Reviewerly/exHarmony huggingface dataset:
Machine learning

In the last few years, especially thanks to the recent advancements in the field of Deep Learning, Machine Learning has drawn a lot of attention. One of the main driving factors of the machine learning hype is related to the fact that it offers a unified framework for introducing intelligent decision-making into many domains. In the following chapters, we will introduce examples of possible applications of machine learning to networking scenarios. Here we will lay the foundation to start diving into the machine learning world. We start by discussing various categories of machine learning algorithms. Then we introduce the needed mathematical notation. Finally, we introduce and discuss the most common algorithms for supervised learning and reinforcement learning.
What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics

AI-powered service robots have gradually developed into popular self-service agents in the health care industry. Though prior research has investigated what affects individuals' adoption of AI-powered service robots in the service industry, few have considered patients' continuance intention for AI-powered service robots at hospitals from the perspective of patients' characteristics. Drawing on the Technology Adoption Model and individual characteristics (i.e., trust in technology and independent personality), we develop a framework testing the factors influencing patients' continuance intention (CI) for AI-powered service robots at hospitals with Intelligent Guide Robots as an example. The model is validated using PLS-SEM analysis and data from 543 patients of a 3-A hospital in Eastern China. The study finds that patients' trust in AI techniques and independent personality positively influence their perception of usefulness (PU), ease-of-use (PEOU), and enjoyment (PE), respectively. Moreover, PU, PEOU, and PE are significant predictors of CI toward AI-powered service robots. PEOU and PE partially mediate the relationship between trust/independent personality and CI. The findings imply that organizations in the healthcare could try their best to increase users' trustworthiness toward AI techniques. In addition, developers could continuously upgrade AI-powered service robots to improve patients’ PE and PEOU.
Robust and reversible color image watermarking based on DFT in the spatial domain

With the purpose of protecting the copyright and integrity of color images, a robust and reversible color image watermarking algorithm in the spatial domain fusing discrete Fourier transform (DFT) is proposed. Based on the relationship between the direct current (DC) component of DFT and the spatial-domain pixel values, the proposed quantization technology is used to embed and extract the watermark in this paper. Firstly, color images are represented in RGB color space, and the red, green, and blue channels of the host image are preprocessed for obtaining image blocks of size 2 × 2 and that of the watermark image are preprocessed for obtaining watermark bit sequences. Secondly, the DC component of DFT in each block is calculated and quantized, and the change of the DC component is assigned to each pixel value by the derived formula. Finally, it is completed to embed a 1-bit watermark into one block in the spatial domain. Meanwhile, a matrix that records the magnitude of the change of pixel value in each block is generated to recover the host image, and the related watermark extraction algorithm is also proposed. The experimental results show that this algorithm can completely extract the watermark and restore the host image without loss under the condition of not being attacked, and it has good invisibility, robustness, security, and embedding payload, particularly its running efficiency is also high.
Process and Outcome-based Evaluation between Virtual Reality-driven and Traditional Construction Safety Training

The emerging digital technologies such as virtual reality (VR) provide an alternative platform for construction safety training. In order to explore how digital-driven technologies affect the effectiveness of safety training, there is a need to empirically test the differences in performance between digital 3D/VR safety training and traditional 2D/paper approach. This research conducted a performance evaluation that emphasises both the training process and learning outcomes of trainees based on researchers’ self-developed immersive construction safety training platform. Data related to physiological indicators such as skin resistance were collected to measure safety performance before and after the training. The detailed measurement indicators included nine categories (e.g., immersion, inspiration) to form a holistic list of evaluation dimensions. The findings revealed that VR-driven immersive safety training outperformed the traditional way for trainees in terms of both process and outcome-based indicators. Results confirmed that safety training was no longer constrained by understanding or memorizing 2D information (texts and images). Instead, trainees experienced a stronger sense of embodied cognition through the immersive experience and multi-sensory engagement by interacting with the VR-driven system. By engaging the theory of embodied cognition, this research provides both the empirical evidence and in-depth analysis of how immersive virtual safety training outperforms traditional training in terms of both training process and outcomes.
Systematic Bibliometric Analysis of Research Hotspots and Trends on the Application of Virtual Reality in Nursing

