Reviewerly/exHarmony huggingface dataset:
Modelling and executing IoT-enhanced business processes through BPMN and microservices

The Internet of Things enables to connect the physical world to digital business processes (BP) and allows a BP to (1) consider real-world data to take more informed business decisions, (2) automate and/or improve BP tasks, and (3) adapt itself to the physical execution environment. We refer to these processes as IoT-enhanced BPs. Although numerous researchers have studied this subject, there are still some challenges to be faced. For instance, the need of a modelling solution that does not increase the notation complexity to facilitate further analysis and engineering decision making, or an execution approach that provides a high degree of independence between the process and the underlying IoT device technology. The objective of this work is defining an approach that (1) considers important intrinsic characteristics of IoT-enhanced BPs at modelling level without growing the complexity of the modelling language, and (2) facilitates the execution of the IoT-enhanced BPs represented in models independently from IoT devices’ technology. To do so, we present a modelling approach that uses standard BPMN concepts to model IoT-enhanced BPs without modifying its metamodel. It applies the Separation of Concern (SoC) design principle: BPMN is used to describe IoT-enhanced BPs while low-level real-world data is captured in an ontology. Finally, a microservice architecture is proposed to execute BPMN models and facilitate its integration with the physical world. This architecture provides high flexibility to support multiples IoT device technologies as well as their evolution and maintenance. The evaluation done allows us to conclude that the application of the SoC principle using BPMN and ontologies facilitates the definition of intrinsic characteristics of IoT-enhanced BPs without increasing the complexity of the BPMN metamodel. Besides, the proposed microservice architecture provides a high degree of decoupling between the created models and the underlying IoT technology.
Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance

Abstract Maritime video surveillance has become an essential part of the vessel traffic services system, intended to guarantee vessel traffic safety and security in maritime applications. To make maritime surveillance more feasible and practicable, many intelligent vision-empowered technologies have been developed to automatically detect moving vessels from maritime visual sensing data (i.e., maritime surveillance videos). However, when visual data is collected in a low-visibility environment, the essential optical information is often hidden in the dark, potentially resulting in decreased accuracy of vessel detection. To guarantee reliable vessel detection under low-visibility conditions, the paper proposes a low-visibility enhancement network (termed LVENet) based on Retinex theory to enhance imaging quality in maritime video surveillance. LVENet is a lightweight deep neural network incorporating a depthwise separable convolution. The synthetically-degraded image generation and hybrid loss function are further presented to enhance the robustness and generalisation capacities of LVENet. Both full-reference and no-reference evaluation experiments demonstrate that LVENet could yield comparable or even better visual qualities than other state-of-the-art methods. In addition, it takes LVENet just 0⋅0045 s to restore degraded images with size 1920 × 1080 pixels on an NVIDIA 2080Ti GPU, which can adequately meet real-time requirements. Using LVENet, vessel detection performance can be greatly improved with enhanced visibility under low-light imaging conditions.
EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow

High-quality training data play a key role in image segmentation tasks. Usually, pixel-level annotations are expensive, laborious and time-consuming for the large volume of training data. To reduce labelling cost and improve segmentation quality, interactive segmentation methods have been proposed, which provide the result with just a few clicks. However, their performance does not meet the requirements of practical segmentation tasks in terms of speed and accuracy. In this work, we propose EdgeFlow, a novel architecture that fully utilizes interactive information of user clicks with edge-guided flow. Our method achieves state-of-the-art performance without any post-processing or iterative optimization scheme. Comprehensive experiments on benchmarks also demonstrate the superiority of our method. In addition, with the proposed method, we develop an efficient interactive segmentation tool for practical data annotation tasks. The source code and tool is avaliable at https://github.com/PaddlePaddle/PaddleSeg.
RISKS OF IMPLEMENTATION OF DIGITAL TECHNOLOGIES INTO EDUCATION

