Reviewerly/exHarmony huggingface dataset:
Data‐driven power system dynamic security assessment under adversarial attacks: Risk warning based interpretation analysis and mitigation

Abstract Power system dynamic security assessment (DSA) has long been essential for protecting the system from the risk of cascading failures and wide‐spread blackouts. The machine learning (ML) based data‐driven strategy is promising due to its real‐time computation speed and knowledge discovery capacity. However, ML algorithms are found to be vulnerable against well‐designed malicious input samples that can lead to wrong outputs. Adversarial attacks are implemented to measure the vulnerability of the trained ML models. Specifically, the targets of attacks are identified by interpretation analysis that the data features with large SHAP values will be assigned with perturbations. The proposed method has the superiority that an instance‐based DSA method is established with interpretation of the ML models, where effective adversarial attacks and its mitigation countermeasure are developed by assigning the perturbations on features with high importance. Later, these generated adversarial examples are employed for adversarial training and mitigation. The simulation results present that the model accuracy and robustness vary with the quantity of adversarial examples used, and there is not necessarily a trade‐off between these two indicators. Furthermore, the rate of successful attacks increases when a greater bound of perturbations is permitted. By this method, the correlation between model accuracy and robustness can be clearly stated, which will provide considerable assistance in decision making.
An improved brain storm optimization with chunking‐grouping method

Summary Brain storm optimization (BSO) is a population‐based intelligence algorithm for optimization problems, which has attracted researchers' growing attention due to its simplicity and efficiency. An improved BSO, called CIBSO, is presented in this article. First of all, a new grouping method, in which the population is partitioned into chunks according to the fitness and recombined to groups, is developed to balance each group with same quality‐level. Afterwards, a new mutation strategy is designed in CIBSO and a learning mechanism is used to adaptively select appropriate strategy. Experiments on the CEC2014 test suite indicate that CIBSO is better or at least competitive performance against the compared BSO variants.
THE MOST IMPORTANT REQUIREMENTS THAT ENSURE EFFECTIVE PEDAGOGICAL CONTROL OF STUDENTS' SELF-EDUCATIONAL ACTIVITIES

Designing the process of self-education must be attributed, first of all, to the management of self-education by the teacher according to the alleged perturbations and by the feedback principle, as well as by the student. This article reveals the proposed model, which is complemented by recommendations for the organization of self-educational activities at the empirical, theoretical and practical stages of mastering educational material. Pedagogical management of self-education on the basis of this model takes place in four stages: preparatory, organizational, activity, analytical. And also among the most important requirements that ensure effective pedagogical control of self-educational activities of students, its objectivity, consistency, complexity, continuity, manufacturability.
INTERCULTURAL COMPETENCE IN THE TEACHING PROCESS OF A FOREIGN LANGUAGE

This article intends to reflect on the findings of an investigation related to intercultural competence, as an inherent element in the teaching-learning of a foreign proposed from the constructivist paradigm with a qualitative approach, based on the researcher's interpretation, in light of the referenced theoretical foundations, in the pedagogical experience and in the analysis of the data. Some strategies could be defined to incorporate the cultural elements when teaching a foreign language.
An empirical study of blockchain‐based models of interest margin of secondary capital bond issuance in the internet of things environment

Abstract The emergence of blockchain technology and the Internet of Things has improved the efficiency and quality of social production, thus accelerating the speed of economic development. The calculation of issue spreads for Tier 2 capital bonds, mainly using a centralized settlement system, ignores the transparency of the data and leads to a high degree of uncertainty in the results of issue spread analysis. The current status of secondary capital bond issuance is analysed and a conceptual definition of blockchain and secondary capital bond is completed as the theoretical support for the subsequent empirical study. Bond issuance data are collected within the wind database as empirical research data, and ordinary bonds are selected as the reference object to put forward three empirical research hypotheses. Utilizing the bond issuance spread research model centered on the blockchain model for empirical analysis, the research results show that the secondary capital bond issuance spread is significantly lower than ordinary bonds.
2D Discrete Wavelet Transformation (2D-DWT) for Nanoscale Morphological Analysis

