Benchmark for Security Testing on Embedded Systems

With the growing popularity of the Internet of Things (IoT), embedded devices continue to integrate more into our daily lives. For this reason, security for embedded devices is a vital issue to address. Attacks such as stack smashing, code injection, data corruption and Return Oriented Programming (ROP) are still a threat to embedded systems. As new methods are developed to defend embedded systems against such attacks, a benchmark to compare these methods is not present. In this work, a benchmark is presented that is aimed at testing the security of new techniques that defend against these common attacks. Two programs are developed that carry three key values needed for a benchmark: realistic embedded application, complex control flow, and being deterministic. The first application is a pin lock system and the second is a compression data logger. A complexity evaluation of the two applications revealed that the pin lock system contained 171 functions and 190 nodes with 252 edges in the control-flow graph, and the compression data logger contained 192 functions and 1,357 nodes with 2,123 edges in the control-flow graph. The current benchmark will be improved in the future by adding more applications with a wider range of complexity

Cross-Modal Data Programming Enables Rapid Medical Machine Learning

Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applying natural language processing pipelines to text reports associated with imaging studies. We propose cross-modal data programming, which generalizes this intuitive strategy in a theoretically-grounded way that enables simpler, clinician-driven input, reduces required labeling time, and improves with additional unlabeled data. In this approach, clinicians generate training labels for models defined over a target modality (e.g. images or time series) by writing rules over an auxiliary modality (e.g. text reports). The resulting technical challenge consists of estimating the accuracies and correlations of these rules; we extend a recent unsupervised generative modeling technique to handle this cross-modal setting in a provably consistent way. Across four applications in radiography, computed tomography, and electroencephalography, and using only several hours of clinician time, our approach matches or exceeds the efficacy of physician-months of hand-labeling with statistical significance, demonstrating a fundamentally faster and more flexible way of building machine learning models in medicine

Spatiotemporal Modeling of Multivariate Signals With Graph Neural Networks and Structured State Space Models

Multivariate signals are prevalent in various domains, such as healthcare, transportation systems, and space sciences. Modeling spatiotemporal dependencies in multivariate signals is challenging due to (1) long-range temporal dependencies and (2) complex spatial correlations between sensors. To address these challenges, we propose representing multivariate signals as graphs and introduce GraphS4mer, a general graph neural network (GNN) architecture that captures both spatial and temporal dependencies in multivariate signals. Specifically, (1) we leverage Structured State Spaces model (S4), a state-of-the-art sequence model, to capture long-term temporal dependencies and (2) we propose a graph structure learning layer in GraphS4mer to learn dynamically evolving graph structures in the data. We evaluate our proposed model on three distinct tasks and show that GraphS4mer consistently improves over existing models, including (1) seizure detection from electroencephalography signals, outperforming a previous GNN with self-supervised pretraining by 3.1 points in AUROC; (2) sleep staging from polysomnography signals, a 4.1 points improvement in macro-F1 score compared to existing sleep staging models; and (3) traffic forecasting, reducing MAE by 8.8% compared to existing GNNs and by 1.4% compared to Transformer-based models

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

Machine learning models that achieve high overall accuracy often make systematic errors on important subsets (or slices) of data. Identifying underperforming slices is particularly challenging when working with high-dimensional inputs (e.g. images, audio), where important slices are often unlabeled. In order to address this issue, recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices on which a model performs poorly. To be useful to a practitioner, these methods must identify slices that are both underperforming and coherent (i.e. united by a human-understandable concept). However, no quantitative evaluation framework currently exists for rigorously assessing SDMs with respect to these criteria. Additionally, prior qualitative evaluations have shown that SDMs often identify slices that are incoherent. In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1,235 slice discovery settings in three input domains (natural images, medical images, and time-series data). Then, motivated by the recent development of powerful cross-modal representation learning approaches, we present Domino, an SDM that leverages cross-modal embeddings and a novel error-aware mixture model to discover and describe coherent slices. We find that Domino accurately identifies 36% of the 1,235 slices in our framework - a 12 percentage point improvement over prior methods. Further, Domino is the first SDM that can provide natural language descriptions of identified slices, correctly generating the exact name of the slice in 35% of settings.Comment: ICLR 2022 (Oral

Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing

Labeled data is a critical resource for training and evaluating machine learning models. However, many real-life datasets are only partially labeled. We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times. Our method uses noisy guesses of the events' end times to train event detection models. Depending on how conservative these guesses are, mislabeled false positives may be introduced into the training set (i.e., negative sequences mislabeled as positives). We further propose a mathematical model for estimating how many inaccurate labels a model is exposed to, based on how noisy the end time guesses are. Finally, we show that neural networks can improve their detection performance by leveraging more training data with less conservative approximations despite the higher proportion of incorrect labels. We adapt sequential versions of MNIST and CIFAR-10 to empirically evaluate our method, and find that our risk-tolerant strategy outperforms conservative estimates by 12 points of mean average precision for MNIST, and 3.5 points for CIFAR. Then, we leverage the proposed training strategy to tackle a real-life application: processing continuous video recordings of epilepsy patients to improve seizure detection, and show that our method outperforms baseline labeling methods by 10 points of average precision

Outil automatique de test de circuits analogiques

Méthodologie et algorithmes pour le test de circuits analogiques -- Méthode automatique de calcul de sensibilité -- Génération de vecteurs de test pour les fautes paramétriques -- Génération de vecteurs de test pour les fautes catastropiques -- Compaction de vecteurs de test -- Insertion de points de test

ABSTRACT Closing the Gap Between Analog and Digital

This paper presents a highly effective method for parallel hard fault simulation and test specification development. The proposed method formulates the fault simulation problem as a problem of estimating the fault value based on the distance between the output parameter distribution of the fault-free and the faulty circuit. We demonstrate the effectiveness and practicality of our proposed method by showing results on different designs. This approach extended by parametric fault testing has been implemented as an automated tools set for IC testing

Parametric fault simulation and test vector generation

Process variation has forever been the major fail cause of analog circuit where small deviations in component values cause large deviations in the measured output parameters. This paper presents a new approach for parametric fault simulation and test vector generation. The proposed approach utilizes the process information and the sensitivity of the circuit principal components in order to generate statistical models of the fault-free and the faulty circuit. The obtained information is then used as a measurement to quantify the testability of the circuit. This approach extended by hard fault testing has been implemented as automated tool set for IC testing called FaultMaxx and TestMaxx