Reviewerly/exHarmony huggingface dataset:
A hierarchical fused fuzzy deep neural network with heterogeneous network embedding for recommendation

The integration of deep learning (DL) and fuzzy learning (FL) is considered a recently emerging and promising research direction in data embedding. The integrated fuzzy neural learning paradigm within deep learning-based recommendation helped improving the performance of both latent feature representation learning and multiple recommendation problem fine-tuning. In general, feature representation learning is considered an important component of modern deep-learning based recommendation systems (RS). However, many well-known deep learning-based recommendation techniques encounter major challenges, which are mainly related to data uncertainty and the presence of noise in the obtained user and item embeddings. Moreover, most of the deep embedding-based RSs have still relied much on the local proximity relationships. These proximity features are obtained from the direct user-item interactions in order to leverage the recommendation outputs. This limitation has led to several difficulties in effectively extracting the rich latent structural features of users and items. In addition, in previous meta-path-based embedding techniques for the HIN-based recommendation approach, the integration between multi-type path-based representation learning for recommendation often encounters challenges concerning feature uncertainty extraction. To overcome the aforementioned problems, in this paper we propose a novel fuzzy-driven HIN embedding model for recommendation, called FuzzyRec. The proposed FuzzyRec has an integrated hierarchical fused neural fuzzy and deep network-based embedding architecture. It supports effective learning of the representations of users and items, as well as reducing feature ambiguity and noise. Extensive experiments demonstrate the effectiveness of our proposed method and its advantages compared with state-of-the-art baselines for recommendation systems.
Transfer learning and fine‐tuned transfer learning methods' effectiveness analyse in the CNN‐based deep learning models

Summary Object detection is a type of application that includes computer vision and image processing technologies, which deal with detecting, tracking, and classifying desired objects in images. Computer vision is a field of artificial intelligence that enables computers and systems to derive information from digital images and take action or suggestions based on that information. CNN is one of the current methods of object detection due to its ease of use and GPU‐supported parallel working features. Due to the aim of completing deep learning model training quickly or due to insufficient dataset, many studies using the transfer learning method are carried out in fields such as medicine, agriculture, and weapons. However, there are very few studies that use the fine‐tuning method and compare transfer learning in terms of effectiveness. By paying attention to the balanced distribution of the data, approximately 100 images of each chess piece type were included in the analysis and a dataset of at least 1000 images was created. The without transfer learning fine‐tune, fine‐tuned transfer learning, transfer learning, fully supervised learning (FSL) and weakly supervised learning (WSL) applied models performances compared. Experimental results show that the fine‐tuned transfer learning applied YOLO V4 model produces more accurate results than the other models in FSL and the transfer learning applied Faster R‐CNN model produces more accurate results than the other models in WSL.
Credit card fraud detection in the era of disruptive technologies: A systematic review

Credit card fraud is becoming a serious and growing problem as a result of the emergence of innovative technologies and communication methods, such as contactless payment. In this article, we present an in-depth review of cutting-edge research on detecting and predicting fraudulent credit card transactions conducted from 2015 to 2021 inclusive. The selection of 40 relevant articles is reviewed and categorized according to the topics covered (class imbalance problem, feature engineering, etc.) and the machine learning technology used (modelling traditional and deep learning). Our study shows a limited investigation to date into deep learning, revealing that more research is required to address the challenges associated with detecting credit card fraud through the use of new technologies such as big data analytics, large-scale machine learning and cloud computing. Raising current research issues and highlighting future research directions, our study provides a useful source to guide academic and industrial researchers in evaluating financial fraud detection systems and designing robust solutions.
Binary Aquila Optimizer for 0–1 knapsack problems

