Reviewerly/exHarmony huggingface dataset:
Healthcare Employee Engagement Using the Internet of Things: A Systematic Overview

Introduction: Healthcare facilities have witnessed deterioration, limited employee engagement, and communication gaps due to a lack of wireless technology. The Internet makes work and life quicker and more intelligent. The Internet of Things (IoT) is a scheme of interconnection equipped with unique identifiers in recent years. Artificial intelligence (AI) and IoT advancement allow employees to develop competent and predictive services and solutions in human resource (HR) practices. This chapter has been formulated to summarise and classify the existing research and better understand the past, present, and future of employee engagement by improving IoT interrelated devices in the healthcare industry.Purpose: This study aims to categorise and overcome the challenges involved in HR practices. Effectively embracing IoT application-connected devices in the healthcare industry can enhance human resources management’s (HRM) role and measure performance assessment to improve employee engagement and productivity.Methodology: In this study, the authors develop propositions dependent on a theory-based review. A systematic analysis was applied to minimise the challenges of HRM. The subject-related articles from different journal sources, like Scopus, Emerald, Web of Science, Springer, etc., were analysed based on engagement criteria. It was graphically recorded in a collective and informative way to emphasise the review outcomes. The study has presented the positive impacts of AI and IoT on engagement in health care.Summary: This chapter accumulated theory-based knowledge about healthcare employee engagement and how IoT-based technology like AI can optimise employees’ engagement effectively. Further, it draws comparative benefits for a workforce to execute performance advancements and create future progressive aspects for healthcare employees.
Multimodal registration of ultrasound and MR images using weighted self-similarity structure vector

Multimodal registration of 2D Ultrasound (US) and 3D Magnetic Resonance (MR) for fusion navigation can improve the intraoperative detection accuracy of lesion. However, multimodal registration remains a challenge because of the poor US image quality. In the study, a weighted self-similarity structure vector (WSSV) is proposed to registrate multimodal images. The self-similarity structure vector utilizes the normalized distance of symmetrically located patches in the neighborhood to describe the local structure information. The texture weights are extracted using the local standard deviation to reduce the speckle interference in the US images. The multimodal similarity metric is constructed by combining a self-similarity structure vector with a texture weight map. Experiments were performed on US and MR images of the liver from 88 groups of data including 8 patients and 80 simulated samples. The average target registration error was reduced from 14.91 ± 3.86 mm to 4.95 ± 2.23 mm using the WSSV-based method. The experimental results show that the WSSV-based registration method could robustly align the US and MR images of the liver. With further acceleration, the registration framework can be potentially applied in time-sensitive clinical settings, such as US-MR image registration in image-guided surgery.
Unsupervised Domain Adaptation Semantic Segmentation of High-Resolution Remote Sensing Imagery With Invariant Domain-Level Prototype Memory

Semantic segmentation is a key technique involved in automatic interpretation of high-resolution remote sensing (HRS) imagery and has drawn much attention in the remote sensing community. Deep convolutional neural networks (DCNNs) have been successfully applied to the HRS imagery semantic segmentation task due to their hierarchical representation ability. However, the heavy dependence on a large number of training data with dense annotation and the sensitiveness to the variation of data distribution severely restrict the potential application of DCNNs for the semantic segmentation of HRS imagery. This study proposes a novel unsupervised domain adaptation semantic segmentation network (MemoryAdaptNet) for the semantic segmentation of HRS imagery. MemoryAdaptNet constructs an output space adversarial learning scheme to bridge the domain distribution discrepancy between the source domain and the target domain and to narrow the influence of domain shift. Specifically, we embed an invariant feature memory module to store invariant domain-level prototype information because the features obtained from adversarial learning only tend to represent the variant feature of current limited inputs. This module is integrated by a category attention-driven invariant domain-level memory aggregation module to current pseudo-invariant feature for further augmenting the representations. An entropy-based pseudo label filtering strategy is used to update the memory module with high-confident pseudo-invariant feature of current target images. Extensive experiments under three cross-domain tasks indicate that our proposed MemoryAdaptNet is remarkably superior to the state-of-the-art methods. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RS-CSU/MemoryAdaptNet-master</uri> .
THE LEGAL CONTROLS FOR THE INDECENT ASSAULT OFFENSE USING ELECTRONIC MEANS BY JORDANIAN LEGISLATION

