Reviewerly/exHarmony huggingface dataset:
Similarity-navigated graph neural networks for node classification

Graph Neural Networks are effective in learning representations of graph-structured data. Some recent works are devoted to addressing heterophily, which exists ubiquitously in real-world networks, breaking the homophily assumption that nodes belonging to the same class are more likely to be connected and restricting the generalization of traditional methods in tasks such as node classification. However, these heterophily-oriented methods still lose efficacy in some typical heterophilic datasets. Moreover, issues on leveraging the knowledge from both node features and graph structure and investigating inherent properties of the datasets still need further consideration. In this work, we first provide insights based on similarity metrics to interpret the long-existing confusion that simple models sometimes perform better than models dedicated to heterophilic networks. Then, sticking to these insights and the classification principle of narrowing the intra-class distance and enlarging the inter-class distance of the sample's embeddings, we propose a Similarity-Navigated Graph Neural Network (SNGNN) which uses Node Similarity matrix coupled with mean aggregation operation instead of the normalized adjacency matrix in the neighborhood aggregation process. Moreover, based on SNGNN, a novel explicitly aggregating mechanism for selecting similar neighbors, named SNGNN+, is devised to preserve distinguishable features and handle the heterophilic problem. Additionally, a variant, SNGNN++, is further designed to adaptively integrate the knowledge from both node features and graph structure for improvement. Extensive experiments are conducted and demonstrate that our proposed framework outperforms the state-of-the-art methods for both small-scale and large-scale graphs regardless of their heterophilic extent. Our implementation is available online.
A Full-Stack View of Probabilistic Computing With p-Bits: Devices, Architectures, and Algorithms

The transistor celebrated its 75${}^\text{th}$ birthday in 2022. The continued scaling of the transistor defined by Moore's Law continues, albeit at a slower pace. Meanwhile, computing demands and energy consumption required by modern artificial intelligence (AI) algorithms have skyrocketed. As an alternative to scaling transistors for general-purpose computing, the integration of transistors with unconventional technologies has emerged as a promising path for domain-specific computing. In this article, we provide a full-stack review of probabilistic computing with p-bits as a representative example of the energy-efficient and domain-specific computing movement. We argue that p-bits could be used to build energy-efficient probabilistic systems, tailored for probabilistic algorithms and applications. From hardware, architecture, and algorithmic perspectives, we outline the main applications of probabilistic computers ranging from probabilistic machine learning and AI to combinatorial optimization and quantum simulation. Combining emerging nanodevices with the existing CMOS ecosystem will lead to probabilistic computers with orders of magnitude improvements in energy efficiency and probabilistic sampling, potentially unlocking previously unexplored regimes for powerful probabilistic algorithms.
A comparative analysis of digital health usage intentions towards the adoption of virtual reality in telerehabilitation

With the rapid development of the metaverse and the problem of non-attendance in traditional rehabilitation, virtual reality in telerehabilitation has become increasingly vital in modern medicine. However, research on determining predictors that influence the public's behavioral intention to adopt VR-based telerehabilitation has not been extensively studied.This study aims to propose a new research model with a comparative analysis on understanding factors affecting the public's behavioral intention to adopt VR in telerehabilitation for different user groups.A total of 215 respondents from the general public completed an online questionnaire to validate the proposed research model. The collected data was analyzed using SPSS and AMOS. The proposed model was additionally validated using CFA and multiple linear regression.This study found that effort expectancy, threat appraisals, and trust had a positive significant influence on the public's behavioral intention to adopt VR in telerehabilitation. However, performance expectancy and facilitating conditions had no significant relationship with behavioral intention. Notably, the average of the primary factors for older adults was generally higher than for younger adults.The present study confirms the applicability of the proposed research model. Our findings contribute up-to-date insights for related stakeholders to minimize implementation failures and develop successful adoption strategies for the future expansion of telerehabilitation.
Glass Segmentation With RGB-Thermal Image Pairs

This paper proposes a new glass segmentation method utilizing paired RGB and thermal images. Due to the large difference between the transmission property of visible light and that of the thermal energy through the glass where most glass is transparent to the visible light but opaque to thermal energy, glass regions of a scene are made more distinguishable with a pair of RGB and thermal images than solely with an RGB image. To exploit such a unique property, we propose a neural network architecture that effectively combines an RGB-thermal image pair with a new multi-modal fusion module based on attention, and integrate CNN and transformer to extract local features and non-local dependencies, respectively. As well, we have collected a new dataset containing 5551 RGB-thermal image pairs with ground-truth segmentation annotations. The qualitative and quantitative evaluations demonstrate the effectiveness of the proposed approach on fusing RGB and thermal data for glass segmentation. Our code and data are available at https://github.com/Dong-Huo/RGB-T-Glass-Segmentation.
Impact of information and communications technology on the development and use of knowledge

