Reviewerly/exHarmony huggingface dataset:
Adapting an English Corpus and a Question Answering System for Slovene

Developing effective question answering (QA) models for less-resourced languages like Slovene is challenging due to the lack of proper training data. Modern machine translation tools can address this issue, but this presents another challenge: the given answers must be found in their exact form within the given context since the model is trained to locate answers and not generate them. To address this challenge, we propose a method that embeds the answers within the context before translation and evaluate its effectiveness on the SQuAD 2.0 dataset translated using both eTranslation and Google Cloud translator. The results show that by employing our method we can reduce the rate at which answers were not found in the context from 56% to 7%. We then assess the translated datasets using various transformer-based QA models, examining the differences between the datasets and model configurations. To ensure that our models produce realistic results, we test them on a small subset of the original data that was human-translated. The results indicate that the primary advantages of using machine-translated data lie in refining smaller multilingual and monolingual models. For instance, the multilingual CroSloEngual BERT model fine-tuned and tested on Slovene data achieved nearly equivalent performance to one fine-tuned and tested on English data, with 70.2% and 73.3% questions answered, respectively. While larger models, such as RemBERT, achieved comparable results, correctly answering questions in 77.9% of cases when fine-tuned and tested on Slovene compared to 81.1% on English, fine-tuning with English and testing with Slovene data also yielded similar performance.
Investigating Cyber Security Awareness (CSA) Amongst Managers in Small and Medium Enterprises (SMEs)

Cyber-attacks have received a lot of attention in recent years due to the increased number of attacks, and subsequently risk of confidentiality, integrity, and availability of critical personal and business data. Attack vectors are evolving and become more complex, raising the need for computer user training on how to safely operate in cyberspace. Unfortunately, cyber security training is not a priority to Small and Medium Enterprises (SMEs) in a developing country such as South Africa as it does not directly improve profits. As a result, SMEs are left vulnerable to ever-changing cyber-attacks. The study investigated the current level of cybersecurity awareness amongst managers in Small and Medium Enterprises (SMEs) in South Africa. Fifteen SMEs residing in the economic hub of South Africa participated in the qualitative study through the use of structured interviews. The results indicated that SMEs are aware of cybersecurity threats as they experience many at the moment, and understand related cybersecurity concepts and possible consequences of a cybersecurity breach. However, budget and time constraints were identified as the main challenges when cybersecurity awareness programs are considered. In addition, IT tasks are often outsourced, negatively influencing the control over the implementation of cybersecurity measures. Although some organizations indicated that they have cyber security policies in place, these policies were often not regularly updated and enforced. As a result, it is recommended that: (a) SMEs should make renewed efforts to foster cybersecurity awareness amongst employees; (b) develop and continuously adjust cybersecurity policies; and (c) prioritize the implementation and education of cybersecurity concepts.
Artificial Intelligence Improves Detection of Supplemental Screening Ultrasound-detected Breast Cancers in Mammography

Despite recent advances in artificial intelligence (AI) software with improved performance in mammography screening for breast cancer, insufficient data are available on its performance in detecting cancers that were initially missed on mammography. In this study, we aimed to determine whether AI software-aided mammography could provide additional value in identifying cancers detected through supplemental screening ultrasound. We searched our database from 2017 to 2018 and included 238 asymptomatic patients (median age, 50 years; interquartile range, 45-57 years) diagnosed with breast cancer using supplemental ultrasound. Two unblinded radiologists retrospectively reviewed the mammograms using commercially available AI software and identified the reasons for missed detection. Clinicopathological characteristics of AI-detected and AI-undetected cancers were compared using univariate and multivariate logistic regression analyses. A total of 253 cancers were detected in 238 patients using ultrasound. In an unblinded review, the AI software failed to detect 187 of the 253 (73.9%) mammography cases with negative findings in retrospective observations. The AI software detected 66 cancers (26.1%), of which 42 (63.6%) exhibited indiscernible findings obscured by overlapping dense breast tissues, even with the knowledge of magnetic resonance imaging and post-wire localization mammography. The remaining 24 cases (36.4%) were considered interpretive errors by the radiologists. Invasive tumor size was associated with AI detection after multivariable analysis (odds ratio, 2.2; 95% confidence intervals, 1.5-3.3; p < 0.001). In the control group of 160 women without cancer, the AI software identified 19 false positives (11.9%, 19/160). Although most ultrasound-detected cancers were not detected on mammography with the use of AI, the software proved valuable in identifying breast cancers with indiscernible abnormalities or those that clinicians may have overlooked.
Synthetic MRI Generation from CT Scans for Stroke Patients

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, grey matter, and cerebrospinal fluid by using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.
Fast and Accurate SNN Model Strengthening for Industrial Applications

