Physical unclonable functions (PUF) are emerging as a promising alternative to traditional cryptographic protocols for IoT authentication. XOR Arbiter PUFs (XPUFs), a group of well-studied PUFs, are found to be secure against machine learning (ML) attacks if the XOR gate is large enough, as both the number of CRPs and the computational time required for modeling n-XPUF increases fast with respect to n, the number of component arbiter PUFs. In this paper, we present a neural network-based method that can successfully attack XPUFs with significantly fewer CRPs and shorter learning time when compared with existing ML attack methods. Specifically, the experimental study in this paper shows that our new method can break the 64-bit 9-XPUF within ten minutes of learning time for all of the tested samples and runs, with magnitudes faster than the fastest existing ML attack method, which takes over 1.5 days of parallel computing time on 16 cores.

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Categorizing Arabic text documents is considered an important research topic in the field of Natural Language Processing (NLP) and Machine Learning (ML). The number of Arabic documents is tremendously increasing daily as new web pages, news articles, social media contents are added. Hence, classifying such documents in specific classes is of high importance to many people and applications. Convolutional Neural Network (CNN) is a class of deep learning that has been shown to be useful for many NLP tasks, including text translation and text categorization for the English language. Word embedding is a text representation currently used to represent text terms as real-valued vectors in vector space that represent both syntactic and semantic traits of text. Current research studies in classifying Arabic text documents use traditional text representation such as bag-of-words and TF-IDF weighting, but few use word embedding. Traditional ML algorithms have already been used in Arabic text categorization, and good results are achieved. In this study, we present a Multi-Kernel CNN model for classifying Arabic news documents enriched with n-gram word embedding, which we call A Superior Arabic Text Categorization Deep Model (SATCDM). The proposed solution achieves very high accuracy compared to current research in Arabic text categorization using 15 of freely available datasets. The model achieves an accuracy ranging from 97.58% to 99.90%, which is superior to similar studies on the Arabic document classification task.

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Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.