Reviewerly/exHarmony huggingface dataset:
MapChain: A Blockchain-Based Verifiable Healthcare Service Management in IoT-Based Big Data Ecosystem

Internet of Things (IoT)-based Healthcare services, which are becoming more widespread today, continuously generate huge amounts of data which is often called big data. Due to the magnitude and intricacy of the data, it is difficult to find valuable information that can be used for decision-making and prediction. Big data systems take on a significant infrastructure service to better serve the purpose of IoT systems and support critical decision making. On the other hand, privacy preservation, data integrity, and identity verification are essential requirements in healthcare big data service management. To overcome these problems, this article offers a scalable computing system that provides verifiable data access mechanism for IoT-enabled health data analytics in the big data ecosystem. There are two primary sub-architectures in the proposed architecture, namely a big data analytics tracking system and a derived blockchain-based data storage/access system. This approach leverages big data systems and blockchain architecture to analyze, and securely store data from IoT-enabled devices and allow verified access to the stored data. The zero-knowledge protocol is used to ensure that no information is accessible to unauthenticated users alongside avoiding data linkability. The results demonstrate the effectiveness of the our method to solve the problems of big data analytics and privacy issues in healthcare.
Dehaze-AGGAN: Unpaired Remote Sensing Image Dehazing Using Enhanced Attention-Guide Generative Adversarial Networks

Remote sensing image dehazing is of great scientific interest and application value in both military and civil fields. In this article, we propose an enhanced attention-guide generative adversarial network (GAN) network, Dehaze-AGGAN, to solve the remote sensing images dehazing problem, which does not require paired training data. Since haze images have a great influence on remote sensing object detection, the dehazing of remote sensing images has become significantly important. Typical image dehazing methods require a hazy input image and its ground truth in a paired manner, while paired training data are usually not available in the field of remote sensing. To solve this problem, we propose the Dehaze-AGGAN network and train it by feeding unpaired clean and hazy images into the model. We present a novel total variation loss combined with the cycle consistency loss to eliminate wave noise and improve the target edge quality in the test dataset. Moreover, we present a new dehazing dataset called remote sensing dehazing dataset (RSD), which contains 7000 simulate and real hazy images including 3500 warship images and 3500 civilian ship images, and evaluate our method in the dataset. We conduct experiments on RSD. Extensive experiments demonstrate that the proposed Dehaze-AGGAN is effective and has strong robustness and adaptability in different settings.
Are consumers more attracted to restaurants featuring humanoid or non-humanoid service robots?

The restaurant industry has witnessed a dramatic rise of service robots. Previous research suggests that humanoid service robots are perceived as social entities and thus might lead to more favorable consumer responses than non-humanoid service robots. However, not all consumers prefer the presence of social entities when eating out. The current research reveals that consumers’ reactions to restaurants featuring humanoid versus non-humanoid service robots depend on whether they will dine out alone (i.e., solo diners) or with others (i.e., group diners). Specifically, the findings suggest that group diners exhibit more favorable attitudes and behavioral intentions toward restaurants featuring humanoid (vs. non-humanoid) service robots, whereas solo diners respond more favorably toward restaurants featuring non-humanoid (vs. humanoid) service robots. Furthermore, anticipated psychological comfort is revealed as the underlying mechanism explaining these effects. • Restaurants can use service robots as a marketing tool to attract consumers. • Consumers respond to service robots as a function of robot type and diner type. • Group diners are more attracted to restaurants featuring humanoid service robots. • Solo diners are more attracted to restaurants featuring non-humanoid service robots. • Anticipated psychological comfort is the underlying mechanism explaining these effects.
Siamese Convolutional Neural Network-Based Twin Structure Model for Independent Offline Signature Verification