In spiking neural networks (SNN), there are emerging security threats, such as adversarial samples and poisoned data samples, which reduce the global model performance. Therefore, it is an important issue to eliminate the impact of malicious data samples on the whole model. In SNNs, a naive solution is to delete all malicious data samples and retrain the entire dataset. In the era of large models, this is impractical due to the huge computational complexity. To address this problem, we present a novel SNN model strengthening method to support fast and accurate removal of malicious data from a trained model. Specifically, we use untrained data that has the same distribution as the training data. We can infer that the untrained data has no effect on the initial model, and the malicious data should have no effect on the final refined model. Thus, we can use the model output of the untrained data with respect to the initial model to guide the final refined model. In this way, we present a stochastic gradient descent method to iteratively determine the final model. We perform a comprehensive performance evaluation on two industrial steel surface datasets. Experimental results show that our model strengthening method can provide accurate malicious data elimination, with speeds 11.7× to 27.2× faster speeds than the baseline method.
Deformation behaviour of high‐strength steel welds using DIC

Abstract This paper focuses on the deformation and strength behaviour of high‐strength steel (HSS) transverse fillet welds. The behaviour of six lap‐welded fillet joints made of high‐strength steels (HSSs) is investigated under tension load through experiment. Due to some weaknesses in the deformation measurement using traditional techniques, the digital image correlation technique (DIC) is used to measure the deformation of transverse fillet weld connections. The development of the deformation on the specimen throughout the loading process was analysed using DIC measurement. The deformation of transverse fillet lap‐welded connections from HSS is always less than 1 mm. The strength calculated from the weld's fracture surface area shows better results than the theoretical weld area because it provides the real strength of the weld. In most cases, the weld failure did not occur along the throat section. Moreover, it is confirmed that the analytical models offered by prEN 1993‐1‐8:2020 are conservative for all the specimens.
Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.
Evaluation on ChatGPT for Chinese Language Understanding

ABSTRACT ChatGPT has attracted extension attention of academia and industry. This paper aims to evaluate ChatGPT in Chinese language understanding capability on 6 tasks using 11 datasets. Experiments indicate that ChatGPT achieved competitive results in sentiment analysis, summary, and reading comprehension in Chinese, while it is prone to factual errors in closed-book QA. Further, on two more difficult Chinese understanding tasks, that is, idiom fill-in-the-blank and cants understanding, we found that a simple chain-of-thought prompt can improve the accuracy of ChatGPT in complex reasoning. This paper further analyses the possible risks of using ChatGPT based on the results. Finally, we briefly describe the research and development progress of our ChatBIT.
A bibliometric review of information technology, artificial intelligence, and blockchain on auditing

AbstractThis study analyzes the state of the art in the literature about information technology, artificial intelligence, and blockchain in auditing. A bibliometric analysis was utilized to depict publishing activity, research trends, and popular topics. In all, 328 studies were published between 2017, and 2022 in the Web of Science core collection, which is one of the most trustworthy databases, and the most important authors, journals, topics, and countries related to the keywords of the study were identified. To visually represent the data, the study employed the software VOSviewer, which graphically displays the relationships and patterns within the literature. This research allows us to suggest potential future research directions that may be useful in reflecting on the significant impact that technology will have on the development of the auditing profession. Four independent research trends were identified: The first trend shows the determinants of independence, and audit quality in an information technology environment. The second trend focuses on the challenges of applying blockchain technology in the auditing, and accounting professions. The third trend is the analysis of accounting data using artificial intelligence technologies. The fourth trend is related to the impact of modern technological systems on auditors' performance in the future.Keywords: Information technologyartificial intelligenceblockchainauditbibliometric analysis Disclosure statementNo potential conflict of interest was reported by the author(s).
Grand challenges in intelligent aerospace systems

SPECIALTY GRAND CHALLENGE article Front. Aerosp. Eng., 12 September 2023Sec. Intelligent Aerospace Systems Volume 2 - 2023 | https://doi.org/10.3389/fpace.2023.1281522
Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification

The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification.Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance.The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set.Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.
Multi-population-based Algorithms with Different Migration Topologies and Their Improvement by Population Re-initialization

In this paper, the possibilities of improving the performance of multi-population-based algorithms were tested. In the proposed approach, it was decided to test various methods and parameters of the population re-initialization mechanism, aimed at improving the diversity of individuals and preventing premature convergence, which is associated with a possible improvement in obtained results. In addition to the standard approach with random re-initialization of a new population, it was decided to test an approach in which selected populations are initialized with the use of modified individuals from better-performing populations. This approach has not been thoroughly tested so far, in particular for many different migration topologies and different population-based algorithms. The presented approach was specifically tested for the MNIA algorithm, eliminating the need to select one specific algorithm for the optimization. The simulations were performed for typical benchmark functions. The results of the simulations allow us to conclude that the proposed approach, depending on the parameters, improved the optimization process.
Test Case Generator for Problems of Complete Coverage and Path Planning for Emergency Response by UAVs