The optimization process entails determining the best values for various system characteristics in order to finish the system design at the lowest possible cost. In general, real-world applications and issues in artificial intelligence and machine learning are discrete, unconstrained, or discrete. Optimization approaches have a high success rate in tackling such situations. As a result, several sophisticated heuristic algorithms based on swarm intelligence have been presented in recent years. Various academics in the literature have worked on such algorithms and have effectively addressed many difficulties. Aquila Optimizer (AO) is one such algorithm. Aquila Optimizer (AO) is a recently suggested heuristic algorithm. It is a novel population-based optimization strategy. It was made by mimicking the natural behavior of the Aquila. It was created by imitating the behavior of the Aquila in nature in the process of catching its prey. The AO algorithm is an algorithm developed to solve continuous optimization problems in their original form. In this study, the AO structure has been updated again to solve binary optimization problems. Problems encountered in the real world do not always have continuous values. It exists in problems with discrete values. Therefore, algorithms that solve continuous problems need to be restructured to solve discrete optimization problems as well. Binary optimization problems constitute a subgroup of discrete optimization problems. In this study, a new algorithm is proposed for binary optimization problems (BAO). The most successful BAO-T algorithm was created by testing the success of BAO in eight different transfer functions. Transfer functions play an active role in converting the continuous search space to the binary search space. BAO has also been developed by adding candidate solution step crossover and mutation methods (BAO-CM). The success of the proposed BAO-T and BAO-CM algorithms has been tested on the knapsack problem, which is widely selected in binary optimization problems in the literature. Knapsack problem examples are divided into three different benchmark groups in this study. A total of sixty-three low, medium, and large scale knapsack problems were determined as test datasets. The performances of BAO-T and BAO-CM algorithms were examined in detail and the results were clearly shown with graphics. In addition, the results of BAO-T and BAO-CM algorithms have been compared with the new heuristic algorithms proposed in the literature in recent years, and their success has been proven. According to the results, BAO-CM performed better than BAO-T and can be suggested as an alternative algorithm for solving binary optimization problems.
A Hybrid Echo State Network for Hypercomplex Pattern Recognition, Classification, and Big Data Analysis

Processing big data with high-dimensional forms is one the most challenging parts of analyzing different signals and systems for various applications, including decision-making, pattern recognition, classification, etc. This procedure can be more problematic when interpreting and modeling complex phenomena. On the other hand, the processing speed in machine learning (ML) methods may not be acceptable, especially for real-time implementations. In other words, the more accurate a signal is, the more latency can occur. Recently, echo state networks (ESN) have shown appropriate precession in signal processing and classifying variables. However, utilization of a reservoir layer affects their speed, particularly for high-dimensional data. This paper presents a powerful ESN method for pattern recognition and classification of complex phenomena based on a new octonion ESN called octonion nonlinear ESN (ONESN). This method includes a modified version of the conventional ESN, in which all computations from the real space are mapped to the octonion space. Consequently, the size of the reservoir and its weights are reduced to 1/8 of its initial amount, increasing the speed of processing. Also, a bilinear filter to improve the performance of the conventional ESN is added to the output of the network.
Scattering Model Guided Adversarial Examples for SAR Target Recognition: Attack and Defense

Deep Neural Networks (DNNs) based Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) systems have shown to be highly vulnerable to adversarial perturbations that are deliberately designed yet almost imperceptible but can bias DNN inference when added to targeted objects. This leads to serious safety concerns when applying DNNs to high-stakes SAR ATR applications. Therefore, enhancing the adversarial robustness of DNNs is essential for applying DNNs to modern real-world SAR ATR systems. Toward building more robust DNN-based SAR ATR models, this article explores the domain knowledge of SAR imaging process and proposes a novel Scattering Model Guided Adversarial Attack (SMGAA) algorithm which can generate adversarial perturbations in the form of electromagnetic scattering response (called adversarial scatterers). The proposed SMGAA consists of two parts: 1) a parametric scattering model and corresponding imaging method and 2) a customized gradient-based optimization algorithm. First, we introduce the effective Attributed Scattering Center Model (ASCM) and a general imaging method to describe the scattering behavior of typical geometric structures in the SAR imaging process. By further devising several strategies to take the domain knowledge of SAR target images into account and relax the greedy search procedure, the proposed method does not need to be prudentially finetuned, and can efficiently find the effective ASCM parameters to fool the SAR classifiers and facilitate the robust model training. Comprehensive evaluations on the MSTAR dataset show that the adversarial scatterers generated by SMGAA are more robust to perturbations and transformations in the SAR processing chain than the currently studied attacks, and are effective to construct a defensive model against the malicious scatterers.
VDPC: Variational density peak clustering algorithm

