Reviewerly/exHarmony huggingface dataset:
Exploring Needs and Design Opportunities for Virtual Reality-based Contour Delineations of Medical Structures

Contour delineation, a critical phase in radiotherapy planning, refers to the process of identifying and segmenting malignant tumors and/or healthy organs from medical images. Today’s contouring software requires oncologists to inspect and contour target of interests by analyzing a stack of planar medical images, which is lengthy, tedious and sometimes error prone. Such design is also in contrast to the stereoscopic nature of medical images. Therefore, it is intuitive to consider bringing contouring into immersive Virtual Reality (VR) space. We present an exploratory study that uses iterative design to understand needs and opportunities to bring contour delineation into an immersive 3D space, such as the one enabled by today’s head-mounted VR displays. We report on the interactions with three medical professionals and three engineering & design experts, and demonstrate the potential for VR–based 3D contouring while studying the benefits of using 3D immersive spaces to augment the process of contour in 2D. Through needs–finding interviews and co–design workshops, we evaluated our initial iterations and proof–of–concept prototypes. We believe that our preliminary findings will benefit researchers and practitioners who are attempting to bring today’s contour delineation processes into an immersive 3D space.
The Nyquist criterion and its relevance in phase-stepping digital shearography: a quantitative study

The Nyquist criterion, or Nyquist sampling theorem, has been used as a gold standard in signal processing and in digital imaging. In this work, we examine its applicability and relevance in phase-stepping digital shearography, which has become a useful tool in the generation of displacement gradient maps of extended objects, and in non-destructive testing (NDT). We show, with experimental data and quantitative measurements from our digital shearography experiments, that it is not necessary to comply strictly with the Nyquist criterion in digital shearography. Operation of the digital imaging device in the sub-Nyquist domain is perfectly feasible, and even desirable, in many cases. This will suggest, to the experimentalist in shearography, the best possible ways to perform image sampling in the case of digital shearography.
A Children-Created Virtual Learning Space Station

We present a virtual learning space station, a design space, which was co-designed by 63 school children from three continents, namely Namibia, Malaysia and Finland. The design space station is developed on the Ohyay platform for children by children to plan, interact and conduct co-design activities. In our hands-on demonstration, attendees can organise/join a design session and test the online collaboration and facilitation tools provided by the space station. Our demo contributes directly to the IMX 2022 theme of “Interactive Media Brings us Together”.
PGE

Although product graphs (PGs) have gained increasing attentions in recent years for their successful applications in product search and recommendations, the extensive power of PGs can be limited by the inevitable involvement of various kinds of errors. Thus, it is critical to validate the correctness of triples in PGs to improve their reliability. Knowledge graph (KG) embedding methods have strong error detection abilities. Yet, existing KG embedding methods may not be directly applicable to a PG due to its distinct characteristics: (1) PG contains rich textual signals, which necessitates a joint exploration of both text information and graph structure; (2) PG contains a large number of attribute triples, in which attribute values are represented by free texts. Since free texts are too flexible to define entities in KGs, traditional way to map entities to their embeddings using ids is no longer appropriate for attribute value representation; (3) Noisy triples in a PG mislead the embedding learning and significantly hurt the performance of error detection. To address the aforementioned challenges, we propose an end-to-end noise-tolerant embedding learning framework, PGE, to jointly leverage both text information and graph structure in PG to learn embeddings for error detection. Experimental results on real-world product graph demonstrate the effectiveness of the proposed framework comparing with the state-of-the-art approaches.
Machine Learning for the Manufacturing of Digital Marketing Techniques and Its Impact on Health Care System

The study explores among the most significant developments in the digital marketing sector has been the emergence of machine learning and artificial intelligence technologies to assist in the streamlining of marketing operations and the increased effectiveness of companies. The manner in which digital marketers collaborate with machine learning-based technologies will determine the direction of marketing in the next. As machine learning and artificial intelligence grow increasingly prevalent in the digital marketing environment, it is critical that the finest digital companies understand how to use machine learning into their digital marketing initiatives in order to remain competitive. As a healthcare organization, you are undoubtedly aware of how difficult it can be to keep up with the digital revolution in the healthcare industry. Selecting which new technologies are worthy the investment in and convincing your staff to embrace changes is frequently the most difficult aspect of the process. Look, adjusting to the digital world necessitates a change in perspective toward one that is more adaptable and threat. It entails letting go of antiquated corporate procedures and placing faith in the fact that disruptions will produce significant benefits.
Analyzing Linter Usage and Warnings Through Mining Software Repositories: A Longitudinal Case Study of JavaScript Packages

