Reviewerly/exHarmony huggingface dataset:
Cutting-edge technology and automation in the pathology laboratory

One of the goals of pathology is to standardize laboratory practices to increase the precision and effectiveness of diagnostic testing, which will ultimately enhance patient care and results. Standardization is crucial in the domains of tissue processing, analysis, and reporting. To enhance diagnostic testing, innovative technologies are also being created and put into use. Furthermore, although problems like algorithm training and data privacy issues still need to be resolved, digital pathology and artificial intelligence are emerging in a structured manner. Overall, for the field of pathology to advance and for patient care to be improved, standard laboratory practices and innovative technologies must be adopted. In this paper, we describe the state-of-the-art of automation in pathology laboratories in order to lead technological progress and evolution. By anticipating laboratory needs and demands, the aim is to inspire innovation tools and processes as positively transformative support for operators, organizations, and patients.
Repetitive research: a conceptual space and terminology of replication, reproduction, revision, reanalysis, reinvestigation and reuse in digital humanities

Abstract This article is motivated by the ‘reproducibility crisis’ that is being discussed intensely in fields such as Psychology or Biology but is also becoming increasingly relevant to Artificial Intelligence, Natural Language Processing and Digital Humanities, not least in the context of Open Science. Using the phrase ‘repetitive research’ as an umbrella term for a range of practices from replication to follow-up research, and with the objective to provide clarity and help establish best practices in this area, this article focuses on two issues: First, the conceptual space of repetitive research is described across five key dimensions, namely those of the research question or hypothesis, the dataset, the method of analysis, the team, and the results or conclusions. Second, building on this new description of the conceptual space and on earlier terminological work, a specific set of terms for recurring scenarios of repetitive research is proposed. For each scenario, its position in the conceptual space is defined, its typical purpose and added value in the research process are discussed, the requirements for enabling it are described, and illustrative examples from the domain of Computational Literary Studies are provided. The key contribution of this article, therefore, is a proposal for a transparent terminology underpinned by a systematic model of the conceptual space of repetitive research.
The effect of rebalancing techniques on the classification performance in cyberbullying datasets

Abstract Cyberbullying detection systems rely increasingly on machine learning techniques. However, class imbalance in cyberbullying datasets, where the percentage of normal labeled classes is higher than that of abnormal labeled ones, presents a significant challenge for classification algorithms. This issue is particularly problematic in two-class datasets, where conventional machine learning methods tend to perform poorly on minority class samples due to the influence of the majority class. To address this problem, researchers have proposed various oversampling and undersampling techniques. In this paper, we investigate the effectiveness of such techniques in addressing class imbalance in cyberbullying datasets. We conduct an experimental study that involves a preprocessing step to enhance machine learning algorithm performance. We then examine the impact of imbalanced data on classification performance for four cyberbullying datasets. To study the classification performance on balanced cyberbullying datasets, we employ four resampling techniques, namely random undersampling, random oversampling, SMOTE, and SMOTE + TOMEK. We evaluate the impact of each rebalancing technique on classification performance using eight well-known classification algorithms. Our findings demonstrate that the performance of resampling techniques depends on the dataset size, imbalance ratio, and classifier used. The conducted experiments proved that there are no techniques that will always perform better the others.
Virtual reality in retailing: a meta-analysis to determine the purchase and non-purchase behavioural intention of consumers

Purpose The purpose of this meta-analysis is to encapsulate the outcomes and generate meaningful conclusions by examining the factors that influence consumers' purchase and non-purchase behaviour intention in a virtual reality retailing context. Design/methodology/approach This study integrates the outcomes from 52 studies, including 403 relationships involving 19,188 samples. The analysis was conducted using R-metafor and AMOS software. Findings The findings indicate that key factors that influence purchase and non-purchase behavioural intentions are virtual reality (VR)characteristics, virtual reality experience and consumer attitudes. VR experience is the strongest predictor for purchase decisions in virtual environment ,while consumer attitude towards VR most strongly influences the non-purchase behaviour of the consumers. Furthermore, the age of the respondents, cultural backgrounds (high vs low power distance) and gender moderate the relationship between consumers' attitudes and purchase and behaviour intentions. Practical implications Marketers can positively influence consumer attitudes and behavioural intentions by prioritizing the design of the virtual environment and facilitating unique experiences (by manipulating different sensory stimuli) in virtual retailing. Originality/value The current meta-analysis reconciles and reinforces the findings in the extant literature and provides a robust empirical generalization of the critical factors that influence consumers' purchase or behavioural intentions in a virtual retailing context.
Financial risk management on a neutral atom quantum processor

