Reviewerly/exHarmony huggingface dataset:
Use of Home-Laboratory Chemistry Sets during the COVID Pandemic to Impart Learning in a Public University: Comparison of Two Cohorts

The COVID pandemic forced teachers to be imaginative, especially with purely practical subjects such as laboratory chemistry. In the Universidad Nacional Autónoma de México, the first-semester chemistry laboratory of the Biology course was taught with a chemistry set to be used at home. The use of the kit was evaluated from the perspective of knowledge acquired, motivation, and attitude. For comparison, groups that did not have the kit were also evaluated. The results for the fall semester 2020, showed that students who were supplied with a home kit remained motivated and had greater confidence in their skills to cope in the chemistry laboratory than did those without it. Family involvement in setting up the materials for the experiments had a beneficial effect on the family dynamic while they were enduring lockdown. The home kit experience was again assessed in the fall semester of 2021 with a new cohort of students; these students were markedly less motivated that those of the previous year’s intake, and their attitudes were less positive. Socioeconomic background showed differences for the first cohort on their learning scores unlike the second cohort, probably attributable to depression, boredom, fear, or uncertainty. The teachers found that their interaction with the students during the experiments with the chemistry kit reinforced theoretical concepts. The experience gained with the chemistry set during the pandemic, together with feedback from the surveys applied to the teachers, gave us the necessary confidence to propose this approach to conduct on formal virtual classes.
Solving simultaneous target assignment and path planning efficiently with time-independent execution

Real-time planning for a combined problem of target assignment and path planning for multiple agents, also known as the unlabeled version of multi-agent path finding (MAPF), is crucial for high-level coordination in multi-agent systems, such as pattern formation by robot swarms. This paper studies two aspects of unlabeled-MAPF: (1) offline scenario: solving large instances using centralized approaches with a small computation time, and (2) online scenario: executing unlabeled-MAPF, despite the timing uncertainties real robots face. For this purpose, we propose TSWAP, a novel sub-optimal complete algorithm that takes an arbitrary initial target assignment and then repeats one-timestep path planning with target swapping. TSWAP can adapt to both offline and online scenarios. We empirically demonstrate that offline TSWAP is highly scalable, and provides near-optimal solutions while reducing runtime by orders of magnitude compared with existing approaches. In addition, we present the benefits of online TSWAP, such as delay tolerance, through real-robot demonstrations.
A Systematic Review of Human Fall Detection Algorithms Using Machine Learning

In the past century, due to healthy lifestyle of people, they suffered from very less disease. As era changes, the life style of people also changes. People are taking unhealthy food in today's era owing to a busy schedule, and they are suffering from various diseases such as human falls. The most common causes of human falls among the elderly include high (low) blood pressure, arthritis, osteoarthritis, coronary heart disease, dementia, depression, and stroke. As a result, intervening early in the event of a fall could cut morbidity and death dramatically. In recent years, the machine learning-based human fall model has made significant progress. A human fall detection model assists the elderly by recognizing the fall and saving their lives. Machine learning (ML)-based models have now become an important research subject because of its capacity to proactively assist the elderly. In this proposed study, the most recent research trends in fall detection system using the machine learning techniques have been analyzed. Moreover, the analysis focuses on the shortcomings associated with ML-based tactics in order to conduct deeper research. The goal of the study is to find out how various type of machine learning algorithms are detecting fall effectively. It is a comprehensive review of existing fall detection models and trends.
What Do Users Think About Abstractions of Ontology-Driven Conceptual Models?

In a previous paper, we proposed an algorithm for ontology-driven conceptual model abstractions [18]. We have implemented and tested this algorithm over a FAIR Catalog of such models represented in the OntoUML language. This provided evidence for the correctness of the algorithm's implementation, i.e., that it correctly implements the model transformation rules prescribed by the algorithm, and its effectiveness, i.e., it is able to achieve high compression (summarization) rates over these models. However, in addition to these properties, it is fundamental to test the validity of this algorithm, i.e., that it achieves what it is intended to do, namely provide summarizing abstractions over these models whilst preserving the gist of the conceptualization being represented. We performed three user studies to evaluate the usefulness of the resulting abstractions as perceived by modelers. This paper reports on the findings of these user studies and reflects on how they can be exploited to improve the existing algorithm.
Brand Building in Metaverse