The widely applied density peak clustering (DPC) algorithm makes an intuitive cluster formation assumption that cluster centers are often surrounded by data points with lower local density and far away from other data points with higher local density. However, this assumption suffers from one limitation that it is often problematic when identifying clusters with lower density because they might be easily merged into other clusters with higher density. As a result, DPC may not be able to identify clusters with variational density. To address this issue, we propose a variational density peak clustering (VDPC) algorithm, which is designed to systematically and autonomously perform the clustering task on datasets with various types of density distributions. Specifically, we first propose a novel method to identify the representatives among all data points and construct initial clusters based on the identified representatives for further analysis of the clusters’ property. Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN. Thus, all the identified initial clusters spreading across different density levels are systematically processed to form the final clusters. To evaluate the effectiveness of the proposed VDPC algorithm, we conduct extensive experiments using 20 datasets including eight synthetic, six real-world, and six image datasets. The experimental results show that VDPC outperforms two classical algorithms (i.e., DPC and DBSCAN) and four state-of-the-art extended DPC algorithms.
Artificial intelligence in cancer research and precision medicine: Applications, limitations and priorities to drive transformation in the delivery of equitable and unbiased care

Artificial intelligence (AI) has experienced explosive growth in oncology and related specialties in recent years. The improved expertise in data capture, the increased capacity for data aggregation and analytic power, along with decreasing costs of genome sequencing and related biologic "omics", set the foundation and need for novel tools that can meaningfully process these data from multiple sources and of varying types. These advances provide value across biomedical discovery, diagnosis, prognosis, treatment, and prevention, in a multimodal fashion. However, while big data and AI tools have already revolutionized many fields, medicine has partially lagged due to its complexity and multi-dimensionality, leading to technical challenges in developing and validating solutions that generalize to diverse populations. Indeed, inner biases and miseducation of algorithms, in view of their implementation in daily clinical practice, are increasingly relevant concerns; critically, it is possible for AI to mirror the unconscious biases of the humans who generated these algorithms. Therefore, to avoid worsening existing health disparities, it is critical to employ a thoughtful, transparent, and inclusive approach that involves addressing bias in algorithm design and implementation along the cancer care continuum. In this review, a broad landscape of major applications of AI in cancer care is provided, with a focus on cancer research and precision medicine. Major challenges posed by the implementation of AI in the clinical setting will be discussed. Potentially feasible solutions for mitigating bias are provided, in the light of promoting cancer health equity.
ACS: Accuracy-based client selection mechanism for federated industrial IoT

This study proposes secure federated learning (FL)-based architecture for the industrial internet of things (IIoT) with a novel client selection mechanism to enhance the learning performance. In order to secure the FL architecture and ensure that available clients are trustworthy, a certificate authority (CA) is adopted. In traditional FL, an aggregation technique known as federated averaging (FedAvg) is utilized to collect local model parameters by selecting a random subset of clients for the training process. However, the random selection may lead to uncertainty and negatively influence the overall FL performance. Moreover, state-of-the-art studies on client selection mainly rely on client’s additional information, which raises a privacy issue. Therefore, a novel client selection mechanism based on client evaluation accuracy called ACS is introduced in this work to improve FL performance while preserving client privacy. Unlike other client selection methods, ACS relies only on the updated local parameter, which is evaluated in the FL server. The proposed ACS considers the highest-performing clients to fasten the convergence time in the FL. Based on the extensive performance evaluation performed in this work using MNIST and F-MNIST datasets with non-independent identically distributed (non-IID) conditions, the adoption of ACS successfully improved the overall performance of FL in terms of accuracy and F1-score with an average of 4.62%. Furthermore, comparative analysis shows that the proposed ACS can achieve specific accuracy with 2.29% lower communication rounds and stable performance compared to other client selection mechanisms.
Explaining the black-box smoothly—A counterfactual approach

