Reviewerly/exHarmony huggingface dataset:
BERT Fine-Tuning the Covid-19 Open Research Dataset for Named Entity Recognition

This study employs the widely used Large Language Model (LLM), BERT, to implement Named Entity Recognition (NER) on the CORD-19 biomedical literature corpus. By fine-tuning the pre-trained BERT on the CORD-NER dataset, the model gains the ability to comprehend the context and semantics of biomedical named entities. The refined model is then utilized on the CORD-19 to extract more contextually relevant and updated named entities. However, fine-tuning large datasets with LLMs poses a challenge. To counter this, two distinct sampling methodologies are proposed to apply on each dataset. First, for the NER task on the CORD-19, a Latent Dirichlet Allocation (LDA) topic modeling technique is employed. This maintains the sentence structure while concentrating on related content. Second, a straightforward greedy method is deployed to gather the most informative data of 25 entity types from the CORD-NER dataset. The study realizes its goals by demonstrating the content comprehension capability of BERT-based models without the necessity of supercomputers, and converting the document-level corpus into a source for NER data, enhancing data accessibility. The outcomes of this research can shed light on the potential progression of more sophisticated NLP applications across various sectors, including knowledge graph creation, ontology learning, and conversational AI.
Differential Privacy for Consumer Data in Retail Data Partnerships

In the context of Industry 4.0 within the retail sector, the pursuit of improved customer experiences through data partnerships has become crucial. Unfortunately, existing frameworks and privacy models do not adequately address consumer privacy protection at the granular level of receipt data. This research paper aims to investigate and propose a privacy-preserving model designed explicitly for retail data partnerships, focusing on receipt data at the multi-granular level. Leveraging the principles of Differential Privacy, we explore techniques to ensure the confidentiality and privacy of sensitive consumer information. Additionally, we delve into selecting an appropriate global sensitivity value, seeking a balance between accuracy and privacy preservation. By addressing the existing gaps in privacy protection within retail data partnerships, this study contributes to establishing robust privacy frameworks and enabling secure collaboration in the retail industry.
Structuring of Discourse and Annotation Method for Contribution Assessment in Collaborative Discussions

Human resource training, screening, and recruiting processes all require low-cost individual evaluation of participants in group discussions. This study aims to explore efficient ways to evaluate verbal discussions based on transcription data in an objective and multi-angle perspective to structure and annotate discussions for quantitative evaluation of the contribution from participants. Using two discussion data sets, this study conducted an experiment in which discussions were classified into a multi-layered discussion structure, and then annotations were further assigned within each layer and evaluated using a rating formula. In this experiment, we evaluated each participant in five evaluation dimensions and the result shows that they can be evaluated from multiple aspects as in conventional evaluation by observers. In addition, we also found that evaluation can be shown in a continuous scoring scope different from the five-point rating scale in conventional evaluation by observers. As only manual annotation is used in this study, it is believed that evaluation can be executed at an even lower cost by automated annotation in future studies.
Enhancing Detection of Daily-Used Face Swap Applications by Using Focused Landmark Analysis

The rapid evolution of user-friendly, high-quality face swap apps has raised security and privacy concerns, particularly on social media. This motivates our proposal of an efficient method to identify faceswap images. Our approach is based on Vision Transformer and exploits Facial Landmark Focusing Image (FAFI), designed to target regions adjacent to facial landmarks to identify anomalies. We conduct experiments on our dataset containing diverse face-swap videos from common applications, capturing real-life contexts. Experimental results demonstrate that FAFI exhibits superior generalization potential compared to Multi-scale Retinex, especially when using the Vision Transformer as a feature extractor, with the accuracy, precision, and recall of 95.29%, 96.45%, and 93.53%, respectively. Furthermore, the visualization using Grad-CAM also demonstrates that our method with Vision Transformer tends to emphasize intricate and detailed abnormal artifacts within the face, providing promising hints to users to identify potential suspected regions in a human face for further evaluation (Code and dataset are available at https://github.com/minhkhoi1026/face-spoofing-dection ).
Differential Privacy Under Membership Inference Attacks

