Reviewerly/exHarmony huggingface dataset:
Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions to optimization problems. The main inspiration for the DA is to simulate the selection of objects from different drawers to create an optimal combination. The optimization process involves a dresser with a given number of drawers, where similar items are placed in each drawer. The optimization is based on selecting suitable items, discarding unsuitable ones from different drawers, and assembling them into an appropriate combination. The DA is described, and its mathematical modeling is presented. The performance of the DA in optimization is tested by solving fifty-two objective functions of various unimodal and multimodal types and the CEC 2017 test suite. The results of the DA are compared to the performance of twelve well-known algorithms. The simulation results demonstrate that the DA, with a proper balance between exploration and exploitation, produces suitable solutions. Furthermore, comparing the performance of optimization algorithms shows that the DA is an effective approach for solving optimization problems and is much more competitive than the twelve algorithms against which it was compared to. Additionally, the implementation of the DA on twenty-two constrained problems from the CEC 2011 test suite demonstrates its high efficiency in handling optimization problems in real-world applications.
Medical visual question answering: A survey

Medical Visual Question Answering~(VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we collect and discuss the publicly available medical VQA datasets up-to-date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. We summarize and discuss their techniques, innovations, and potential improvements. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions. Our goal is to provide comprehensive and helpful information for researchers interested in the medical visual question answering field and encourage them to conduct further research in this field.
FTX Collapse and systemic risk spillovers from FTX Token to major cryptocurrencies

We examine the dynamic lower tail dependence and downside risk spillover between the FTX Token and seven major cryptocurrencies using Rotated Gumbel copula and GARCH copula quantile regression-based ∆CoVaR models. Daily data is analyzed from May 1, 2020 to December 31, 2022. The results show a strong evidence of risk spillover effects from FTX Token to crypto markets. Solana, followed by Cardano, displays the largest downside risk spillover. Tether and Bitcoin are affected least by the FTX fallout, receiving the lowest downside risk spillovers. Furthermore, the dynamic risk spillover effects are heterogeneous over time and comparatively different for each cryptocurrency.
Relative margin induced support vector ordinal regression

As a classical ordinal regression model, support vector ordinal regression (SVOR) finds (r-1) parallel discriminant hyperplanes via maximizing the minimal margins between different ranks. The ordinal relation is guaranteed by explicit or implicit constraints. However, the minimal margin between adjacent ranks is only determined by minor patterns near the margin hyperplanes and others have no influence on the discriminant hyperplane learning. In order to reflect the contributions of these patterns, this paper proposes relative margin induced support vector ordinal regression (RMSVOR) models, in which the margin between a pattern and a discriminant hyperplane is depicted by a function of relative margin information to reflect its contribution on this hyperplane. The relative margin information is estimated by nearest neighbor chain to reflect the prior knowledge of the pattern in training set. The experiments, performed on discretized regression datasets and real ordinal regression datasets, demonstrate that RMSVOR is superior to previous ordinal regression models (SVOR, NPSVOR and NPHORM) and canonical multi-class classification models (OvASVM and OvOSVM).
A Comprehensive Cybersecurity Framework for Present and Future Global Information Technology Organizations

This chapter examines how education, technology, national and international regulations contribute to a comprehensive cybersecurity framework for present and future global IT companies. IT-driven enterprises may utilize the following security recommendations. Businesses who seek to examine their external and internal security with security upload and establish settings for success regardless of location must solve these issues. To produce more effective legislation, education efforts, and technologies that are resistant to cyberattacks, this work explores fundamental research gaps in cybersecurity and demonstrates how cybersecurity may be divided into these three fundamental categories and integrated to tackle problems such as the creation of training environments for authentic cybersecurity situations. It will explain links between technology and certification and discuss legislative standards and instructional frameworks for merging criteria for system accreditation and cybersecurity. The study finishes with wireless network security recommendations.
Blockchain Technology in Renovating Healthcare

The positive effect of technology on health care is known as digital transformation. Wearable fitness technology, telemedicine, and AI-enabled medical devices are just a few examples of how health care is undergoing a digital transformation. These are intended to revolutionize the health-care industry by improving patient care, streamlining operations, and lowering costs. They are still facing significant challenges in patient data security and privacy, invoicing and payment processing, medical supply chain, and drug integrity. Blockchain technology can absolve the healthcare industry from facing these challenges; it can establish a blockchain of medical records. Blockchain is considered highly secure, transparent, and immune to hackers due to its digital encryption. This article examined the potential of blockchain technology to improve the security, privacy, and interoperability of healthcare data, and it suggested a few advanced uses of blockchain in the health care domain after a thorough analysis of the existing major challenges in the sector.
Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms

Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms. A robust large-scale study of how the performance of these platforms scales with the number of qubits is key to providing quantum solutions to challenging industry problems. This work benchmarks the runtime and accuracy for a representative sample of specialized high-performance simulated and physical quantum processing units. Results show the QMware simulator can reduce the runtime for executing a quantum circuit by up to 78% compared to the next fastest option for algorithms with fewer than 27 qubits. The AWS SV1 simulator offers a runtime advantage for larger circuits, up to the maximum 34 qubits available with SV1. Beyond this limit, QMware can execute circuits as large as 40 qubits. Physical quantum devices, such as Rigetti's Aspen-M2, can provide an exponential runtime advantage for circuits with more than 30 qubits. However, the high financial cost of physical quantum processing units presents a serious barrier to practical use. Moreover, only IonQ's Harmony quantum device achieves high fidelity with more than four qubits. This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.
A decision-making framework for blockchain platform evaluation in spherical fuzzy environment

Blockchain systems with critical features (e.g., security, flexibility against attacks, and data traceability) have created possibilities for users to develop innovative economic and business models. Despite the abundance and variety of features of blockchain technology, choosing a suitable and reliable blockchain platform is somewhat challenging. Several criteria, including profitability, sustainability, and popularity, should be considered to identify such platforms. Accordingly, this study investigates the blockchain platform evaluation and selection as a multi-criteria decision-making (MCDM) problem. In this regard, we propose an integrated approach based on the extended version of MCDM methods in a spherical fuzzy environment. In the developed approach, the main criteria are weighted using the developed spherical fuzzy best-worst method (SF-BWM) to reduce uncertainty in pairwise comparisons. Then, the 15 blockchain platforms are analyzed and ranked using spherical fuzzy measurement of alternatives and ranking according to the compromise solution (SF-MARCOS). The outputs of the SF-BWM-MARCOS approach imply that Bitcoin and Ethereum are placed in the first and second priorities, respectively. The comparison of the results with other MCDM methods and sensitivity analysis demonstrates the performance of the proposed approach and its ranking stability in various scenarios.
Fitness-Distance-Constraint (FDC) based guide selection method for constrained optimization problems

In the optimization of constrained type problems, the main difficulty is the elimination of the constraint violations in the evolutionary search process. Evolutionary algorithms are designed by default according to the requirements of unconstrained and continuous global optimization problems. Since there are no constraint functions in these type of problems, the constraint violations are not considered in the design of the guiding mechanism of evolutionary algorithms. In this study, two new methods were introduced to redesign the evolutionary algorithms in accordance with the requirements of constrained optimization problems. These were (i) constraint space-based, called Fitness-Distance-Constraint (FDC), selection method and (ii) dynamic guiding mechanism. Firstly, thanks to the FDC guide selection method, the constraint violation values of the individuals in the population were converted into score values and the individuals who increase the diversity in the search process were selected as guide. On the other hand, in dynamic guiding mechanism, the FDC method was applied in case of constraint violation, otherwise the default guide selection method was used The proposed methods were used to redesign the guiding mechanism of adaptive guided differential evolution (AGDE), a current evolutionary algorithm, and the FDC-AGDE algorithm was designed. The performance of the FDC-AGDE was tested on eleven different constrained real-world optimization problems. The results of the FDC-AGDE and AGDE were evaluated using the Friedman and Wilcoxon test methods. According to Wilcoxon pairwise results, the FDC-AGDE showed better performance than the AGDE in nine of the eleven problems and equal performance in two of the eleven problems. Moreover, the proposed algorithm was compared with the competitive and up-to-date MHS algorithms in terms of the results of Friedman test, Wilcoxon test, feasibility rate, and success rate. According to Friedman test results, the first three algorithms were the FDC-AGDE, LSHADE-SPACMA, and AGDE algorithms with the score of 2.69, 4.05, and 4.34, respectively. According to the mean values of the success rates obtained from the eleven problems, the FDC-AGDE, LSHADE-SPACMA, and AGDE algorithms ranked in the first three with the success rates of 67%, 48% and 28%, respectively. Consequently, the FDC-AGDE algorithm showed a superior performance comparing with the competing MHS algorithms. According to the results, it is expected that the proposed methods will be widely used in the constrained optimization problems in the future.
MaCySTe: A virtual testbed for maritime cybersecurity