We propose a BlackBox Counterfactual Explainer, designed to explain image classification models for medical applications. Classical approaches (e.g., , saliency maps) that assess feature importance do not explain how imaging features in important anatomical regions are relevant to the classification decision. Such reasoning is crucial for transparent decision-making in healthcare applications. Our framework explains the decision for a target class by gradually exaggerating the semantic effect of the class in a query image. We adopted a Generative Adversarial Network (GAN) to generate a progressive set of perturbations to a query image, such that the classification decision changes from its original class to its negation. Our proposed loss function preserves essential details (e.g., support devices) in the generated images. We used counterfactual explanations from our framework to audit a classifier trained on a chest X-ray dataset with multiple labels. Clinical evaluation of model explanations is a challenging task. We proposed clinically-relevant quantitative metrics such as cardiothoracic ratio and the score of a healthy costophrenic recess to evaluate our explanations. We used these metrics to quantify the counterfactual changes between the populations with negative and positive decisions for a diagnosis by the given classifier. We conducted a human-grounded experiment with diagnostic radiology residents to compare different styles of explanations (no explanation, saliency map, cycleGAN explanation, and our counterfactual explanation) by evaluating different aspects of explanations: (1) understandability, (2) classifier's decision justification, (3) visual quality, (d) identity preservation, and (5) overall helpfulness of an explanation to the users. Our results show that our counterfactual explanation was the only explanation method that significantly improved the users' understanding of the classifier's decision compared to the no-explanation baseline. Our metrics established a benchmark for evaluating model explanation methods in medical images. Our explanations revealed that the classifier relied on clinically relevant radiographic features for its diagnostic decisions, thus making its decision-making process more transparent to the end-user.
UML Profile for Messaging Patterns in Service-Oriented Architecture, Microservices, and Internet of Things

The exchange of information among information technology (IT) systems is inevitable. Service fulfillment often involves sending and receiving messages. The article presents a set of messaging patterns for service-oriented architecture, microservices, and messaging protocols for the Internet of Things. The paper describes selected patterns that are the result of current research work. In addition, patterns introduced in open-source frameworks such as ZeroMQ have also been included. Moreover, the set includes Enterprise Integration Patterns. All considered messaging patterns have been described using the stereotype extensibility mechanism of the Unified Modeling Language (UML), and their complete set has been included in the new UML Profile for Messaging Patterns. The paper also shows the manner of integration flow modeling. In the illustrative examples, both the integration flow modeling diagram and the profile have been used to describe the communication in the context of the Integrated services view of the 1+5 architectural views model. The profile has been designed in the visual paradigm tool and revealed in a public repository for the community.
Analysis of driving factors of spatial distribution of heavy metals in soil of non-ferrous metal smelting sites: Screening the geodetector calculation results combined with correlation analysis

Heavy metals (HMs) discharged from smelting production may pose a major threat to human health and soil ecosystems. In this study, the spatial distribution characteristics of HMs in the soil of a non-ferrous metal smelting site were assessed. This study employed the geodetector (GD) by optimizing the classification condition and supplementing the correlation analysis (CA). The contribution of driving factors, such as production workshop distributions, hydrogeological conditions, and soil physicochemical properties, to the distribution of HMs in soil in the horizontal and vertical dimensions was assessed. The results showed that the main factors underlying the spatial distribution of As, Cd, Hg, Pb, Sb, and Zn in the horizontal direction were the distance from the sintering workshop (the maximum q value of that factor, q=0.28), raw material yard (q=0.14), and electrolyzer (q=0.29), while those in the vertical direction were the soil moisture content (q=0.17), formation lithology (q=0.12), and soil pH (q=0.06). The findings revealed that the CA is a simple and effective method to supplement the GD analysis underlying the spatial distribution characteristics of HMs at site scale. This study provides useful suggestions for environmental management to prevent HMs pollution and control HMs in the soil of non-ferrous metal smelting sites.
VToonify

Generating high-quality artistic portrait videos is an important and desirable task in computer graphics and vision. Although a series of successful portrait image toonification models built upon the powerful StyleGAN have been proposed, these image-oriented methods have obvious limitations when applied to videos, such as the fixed frame size, the requirement of face alignment, missing non-facial details and temporal inconsistency. In this work, we investigate the challenging controllable high-resolution portrait video style transfer by introducing a novel VToonify framework. Specifically, VToonify leverages the mid- and high-resolution layers of StyleGAN to render high-quality artistic portraits based on the multi-scale content features extracted by an encoder to better preserve the frame details. The resulting fully convolutional architecture accepts non-aligned faces in videos of variable size as input, contributing to complete face regions with natural motions in the output. Our framework is compatible with existing StyleGAN-based image toonification models to extend them to video toonification, and inherits appealing features of these models for flexible style control on color and intensity. This work presents two instantiations of VToonify built upon Toonify and DualStyleGAN for collection-based and exemplar-based portrait video style transfer, respectively. Extensive experimental results demonstrate the effectiveness of our proposed VToonify framework over existing methods in generating high-quality and temporally-coherent artistic portrait videos with flexible style controls. Code and pretrained models are available at our project page: www.mmlab-ntu.com/project/vtoonify/.