Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss

We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data dimension scales. In particular, we consider a general approach where changes are detected by comparing the distribution of the log-likelihood of the datastream over different time windows. Despite the fact that this approach constitutes the frame of several change-detection methods, its effectiveness when data dimension scales has never been investigated, which is indeed the goal of our paper. We show that the magnitude of the change can be naturally measured by the symmetric Kullback-Leibler divergence between the pre- and post-change distributions, and that the detectability of a change of a given magnitude worsens when the data dimension increases. This problem, which we refer to as \emph{detectability loss}, is due to the linear relationship between the variance of the log-likelihood and the data dimension. We analytically derive the detectability loss on Gaussian-distributed datastreams, and empirically demonstrate that this problem holds also on real-world datasets and that can be harmful even at low data-dimensions (say, 10)

Class Distribution Monitoring for Concept Drift Detection

We introduce Class Distribution Monitoring (CDM), an effective concept-drift detection scheme that monitors the class-conditional distributions of a datastream. In particular, our solution leverages multiple instances of an online and nonparametric change-detection algorithm based on QuantTree. CDM reports a concept drift after detecting a distribution change in any class, thus identifying which classes are affected by the concept drift. This can be precious information for diagnostics and adaptation. Our experiments on synthetic and real-world datastreams show that when the concept drift affects a few classes, CDM outperforms algorithms monitoring the overall data distribution, while achieving similar detection delays when the drift affects all the classes. Moreover, CDM outperforms comparable approaches that monitor the classification error, particularly when the change is not very apparent. Finally, we demonstrate that CDM inherits the properties of the underlying change detector, yielding an effective control over the expected time before a false alarm, or Average Run Length (ARL0)

Nonparametric and Online Change Detection in Multivariate Datastreams Using QuantTree

We address the problem of online change detection in multivariate datastreams, and we introduce QuantTree Exponentially Weighted Moving Average (QT-EWMA), a nonparametric change-detection algorithm that can control the expected time before a false alarm, yielding a desired Average Run Length (ARL 0 ). Controlling false alarms is crucial in many applications and is rarely guaranteed by online change-detection algorithms that can monitor multivariate datastreams without knowing the data distribution. Like many change-detection algorithms, QT-EWMA builds a model of the data distribution, in our case a QuantTree histogram, from a stationary training set. To monitor datastreams even when the training set is extremely small, we propose QT-EWMA-update, which incrementally updates the QuantTree histogram during monitoring, always keeping the ARL0 under control. Our experiments, performed on synthetic and real-world datastreams, demonstrate that QT-EWMA and QT-EWMA-update control the ARL0 and the false alarm rate better than state-of-the-art methods operating in similar conditions, achieving lower or comparable detection delays

Perception Visualization: Seeing Through the Eyes of a DNN

Artificial intelligence (AI) systems power the world we live in. Deep neural networks (DNNs) are able to solve tasks in an ever-expanding landscape of scenarios, but our eagerness to apply these powerful models leads us to focus on their performance and deprioritises our ability to understand them. Current research in the field of explainable AI tries to bridge this gap by developing various perturbation or gradient-based explanation techniques. For images, these techniques fail to fully capture and convey the semantic information needed to elucidate why the model makes the predictions it does. In this work, we develop a new form of explanation that is radically different in nature from current explanation methods, such as Grad-CAM. Perception visualization provides a visual representation of what the DNN perceives in the input image by depicting what visual patterns the latent representation corresponds to. Visualizations are obtained through a reconstruction model that inverts the encoded features, such that the parameters and predictions of the original models are not modified. Results of our user study demonstrate that humans can better understand and predict the system's decisions when perception visualizations are available, thus easing the debugging and deployment of deep models as trusted systems.Comment: Accepted paper at BMVC 2021 (Proceedings not available yet

An ensemble approach to estimate the fault-time instant

Since systems are prone to faults, fault detection and isolation are essential activities to be considered in safety-critical applications. In this direction, the availability of a sound estimate of the time instant the fault occurred is a precious information that a diagnosis system can fruitfully exploit, e.g., to identify information consistent with the faulty state. Unfortunately,any fault-detection system introduces a structural delay that,typically, increases in correspondence of subtle faults (e.g., those characterized by a small magnitude) with a consequence that the fault-occurrence time is overestimated. In this paper we propose an ensemble approach to estimate the time instant a fault occurred. We focus on systems that can be described as ARMA models and faults inducing an abrupt change in the model coefficients.Postprint (published version

Open-Set Recognition: an Inexpensive Strategy to Increase DNN Reliability

Deep Neural Networks (DNNs) are nowadays widely used in low-cost accelerators, characterized by limited computational resources. These models, and in particular DNNs for image classification, are becoming increasingly popular in safety-critical applications, where they are required to be highly reliable. Unfortunately, increasing DNNs reliability without computational overheads, which might not be affordable in low-power devices, is a non-trivial task. Our intuition is to detect network executions affected by faults as outliers with respect to the distribution of normal network's output. To this purpose, we propose to exploit Open-Set Recognition (OSR) techniques to perform Fault Detection in an extremely low-cost manner. In particuar, we analyze the Maximum Logit Score (MLS), which is an established Open-Set Recognition technique, and compare it against other well-known OSR methods, namely OpenMax, energy-based out-of-distribution detection and ODIN. Our experiments, performed on a ResNet-20 classifier trained on CIFAR-10 and SVHN datasets, demonstrate that MLS guarantees satisfactory detection performance while adding a negligible computational overhead. Most remarkably, MLS is extremely convenient to configure and deploy, as it does not require any modification or re-training of the existing network. A discussion of the advantages and limitations of the analysed solutions concludes the paper

A distributed Self-adaptive Nonparametric Change-Detection Test for Sensor/Actuator Networks

Abstract. The prompt detection of faults and, more in general, changes in stationarity in networked systems such as sensor/actuator networks is a key issue to guarantee robustness and adaptability in applications working in reallife environments. Traditional change-detection methods aiming at assessing the stationary of a data generating process would require a centralized availability of all observations, solution clearly unacceptable when large scale networks are considered and data have local interest. Differently, distributed solutions based on decentralized change-detection tests exploiting information at the unit and cluster level would be a solution. This work suggests a novel distributed change-detection test which operates at two-levels: the first, running on the unit, is particularly reactive in detecting small changes in the process generating the data, whereas the second exploits distributed information at the cluster-level to reduce false positives. Results can be immediately integrated in the machine learning community where adaptive solutions are envisaged to address changes in stationarity of the considered application. A large experimental campaign shows the effectiveness of the approach both on synthetic and real data applications.

Change Detection in Multivariate Datastreams: Likelihood and Detectability Loss

We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data dimension scales. In particular, we consider a general approach where changes are detected by comparing the distribution of the log-likelihood of the datastream over different time windows. Despite the fact that this approach constitutes the frame of several change-detection methods, its effectiveness when data dimension scales has never been investigated, which is indeed the goal of our paper. We show that the magnitude of the change can be naturally measured by the symmetric Kullback-Leibler divergence between the pre- and post-change distributions, and that the detectability of a change of a given magnitude worsens when the data dimension increases. This problem, which we refer to as \emphdetectability loss, is due to the linear relationship between the variance of the log-likelihood and the data dimension. We analytically derive the detectability loss on Gaussian-distributed datastreams, and empirically demonstrate that this problem holds also on real-world datasets and that can be harmful even at low data-dimensions (say, 10)