Reviewerly/exHarmony huggingface dataset:
A Dual-Stage Noise Training Scheme for Breast Ultrasound Image Classification

Breast cancer is one of the most common types of cancer and among the top leading causes of death among women all around the world. An early diagnosis is extremely critical, and in-time treatments can greatly help to prevent the cancer cells from spreading. Mammography, MRI, biopsy, ultrasound, etc., are recognized as effective imaging tests for breast cancer. However, the diagnosis entirely depends on the experience and expertise of the radiologist. In the last decade, computer-aided diagnosis (CAD) systems have been developed as a secondary reference to provide an objective analysis of medical images. With the help of deep learning (DL) and convolutional neural networks (CNNs), the accuracy of intelligent systems on image tasks has been perpetually improved. In this work, we specifically focus on the breast ultrasound image classification task. We apply transfer learning to four widely used backbone CNN architectures in the medical image classification field: AlexNet, ResNet-18, ResNet-50, and VGG16. They are fine-tuned on carefully constructed noisy datasets, and the test results suggest that they all acquire remarkable noise resistance, and this immunity is almost invariant to noise intensity. We systematically formalize our methodology as a dual-stage noise training scheme and provide empirical parameter configurations for each backbone CNN. This scheme enjoys being simple, effective, and universal. We believe this study will benefit the development of the robust design of DL models in the medical area.
From Natural Language Texts to RDF Triples: A Novel Approach to Generating e-Commerce Knowledge Graphs

The use of Knowledge Graphs (KGs) has gained traction in various software systems to improve data handling. E-commerce platforms, for example, have leveraged KGs to perform a range of tasks, including advertisement and product recommendation. However, generating accurate, trustworthy, and scalable RDF triples for populating KGs remains a challenging and error-prone task, especially given the lack of accurate and complete data. This article presents the QART framework, a natural language processing-based approach for generating RDF triples from e-commerce product Q &A. Our QART framework leverages templates to extract entities and intents and generates summarized sentences useful to populate KGs. Our experimental results demonstrate results concerning how we fine-tuned and used few-shots prompts in models such as T5, PTT5, GPT Neo and Bloom in an e-commerce dataset for the summarization task. Our evaluations identified key challenges in building the framework. Our contribution paves the way for the development of automatic mechanisms for text-to-triple transformation in e-commerce systems.
Measuring Augmented Reality and Virtual Reality Trajectory in the Training Environment

Corporate learning professionals understand that technology has changed the way we learn, work and interact with one another. The last vestige of the corporate training environment left mostly unchanged was in-person training. Until the last decade, in-person training with real world simulation was understood to achieve the highest knowledge transfer through experiential learning. With COVID-19 and the prevalence of hybrid work, companies started relying on technology in more ways, including how virtual simulation can replace in-person training. As augmented and virtual reality enter more workplaces across the United States, there is an opportunity to learn from the industries and workplaces who have utilized augmented and virtual reality in corporate training for over a decade. The healthcare, aviation, construction and military have utilized augmented and virtual reality in the training environment for a number of years. These industries are increasingly replacing live simulation and in-person learning with augmented and virtual reality. This study investigates aviation, one of the industries at the forefront of augmented and virtual reality programs in corporate training. Flight attendants are the population tasked with safety and security of passengers on commercial airlines. In this study, in-flight virtual reality training is compared to in-flight real life simulation training to understand what impact, positive or negative, virtual reality has on knowledge transfer.
Comparative Assessment of Deep End-To-End, Deep Hybrid and Deep Ensemble Learning Architectures for Breast Cancer Histological Classification

Breast cancer (BC) is considered one of the major public health issues and a leading cause of death among women in the world. Its early diagnosis using histological imaging modalities can significantly help to increase the chances of survival rate by choosing the adequate treatment. This research work is a comparative study that assesses and compares the performances of: (1) seven deep end-to-end architectures using seven deep learning models including VGG16, VGG19, ResNet 50, MobileNet V2, Inception V3, Inception ResNet V2 and DenseNet 201, (2) a deep hybrid architectures combining the deep learning model DenseNet 201 for feature extraction and MLP for classification and (3) a deep end-to-end heterogeneous ensembles of seven deep learning models used as base learners including VGG16, VGG19, ResNet50, Inception V3, Inception ResNet V2, Xception, and MobileNet V2 and combined with weighted voting over a hematoxylin and eosin (H&E)-stained pathological images. The experiments were conducted using the public BreakHis dataset, 5-fold cross validation method, four metrics of performances, the Scott Knott statistical test and the Borda Count voting method. The results showed that the deep end-to-end architecture DenseNet 201 is outperforming the others architectures with an accuracy value of 94,9%, 94,4%, 93,1%, 93,1% and 98,07% for the MF 40X,100X, 200X and 400X respectively, The comparison of the deep end-to-end, hybrid and end-to-end ensemble learning architectures proved the potential of the deep learning model DenseNet 201 for both feature extraction and classification since it gave the best results and outperformed the others.
Gradient-Based Neural Architecture Search: A Comprehensive Evaluation

