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Wavelet-based Temporal Forecasting Models of Human Activities for Anomaly Detection

arXiv.org Artificial Intelligence

This paper presents a novel approach for temporal modelling of long-term human activities based on wavelet transforms. The model is applied to binary smart-home sensors to forecast their signals, which are used then as temporal priors to infer anomalies in office and Active & Assisted Living (AAL) scenarios. Such inference is performed by a new extension of Hybrid Markov Logic Networks (HMLNs) that merges different anomaly indicators, including activity levels detected by sensors, expert rules and the new temporal models. The latter in particular allow the inference system to discover deviations from long-term activity patterns, which cannot by detected by simpler frequency-based models. Two new publicly available datasets were collected using several smart-sensors to evaluate the wavelet-based temporal models and their application to signal forecasting and anomaly detection. The experimental results show the effectiveness of the proposed techniques and their successful application to detect unexpected activities in office and AAL settings.


Exploiting Class Learnability in Noisy Data

arXiv.org Machine Learning

In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged Web data. Noisy labels occur frequently in data sets harvested via these means, sometimes resulting in entire classes of data on which learned classifiers generalize poorly. For real world applications, we argue that it can be beneficial to avoid training on such classes entirely. In this work, we aim to explore the classes in a given data set, and guide supervised training to spend time on a class proportional to its learnability. By focusing the training process, we aim to improve model generalization on classes with a strong signal. To that end, we develop an online algorithm that works in conjunction with classifier and training algorithm, iteratively selecting training data for the classifier based on how well it appears to generalize on each class. Testing our approach on a variety of data sets, we show our algorithm learns to focus on classes for which the model has low generalization error relative to strong baselines, yielding a classifier with good performance on learnable classes.



Data Cleansing and Exploration for the New Era with Optimus

@machinelearnbot

Data scientists, data analysts, business analyst, owners of a data driven company, what do they have in common? They all need to be sure that the data that they'll be consuming is at its optimal stage. Right now with the emergence of Big Data, Machine Learning, Deep Learning and Artificial Intelligence (The New Era as I call it) almost every company or entrepreneur wants to create a solution that uses data to predict or analyze. Until now there was no solution to the common problem for all data driven projects for the New Era - Data cleansing and exploration. With Optimus we are launching an easy to use, easy to deploy to production, and open source framework to clean and analyze data in a parallel fashion using state of the art technologies.


Python Training Python For Data Science Learn Python

@machinelearnbot

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