Reviewerly/exHarmony huggingface dataset:
Healthcare professionals’ digital health competence and its core factors; development and psychometric testing of two instruments

Healthcare professionals’ digital health competence is an important phenomenon to study as healthcare practices are changing globally. Recent research aimed to define this complex phenomenon and identify the current state of healthcare professionals’ competence in digitalisation but did not include an overarching outlook when measuring digital health competence of healthcare professionals. The purpose of this study was to develop and psychometrically validate two self-assessed instruments measuring digital health competence and factors associating with it. The study followed three phases of instrument development and validation: 1) conceptualisation and item pool generation; 2) content validity testing and pilot study; and 3) construct validity and reliability testing. The conceptual background of the instruments was based on individual interviews conducted with healthcare professionals (n = 20) and previous systematic reviews. A total of 17 experts assessed the instrument’s content validity. Face validity was evaluated by a group of healthcare professionals (n = 20). Data collection from 817 professionals took place in spring-summer 2022 in nine organisations. Construct validity was confirmed with exploratory factor analysis. Cronbach’s alpha was used to assess the internal consistency of the instruments. The instrument development and validation process resulted in two instruments: DigiHealthCom and DigiComInf. DigiHealthCom included 42 items in 5 factors related to digital health competence, and DigiComInf included 15 items in 3 factors related to educational and organisational factors associated with digital health competence. The DigiHealthCom instrument explained 68.9 % of the total variance and the factors’ Cronbach alpha values varied between 0.91 and 0.97. The DigiComInf instrument explained 59.6 % of the total variance and the factors’ Cronbach alpha values varied between 0.76 and 0.88. The two instruments gave valid and reliable results in psychometric testing. The instruments could be used to evaluate healthcare professionals’ digital health competence and associated factors.
All-optical image edge detection based on the two-dimensional photonic spin Hall effect in anisotropic metamaterial

Optical image processing based on the photonic spin Hall effect (SHE) has been gaining significant attention as a convenient and an accurate way for image edge detection. However, the recent edge imaging techniques depending on optical differentiation is mainly achieved by modulation of one-dimensional photonic SHE. Here, we theoretically predict the two-dimensional photonic SHE in the anisotropic metamaterial, and find that its longitudinal and transverse displacements exhibit spin-dependent property at filling factors with increasing incidence angle. As the transverse and in-plane displacements induced by the photonic SHE can be controlled by the filling factor of the crystal structure, the optical axis angle, and the incident angle, this intrinsical effect can be used to realize a tunable edge imaging. Interestingly, by changing the optical axis of the anisotropic metamaterial, the in-plane displacements are equal to the transverse displacements for a certain filling factor and the corresponding incident angle. Therefore, we propose a two-dimensional image edge detection method based on the photonic SHE in anisotropic metamaterial. Further numerical results validate the theoretical proposal.
<scp>DBF‐YOLO</scp>: <scp>UAV</scp> Small Targets Detection Based on Shallow Feature Fusion

Driven by deep learning, great breakthroughs had been made in the field of target detection. Small target detection algorithms were widely used in industry, agriculture and other fields. But the small target had few available features and the loss of small target detail information in feature extraction. So it led to the low accuracy of the small target detection algorithms. In this paper, we proposed DBF‐YOLO algorithm based on the classical YOLOV5. The classical YOLOV5 algorithm with high speed. The detection speed of the minimum model could reach 24 ms. However, the deep network structure led to the low detection accuracy of small targets. Our proposed DBF‐YOLO algorithm was an improvement on the problem of small target information being lost. The main contributions of this article were mainly: First, a shallow feature extraction network was introduced in P1 layer, more details of small targets could be well retained. Second, by adding the feature fusion network of shallow feature map and the detection output part in the FPN + PAN layers, the algorithm's accuracy and generalization ability were significantly enhanced. Compared to YOLOV5, the performance of the DBF‐YOLO algorithm was significantly improved. On the validation set, mAP@0.5 and mAP@0.5:0.95 were increased by 8.80 and 5.90%, respectively. Recall was increased from the initial 34.50–41.80%. Precision was increased from initial 44.20 to 50.70%. On the test set, mAP@0.5 and mAP@0.5:0.95 were increased by 6.40 and 3.90%, respectively. Recall was increased 5.10%. Precision was increased 6.60%. Experiments had shown that the improved algorithm achieved good results in accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
High-throughput and area-efficient architectures for image encryption using PRINCE cipher