In recent years, the rise of the metaverse has presented businesses with a new platform for brand building. Metaverse is a virtual world that allows people to interact with each other in a simulated environment. It is becoming increasingly popular due to the advancements in technology and the pandemic situation. In this chapter, the authors aim to explore the strategies that businesses can use to build their brand in the metaverse. They will also investigate the benefits of brand building in the metaverse and its impact on businesses.
Commercial Metaverse and the Customer

The metaverse has made customer experience personalization a top priority ever since it was founded. Users can customize their avatars and live experiences to create something truly unique. unlike any other Brands will be able to offer augmented reality in the metaverse as well. Therefore, brands must invest in enhancing the customer experience throughout the entire buying journey, including discovery, acquisition, and decision-making. To accomplish this, they must gather and examine audience data with the intention of personalization. Our research has two main goals. The first is Discussing the potential of the metaverse for digital marketing and examining the emerging technologies that can be used in marketing. The second goal of this research is to determine how Moroccan businesses could utilize metaverse in digital marketing, beyond the technical aspects and tools. What position should Moroccan businesses take in the world of cutting-edge technologies? A questionnaire was sent to 20 companies to answer the research questions.
Challenges and Opportunities to the Media and Entertainment Industry in Metaverse

Metaverse is the buzz word in the entertainment and media industry. Metaverse is a virtual world that offers another dimension for interacting with other users. It acts as a tool to complement the actual world by means of holograms and avatars. It has developed as an updated and embodied version of the internet. It is expected to be a highly immersive experience that is prodigious, new, and a successor to the mobile internet. In this chapter, the main study focus will be on the emerging opportunities and challenges to the metaverse in media and entertainment industry and will provide more detailed scoping review of the concept. The findings from the literature conceptualize the prevailing opportunities and upcoming challenges for metaverse in media and entertainment industry and offer guidance in the form of future research directions.
Merging Big Data With the Metaverse

A shared virtual universe called a metaverse is produced when various virtual worlds and augmented reality settings collide. The enormous amounts of data, processing power, and connectivity needed for a fully realized metaverse may be too much for traditional computer systems and networking technologies to handle. In order to enable the metaverse's expansion and evolution into a wholly immersive and linked reality, there is an increasing need to create new technologies, protocols, and architectures. The authors in this chapter suggest a brand-new design with a number of layers, starting with the data collecting layer and ending with the data governance layer. This chapter makes an effort to suggest a novel architecture that is made up of numerous layers, from the layer of data collecting to the layer of data governance. Although the idea has been around for a while, current technological developments have made it easier than ever to build a real metaverse.
A Study of AR and VR in the Real World

The most well-liked and developed technologies for the future are augmented reality and virtual reality, known as AR and VR. When you use high-tech equipment, you just enter another universe and engage with it. With data, visuals, and customised experiences superimposed on the real world, virtual reality transports us to parallel realms, and augmented reality brings the real world to life. In virtual reality, it is easy to observe how a software programme can produce a virtual environment that is mostly driven by sight and sound. The user's experience of virtual reality is presented to them in a way that makes them see it as real. AR, on the other hand, transfers aspects from the virtual world to the physical one. Augmented reality serves as a link between the physical and digital worlds. This chapter focuses on better understanding of augmented reality and virtual reality concepts. The chapter discusses the functions of AR and VR, their impact on society, the challenges of implementing AR and VR, and their impact on customer purchase behavior.
CDM: A coupled deformable model for image segmentation with speckle noise and severe intensity inhomogeneity

Speckle noise and intensity inhomogeneity are always challenging issues in the area of image segmentation, especially when both difficulties appear simultaneously. Consequently, the majority of existing deformable models yield inadequate results in this situation. This paper aims to address these issues by combining a bias correction term and a despeckling term into a single variational level set architecture, which not only corrects the severe intensity inhomogeneity but also performs image despeckling and segmentation simultaneously. Specifically, edge and region synergetic terms are defined to correct the severe intensity inhomogeneity during image segmentation that eradicates their shortcomings as well as retains their advantages. Further, a despeckling term is constructed, which effectively suppresses the noise while enhancing image details. Since the lack of thorough investigation on despeckling of images degraded by Rayleigh noise distribution, which usually appear in ultrasound (US) images, so we included it in the coupled deformable model (CDM). Moreover, region-based terms eradicate complex re-initialization and numerical instability during the level set evolution, as a result, no distance regularization term is needed. Additionally, Schauder’s fixed point theorem is used to demonstrate the well-posedness of the present system. Extensive numerical experiments on real and synthetic images with speckle noise and severe inhomogeneous intensity demonstrate that the CDM exhibits superior results compared to classical and some recent deformable models in terms of accuracy, robustness, and various quality measures.
BEAUD: A Browser Extension to Automatize End-User Deeds