Unmanned Aerial Vehicles (UAVs) can aid rescue workers during operational emergency response procedures in tasks such as communication delivery or aerial reconnaissance of the area. Before a UAV team starts operating in the natural environment, multiple simulations aimed at experimental verification of their paths’ effectiveness are necessary. Simulations require demanding test cases that exhibit different types of problem complexity and are easily controlled by a simple set of parameters. We develop a model of the problem adjusted to the specific requirements of the optimization algorithm. Then, we generate a set of problem-specific benchmarks using the model and available statistical information about population density levels for the entire area of Poland. The proposed generator divides the selected area into convex regions with a given population density level. Depending on the parameter settings, it generates more than one map of convex regions from a single raw data. The simulation results show the diversity of the obtained test cases and their main features.
An automated materials and processes identification tool for material informatics using deep learning approach

This article reports a tool that enables Materials Informatics, termed as MatRec, via a deep learning approach. The tool captures data, makes appropriate domain suggestions, extracts various entities such as materials and processes, and helps to establish entity-value relationships. This tool uses keyword extraction, a document similarity index to suggest relevant documents, and a deep learning approach employing Bi-LSTM for entity extraction. For example, materials and processes for electrical charge storage under an electric double layer capacitor (EDLC) mechanism are demonstrated herewith. A knowledge graph approach finds and visualizes different latent knowledge sets from the processed information. The MatRec received an F1 score of 9̃6% for entity extraction, 8̃3% for material-value relationship extraction, and 8̃7% for process-value relationship extraction, respectively. The proposed MatRec could be extended to solve material selection issues for various applications and could be an excellent tool for academia and industry.
Basic Research Perspective on Quantum Information Science for the Aerospace Community

Scientific and technological progress has repeatedly revolutionized society, from the invention of steam-power-driven machines that brought forth the Industrial Age to the development of the transistor that sparked the Information Age. As societies and economies become more connected through global communication networks and reliant on computational power, the demand for faster, more efficient, and secure information capabilities escalates commensurately. Enter Quantum Information Science (QIS) (QIS), a field founded on the physical laws of quantum mechanics that introduce extraordinary different and powerful paradigms for generating, manipulating, measuring, securing, and processing information. While many quantum effects are generally well understood and form the basis of widely exploited current technology, QIS is still a richly underexplored research area. This overview provides a basic research perspective on the foundational resources that empower QIS, the main challenges in exploiting QIS for practical applications, and the need for input from researchers outside quantum physics, such as those from the aerospace community, to stimulate new ideas and advance progress in QIS. This paper introduces key QIS concepts and continues with discussions on each of the following subareas: sensing, computation and algorithms, networks, and information security. The paper concludes with an outlook of the field as it relates to aerospace.
Blockchain-Based Triple-Entry Accounting: A Systematic Literature Review and Future Research Agenda

ABSTRACT Recent cases, such as Wirecard, Luckin Coffee, and Steinhoff, highlight the vulnerability of current accounting practices and question their suitability to prevent criminal behavior. Triple-entry accounting (TEA) is a possible answer against accounting fraud and financial manipulation that is increasingly discussed in research and practice. By being based on blockchain technology, TEA is tamper-proof and, thus, might be an effective instrument against manipulation in accounting. However, despite the potential of TEA, research on this topic is scattered and mostly isolated from each other. By conducting a systematic literature review, we synthesize and summarize current research on blockchain-based TEA. Hereby, we find that current research is almost entirely theoretical. Up to now, real-world implementations and their evaluation are still missing in both research and practice. To address these gaps, we develop guiding research questions for interested scholars to pave the way for TEA’s practical use. JEL Classifications: M40; M41.
Early breast cancer detection and differentiation tool based on tissue impedance characteristics and machine learning

During Basic screening, it is challenging, if not impossible to detect breast cancer especially in the earliest stage of tumor development. However, measuring the electrical impedance of biological tissue can detect abnormalities even before being palpable. Thus, we used impedance characteristics data of various breast tissue to develop a breast cancer screening tool guided and augmented by a deep learning (DL). A DL algorithm was trained to ideally classify six classes of breast cancer based on electrical impedance characteristics data of the breast tissue. The tool correctly predicted breast cancer in data of patients whose breast tissue impedance was reported to have been measured when other methods detected no anomaly in the tissue. Furthermore, a DL-based approach using Long Short-Term Memory (LSTM) effectively classified breast tissue with an accuracy of 96.67%. Thus, the DL algorithm and method we developed accurately augmented breast tissue classification using electrical impedance and enhanced the ability to detect and differentiate cancerous tissue in very early stages. However, more data and pre-clinical is required to improve the accuracy of this early breast cancer detection and differentiation tool.