The development and capitalisation of knowledge have been defining the features of economic development in recent decades. This global trend leads to a constant search for success factors in knowledge economies. The Global Knowledge Index (GKI) is the leading international indicator of the quality of knowledge capital and the ability to build effective knowledge economies. Using GKI as the primary research indicator fostering knowledge-based economies, we assessed the role of information and communications technology (ICT) factors in its formation. GKI depends on subjective assessments of the impact of ICT factors on the development and use of knowledge set by experts. Here, we evaluate the effect of elements that are subject to statistical reporting and, therefore, most objectively reflect existing ICT trends in enterprises. Using the method of correlation-regression analysis and data from EU countries, we estimated the relationship of seven selected factors in the formation of GKI, establishing that significant links exist with six of seven studied elements. The closest links are with the indicators 'use of computers and the internet by employees' and 'enterprises with a website'. Additionally, continuing to test the hypothesis of a significant contribution of ICT to modern changes in the knowledge economy, we investigated their impact on GKI based on ICT development indicators assessed in the Network Readiness Index. The effect of this indicator is extremely high (R2 = 0.9287). Generally, the influence of ICT factors on knowledge development, and hence knowledge-based economies, allows one to identify the critical determinants of the success of leading countries. Our approach can be used to periodically monitor and select the most influential factors. Through reasonable efforts to stimulate the development of ICT, strengthening the role of such factors can make full use of current opportunities to create a knowledge-oriented economy.
Reinforcement learning-based multi-strategy cuckoo search algorithm for 3D UAV path planning

Unmanned aerial vehicles are applied extensively in various fields due to their advantages of low-cost, high-maneuverability, and easy-operation. However, the path planning problem of unmanned aerial vehicles, which directly determines the flight safety and efficiency, still remains challenging when building and optimizing the path model. To further study the path planning problem, we firstly construct it as a constrained optimization problem. The objective function considers the costs of path length and threat, and the constraints involve the collision and turning angle. Additionally, we employ the theory of B-Spline curve to represent the planned paths to facilitate the optimization of established model. Then, aiming at the poor searchability and slow convergence speed of current optimization methods, we propose a reinforcement learning-based multi-strategy cuckoo search algorithm. Specifically, we establish an innovative reinforcement learning-based multi-strategy mechanism and a reinforced switch parameter based on the theory of reinforcement learning. To verify the effectiveness of the proposed algorithm, extensive experiments are carried out on the CEC'17 benchmark test and different three-dimensional path planning problems. Detailed statistical analysis of the experimental results confirm the superiority of our proposed algorithm to the other well-established algorithms.
An improved gorilla troops optimizer for global optimization problems and feature selection

The Artificial Gorilla Groups Optimizer (GTO) is a novel metaheuristic algorithm that takes its cues from the collective intelligence of wild gorilla troops. Although it has shown promise in solving various real-world applications, it may still get stuck at local optima and premature convergence when dealing with more complex optimization tasks. Because of these shortcomings, this research proposes a new modified Gorilla Groups Optimizer (mGTO) method, employing a set of operators to achieve a more stable balance between exploitation and exploration. These operators are the elite Opposition Based-Learning (EOBL), Cauchy Inverse Cumulative (CICD) Distribution Operator, and tangent Flight (TFO). Each operator is used to achieve a specific task during the search process. The EOBL aims to enhance the diversity of the population, which leads to discovering the feasible region. Whereas the integration between CICD and TFO is used to improve the population’s exploitation ability, which leads to an increase in the convergence rate. To validate the efficiency of the presented method, called mGTO, a set of experimental series is conducted using the CEC2020 benchmark and constraint design engineering problems. In addition, its applicability is assessed by implementing mGTO as a feature selection technique and applying it to improve the classification accuracy of sixteen datasets. The results of mGTO are also compared with those produced by other well-known meta-heuristic techniques. The statistical validity of the performance is also verified using Wilcoxon’s rank-sum test. The experimental results and comparison analysis reveal the consistent and better performance of the proposed mGTO method to solve optimization problems.
High-quality digital distance teaching during COVID-19 school closures: Does familiarity with technology matter?

