In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

/pdf/an-empirical-survey-of-data-augmentation-for-time-series-5bdy19uq2z.pdf

An empirical survey of data augmentation for time series classification with neural networks.

https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1189&context=electricalpub

Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

Simulation-based reinforcement learning approaches are leading the next innovations in legged robot control. However, the resulting control policies are still not applicable on soft and deformable terrains, especially at high speed. The primary reason is that reinforcement learning approaches, in general, are not effective beyond the data distribution: The agent cannot perform well in environments that it has not experienced. To this end, we introduce a versatile and computationally efficient granular media model for reinforcement learning. Our model can be parameterized to represent diverse types of terrain from very soft beach sand to hard asphalt. In addition, we introduce an adaptive control architecture that can implicitly identify the terrain properties as the robot feels the terrain. The identified parameters are then used to boost the locomotion performance of the legged robot. We applied our techniques to the Raibo robot, a dynamic quadrupedal robot developed in-house. The trained networks demonstrated high-speed locomotion capabilities on deformable terrains: The robot was able to run on soft beach sand at 3.03 meters per second although the feet were completely buried in the sand during the stance phase. We also demonstrate its ability to generalize to different terrains by presenting running experiments on vinyl tile flooring, athletic track, grass, and a soft air mattress. Description An adaptive locomotion strategy and terrain simulation enable agile and robust quadrupedal locomotion on deformable terrains.

Learning quadrupedal locomotion on deformable terrain

Network intrusion detection focuses on classifying network traffic as either normal or attack carrier. The classification is based on information extracted from the network flow packets. This is a complex classification problem with unbalanced datasets and noisy data. This work extends the classic radial basis function (RBF) neural network by including it as a policy network in an offline reinforcement learning algorithm. With this approach, all parameters of the radial basis functions (along with the network weights) are learned end-to-end by gradient descent without external optimization. We further explore how additional dense hidden-layers, and the number of radial basis kernels influence the results. This novel approach is applied to five prominent intrusion detection datasets (NSL-KDD, UNSW-NB15, AWID, CICIDS2017 and CICDDOS2019) achieving better performance metrics than alternative state-of-the-art models. Each dataset provides different restrictions and challenges allowing a better validation of results. Analysis of the results shows that the proposed architectures are excellent candidates for designing classifiers with the constraints imposed by network intrusion detection. We discuss the importance of dataset imbalance and how the proposed methods may be critically important for unbalanced datasets.

Network Intrusion Detection Based on Extended RBF Neural Network With Offline Reinforcement Learning

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among ML practitioners. This paper explains the DevOps and MLOps processes relevant to the implementation of MLOps. The contribution of this paper towards the MLOps framework is threefold: First, we review the state of the art in MLOps by analyzing the related work in MLOps. Second, we present an overview of the leading DevOps principles relevant to MLOps. Third, we derive an MLOps framework from the MLOps theory and apply it to a time-series forecasting application in the hourly day-ahead electricity market. The paper concludes with how MLOps could be generalized and applied to two more use cases with minor changes.

/pdf/from-devops-to-mlops-overview-and-application-to-electricity-3e1hv67a.pdf

From DevOps to MLOps: Overview and Application to Electricity Market Forecasting

Machine leaning (ML) models must be accurate to produce quality AI solutions. There must be high accuracy in the data and with the model that is built using the data. Online machine learning algorithms fits naturally with use cases that involves time series data. In online environments the data distribution can change over time producing what is known as concept drift. Real-life, real-time, machine learning algorithms operating in dynamic environments must be able to detect any drift or changes in the data distribution and adapt and update the ML model in the face of data that changes over time. In this paper we present the work of a simulated drift added to time series ML models. We simulate drift on Multiplayer perceptron (MLP), Long Short Term Memory (LSTM), Convolution Neural Networks (CNN) and Gated Recurrent Unit (GRU). Results show ML models with flavors of recurrent neural network (RNN) are less sensitive to drift compared to other models. By adding noise to the training set, we can recover accuracy of the model in the face of drift.

Mitigating Drift in Time Series Data with Noise Augmentation

this work is a continuation of an ongoing effort to increase the robustness of the deep neural network, and thus mitigate possible adversarial examples. In our previous work, the emphasis was placed on denoising the input dataset by adding colored noise before processing. In that work, the evaluation made with the empirical robustness score, resulted in a 1% improvement on average for individual noise and a 3.74% improvement on average for ensemble noise. The aim of this paper is to demonstrate the effective robustness of a well-designed radial basis function neural network in tackling adversarial examples. With the empirical robustness as a metric, the results show a 72.5% increase with Fast Gradient Sign Method (FGSM) attack on the MNIST dataset in comparison to a simple deep network and a 6.4 % increase with FGSM on the CIFAR10 dataset.

Radial Basis Function Network: Its Robustness and Ability to Mitigate Adversarial Examples

Cybersecurity is critically important to nations and societies. However, it has proven difficult for cybersecurity researchers to quantitatively and experimentally analyze, compare and validate the effectiveness of various offensive and defensive mechanisms. The Cyber Analysis, Simulation and Experimentation Environment (CASE-V) project is focused on developing a secure, highly available, feature rich and flexible framework and testbed. CASE-V includes two foundational platforms: OpenStack cloud and Hadoop big data. This paper presents the architectural design and implementation of the Version 1 CASE-V testbed which is initially operational with ongoing refinements and enhancements underway. It also describes an Automation and User Interface (UI) framework for the testbed to enhance usability and operational robustness, and its prototype implementation.

Building a Cybersecurity Research and Experimentation Testbed

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Mitigating Drift in Time Series Data with Noise Augmentation

Radial Basis Function Network: Its Robustness and Ability to Mitigate Adversarial Examples

Building a Cybersecurity Research and Experimentation Testbed