Reviewerly/exHarmony huggingface dataset:
Algorithms in Low-Code-No-Code for Research Applications: A Practical Review

Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20 years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub mentioned that the future of coding is no coding at all. This paper systematically reviewed several of the recent studies using mainstream LCNC platforms to understand the area of research, the LCNC platforms used within these studies, and the features of LCNC used for solving individual research questions. We identified 23 research works using LCNC platforms, such as SetXRM, the vf-OS platform, Aure-BPM, CRISP-DM, and Microsoft Power Platform (MPP). About 61% of these existing studies resorted to MPP as their primary choice. The critical research problems solved by these research works were within the area of global news analysis, social media analysis, landslides, tornadoes, COVID-19, digitization of process, manufacturing, logistics, and software/app development. The main reasons identified for solving research problems with LCNC algorithms were as follows: (1) obtaining research data from multiple sources in complete automation; (2) generating artificial intelligence-driven insights without having to manually code them. In the course of describing this review, this paper also demonstrates a practical approach to implement a cyber-attack monitoring algorithm with the most popular LCNC platform.
Framework for Implementation of Smart Driver Assistance System Using Augmented Reality

This research is to investigate momentum innovation for constantly detecting street signs from a moving vehicle. The most encouraging innovation for perceptive vehicle frameworks is vision sensors and image preparation, therefore this is examined the most thoroughly. Various handling calculations and study the world over concerned with sign acknowledgment are investigated. A functional framework has also been implemented using a regular web-camera installed in a testing car. This framework is limited to speed signs and achieves outstanding displays due to rapid but hearty computations. The division is based on shading data, and the recognition is based on a model coordinating computation. The human–computer interface is a voice that announces which sign has been discovered. Driver carelessness is a key element that commonly results in distortion of surroundings, for example, erroneous recognition or disregard of street and traffic signs. Although significant progress has been made in developing a vehicle capable of autonomous guided driving, development has been slow because to concerns of speed, safety, and the ever-changing complexity of the on-road situation. Street plays a key role for the motorist in gathering data from the sign and then acting in a similar fashion.
Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.
Improved wild horse optimization with levy flight algorithm for effective task scheduling in cloud computing

Abstract Cloud Computing, the efficiency of task scheduling is proportional to the effectiveness of users. The improved scheduling efficiency algorithm (also known as the improved Wild Horse Optimization, or IWHO) is proposed to address the problems of lengthy scheduling time, high-cost consumption, and high virtual machine load in cloud computing task scheduling. First, a cloud computing task scheduling and distribution model is built, with time, cost, and virtual machines as the primary factors. Second, a feasible plan for each whale individual corresponding to cloud computing task scheduling is to find the best whale individual, which is the best feasible plan; to better find the optimal individual, we use the inertial weight strategy for the Improved whale optimization algorithm to improve the local search ability and effectively prevent the algorithm from reaching premature convergence. To deliver services and access to shared resources, Cloud Computing (CC) employs a cloud service provider (CSP). In a CC context, task scheduling has a significant impact on resource utilization and overall system performance. It is a Nondeterministic Polynomial (NP)-hard problem that is solved using metaheuristic optimization techniques to improve the effectiveness of job scheduling in a CC environment. This incentive is used in this study to provide the Improved Wild Horse Optimization with Levy Flight Algorithm for Task Scheduling in cloud computing (IWHOLF-TSC) approach, which is an improved wild horse optimization with levy flight algorithm for cloud task scheduling. Task scheduling can be addressed in the cloud computing environment by utilizing some form of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. The proposed IWHOLF-TSC technique constructs a multi-objective fitness function by reducing Makespan and maximizing resource utilization in the CC platform. The IWHOLF-TSC technique proposed combines the wild horse optimization (WHO) algorithm and the Levy flight theory (LF). The WHO algorithm is inspired by the social behaviours of wild horses. The IWHOLF-TSC approach's performance can be validated, and the results evaluated using a variety of methods. The simulation results revealed that the IWHOLF-TSC technique outperformed others in a variety of situations.
Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds

