Reviewerly/exHarmony huggingface dataset:
SAR-to-Optical Image Translation and Cloud Removal Based on Conditional Generative Adversarial Networks: Literature Survey, Taxonomy, Evaluation Indicators, Limits and Future Directions

Due to the limitation of optical images that their waves cannot penetrate clouds, such images always suffer from cloud contamination, which causes missing information and limitations for subsequent agricultural applications, among others. Synthetic aperture radar (SAR) is able to provide surface information for all times and all weather. Therefore, translating SAR or fusing SAR and optical images to obtain cloud-free optical-like images are ideal ways to solve the cloud contamination issue. In this paper, we investigate the existing literature and provides two kinds of taxonomies, one based on the type of input and the other on the method used. Meanwhile, in this paper, we analyze the advantages and disadvantages while using different data as input. In the last section, we discuss the limitations of these current methods and propose several possible directions for future studies in this field.
A Systematic Review on Imbalanced Learning Methods in Intelligent Fault Diagnosis

The theoretical developments of data -driven fault diagnosis methods have yielded fruitful achievements and significantly benefited industry practices. However, most methods are developed based on the assumption of data balance, which is incompatible with engineering scenarios. First, the normal state accounts for the majority of the equipment’s lifespan; second, the probability of various faults varies, both of which result in an imbalance in the data. The consequence of data imbalance in intelligent fault diagnosis methods has attracted extensive attention from the research community, and a significant number of papers have been published. Nevertheless, a comprehensive review of achievements in this field is still missing, and the research perspectives have not been thoroughly investigated. To end this, we review and discuss all the research achievements in fault diagnosis under data imbalance in this survey, based on to the best of our knowledge. First, the existing imbalanced learning methods are classified into three categories: data processing methods, model construction methods, and training optimization methods. Then, the three methodologies are introduced and discussed in detail: the data processing method is to optimize the inputs of the intelligent fault diagnosis model so that the imbalance rate of the sample set involved in training is reduced; the model construction method is to design the structure and the features of the intelligent fault diagnosis model so that the model itself is resistant to the effects of imbalance; the training optimization method is an optimization of the training process for intelligent fault diagnosis models, raising the importance of the minority class in the training. Finally, this survey summarizes the prospects of the imbalanced learning problem in intelligent fault diagnosis, discusses the possible solutions, and provides some recommendations.
Trans-AI/DS: transformative, transdisciplinary and translational artificial intelligence and data science

Abstract After the many ups and downs over the past 70 years of AI and 50 years of data science (DS), AI/DS have migrated into their new age. This new-generation AI/DS build on the consilience and universology of science, technology and engineering. In particular, it synergizes AI and data science, inspiring Trans-AI/DS (i.e., Trans-AI, Trans-DS and their hybridization) thinking, vision, paradigms, approaches and practices. Trans-AI/DS feature their transformative (or transformational), transdisciplinary , and translational AI/DS in terms of thinking, paradigms, methodologies, technologies, engineering, and practices. Here, we discuss these important paradigm shifts and directions. Trans-AI/DS encourage big and outside-the-box thinking beyond the classic AI, data-driven, model-based, statistical, shallow and deep learning hypotheses, methodologies and developments. They pursue foundational and original AI/DS thinking, theories and practices from the essence of intelligences and complexities inherent in humans, nature, society, and their creations.
A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization

Survey/review study A Survey of Algorithms, Applications and Trends for Particle Swarm Optimization Jingzhong Fang 1, Weibo Liu 1,*, Linwei Chen 2, Stanislao Lauria 1, Alina Miron 1, and Xiaohui Liu 1 1 Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom 2 The School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom * Correspondence: Weibo.Liu2@brunel.ac.uk Received: 18 October 2022 Accepted: 28 November 2022 Published: 27 March 2023 Abstract: Particle swarm optimization (PSO) is a popular heuristic method, which is capable of effectively dealing with various optimization problems. A detailed overview of the original PSO and some PSO variant algorithms is presented in this paper. An up-to-date review is provided on the development of PSO variants, which include four types i.e., the adjustment of control parameters, the newly-designed updating strategies, the topological structures, and the hybridization with other optimization algorithms. A general overview of some selected applications (e.g., robotics, energy systems, power systems, and data analytics) of the PSO algorithms is also given. In this paper, some possible future research topics of the PSO algorithms are also introduced.
High-Efficiency Blockchain-Based Supply Chain Traceability