The article deals with the historical background of transition from informatization of education to its digitalization: from the stage of fragmentary application of information technologies in the educational process, the consolidation of tasks solved with their help, the development of e-learning, equipping universities with e-IOS and the use of its resources in educational activities to the stage of introduction of new digital technologies in the educational process. Theoretical analysis revealed the essence of the term digitalization, established a list of significant digital technologies for the development of society (cloud, cognitive, big data technologies and the Internet of things), defined and systematized list of key digital competencies of modern specialists (skills of working with digital technologies, the ability to diverse and effective online communication, analytical skills, critical and flexible thinking, multitasking skills, integrated work in interprofessional teams). Empirical data of the pilot study confirmed the need for the formation of digital competencies, changes in the content of training, equipping teachers with relevant knowledge, skills, competencies. Along with positive aspects of digitalization of education, the article presents possible negative changes in this area (loss of basic cognitive competencies, reduction of the general level of training, reduction of the need for an intellectual specialist, departure from the fundamental, redistribution of the functions of administration of universities and teachers, high requirements for the psychological qualities of the teacher, reduction of personal contacts, the growth of conflicts, leakage of talented youth and teachers abroad, reduction of the contingent of higher education, loss of the status of domestic higher education, reducing the number of students). The task of ensuring the quality of digital education and ways the actual pedagogical research (the digital model of the teacher; technology of development of digital educational content, methods of evaluation of its quality with ergonomic, health, psychological and pedagogical requirements; methods of monitoring of progress and the formation of educational outcomes in terms of the transfer of learning process to the global network; means of ensuring the quality of training, prevention of possible risks of its reduction) are actualized.
Application of Robust Zero-Watermarking Scheme Based on Federated Learning for Securing the Healthcare Data

The privacy protection and data security problems existing in the healthcare framework based on the Internet of Medical Things (IoMT) have always attracted much attention and need to be solved urgently. In the teledermatology healthcare framework, the smartphone can acquire dermatology medical images for remote diagnosis. The dermatology medical image is vulnerable to attacks during transmission, resulting in malicious tampering or privacy data disclosure. Therefore, there is an urgent need for a watermarking scheme that doesn't tamper with the dermatology medical image and doesn't disclose the dermatology healthcare data. Federated learning is a distributed machine learning framework with privacy protection and secure encryption technology. Therefore, this paper presents a robust zero-watermarking scheme based on federated learning to solve the privacy and security issues of the teledermatology healthcare framework. This scheme trains the sparse autoencoder network by federated learning. The trained sparse autoencoder network is applied to extract image features from the dermatology medical image. Image features are undergone to two-dimensional Discrete Cosine Transform (2D-DCT) in order to select low-frequency transform coefficients for creating zero-watermarking. Experimental results show that the proposed scheme has more robustness to the conventional attack and geometric attack and achieves superior performance when compared with other zero-watermarking schemes. The proposed scheme is suitable for the specific requirements of medical images, which neither changes the important information contained in medical images nor divulges privacy data.
Cryptocurrency Scams: Analysis and Perspectives

Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades amount to dozens of USD billions. The pseudonymity features of these cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the research on the analysis of their effects, and the development of techniques to counter them. However, doing research in this field requires addressing several challenges: for instance, although a few data sources about cryptocurrency scams are publicly available, they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse cryptocurrency scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams.We devise an automatic tool that recognises scams and classifies them according to our taxonomy.We assess the effectiveness of our tool through standard performance metrics.We also give an in-depth analysis of the classification results, offering several insights into threat types, from their features to their connection with other types. Finally, we provide a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams.
An ITO–graphene heterojunction integrated absorption modulator on Si-photonics for neuromorphic nonlinear activation