With the emergence of the metaverse, virtual reality, as a digital technology, must be getting hotter. High quality virtual reality related nursing knowledge scene learning is gradually replacing traditional education and intervention skills.This systematic study aimed to gain insights into the overall application of virtual reality technology in the study of nursing.Citations downloaded from the Web of Science Core Collection database for use in VR in nursing publications published from January 1, 2012, to December 31, 2021, were considered in the research. Information retrieval was analyzed using https://bibliometric.com/app, CiteSpace.5.8. R3, and VOS viewer.A total of 408 institutions from 95 areas contributed to relevant publications, of which the United States is the most influential country in this research field. The clustering labels of cited documents were obtained from the citing documents. Virtual simulation, virtual learning, clinical skills, and dementia are the clustering labels of co-cited documents. The burst keywords represented the research frontiers in 2020-2021, which were knowledge and simulation.Virtual nursing has had an impact on both nurses and clients. With the emergence of the concept of the metaverse, the research and application of virtual reality technology in nursing will gradually increase.
Effects of immersive virtual reality classrooms on students' academic achievement, motivation and cognitive load in science lessons

Abstract Background Immersive virtual reality (IVR) applications that support student learning have gained increasing interest. However, empirical studies exploring the educational potential of using IVR in primary school science classrooms are lacking. Objectives This study developed a series of IVR science lessons for primary school students and examined the effects of these lessons on learning outcomes. Methods Our mixed‐method approach employed pre‐ and post‐tests to measure academic achievement, questionnaires to measure motivation and cognitive load, and semi‐structured interviews to further explore students' feelings and attitudes about IVR science lessons. Participants included 362 Grade 4 students assigned to either the experimental (IVR‐based classroom) or control (traditional classroom) groups. The experimental group engaged in IVR science lessons using Head‐Mounted Displays; the control group learned the same material through traditional methods. Results and conclusions The results showed that the IVR‐based classroom significantly improved primary students' academic achievement and science motivation and decreased their cognitive load. Moreover, the experimental group reported positive experiences with the IVR science lessons. Takeaways These findings help unpack the relationships between IVR and academic achievement, science motivation, and cognitive load in primary school science lessons, thus providing insight on integrating IVR into existing classrooms.
An intelligent metaphor-free spatial information sampling algorithm for balancing exploitation and exploration

In this paper, we propose an intelligent scheme and design a spatial information sampling algorithm (SIS) to achieve a balance between exploitation and exploration more efficiently. In SIS, by creatively using a chaotic map, we divide the population of the algorithm into external and internal parts and activate the corresponding exploitation or exploration operations according to the location where the optimal individuals appear in the population. The intelligent scheme makes full use of the spatial information in the solution space where the algorithm’s population is located. It also gives the algorithm the authority to start exploitation or exploration operations autonomously based on the information in the space, which increases the flexibility of the algorithm in complex optimization problems. This novel scheme not only further promotes the intelligence of the algorithm in a simple architecture but also creates a new way of balancing exploitation and exploration in the optimization process. We test the SIS on IEEE CEC2017, IEEE CEC2011, an artificial neural model training problem, and a position optimization problem of wave energy converters. When compared to other state-of-the-art meta-heuristics, the results of the Wilcoxon signed-rank test, Wilcoxon rank-sum test, and Friedman test revealed that SIS possesses superior solutions in terms of global optimality, avoidance of local minima, and solution quality. The source code of SIS is released at https://toyamaailab.github.io/.
Electronic Healthcare Data Record Security Using Blockchain and Smart Contract

An electronic health record (EHR) is a technology that allows you to keep track of your health information. It keeps computerized records of several healthcare organizations. Records are exchanged via enterprise-wide data systems as well as other networking technologies and exchanges. Patients nowadays expect immediate access to their health information. However, the health sector comes with immediate access to data, and there are worries about the privacy and security of medical records of patients. As a result, a blockchain-based solution can assist in resolving this issue. The blockchain has the potential to beat the conventional centralized system, which suffers from a severe lack of accessibility. This is a decentralized technology that has recently been presented to provide a new viewpoint on data security and system efficiency. This paper presents a blockchain-based system that helps the patient’s data be managed and secured into a single record held by the patient. This system was developed using the Ethereum network using Ganache, as well as programming languages, tools, and techniques such as Solidity and web3.js. The measured approach suggested in this paper uses this platform to store patients’ data and execute functions in a decentralized system using blockchain smart contracts. Transactions are communicated through the smart contract once it has been launched, providing security and privacy features. Furthermore, the transaction’s desired alterations can be verified and transmitted to the entire distributed network. There is also a cryptocurrency wallet (MetaMask) that holds a centrally controlled, private information system in which records can be quickly accessed and secured by authorities. Doctors and patients can access the system through the wallet. Moreover, all the data of the doctor and patient will be secured and managed through this system. This proposed system is aimed at doing things such as the following: blockchain technology allows users to obtain the same data at the same time, increasing efficiency, developing credibility, and reducing barriers. It enables the secure storage of data by setting specific access for users. Additionally, this proposed system facilitates the secure transfer of patient medical records. Finally, this paper describes a health-record system and a new protocol that are quick and secure to use. It allows greater openness and ownership of sensitive data to be recorded and secured and also promotes the healthcare sector with blockchain.
Drug Recommendation System for Diabetes Using a Collaborative Filtering and Clustering Approach: Development and Performance Evaluation

