Reviewerly/exHarmony huggingface dataset:
Automated Diagnosis of Prostate Cancer Using mpMRI Images: A Deep Learning Approach for Clinical Decision Support

Prostate cancer (PCa) is a significant health concern for men worldwide, where early detection and effective diagnosis can be crucial for successful treatment. Multiparametric magnetic resonance imaging (mpMRI) has evolved into a significant imaging modality in this regard, which provides detailed images of the anatomy and tissue characteristics of the prostate gland. However, interpreting mpMRI images can be challenging for humans due to the wide range of appearances and features of PCa, which can be subtle and difficult to distinguish from normal prostate tissue. Deep learning (DL) approaches can be beneficial in this regard by automatically differentiating relevant features and providing an automated diagnosis of PCa. DL models can assist the existing clinical decision support system by saving a physician’s time in localizing regions of interest (ROIs) and help in providing better patient care. In this paper, contemporary DL models are used to create a pipeline for the segmentation and classification of mpMRI images. Our DL approach follows two steps: a U-Net architecture for segmenting ROI in the first stage and a long short-term memory (LSTM) network for classifying the ROI as either cancerous or non-cancerous. We trained our DL models on the I2CVB (Initiative for Collaborative Computer Vision Benchmarking) dataset and conducted a thorough comparison with our experimental setup. Our proposed DL approach, with simpler architectures and training strategy using a single dataset, outperforms existing techniques in the literature. Results demonstrate that the proposed approach can detect PCa disease with high precision and also has a high potential to improve clinical assessment.
Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis

This review furnishes an exhaustive analysis of the latest advancements in deep learning techniques applied to whole slide images (WSIs) in the context of cancer prognosis, focusing specifically on publications from 2019 through 2023. The swiftly maturing field of deep learning, in combination with the burgeoning availability of WSIs, manifests significant potential in revolutionizing the predictive modeling of cancer prognosis. In light of the swift evolution and profound complexity of the field, it is essential to systematically review contemporary methodologies and critically appraise their ramifications. This review elucidates the prevailing landscape of this intersection, cataloging major developments, evaluating their strengths and weaknesses, and providing discerning insights into prospective directions. In this paper, a comprehensive overview of the field aims to be presented, which can serve as a critical resource for researchers and clinicians, ultimately enhancing the quality of cancer care outcomes. This review’s findings accentuate the need for ongoing scrutiny of recent studies in this rapidly progressing field to discern patterns, understand breakthroughs, and navigate future research trajectories.
A dynamic spatial distributed information clustering method for aluminum electrolysis cell

Distributed anode current (DAC) is a high-dimensional spatial-distributed signal that can be measured online in the industrial aluminum electrolysis process. The difference of physicochemical properties in different spatial regions in an aluminum electrolysis cell can be obtained by spatial clustering analysis of DAC data. In this study, a dynamic spatial distributed information clustering method (DSDIC) for aluminum electrolysis cell is proposed. This method can effectively capture the complex dynamic spatio-temporal correlations in DAC. Firstly, the dynamic graph is identified to capture the complex dynamicity of the DAC. Then, the anode-spatial structure information (ASSI) extends the one-dimensional current signal generated by each carbon anode into a feature matrix to achieve the fusion of data and spatial structure knowledge. Finally, the adjacency matrix of dynamic graph performs low-pass filtering on the feature matrix to obtain low-frequency information that is beneficial to downstream learning tasks. Meanwhile, a fixed graph structure based on process mechanism knowledge is designed to capture the spatial correlation caused by external periodic operations in industrial process. The experimental results on the actual industrial aluminum electrolysis datasets show that our method improves the clustering accuracy by 3.96% compared with existing clustering methods.
Exploring the differences in adversarial robustness between ViT- and CNN-based models using novel metrics

Deep-learning models have demonstrated remarkable performance in a variety of fields, owing to advancements in computational power and the availability of extensive datasets for training large-scale models. Nonetheless, these models inherently possess a vulnerability wherein even small alterations to the input can lead to substantially different outputs. Consequently, it is imperative to assess the robustness of deep-learning models prior to relying on their decision-making capabilities. In this study, we investigate the adversarial robustness of convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid CNNs +ViTs, which represent prevalent architectures in computer vision. Our evaluation is grounded on four novel model-sensitivity metrics that we introduce. These metrics are evaluated in the context of random noise and gradient-based adversarial perturbations. To ensure a fair comparison, we employ models with comparable capacities within each group and conduct experiments separately, utilizing ImageNet-1K and ImageNet-21K as pretraining data. Our fair experimental results provide empirical evidence that ViT-based models exhibit higher adversarial robustness than CNN-based counterparts, helping to dispel doubts about the findings of prior studies. Additionally, we introduce novel metrics that contribute new insights into the previously unconfirmed characteristics of these models.
Merge vs. "Lerge:" Problems of association

