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AMS Publications Support for Open, Transparent, and Equitable Research

Weather and Forecasting 2023年第11期38卷 2153-2154页

作者： Schuster, Douglas Friedman, Michael AMS Board on Open Science Data and Software AMS Publications Associate Director–Publishing Technology

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DP-PartFIM: Frequent Itemset Mining using Differential Privacy and Partition

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IEEE Transactions on Emerging Topics in Computing 2024年 1-11页

作者： Liu, Xinyu Gan, Wensheng Yu, Lele Liu, Yining School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China College of Cyber Security Jinan University Guangzhou China

Itemset mining is a popular data mining technique for extracting interesting and valuable information from large datasets. However, since datasets contain sensitive private data, it is not permitted to directly mine the data or share the mining results. Previous privacy-preserving frequent itemset mining research was not efficient because of the use of privacy budgets or long transaction truncation strategies, which are impractical for large datasets. In this paper, we propose a more efficient partition mining technology, DP-PartFIM, based on differential privacy, which protects privacy while mining data. DP-PartFIM uses partition mining to mine frequent itemsets and constructs vertical data storage formats for each partition, which makes the algorithm equally efficient for large datasets. To protect data privacy, DP-PartFIM adds Laplace noise to support candidate itemsets. The experimental results show that, compared with the classical privacy-preserving itemset mining methods, DP-PartFIM better guarantees data utility and privacy. IEEE

关键词： Differential privacy

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Random logistic machine (RLM): Transforming statistical models into machine learning approach

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Communications in Statistics - Theory and Methods 2024年第21期53卷 7517-7525页

作者： Li, Yu-Shan Guo, Chao-Yu Division of Biostatistics and Data Science Institute of Public Health College of Medicine National Yang Ming Chiao Tung University Taipei Taiwan

data science is booming with big data, and machine learning provides better predictive analyses. However, conventional statistical models can effortlessly interpret the effect estimates, and the prediction models are generally in a closed form. Therefore, this research integrates the logistic regression model’s core with the Random Forest structure to create a blended novel machine learning method, the Random Logistic Machine (RLM). In this way, the new approach preserves the statistical and machine learning advantages. Computer simulation studies examined the predictive ability of RLM, random forest, and Logistic Regression under various scenarios. The results showed that the RLM delivers a comparable performance to Random Forests and Logistic Regression. An application to the Breast Cancer Wisconsin (Diagnostic) data Set also demonstrates the superior performance of the new approach. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Logistic regression

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Kalman filter based on a fractional discrete-time stochastic augmented CoVid-19 model

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Journal of Biosafety and Biosecurity 2024年第2期6卷 76-98页

作者： Mohammad Ghani Dwi Rantini Maryamah Data Science Technology Faculty of Advanced Technology and MultidisciplineUniversitas AirlanggaSurabaya 60115Indonesia

In this paper,we study the dynamics of the CoVid-19 outbreak in Semarang,Indonesia,using a fractional CoVid-19 *** first determine the effects of the isolation rateand infection rate b on the reproduction number R0 and infected number *** find that R0 is directly proportional to b and inversely proportional to.For V,the effect of physical distancing is not as significant as changing.Asincreases,V decreases,the number of susceptible individuals increases,the number of quarantined individuals decreases sharply,and the number of recovered individuals ***,the effect of vaccination is also *** combination of physical distancing,isolation,and vaccination has a significant impact on reducing the number of infected *** of dynamical systems allows us to understand the characteristics of our model,such as its boundedness and non-negativity,the existence of equilibrium points,the existence and uniqueness of solutions,and the local and global *** validate our fractional CoVid-19 model,we introduce the fractional extended Kalman filter(FracEKF)as a prediction method and compare the results against reported CoVid-19 *** is a modified version of the basic extended Kalman filter with a time-fractional memory *** prediction results illustrate the accuracy of this model in terms of the root mean square error(RMSE),normalized root mean square error(NRMSE),and mean absolute percentage error(MAPE)for each ***reproduces the trends observed in the reported data for the number of infected individuals,i.e.,whenincreases,the infected number ***,a higher fractional-order results in higher model ***,higher values of the process noise Qf give smaller errors,whereas higher values of the observation noise Rf produce higher *** and the fractional-order a are inversely proportional to RMSE;NRMSE,and MAPE,whereas Rf is directly proportional to RMSE;NRMSE,and MAPE