One of the challenges in deep learning involves discovering the optimal architecture for a specific task. This is effectively tackled through Neural Architecture Search (NAS). Neural Architecture Search encompasses three prominent approaches—reinforcement learning, evolutionary algorithms, and gradient descent—that have demonstrated noteworthy potential in identifying good candidate architectures. However, approaches based on reinforcement learning and evolutionary algorithms often necessitate extensive computational resources, requiring hundreds of GPU days or more. Therefore, we confine this work to a gradient-based approach due to its lower computational resource demands. Our objective encompasses identifying the optimal gradient-based NAS method and pinpointing opportunities for future enhancements. To achieve this, a comprehensive evaluation of the use of four major Gradient descent-based architecture search methods for discovering the best neural architecture for image classification tasks is provided. An overview of these gradient-based methods, i.e., DARTS, PDARTS, Fair DARTS and Att-DARTS, is presented. A theoretical comparison, based on search spaces, continuous relaxation strategy and bi-level optimization, for deriving the best neural architecture is then provided. The strong and weak features of these methods are also listed. Experimental results for comparing the error rate and computational cost of these gradient-based methods are analyzed. These experiments involved using bench marking datasets CIFAR-10, CIFAR-100 and ImageNet. The results show that PDARTS is better and faster among the examined methods, making it a potent candidate for automating Neural Architecture Search. By effectively conducting a comparative analysis, our research provides valuable insights and future research directions to address the criticism and gaps in the literature.
Statistics and Causation

Kullback (1959) points out that information theory is a branch of the mathematical theory of probability and statistics; insofar as bioinformatics is a branch of information theory, it follows that statistics is superordinate to bioinformatics.
Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence Encoders

The fast growth of data in the academic field has contributed to making recommendation systems for scientific papers more popular. Content-based filtering (CBF), a pivotal technique in recommender systems (RS), holds particular significance in the realm of scientific publication recommendations. In a content-based scientific publication RS, recommendations are composed by observing the features of users and papers. Content-based recommendation encompasses three primary steps, namely, item representation, user modeling, and recommendation generation. A crucial part of generating recommendations is the user modeling process. Nevertheless, this step is often neglected in existing content-based scientific publication RS. Moreover, most existing approaches do not capture the semantics of user models and papers. To address these limitations, in this paper we present a transparent Recommendation and Interest Modeling Application (RIMA), a content-based scientific publication RS that implicitly derives user interest models from their authored papers. To address the semantic issues, RIMA combines word embedding-based keyphrase extraction techniques with knowledge bases to generate semantically-enriched user interest models, and additionally leverages pretrained transformer sentence encoders to represent user models and papers and compute their similarities. The effectiveness of our approach was assessed through an offline evaluation by conducting extensive experiments on various datasets along with user study (N = 22), demonstrating that (a) combining SIFRank and SqueezeBERT as an embedding-based keyphrase extraction method with DBpedia as a knowledge base improved the quality of the user interest modeling step, and (b) using the msmarco-distilbert-base-tas-b sentence transformer model achieved better results in the recommendation generation step.
ImWIP: Open-source image warping toolbox with adjoints and derivatives

Image warping is a popular tool for modeling deformation and motion in digital images. Inversion of such models requires two related operators: adjoint and differentiated image warping. Many applications rely on these operators, often through ad hoc and approximate implementations, which leads to a suboptimal quality and convergence speed, and hinders development of and comparison across different applications.In this work, we present an open-source image warping toolbox called ImWIP (Image Warping for Inverse Problems) that overcomes these issues. It implements differentiable image warping operators, together with their exact adjoints and derivatives (up to floating point errors). ImWIP is demonstrated on examples from X-ray computed tomography and magnetic resonance imaging, and is shown to improve both reconstruction quality and convergence speed compared to state-of-the-art warping methods.
Continual learning classification method with human-in-the-loop