Web Augmentation allows anyone, computer-literate or not, to adapt the content, style or behavior of any web page. Searching for information is not always an undemanding task. The information searches carried out by users cause frustration when they are carried out frequently. Even more so when the interactions accomplished are numerous or when these interactions lead to errors. In this paper, we present BEAUD, a Web Augmentation extension that allows automating the information search processes that users make on the web. This tool drastically reduces the interactions that users make to the minimum, reducing errors and frustration.
Augmented Reality Surgical Navigation System Integrated with Deep Learning

Most current surgical navigation methods rely on optical navigators with images displayed on an external screen. However, minimizing distractions during surgery is critical and the spatial information displayed in this arrangement is non-intuitive. Previous studies have proposed combining optical navigation systems with augmented reality (AR) to provide surgeons with intuitive imaging during surgery, through the use of planar and three-dimensional imagery. However, these studies have mainly focused on visual aids and have paid relatively little attention to real surgical guidance aids. Moreover, the use of augmented reality reduces system stability and accuracy, and optical navigation systems are costly. Therefore, this paper proposed an augmented reality surgical navigation system based on image positioning that achieves the desired system advantages with low cost, high stability, and high accuracy. This system also provides intuitive guidance for the surgical target point, entry point, and trajectory. Once the surgeon uses the navigation stick to indicate the position of the surgical entry point, the connection between the surgical target and the surgical entry point is immediately displayed on the AR device (tablet or HoloLens glasses), and a dynamic auxiliary line is shown to assist with incision angle and depth. Clinical trials were conducted for EVD (extra-ventricular drainage) surgery, and surgeons confirmed the system's overall benefit. A "virtual object automatic scanning" method is proposed to achieve a high accuracy of 1 ± 0.1 mm for the AR-based system. Furthermore, a deep learning-based U-Net segmentation network is incorporated to enable automatic identification of the hydrocephalus location by the system. The system achieves improved recognition accuracy, sensitivity, and specificity of 99.93%, 93.85%, and 95.73%, respectively, representing a significant improvement from previous studies.
Analysis of emergency remote teaching in formal education: crosschecking three contemporary techno-pedagogical frameworks

During the COVID-19 pandemic outbreak many countries around the world were forced to turn to Emergency Remote Teaching (ERT) and upscale the use of digital technologies for learning, teaching and assessment. The current study analysed field reports from 89 elementary and secondary Hebrew-speaking and Arabic-speaking information and communication technology schools in Israel, representing the cultural, ethnic, and religious diversity of the education system. The qualitative analysis of the collected data was based on three well established contemporary models of technology integration and Digital Competence of Educators (DigCompEdu): the International Society for Technology in Education, Technological Pedagogical Content Knowledge and DigCompEdu. The analysis (n&nbsp;= 872 statements) yielded aspects in the teachers’ reports that correspond with the theoretical models, alongside aspects that extend these models to ERT and aspects that were missing from the reports. Finally, based on our findings and previous work we suggested a comprehensive framework for ERT that can be used to design teachers’ professional development necessary for effective remote teaching in both emergency and routine times.
Application of Multitemporal Change Detection in Radar Satellite Imagery Using REACTIV-Based Method for Geospatial Intelligence

Constant monitoring of airports and aviation bases has become one of the priorities in today's strategic security. It results in the necessity to develop the potential of satellite Earth observation systems and to intensify the efforts to develop the technologies of processing SAR data, in particular in the aspect of detecting changes. The aim of this work is to develop a new algorithm based on the modified core REACTIV in the multitemporal detection of changes in radar satellite imagery. For the purposes of the research works, the new algorithm implemented in the Google Earth Engine environment has been transformed so that it would meet the requirements posed by imagery intelligence. The assessment of the potential of the developed methodology was performed based on the analysis of the three main aspects of change detection: analysis of infrastructural changes, analysis of military activity, and impact effect evaluation. The proposed methodology enables automated detection of changes in multitemporal series of radar imagery. Apart from merely detecting the changes, the method also allows for the expansion of the change analysis result by adding another dimension: the determination of the time of the change.
Comprehensive Survey of Sensor Data Verification in Internet of Things