Membership inference attacks are used as an audit tool to quantify training data leaks in machine learning models. Protection can be provided by anonymizing the training data or using training functions with differential privacy. Depending on the context, such as building data collection services for central machine learning models or responding to queries from end users, data scientists can choose between local and global differential privacy parameters. Different types of differential privacy have different epsilon values that reflect different mechanisms, making it difficult for data scientists to select appropriate differential privacy parameters and avoid inaccurate conclusions. The experiments in this paper show the relative privacy-accuracy trade-off of local and global differential privacy mechanisms under a white-box membership inference attack. While membership inference only reflects the lower bound for inference risk, and differential privacy formulates the upper bound, the experiments in this study with some datasets show that the trade-off between accuracy and privacy is similar for both types of mechanisms, although there is a large difference in their upper bounds. This suggests that the upper bound is far from the practical susceptibility to membership inference. Therefore, a small epsilon value in global differential privacy and a large epsilon value in local differential privacy lead to the same risk of membership inference. In addition, the risks from membership inference attacks are not uniform across all classes, especially when the training dataset in machine learning models is skewed.
Multiple Vehicles Detection and Tracking Using Ground and Aerial Images for Vehicular Management System

Vehicle Management Systems (VMSs) are crucial in location-based services for optimizing transportation efficiency, traffic flow, and overall mobility for individuals and businesses. To further enhance their performance, smart VMSs must leverage existing traffic data it gathers by detecting and tracking vehicles periodically. However, despite recent advancements in traffic monitoring, there remains a need for an efficient and up-to-date system, especially in dense traffic or situations requiring aerial monitoring. This paper focuses on the importance of detecting and tracking multiple vehicles in such systems, presenting a two-way validation architecture that integrates CCTV and drone images to enhance accuracy and authenticity. The architecture utilizes the YOLO pretrained model for CCTV images, which is subsequently fine-tuned for drone images. Results demonstrate an accuracy of 99% and a mean average precision of up to 97%. Lastly, the proposed architecture is used for two real-time applications of vehicle counter and lane detection which shows its validity and ability in aforementioned situations.
Breast Cancer Classification on Mammograms Using Vision Transformer

Breast cancer is the most commonly diagnosed cancer and the fifth leading cause of death in women. Early detection of this disease not only increases the survival rate but also reduces the cost of treatment. Mammography (X-ray mammography) is the current imaging method to identify and diagnose breast malignancies. In this work, we propose a classification technique based on several network architectures, including NasNetLarge, MobileNetV2, InceptionV3, DenseNet, and Vision Transformer to classify mammograms as normal, benign, or malignant. Experimental results show that the accuracy of the proposed models is up to 99%. The support of mammograms screening containing lesions will help doctors focus more on analyzing results. This helps the accuracy of diagnosis to increase and gives timely treatment direction.
Web Browsers’ Support for Managing Cookies. An Experiment Report

In this paper we analyze the support currently provided by the most-widely used web browsers for managing cookies. To carry out our study we have first designed an experiment, and then we have carried out this experiment in each of the selected web browsers—or, more precisely, in each of the configurations related to cookies currently supported by each of the selected web browsers. The main take-away from this experiment is that the current trend among web browsers of leaving the responsibility of handling cookies to the browsers’ end-users is not practical (and, therefore, not effective), simply because the understanding required for setting up the browsers’ configurations related to cookies is beyond what can be reasonably expected from typical browsers’ end-user. In addition, we have carried out a survey whose preliminary results seem to validate the main conclusion from our experiment.
Deep Learning Techniques for Segmenting Breast Lesion Regions and Classifying Mammography Images

Breast cancer is currently one of the leading causes of death in many countries worldwide. Detecting breast masses early can provide higher chances of survival for patients. However, determining and segmenting benign or malignant breast masses is becoming a challenging issue. Currently, there are a wide range of Convolutional Neural Networks used to address breast mass segmentation and breast cancer classification issues, such as U-Net, SegNet, Mask R-CNN, for segmentation, and Convnet, CNN, ResNet, for classification. However, these solutions are still not effective enough. Therefore, we have solved this problem by applying modern model called Segment Anything Model to predict breast tumor segmentation masks to help doctors identify and evaluate breast tumors and two models EfficientNet B0 combined with Focal Loss and Vision Transformer base to classify breast images as benign or malignant. The experimental results show those modern models achieved high performance with an Intersection over Union score of 96.59% on the CIBS-DDSM dataset. Additionally, the classification model achieved an accuracy of 100% and F1-scores of 100% on the DDSM dataset, outperforming other models. Our technique helps support doctors in identifying breast masses in images and provides reliable predictions for diagnostic purposes, thus improving the effectiveness of breast cancer detection.
Pedestrian Intention Prediction and Style Recognition in Bird’s-Eye View