Digital image processing nowadays is widely used in various applications in micro to macro scale such as Nano-structure for medical, defense, natural resource management, security purposes. This article reviewed the 2-Dimensional Discrete Wavelet Transformation (2D-DWT) for morphological analysis of Scanning Electron Microscope (SEM) image of Ni-P-CNF nanocomposite plated on mild steel substrate (grade AISI1040) for analyzing the multi-level decomposition, denoising and compression test. It was concluded that the 2D-DWT method is more efficient and precise as compared to the conventional methods like Power Spectral Density (PSD) and histogram equivalence. These methods are limited by the Heisenberg uncertainty principle, whereas, the wavelet theorem provides a multi-resolution analysis. The wavelet function can capture localized characteristics and transients in the data since it is often localized in both time and frequency. These features make wavelets ideal for storing transient and steady-state components of a signal or image, allowing them to simultaneously offer excellent time and frequency localization. SEM images usually contain huge information which can lead to computational complexities. 2D-DWT is a very effective tool to de-noise the image. In order to test its efficiency, we have intentionally added some noise in the image and de-noise it. Also, we have compressed the image at different at different levels. This study provides the utility of the 2D-DWT for image processing as well as compared with other approaches for image decomposition, denoising and image compression.
Distributionally Robust Optimization and Invariant Representation Learning for Addressing Subgroup Underrepresentation: Mechanisms and Limitations

Spurious correlation caused by subgroup underrepresentation has received increasing attention as a source of bias that can be perpetuated by deep neural networks (DNNs). Distributionally robust optimization has shown success in addressing this bias, although the underlying working mechanism mostly relies on upweighting under-performing samples as surrogates for those underrepresented in data. At the same time, while invariant representation learning has been a powerful choice for removing nuisance sensitive features, it has been little considered in settings where spurious correlations are caused by significant underrepresentation of subgroups. In this paper, we take the first step to better understand and improve the mechanisms for debiasing spurious correlation due to subgroup underrepresentation in medical image classification. Through a comprehensive evaluation study, we first show that 1) generalized reweighting of under-performing samples can be problematic when bias is not the only cause for poor performance, while 2) naive invariant representation learning suffers from spurious correlations itself. We then present a novel approach that leverages robust optimization to facilitate the learning of invariant representations at the presence of spurious correlations. Finetuned classifiers utilizing such representation demonstrated improved abilities to reduce subgroup performance disparity while maintaining high average and worst-group performance.
Mitigating Bias in MRI-Based Alzheimer’s Disease Classifiers Through Pruning of Deep Neural Networks

With the increasing prevalence of Alzheimer's disease among the elderly population, the development of machine learning models for early identification is crucial. However, it has been observed that these models may exhibit inherent biases, leading to performance disparities across protected attributes such as age and gender. In this study, we propose a model pruning method aimed at reducing bias and achieving model fairness in a pre-trained neural network designed for Alzheimer's disease identification. Our pruning method takes into account both bias reduction and model performance. The results demonstrate that our proposed pruning method significantly reduces disparities between age and gender attribute compared to the baseline model. For disparity in age attribute, our method successfully reduces disparity across multiple metrics, such as true positive rate ( $$-7.3\%$$ ). Similarly, for gender attribute, our approach mitigated bias in evaluation metrics, such as true negative rate ( $$-5.0\%$$ ). The results also show that our method maintains or even improves the model's performance and outperforms other methods across most evaluation metrics. These compelling findings underscore the immense potential of our approach in effectively mitigating gender and age disparities in MRI-based Alzheimer's disease identification.
How You Split Matters: Data Leakage and Subject Characteristics Studies in Longitudinal Brain MRI Analysis

Deep learning models have revolutionized the field of medical image analysis, offering significant promise for improved diagnostics and patient care. However, their performance can be misleadingly optimistic due to a hidden pitfall called 'data leakage'. In this study, we investigate data leakage in 3D medical imaging, specifically using 3D Convolutional Neural Networks (CNNs) for brain MRI analysis. While 3D CNNs appear less prone to leakage than 2D counterparts, improper data splitting during cross-validation (CV) can still pose issues, especially with longitudinal imaging data containing repeated scans from the same subject. We explore the impact of different data splitting strategies on model performance for longitudinal brain MRI analysis and identify potential data leakage concerns. GradCAM visualization helps reveal shortcuts in CNN models caused by identity confounding, where the model learns to identify subjects along with diagnostic features. Our findings, consistent with prior research, underscore the importance of subject-wise splitting and evaluating our model further on hold-out data from different subjects to ensure the integrity and reliability of deep learning models in medical image analysis.
Studying the Effects of Sex-Related Differences on Brain Age Prediction Using Brain MR Imaging