<title xmlns="http://www.ncbi.nlm.nih.gov/JATS1" /> <p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">It is shown that the proposal of identifying Merge and the Leibnizian addition operator runs up against the obstacle that the latter is associative while the former is not. The confound is attributed to insufficient appreciation of the difference between a calculus for natural language syntax and a calculus of concepts.
Exploration of potential geothermal fields using MAXENT and AHP: A case study of the Büyük Menderes Graben

In today's world, geothermal energy is essential as one of the alternative energy sources because it is renewable and does not harm the environment or the atmosphere. Büyük Menderes Graben (BMG) has significant geothermal potential in the Aegean Region of Turkey. The Maximum Entropy (MaxEnt) Method and Multi-criteria Decision Analysis (MCDA) were used in this study to define potential geothermal areas. The Geographical Information Systems (GIS) Based Maxent Method is the machine learning method. The decision analysis stage of the MCDA employed the Analytic Hierarchy Process (AHP). A geothermal favorability map was produced by combining weighted layers of standardized data according to the AHP. The Maxent Method created the other geothermal favorability map that self-estimates the weight of criteria. The Natural Breaks Jenks Method was used to categorize both maps. The findings of our study were compared with the locations of the geothermal resources in BMG. Both methods result in high accuracy, but the MaxEnt method has a high sensitivity to the geological parameters (cap rock geology and fault) of the geothermal system. So, the high gain values ensured the target was determined more precisely in the Maxent method. The MaxEnt method in our study is a guide for the exploration of new geothermal fields. Further geothermal explorations in the BMG will increase the potential of thermal tourism, housing heating, greenhouse, and balneological applications and contribute to renewable energy production in Turkey.
MRC-Sum: An MRC framework for extractive summarization of academic articles in natural sciences and medicine

Extractive summarization for academic articles in natural sciences and medicine has attracted attention for a long time. However, most existing extractive summarization models often process academic articles with sentence classification models, which are hard to produce comprehensive summaries. To address this issue, we explore a new view to solve the extractive summarization of academic articles in natural sciences and medicine by taking it as a question-answering process. We propose a novel framework, MRC-Sum, where the extractive summarization for academic articles in natural sciences and medicine is cast as an MRC (Machine Reading Comprehension) task. To instantiate MRC-Sum, article-summary pairs in the summarization datasets are firstly reconstructed into (Question, Answer, Context) triples in the MRC task. Several questions are designed to cover the main aspects (e.g. Background, Method, Result, Conclusion) of the articles in natural sciences and medicine. A novel strategy is proposed to solve the problem of the non-existence of the ground truth answer spans. Then MRC-Sum is trained on the reconstructed datasets and large-scale pre-trained models. During the inference stage, four answer spans of the predefined questions are given by MRC-Sum and concatenated to form the final summary for each article. Experiments on three publicly available benchmarks, i.e., the Covid, PubMed, and arXiv datasets, demonstrate the effectiveness of MRC-Sum. Specifically, MRC-Sum outperforms advanced extractive summarization baselines on the Covid dataset and achieves competitive results on the PubMed and arXiv datasets. We also propose a novel metric, COMPREHS, to automatically evaluate the comprehensiveness of the system summaries for academic articles in natural sciences and medicine. Abundant experiments are conducted and verified the reliability of the proposed metric. And the results of the COMPREHS metric show that MRC-Sum is able to generate more comprehensive summaries than the baseline models.
Encoder–decoder cycle for visual question answering based on perception-action cycle

In this study, we propose a novel encoder–decoder cycle (EDC) framework inspired by the human learning process called the perception-action cycle to tackle challenging problems such as visual question answering (VQA) and visual relationship detection (VRD). EDC considers the understanding of the visual features of an image as perception and the act of answering the question regarding that image as an action. In the perception-action cycle, information is primarily collected from the environment and then passed to sensory structures in the brain to form an understanding of the environment. Acquired knowledge is then passed to motor structures to perform an action on the environment. Next, sensory structures perceive the altered environment and improve their understanding of the surrounding world. This process of understanding the environment, performing an action correspondingly, and then re-evaluating the initial understanding occurs cyclically in human life. EDC initially mimics this mechanism of introspection by comprehending and refining visual features to acquire the proper knowledge for answering the question. Subsequently, it decodes visual and language features into answer features, feeding them back cyclically to the encoder. In the VRD task, EDC decodes visual features to generate predicate features. We evaluate the proposed framework on the TDIUC, VQA 2.0, and VRD datasets, which outperforms the state-of-the-art models on the TDIUC and VRD datasets.
First- and second-order high probability complexity bounds for trust-region methods with noisy oracles