Machine learning models capable of handling the large data sets collected in the financial world can often become black boxes expensive to run. The quantum computing paradigm suggests new optimization techniques that, combined with classical algorithms, may deliver competitive, faster, and more interpretable models. In this paper we propose a quantum-enhanced machine learning solution for the prediction of credit rating downgrades, also known as fallen-angels forecasting in the financial risk management field. We implement this solution on a neutral atom quantum processing unit with up to 60 qubits on a real-life data set. We report performance that is competitive with the state-of-the-art random forest benchmark, whereas our model achieves better interpretability and comparable training times. We examine how to improve performance in the near term, validating our ideas with tensor-networks-based numerical simulations.
A CNN-Transformer Hybrid Network for Multi-scale object detection

Recently, Transformer-based methods have been the main framework in various computer vision tasks. Vision transformers achieve object detection based on sequence of visual tokens, lacking the ability of extracting local context and dealing with scale variance. To tackle this problem, we propose a hybrid CNN-transformer model with multiple dual-branch transformer blocks in which transformer branch captures global dependences and the CNN branch enhances local context. As convolution branch and transformer branch pay attention to different-level information, we combine the output features of the CNN branch and transformer branch with adaptive weights calculated from the visual content. Moreover, instead of detecting objects from transformer outputs directly, we introduce a feature aggregation module to fuse different levels features and construct feature pyramid based upon these multi-level features. The proposed feature aggregation module alleviates semantic gap between high-level and low-level features. Experimental results on the MS COCO dataset show that our method significantly improves the performance of multi-scale object detection.
UAV Path Planning with an Adaptive Hybrid PSO

Optimized Path planning plays a significant role in enabling autonomous operation of Unmanned Aerial Vehicles (UAVs). Finding the shortest path is one of many NP-hard problems for which metaheuristic algorithms have shown promising results. Particle swarm optimization (PSO) is a frequently used metaheuristic approach for robotic path planning. PSO, however, has significant flaws like poor exploration capacity and a lack of diversity in the particles. PSO may become vulnerable to local minima due to either of these constraints. Inertia weight formulation and hybridization with other metaheuristic algorithms are two popular strategies for overcoming PSO's drawbacks. This study presents the aw-GPSO adaptive hybrid PSO algorithm, which is applied for offline 3D path planning of UAVs. To enhance the exploration capability of the PSO algorithm and improve particle diversity, we incorporate the Grey Wolf Optimization (GWO) algorithm and introduce a time-varying adaptive inertia weight parameter. To showcase the enhancements achieved, we conducted simulations of our proposed approach and conducted a comparative analysis with recent variants of PSO utilized in UAV path planning.
Towards explainable motion prediction using heterogeneous graph representations

Motion prediction systems play a crucial role in enabling autonomous vehicles to navigate safely and efficiently in complex traffic scenarios. Graph Neural Network (GNN)-based approaches have emerged as a promising solution for capturing interactions among dynamic agents and static objects. However, they often lack transparency, interpretability and explainability — qualities that are essential for building trust in autonomous driving systems. In this work, we address this challenge by presenting a comprehensive approach to enhance the explainability of graph-based motion prediction systems. We introduce the Explainable Heterogeneous Graph-based Policy (XHGP) model based on an heterogeneous graph representation of the traffic scene and lane-graph traversals. Distinct from other graph-based models, XHGP leverages object-level and type-level attention mechanisms to learn interaction behaviors, providing information about the importance of agents and interactions in the scene. In addition, capitalizing on XHGP’s architecture, we investigate the explanations provided by the GNNExplainer and apply counterfactual reasoning to analyze the sensitivity of the model to modifications of the input data. This includes masking scene elements, altering trajectories, and adding or removing dynamic agents. Our proposal advances towards achieving reliable and explainable motion prediction systems, addressing the concerns of users, developers and regulatory agencies alike. The insights gained from our explainability analysis contribute to a better understanding of the relationships between dynamic and static elements in traffic scenarios, facilitating the interpretation of the results, as well as the correction of possible errors in motion prediction models, and thus contributing to the development of trustworthy motion prediction systems. The code to reproduce this work is publicly available at https://github.com/sancarlim/Explainable-MP/tree/v1.1.
Face Recognition Using Machine Learning