Linters are static analysis tools supporting developers by automatically detecting possible code errors, bad coding practices, violations of coding conventions, and styling issues while offering actionable advice on how to resolve these concerns to prevent inconsistencies in the codebase, errors, and degrading software quality. They are especially beneficial for software projects based on dynamically-typed languages, e.g., JavaScript, which are more prone to development errors and inconsistencies due to languages’ dynamic nature. However, little is known about how their usage and generated warnings evolve throughout the entire development histories of real-world software projects. This paper performs source code-based commit history analysis on 15 JavaScript npm packages. The empirical analysis of 16,562 commits revealed that the choice of linter and their configurations are rarely subjected to change once introduced to a package. When configurations do change, most modifications are minor. Unresolved violations of rules, which produce warnings, generally increase. Though, the density of warnings was found to be slightly decreasing with the maturity of a package – overall and for each distinct group of warnings.
Recent Updates to the National Solar Radiation Database (NSRDB)&amp;#160;&amp;#160;

&amp;lt;p&amp;gt;For over 20 years, the National Solar Radiation Database (NSRDB), covering most of the western hemisphere, has been a source of public data for many solar energy applications. Recent improvements in satellite technology and machine-learning-based remote sensing methods have added tremendous value to the NSRDB in terms of both the quantity and quality of the data.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;For example, the historical NSRDB data that is available from 1998 to present with one year lag is processed on a nominal 4x4 km grid spacing at a 30min frequency. Beginning in 2018, the NSRDB has additional datasets at 2x2 km 5min resolution available for the Continental United States, Hawaii, Mexico, and the Caribbean Islands, and at a 2x2 km 10min resolution available for North and South America from +60 to -60 degrees latitude. The improved spatiotemporal resolution should be a great asset to our stakeholders, especially for the analysis of utility scale solar installations which typically desire a higher resolution than the previously available 4x4 km 30min data.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Moreover, we have developed new methods for the prediction of cloud properties from satellite data using physics-guided machine learning. These methods were originally developed to compensate for the limitations of traditional cloud property retrieval algorithms, but they have proven to be generally more accurate than the traditional algorithms. The results demonstrate higher accuracy in the modeled irradiance that is expected to be helpful for a wide variety of solar energy applications.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In summary, the goal of the NSRDB is to provide the public with the highest-quality freely-available solar irradiance data possible. In this context, the NSRDB continues to evolve and push the envelope of what a public solar dataset can be. We think these recent advancements are important contributions to the solar energy community, and we hope that they will be fully taken advantage of.&amp;lt;/p&amp;gt;
A Quick Performance Assessment for Artificial Electric Field Algorithm

There are several effective metaheuristic algorithms in this literature for solving complex problems from different fields. In this work, Artificial Electric Field Algorithm (AEFA) is compared with the state-of-art algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Harmony Search (HS) algorithms. The values are studied based on convergence at the global optimum and resilience in tracking shifting optimum value. To accomplish this purpose, a series of benchmark functions with static and dynamic properties are employed. The results show that AEFA performs well in both cases.
Economics of Supply Chain Cyberattacks

Supply chain cyberattacks are being regarded as a major problem with substantial costs to businesses and national economies. According to cybersecurity firm Kaspersky, the average economic costs associated with a supply chain attack on an enterprise was $1.4 M USD in 2021, which was higher than all other types of cyberattacks.1
Research on Intelligent Recognition Algorithm of College Students’ Classroom Behavior Based on Improved SSD

This paper takes the classroom images of more than 50 classrooms in a university for nearly 4 months in a semester as the research object. The LabelImg manual annotation method is used to construct a detection data set including four behavioral states: listening to class, taking notes, playing with mobile phones, and sleeping. In order to effectively improve the detection accuracy of the data annotation model, we used data enhancement techniques such as cropping, rotation, and shading transformation to expand the number of dataset. Based on this dataset, an improved SSD model based on deep learning target detection technology is adopted. ResNet module is used to solve the problem that VGG module has poor detection results of students’ behavior state in picture analysis, FPN module is added to build RF-SSD detection model to improve the efficiency of image recognition to solve the problem of the low efficiency of small target recognition in the back of class. The experimental results show that RF-SSD has a great improvement in feature extraction ability and small target recognition accuracy compared with native SSD in self-constructed dataset, and can provide technical support and new ideas and methods for teaching management in universities.
Examining Techniques to Solving Imbalanced Datasets in Educational Data Mining Systems