Survey/review study Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds Xiang Li 1, Minglei Li 1, Pengfei Yan 1, Guanyi Li 1, Yuchen Jiang 1, Hao Luo 1,*, and Shen Yin 2 1 Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China 2 Department of Mechanical and Industrial Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim 7034, Norway * Correspondence: hao.luo@hit.edu.cn Received: 16 October 2022 Accepted: 25 November 2022 Published: 27 March 2023 Abstract: With the improvement of hardware computing power and the development of deep learning algorithms, a revolution of "artificial intelligence (AI) + medical image" is taking place. Benefiting from diversified modern medical measurement equipment, a large number of medical images will be produced in the clinical process. These images improve the diagnostic accuracy of doctors, but also increase the labor burden of doctors. Deep learning technology is expected to realize an auxiliary diagnosis and improve diagnostic efficiency. At present, the method of deep learning technology combined with attention mechanism is a research hotspot and has achieved state-of-the-art results in many medical image tasks. This paper reviews the deep learning attention methods in medical image analysis. A comprehensive literature survey is first conducted to analyze the keywords and literature. Then, we introduce the development and technical characteristics of the attention mechanism. For its application in medical image analysis, we summarize the related methods in medical image classification, segmentation, detection, and enhancement. The remaining challenges, potential solutions, and future research directions are also discussed.
Synthetic data generation: State of the art in health care domain

Recent progress in artificial intelligence and machine learning has led to the growth of research in every aspect of life including the health care domain. However, privacy risks and legislations hinder the availability of patient data to researchers. Synthetic data (SD) has been regarded as a privacy-safe alternative to real data and has lately been employed in many research and academic endeavors. This growing body of research needs to be consolidated for the researchers and practitioners to gain a quick and fruitful comprehension of the state of the art in synthetic data generation in health care. The purpose of this study is to collate and synthesize the current state of synthetic data generation following a narrative review of 70 peer-reviewed studies discussing privacy-preserving synthetic medical data generation techniques. The literature shows the effectiveness of synthetic datasets for different applications in research, academics, and testing according to existing statistical and task-based utility metrics. However, the focus on longitudinal synthetic data seems deficient. Moreover, a unified metric for generic quality assessment of synthetic data is lacking. The results of this review will serve as a quick reference guide for the researchers and practitioners in the healthcare domain to select a suitable synthetic data strategy for their application based on its strengths and weaknesses and pave the path for further research and development in healthcare.
Key concepts in artificial intelligence and technologies 4.0 in services

Abstract The emerging Industry 4.0 technologies that are impacting the global economy also represent an extraordinary opportunity to increase customer value in the service sector. Indeed, the ongoing Fourth Industrial Revolution differs from previous technologies in three main ways: (1) technological developments overcomes humans’ capabilities such that humans or even companies are no longer controlling technology; (2) customers embrace life in new technology-made environments, and (3) the boundaries between human and technology become to be blurred. This document explains these novel insights and defines the key AI-related concepts linked to each of these three distinctive aspects of Technologies 4.0 in services.
Federated learning in smart cities: Privacy and security survey

Over the last decade, smart cities (SC) have been developed worldwide. Implementing big data and the internet of things improves the monitoring and integration of different infrastructure systems in SC and thus makes our cities more efficient, livable, and sustainable. However, big data is more vulnerable to attacks and hacking, posing significant challenges to the SC's privacy and security. Moreover, more regulations and rules about protecting user data are being enforced worldwide. Compared with centralized machine learning, federated learning (FL) provides a natural method to protect users' privacy by distributing learning over decentralized data and offers artificial intelligence advantages for sensitive, heterogeneous data domains. Researchers have recently implemented FL, motivated by the improvement in privacy and security of integrated systems in SC. However, many technological problems and risks have been identified. This study provides a comprehensive review of FL's application to improve privacy and security in SC systems (i.e., transportation, healthcare, communication, etc.). The benefits, drawbacks, open research issues and future directions of the implementation of FL in SC are thoroughly discussed. The study concluded that the existing FL systems required comprehensive testing for different types of attacks, further improving the data protection and performance in SC.
Deep fusion of human-machine knowledge with attention mechanism for breast cancer diagnosis