The classification problem is essential to machine learning, often used in fault detection, condition monitoring, and behavior recognition. In recent years, due to the rapid development of incremental learning, reinforcement learning, transfer learning, and continual learning algorithms, the contradiction between the classification model and new data has been alleviated. However, due to the lack of feedback, most classification algorithms take long to search and may deviate from the correct results. Because of this, we propose a continual learning classification method with human-in-the-loop (H-CLCM) based on the artificial immune system. H-CLCM draws lessons from the mechanism that humans can enhance immune response through various intervention technologies and brings humans into the test learning process in a supervisory role. The human experience is integrated into the test phase, and the parameters corresponding to the error identification data are adjusted online. It enables it to converge to an accurate prediction model at the lowest cost and to learn new data categories without retraining the classifier.•All necessary steps and formulas of H-CLCM are provided.•H-CLCM adds manual intervention to improve the classification ability of the model.•H-CLCM can recognize new types of data.
Modal Reconstruction Based on Arbitrary High-Order Zernike Polynomials for Deflectometry

Deflectometry is a non-destructive, full-field phase measuring method, which is usually used for inspecting optical specimens with special characteristics, such as highly reflective or specular surfaces, as well as free-form surfaces. One of the important steps in the Deflectometry method is to retrieve the surface from slope data of points on the sample map or surface reconstruction. This paper proposes a modal reconstruction method using an adjustable number of Zernike polynomials. In addition, the proposed method enables the analyses on practical surfaces that require an infinite number of Zernike terms to be represented. Experiments on simulated surfaces indicated that the algorithm is able to reveal the number of major-contributing Zernike terms, as well as reconstruct the surface with a micrometer-scale from slope data with a signal-to-noise ratio of 10.
Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory

To address the challenges of low-light images, such as low brightness, poor contrast, and high noise, a network model based on deep learning and Retinex theory is proposed. The model consists of three modules: image decomposition, illumination enhancement, and color restoration. In the image decomposition module, dilated convolutions and residual connections are employed to mitigate the issue of detail loss during the decomposition process. The illumination enhancement module utilizes a set of mapping curves to enhance the illumination map. The color restoration module employs a weighted fusion of a 3D lookup table (3DLUT) to mitigate color distortion in the images. The experimental results demonstrate that the proposed algorithm effectively improves the brightness and contrast of low-light images while addressing the issues of detail loss and color distortion. Compared to other algorithms, it achieves better subjective and objective evaluations.
Enhancing the Transferability of Targeted Attacks with Adversarial Perturbation Transform

The transferability of adversarial examples has been proven to be a potent tool for successful attacks on target models, even in challenging black-box environments. However, the majority of current research focuses on non-targeted attacks, making it arduous to enhance the transferability of targeted attacks using traditional methods. This paper identifies a crucial issue in existing gradient iteration algorithms that generate adversarial perturbations in a fixed manner. These perturbations have a detrimental impact on subsequent gradient computations, resulting in instability of the update direction after momentum accumulation. Consequently, the transferability of adversarial examples is negatively affected. To overcome this issue, we propose an approach called Adversarial Perturbation Transform (APT) that introduces a transformation to the perturbations at each iteration. APT randomly samples clean patches from the original image and replaces the corresponding patches in the iterative output image. This transformed image is then used to compute the next momentum. In addition, APT could seamlessly integrate with other iterative gradient-based algorithms, incurring minimal additional computational overhead. Experimental results demonstrate that APT significantly enhances the transferability of targeted attacks when combined with traditional methods. Our approach achieves this improvement while maintaining computational efficiency.
Malicious Contract Detection for Blockchain Network Using Lightweight Deep Learning Implemented through Explainable AI

A smart contract is a digital contract on a blockchain. Through smart contracts, transactions between parties are possible without a third party on the blockchain network. However, there are malicious contracts, such as greedy contracts, which can cause enormous damage to users and blockchain networks. Therefore, countermeasures against this problem are required. In this work, we propose a greedy contract detection system based on deep learning. The detection model is trained through the frequency of opcodes in the smart contract. Additionally, we implement Gredeeptector, a lightweight model for deployment on the IoT. We identify important instructions for detection through explainable artificial intelligence (XAI). After that, we train the Greedeeptector through only important instructions. Therefore, Greedeeptector is a computationally and memory-efficient detection model for the IoT. Through our approach, we achieve a high detection accuracy of 92.3%. In addition, the file size of the lightweight model is reduced by 41.5% compared to the base model and there is little loss of accuracy.
Impulsive Aggression Break, Based on Early Recognition Using Spatiotemporal Features