关键词： stability. fractional inverse

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A Hybrid Ensemble Approach for Depression Detection: Combining Deep Learning and Machine Learning 4

A Hybrid Ensemble Approach for Depression Detection: Combini...

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4th IEEE International Conference on data science and Computer Application, ICDSCA 2024

作者： Shan, Richard Department of Data Science North Carolina School of Science and Mathematics DurhamNC United States

ISBN: (纸本)9798350368239

The depression detection from text data represents a pragmatic application of machine learning and natural language processing in the mental health field. This paper presents a hybrid ensemble learning approach that combines conventional machine learning models, namely Naive Bayes, Random Forest, XGBoost, and Logistic Regression, with deep learning models, including the Long Short-Term Memory (LSTM) and Multi-Layer Perceptron networks. The hard voting for ensembling results in the ensemble model outperforming individual models on precision, on a par with the best model in terms of accuracy, F1-score, and recall. The evaluation of the models on a dataset of depression-related text posts indicates that the ensemble significantly reduces both false positives and false negatives, shown in the confusion matrix. The LSTM model, integrated into the ensemble, captures sequential dependencies in text, contributing to improved depression classification. This study demonstrates the interplay potential of machine learning and deep learning techniques to improve the balance and robustness of depression detection systems. ©2024 IEEE.

关键词： Contrastive Learning

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CONTESTS: a Framework for Consistency Testing of Span Probabilities in Language Models

CONTESTS: a Framework for Consistency Testing of Span Probab...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wagner, Eitan Slavutsky, Yuli Abend, Omri School of Computer Science and Engineering Department of Statistics and Data Science Hebrew University of Jerusalem Israel

ISBN: (纸本)9798891761643

Although language model scores are often treated as probabilities, their reliability as probability estimators has mainly been studied through calibration, overlooking other aspects. In particular, it is unclear whether language models produce the same value for different ways of assigning joint probabilities to word spans. Our work introduces a novel framework, ConTestS (Consistency Testing over Spans), involving statistical tests to assess score consistency across interchangeable completion and conditioning orders. We conduct experiments on post-release real and synthetic data to eliminate training effects. Our findings reveal that both Masked Language Models (MLMs) and autoregressive models exhibit inconsistent predictions, with autoregressive models showing larger discrepancies. Larger MLMs tend to produce more consistent predictions, while autoregressive models show the opposite trend. Moreover, for both model types, prediction entropies offer insights into the true word span likelihood and therefore can aid in selecting optimal decoding strategies. The inconsistencies revealed by our analysis, as well their connection to prediction entropies and differences between model types, can serve as useful guides for future research on addressing these limitations. © 2024 Association for Computational Linguistics.

关键词： Prediction models

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Self-Supervised Learning for Facial Skin Lesion Classification

Self-Supervised Learning for Facial Skin Lesion Classificati...

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2025 International Conference on New Trends in Computing sciences, ICTCS 2025

作者： Bdair, Tariq Alassi, Reem Taweel, Saja Kalha, Ruby Data Science Department PSUT Amman Jordan

ISBN: (纸本)9798331523411

Due to its sensitivity, facial skin lesion diagnosis and treatment represent a huge challenge for patients and dermatologists. Although deep-learning-based approaches have shown remarkable advances in addressing diseases and skin cancers, facial skin lesion detection has lacked effort in this direction and has not been investigated thoroughly. This paper addresses this problem by proposing DermTech, a self-supervised deep learning framework. Our framework consists of different parts that contribute to precisely identifying five common facial skin conditions;Rosacea, Eczema, Acne, Milia, and Melasma. DermTech utilizes a Vision Transformer (ViT) combined with contrastive learning techniques to enhance feature representation. Additionally, it introduces reconstruction loss, which is combined with contrastive loss in a novel multi-tasking framework, to enable strong classification abilities for various skin issues. Our experiments show that each proposed contribution leads to improvement in performance and reaches a total accuracy of 76.70%. ©2025 IEEE.