Supply chain traceability refers to product tracking from the source to customers, demanding transparency, authenticity, and high efficiency. In recent years, blockchain has been widely adopted in supply chain traceability to provide transparency and authenticity, while the efficiency issue is understudied. In practice, as the numerous product records accumulate, the time- and storage- efficiencies will decrease remarkably. To the best of our knowledge, this paper is the first work studying the efficiency issue in blockchain-based supply chain traceability. Compared to the traditional method, which searches the records stored in a single chunk sequentially, we replicate the records in multiple chunks and employ parallel search to boost the time efficiency. However, allocating the record searching primitives to the chunks with maximized parallelization ratio is challenging. To this end, we model the records and chunks as a bipartite graph and solve the allocation problem using a maximum matching algorithm. The experimental results indicate that the time overhead can be reduced by up to 85.1% with affordable storage overhead.
Gaussian mixture continuously adaptive regression for multimode processes soft sensing under time-varying virtual drift

Due to time-varying virtual drift in multimode processes, the performance of soft sensors will degrade after online deployment. Traditional adaptive mechanisms have been developed to address this issue, but all have limitations in the real-world scenario. Therefore, this paper explores domain adaptation as a new adaptive mechanism for its unsupervised knowledge calibration effect, and a Gaussian mixture continuously adaptive regression (GMCAR) is proposed for soft sensor modeling. First, we extend Gaussian domain adaptation to Gaussian mixture domain adaptation, in which the target domain could transfer knowledge from multiple Gaussian source domains with different posterior probabilities. Furthermore, a new sensitivity-based continuous domain adaptation is presented to efficiently and naturally update the Gaussian mixture domain adaptation by exploring existing knowledge and using the continuity of data streams. As well, an adaptive Gaussian mixture regression is designed to continuously update the soft sensing model according to the current process state. Finally, experimental results on the TE benchmark process and a real air separation process demonstrate that the proposed GMCAR-based soft sensor achieves state-of-the-art performance under time-varying virtual drift.
Exploring the hyperledger blockchain technology disruption and barriers of blockchain adoption in petroleum supply chain

Blockchain is an emerging technology that can build technology innovation on core front line operations in the petroleum industry. In the present study, we explore the research questions: how hyperledger fabric blockchain can disrupt the petroleum supply chain, the prominent barriers to blockchain adoption. Hyperledger blockchain is a private network in which information sharing through only permissioned participants feasible. The study takes inputs from industry and academic experts to explore blockchain's role in every segment of the petroleum industry. It employs a hesitant fuzzy set theory to explore the prominent barriers of blockchain adoption. The research findings identify that blockchain has significant exposure in land management, asset history records, financial records, supply chain management, etc. The prominent barriers to blockchain adoption are lack of general standards, lack of trust among partners, and lack of understanding. In particular, this research gives the petroleum industry blockchain-based digitization and highlights future research directions in technology.
Free-Space Quantum Secure Direct Communication: Basics, Progress, and Outlook

Quantum secure direct communication (QSDC) leverages quantum states to transmit information securely and reliably over a noisy, lossy, and wiretapped quantum channel. It has been proven to be information-theoretically secure. Recent experiments show that QSDC can enable long-distance communication and large-scale networking with existing technologies. In particular, free-space QSDC has the unique advantages and prospects for practical large-scale application. Here, we review the QSDC basics and the progress of free-space QSDC. The feasibility of satellite-based QSDC and the future directions of free-space QSDC are discussed. We propose a practical and feasible route that can make immediate application of QSDC in real life.
Role of Blockchain in Health Care: A Comprehensive Study

There has been a lot of interest in blockchain technology in a lot of different fields over the last decade, including finance, government, energy and health. This article gives an in-depth look at how blockchain can be used in the healthcare sector. In reality, research in this area is moving very quickly. People can now use blockchain-based technology for sharing remote patient monitoring, the supply chain for medicines, electronic medical records and so on. Under this paper, we want to show how blockchain can be used in the healthcare industry and how much research has been done in this field.
Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations

Machine learning methods are widely used within the medical field. However, the reliability and efficacy of these models is difficult to assess, making it difficult for researchers to identify which machine-learning model to apply to their dataset. We assessed whether variance calculations of model metrics (e.g., AUROC, Sensitivity, Specificity) through bootstrap simulation and SHapely Additive exPlanations (SHAP) could increase model transparency and improve model selection. Data from the England National Health Services Heart Disease Prediction Cohort was used. After comparison of model metrics for XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boosting, XGBoost was used as the machine-learning model of choice in this study. Boost-strap simulation (N = 10,000) was used to empirically derive the distribution of model metrics and covariate Gain statistics. SHapely Additive exPlanations (SHAP) to provide explanations to machine-learning output and simulation to evaluate the variance of model accuracy metrics. For the XGBoost modeling method, we observed (through 10,000 completed simulations) that the AUROC ranged from 0.771 to 0.947, a difference of 0.176, the balanced accuracy ranged from 0.688 to 0.894, a 0.205 difference, the sensitivity ranged from 0.632 to 0.939, a 0.307 difference, and the specificity ranged from 0.595 to 0.944, a 0.394 difference. Among 10,000 simulations completed, we observed that the gain for Angina ranged from 0.225 to 0.456, a difference of 0.231, for Cholesterol ranged from 0.148 to 0.326, a difference of 0.178, for maximum heart rate (MaxHR) ranged from 0.081 to 0.200, a range of 0.119, and for Age ranged from 0.059 to 0.157, difference of 0.098. Use of simulations to empirically evaluate the variability of model metrics and explanatory algorithms to observe if covariates match the literature are necessary for increased transparency, reliability, and utility of machine learning methods. These variance statistics, combined with model accuracy statistics can help researchers identify the best model for a given dataset.
PDF-UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network

Rapid and precise segmentation of breast tumors is a severe challenge for the global research community to diagnose breast cancer in younger females. An ultrasound system is a non-invasive and efficient way of breast screening. The area, shape, and texture of different breast tumors play a vital role for clinicians in making accurate diagnostic decisions. Furthermore, the limited availability of breast tumor annotated datasets is another challenge for properly training deep neural networks. This research proposes a semi-supervised learning-based method, which incorporates a Data expansion network (DEN), Probability map generator network (PMG), and U-shaped pyramid-dilated fusion network (PDF-UNet) for accurate breast tumor segmentation. The first DEN network is trained on breast unannotated tumor images and generates synthetic images for the data expansion task. The second PMG network generates corresponding probability map images against synthetic unannotated images. Finally, we proposed a segmentation network (PDF-UNet), a modified variant of UNet, to segment the breast tumor images. The results demonstrate that compared with classical UNet, our proposed PDF-UNet achieves an increment of DSC (2.42%) on the Mendeley dataset and an increment of DSC (1.52%) observed on the SIIT dataset. The results reflect that the proposed method is effective when annotated breast ultrasound data is insufficient to train the network. Furthermore, the proposed method can be helpful in relieving the annotation burden of radiologists. The implementation source code is available at GitHub: https://github.com/ahmedeqbal/PDF-UNet.
Block Image Encryption Algorithm Based on Novel Chaos and DNA Encoding

To solve the problem of the low secret space and security of some image schemes, a novel 4D chaotic system is derived in this paper. Compared with other similar chaotic systems, the new system only has one equilibrium point and can exhibit hyperchaotic characteristics, under some parameter space. The system has better dynamic characters represented by calculation of the Lyapunov exponents, phase planes, and visual 0–1 test diagram. In this study, a novel image encryption algorithm is employed based on the new dynamic system, Zigzag transform, and DNA operation. Based on the improved Zigzag transformation, the plain image is block-scrambled, and DNA encoded with the treated chaotic sequences. The transformation efficiency is improved by combining multiple images at the same time. Numerical analysis has been carried out; the results show that our algorithm achieves much better performance in security, i.e., with enhanced pseudo-randomness, higher key sensitivity, weak correlation, fairly large key space, higher security, and a stronger ability to resist various attacks. Through visual analysis, the algorithm is deemed safe and effective for digital images.
Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach

The data-driven method is a promising way to predict the energy consumption of buildings, however suffering from the data shortage problem in various scenarios. Even though transfer learning can improve the few-shot prediction performance by utilizing other buildings’ data, the centralized approach poses potential privacy risks. To tackle this issue, the paper proposes a privacy-preserving knowledge sharing framework to facilitate the few-shot building energy prediction based on federated learning. First, a private data aggregation scheme is established to encrypt the sensitive data with shared random masks and guarantee the privacy of the data preprocessing and model optimization. Then, to alleviate the intrinsic data heterogeneity, a dynamical clustering federated learning algorithm is proposed to implement the intra-cluster and inter-cluster knowledge sharing along with the iterative clustering process for participating buildings. Finally, the network-based transfer learning approach is incorporated into the distributed framework to establish the customized model based on trained cluster models and further boost the prediction performance for each building. Extensive experiments on the Building Data Genome Project 2 (BDGP2) dataset indicate that the federated approach witnesses a desirable prediction performance while preserving the privacy of building occupants.
The effects of virtual reality (VR) and augmented reality (AR) on senior tourists’ experiential quality, perceived advantages, perceived enjoyment, and reuse intention