&lt;div&gt;In this article, pedestrian crossing intention and pedestrian crossing style are studied by means of statistical theory and artificial neural network. Feature parameters such as the average speed of pedestrians, pedestrian attention to vehicles, and vehicle arrival speed are extracted before and during the time pedestrians cross the street from a bird’s-eye view. Based on these parameters, an artificial neural network is used to predict the pedestrian crossing intention. K-means statistical method was used to cluster the pedestrian crossing styles, and the results showed that clustering the crossing styles into three categories, conservative, cautious, and adventurous, has a better classification effect, and the crossing behaviors of different types of pedestrians were analyzed. A random forest-based model is used to identify pedestrian crossing styles, the prediction accuracy reaches 91.83% and the recognition accuracy reaches 93.3%. The research content of this article can provide the foundation for intelligent driving pedestrian prediction, pedestrian crossing scene simulation, and traffic management.&lt;/div&gt;
Comparison of Machine Learning Models to Classify Documents on Digital Development

Automated document classification is a trending topic in Natural Language Processing (NLP) due to the extensive growth in digital databases. However, a model that fits well for a specific classification task might perform weakly for another dataset due to differences in the context. Thus, training and evaluating several models is necessary to optimise the results. This study employs a publicly available document database on worldwide digital development interventions categorised under twelve areas. Since digital interventions are still emerging, utilising NLP in the field is relatively new. Given the exponential growth of digital interventions, this research has a vast scope for improving how digital-development-oriented organisations report their work. The paper examines the classification performance of Machine Learning (ML) algorithms, including Decision Trees, k-Nearest Neighbors, Support Vector Machine, AdaBoost, Stochastic Gradient Descent, Naive Bayes, and Logistic Regression. Accuracy, precision, recall and F1-score are utilised to evaluate the performance of these models, while oversampling is used to address the class-imbalanced nature of the dataset. Deviating from the traditional approach of fitting a single model for multiclass classification, this paper investigates the One vs Rest approach to build a combined model that optimises the performance. The study concludes that the amount of data is not the sole factor affecting the performance; features like similarity within classes and dissimilarity among classes are also crucial.
Hybridization of Modified Grey Wolf Optimizer and Dragonfly for Feature Selection

There are numerous techniques designed to enhance the performance of machine learning models, with feature selection being one of the key strategies. Although many feature selection methods exist, our study presents a novel hybrid approach that merges two metaheuristic techniques: the Modified Grey Wolf Optimizer (MGWO) and the Dragonfly Algorithm (DA). This innovative method not only boosts the model's performance but also emphasizes the most pertinent features. Our experimental results showcase robust model performance, achieving an F1-score of 90% on our experimental dataset, surpassing other approaches. Further results and discussions are provided in this paper, .
Chaotic Mountain Gazelle Optimizer (CMGO): A Robust Optimization Algorithm for K-Means Clustering of Diverse Data Types

Addressing challenges in data clustering for diverse data types, we introduce the Chaos Mountain Gazelle Optimizer (CMGO). This enhanced Mountain Gazelle Optimizer (MGO) is tailored for K-means clustering solutions. Noticing a skew in MGO's strategy distribution, we integrated a chaotic map into the Territorial Solitary Males strategy and omitted the Migration to Search for Food strategy. This adjustment increases exploration and curtails exploitation, improving CMGO's effectiveness in clustering complex datasets. We implemented the Gower distance technique to navigate K-means clustering's limitations with categorical and binary data. Tests on numeric, binary, categorical, and mixed data underscore the clustering's versatility. We evaluated CMGO against 14 algorithms on 28 UCI and OpenML datasets using the F-Measure metric and the tied rank test for statistical significance ranking. CMGO outperforms the original MGO and other tested algorithms in clustering pure numeric and categorical data, securing first place, and third for mixed data. Thus, CMGO emerges as a robust, efficient K-means optimizing method for complex, diverse datasets.
Exploration of the Feasibility and Applicability of Domain Adaptation in Machine Learning-Based Code Smell Detection

Machine learning-based code smell detection was introduced to mitigate the limitations of the heuristic-based approach and the subjectivity issues. Due to limited choices of the publicly available datasets, most of the machine learning-based classifiers were trained by the earlier versions of open-source projects that no longer represent the characteristics and properties of modern programming languages. Our experiments exhibit the feasibility and applicability of using a machine learning classifier well-trained on the earlier versions of open-source projects to classify four types of code smells, i.e., god class, data class, feature envy, and long method, in modern Java open-source projects without extensive feature engineering. The performance produced by the supervised machine learning algorithms was evaluated and compared. Particle swarm optimization and Bayesian optimization were adopted to enhance the performance of the machine learning classifiers, i.e., decision tree and random forest. The experimental results indicated that the machine learning-based code smell classifiers adapt poorly to the different target domain. The hyper-parameter optimization slightly improves the performance of the machine learning classifiers when classifying feature envy, god class, and long method in a modern Java project.
Death comes but why: A multi-task memory-fused prediction for accurate and explainable illness severity in ICUs