Internet of Things (IoT) has gained popularity in recent years and has engulfed nearly every industry. The widespread use of numerous ubiquitous computing devices in the low-resource domain has resulted in a new set of privacy and security concerns. To address the problem of security in resource-constrained devices, many lightweight algorithms have been developed. This paper proposes optimized hardware implementations of the lightweight PRINCE block cipher, with the aim of providing adequate security while maximizing resource efficiency. The proposed architecture uses fewer resources and provides a reasonable trade-off between area footprint and efficiency. In the proposed unrolled pipelined architecture, the encryption round is divided into three sub-stages, with registers inserted in between. Using this design approach, the operating frequency is greatly improved. As a result, this architecture adapts itself effectively to high-performance applications. This paper also proposes serial-based and round-based architectures for resource-constrained devices. The proposed unrolled sub pipeline PRINCE block cipher is implemented on the Virtex-6-FF784 and Virtex-4-FF668 FPGA device families and achieved substantial improvements in throughput of 13.057% and 113%, respectively, as well as efficiency of 8.109% and 113.734% respectively. The proposed architecture is evaluated on a variety of grayscale images, and the security analysis is performed using MATLAB software. Aside from that, the proposed architecture uses the CBC-mode of operation. The security analysis and encryption outputs show that the proposed architecture is an effective choice for image encryption and provides sufficient security to the cipher images.
Energy-Efficient Federated Learning With Resource Allocation for Green IoT Edge Intelligence in B5G

An edge intelligence-aided Internet-of-Things (IoT) network has been proposed to accelerate the response of IoT services by deploying edge intelligence near IoT devices.The transmission of data from IoT devices to the edge nodes leads to large network traffic in the wireless connections.Federated Learning (FL) is proposed to solve the high computational complexity by training the model locally on IoT devices and sharing the model parameters in the edge nodes.This paper focuses on developing an efficient integration of joint edge intelligence nodes depending on investigating an energy-efficient bandwidth allocation, computing Central Processing Unit (CPU) frequency, optimization transmission power, and the desired level of learning accuracy to minimize the energy consumption and satisfy the FL time requirement for all IoT devices.The proposal efficiently optimized the computation frequency allocation and reduced energy consumption in IoT devices by solving the bandwidth optimization problem in closed form.The remaining computational frequency allocation, transmission power allocation, and loss could be resolved with an Alternative Direction Algorithm (ADA) to reduce energy consumption and complexity at every iteration of FL time from IoT devices to edge intelligence nodes.The simulation results indicated that the proposed ADA can adapt the central processing unit frequency and power transmission control to reduce energy consumption at the cost of a small growth of FL time.
Reaction of Carbon Dioxide Gas Absorption with Suspension of Calcium Hydroxide in Slurry Reactor

Chemical phenomena involving three phases (solid, liquid, and gas) are often found in the industry. Carbonate (CaCO3) is widely used in industries as a powder-making material in the cosmetic industry, a pigment in the paint industry, and filler in the paper and rubber industry. This research aim to study the ordering process carbonate deposits (CaCO3) from the absorption process of CO2 gas with Ca(OH)2 suspension. The absorption reaction of CO2 gas with Ca(OH)2 suspension was carried out in a stirred slurry tank reactor. Initially, the reactor containing water was heated to a certain temperature, then Ca(OH)2 was added to the reactor. Furthermore, CO2 gas with a certain flow rate and temperature (according to the reactor temperature) is flown with the help of a gas distributor. Samples were taken every 1 min until the concentration of Ca(OH)2 could not be detected (completely reacted). The variables in this study were: stirrer rotation speed (5.66711.067 rps), CO2 gas flow rate (34.0127–60.5503 c/s), and temperature (30–50°C). The mass transfer coefficient and the reaction rate coefficient were determined by minimizing Sum of Squares of Errors (SSE). This experimental process follows a dynamic regime. A dimensionless number relationship for the gas-liquid mass transfer for the value range is Re1 = 18928.76-38217.20, Sh = 0.07928 Reg0.4383 Rel0.4399 Sc0.6415 with an error of 5.19%. The dimensionless number relationship for solid-liquid mass transfer is Sh = 0.0001179 Reg0.4674 Rel0.5403 Sc1.444 with an error of 7.31%. The relationship between the reaction rate constant and the temperature in the 30-50 °C range can be approximated by the Arrhenius equation, namely kr = 1771000 e-2321.4/T cm3/mgmol/s with an error of 3.63%. Doi: 10.28991/ESJ-2023-07-02-02 Full Text: PDF
Unsupervised domain adaptation via progressive positioning of target-class prototypes