<h2>Abstract</h2> The increasing reliance on technology and automation in the maritime sector has made it more vulnerable to cyberattacks. Cyber threats to ships and offshore platforms can have serious consequences, including accidents leading to loss of life, asset damage, or environmental impact. One primary means of countering these threats is developing and testing countermeasures tailored to the shipboard infrastructures and training the personnel. To this aim, we present MaCySTe, an open-source project that provides a testbed reproducing the network infrastructure and core components of the onboard cyber–physical systems. As it interfaces with ship simulators, it allows for realistic testing and training sessions involving cybersecurity and maritime operators. We expect that MaCySTe will contribute to fostering cybersecurity protection and awareness in this critical sector.
Exploring the Potential of Metaverse Technology in Healthcare: Applications, Challenges, and Future Directions

In recent times, the emergence of the Metaverse has garnered worldwide attention as an innovative digital space that holds immense potential to provide a wide range of health services to medical professionals and patients. With increasing stress on healthcare systems, it has become crucial to explore the latest and cost-effective solutions that can provide fast and reliable medical services. The focus of this study, therefore, is to explore applications of metaverse in various health care systems and elaborate on how it can efficiently improve the clinical management of patients. Consequently, an in-depth assessment of the metaverse has been carried out, while covering its core fundamentals, key technologies, and diverse applications in healthcare and medicine, including but not limited to, emergency response learning, hands-on experience in anatomy learning, orthopaedics, paediatrics and so on. To carry out the study, we have used an exploratory approach to analyze qualitative data on healthcare metaverse services in our systematic review. Relevant articles from scientific databases such as Web of Science, Springer, Scopus, and IEEE have been identified, and the analysis has been conducted using the PRISMA reporting guideline to ensure transparent and comprehensive reporting. The results of the study suggest that the metaverse has the potential to transform healthcare systems by introducing novel methods for delivering healthcare services. Metaverse’s AR/VR technologies can enable remote medical consultations and training, benefiting patients and healthcare professionals. Additionally, patients can access health-related information and resources, empowering them to manage their health better and make more informed decisions.
Deep attention SMOTE: Data augmentation with a learnable interpolation factor for imbalanced anomaly detection of gas turbines

Anomaly detection of gas turbines faces the significant challenges of data imbalance and inter-class overlap. In this paper, we develop a novel data augmentation method, namely deep attention synthetic minority over-sampling technique with the Encoder-Decoder (DA-SMOTE-ED), which serves as a key step in our hybrid re-sampling scheme. To reduce the risk of generating noise data, on one hand, the DA-SMOTE-ED leverages an Encoder-Decoder to learn a class-separable feature space to weaken the effect of inter-class overlap. On the other hand, an attention module is applied to assign proper interpolation factors to generate synthetic samples that stay off the aggregation area of normal samples. Moreover, synthetic samples are generated in the learnable feature space, mapped back to the original space, and merged with under-sampled samples to form the balanced dataset. Finally, the superiority of the developed method is validated through two case studies including the real monitoring data of gas turbines and the modified version of the commercial modular aero-propulsion system simulation (C-MAPPS) dataset. More specifically, its average balanced accuracy is 91.77 % on the gas turbine dataset, yielding 3.67 %, 6.4 %, and 5.56 % improvements compared to the SMOTE-ENN, TimeGAN, and AugmentTS, respectively.
Fine-tuned YOLOv5 for real-time vehicle detection in UAV imagery: Architectural improvements and performance boost

Nowadays, Unmanned Aerial Vehicles (UAVs) have become useful for various civil applications, such as traffic monitoring and smart parkings, where real-time vehicle detection and classification is one of the key tasks. There are many challenges in detecting vehicles including small size objects and the variety in the UAV’s altitude and angle. As classic object detection solutions have limitations in confronting these challenges, recent methods are developed based on convolutional neural networks and their ability in effective feature learning. Due to the computational complexity in these networks and the need for accurate and real-time object detection, balancing the accuracy and inference speed is obligatory for efficiency. This paper aims to propose an accurate, efficient and real-time vehicle detection network based on the successful YOLOv5 object detection model. This is done by improving the structure of the model, adding attention mechanism and using an adaptive bounding box regression loss function. Also, considering the need for real-time inference speed, the depth and width of the model was balanced and ghost convolution was incorporated into the Neck unit to further improve the balance between accuracy and inference speed. The proposed method is evaluated on three different urban UAV imagery datasets, VisDrone, CARPK and VAID, specifically intended for civil applications. Comparing the obtained results from the proposed method with YOLOv5 baseline models, it achieved 3.52% higher mAP50 and 207.15% higher FPS than YOLOv5X on VisDrone dataset, while it is much smaller in size and GFLOPS. Totally, the proposed network outcomes show how the applied structural and conceptual modifications can upgrade the YOLO family towards being small in size, high in accuracy and fast in inference speed.
ChatGPT: A Valuable Tool for Emergency Medical Assistance