During the COVID-19-related school closures teachers and students were challenged to suddenly switch to digital teaching at a distance. In particular, the challenge was to organize high-quality teaching in which students stay on task. Familiarity with technology may have helped to master the situation. However, only few studies so far have examined the quality of digital distance teaching (e.g., cognitive activation) during school closures and its relation to students' learning (e.g., effort investment). Moreover, systematic research concerning the role of familiarity with technology-enhanced teaching and learning acquired during face-to-face teaching is yet lacking. In our study, we used data from 729 ninth graders to investigate how student-observed learning activities when using technology at a distance were related to students' effort in learning in two subjects (mathematics, German). In addition, we examined whether student-perceived cognitive activation mediated this relation. Finally, the sample provides the unique opportunity to examine the role of familiarity, as some of the classes had been randomly equipped with tablet computers one year before the school closures and thus had the opportunity to gain familiarity with using technology in the classroom. Results from structural equation models showed that student-observed learning activities were associated with students' learning effort in both subjects. Student-perceived cognitive activation mediated this association. Familiarity with face-to-face technology-enhanced teaching gained before the COVID-19 pandemic appeared to be less important for high-quality digital distance teaching. Thus, infrastructural measures, such as equipping schools with digital devices so that teachers and students can familiarize themselves with technology, do not seem to be decisive for high-quality digital (distance) teaching-at least in the case of short-term change from face-to-face to digital distance teaching, as was necessary during the COVID-19 pandemic.
A multi-stage underwater image aesthetic enhancement algorithm based on a generative adversarial network

Existing underwater image enhancement algorithms rely on paired datasets, which enhance underwater images by learning the mapping relationship between low-quality and high-quality data. However, currently, high-quality data (which are called real data) are artificially selected by the dataset builders from the results of previous algorithms, and there are no real paired data in the true sense. In this paper, we used CycleGAN for underwater image enhancement, which is unsupervised learning. We designed the aesthetic loss and style consistency loss to constrain the generated image to make it more consistent with perception by human eyes and to improve the contrast. We used a two-stage generative network structure to compensate for the loss of information during the enhancement process and enhanced the details. We verified the superiority of our algorithm in the subjective and aesthetic aspects through a large number of comparative and ablation experiments as well as subjective and objective analyses.
Efficient Image Encryption Scheme Using Novel 1D Multiparametric Dynamical Tent Map and Parallel Computing

In order to ensure reliable and secure image exchange, chaotic systems are often considered for their good performance in information security. In this work, we first propose an extended version of a chaotic tent map (TM)—the multiparametric 1D tent map (MTM). The latter contains six control parameters defined over an unlimited range. These parameters strongly influence the MTM output when they are slightly modified by ∓10−13, which makes MTM stronger than the existing TM and other 1D chaotic maps in terms of security key space. Then, this paper proposes a simple, yet powerful method to make uniform the distribution of chaotic sequence values, making the latter suitable for use in cryptosystems. Next, a new image cryptosystem is introduced based on MTM and parallel computing. This computing mode is incorporated to boost the security level of our scheme and to speed up its runtime. Indeed, in only one running round, our encryption scheme generates a security key of space equal to 1078×n with n indicating the number of the available CPU cores. Therefore, the suggested scheme achieves a good trade-off between safety and efficiency. The results of the performed comparisons and numerical experiments indicate on the one hand that MTM exhibits good chaotic characteristics in comparison to its original version. On the other hand, the suggested cryptosystem demonstrates good strength against various kinds of attacks (brute force, statistical, classical, noise, differential, etc.). Furthermore, comparison with similar schemes indicate that the proposed scheme is competitive in terms of execution time and superior in terms of security level.
Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches

In light of the rapidly expanding solar photovoltaic (PV) sector, it is important to provide a deeper understanding of solar energy resources to successfully implement solar energy projects. In this study, an interpretable machine learning model based on extreme gradient boosting (XGBoost) optimized by particle swarm optimization (PSO) algorithms was developed to estimate global solar radiation. The results show that the proposed PSO-XGBoost model possesses the most superior accuracy and stability, with the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of 0.953, 1.597 MJ·m−2·day−1, 1.138 MJ·m−2·day−1, and 10.500%, respectively. With the geographic information system (GIS) -based approaches, a 50 km by 50 km spatial resolution map of long-term national average solar radiation resources was generated based on the reconstructed solar radiation dataset, as well as the PV power potential map. The findings reveal that the nationwide annual mean solar radiation resources were decreasing at an estimated attenuation of −0.83 W·m−2·decade−1, with a downward trend of the greatest magnitude of −1.83 W·m−2·decade−1 for summer. China’s long-term average yearly PV power potential reached 285.00 kWh·m−2, indicating a spatial pattern of higher potentials in the northwestern and northern provinces, while lower values in the southeastern provinces. Moreover, the PV power potential in China decreased by 1.69 kWh·m−2·decade−1 from 1961 to 2016, with an attenuation of above 5 kWh·m−2·decade−1 in heavily polluted regions. During the 2010s, 30 out of the 31 provinces experienced a reduction in the PV power potential between 0.25% and 10.27%, with an average national reduction of 2.88%, compared to the 1960s scenario. Also, policy recommendations for long-term PV project deployment were given regarding the regional mismatch between PV power potential and installed capacity in China.
A comparative study of evaluating and benchmarking sign language recognition system-based wearable sensory devices using a single fuzzy set