The significant progress in the field of information technology has reduced the distances between individuals and created a digital society that has effectively contributed to the development of societies and made the world more flexible and simple; however, the rapid growth of information technology has been met with negative attitudes by the misuse of this technology, including the infringement of the protection rights of individuals by committing various cyber offenses, including the offense of indecent assault using electronic means, accordingly, the Jordanian Penal Law will deal with this crime in consultation with the Jordanian Cybercrime Law. Therefore, the applied approach was followed in this study. As a result, it came out with several results and recommendations, the most important of which is the need for the Jordanian legislators to intervene with a legal text that defines acts and forms of conduct in the small offense of indecent assault and sets its punishment to prevent conflict and by the principle of "nullum crimen, nulla poena sine lege."
Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence

Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers.
Unified Transformer Network for Multi-Weather Image Restoration

Vision based applications routinely involve restoration as a preprocessing step, making it impossible to have separate architectures for different types of weather restoration. But, most of the existing methods focus on weather specific application. Further, the methods for multi-weather image restoration have high computational constraints. To overcome these limitations, we propose a compact transformer based network, with 4.5M parameters (1/10 $$^{th}$$ of the existing method) for unified (simultaneous) removal of rain, snow and hazy effect with single set of trained parameters. We propose two parallel streams to handle the degradations: First, original resolution transformer stream (ORTS) focuses mainly on extracting fine level features through original scales of the inputs. Second, multi-level feature aggregation stream (MFAS) learns different sizes of the weather degradations. Further, it also uses coarse outputs from the first stream and utilizes edge boosting skip connections (EBSC) for propagating crucial edge details essential for image restoration. Finally, we present a memory replay training approach for generalization of the proposed network on multi-weather degraded scenarios. Substantial experiments on synthetic as well as real-world images, along with extensive ablation studies, demonstrate that the proposed method performs competitively with the existing methods for multi-weather image restoration. The code is provided at https://github.com/AshutoshKulkarni4998/UMWTransformer .
Multi-Agent Coverage Path Planning using a Swarm of Unmanned Aerial Vehicles

In recent years, rapid technological advancements in unmanned aerial vehicles (UAVs) have propelled their applications to a wide range of areas such as agriculture, mapping & surveying, surveillance, and many more. A swarm of UAVs can efficiently accomplish the goals rather than a single UAV due to its ability to cover a larger area. Multi-agent coverage path planning (CPP) is the process of determining efficient coverage paths for the swarm of UAVs to completely cover an area of interest. A cellular grid is formed using hexagonal decomposition to ensure complete coverage, with the centroid of each cell acting as a waypoint. The k-means clustering algorithm is implemented to divide the set of waypoints such that the workload is uniformly distributed among the UAVs. Multi-agent CPP is transformed into multiple single-agent CPP, thereby alleviating the exploratory complexity of multi-agent CPP. A mixed-integer linear programming based vehicle routing problem is formed to optimize the path of each UAV considering various constraints.
A novel hyperspectral remote sensing tool for detecting and analyzing human materials in the environment: a geoenvironmental approach to aid in emergency response

Abstract Geology is the backdrop against which environmental disasters, emergencies and conflict occur. A search and discovery tool is developed to aid in investigations, search and rescue operations, and emergency response operations. The primary goal is to provide a software tool for the interpretations of hyperspectral remote sensing images in the context of investigations and emergency response operations in a wide range of outdoor settings (e.g., streams, semiarid settings, urban). Data is also translatable to indoor forensic hyperspectral imaging and reflective spectroscopy work. Data was collected on geologic materials, human materials, and other relevant items by staff and the accompanying software tool was developed by L3Harris Geospatial. A description of the spectral search and discovery tool is provided and is a customized ENVI extension written using the IDL programming language designed to help users find custom targets within hyperspectral imagery. The tool is free of charge and can be accessed here .
Natural and Artificial Dynamics in GNNs: A Tutorial