The high demand for machine intelligence of doubling every three months is driving novel hardware solutions beyond charging of electrical wires, given a resurrection to application specific integrated circuit (ASIC)-based accelerators. These innovations include photonic-based ASICs (P-ASICs) due to prospects of performing optical linear (and also nonlinear) operations, such as multiply–accumulate for vector matrix multiplications or convolutions, without iterative architectures. Such photonic linear algebra enables picosecond delay when photonic integrated circuits are utilized via “on-the-fly” mathematics. However, the neuron’s full function includes providing a nonlinear activation function, known as thresholding, to enable decision making on inferred data. Many P-ASIC solutions perform this nonlinearity in the electronic domain, which brings challenges in terms of data throughput and delay, thus breaking the optical link and introducing increased system complexity via domain crossings. This work follows the notion of utilizing enhanced light–matter interactions to provide efficient, compact, and engineerable electro-optic neuron nonlinearity. Here, we introduce and demonstrate a novel electro-optic device to engineer the shape of this optical nonlinearity to resemble a leaky rectifying linear unit—the most commonly used nonlinear activation function in neural networks. We combine the counter-directional transfer functions from heterostructures made out of two electro-optic materials to design a diode-like nonlinear response of the device. Integrating this nonlinearity into a photonic neural network, we show how the electrostatics of this thresholder’s gating junction improves machine learning inference accuracy and the energy efficiency of the neural network.
FedDL

Deep learning has been increasingly applied to improve human activity recognition (HAR) accuracy and reduce the human efforts of handcrafted feature extractions. Federated Learning (FL) is an emerging learning paradigm that enables the collaborative learning of a global model without exposing users' raw data. However, existing FL approaches yield unsatisfactory HAR performance as they fail to dynamically aggregate models according to the statistical diversity of users' data. In this paper, we propose FedDL, a novel federated learning system for HAR that can capture the underlying user relationships and apply them to learn personalized models for different users dynamically. Specifically, we design a dynamic layer sharing scheme that learns the similarity among users' model weights to form the sharing structure and merges models accordingly in an iterative, bottom-up layer-wise manner. FedDL merges local models based on the dynamic sharing scheme, significantly speeding up the convergence while maintaining high accuracy. We have implemented FedDL and evaluated using a new data set we collected using LiDAR and four public real-world datasets involving 178 users in total. The results show that FedDL outperforms several state-of-the-art FL paradigms in terms of model accuracy (by more than 15%), converging rate (by more than 70%), and communication overhead (about 30% reduction). Moreover, the testing results on the datasets of different scales show that FedDL has high scalability and hence can be deployed for large-scale real-world applications.
Towards emotive sensory Web in virtual health care: Trends, technologies, challenges and ethical issues

With fast-paced technological advancements coupled with increasing demands for virtual health care especially during the pandemic, health systems globally are on the cusp of transforming healthcare through emerging technologies. Such paradigm shift in digital healthcare requires effective integration of information, computing, communication, and connectivity technologies sharing health data through the web. The current technologies deployed in healthcare service delivery lack emotional competencies despite the growing demands for capturing and monitoring patients' affections, feelings and emotions. The importance of the emotive Sensory web has been gaining relevance in healthcare and society to remotely monitor patients’ emotional competencies during the pandemic like COVID-19 to minimize infections. In this respect, this paper discusses the significant benefits, trends, technologies, perceived challenges, and ethical issues of the emotive sensory Web in virtual health care. The study revealed that emerging technologies such as artificial intelligence, Internet of Things, Internet of Medical Things, 5G technology, Industry 5.0, big data, cloud computing are paramount in the development and implementation of sensory emotive Web in virtual health care. However, the standardization and synchronization of communication protocols, as well as devices interoperability issues, threatens the integration of these technologies. While the need for emotive sensory Web in virtual health care continues to increase, there are social, technological, infrastructural, economic barriers, security and ethical issues that hinder its implementation. Therefore, there is a need for developing technological infrastructure that supports the integration of appropriate technologies; crafting regional and international legal and regulatory policy through WHO; and standardization and interoperability of sensory devices.
A Concise Overview on Solar Resource Assessment and Forecasting