The study of human behaviors aims to gain a deeper perception of stimuli that control decision making. To describe, explain, predict, and control behavior, human behavior can be classified as either non-aggressive or anomalous behavior. Anomalous behavior is any unusual activity; impulsive aggressive, or violent behaviors are the most harmful. The detection of such behaviors at the initial spark is critical for guiding public safety decisions and a key to its security. This paper proposes an automatic aggressive-event recognition method based on effective feature representation and analysis. The proposed approach depends on a spatiotemporal discriminative feature that combines histograms of oriented gradients and dense optical flow features. In addition, the principal component analysis (PCA) and linear discriminant analysis (LDA) techniques are used for complexity reduction. The performance of the proposed approach is analyzed on three datasets: Hockey-Fight (HF), Stony Brook University (SBU)-Kinect, and Movie-Fight (MF), with accuracy rates of 96.5%, 97.8%, and 99.6%, respectively. Also, this paper assesses and contrasts the feature engineering and learned features for impulsive aggressive event recognition. Experiments show promising results of the proposed method compared to the state of the art. The implementation of the proposed work is available here.
Q8VaxStance: Dataset Labeling System for Stance Detection towards Vaccines in Kuwaiti Dialect

The Kuwaiti dialect is a particular dialect of Arabic spoken in Kuwait; it differs significantly from standard Arabic and the dialects of neighboring countries in the same region. Few research papers with a focus on the Kuwaiti dialect have been published in the field of NLP. In this study, we created Kuwaiti dialect language resources using Q8VaxStance, a vaccine stance labeling system for a large dataset of tweets. This dataset fills this gap and provides a valuable resource for researchers studying vaccine hesitancy in Kuwait. Furthermore, it contributes to the Arabic natural language processing field by providing a dataset for developing and evaluating machine learning models for stance detection in the Kuwaiti dialect. The proposed vaccine stance labeling system combines the benefits of weak supervised learning and zero-shot learning; for this purpose, we implemented 52 experiments on 42,815 unlabeled tweets extracted between December 2020 and July 2022. The results of the experiments show that using keyword detection in conjunction with zero-shot model labeling functions is significantly better than using only keyword detection labeling functions or just zero-shot model labeling functions. Furthermore, for the total number of generated labels, the difference between using the Arabic language in both the labels and prompt or a mix of Arabic labels and an English prompt is statistically significant, indicating that it generates more labels than when using English in both the labels and prompt. The best accuracy achieved in our experiments in terms of the Macro-F1 values was found when using keyword and hashtag detection labeling functions in conjunction with zero-shot model labeling functions, specifically in experiments KHZSLF-EE4 and KHZSLF-EA1, with values of 0.83 and 0.83, respectively. Experiment KHZSLF-EE4 was able to label 42,270 tweets, while experiment KHZSLF-EA1 was able to label 42,764 tweets. Finally, the average value of annotation agreement between the generated labels and human labels ranges between 0.61 and 0.64, which is considered a good level of agreement.
Classification of Events in Selected Industrial Processes Using Weighted Key Words and K-Nearest Neighbors Algorithm

The problem of classifying events in the industry is related to a large amount of accumulated text data including, among others, communication between the company and the client, whose expectations regarding the quality of its service are constantly growing. The currently used solutions for handling incoming requests have numerous disadvantages; they imply additional costs for the company and often a high level of customer dissatisfaction. A partial solution to this problem may be the automation of event classification; for example, by means of an expert IT system. The presented work proposes the solution to the problem of classifying text events. For this purpose, textual descriptions of events were used, which were collected for many years by companies from many different industries. A large part of text events are various types of problems reported by company customers. As part of this work, a complex text-classification process was constructed by using the K-Nearest Neighbors algorithm. The demonstrated classification process uses two novel proposed mechanisms: the dynamic extension of stop list and weighted keywords. Both of the mechanisms aim to improve the classification performance by solving typical problems that occur when using a fixed stop list and a classical keyword extraction approach by using TF or TF-IDF methods. Finally, the Text Events Categorizer system that implements the proposed classification process was described.
Towards Feasible Capsule Network for Vision Tasks

Capsule networks exhibit the potential to enhance computer vision tasks through their utilization of equivariance for capturing spatial relationships. However, the broader adoption of these networks has been impeded by the computational complexity of their routing mechanism and shallow backbone model. To address these challenges, this paper introduces an innovative hybrid architecture that seamlessly integrates a pretrained backbone model with a task-specific capsule head (CapsHead). Our methodology is extensively evaluated across a range of classification and segmentation tasks, encompassing diverse datasets. The empirical findings robustly underscore the efficacy and practical feasibility of our proposed approach in real-world vision applications. Notably, our approach yields substantial 3.45% and 6.24% enhancement in linear evaluation on the CIFAR10 dataset and segmentation on the VOC2012 dataset, respectively, compared to baselines that do not incorporate the capsule head. This research offers a noteworthy contribution by not only advancing the application of capsule networks, but also mitigating their computational complexities. The results substantiate the feasibility of our hybrid architecture, thereby paving the way for a wider integration of capsule networks into various computer vision tasks.