Abstract Predicting the severity of an illness is crucial in intensive care units (ICUs) if a patient‘s life is to be saved. The existing prediction methods often fail to provide sufficient evidence for time-critical decisions required in dynamic and changing ICU environments. In this research, a new method called MM-RNN (multi-task memory-fused recurrent neural network) was developed to predict the severity of illnesses in intensive care units (ICUs). MM-RNN aims to address this issue by not only predicting illness severity but also generating an evidence-based explanation of how the prediction was made. The architecture of MM-RNN consists of task-specific phased LSTMs and a delta memory network that captures asynchronous feature correlations within and between multiple organ systems. The multi-task nature of MM-RNN allows it to provide an evidence-based explanation of its predictions, along with illness severity scores and a heatmap of the patient’s changing condition. The results of comparison with state-of-the-art methods on real-world clinical data show that MM-RNN delivers more accurate predictions of illness severity with the added benefit of providing evidence-based justifications.
Super Resolution of Satellite-Derived Sea Surface Temperature Using a Transformer-Based Model

Sea surface temperature (SST) is one of the most important factors related to the ocean and the climate. In studying the domains of eddies, fronts, and current systems, high-resolution SST data are required. However, the passive microwave radiometer achieves a higher spatial coverage but lower resolution, while the thermal infrared radiometer has a lower spatial coverage but higher resolution. In this paper, in order to improve the performance of the super-resolution SST images derived from microwave SST data, we propose a transformer-based SST reconstruction model comprising the transformer block and the residual block, rather than purely convolutional approaches. The outputs of the transformer model are then compared with those of the other three deep learning super-resolution models, and the transformer model obtains lower root-mean-squared error (RMSE), mean bias (Bias), and robust standard deviation (RSD) values than the other three models, as well as higher entropy and definition, making it the better performing model of all those compared.
A diffusion-based kernel density estimator (diffKDE, version 1) with optimal bandwidth approximation for the analysis of data in geoscience and ecological research

Abstract. Probability density functions (PDFs) provide information about the probability of a random variable taking on a specific value. In geoscience, data distributions are often expressed by a parametric estimation of their PDF, such as, for example, a Gaussian distribution. At present there is growing attention towards the analysis of non-parametric estimation of PDFs, where no prior assumptions about the type of PDF are required. A common tool for such non-parametric estimation is a kernel density estimator (KDE). Existing KDEs are valuable but problematic because of the difficulty of objectively specifying optimal bandwidths for the individual kernels. In this study, we designed and developed a new implementation of a diffusion-based KDE as an open source Python tool to make diffusion-based KDE accessible for general use. Our new diffusion-based KDE provides (1) consistency at the boundaries, (2) better resolution of multimodal data, and (3) a family of KDEs with different smoothing intensities. We demonstrate our tool on artificial data with multiple and boundary-close modes and on real marine biogeochemical data, and compare our results against other popular KDE methods. We also provide an example for how our approach can be efficiently utilized for the derivation of plankton size spectra in ecological research. Our estimator is able to detect relevant multiple modes and it resolves modes that are located closely to a boundary of the observed data interval. Furthermore, our approach produces a smooth graph that is robust to noise and outliers. The convergence rate is comparable to that of the Gaussian estimator, but with a generally smaller error. This is most notable for small data sets with up to around 5000 data points. We discuss the general applicability and advantages of such KDEs for data–model comparison in geoscience.
Theoretical Analysis of Edit Distance Algorithms

To what extent have the techniques for theoretical analysis of edit distance algorithms achieved their goals?
Thus Spake ChatGPT

On the reliability of AI-based chatbots for science communication.
Adjustable accuracy of phase steps in a double-aperture common-path interferometer by rotating and displacing grating at Fourier plane

Detuning errors limit the accuracy of phase extraction in phase-shifting interferometry (PSI). To improve this accuracy, the systems to control the phase shifter dispositive must be highly accurate, involving high technology, exhaustive calibration, high cost, and constant maintenance. This manuscript presents a method for improving the accuracy of the phase step generated at the Fourier plane by rotating and displacing the grating in a double-aperture common-path interferometer. It is demonstrated both the detuning errors, and the phase extraction uncertainty decrease with the grating rotation angle. Its experimental implementation is very simple, as it does not require any sophisticated mechanical system to improve the accuracy, no exhaustive calibration, and no additional mechanical and optical dispositive. With the present approach, the PSI method could be achieved very simply and with high accuracy by using a low-accuracy actuator. The theoretical model and experimental results are presented to demonstrate the present proposal.&#xD