The Internet of Things (IoT) has been a critical and emerging technology platform enabling the development and deployment of smart devices to solve real-world challenges and issues.In addition, IoT applications have gathered various and numerous data from various sources, such as sensor data from IoT devices, system data, status information of IoT devices, and others.However, the quality of sensor data should be thoroughly verified to exploit the benefits of sensor data fully.Due to the importance of sensor data quality, we comprehensively provide a survey on the verification of IoT sensor data in this paper.Thus, we conduct a survey of sensor data verification in IoT regarding six aspects.We focus on: 1) anomaly classification; 2) sensor data verification frameworks; 3) sensor data verification methods (including anomaly detection and anomaly correction); 4) evaluation methods to assess sensor data verification methods; 5) technology and tools to build sensor data verification systems; and 6) challenges and future research directions.In addition, we identify advantages and disadvantages of the methods in anomaly detection and correction.Hence, this survey provides a comprehensive and better understanding of sensor data verification in IoT, building background knowledge in related topics.
The Effect of Work from Home on Adaptive Performance and Moderated by Humble Leadership

Abstract The Covid-19 pandemic has changed the norms of daily life for individuals around the world. This change causes employees to work from home (WFH). This new norm has opened up the landscape for the advantages of WFH and adapting oneself in performing tasks. In addition to the advantages of WFH, and leadership style and work autonomy also have the potential to assist employees to adapt the flexible work environment. Therefore, the purpose of the study is to examine the effect of WFH on adaptive performance among employees in Malaysia. This study examined the effects of humble leadership, and its moderating effects on the association between WFH and employees’ adaptive performance. The field study was conducted in Malaysia among public and private sector employees and total 200 participants were approached. The Partial Least Square (PLS) technique was used to test the hypothesized relationships among variables. The results of this study indicated that the WFH and humble leadership influenced adaptive performance and employees who perceived high humble leadership strengthen the relationship between WFH and adaptive performance. The study sheds new light on the advantages of WFH and humble leadership on adaptive performance. The theoretical and practical implications are discussed.
Random Walk Graph Auto-Encoders With Ensemble Networks in Graph Embedding

Recently graph auto-encoders have received increasingly widespread attention as one of the important models in the field of deep learning. Existing graph auto-encoder models only use graph convolutional neural networks (GCNs) as encoders to learn the embedding representation of nodes. However, GCNs are only suitable for transductive learning, have poor scalability and shallow models with a poor perceptual field, and have limitations in node feature extraction. To alleviate these problems, we propose to use an adaptive weight integration graph attention network (GAT) and GCN’s random walk graph auto-encoder (EGRWR-GAE) to better learn the embedding representation of nodes. There is a large amount of noise in the graph data, which interferes with feature extraction and the GAT model is sensitive to noisy data, we propose a random walk graph auto-encoder (EGSRWR-GAE) that integrates GAT, GCN, and self-supervised graph attention networks (SuperGAT) using adaptive weights. The effectiveness of our model is well demonstrated by three publicly available datasets (Cora, Citeseer, and Pubmed) with optimizations of up to 2.2% on the link prediction task and up to 12.9% on the node clustering task.
Editorial to the Special Issue “The Impact of Digitalization on International Law”

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Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

The increasing usage of unmanned aerial vehicles (UAVs) and their variants carrying complex weapon systems, known as unmanned combat aerial vehicles (UCAVs), has triggered a global revolution in complex military and commercial operations and has attracted researcher attention from different engineering disciplines in order to solve challenging problems regarding these modern vehicles. Path planning is a challenging problem for UAV and UCAV systems that requires the calculation of an optimal solution by considering enemy threats, total flight length, fuel or battery consumption, and some kinematic properties such as turning or climbing angles. In this study, the immune plasma (IP or IPA) algorithm, one of the most recent nature-inspired intelligent optimization methods, was modified by changing the default plasma transfer operations with a newly proposed technique called the extended treatment approach; extended IPA (ExtIPA) was then introduced as a path planner. To analyze the solving capabilities of the ExtIPA, 16 cases from five battlefield scenarios were tested by assigning different values to the algorithm-specific control parameters. The paths calculated with ExtIPA were compared with the paths found by planners on the basis of other intelligent optimization techniques. Comparative studies between ExtIPA and other techniques allowed for stating that the extended treatment approach significantly contributes to both the convergence speed and qualities of the obtained solutions and helps ExtIPA in performing better than its rivals in most cases.