Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS). In its basic form, an anomaly detection module monitors the sensor measurements and actuator states from the physical plant, and detects anomalies in these measurements to identify abnormal operation status. Nevertheless, building effective anomaly detection models for CPS is rather challenging as the model has to accurately detect anomalies in presence of highly complicated system dynamics and unknown amount of sensor noise. In this work, we propose a novel time series anomaly detection method called Neural System Identification and Bayesian Filtering (NSIBF) in which a specially crafted neural network architecture is posed for system identification, i.e., capturing the dynamics of CPS in a dynamical state-space model; then a Bayesian filtering algorithm is naturally applied on top of the "identified" state-space model for robust anomaly detection by tracking the uncertainty of the hidden state of the system recursively over time. We provide qualitative as well as quantitative experiments with the proposed method on a synthetic and three real-world CPS datasets, showing that NSIBF compares favorably to the state-of-the-art methods with considerable improvements on anomaly detection in CPS.

The spectacular success of deep generative models calls for quantitative tools to measure their statistical performance. Divergence frontiers have recently been proposed as an evaluation framework for generative models, due to their ability to measure the quality-diversity trade-off inherent to deep generative modeling. However, the statistical behavior of divergence frontiers estimated from data remains unknown to this day. In this paper, we establish non-asymptotic bounds on the sample complexity of the plug-in estimator of divergence frontiers. Along the way, we introduce a novel integral summary of divergence frontiers. We derive the corresponding non-asymptotic bounds and discuss the choice of the quantization level by balancing the two types of approximation errors arisen from its computation. We also augment the divergence frontier framework by investigating the statistical performance of smoothed distribution estimators such as the Good-Turing estimator. We illustrate the theoretical results with numerical examples from natural language processing and computer vision.

An increasing number of machine learning models have been deployed in domains with high stakes such as finance and healthcare. Despite their superior performances, many models are black boxes in nature which are hard to explain. There are growing efforts for researchers to develop methods to interpret these black-box models. Post hoc explanations based on perturbations, such as LIME, are widely used approaches to interpret a machine learning model after it has been built. This class of methods has been shown to exhibit large instability, posing serious challenges to the effectiveness of the method itself and harming user trust. In this paper, we propose S-LIME, which utilizes a hypothesis testing framework based on central limit theorem for determining the number of perturbation points needed to guarantee stability of the resulting explanation. Experiments on both simulated and real world data sets are provided to demonstrate the effectiveness of our method.

Explaining the foundations for predictions obtained from graph neural networks (GNNs) is critical for credible use of GNN models for real-world problems. Owing to the rapid growth of GNN applications, recent progress in explaining predictions from GNNs, such as sensitivity analysis, perturbation methods, and attribution methods, showed great opportunities and possibilities for explaining GNN predictions. In this study, we propose a method to improve the explanation quality of node classification tasks that can be applied in a post hoc manner through aggregation of auxiliary explanations from important neighboring nodes, named SEEN. Applying SEEN does not require modification of a graph and can be used with diverse explainability techniques due to its independent mechanism. Experiments on matching motif-participating nodes from a given graph show great improvement in explanation accuracy of up to 12.71% and demonstrate the correlation between the auxiliary explanations and the enhanced explanation accuracy through leveraging their contributions. SEEN provides a simple but effective method to enhance the explanation quality of GNN model outputs, and this method is applicable in combination with most explainability techniques.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as facultymembers doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

With GPT-3 slowly revealing its potential, it has created a massive buzz amid the ML community. While developers are trying their hands on some of the exciting applications of GPT-3, many are expressing their astonishment with the kind of possibilities it can bring for humanity. Apart from writing codes, writing stories, and developing websites, many developers have played around with GPT-3 to bring out some fun applications. About: OthersideAI is an online tool that generates full-length emails according to the preference, only by typing the essential pointers that need to be covered. With GPT-3, the tool helps users create well-written, concise, convincing emails 4x faster than before.

If the potential and possibility of artificial intelligence has always fascinated you, get ready for the perfect bundle to fill the next few weeks with! Humble Bundle teamed up with Morgan & Claypool to bring you insights into AI and its applications into autonomous vehicles, conversational systems, and more! Pick up this bundle and you'll enjoy discovering eBooks like Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls, Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production, and Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots. Your purchase of this bundle helps support a charity of your choice. This bundle launched on June 14 at 11:00 am PST and lasts through July 05, 2021.

In the last decade, advances in data science and engineering have made possible the development of various data products across industry. Problems that not so long ago were treated as very difficult for machines to tackle are now solved (to some extent) and available at large scale capacities. These include many perceptual-like tasks in computer vision, speech recognition, and natural language processing (NLP). Nowadays, we can contract large-scale deep learning-based vision systems that can recognize and verify faces on images and videos. In the same way, we can take advantage of large-scaled language models to build conversational bots, analyze large bodies of text to find common patterns, or use translation systems that can work on nearly any modern language.

The authors of this blog are Stan Zwinkels & Ted de Vries Lentsch. This blog aims to present our attempt to create a detection algorithm for detecting ripe flowers of the Alstroemeria genus Morado. Throughout this blog, we explain our process to create a dataset and detection model that achieves an F1 score of more than 0.75. This blog is part of the course Seminar Computer Vision By Deep Learning (CS4245) 2021 from the Delft University of Technology. Creating the dataset has been carried out in collaboration with the company Hoogenboom Alstroemeria.

We have published 7k books, videos, articles, and tutorials. If you've been following developments in deep learning and natural language processing (NLP) over the past few years then you've probably heard of something called BERT; and if you haven't, just know that techniques owing something to BERT will likely play an increasing part in all our digital lives. BERT is a state-of-the-art embedding model published by Google, and it represents a breakthrough in the field of NLP by providing excellent results on many NLP tasks, including question answering, text generation, sentence classification, and more. Here we are going to look at what BERT is and see what is distinctive about it, by looking in a relatively high-level way (eschewing the underlying linear algebra) at the internal workings of the BERT model. By the end you should have if not a detailed understanding then at least a strong sense of what underpins this modern approach to NLP and other methods like it.