In the big data era, the relationship between entities becomes more complex. Therefore, graph (or network) data attracts increasing research attention for carrying complex relational information. For a myriad of graph mining/learning tasks, graph neural networks (GNNs) have been proven as effective tools for extracting informative node and graph representations, which empowers a broad range of applications such as recommendation, fraud detection, molecule design, and many more. However, real-world scenarios bring pragmatic challenges to GNNs. First, the input graphs are evolving, i.e., the graph structure and node features are time-dependent. Integrating temporal information into the GNNs to enhance their representation power requires additional ingenious designs. Second, the input graphs may be unreliable, noisy, and suboptimal for a variety of downstream graph mining/learning tasks. How could end-users deliberately modify the given graphs (e.g., graph topology and node features) to boost GNNs' utility (e.g., accuracy and robustness)? Inspired by the above two kinds of dynamics, in this tutorial, we focus on topics of natural dynamics and artificial dynamics in GNNs and introduce the related works systematically. After that, we point out some promising but under-explored research problems in the combination of these two dynamics. We hope this tutorial could be beneficial to researchers and practitioners in areas including data mining, machine learning, and general artificial intelligence.
Measuring Interpersonal Trust towards Virtual Humans with a Virtual Maze Paradigm

Virtual humans, including virtual agents and avatars, play an increasingly important role as VR technology advances. For example, virtual humans are used as digital bodies of users in social VR or as interfaces for AI assistants in online financing. Interpersonal trust is an essential prerequisite in real-life interactions, as well as in the virtual world. However, to date, there are no established interpersonal trust measurement tools specifically for virtual humans in virtual reality. This study fills the gap, by contributing a novel validated behavioural tool to measure interpersonal trust towards a specific virtual social interaction partner in social VR. This validated paradigm is inspired by a previously proposed virtual maze task that measures trust towards virtual characters. In the current study, a variant of this paradigm was implemented. The task of the users (the trustors) is to navigate through a maze in virtual reality, where they can interact with a virtual human (the trustee). They can choose to 1) ask for advice and 2) follow the advice from the virtual human if they want to. These measures served as behavioural measures of trust. We conducted a validation study with 70 participants in a between-subject design. The two conditions did not differ in the content of the advice but in the appearance, tone of voice and engagement of the trustees (alleged as avatars controlled by other participants). Results indicate that the experimental manipulation was successful, as participants rated the virtual human as more trustworthy in the trustworthy condition than in the untrustworthy condition. Importantly, this manipulation affected the trust behaviour of our participants, who, in the trustworthy condition, asked for advice more often and followed advice more often, indicating that the paradigm is sensitive to assessing interpersonal trust towards virtual humans. Thus, our paradigm can be used to measure differences in interpersonal trust towards virtual humans and may serve as a valuable research tool to study trust in virtual reality.
Underwater optical image enhancement based on super-resolution convolutional neural network and perceptual fusion

Underwater optical images often have serious quality degradations and distortions, which hinders the development of underwater optics and vision systems. Currently, there are two mainstream solutions: non-learning based and learning-based. Both have their advantages and disadvantages. To fully integrate the advantages of both, we propose an enhancement method based on superresolution convolutional neural network (SRCNN) and perceptual fusion. First, we introduce a weighted fusion BL estimation model with a saturation correction factor (SCF-BLs fusion), the accuracy of image prior information is improved effectively. Next, a refined underwater dark channel prior (RUDCP) is proposed, which combines guided filtering and an adaptive reverse saturation map (ARSM) to restore the image, which not only preserves edge details but also avoids the interference of artificial light. Then, the SRCNN fusion adaptive contrast enhancement is proposed to enhance the colour and contrast. Finally, to further enhance image quality, we employ efficient perceptual fusion to blend the different resulting outputs. Extensive experiments demonstrate that our method has outstanding visual results in underwater optical image dehazing, color enhancement and is artefact- and halo-free.
SERIOUS GAMES IN ENGINEERING EDUCATION