Diabetes is a public health problem worldwide. Although diabetes is a chronic and incurable disease, measures and treatments can be taken to control it and keep the patient stable. Diabetes has been the subject of extensive research, ranging from disease prevention to the use of technologies for its diagnosis and control. Health institutions obtain information required for the diagnosis of diabetes through various tests, and appropriate treatment is provided according to the diagnosis. These institutions have databases with large volumes of information that can be analyzed and used in different applications such as pattern discovery and outcome prediction, which can help health personnel in making decisions about treatments or determining the appropriate prescriptions for diabetes management.The aim of this study was to develop a drug recommendation system for patients with diabetes based on collaborative filtering and clustering techniques as a complement to the treatments given by the treating doctor.The data set used contains information from patients with diabetes available in the University of California Irvine Machine Learning Repository. Data mining techniques were applied for processing and analysis of the data set. Unsupervised learning techniques were used for dimensionality reduction and patient clustering. Drug predictions were obtained with a user-based collaborative filtering approach, which enabled creating a patient profile that can be compared with the profiles of other patients with similar characteristics. Finally, recommendations were made considering the identified patient groups. The performance of the system was evaluated using metrics to assess the quality of the groups and the quality of the predictions and recommendations.Principal component analysis to reduce the dimensionality of the data showed that eight components best explained the variability of the data. We identified six groups of patients using the clustering algorithm, which were evenly distributed. These groups were identified based on the available information of patients with diabetes, and then the variation between groups was examined to predict a suitable medication for a target patient. The recommender system achieved good results in the quality of predictions with a mean squared error metric of 0.51 and accuracy in the quality of recommendations of 0.61, which is acceptable.This work presents a recommendation system that suggests medications according to drug information and the characteristics of patients with diabetes. Some aspects related to this disease were analyzed based on the data set used from patients with diabetes. The experimental results with clustering and prediction techniques were found to be acceptable for the recommendation process. This system can provide a novel perspective for health institutions that require technologies to support health care personnel in the management of diabetes treatment and control.
AutoML-ID: automated machine learning model for intrusion detection using wireless sensor network

Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions. However, the major drawback of these techniques, including the manual selection of the optimal hyperparameters, is that they depend highly on the problem and demand application-specific expertise. In this paper, we introduced Automated Machine Learning (AutoML) model to automatically select the machine learning model (among support vector regression, Gaussian process regression, binary decision tree, bagging ensemble learning, boosting ensemble learning, kernel regression, and linear regression model) and to automate the hyperparameters optimisation for accurate prediction of numbers of k-barriers for fast intrusion detection and prevention using Bayesian optimisation. To do so, we extracted four synthetic predictors, namely, area of the region, sensing range of the sensor, transmission range of the sensor, and the number of sensors using Monte Carlo simulation. We used 80% of the datasets to train the models and the remaining 20% for testing the performance of the trained model. We found that the Gaussian process regression performs prodigiously and outperforms all the other considered explainable machine learning models with correlation coefficient (R = 1), root mean square error (RMSE = 0.007), and bias = - 0.006. Further, we also tested the AutoML performance on a publicly available intrusion dataset, and we observed a similar performance. This study will help the researchers accurately predict the required number of k-barriers for fast intrusion detection and prevention.
Ant colony optimization for path planning in search and rescue operations