While utilizing machine learning models, one of the most crucial aspects is how bias and fairness affect model outcomes for diverse demographics. This becomes especially relevant in the context of machine learning for medical imaging applications as these models are increasingly being used for diagnosis and treatment planning. In this paper, we study biases related to sex when developing a machine learning model based on brain magnetic resonance images (MRI). We investigate the effects of sex by performing brain age prediction considering different experimental designs: model trained using only female subjects, only male subjects and a balanced dataset. We also perform evaluation on multiple MRI datasets (Calgary-Campinas(CC359) and CamCAN) to assess the generalization capability of the proposed models. We found disparities in the performance of brain age prediction models when trained on distinct sex subgroups and datasets, in both final predictions and decision making (assessed using interpretability models). Our results demonstrated variations in model generalizability across sex-specific subgroups, suggesting potential biases in models trained on unbalanced datasets. This underlines the critical role of careful experimental design in generating fair and reliable outcomes.
Gradient-Based Enhancement Attacks in Biomedical Machine Learning

The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve performance via recently-developed "enhancement attacks" may be a greater threat to biomedical machine learning. In the spirit of developing attacks to better understand trustworthiness, we developed two techniques to drastically enhance prediction performance of classifiers with minimal changes to features: 1) general enhancement of prediction performance, and 2) enhancement of a particular method over another. Our enhancement framework falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities between original and enhanced data (Pearson's $$r's>0.99$$ ). Similarly, the method-specific enhancement framework was effective in falsely improving the performance of one method over another. For example, a simple neural network outperformed logistic regression by 17% on our enhanced dataset, although no performance differences were present in the original dataset. Crucially, the original and enhanced data were still similar ( $$r=0.99$$ ). Our results demonstrate the feasibility of minor data manipulations to achieve any desired prediction performance, which presents an interesting ethical challenge for the future of biomedical machine learning. These findings emphasize the need for more robust data provenance tracking and other precautionary measures to ensure the integrity of biomedical machine learning research. The code is available at https://github.com/mattrosenblatt7/enhancement_EPIMI .
A Study of Age and Sex Bias in Multiple Instance Learning Based Classification of Acute Myeloid Leukemia Subtypes

Accurate classification of Acute Myeloid Leukemia (AML) subtypes is crucial for clinical decision-making and patient care. In this study, we investigate the potential presence of age and sex bias in AML subtype classification using Multiple Instance Learning (MIL) architectures. To that end, we train multiple MIL models using different levels of sex imbalance in the training set and excluding certain age groups. To assess the sex bias, we evaluate the performance of the models on male and female test sets. For age bias, models are tested against underrepresented age groups in the training data. We find a significant effect of sex and age bias on the performance of the model for AML subtype classification. Specifically, we observe that females are more likely to be affected by sex imbalance dataset and certain age groups, such as patients with 72 to 86 years of age with the RUNX1::RUNX1T1 genetic subtype, are significantly affected by an age bias present in the training data. Ensuring inclusivity in the training data is thus essential for generating reliable and equitable outcomes in AML genetic subtype classification, ultimately benefiting diverse patient populations.
A robust kernel least mean square algorithm and its quantization

To further improve the performance of the kernel adaptive filtering algorithm in a non-Gaussian environment, a robust kernel least mean square algorithm is proposed, and the effectiveness of the root cost function and the convergence of the algorithm is theoretically analyzed. An improved online vector quantization criterion is then applied to the proposed algorithm to suppress the linearly growing network size. Finally, the different performances of the algorithm of this paper and other kernel adaptive filtering algorithms as well as this paper’s algorithm before and after quantization are compared in Mackey Glass chaotic time series as well as in system identification, confirming the superiority of the algorithm of this paper and the improved online vector quantization criterion.
A Two-Stage Three-Dimensional Attention Network for Lightweight Image Super-Resolution

In recent years, single image super-resolution (SISR) methods using convolutional neural networks (CNN) have achieved satisfactory performance. Nevertheless, the large model scale and the slow inference speed of these methods greatly limit the application scenarios. In this paper, we propose a two-stage three-dimensional attention network (ATTNet) for lightweight image super-resolution. First, we put forward the spatial feature encoder–decoder (SFE-D) with a spatial attention mechanism. Next, the channel transposed attention module (CTAM) with a channel self-attention mechanism is designed. Both the modules are used for fine feature extraction in the low-resolution stage. Finally, the content-based pixel recombination module (CPRM) is proposed to reconstruct the detailed content with a joint attention mechanism in the high-resolution stage. According to our experimental results, significant performance in terms of the quantitative metrics and the subjective visual quality can be achieved on average compared with the state-of-the-art lightweight SISR algorithms.
Pipelined CNN Inference on Heterogeneous Multi-processor System-on-Chip