Currently, the physician-patient relationship is becoming a triad, with patients relying more on online symptom research. In November 2022, Chat Generative Pretrained Transformer (ChatGPT), a chat-based artificial intelligence (AI) language model, was introduced. Since its launch, it has rapidly gained millions of users and proven capable of handling complex writing tasks, from composing essays to solving complicated problems.1 Although its effect on the education system may seem controversial, its widespread popularity presents an opportunity for medical professionals to harness its potential.
Feature: The Profession of Teaching English in the Two-Year College: Findings from the 2019 TYCA Workload Survey

In fall 2019, the Two-Year College English Association distributed a survey to two-year college English faculty across the United States through professional listservs, regional distribution lists, and social media platforms. This report summarizes the key data derived from 1,062 responses to close-ended questions about workload related to teaching, service, leadership, and professional development. The report discusses the demographic profile, employment status, and contractual obligations in course assignments of the two-year college English faculty who responded. It also summarizes Information about respondents’ overload teaching, their autonomy within their teaching responsibilities, and the kinds of service and professional development activities in which they engaged.
Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records

Conducting a systematic review demands a significant amount of effort in screening titles and abstracts. To accelerate this process, various tools that utilize active learning have been proposed. These tools allow the reviewer to interact with machine learning software to identify relevant publications as early as possible. The goal of this study is to gain a comprehensive understanding of active learning models for reducing the workload in systematic reviews through a simulation study.The simulation study mimics the process of a human reviewer screening records while interacting with an active learning model. Different active learning models were compared based on four classification techniques (naive Bayes, logistic regression, support vector machines, and random forest) and two feature extraction strategies (TF-IDF and doc2vec). The performance of the models was compared for six systematic review datasets from different research areas. The evaluation of the models was based on the Work Saved over Sampling (WSS) and recall. Additionally, this study introduces two new statistics, Time to Discovery (TD) and Average Time to Discovery (ATD).The models reduce the number of publications needed to screen by 91.7 to 63.9% while still finding 95% of all relevant records (WSS@95). Recall of the models was defined as the proportion of relevant records found after screening 10% of of all records and ranges from 53.6 to 99.8%. The ATD values range from 1.4% till 11.7%, which indicate the average proportion of labeling decisions the researcher needs to make to detect a relevant record. The ATD values display a similar ranking across the simulations as the recall and WSS values.Active learning models for screening prioritization demonstrate significant potential for reducing the workload in systematic reviews. The Naive Bayes + TF-IDF model yielded the best results overall. The Average Time to Discovery (ATD) measures performance of active learning models throughout the entire screening process without the need for an arbitrary cut-off point. This makes the ATD a promising metric for comparing the performance of different models across different datasets.
Evolutionary-state-driven multi-swarm cooperation particle swarm optimization for complex optimization problem

Particle swarm optimization (PSO) has been widely used to solve various practical engineering problems, but it has shortcomings such as premature convergence and poor global search ability. To solve these problems, evolutionary-state-driven multi-swarm cooperation particle swarm optimization (ESD-PSO) is proposed, using an adaptive multi-swarm cooperation mechanism (AMC) to improve the information exchange efficiency among all sub-swarms. In AMC, the population is randomly divided into multiple equal sub-swarms, and the best particle of a sub-swarm is used to evaluate its evolutionary state. When the number of stagnant sub-swarms reaches a threshold, all sub-swarms are adaptively regrouped. In addition, if a sub-swarm is stagnant for a long time without reaching the regroup condition, a stagnation compensation strategy (SCS) is triggered to enhance the stagnant sub-swarm diversity. In this case, a compensation sample is generated to help the stagnant sub-swarm to again search for promising regions. A competitive disturbance mechanism (CDM) is used to increase the solution accuracy. The performance of ESD-PSO is evaluated on the CEC2013 and CEC2017 test suites, and three engineering optimization problems. Experimental results show that ESD-PSO outperforms compared PSO variants.