In this paper, we present convergence guarantees for a modified trust-region method designed for minimizing objective functions whose value and gradient and Hessian estimates are computed with noise. These estimates are produced by generic stochastic oracles, which are not assumed to be unbiased or consistent. We introduce these oracles and show that they are more general and have more relaxed assumptions than the stochastic oracles used in prior literature on stochastic trust-region methods. Our method utilizes a relaxed step acceptance criterion and a cautious trust-region radius updating strategy which allows us to derive exponentially decaying tail bounds on the iteration complexity for convergence to points that satisfy approximate first- and second-order optimality conditions. Finally, we present two sets of numerical results. We first explore the tightness of our theoretical results on an example with adversarial zeroth- and first-order oracles. We then investigate the performance of the modified trust-region algorithm on standard noisy derivative-free optimization problems.
Multi-scale broad collaborative filtering for personalized recommendation

Recently, neighborhood-based collaborative filtering has been increasingly used in personalized recommender systems. However, inevitably, the neighborhood selection is based on a single scale, i.e. selecting a fixed number of the nearest users/items. To solve this problem, we propose a new recommender system called Multi-scale Broad Collaborative Filtering (MBCF). The main contribution lies in designing a multi-scale collaborative vector for capturing the rich information from different numbers of the nearest users/items. However, it is undesirable to input the low-dimensional multi-scale collaborative vector directly into the Deep Neural Networks (DNNs), which can easily lead to overfitting. For this reason, instead of DNNs, the Broad Learning System (BLS) is adopted as the mapping function to learn the complex nonlinear relationships between users and items, which can avoid the above problems while obtaining very satisfactory recommendation performance. Extensive experiments on eight benchmark datasets demonstrate the effectiveness of the proposed MBCF algorithm.
A comprehensive review of extreme learning machine on medical imaging

The feedforward neural network based on randomization has been of great interest in the scientific community, particularly extreme learning machines, due to its simplicity, training speed, and levels of accuracy comparable to traditional learning algorithms. Extreme learning machines (ELMs) are a type of artificial neural network (ANN) with one or more hidden layers that are trained under supervised, unsupervised, or semi-supervised learning approaches. These networks are widely used in various research areas, such as medical image processing (MI). This research work presents an exhaustive review of extreme learning machines (ELM) and medical image processing (MI), due to the high impact that these networks have had on the scientific community and the importance of MI for physicians who use them to diagnose different injuries and diseases. First, the theoretical construct of ELMs is developed based on the types of supervised, unsupervised, and semi-supervised learning. Then, the importance of MI for the diagnosis of a disease or classification of the most commonly used imaging modalities is analyzed for articles concerning radiography, computed tomography (CT), magnetic resonance (MR), ultrasound (US), and mammography (MG). Next, the reference data sets linked to various human body organs, such as the brain, lungs, skin, eyes, breasts, and cervix are described. Then, a review, analysis, and classification of the development of the last 6 years (2017–2022) of ELMs, based on learning types and MI, is performed. With the information obtained above, a construction of summary tables of the articles, classified according to the type of learning, is performed, highlighting the organ, reference, year, methodology, database, modality, and results. Finally, the discussion, conclusions and challenges related to this topic are presented. The findings indicate that the review articles reported in the literature have not addressed the relationship between ELMs and medical imaging in depth and have excluded key aspects, which are developed in this article. These aspects include a comprehensive analysis of the most popular imaging modalities, a detailed description of both the most popular databases and the most relevant databases for the machine learning community and, finally, the incorporation of schemes that explain the fundamentals of the main learnings considered when generating ELM-based trained smart models, which can be useful for medical image processing.
A modified smell agent optimization for global optimization and industrial engineering design problems