The educational data mining research attempts have contributed in developing policies to improve student learning in different levels of educational institutions. One of the common challenges to building accurate classification and prediction systems is the imbalanced distribution of classes in the data collected. This study investigates data-level techniques and algorithm-level techniques. Six classifiers from each technique are used to explore their effectiveness to handle the imbalanced data problem while predicting students’ graduation grade based on their performance at the first stage. The classifiers are tested using the k-fold cross-validation approach before and after applying the data-level and algorithm-level techniques. For the purpose of evaluation, various evaluation metrics have been used such as accuracy, precision, recall, and f1-score. The results showed that the classifiers do not perform well with imbalanced dataset, and the performance could be improved by using these techniques. As for the level of improvement, it varies from one technique to another. Additionally, the results of the statistical hypothesis testing confirmed that there were no statistically significant differences for classifiers of the two techniques.
Immersive Media-Based Tourism Emerging Challenge of VR Addiction Among Generation Z

The virtual reality (VR) applications in entertainment and tourism industry have become growingly intense among generation Z. Interestingly, some pilot research on tourism studied concluded the positive impact of its flow experience on adoption of VR tourism, which is also driving the risk of immersive addictive. In the context of tourism and information and communication technology (ICT)-based innovation, there is a lack of immersive addictive behavior (IAB)-related literature. In addition, during the currently ongoing pandemic crisis, VR technology has gained particular importance in the tourism industry among generation Z. The present venture underlines the mechanism of IAB, investigates the VR addiction while underlining the cognitive abilities of individuals. This study applies empirical framework of cognitive–behavioral model. Results demonstrate that in the case of VR tourism, the immersive experience (presence and flow) determines the addictive behavior. Furthermore, VR imagery (VI), psychological curiosity (PC), and VR convenience (VRC) have significant influence on the VR presence and immersive flow. Moreover, the practical and theoretical implications have been discussed in the current research to prevent IAB.
Static Obstacle Avoidance for Rover Vehicles using Model Predictive Controller

In this paper, a Model Predictive Controller (MPC) is implemented to control and predict the future position, angle, and velocity of Rover Vehicle (RV) for avoiding static obstacle. A communication has been established between IoT device and Rover Vehicle (RV), where real time data has been fetched. In the architecture of these vehicles, there are three basic modules such as perception, network, and application. In the perception module, different types of sensors, such as, Ultrasonic Sensor (US), Lidar Sensor (LS) are embedded in it. In the network, devices such as Bluetooth, Wi-Fi relate to main server. Lastly, Vehicle Global ID is generated for communicating different devices on the application module present here. A Simulink model is created in the MATLAB platform where eight different obstacle cases of variable length and width have been considered. By applying the adaptive property of MPC, position and heading angle is optimized at each instant by considering area of static obstacles. Simulation results show that the proposed MPC, the controller has achieved better performance than other controllers like PID and Model Reference Adaptive Controller (MRAC).
Utilizing mobile-learning and CAP(E) lesson framework in improving the productive skills of learners in a hybrid environment

Purpose Research advocates for the use of good teaching practices and approaches while integrating technology in digitally enhanced learning. This is on the premise that previous studies on mobile learning have neglected this aspect of technology integration resulting in numerous challenges. Moreover, there is evidence in the literature showing a scarcity of studies on the use of mobile learning in teaching productive skills. On the other hand, linguists recommend the use of responsive lesson design frameworks in language teaching, claiming effectiveness in teaching all language skills. However, responsive lesson design frameworks are yet to be implemented in a classroom setting. To bridge these gaps in scientific literature, our study decides to utilize the CAPE framework as a good teaching method for improving the productive skills of students in mobile-based instruction. Design/methodology/approach This study utilizes a mixed-methods research design with an experimental approach. Post-tests and interviews were employed to elicit information from the student-participants on the objective of the study. Findings Following the analysis of the collected data, notable findings were obtained. While there was evidence to show that the students perceived m-learning as boring and ineffective when incorporated with a traditional lesson framework, our study unveiled that students showed a different perception when incorporated with the CAPE framework. Practical implications Our study unveils that integrating responsive lesson frameworks with m-learning improves the speaking and writing skills of students. Originality/value This study provides empirical evidence to show the role of good teaching practices like integrating responsive LDFs and mobile learning in improving the productive skills of students. This study is the first to investigate the integration of CAPE and mobile learning in enhancing expressive skills.
The Role of Artificial Intelligence in the Insurance Industry of India