In search and rescue operations, an efficient search path, colloquially understood as a path maximizing the probability of finding survivors, is more than a path planning problem. Maximizing the objective adequately, i.e., quickly enough and with sufficient realism, can have substantial positive impact in terms of human lives saved. In this paper, we address the problem of efficiently optimizing search paths in the context of the NP-hard optimal search path problem with visibility, based on search theory. To that end, we evaluate and develop ant colony optimization algorithm variants where the goal is to maximize the probability of finding a moving search object with Markovian motion, given a finite time horizon and finite resources (scans) to allocate to visible regions. Our empirical results, based on evaluating 96 variants of the metaheuristic with standard components tailored to the problem and using realistic size search environments, provide valuable insights regarding the best algorithm configurations. Furthermore, our best variants compare favorably, especially on the larger and more realistic instances, with a standard greedy heuristic and a state-of-the-art mixed-integer linear program solver. With this research, we add to the empirical body of evidence on an ant colony optimization algorithms configuration and applications, and pave the way to the implementation of search path optimization in operational decision support systems for search and rescue.
SGKT: Session graph-based knowledge tracing for student performance prediction

Knowledge tracing is a modeling method of students’ knowledge mastery. The deep knowledge tracing (DKT) model uses long short-term memory (LSTM) to process the sequence data of students exercises. However, the LSTM-based model pays more attention to the short-term response status of students while ignoring the long-term learning process. Moreover, existing graph-based knowledge tracing models focus on the static relationship between exercises and skills, ignoring the dynamic graphs formed by students exercises in a session. In this work, we propose a novel knowledge tracing model which is based on an exercise session graph, named session graph based knowledge tracing (SGKT). The session graph is used to model the students’ answering process. In addition, a relationship graph is used to model the relationship between exercises and skills. Then we use gated graph neural networks to obtain the students’ knowledge state from the session graph and use graph convolutional networks to obtain the embedding representations of exercises and skills in the relationship graph. Next, through the interaction mechanism, multiple interaction states composed of knowledge states and embedding representations are obtained. The attention mechanism is used to find the focus from these states and make predictions. Experiments are conducted on three publicly available datasets and the results show that our approach has advantages over some existing baseline methods.
HGNN$^+$: General Hypergraph Neural Networks

Graph Neural Networks have attracted increasing attention in recent years. However, existing GNN frameworks are deployed based upon simple graphs, which limits their applications in dealing with complex data correlation of multi-modal/multi-type data in practice. A few hypergraph-based methods have recently been proposed to address the problem of multi-modal/multi-type data correlation by directly concatenating the hypergraphs constructed from each single individual modality/type, which is difficult to learn an adaptive weight for each modality/type. In this paper, we extend the original conference version HGNN, and introduce a general high-order multi-modal/multi-type data correlation modeling framework called HGNN + to learn an optimal representation in a single hypergraph based framework. It is achieved by bridging multi-modal/multi-type data and hyperedge with hyperedge groups. Specifically, in our method, hyperedge groups are first constructed to represent latent high-order correlations in each specific modality/type with explicit or implicit graph structures. An adaptive hyperedge group fusion strategy is then used to effectively fuse the correlations from different modalities/types in a unified hypergraph. After that a new hypergraph convolution scheme performed in spatial domain is used to learn a general data representation for various tasks. We have evaluated this framework on several popular datasets and compared it with recent state-of-the-art methods. The comprehensive evaluations indicate that the proposed HGNN + framework can consistently outperform existing methods with a significant margin, especially when modeling implicit data correlations. We also release a toolbox called THU-DeepHypergraph for the proposed framework, which can be used for various of applications, such as data classification, retrieval and recommendation.
Constructing a 3D Exponential Hyperchaotic Map with Application to PRNG

Some weaknesses of 1D chaotic maps, such as lacking of ergodicity, multiple bifurcations, dense periodic windows, and short iteration period, limit their practical applications in cryptography. A higher-dimensional chaotic map with ergodicity can solve these problems. Based on 1D quadratic map, a 3D exponential hyperchaotic map (3D-EHCM) is constructed, and its dynamic behaviors, such as phase diagram, Lyapunov exponent spectrum, Kolmogorov entropy (KE), correlation dimension, approximate entropy and randomness, are analyzed and tested. The results demonstrate that the 3D-EHCM has ergodicity in a larger range of control parameter, and its state points have a longer period. To counteract dynamical degradation and make it suitable for a PRNG, the periodic point detection and random impulsive perturbation are applied to lengthen the aperiodic time sequence, and statistical results demonstrate that a full-period sequence can be obtained.