Domain adaptation transfers knowledge from the source domain to the target domain. The existing methods reduce the domain discrepancy by aligning domain distribution. To align the two domains at category level, a pseudo labeling approach is often adopted. However, unreliable pseudo labels may cause negative transfer problems, which hinders further improvement of domain adaptation methods. To solve this problem, we propose a new unsupervised domain adaptation method via Progressive Positioning of Target-Class Prototypes (P2TCP), in this paper. P2TCP applies the knowledge of the source domain to locate the target class prototypes, then predicts the target samples through exploiting the structural information within the target domain. Inspired by the curriculum learning, we further propose an adaptive-dual label filtering method to improve the model with iteration by an easy-to-hard strategy. Extensive experiments reveal that our method achieves the state-of-the-art on the four benchmark datasets. Our code is available at P2TCP.
Hertzbleed: Turning Power Side-Channel Attacks Into Remote Timing Attacks on x86

Power side-channel attacks exploit data-dependent variations in a CPU’s power consumption to leak secrets. In this article, we show that on modern CPUs, power side-channel attacks can be turned into timing attacks that can be mounted without access to any power measurement interface. This discovery exploits how, under certain circumstances, the dynamic frequency scaling of modern x86 CPU depends on the current power consumption (and hence, data). We demonstrate that this “frequency side channel” is a real threat to the security of cryptographic software. First, we reverse engineer the dependency between data, power, and frequency on a modern x86 CPU—finding, among other things, that differences as small as a set bit’s position in a word can be distinguished through frequency changes. Second, we describe a novel chosen-ciphertext attack against (constant-time implementations of) supersingular isogeny key encapsulation that allows full key extraction via remote timing.
The Chatbots Are Invading Us: A Map Point on the Evolution, Applications, Opportunities, and Emerging Problems in the Health Domain

The inclusion of chatbots is potentially disruptive in society, introducing opportunities, but also important implications that need to be addressed on different domains. The aim of this study is to examine chatbots in-depth, by mapping out their technological evolution, current usage, and potential applications, opportunities, and emerging problems within the health domain. The study examined three points of view. The first point of view traces the technological evolution of chatbots. The second point of view reports the fields of application of the chatbots, giving space to the expectations of use and the expected benefits from a cross-domain point of view, also affecting the health domain. The third and main point of view is that of the analysis of the state of use of chatbots in the health domain based on the scientific literature represented by systematic reviews. The overview identified the topics of greatest interest with the opportunities. The analysis revealed the need for initiatives that simultaneously evaluate multiple domains all together in a synergistic way. Concerted efforts to achieve this are recommended. It is also believed to monitor both the process of osmosis between other sectors and the health domain, as well as the chatbots that can create psychological and behavioural problems with an impact on the health domain.
An efficient Intra-Inter pixel encryption scheme to secure healthcare images for an IoT environment

Digital images are being frequently used for diagnosis in clinics today. Diagnostic images with identifying patient data are stored and transmitted across open networks. Security measures are therefore becoming more and more crucial to safeguard the confidentiality of sensitive patient data. In medical imaging, adjacent pixels show a strong degree of connection. This paper presents an efficient intra-inter bit-level pixel operation-based image encryption method that eliminates pixel correlation and provides strong security. In this method, after gathering medical image data from IoT sensors, the intra bit-level pixel permutation and diffusion operations are executed separately and in parallel in each of the bit planes, and the inter bit-level pixel permutation and diffusion operations are then performed in the concatenated bit-planes. The bit-level operation not only permutes but also diffuses the pixels at the same time. As a result, the security of the algorithm is increased by executing several pixel permutations and diffusions. In addition to this, the approach is effective due to the parallel execution of bit-plane permutation and diffusion. The simulation results and the security analysis indicate the superior performance of the proposed algorithm.
2023 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

Blockchain technology has already established itself for developing various new complex decentralized software systems.For example, integrating blockchains, Artificial Intelligence (AI), and the Internet-of-Things (IoT) provides a promising new research and development direction that has generated significant research funding and venture capital investments.Each of these technologies is coming forward with a new set of engineering challenges (e.g., understanding the requirements related to trust and ethics, system resilience towards unpredictable market behavior, or development of ultra-long-term software systems).At the same time, various blockchain systems are going through turbulent swings in value, culture, trustworthiness, illegal activity, public perception, supporting market infrastructure, and censorship resistance.This keynote will present a selection of our recent work ranging from engineering trust to building censorship-resistant federated learning on decentralized permissionless blockchains.The common theme is understanding how various innovations, illegal activities, and system manipulations lead to blockchain system turbulences and vice versa.The keynote discusses the feasibility of engineering mechanisms to predict and dampen the effects of blockchain turbulences using market mechanisms and machine learning.The goal is to increase fundamental blockchain system trustworthiness, reliability, and public acceptability, while increasing the value and benefits of complex blockchain systems.
Efficient Face Recognition System for Identifying Lost People

