, we can vary the style strength of the generated sentence by changing the style of the embedding expression.Therefore, our approach not only controls the strength of the style, but also simplifies the model architecture.Experimental results show that our approach achieves better style transfer performance and content preservation than previous approaches.
Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Study

Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31–48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75–78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review.
Relationships between using modern ICT educational resources and schoolchildren’s academic performance

Introduction. The article investigates the contradictory problem of creating a cognitive educational environment within the context of preventing schoolchildren from using modern information technologies in the classroom in Russian schools. The purpose of the article is to analyze the relationships between the use of ICT resources by schoolchildren and their academic achievements. Materials and Methods. The study used both qualitative and quantitative methods. Based on Russian data from the international comparative study PISA 2015 (n = 6036) and 2018 (n = 7608) factorial and regression analysis was carried out. Qualitative analysis was conducted on the basis of data collected via interviews with teachers in focus groups (n = 91) at 10 schools in Moscow (the Russian Federation). Results. The data obtained from the interviews with teachers in Moscow schools reveal their negative attitude to the use of electronic devices by school students. The teachers believe that it deteriorates students’ cognitive and metacognitive skills. The factor analysis of the students’ survey data made it possible to distinguish two groups of variables: (1) the use of ICT resources for educational purposes; (2) the game format of their use. Regression analysis showed that the game format of using ICT resources does not worsen the academic performance. Moreover, frequent usage of ICT recourses for educational purposes significantly correlates with high academic achievements. Conclusions. It is concluded that the use of ICT resources as a game tool does not lead to a deterioration in academic performance. At the same time, the frequent use of electronic resources for educational purposes can be an important step towards reducing the risks of academic failure and can contribute to increasing students’ academic achievements. Keywords Modern educational environment; IT-technologies; IT-resources; Use of electronic devices in schools; Learning outcomes; Academic achievements; Socio-economic status of the family.
We Have So Much in Common: Modeling Semantic Relational Set Abstractions in Videos

Identifying common patterns among events is a key capability for human and machine perception, as it underlies intelligent decision making. Here, we propose an approach for learning semantic relational set abstractions on videos, inspired by human learning. Our model combines visual features as input with natural language supervision to generate high-level representations of similarities across a set of videos. This allows our model to perform cognitive tasks such as set abstraction (which general concept is in common among a set of videos?), set completion (which new video goes well with the set?), and odd one out detection (which video does not belong to the set?). Experiments on two video benchmarks, Kinetics and Multi-Moments in Time, show that robust and versatile representations emerge when learning to recognize commonalities among sets. We compare our model to several baseline algorithms and show that significant improvements result from explicitly learning relational abstractions with semantic supervision. Code and models are available online (Project website: abstraction.csail.mit.edu ).
Single image dehazing based on bright channel prior model and saliency analysis strategy

Haze is a common atmospheric phenomenon that causes poor visibility in outdoor images, which greatly limits image application in later stages. Therefore, haze removal has become the first and most indispensable step when dealing with degraded images. In this paper, we propose a novel bright channel prior (BCP) model and a saliency analysis strategy for haze removal. First, we obtain a more robust and accurate atmospheric light by a superpixel-based dark channel method. Second, we utilize the dark channel prior (DCP) to handle dark regions in hazy images. However, the DCP often mistakes white regions for opaque haze and thus causes serious colour distortion and halo effects. To solve this problem, a new BCP is proposed to accurately estimate the transmission of bright regions in hazy images. Third, we fuse the DCP and BCP using a multiscale fusion strategy with Laplacian pyramid representation to gain the correct transmission information for both bright and dark regions. Finally, a novel saliency analysis strategy for transmission refinement is proposed, so that the texture details can remain present to the greatest extent in the restored images. The experimental results illustrate that our proposed method performs well in restoring images containing bright objects.
Dialect Identification under Domain Shift: Experiments with Discriminating Romanian and Moldavian

This paper describes a set of experiments for discriminating between two closely related language varieties, Moldavian and Romanian, under a substantial domain shift. The experiments were conducted as part of the Romanian dialect identification task in the VarDial 2020 evaluation campaign. Our best system based on linear SVM classifier obtained the first position in the shared task with an F1 score of 0.79, supporting the earlier results showing (unexpected) success of machine learning systems in this task. The additional experiments reported in this paper also show that adapting to the test set is useful when the training data comes from another domain. However, the benefit of adaptation becomes doubtful even when a small amount of data from the target domain is available.
Survey on Reversible Watermarking Techniques for Medical Images