Face recognition is a technology this involves identify or verify an individual identity by analyzing their facial features. It has gained significant popularity and is widely used in various fields include security system, law digital authentication. The process of face detection typically involve several step. First, a face detection algorithm locates and extracts face features from an images or video. Then, the system analyzes the face characteristics, such as the distances between the eye, the shape of the noses, and the contours of the faces, to creates a unique facial template or signature. This template is compared to a data of known faces to find a match. If a match is found, the systems can provides the person's identity or indicate a potential match. Face recognition algorithms use sophisticated techniques like machine learning and deep neural networks to improve accuracy and handle variations in lighting conditions, pose, and facial expressions.
Dissipative phase transitions in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-photon driven quantum nonlinear resonators

We investigate and characterize the emergence of finite-component dissipative phase transitions (DPTs) in nonlinear photon resonators subject to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>-photon driving and dissipation. Exploiting a semiclassical approach, we derive general results on the occurrence of second-order DPTs in this class of systems. We show that for all odd <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>, no second-order DPT can occur while, for even <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math>, the competition between higher-order nonlinearities determines the nature of the criticality and allows for second-order DPTs to emerge only for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>4</mml:mn></mml:math>. As pivotal examples, we study the full quantum dynamics of three- and four-photon driven-dissipative Kerr resonators, confirming the prediction of the semiclassical analysis on the nature of the transitions. The stability of the vacuum and the typical timescales needed to access the different phases are also discussed. We also show a first-order DPT where multiple solutions emerge around zero, low, and high-photon numbers. Our results highlight the crucial role played by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>w</mml:mi><mml:mi>e</mml:mi><mml:mi>a</mml:mi><mml:mi>k</mml:mi></mml:math> symmetries in triggering critical behaviors, providing a Liouvillian framework to study the effects of high-order nonlinear processes in driven-dissipative systems, that can be applied to problems in quantum sensing and information processing.
A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning

The aim of this paper is to investigate the ethical and responsible use of AI chatbots in education in support of critical thinking, cognitive flexibility and self-regulation in terms of their potential to enhance and motivate teaching and learning in contemporary education environments. AI chatbots such as ChatGPT by OpenAI appear to be improving in conversational and other capabilities and this paper explores such advances using version 4. Based on a review of the research literature, a conceptual framework is formulated for responsible use of AI chatbots in education supporting cognitive flexibility in AI-rich learning environments. The framework is then operationalized for use in this paper through the development of exemplars for math, english language arts (ELA), and studying with ChatGPT to close learning gaps in an effort to foster more ethical and responsible approaches to the design and development of AI chatbots for application and use in teaching and learning environments. This paper extends earlier foundational work on cognitive flexibility and AI chatbots as well as earlier work on cognitive flexibility in support of creativity and innovation with AI chatbots in urban civic spaces.
MAG-Res2Net: a novel deep learning network for human activity recognition

Abstract Objective. Human activity recognition (HAR) has become increasingly important in healthcare, sports, and fitness domains due to its wide range of applications. However, existing deep learning based HAR methods often overlook the challenges posed by the diversity of human activities and data quality, which can make feature extraction difficult. To address these issues, we propose a new neural network model called MAG-Res2Net, which incorporates the Borderline-SMOTE data upsampling algorithm, a loss function combination algorithm based on metric learning, and the Lion optimization algorithm. Approach. We evaluated the proposed method on two commonly utilized public datasets, UCI-HAR and WISDM, and leveraged the CSL-SHARE multimodal human activity recognition dataset for comparison with state-of-the-art models. Main results. On the UCI-HAR dataset, our model achieved accuracy, F1-macro, and F1-weighted scores of 94.44%, 94.38%, and 94.26%, respectively. On the WISDM dataset, the corresponding scores were 98.32%, 97.26%, and 98.42%, respectively. Significance. The proposed MAG-Res2Net model demonstrates robust multimodal performance, with each module successfully enhancing model capabilities. Additionally, our model surpasses current human activity recognition neural networks on both evaluation metrics and training efficiency. Source code of this work is available at: https://github.com/LHY1007/MAG-Res2Net .
A Hybrid Particle Whale Optimization Algorithm with application to workflow scheduling in cloud–fog environment

Workflow scheduling aims to optimize task allocation and execution in cloud-fog computing environments. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complexity and variability of tasks and resources involved. Although meta-heuristic algorithms have been proposed to address these challenges, they often suffer from being trapped in local optima, failing to achieve the global optimal solution. Our work used a hybrid approach “Particle Whale Optimization Algorithm” (PWOA) to overcome the above-mentioned problem by combining Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). PSO’s limitation in high-dimensional search spaces, characterized by slow convergence toward the global optimum, and the population-based WOA algorithm’s limited exploration of search spaces are overcome by the proposed algorithm PWOA. The proposed algorithm PWOA overcomes the limitations of PSO and WOA. The objective of the PWOA algorithm is to minimize the Total Execution Time (TET) and Total Execution Cost (TEC) associated with dependent tasks in a cloud-fog computing environment. To evaluate the performance of the PWOA algorithm, extensive simulations are conducted using test cases (30, 50, 100 and 1000) from five different scientific workflows: Cybershake, Epigenomics, Inspiral, Montage and Sipht. These workflows encompassed varying numbers of assigned tasks. The simulation results demonstrated that the proposed PWOA algorithm outperformed the standard PSO and WOA algorithms to improve TET and TEC across all the diverse workflows. This study also provides a comparative view of the effectiveness of the PWOA algorithm in enhancing workflow scheduling performance.
Does Reinforcement Learning Improve Outcomes for Critically Ill Patients? A Systematic Review and Level-of-Readiness Assessment

OBJECTIVE: Reinforcement learning (RL) is a machine learning technique uniquely effective at sequential decision-making, which makes it potentially relevant to ICU treatment challenges. We set out to systematically review, assess level-of-readiness and meta-analyze the effect of RL on outcomes for critically ill patients. DATA SOURCES: A systematic search was performed in PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection, Elsevier/SCOPUS and the Institute of Electrical and Electronics Engineers Xplore Digital Library from inception to March 25, 2022, with subsequent citation tracking. DATA EXTRACTION: Journal articles that used an RL technique in an ICU population and reported on patient health-related outcomes were included for full analysis. Conference papers were included for level-of-readiness assessment only. Descriptive statistics, characteristics of the models, outcome compared with clinician’s policy and level-of-readiness were collected. RL-health risk of bias and applicability assessment was performed. DATA SYNTHESIS: A total of 1,033 articles were screened, of which 18 journal articles and 18 conference papers, were included. Thirty of those were prototyping or modeling articles and six were validation articles. All articles reported RL algorithms to outperform clinical decision-making by ICU professionals, but only in retrospective data. The modeling techniques for the state-space, action-space, reward function, RL model training, and evaluation varied widely. The risk of bias was high in all articles, mainly due to the evaluation procedure. CONCLUSION: In this first systematic review on the application of RL in intensive care medicine we found no studies that demonstrated improved patient outcomes from RL-based technologies. All studies reported that RL-agent policies outperformed clinician policies, but such assessments were all based on retrospective off-policy evaluation.
Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms

Abstract Objectives To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists. Materials and methods All mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24 months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AI sens ) and specificity (AI spec ) of first readers. Accuracy measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and recall rate (RR). Results The sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AI sens had lower specificity (97.5% vs 97.7%; p &lt; 0.0001) and PPV (17.5% vs 18.7%; p = 0.01) and a higher RR (3.0% vs 2.8%; p &lt; 0.0001) than first readers. AI spec was comparable to first readers in terms of all accuracy measures. Both AI sens and AI spec detected significantly fewer screen-detected cancers (1166 (AI sens ), 1156 (AI spec ) vs 1252; p &lt; 0.0001) but found more interval cancers compared to first readers (126 (AI sens ), 117 (AI spec ) vs 39; p &lt; 0.0001) with varying types of cancers detected across multiple subgroups. Conclusion Standalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers. Clinical relevance statement Replacing first readers with AI with an appropriate cut-off score could be feasible. AI-detected cancers not detected by radiologists suggest a potential increase in the number of cancers detected if AI is implemented to support double reading within screening, although the clinicopathological characteristics of detected cancers would not change significantly. Key Points • Standalone AI cancer detection was compared to first readers in a double-read mammography screening population. • Standalone AI matched at first reader specificity showed no statistically significant difference in overall accuracy but detected different cancers. • With an appropriate threshold, AI-integrated screening can increase the number of detected cancers with similar clinicopathological characteristics.