One of the toughest biometrics and document forensics problems is confirming a signature’s authenticity and legal identity. A forgery may vary from a genuine signature by specific distortions. Therefore, it is necessary to continuously monitor crucial distinctions between real and forged signatures for secure work and economic growth, but this is particularly difficult in writer-independent tasks. We thus propose an innovative and sustainable writer-independent approach based on a Siamese neural network for offline signature verification. The Siamese network is a twin-like structure with shared weights and parameters. Similar and dissimilar images are exposed to this network, and the Euclidean distances between them are calculated. The distance is reduced for identical signatures, and the distance is increased for different signatures. Three datasets, namely GPDS, BHsig260 Hindi, and BHsig260 Bengali datasets, were tested in this work. The proposed model was analyzed by comparing the results of different parameters such as optimizers, batch size, and the number of epochs on all three datasets. The proposed Siamese neural network outperforms the GPDS synthetic dataset in the English language, with an accuracy of 92%. It also performs well for the Hindi and Bengali datasets while considering skilled forgeries.
Massive data language models and conversational artificial intelligence: Emerging issues

Summary Google’s LaMDA, Open AI’s GPT‐3, and Meta’s BlenderBot are artificial intelligence (AI)‐based chatbots, that have been trained on billions of documents creating the notion of “massive data.” These systems use human‐generated documents to capture words and relationships between words that people use when they communicate. This paper examines some of the similarities of these systems and the emerging issues regarding these massive data language models, including whether they are sentient, the use and impact of scale, information use and ownership, and explanations of discussions and answers. This paper also directly investigates some artifacts of Google’s LaMDA and compares them with Meta’s BlenderBot. Finally, this paper examines emerging issues and questions deriving from our analysis.
The multidimensional effect of financial development on trade in Africa: The role of the digital economy

The issue of how financial development affects international trade has gained much attention in the literature, both theoretical and empirical, without investigating the various transmission channels of financial development on international trade. Significantly, how the digital economy modulates the effects of financial development on trade remains unexplored. Our study addresses these research gaps using panel data from 47 African economies spanning 1990 to 2019. Our findings based on the dynamic system GMM approach show that the direct impacts of financial development and the digital economy have been crucial in driving international trade in Africa in the short and long runs. Second, in the short and long runs, the digital economy serves as a vital channel through which financial development has the most significant impact on trade in Africa. Third, the robustness checks by excluding countries endowed with natural resources in Africa provide robust evidence that digital technology and finance matter for trade. Further, the study discloses an important novelty in that the marginal effects on trade increase when financial development in Africa interacts with the digital economy. In light of these findings, we conclude that policy reforms should focus on policies that encourage the development of the digital economy and its use in the financial sector in Africa.
Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self-supervised techniques in histopathological image analysis

Histopathological images contain abundant phenotypic information and pathological patterns, which are the gold standards for disease diagnosis and essential for the prediction of patient prognosis and treatment outcome. In recent years, computer-automated analysis techniques for histopathological images have been urgently required in clinical practice, and deep learning methods represented by convolutional neural networks have gradually become the mainstream in the field of digital pathology. However, obtaining large numbers of fine-grained annotated data in this field is a very expensive and difficult task, which hinders the further development of traditional supervised algorithms based on large numbers of annotated data. More recent studies have started to liberate from the traditional supervised paradigm, and the most representative ones are the studies on weakly supervised learning paradigm based on weak annotation, semi-supervised learning paradigm based on limited annotation, and self-supervised learning paradigm based on pathological image representation learning. These new methods have led a new wave of automatic pathological image diagnosis and analysis targeted at annotation efficiency. With a survey of over 130 papers, we present a comprehensive and systematic review of the latest studies on weakly supervised learning, semi-supervised learning, and self-supervised learning in the field of computational pathology from both technical and methodological perspectives. Finally, we present the key challenges and future trends for these techniques.
Research on public opinion sentiment classification based on attention parallel dual-channel deep learning hybrid model

