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Benefits of a Unified CNAPP and XDR Platform

In this episode of the "Cybersecurity Insights" podcast, Uptycs CEO Ganesh Pai discusses unifying XDR and CNAPP to improve visibility and explains the ....

Ganesh Pai , Akamai Technologies , Sonus Networks , Carrier Products , Before Akamai , Outlier Detection , Anomaly Detection ,

Use ChatGPT Code Interpreter for data analysis and more

This guide provides more information on how you can use ChatGPT Code Interpreter for data analysis by uploading multiple files to analyze ....

Manual Data , Publicly Available , Handling Large , Data Formats , Data Cleaning , Handling Missing , Outlier Detection , Feature Selection , Ensuring Data , Machine Learning , Understanding Customer , Time Series , Latent Dirichlet Allocation , Size Limit ,

"Deep One-Class Hate Speech Detection Model" by Saugata Bose and Guoxin Su

Hate speech detection for social media posts is considered as a binary classification problem in existing approaches, largely neglecting distinct attributes of hate speeches from other sentimental types such as “aggressive” and “racist”. As these sentimental types constitute a significant major portion of data, the classification performance is compromised. Moreover, those classifiers often do not generalize well across different datasets due to a relatively small number of hate-class samples. In this paper, we adopt a one-class perspective for hate speech detection, where the detection classifier is trained with hate-class samples only. Our model employs a BERT-BiLSTM module for feature extraction and a one-class SVM for classification. A comprehensive evaluation with four benchmarking datasets demonstrates the better performance of our model than existing approaches, as well as the advantage of training our model with a combination of the four datasets. ....

Date Class , Ne Class Svm , Outlier Detection , Transfer Learning ,

"Bayesian analysis of De distributions in optical dating: Towards a rob" by Bo Li, Zenobia Jacobs et al.

In optical dating, especially single-grain dating, various patterns of distributions in equivalent dose (De) are usually observed and analysed using different statistical models. None of these methods, however, is designed to deal with outliers that do not form part of the population of grains associated with the event of interest (the ‘target population’), despite outliers being commonly present in single-grain De distributions. In this paper, we present a Bayesian method for detecting De outliers and making allowance for them when estimating the De value of the target population. We test this so-called Bayesian outlier model (BOM) using data sets obtained for individual grains of quartz from sediments deposited in a variety of settings, and in simulations. We find that the BOM is suitable for single-grain De distributions containing outliers that, for a variety of reasons, do not form part of the target population. For example, De outliers may be associated with grains that have ....

Page Models , Bayesian Inference , Equivalent Doses , Outlier Detection , Single Grains ,

E&P Plus Tech Trends: Data Analytics and Cloud Computing Technologies

E&P Plus Tech Trends: Data Analytics and Cloud Computing Technologies
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