Convolutional neural networks (CNNs)-based inference is a quintessential component in mobile machine learning applications. Privacy and real-time response requirements require applications to perform inference on the mobile (edge) devices themselves. Heterogeneous multi-processor system-on-chips (HMPSoCs) within the edge devices enable high-throughput, low-latency edge inference. An HMPSoC contains several processing cores, each capable of independently performing CNN inference. However, to meet stringent performance requirements, an application must simultaneously involve all core types in inferencing. A software-based CNN inference pipeline design allows for synergistic engagement of all the cores in an HMPSoC for a high-throughput and low-latency CNN inference. In this chapter, we present two different CNN inference pipeline designs. The first design creates a pipeline between two different types of CPU cores. The second design extends the pipeline from CPU to GPU. We also provide a future perspective and research directions on the subject.
An Efficient Approach of Assessing Quality of Blurred Image

This paper presents a method for evaluating the quality of images altered by Gaussian blur. The method is based on the observation of bokeh mode images where the region of interest (foreground) is sharp, while the remaining parts (background) are intentionally blurred to enhance the perceptual quality of the image. The blurriness of the background increases attention towards the foreground part of the image. The proposed quality metric is obtained by combining the attention factor and the sharpness of the region of interest. The accuracy, in terms of Spearman’s-rank-order correlation-coefficient (SROCC), for popular and publicly available databases such as LIVE, VCL, TID2008, CSIQ, and TID2013, is 0.963, 0.925, 0.900, 0.930, and 0.930, respectively. The proposed method achieves high and consistent Spearman’s rank-order correlation coefficient (SROCC) values compared to the majority of state-of-the-art algorithms. Furthermore, in terms of speed, the proposed method surpasses other state-of-the-art methods. The MATLAB code of the proposed metric is publicly available at https://drive.google.com/drive/folders/1SRmUp0N157Ati9l3kV13uoCxw5PhMgQn?usp=sharing .
Boruta Feature Selection Method for Optimizing a Case-Based Reasoning Model to Predict Heart Disease

Nowadays, heart diseases are becoming a major problem, with which a significant part of the world population is affected. The field of medicine may significantly benefit from prediction systems using artificial intelligence techniques by making the disease prediction more accurate and faster. This paper aims to improve the predictive performance of cardiac disease diagnosis through the use of the case-based reasoning (CBR) approach, specifically focusing on its two phases: retrieval and reuse. Additionally, we aim to optimize the selection of attributes in cardiac dataset by using the Boruta method. Our approach uses various models including machine learning and deep learning models, in addition to hybrid models in the retrieval phase to accurately predict the presence or absence of a cardiac disease among patients. A robust reuse measure is used to verify the validity of the retrieved solutions and determine the necessity of applying the reuse algorithm. The results showed a significant improvement in predictive precision, with the highest accuracy achieved by the hybrid 1D CNN–SVM model on cardiac datasets. The effectiveness of the suggested approach is discussed by comparing the results with different search methods.
A New Lightweight Script Independent Scene Text Style Transfer Network

Scene text style transfer without a language barrier is an open challenge for the video and scene text recognition community because this plays a vital role in poster, web design, augmenting character images, and editing characters to improve scene text recognition performance and usability. This work presents a new model, called Script Independent Scene Text Style Transfer Network (SISTSTNet), for extracting scene characters and transferring text style simultaneously. The SISTSTNet performs mapping in language-independent feature space for transferring style. It is designed based on a Style Parameter Network and Target Encoder Network through lightweight MobileNetv3 convolutional and residual blocks to capture the style and shape to generate target characters. Similarly, a generative model is explored through the Visual Geometry Group (VGG) network for character replacement. The SISTSTNet is flexible and works on different languages and arbitrary examples in a neat and unified fashion. The experimental results on images in various languages, namely, English, Chinese, Hindi, Russian, Japanese, Arabic, Greek, and Bengali and cross-language validation demonstrate the effectiveness of the proposed method. The performance of the method is superior compared to the state-of-the-art methods in terms of quality measures, language independence, shape-preserving, and efficiency. The code and dataset will be released to the public to support reproducibility.
Unveiling Fairness Biases in Deep Learning-Based Brain MRI Reconstruction

Deep learning (DL) reconstruction particularly of MRI has led to improvements in image fidelity and reduction of acquisition time. In neuroimaging, DL methods can reconstruct high-quality images from undersampled data. However, it is essential to consider fairness in DL algorithms, particularly in terms of demographic characteristics. This study presents the first fairness analysis in a DL-based brain MRI reconstruction model. The model utilises the U-Net architecture for image reconstruction and explores the presence and sources of unfairness by implementing baseline Empirical Risk Minimisation (ERM) and rebalancing strategies. Model performance is evaluated using image reconstruction metrics. Our findings reveal statistically significant performance biases between the gender and age subgroups. Surprisingly, data imbalance and training discrimination are not the main sources of bias. This analysis provides insights of fairness in DL-based image reconstruction and aims to improve equity in medical AI applications.