Recent research has focused on developing real-time sign language recognition systems (SLRSs) based on gesture recognition to classify hand motions into their equivalent meaning in spoken language, but no comprehensive system with all desirable features has been presented. The existence of different systems has hindered the process of selecting the most preferred system. Therefore, many researchers have compared and evaluated several recognition systems to identify the best one using multicriteria decision-making methods. These studies extended the fuzzy decision by opinion score method (FDOSM) using a single Likert scale under the Pythagorean fuzzy set (PFS) or one of its extensions. However, no comparative study has examined the influence of using multiple Likert scales with a single fuzzy set. Furthermore, the effect of employing multiple Likert scales on benchmarking results is a challenging task. Therefore, this paper examines the three Likert scales (five-, seven- and ten-point) under the same fuzzy environment. This paper extends FDOSM into PFSs based on the power Bonferroni mean (PBM) operator (named PFDOSM-PBM) to benchmark the real-time SLRS. The decision matrix is constructed based on 30 real-time SLRS-based wearable sensory devices and the 11 evaluation criteria. The results reveal that the five-point Likert scale is superior to other scales (i.e., seven- and ten-point) as it is flexible, easy to use and generates more accurate findings on the basis of uncertainty compared to other scales. Systematic ranking and comparative analysis are conducted to validate and evaluate the proposed method.
ManuChain II: Blockchained Smart Contract System as the Digital Twin of Decentralized Autonomous Manufacturing Toward Resilience in Industry 5.0

In Industry 5.0 vision, machines are empowered with the interaction capability to autonomously make local decisions and coordinate with each other as well as humans. However, how to form a group consensus on the rapid self-organizing of the manufacturing process is critical for achieving manufacturing resilience under disturbances and disruptions. Based on our formerly developed system ManuChain (Leng et al., 2020), this article proposes a blockchained smart contract system (BSCS), named ManuChain II, as the digital twin of a decentralized autonomous manufacturing system for achieving resilience in Industry 5.0. A blockchain-secured multiagent system architecture together with a product data model is established to form the BSCS. The BSCS could prevent tampering with data and enhance the transparency of the product manufacturing process. In BSCS, two types of blockchained smart contracts (SCs) are established with a bi-level interplay computing architecture. The lower-level contracts perform the predefined and learned patterns of task coordination for achieving resilience under internal disruptions. The upper-level contracts are incorporated with communication-efficient decentralized deep-learning algorithms for the learning, updating, transferring, and sharing of coordination patterns used in the autonomous decision in lower-level SCs, thereby achieving the continuous improvement of the system’s decentralized autonomous intelligence. Via incorporating the decentralized deep learning algorithms into blockchained SCs, this study reveals a new way to realize the self-organizing intelligence of the manufacturing system for enhancing resilience toward Industry 5.0.
An Improved YOLOv5-Based Underwater Object-Detection Framework

To date, general-purpose object-detection methods have achieved a great deal. However, challenges such as degraded image quality, complex backgrounds, and the detection of marine organisms at different scales arise when identifying underwater organisms. To solve such problems and further improve the accuracy of relevant models, this study proposes a marine biological object-detection architecture based on an improved YOLOv5 framework. First, the backbone framework of Real-Time Models for object Detection (RTMDet) is introduced. The core module, Cross-Stage Partial Layer (CSPLayer), includes a large convolution kernel, which allows the detection network to precisely capture contextual information more comprehensively. Furthermore, a common convolution layer is added to the stem layer, to extract more valuable information from the images efficiently. Then, the BoT3 module with the multi-head self-attention (MHSA) mechanism is added into the neck module of YOLOv5, such that the detection network has a better effect in scenes with dense targets and the detection accuracy is further improved. The introduction of the BoT3 module represents a key innovation of this paper. Finally, union dataset augmentation (UDA) is performed on the training set using the Minimal Color Loss and Locally Adaptive Contrast Enhancement (MLLE) image augmentation method, and the result is used as the input to the improved YOLOv5 framework. Experiments on the underwater datasets URPC2019 and URPC2020 show that the proposed framework not only alleviates the interference of underwater image degradation, but also makes the mAP@0.5 reach 79.8% and 79.4% and improves the mAP@0.5 by 3.8% and 1.1%, respectively, when compared with the original YOLOv8 on URPC2019 and URPC2020, demonstrating that the proposed framework presents superior performance for the high-precision detection of marine organisms.