The research on sentiment classification of online public opinion is helpful to the management and control of online public opinions. In the matter of the problems of previous sentiment analysis research that it is difficult to well capture text sentiment features and to identify words ambiguity, an Attention Parallel Dual-channel Deep Learning Hybrid Model (ADDHM) is proposed. Bidirectional Encoder Representations from Transformers (BERT) is applied to extract semantic features and training text vector representation. Convolutional Neural Network (CNN) and Bidirectional Long Short-term Memory (BiLSTM), introducing the attention mechanism, form a dual-channel model to extract text semantic features so as to enrich the words meaning and improve the classification level. Microblog public opinion is taken as an experiment case and hyperparameters are adjusted to find the optimal hyperparameter combination. Six comparison models are selected to verify the validity of ADDHM on four data sets. The classification accuracy of the proposed model on the four experimental data sets are respectively 96.68%, 88.86%, 89.64% and 92.72%, which are superior to the comparison model, and the ROC curve performance of the model is also the best. The performance of ADDHM is significantly different from that of the comparison models. ADDHM can effectively optimize the expression of text features and enhance the capacity of extracting text sentiment feature. It has better classification effect and is more befitting for sentiment classification of online public opinion comments.
Virtual spaces as the future of consumption in tourism, hospitality and events

Purpose Virtual spaces, commonly referred to as the Metaverse, are predicted to disrupt consumption patterns in tourism, hospitality and events (THE) by shifting some user experiences to a virtual world. Scholarly investigations are necessitated to aid in an understanding of virtual spaces and the implications of their consumption for THE industries. This viewpoint outlines a provisional research agenda on virtual spaces. Design/methodology/approach To inform its arguments, this viewpoint draws upon academic and grey literature surrounding the emerging topic of the Metaverse in THE industries. Findings The research agenda should consider four perspectives representing different actors of THE value chain, i.e. developers/suppliers, THE business professionals, customers and policymakers. The research agenda should also incorporate the wider spillover effects of consumption of virtual spaces which may stretch well beyond THE industries. Originality/value This viewpoint outlines some research directions which may aid different actors of THE value chain alongside academics in better understanding the emerging phenomenon of virtual spaces and comprehend the opportunities and challenges associated with their uptake by THE industries.
A Comprehensive Review of Bat Inspired Algorithm: Variants, Applications, and Hybridization

Bat algorithm (BA) is one of the promising metaheuristic algorithms. It proved its efficiency in dealing with various optimization problems in diverse fields, such as power and energy systems, economic load dispatch problems, engineering design, image processing and medical applications. Thus, this review introduces a comprehensive and exhaustive review of the BA, as well as evaluates its main characteristics by comparing it with other optimization algorithms. The review paper highlights the performance of BA in different applications and the modifications that have been conducted by researchers (i.e., variants of BA). At the end, the conclusions focus on the current work on BA, highlighting its weaknesses, and suggest possible future research directions. The review paper will be helpful for the researchers and practitioners of BA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining and clustering. As well, it is wealthy in research on health, environment and public safety. Also, it will aid those who are interested by providing them with potential future research.
AI in Blockchain Towards Realizing Cyber Security

Blockchain and artificial intelligence are two technologies that, when combined, have the ability to help each other realize their full potential. Blockchains can guarantee the accessibility and consistent admittance to integrity safeguarded big data indexes from numerous areas, allowing AI systems to learn more effectively and thoroughly. Similarly, artificial intelligence (AI) can be used to offer new consensus processes, and hence new methods of engaging with Blockchains. When it comes to sensitive data, such as corporate, healthcare, and financial data, various security and privacy problems arise that must be properly evaluated. Interaction with Blockchains is vulnerable to data credibility checks, transactional data leakages, data protection rules compliance, on-chain data privacy, and malicious smart contracts. To solve these issues, new security and privacy-preserving technologies are being developed. AI-based blockchain data processing, either based on AI or used to defend AI-based blockchain data processing, is emerging to simplify the integration of these two cutting-edge technologies.
A hybrid optimization framework for UAV reconnaissance mission planning