Breast cancer is a common disease worldwide that poses a significant threat to the health of women. Many researchers have developed computer-aided diagnosis (CAD) systems to help clinicians identify breast cancer. However, the existing methods ignore the combination of image information and human clinical description in CAD systems. In this paper, we propose a novel breast tumor classification system based on spatial attention and cross-semantic human–machine knowledge fusion. Our system pairs the ultrasound image and human scoring data as the input of the network and maps them to the same feature space. Then a human–machine knowledge aggregation network based on channel attention is proposed to fuse features from images and human descriptions to classify breast tumors. In the image feature extraction process, we propose a spatial attention convolution neural network to automatically focus on the key regions of the image related to classification. We have conducted cross-validation experiments, and the comparative results have shown that our method can effectively improve classification performance and achieve the highest value in five evaluation metrics.
Dimension improvements based adaptation of control parameters in Differential Evolution: A fitness-value-independent approach

In this paper, we proposed a novel fitness-value-independent Differential Evolution to tackle complex real-parameter single-objective optimization. There were three innovations in the algorithm: First, dimension improvements based adaptation schemes for control parameters F and CR were advanced in this paper. Different from those fitness value improvements based parameter control in the recent winner DE variants, our algorithm has fitness-value-independent characteristic, therefore, it can be applied in much wider optimization scenarios especially for those that the exact fitness values of the objectives are unavailable. Second, a combined parabolic–linear reduction scheme for population size is employed in the algorithm based on the observation that the slower reduction of population size at the earlier stage of the evolution usually helps to get better perception of the objectives while a linear reduction of population size in the later stage helps to get better exploitation. Third, an indicator is proposed to monitor the diversity of the population and a corresponding population enhancement technique is launched when the diversity is detected bad. The first two innovations have the Parameter adaptive characteristic of the DE algorithm while the third innovation refers to population enhancement technique, therefore, we name our algorithm “the PaDE-pet algorithm”. Then, the algorithm is validated under a larger test suite containing 88 benchmarks from CEC2013, CEC2014 and CEC2017 test suites for real-parameter single objective optimization, which may avoid over-fitting problem in comparison with employing a test suite containing a small number of benchmarks. The experiments support the superiority of the novel PaDE-pet algorithm in comparison with several recently proposed powerful DE variants.
A reinforcement learning-based hybrid Aquila Optimizer and improved Arithmetic Optimization Algorithm for global optimization

This study constructs a reinforcement learning-based hybrid algorithm for Aquila Optimizer (AO) and improved Arithmetic Optimization Algorithm (IAOA). The point of the hybrid algorithm is that Q-learning can dynamically select the AO and the IAOA at different stages for different problems. In Arithmetic Optimization Algorithm (AOA), the mathematical optimization acceleration (MOA) function is restructured to balance global search and local exploitation, which can effectively stay away from the local optimum. Moreover, an improved reward function is modeled for Q-learning, which makes our hybrid algorithm more efficient and accurate. A set of benchmark functions and two engineering optimization problems are employed to test the performance of the proposed hybrid algorithm in this paper. Compared with other algorithms, the results show that the proposed hybrid algorithm has higher convergence speed and accuracy.
Blockchain-Aided Secure Semantic Communication for AI-Generated Content in Metaverse

The construction of virtual transportation networks requires massive data to be transmitted from edge devices to Virtual Service Providers (VSP) to facilitate circulations between the physical and virtual domains in Metaverse. Leveraging semantic communication for reducing information redundancy, VSPs can receive semantic data from edge devices to provide varied services through advanced techniques, e.g., AI-Generated Content (AIGC), for users to explore digital worlds. But the use of semantic communication raises a security issue because attackers could send malicious semantic data with similar semantic information but different desired content to break Metaverse services and cause wrong output of AIGC. Therefore, in this paper, we first propose a blockchain-aided semantic communication framework for AIGC services in virtual transportation networks to facilitate interactions of the physical and virtual domains among VSPs and edge devices. We illustrate a training-based targeted semantic attack scheme to generate adversarial semantic data by various loss functions. We also design a semantic defense scheme that uses the blockchain and zero-knowledge proofs to tell the difference between the semantic similarities of adversarial and authentic semantic data and to check the authenticity of semantic data transformations. Simulation results show that the proposed defense method can reduce the semantic similarity of the adversarial semantic data and the authentic ones by up to 30% compared with the attack scheme.
Relay-Assisted Federated Edge Learning: Performance Analysis and System Optimization