In the world, a countless number of people are missing every day which includes kids, teens, mentally challenged, old-aged people with Alzheimer's, etc. Most of them remain untraced. This paper proposes a system that would help the police and the public by accelerating the process of searching using face recognition. When a person goes missing, the people related to that person or the police can upload the picture of the person which will get stored in the database. When the public encounter a suspicious person, they can capture and upload the picture of that person into our portal. The face recognition model in our system will try to find a match in the database with the help of face encodings. It is performed by comparing the face encodings of the uploaded image to the face encodings of the images in the database. If a match is found, it will be notified to the police and the people related to that person along with the location of where the person is found. The face recognition model that we have used maintains an accuracy of 99.38% on the Labelled Faces in the Wild Benchmark which comprises of 13,000 images [1].
Anomaly Detection in 6G Networks Using Machine Learning Methods

While the cloudification of networks with a micro-services-oriented design is a well-known feature of 5G, the 6G era of networks is closely related to intelligent network orchestration and management. Consequently, artificial intelligence (AI), machine learning (ML), and deep learning (DL) have a big part to play in the 6G paradigm that is being imagined. Future end-to-end automation of networks requires proactive threat detection, the use of clever mitigation strategies, and confirmation that 6G networks will be self-sustaining. To strengthen and consolidate the role of AI in safeguarding 6G networks, this article explores how AI may be employed in 6G security. In order to achieve this, a novel anomaly detection system for 6G networks (AD6GNs) based on ensemble learning (EL) for communication networks was redeveloped in this study. The first stage in the EL-ADCN process is pre-processing. The second stage is the feature selection approach. It applies the reimplemented hybrid approach using a comparison of the ensemble learning and feature selection random forest algorithms (CFS-RF). NB2015, CIC_IDS2017, NSL KDD, and CICDDOS2019 are the three datasets, each given a reduced dimensionality, and the top subset characteristic for each is determined separately. Hybrid EL techniques are used in the third step to find intrusions. The average voting methodology is employed as an aggregation method, and two classifiers—support vector machines (SVM) and random forests (RF)—are modified to be used as EL algorithms for bagging and adaboosting, respectively. Testing the concept of the last step involves employing classification forms that are binary and multi-class. The best experimental results were obtained by applying 30, 35, 40, and 40 features of the reimplemented system to the three datasets: NSL_KDD, UNSW_NB2015, CIC_IDS2017, and CICDDOS2019. For the NSL_KDD dataset, the accuracy was 99.5% with a false alarm rate of 0.0038; the accuracy was 99.9% for the UNSW_NB2015 dataset with a false alarm rate of 0.0076; and the accuracy was 99.8% for the CIC_IDS2017 dataset with a false alarm rate of 0.0009. However, the accuracy was 99.95426% for the CICDDOS2019 dataset, with a false alarm rate of 0.00113.
Metaverse in hospitality and tourism: a critical reflection

Purpose This study aims to discuss the current context, scope and impacts of a metaverse in the hospitality and tourism industry. Although existing literature discussed the potentials of the metaverse in this context, the ways the metaverse work is still being defined and accessing a complete metaverse is still not yet possible. This existing knowledge will become increasingly sophisticated and complex as developments in the metaverse continue, eventually contributing to a knowledge gap in knowledge, and its implications in shaping how the future digital environment should take form. Design/methodology/approach This work is based on a critical reflection of the existing developments and applications of the metaverse. Drawing from authors’ experiences, and synthesis of existing works and narratives, this work discusses the applications of the metaverse, critical factors for considerations and applications of the metaverse and proposes the way forward for potential users. Findings The metaverse provides new opportunities for the hospitality and tourism industry but the impact of the technology may not be felt immediately. The real challenge lies in developing a responsible digital environment for users and suppliers. Although the aspects to be considered are many, a lack of preparedness is a great obstacle. Research limitations/implications This paper provides a comprehensive evaluation of how the metaverse can be applied in the hospitality and tourism sector aiming to provide diverse stakeholders insights into the associated opportunities and pitfalls. Originality/value To the best of the authors’ knowledge, this paper is among the first attempts to critically reflect on the possibilities of the metaverse, and contributes to the discussion on the attributes of the metaverse for tourism and hospitality (e.g. SSIs, decentralization) and includes discussion on special needs users, sustainable usage and climate change, and presents several agendas for further actions.
Predictive Mining of Comparable Entities from the Web

Comparing entities is an important part of decision making. Several approaches have been reported for mining comparable entities from Web sources to improve user experience in comparing entities online.However, these efforts extract only entities explicitly compared in the corpora, and may exclude entities that occur less-frequently but potentially comparable. To build a more complete comparison machine that can infer such missing relations, here we develop a solutionto predict transitivity of known comparable relations. Named CliqueGrow, our approach predicts missing links given a comparable entity graph obtained from versus query logs. Our approach achieved the highest F1-score among five link prediction approaches and a commercial comparison engine provided by Yahoo!.