Abstract This paper introduces an Improved Smell Agent Optimization Algorithm (mSAO), a new and enhanced metaheuristic designed to tackle complex engineering optimization issues by overcoming the shortcomings of the recently introduced Smell Agent Optimization Algorithm. The proposed mSAO incorporates the jellyfish swarm active–passive mechanism and novel random operator in the elementary SAO. The objective of modification is to improve the global convergence speed, exploration–exploitation behaviour, and performance of SAO, as well as provide a problem-free method of global optimization. For numerical validation, the mSAO is examined using 29 IEEE benchmarks with varying degrees of dimensionality, and the findings are contrasted with those of its basic version and numerous renowned recently developed metaheuristics. To measure the viability of the mSAO algorithm for real-world applications, the algorithm was employed to solve to resolve eight challenges drawn from real-world scenarios including cantilever beam design, multi-product batch plant, industrial refrigeration system, pressure vessel design, speed reducer design, tension/compression spring, and three-bar truss problem. The computational analysis demonstrates the robustness of mSAO relatively in finding optimal solutions for mechanical, civil, and industrial design problems. Experimental results show that the suggested modifications lead to an improvement in solution quality by 10–20% of basic SAO while solving constraint benchmarks and engineering problems. Additionally, it contributes to avoiding local optimal stuck, and premature convergence limitations of SAO and simultaneously.
Unleashing the Potentials of Artificial Intelligence for Micro, Small, and Medium Enterprises: A Systematic Literature Review

Artificial intelligence has undergone rapid evolution, reaching a level of sophistication where it is now considered a personal assistant, aiding humans in their daily tasks. The potential of artificial intelligence can be observed in almost every sector of life including the business sector. Limited resources and skill capabilities especially for micro, small, and medium enterprises tend to hinder progress. However, given the current advanced level of artificial intelligence, it has emerged as an innovative and cost-effective solution to support business actors. This research systematically examines and discusses the potential of utilizing artificial intelligence in the business sector from credible and scientific sources. A systematic literature review methodology is used for this study. The review encompasses Scopus-indexed journals published between 2019 and 2023, with additional open-access publications. Based on the research findings, 106 studies were found, however, after the screening process the number of studies is reduced to 13 articles. Customer churn management emerged as the most prominent utilization of artificial intelligence. On the other hand, from a technological perspective, optimization technique emerged as the most frequently addressed topic in the examined studies.
Indistinguishable entangled fermions: basics and future challenges

The study of entanglement in systems composed of identical particles raises interesting challenges with far-reaching implications in both, our fundamental understanding of the physics of composite quantum systems, and our capability of exploiting quantum indistinguishability as a resource in quantum information theory. Impressive theoretical and experimental advances have been made in the last decades that bring us closer to a deeper comprehension and to a better control of entanglement. Yet, when it involves composites of indistinguishable quantum systems, the very meaning of entanglement, and hence its characterization, still finds controversy and lacks a widely accepted definition. The aim of the present paper is to introduce, within an accessible and self-contained exposition, the basic ideas behind one of the approaches advanced towards the construction of a coherent definition of entanglement in systems of indistinguishable particles, with focus on fermionic systems. We also inquire whether the corresponding tools developed for studying entanglement in identical-fermion systems can be exploited when analysing correlations in distinguishable-party systems, in which the complete information of the individual parts is not available. Further, we open the discussion on the broader problem of constructing a suitable framework that accommodates entanglement in the presence of generalized statistics. This article is part of the theme issue 'Identity, individuality and indistinguishability in physics and mathematics'.
A Machine Learning Framework for Shuttlecock Tracking and Player Service Fault Detection

Shuttlecock tracking is required for examining the trajectory of the shuttle-cock in badminton matches. Player Service Fault Detection identifies service faults during badminton matches. The match point scored by players is analyzed by the first referee based on the shuttlecock landing point and player service faults. If the first referee cannot decide, they use technology such as a third umpire system to assist. The current challenge with the third umpire system is based on the high number of marginal errors in predicting the match score. This research proposes a Machine Learning Framework to improve the accuracy of Shuttlecock Tracking and player service fault detection. The proposed framework combines a shuttlecock trajectory model and a player service fault model. The shuttlecock trajectory model is implemented using a pre-trained Convolutional Neural Network (CNN), namely Track-Net. The player service fault detection model uses Google MediaPipe Pose. A Random Forest classifier is used to classify the player's service faults. The framework is trained using the badminton world federation channel dataset. The dataset consists of 100000 images of badminton players and shuttlecock positions. The models are evaluated using a confusion matrix based on loss, accuracy, precision, recall, and F1 scores. Results demonstrate that the optimized TrackNet model has an accuracy of 90%, which is 5% more with 2.84% less positioning error compared to the current state of the art. The player service fault detection model can classify player faults with 90% accuracy using Google MediaPipe Pose, 10% more compared to the Openpose model. The machine learning framework for shuttlecock tracking and player service fault detection is of use to referees and the Badminton World Federation (BWF) for improving referee decision-making.