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Free, publicly-accessible full text available December 1, 2022
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Recent work on explainable clustering allows describing clusters when the features are interpretable. However, much modern machine learning focuses on complex data such as images, text, and graphs where deep learning is used but the raw features of data are not interpretable. This paper explores a novel setting for performing clustering on complex data while simultaneously generating explanations using interpretable tags. We propose deep descriptive clustering that performs sub-symbolic representation learning on complex data while generating explanations based on symbolic data. We form good clusters by maximizing the mutual information between empirical distribution on the inputs and the induced clusteringmore »Free, publicly-accessible full text available August 1, 2022
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With their growing popularity, Internet-of-Things (IoT) devices have become attractive targets for attack. Like most modern software systems, IoT device firmware depends on external third-party libraries extensively, increasing the attack surface of IoT devices. Furthermore, we find that the risk is compounded by inconsistent library management practices and delays in applying security updates—sometimes hundreds of days behind the public availability of critical patches—by device vendors. Worse yet, because these dependencies are "baked into" the vendor-controlled firmware, even security-conscious users are unable to take matters into their own hands when it comes to good security hygiene. We present Capture, a novelmore »Free, publicly-accessible full text available August 1, 2022
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Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entiremore »Free, publicly-accessible full text available July 19, 2022
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Denial of Service (DoS) is one of the common attempts in security hacking for making computation resources unavailable or to impair geographical networks. In this paper, we detect Denial of Service (DoS) attack from publicly available datasets using Logistic regression, Naive Bayes algorithm and artificial neural networks. The results from our experiments indicate that the accuracy, ROC curve and balanced accuracy of artificial neural network were higher than Naive Bayes algorithm and logistic regression for slightly imbalanced distribution dataset.