ABSTRACTABSTRACTThis study investigated the use of virtual reality (VR) and augmented reality (AR) virtual tourism to overcome problems and limitations in the senior tourism market. We explored the attributes of VR and AR in the context of vitalizing senior tourism, and the effects of such attributes on older adults' experience, perceived advantage, enjoyment, and reuse intention. Using mixed methods (i.e. qualitative and quantitative approaches), we developed four attributes of VR and AR for senior tourism and analyzed the effects of these attributes on older adults' emotional and behavioural responses. Analysis revealed significant relationships between the variables presented. These results offer meaningful insights about the use of VR and AR in virtual tourism to effectively activate senior tourism.KEYWORDS: Technologyvirtualvirtual reality (VR)augmented reality (AR)senior Disclosure statementNo potential conflict of interest was reported by the author(s).
LSTM-ReGAT: A network-centric approach for cryptocurrency price trend prediction

Predicting price trends of cryptocurrencies is a challenging task due to the highly speculative cryptocurrency market. Prior studies mainly investigate predictors such as historical trading data, macroeconomic development, and public interests in cryptocurrencies for price trend prediction while ignoring the predictive role of the relations between cryptocurrencies. In fact, the price movement of a cryptocurrency may be affected by those of other cryptocurrencies, thus, incorporating cryptocurrency interrelations can further improve the prediction performance. In this paper, we propose a novel end-to-end model for price trend prediction using long short-term memory (LSTM) and relationwise graph attention network (ReGAT), in which both individual cryptocurrency features and cryptocurrency relations are considered. Specifically, a cryptocurrency network is built by using shared features between cryptocurrencies. Further, LSTM is used to profile sequential patterns of individual cryptocurrency features. To fully extract network features, ReGAT is designed to aggregate information conveyed by different types of relations while automatically differentiating their importance. Finally, the conventional and network features are concatenated to predict the price trend. The effectiveness of LSTM-ReGAT is validated using real-world cryptocurrency market data. The trading simulations for Bitcoin and portfolios reveal that our model obtains the highest profits. Our study provides insightful implications for investment decision support in cryptocurrency market.
Doubly contrastive representation learning for federated image recognition

This paper focuses on the problem of personalized federated learning (FL) with the schema of contrastive learning (CL), which is to implement collaborative pattern classification by many clients. The traditional FL frameworks mostly facilitate the global model for the server and the local models for the clients to be similar, often ignoring the data heterogeneity of the clients. Aiming at achieving better performance in clients, this study introduces a personalized federated contrastive learning model, dubbed PerFCL, by proposing a new approach to doubly contrastive representation learning (DCL). Concretely, PerFCL borrows the DCL scheme, where one CL loss compares the shared parts of local models with the global model and the other CL loss compares the personalized parts of local models with the global model. To encourage the difference between the two parts, we created a double optimization problem composed of maximizing the comparison agreement for the former and minimizing the comparison agreement for the latter. We evaluated the proposed model on three publicly available data sets for federated image classification. Experiment results show that PerFCL benefits from the proposed DCL strategy and performs better than the state-of-the-art federated-learning models.
A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting

Due to the intermittency and fluctuation of solar energy, its exponential growth presents serious challenges to the power system. Therefore, photovoltaic (PV) power forecasting, including solar irradiance forecasting, has become a necessary prerequisite for the grid connection of photovoltaic power stations. However, traditional 2D convolution networks are less effective in extracting spatial features, especially limited in handling long-term dependencies. To address these problems, in this paper, a novel ultra-short-term solar irradiance forecasting method based on a 3D Convolutional Long Short-Term Memory and 3D Convolutional Neural Networks (3D ConvLSTM-CNN) hybrid model is proposed by processing multiple consecutive all-sky images with various color channels, the spatial information of different color channel images can better extract different types of cloud information, and the 3D ConvLSTM-CNN can take into account the temporal information. The temporal and spatial features of the sky image are extracted from multiple images at different times, simultaneously, and the textual meteorological features of the corresponding images fused via the LSTM hybrid network input model to finally establish the model for forecasting the next moment. All-sky image data and irradiance data collected by Yunnan Normal University are used to test and verify the model. The experimental results indicate that the proposed method has a promising performance and achieves 28.2%, 34.8%, 19.9%, 42.7%, and 68.3% improvement on nRMSE, MAPE, SMAPE, MedAPE, and R2 over the persistence model for 5-min ahead global horizontal irradiance (GHI) prediction.