Reviewerly/exHarmony huggingface dataset:
Extreme learning machine based transfer learning for aero engine fault diagnosis

Aero engine fault diagnosis is very important to ensure flight safety. However, the lack of fault data limits aero engine fault diagnosis. Fortunately, transfer learning can transfer data information from other fields to the target field, so as to solve the problem of lack of data in the target field. Therefore, according to the idea of transfer learning, this paper proposes two cross-domain aero engine fault diagnosis methods, one-stage-transfer-learning ELM (OSTL-ELM) and two-stage-transfer-learning ELM (TSTL-ELM). These methods are both based on extreme learning machine (ELM), which has fast training speed and good real-time diagnosis. OSTL-ELM uses one stage to extract information from two domains, and TSTL-ELM uses two stages, which realizes the individually adaptation of the target domain. The network weights of the two methods are generated by calculation rather than iteration. Only a small amount of target domain data is needed, and high diagnostic accuracy can be obtained. Finally, the simulation experiment of aero engine degradation state fault diagnosis is designed, and the simulation experiment is carried out under four working conditions. The results show that the two methods are effective in fault diagnosis, and TSTL-ELM has better performance.
Artificial Neural Network training using metaheuristics for medical data classification: An experimental study

The Artificial Neural Network (ANN) is an important machine learning tool used in medical data classification for disease diagnosis. The learning algorithm in ANN training plays a crucial role in classification performance. Various approaches have been successfully applied as a learning algorithm for ANN training. This paper performs an experimental study that investigates the performance of different metaheuristics as learning algorithms to train the ANN for medical data classification tasks. The experiments are carried out on 15 well-known medical datasets. A comparative study is conducted with the classical Levenberg–Marquardt (LM) and other thirteen recent and relevant metaheuristics. Different evaluation criteria such as accuracy, sensitivity, specificity, precision, Geometric Mean, F-Measure, false-positive rate (FPR) are considered for performance estimation. The classification results are analyzed using Multi-Criteria Decision Making (MCDM) method, and the results with analysis establish that the Equilibrium Optimizer algorithm outperforms all the other algorithms included in the comparative study.
Blockchain-Based Digital Twins Collaboration for Smart Pandemic Alerting: Decentralized COVID-19 Pandemic Alerting Use Case

Emerging technologies such as digital twins, blockchain, Internet of Things (IoT), and Artificial Intelligence (AI) play a vital role in driving the industrial revolution in all domains, including the healthcare sector. As a result of COVID-19 pandemic outbreak, there is a significant need for medical cyber-physical systems to adopt these emerging technologies to combat COVID-19 paramedic crisis. Also, acquiring secure real-time data exchange and analysis across multiple participants is essential to support the efforts against COVID-19. Therefore, we have introduced a blockchain-based collaborative digital twins framework for decentralized epidemic alerting to combat COVID-19 and any future pandemics. The framework has been proposed to bring together the existing advanced technologies (i.e., blockchain, digital twins, and AI) and then provide a solution to decentralize epidemic alerting to combat COVID-19 outbreaks. Also, we have described how the conceptual framework can be applied in the decentralized COVID-19 pandemic alerting use case.
An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist's expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.
Augmented Reality and Engineering Education: A Systematic Review

Augmented reality (AR) for learning is a relevant topic that has recently received considerable attention. However, the current literature lacks a survey of AR-based educational approaches and experiences in the specific field of engineering studies. Five research questions were addressed: RQ1) engineering studies where AR is used; RQ2) types of educational activities where AR is used; RQ3) evaluation of its impact on students and instructors; RQ4) relevant characteristics of AR apps; and RQ5) their degree of interactivity. Regarding RQ1, it is concluded that AR has been mainly used in technical drawing, electronics, and construction. Concerning RQ2, AR apps have assisted in lectures, exercise classes, and laboratories. However, the preferred educational activity varies for each discipline. Regarding RQ3, it has been found that AR apps have been evaluated with respect to students&#x2019; or instructors&#x2019; perceptions and students&#x2019; academic performance. In general, the perceptions are positive, but students criticize some technical elements. Moreover, academic performance is increased in most studies. Finally, regarding RQ4 and RQ5, AR apps do not achieve the highest levels of functional characteristics and have low degrees of interactivity. The systematic review indicates that there is plenty of room for the future use of AR in engineering studies, but each engineering area must identify adequate educational purposes. It is also recommended to assess apps through objective measures, more structured constructs, and validated scales. Finally, higher functional characteristics and interactivity should be encouraged to exploit the full potential of AR.
Machine learning models and over-fitting considerations

Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes. Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor generalizability, which smaller datasets can be more prone to. In an effort to educate readers interested in artificial intelligence and model-building based on machine-learning algorithms, we outline important details on cross-validation techniques that can enhance the performance and generalizability of such models.
Criminology Towards the Metaverse: Cryptocurrency Scams, Grey Economy and the Technosocial

Abstract Online markets in cryptocurrency represent a sprawling and eclectic alternative financial system, selling cutting edge techno-investment schemes that are complex and high risk. Crime control is almost entirely absent from this new crypto economy, and it is full of scams. This paper draws on an ethnography of crypto trading to review the main types of scam, suggesting that the grey economy of cryptocurrency trading is part of a wider evolution of society towards the technosocial, and beyond that perhaps towards the metaversal.
Assessment of the potential of floating solar photovoltaic panels in bodies of water in mainland Spain

This article presents the potential of floating photovoltaic solar energy in Spain, a country with a high solar energy resource and a large water surface area for its deployment, for the first time. Geodata for natural, artificial, and highly modified bodies of freshwater, along with environmental geospatial datasets, were used to calculate electricity generation, taking into account the positive water-cooling effect. The results revealed that Spain could meet about 31% of its electricity demand by covering only 10% of the available water surface area. Deployment of the country's full floating photovoltaic potential could reduce non-renewable electricity generation by 81% and greenhouse gas emissions by 6%, thereby helping to meet the European Union 2030 target. Spanish regions could benefit from this renewable energy, not only by reducing their dependence on non-renewable resources, but also by balancing their electricity generation and demand. The potential of this renewable energy technology is higher in southern regions and particularly in Extremadura, where the electricity generation potential is three times the electricity demand. A detailed analysis of the floating photovoltaic potential in three dam reservoirs, the Borbollón, La Pedrera and Guadalcacín, is also presented for four coverage scenarios. The results highlight the importance of including water depth restrictions on floating photovoltaic module operation and variations in reservoir water level in future assessments, rather than simply applying a fixed percentage of coverage.
An Efficient Blockchain-Based Hierarchical Data Sharing for Healthcare Internet of Things

With the assistance of the Internet of Things, the fast developing Healthcare Internet of Things (H-IoT) has promoted the healthcare ecosystem into the era of Health 5.0 and enables many promising medical applications, such as remote healthcare that is crucial in pandemic (e.g., coronavirus disease 2019). Healthcare participants can make accurate diagnosis, treatment, and research based on the shared personal health records (PHRs) sensed from remote H-IoT devices. However, current H-IoT systems fall short of a secure and trustworthy PHR sharing service in remote healthcare, which is able to prevent user privacy leakage and PHR integrity violation together with high efficiency in key distribution alongside efficient data retrieval and fine-grained access control. In response, we present a blockchain-based hierarchical data sharing framework (BHDSF) to provide fine-grained access control and efficient retrieval over encrypted PHRs with low consumed hierarchical key distribution and key leakage resistance. Compared with the existing solutions, the BHDSF takes both untrusted cloud and malicious auditor into consideration simultaneously and achieves trustworthy PHR integrity auditing and metadata verification by leveraging the blockchain technique. Besides, the BHDSF enables efficiently aggregative authentication for the trustworthiness of source records from H-IoT devices, which is lacked in most of the existing data sharing frameworks. Finally, we demonstrate the feasibility of the BHDSF by conducting extensive empirical tests over a real-world dataset.
Understanding the user experience of customer service chatbots: An experimental study of chatbot interaction design