In recent years, the growth of the Internet along with multimedia data delivery has caused the misuse and even alteration of the original digital content. Multimedia data has a big share of the total data transmitted over the Internet every day. Such multimedia data is vulnerable to misuse and theft. Digital watermarking has been used traditionally to protect multimedia data by authenticating the content and by providing copyright protection. The downside of digital watermarking, however, is that it always introduces some amount of signal distortion. The distortion caused is imperceptible and doesn’t create an issue in general. But, in certain applications like the medical field, such loss of information is intolerable because the distorted information might affect the analysis of medical images and the subsequent inferences drawn from this analysis. Reversible watermarking techniques ensure that there is no information loss in the watermarking process and as such are suitable for such applications. This chapter presents an overview of reversible digital watermarking, the classification of different reversible watermarking techniques with special emphasis on the state-of-the-art reversible watermarking techniques developed for maintaining the integrity and authenticity of medical image data.
YOLOv3 Remote Sensing SAR Ship Image Detection

Deep learning has proved to be useful for ship detection in SAR satellite images. SAR is a satellite that can be used to capture images from Earth’s surface even in unfavorable weather conditions. Although many object detection models have been applied to this problem previously, we wanted to use a model that was fast and accurate. In this paper, we use You Only Look Once version-3 (YOLOv3) and compare its results with You Only Look Once version-2 (YOLOv2). The motivation was to improve upon the results of YOLOv2. The results show that YOLOv3 achieved 90.25 average precision (AP) compared to 90.05 AP of YOLOv2. Furthermore, YOLOv3 gave an inference time of 22 ms against 25 ms of YOLOv2. The dataset used is A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds; the dataset consists of 43,819 images of 256 × 256 pixels. The dataset contains Gaofen-3 and Sentinel-1 satellite images.
Security and Privacy of E-health Data

Information and Communication Technology (ICT) advancements have eased the life of mankind in many ways. Modern-day gadgets and tools have helped immensely in our daily life and have increased the life standards. Many tools and techniques are being used to communicate and disseminate information in the form of text, audio, video and what not. Effects of ICT can be visualized in home and domestic activities, education healthcare, commerce, banking and employment. Medical fraternity across the globe has heavily invested in ICT to provide better quality health-related services throughout the world in general and within their respective countries in particular. Medical experts uses e-healthcare systems, a combination of multimedia and digital technologies to facilitate health improvement and healthcare services which include diagnosis, application, training and implementation. The medical information stored in electronic form known as electronic health record have replaced traditional paper-based systems. Electronic health record systems, a critical part of e-health system, are prone to the threats which make security and privacy of patient’s health data vulnerable. Unauthorized access of electronic gadgets, data manipulation, denial-of-service attacks and phishing are common threats to the e-healthcare systems. Different types of users are accessing this vital information from the electronic healthcare systems which make it imperative to implement role-based access control mechanism. In this chapter, privacy-related issues of e-health data and security models used at different phases have been discussed.
A probe into the application of English euphemism in college classroom teaching under multimedia network technology

With the rapid development of modern computer technology, computer information management system has basically replaced the information statistics based on handwritten records. Due to the rapid development of computer technology, big changes have happened in people's way of life. Accordingly, along with the network technology and the computer technology development, the teaching method also develops rapidly. From the initial projection technology to the current multimedia classroom, microphone audio screen is available. At present, most online teaching platforms provide students with a unified teaching process, and cannot provide corresponding learning resources according to the different characteristics of learners. In order to ameliorate the efficiency of educational information, it is necessary to arouse the enthusiasm of students in the process of learning, and a variety of multimedia network learning platforms begin to be established. English euphemism is also very important in English teaching, which can improve teachers' teaching level, and secondly, improve students' oral English communication level. In this paper, a network teaching system embedded in English euphemism database is proposed to provide more convenience and resources for teachers and students to learn English. Through the technology of network, database and so on, the design of the network teaching platform is realized successfully. Finally, through the actual test and investigation, the utility of the system is fully affirmed. The application of English euphemism teaching in multimedia has a very profound foundation.