The topic of Serious Games in engineering education, especially in universities have not been fully explored, even though Serious Games have been known as an educational method for a very long time. This article touches upon the issue of applying methods of Serious Games for future engineering education. This article presents an analysis of modern videogames, that can be recommended for engineering students and practices that these games use, that can be used for creating such kind of in-direct learning experience. Modern well-designed puzzle videogames enhance problem-solving skills, and in this article, particular methods of how to create a good game for effective learning are inspected. Serious Games can be greatly effective in studying such disciplines as programming and can be used to grant future engineers better understanding of how their theoretical knowledge can be applied on practice. It can be very beneficial for a student to be able to test out their skills and knowledge in a safe simulated environment that does not have as much stress, as an actual test. Games are often seen as not the best way of studying, but there are a great way to motivate students. The effectiveness of interactive eLearning for engineers is brought up to prove that gaming can be a very efficient practice and should be considered as a part of the university course. In the end of this article, there are suggestions of possible methodology for universities to create their own educational games on the base of existing engineering subjects, using best practices from modern videogames that are popular among university students demographic.
LIBQUEST - REVITALIZE LIBRARIES AND READING THROUGH GAMIFICATION

Digital technology has already become a necessity of everyday life for any average person in developed countries. Starting from simple tasks like calculus or grammar, to more complex ones like navigation, simulations and appointments, nowadays we rely more and more on technology like smartphones, smartwatches and smart assistants. As this may seem to improve our life and help us in everyday tasks, it may have a negative impact in other areas like culture, education, focus, sustained brain-use or imagination. Libraries, a pillar of culture and education, lose popularity every day in favour of digital content and feeds. This article pioneers the design and the implementation of a modern cultural space to encourage reading and involvement in cultural activities through gamification in a mixed reality context. It is part of the Lib2Life research project that is being carried out by a strategic partnership between national libraries and universities to explore ways that can revitalize libraries and cultural heritage using digital technology. The paper presents a detailed solution design and the refining iterations on the initial concept of adding mixed reality game quests inside national libraries activity. The quests will get users to involve in captivating adventure games the will require them to use digital technology alongside real libraries or cultural events in order to solve the puzzle. Furthermore, the solution has a user-centered perspective, where the user can both access the content and contribute with his own content, that increases the potential to create virtual communities that benefit and contribute at the same time. This work is supported by a grant of the Romanian Ministery of Research and Innovation, CCCDI - UEFISCDI, project number PN-III-P1-1.2-PCCDI-2017-0689 / ,,Lib2Life- Revitalizarea bibliotecilor si a patrimoniului cultural prin tehnologii avansate" / "Revitalizing Libraries and Cultural Heritage through Advanced Technologies", within PNCDI III.
Data Sharing Enters a New Era

In this commentary, the authors discuss the challenges in data sharing that were not addressed in the recent directive on data availability from the White House Office of Science and Technology Policy.
Analysis of Credit Card Fraud Data Using Various Machine Learning Methods

The credit card details of customers are very important. Due to online transactions, fraud is also increasing by those who have unauthorized access. Various machine learning algorithms are used to perform an analysis on the credit card fraud information. The analysis of the transaction details can be done during the transaction, or the details can be fetched from stored databases. In this chapter, different tree methods are used to perform the analysis and evaluate the performance. An accuracy of about 99.9% without Principal Component Analysis (PCA) and 99.8% of accuracy with PCA is obtained. The analysis is performed using various algorithms like the K-nearest neighbor method (KNN), the support vector machine (SVM) method, the Gaussian naive Bayesian algorithm, and the logistic regression (LR) algorithm. The analysis was done using the option of principal component analysis and without principal component analysis with good results of about 99.9% accuracy obtained for the SVM, KNN, and LR algorithms.
MLOps: Overview of Current State and Future Directions

This article presents and discuss the industrialization process of ML (Machine Learning) projects with a focus on the principles of MLOps (Machine Learning Operations) and the challenges encountered when putting an ML project into production. The paper also proposes a set of tools used in an MLOps context to facilitate the deployment of ML projects and their production release. This paper is a guide to discover the MLOps domain in its theoretical (MLOps concepts, pipeline and life cycle) and practical (technical and tools) aspects. MLOps must provide answers to the use of ML applications hosted on servers with high performance, also for applications embedded in equipment with minimal sizing, since we are talking about the fourth industrial revolution with the increase of number of sensors in the world generating a mass of data that must be efficiently processed and analyzed.