Understanding the user experience of chatbots for customer service is essential to realize the potential of this technology. Such chatbots are typically designed for efficient and effective interactions, accentuating pragmatic quality, and there is a need to understand how to make these more pleasant and engaging, strengthening hedonic quality. One promising approach is to design for more humanlike chatbot interactions, that is, interactions resembling those of skilled customer service personnel. In a randomized experiment (n = 35) we investigated two chatbot interaction design features that may strengthen the impression of a humanlike character: (a) topic-led conversations, encouraging customer reflection, in contrast to task-led conversations, aiming for efficient goal completion, and (b) free text interaction, where users interact mainly using their own words, rather than button interaction, where users mainly interact through predefined answer alternatives. dependent variables were participant perceptions of anthropomorphism and social presence, two key concepts related to chatbot human likeness, in addition to pragmatic quality and hedonic quality. To further explore user perceptions of the interaction designs, the study also included semi-structured interviews. Topic-led conversations were found to strengthen anthropomorphism and hedonic quality. A similar effect was not found for free text interaction, reportedly due to lack in chatbot flexibility and adaptivity. Implications for theory and practice are suggested.
Sign Pose-based Transformer for Word-level Sign Language Recognition

In this paper we present a system for word-level sign language recognition based on the Transformer model. We aim at a solution with low computational cost, since we see great potential in the usage of such recognition system on hand-held devices. We base the recognition on the estimation of the pose of the human body in the form of 2D landmark locations. We introduce a robust pose normalization scheme which takes the signing space in consideration and processes the hand poses in a separate local coordinate system, independent on the body pose. We show experimentally the significant impact of this normalization on the accuracy of our proposed system. We introduce several augmentations of the body pose that further improve the accuracy, including a novel sequential joint rotation augmentation. With all the systems in place, we achieve state of the art top-l results on the WLASL and LSA64 datasets. For WLASL, we are able to successfully recognize 63.18 % of sign recordings in the 100-gloss subset, which is a relative improvement of 5 % from the prior state of the art. For the 300-gloss subset, we achieve recognition rate of 43.78 % which is a relative improvement of 3.8 %. With the LSA64 dataset, we report test recognition accuracy of 100 %.
An effective multiple-image encryption algorithm based on 3D cube and hyperchaotic map

In accordance with the principle that the cube graph can be obtained by superimposing planes, a multiple-image encryption scheme based on hyperchaotic map and 3D cube is designed in this paper. Multiple images are segmented by columns, and a regular cube is obtained by stacking multiple fixed-size planes. Each stacked plane is considered as a stacked plane of the cube along the z-axis direction. Firstly, each tangent plane is rotated by different angles in the counterclockwise direction and then DNA encoded, followed by face swap, row swap, and column swap in turn. Then, the DNA-encoded image cube is subjected to DNA addition with the chaotic cube. Finally, two DNA sequences are randomly selected for DNA mutation. The mutated cube is decoded into a digital cube by DNA and the multiple-image encryption algorithm is completed. Theoretically, the cube used in the encryption process can be infinitely large, which means that the number and the size of 2D images that can be encrypted can be unrestricted. Experimental simulations and performance tests prove that the proposed image encryption scheme is effective and secure.
Procurement, traceability and advance cash credit payment transactions in supply chain using blockchain smart contracts

Timely payment is necessary for the efficient delivery of goods and services to all entities in the supply chain. Traditionally, there has been a lot of manual processes related to payment. Nowadays the technology advancements make the payment process in electronic form. Even the electronic form is not easily accessible to all parties in the supply chain. Each entity of the supply chain maintains separate transaction records that remain difficult, inefficient, and error prone. Often companies face difficulties in monitoring the status of shipments and linking them to payments. In the real world, the suppliers offer buyers three terms of payment of purchase costs. The buyer pays the supplier as advance payment, cash, or credit. This research considers that the supplier ships the goods to the buyer by means of third-party logistics services. The supplier initially pays an advance to the external logistics provider while shipping the product to the buyer. Once the buyer verify that the goods are received in correct quantity, the supplier shall pay the balance amount to the third-party logistics provider. This research proposes a blockchain-based smart contract for supply chain transactions. This paper presents a general platform for stakeholders in the supply chain through an Ethereum based smart contract with comprehensive interaction algorithm that enables decentralized authorization, process automation, and information sharing between the supply chain stakeholders. The proposed smart contract benefits the supplier by lowering the risk of non-payment during the credit period. In addition, the buyer benefits from on-time delivery performance and product tracking. Finally, the 3PL significantly reduces their costs by lowering the overall cost by lowering the cost of paperwork and usage of software for product tracking. Adopting a blockchain-based solution in supply chain transaction considering ACC payment is cost-effective simple, secure, trustworthy, and transparent.
Region Similarity Representation Learning