In this paper, we study a relay-assisted federated edge learning (FEEL) network under latency and bandwidth constraints. In this network, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> users collaboratively train a global model assisted by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> intermediate relays and one edge server. We firstly propose partial aggregation and spectrum resource multiplexing at the relays in order to improve the communication of the relay-assisted FEEL system. Furthermore, we derive analytical and asymptotic expressions of the system outage probability and convergence rate. For the purpose of improving the system performance, we further optimize the relay-assisted FEEL network by maximizing the number of users who participate in each round of federated learning, through allocation of the wireless bandwidth among users and relays. Specifically, two bandwidth allocation (BA) schemes have been proposed, assuming either instantaneous or statistical channel state information (CSI). Simulations show the advantages of the proposed BA schemes over other benchmarks, regarding the accuracy and convergence rate of the considered relay-assisted FEEL network.
Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform

Accurate daily solar radiation prediction is a crucial task for the management and generation of solar energy as one of the alternatives to fossil fuels. In this study, the prediction accuracy of new machine learning methods, wavelet long short-term memory (WLSTM), wavelet multi-layer perceptron artificial neural network (WMLPANN), long short-term memory (LSTM), multi-layer perceptron artificial neural network (MLPANN), and multivariate adaptive regression splines (MARS), was assessed for modeling daily solar radiation using various input combinations of climatic data of maximum and minimum relative humidity, potential evapotranspiration, maximum and minimum temperature, precipitation and wind speed from two stations, Brownstown and Carbondale located in Illinois, USA. For accurate assessment of prediction accuracy of the proposed models, four reliable statistical metrics, root mean squared error (RMSE), Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and One-Tailed Wilcoxon Signed-Rank Test were employed. Comparison of results, based on the RMSE values, indicated that the WLSTM method performed better than the WMLPANN, LSTM, MLPANN and MARS methods in the estimation of solar radiation values at both stations. The average RMSE values of WMLPANN, LSTM, MLPANN, and MARS approaches was decreased by 6%, 4%, 7.3%, and 13.5% using WLSTM method at Brownstown Station, by 6%, 5.2%, 13.2%, and 14.3% at Carbondale Station, respectively. The overall results during the testing phase of both stations revealed the successful application of hybridization of the LSTM model with the wavelet transform technique for improving the prediction accuracy of solar radiation based on climatic parameters.
Integration of blockchain-enabled closed-loop supply chain and robust product portfolio design

Blockchain technology is one of the latest financial concepts influencing international trades, which contributes to speeding the supply chains by managing faster and more accurate financial transactions. Moreover, the product portfolio is highly effective on the financial flow of the supply chain. Accordingly, in this research, a comprehensive framework is proposed to design a blockchain-enabled closed-loop supply chain (BCSC), considering the role of the product portfolio. First, a mathematical model for robust product portfolio design is proposed. Next, a closed-loop supply chain network is designed where the financial flow via blockchain is of concern. To optimize this supply chain, the change in equity, as well as satisfaction from the blockchain network, is maximized. An exact solution method using GAMS software is applied to optimize the proposed mathematical model. The results obtained by implementation and analysis of this proposed framework indicate that by designing a product portfolio based on a robust model, BCSC would achieve the ideal value for its financial indexes, the change in equity in specific. Comparison of this newly proposed framework with the integrated portfolio-supply chain model reveals a 0.33% average error and a 1.5% reduction in lost revenue. It is concluded that this proposed framework assists supply chain managers in reducing their inaccurate decisions in both the physical and financial flow considerably.