Introduction: Artificial intelligence (AI), the engineering of brilliant machinery, performs intelligent human intelligence tasks, such as learning and problem-solving. Insurance is a financial protection policy either for individuals or entities to reimburse losses from the insured company. The role of AI in insurance always helps enhance customer services and understand their behaviour.Purpose: This chapter aims to determine the role of AI in the insurance industry in India. The insurance industry is expanding very fast, and to further increase its horizons, the part of the technology of AI is essential. However, this sector has initiated using AI technology and is expanding its scope to benefit the customers.Methodology: The authors selected research papers of the last five years to review and determine how the technology changed during the period and how an increase in AI benefits the industry and facilitates delivering the best services, and understanding the customer’s needs and behaviour.Findings: It has been found that the industry is moving very fast and adopting the AI technology methods to enhance customer services, betterment for growing India, and serve insurance services to the nation efficiently.
Towards High-Fidelity Face Self-Occlusion Recovery via Multi-View Residual-Based GAN Inversion

Face self-occlusions are inevitable due to the 3D nature of the human face and the loss of information in the projection process from 3D to 2D images. While recovering face self-occlusions based on 3D face reconstruction, e.g., 3D Morphable Model (3DMM) and its variants provides an effective solution, most of the existing methods show apparent limitations in expressing high-fidelity, natural, and diverse facial details. To overcome these limitations, we propose in this paper a new generative adversarial network (MvInvert) for natural face self-occlusion recovery without using paired image-texture data. We design a coarse-to-fine generator for photorealistic texture generation. A coarse texture is computed by inpainting the invisible areas in the photorealistic but incomplete texture sampled directly from the 2D image using the unrealistic but complete statistical texture from 3DMM. Then, we design a multi-view Residual-based GAN Inversion, which re-renders and refines multi-view 2D images, which are used for extracting multiple high-fidelity textures. Finally, these high-fidelity textures are fused based on their visibility maps via Poisson blending. To perform adversarial learning to assure the quality of the recovered texture, we design a discriminator consisting of two heads, i.e., one for global and local discrimination between the recovered texture and a small set of real textures in UV space, and the other for discrimination between the input image and the re-rendered 2D face images via pixel-wise, identity, and adversarial losses. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods in face self-occlusion recovery under unconstrained scenarios.
«Digital Foresight» – educational practice with a collective work designer in a hybrid learning environment

Introduction. The article deals with the issues related to the aspects of methodological support of pedagogical activity in the digital educational environment and the organization of educational cooperation using the designer of collective work in the conditions of hybrid learning. Materials and Methods. The designer «Digital Foresight» is a set of cards for designing a lesson; when implementing training in a hybrid format, a symbiosis of already well-known and proven technologies becomes relevant: distance and e-learning, integration of elements of mixed learning; the method of generalizing pedagogical experience in remote, hybrid and mixed formats, as well as mass surveys of participants in the educational process. Results. The paper substantiates the relevance of the introduction of the technology of educational cooperation in the digital educational environment and the importance of organizing group work of teachers in a hybrid learning environment. Discussion and Conclusions. The materials of the article are aimed at updating the model of the methodological support system in conditions of uncertainty; the use of the designer in professional activity will increase the competence of the teacher in the field of digital technologies.
Online Reflective Learning for Robust Medical Image Segmentation

Deep segmentation models often face the failure risks when the testing image presents unseen distributions. Improving model robustness against these risks is crucial for the large-scale clinical application of deep models. In this study, inspired by human learning cycle, we propose a novel online reflective learning framework (RefSeg) to improve segmentation robustness. Based on the reflection-on-action conception, our RefSeg firstly drives the deep model to take action to obtain semantic segmentation. Then, RefSeg triggers the model to reflect itself. Because making deep models realize their segmentation failures during testing is challenging, RefSeg synthesizes a realistic proxy image from the semantic mask to help deep models build intuitive and effective reflections. This proxy translates and emphasizes the segmentation flaws. By maximizing the structural similarity between the raw input and the proxy, the reflection-on-action loop is closed with segmentation robustness improved. RefSeg runs in the testing phase and is general for segmentation models. Extensive validation on three medical image segmentation tasks with a public cardiac MR dataset and two in-house large ultrasound datasets show that our RefSeg remarkably improves model robustness and reports state-of-the-art performance over strong competitors.