We present Region Similarity Representation Learning (ReSim), a new approach to self-supervised representation learning for localization-based tasks such as object detection and segmentation. While existing work has largely focused on solely learning global representations for an entire image, ReSim learns both regional representations for localization as well as semantic image-level representations. ReSim operates by sliding a fixed-sized window across the overlapping area between two views (e.g., image crops), aligning these areas with their corresponding convolutional feature map regions, and then maximizing the feature similarity across views. As a result, ReSim learns spatially and semantically consistent feature representation throughout the convolutional feature maps of a neural network. A shift or scale of an image region, e.g., a shift or scale of an object, has a corresponding change in the feature maps; this allows downstream tasks to leverage these representations for localization. Through object detection, instance segmentation, and dense pose estimation experiments, we illustrate how ReSim learns representations which significantly improve the localization and classification performance compared to a competitive MoCo-v2 baseline: $ + 2.7AP_{75}^{bb}VOC$, $ + 1.1AP_{75}^{bb}COCO$, and +1.9 AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mk</sup> Cityscapes. Code and pre-trained models are released at: https://github.com/Tete-Xiao/ReSim
A Simple Feature Augmentation for Domain Generalization

The topical domain generalization (DG) problem asks trained models to perform well on an unseen target domain with different data statistics from the source training domains. In computer vision, data augmentation has proven one of the most effective ways of better exploiting the source data to improve domain generalization. However, existing approaches primarily rely on image-space data augmentation, which requires careful augmentation design, and provides limited diversity of augmented data. We argue that feature augmentation is a more promising direction for DG. We find that an extremely simple technique of perturbing the feature embedding with Gaussian noise during training leads to a classifier with domain-generalization performance comparable to existing state of the art. To model more meaningful statistics reflective of cross-domain variability, we further estimate the full class-conditional feature covariance matrix iteratively during training. Subsequent joint stochastic feature augmentation provides an effective domain randomization method, perturbing features in the directions of intra-class/cross-domain variability. We verify our proposed method on three standard domain generalization benchmarks, Digit-DG, VLCS and PACS, and show it is outperforming or comparable to the state of the art in all setups, together with experimental analysis to illustrate how our method works towards training a robust generalisable model.
Hand gesture recognition based on a Harris Hawks optimized Convolution Neural Network

Hand gestures are an effective method of communication, especially when we are communicating with people who cannot understand our spoken language. Furthermore, it is a key aspect to human–computer interaction. Understanding hand gestures is very important to ensure that listeners understand what speakers are attempting to communicate. Even though several researchers have proposed deep learning-based models for hand gesture recognition, the hyper-parameter tuning of these models is a relatively unexplored area. In this work, Convolutional Neural Networks (CNN) are used to classify hand gesture images. To tune the hyper-parameters of the CNN, a recently developed metaheuristic algorithm, namely, the Harris Hawks Optimization (HHO) algorithm, is used. Our in-depth comparative analysis proves that the proposed HHO-CNN hybrid model outperforms the existing models by attaining an Accuracy of 100%.
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing and speech recognition. However, their superior performance comes at the considerable cost of computational complexity, which greatly hinders their applications in many resource-constrained devices, such as mobile phones and Internet of Things (IoT) devices. Therefore, methods and techniques that are able to lift the efficiency bottleneck while preserving the high accuracy of DNNs are in great demand in order to enable numerous edge AI applications. This paper provides an overview of efficient deep learning methods, systems and applications. We start from introducing popular model compression methods, including pruning, factorization, quantization as well as compact model design. To reduce the large design cost of these manual solutions, we discuss the AutoML framework for each of them, such as neural architecture search (NAS) and automated pruning and quantization. We then cover efficient on-device training to enable user customization based on the local data on mobile devices. Apart from general acceleration techniques, we also showcase several task-specific accelerations for point cloud, video and natural language processing by exploiting their spatial sparsity and temporal/token redundancy. Finally, to support all these algorithmic advancements, we introduce the efficient deep learning system design from both software and hardware perspectives.