Applying unmanned aerial vehicles (UAVs) in military reconnaissance missions arises in recent years. It refers to using finite UAVs to reconnoiter some enemy targets with limited battery power, aiming at collecting valuable information as much as possible. While existing methods like heuristics and intelligent optimization methods have been widely applied, their performance on large-scale reconnaissance mission planning problems (RMPPs) is still unsatisfactory. This study proposes a hybrid optimization framework, namely, EA-DRL, to improve the optimization effect based on a problem decomposition strategy. In detail, the RMPP is decomposed into a target selection subproblem and a path planning subproblem. An evolutionary algorithm and a deep reinforcement learning method are proposed to solve them, respectively. Updating solutions to these two subproblems iteratively eventually completes the optimization of the given RMPP. Experimental results on different types of RMPP instances show great effectiveness and strong generalization ability of the proposed framework EA-DRL. • A decomposition strategy for UAV reconnaissance mission planning problems (RMPPs). • A hybridization of evolutionary algorithms and deep reinforcement learning methods. • A useful method which can be extended for solving multi-objective RMPPs. • An efficient population initialization method.
Automatic Data Clustering by Hybrid Enhanced Firefly and Particle Swarm Optimization Algorithms

Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optimization algorithm utilizing ten UCI Machine Learning Repository datasets and eight Shape sets for performance evaluation. In addition to this, two clustering validity measures, Compact-separated and David–Bouldin, have been used for analyzing the efficiency of these algorithms. The experimental results show that the two proposed hybrid algorithms outperform the existing hybrid firefly particle swarm optimization algorithm.
Online tool wear monitoring by super-resolution based machine vision

The tool condition has been a major concern in modern computer numerical control (CNC) machining due to its direct effects on the quality of final product both in the surface and dimensional integrity. The conventional machine vision-based tool condition monitoring (TCM) approaches cannot meet the high precision requirement in micro machining, as the cutting parameters are in micro scale and the spindle works in high rotation speed which makes online tool wear measurement quite difficult. To meet these challenges, this study develops a single image super-resolution (SISR) approach for direct tool wear estimation in micro-milling. Motivated by the self-similarity of tool wear image morphology, this study proposes a sparse decomposition framework by learning dictionaries from the tool wear image pyramid. Based on their multi-scale invariant properties, the similar image patches of coarse scales can be retrieved from fine scales to reconstruct the high-resolution image fastly and in high quality. The reconstructed high-resolution image then can be conveniently applied to wear monitoring, and overcomes the image acquisition deficiencies of the conventional machine vision-based monitoring approaches. Experimental results validate this approach for the tool wear area estimation as well as its generalization of the wear width with regarding to the conventional manual measurements.
Classification of Malaria Using Object Detection Models

Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction.
Classification of earthquakes, explosions and mining-induced earthquakes based on XGBoost algorithm

The classification of low-magnitude tectonic earthquakes, explosions and mining-induced earthquakes is an important task in regional earthquake monitoring. Seismic events occurring at local and regional distances are classified primarily based on the characteristics of their waveform. We established 36-dimensional and 201-dimensional datasets by using feature extraction and amplitude spectral analysis. The Extreme Gradient Boosting (XGBoost) supervised algorithm is introduced for the discrimination of couples-class and three-class. The accuracies in the earthquakes/explosions discrimination with feature extraction dataset and amplitude spectrum dataset are 97.48% and 95.12%, respectively, which shows that feature extraction can effectively quantify the differences between earthquakes and explosions. For the classification of earthquakes/mining-induced earthquakes and explosions/mining-induced earthquakes, the performance of XGBoost with the amplitude spectrum dataset is greater, with accuracies of 99.24% and 95.33%, respectively. In the classification of the three types of events, the accuracies of XGBoost are 96.41% for earthquakes, 90.38% for explosions, and 94.04% for mining-induced earthquakes. The performance indices of XGBoost for different input parameters are invariably greater than those of the support vector machine (SVM), with stable classification ability, suggesting that the XGBoost model has good prospects for application in seismic event classification.