关键词： Computer Vision Facial Skin Classification Masked Auto-Encoders Self-Supervised Learning Vision Transformer

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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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Human Activity Recognition via Wi-Fi CSI and VAE-CNN-BiLSTM Hybrid Deep Learning

Human Activity Recognition via Wi-Fi CSI and VAE-CNN-BiLSTM ...

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2024 International Conference on Brain Computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Aparna, G.B. Poulose, Alwin School of Data Science Indian Institute of Science Education and Research Kerala Thiruvananthapuram695551 India

ISBN: (纸本)9798331540067

Human Activity Recognition (HAR) is vital across multiple applications, such as healthcare monitoring, smart home systems, and surveillance. Recently, Wi-Fi channel state information (CSI) has gained attention as a valuable data source for HAR, owing to its non-intrusive nature and capacity to capture detailed spatial information. This paper introduces a deep learning framework that combines variational autoencoders (VAEs), convolutional neural networks (CNNs), and bidirectional long short-term memory (BiLSTM) networks to achieve high-accuracy HAR using Wi-Fi CSI data. The proposed hybrid VAE-CNN-BiLSTM model utilizes spatial information encoded in CSI matrices to extract discriminative features crucial for recognizing different activities. The VAE component captures a compressed representation of the high-dimensional CSI data, enhancing the model's robustness and generalization. The CNN layers then capture spatial features from raw CSI data, identifying both local and global patterns, which are further processed by the BiLSTM layers to capture the temporal dependencies and dynamic patterns associated with human activities. To evaluate the model, we conducted experiments on a publicly available Wi-Fi CSI dataset for human activity recognition in both line-of-sight (LOS) and non-line-of-sight (NLOS) indoor environments, comparing its performance with other state-of-the-art deep learning models. The results indicate that the hybrid VAE-CNN-BiLSTM model achieves superior accuracy, with classification rates of 88.9% in LOS and 88.2% in NLOS scenarios, demonstrating its effectiveness in accurately distinguishing various human activities. © 2024 IEEE.

关键词： Wi-Fi

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Identifying Affected Libraries and Their Ecosystems for Open Source Software Vulnerabilities 24

Identifying Affected Libraries and Their Ecosystems for Open...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Wu, Susheng Song, Wenyan Huang, Kaifeng Chen, Bihuan Peng, Xin School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Shanghai China

ISBN: (纸本)9798400702174

Software composition analysis (SCA) tools have been widely adopted to identify vulnerable libraries used in software applications. Such SCA tools depend on a vulnerability database to know affected libraries of each vulnerability. However, it is labor-intensive and error prone for a security team to manually maintain the vulnerability database. While several approaches adopt extreme multi-label learning to predict affected libraries for vulnerabilities, they are practically ineffective due to the limited library labels and the unawareness of ecosystems. To address these problems, we first conduct an empirical study to assess the quality of two fields, i.e., affected libraries and their ecosystems, for four vulnerability databases. Our study reveals notable inconsistency and inaccuracy in these two fields. Then, we propose HOLMES to identify affected libraries and their ecosystems for vulnerabilities via a learning-to-rank technique. The key idea of HOLMES is to gather various evidences about affected libraries and their ecosystems from multiple sources, and learn to rank a pool of libraries based on their relevance to evidences. Our extensive experiments have shown the effectiveness, efficiency and usefulness of HOLMES © 2024 ACM.

关键词： Open source software

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