Abstract China’s recently announced directive on tackling climate change, namely, to reach carbon peak by 2030 and to achieve carbon neutrality by 2060, has led to an unprecedented nationwide response among the academia and industry. Under such a directive, a rapid increase in the grid penetration rate of solar in the near future can be fully anticipated. Although solar radiation is an atmospheric process, its utilization, as to produce electricity, has hitherto been handled by engineers. In that, it is thought important to bridge the two fields, atmospheric sciences and solar engineering, for the common good of carbon neutrality. In this überreview, all major aspects pertaining to solar resource assessment and forecasting are discussed in brief. Given the size of the topic at hand, instead of presenting technical details, which would be overly lengthy and repetitive, the overarching goal of this review is to comprehensively compile a catalog of some recent, and some not so recent, review papers, so that the interested readers can explore the details on their own.
Quantized Federated Learning Under Transmission Delay and Outage Constraints

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various communication schemes have been proposed to expedite the FL process, most of them have assumed ideal wireless channels which provide reliable and lossless communication links between the server and mobile clients. Unfortunately, in practical systems with limited radio resources such as constraint on the training latency and constraints on the transmission power and bandwidth, transmission of a large number of model parameters inevitably suffers from quantization errors (QE) and transmission outage (TO). In this paper, we consider such non-ideal wireless channels, and carry out the first analysis showing that the FL convergence can be severely jeopardized by TO and QE, but intriguingly can be alleviated if the clients have uniform outage probabilities. These insightful results motivate us to propose a robust FL scheme, named FedTOE, which performs joint allocation of wireless resources and quantization bits across the clients to minimize the QE while making the clients have the same TO probability. Extensive experimental results are presented to show the superior performance of FedTOE for deep learning-based classification tasks with transmission latency constraints.
Cloud Removal in Remote Sensing Images Using Generative Adversarial Networks and SAR-to-Optical Image Translation

Satellite images are often contaminated by clouds. Cloud removal has received special attention due to the wide range of satellite image applications. As the clouds thicken, the process of removing them becomes more challenging. In such cases, using auxiliary images, such as near-infrared or synthetic aperture radar (SAR), for reconstructing is common. In this study, we attempt to solve the problem using two generative adversarial networks (GANs): the first translates SAR images to optical images and the second removes clouds using the translated images of prior GAN. Also, we propose dilated residual inception blocks (DRIBs) instead of vanilla U-net in the generator networks and use structural similarity index measure (SSIM) in addition to the L1 loss function. Reducing the number of downsamplings and expanding receptive fields by dilated convolutions increased the quality of output images. We used the SEN1-2 dataset to train and test both GANs, and we made cloudy images by adding synthetic clouds to optical images. In addition, we used the SEN12MS-CR dataset to test network performance to remove real clouds. The restored images are evaluated using PSNR, SSIM, SAM, MAE, RMSE, and <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>. We compared the proposed method with state-of-the-art deep learning models and achieved more accurate results in both SAR-to-optical image translation and cloud removal parts.
Securing Environmental IoT Data Using Masked Authentication Messaging Protocol in a DAG-Based Blockchain: IOTA Tangle

The demand for the digital monitoring of environmental ecosystems is high and growing rapidly as a means of protecting the public and managing the environment. However, before data, algorithms, and models can be mobilized at scale, there are considerable concerns associated with privacy and security that can negatively affect the adoption of technology within this domain. In this paper, we propose the advancement of electronic environmental monitoring through the capability provided by the blockchain. The blockchain’s use of a distributed ledger as its underlying infrastructure is an attractive approach to counter these privacy and security issues, although its performance and ability to manage sensor data must be assessed. We focus on a new distributed ledger technology for the IoT, called IOTA, that is based on a directed acyclic graph. IOTA overcomes the current limitations of the blockchain and offers a data communication protocol called masked authenticated messaging for secure data sharing among Internet of Things (IoT) devices. We show how the application layer employing the data communication protocol, MAM, can support the secure transmission, storage, and retrieval of encrypted environmental sensor data by using an immutable distributed ledger such as that shown in IOTA. Finally, we evaluate, compare, and analyze the performance of the MAM protocol against a non-protocol approach.
MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally.