One-Class Classification: Taxonomy of Study and Review of Techniques

One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class. The OCC problem has been considered and applied under many research themes, such as outlier/novelty detection and concept learning. In this paper we present a unified view of the general problem of OCC by presenting a taxonomy of study for OCC problems, which is based on the availability of training data, algorithms used and the application domains applied. We further delve into each of the categories of the proposed taxonomy and present a comprehensive literature review of the OCC algorithms, techniques and methodologies with a focus on their significance, limitations and applications. We conclude our paper by discussing some open research problems in the field of OCC and present our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure

Affect-driven Engagement Measurement from Videos

In education and intervention programs, person's engagement has been identified as a major factor in successful program completion. Automatic measurement of person's engagement provides useful information for instructors to meet program objectives and individualize program delivery. In this paper, we present a novel approach for video-based engagement measurement in virtual learning programs. We propose to use affect states, continuous values of valence and arousal extracted from consecutive video frames, along with a new latent affective feature vector and behavioral features for engagement measurement. Deep learning-based temporal, and traditional machine-learning-based non-temporal models are trained and validated on frame-level, and video-level features, respectively. In addition to the conventional centralized learning, we also implement the proposed method in a decentralized federated learning setting and study the effect of model personalization in engagement measurement. We evaluated the performance of the proposed method on the only two publicly available video engagement measurement datasets, DAiSEE and EmotiW, containing videos of students in online learning programs. Our experiments show a state-of-the-art engagement level classification accuracy of 63.3% and correctly classifying disengagement videos in the DAiSEE dataset and a regression mean squared error of 0.0673 on the EmotiW dataset. Our ablation study shows the effectiveness of incorporating affect states in engagement measurement. We interpret the findings from the experimental results based on psychology concepts in the field of engagement.Comment: 13 pages, 8 figures, 7 table

FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networks

Federated Learning (FL) and Split Learning (SL) are privacy-preserving Machine-Learning (ML) techniques that enable training ML models over data distributed among clients without requiring direct access to their raw data. Existing FL and SL approaches work on horizontally or vertically partitioned data and cannot handle sequentially partitioned data where segments of multiple-segment sequential data are distributed across clients. In this paper, we propose a novel federated split learning framework, FedSL, to train models on distributed sequential data. The most common ML models to train on sequential data are Recurrent Neural Networks (RNNs). Since the proposed framework is privacy preserving, segments of multiple-segment sequential data cannot be shared between clients or between clients and server. To circumvent this limitation, we propose a novel SL approach tailored for RNNs. A RNN is split into sub-networks, and each sub-network is trained on one client containing single segments of multiple-segment training sequences. During local training, the sub-networks on different clients communicate with each other to capture latent dependencies between consecutive segments of multiple-segment sequential data on different clients, but without sharing raw data or complete model parameters. After training local sub-networks with local sequential data segments, all clients send their sub-networks to a federated server where sub-networks are aggregated to generate a global model. The experimental results on simulated and real-world datasets demonstrate that the proposed method successfully train models on distributed sequential data, while preserving privacy, and outperforms previous FL and centralized learning approaches in terms of achieving higher accuracy in fewer communication rounds

Classification and Decision-Theoretic Framework for Detecting and Reporting Unseen Falls

Detecting falls is critical for an activity recognition system to ensure the well being of an individual. However, falls occur rarely and infrequently, therefore sufficient data for them may not be available during training of the classifiers. Building a fall detection system in the absence of fall data is very challenging and can severely undermine the generalization capabilities of an activity recognition system. In this thesis, we present ideas from both classification and decision theory perspectives to handle scenarios when the training data for falls is not available. In traditional decision theoretic approaches, the utilities (or conversely costs) to report/not-report a fall or a non-fall are treated equally or the costs are deduced from the datasets, both of which are flawed. However, these costs are either difficult to compute or only available from domain experts. Therefore, in a typical fall detection system, we neither have a good model for falls nor an accurate estimate of utilities. In this thesis, we make contributions to handle both of these situations. In recent years, Hidden Markov Models (HMMs) have been used to model temporal dynamics of human activities. HMMs are generally built for normal activities and a threshold based on the log-likelihood of the training data is used to identify unseen falls. We show that such formulation to identify unseen fall activities is ill-posed for this problem. We present a new approach for the identification of falls using wearable devices in the absence of their training data but with plentiful data for normal Activities of Daily Living (ADL). We propose three 'X-Factor' Hidden Markov Model (XHMMs) approaches, which are similar to the traditional HMMs but have ``inflated'' output covariances (observation models). To estimate the inflated covariances, we propose a novel cross validation method to remove 'outliers' or deviant sequences from the ADL that serves as proxies for the unseen falls and allow learning the XHMMs using only normal activities. We tested the proposed XHMM approaches on three activity recognition datasets and show high detection rates for unseen falls. We also show that supervised classification methods perform poorly when very limited fall data is available during the training phase. We present a novel decision-theoretic approach to fall detection (dtFall) that aims to tackle the core problem when the model for falls and information about the costs/utilities associated with them is unavailable. We theoretically show that the expected regret will always be positive using dtFall instead of a maximum likelihood classifier. We present a new method to parameterize unseen falls such that training situations with no fall data can be handled. We also identify problems with theoretical thresholding to identify falls using decision theoretic modelling when training data for fall data is absent, and present an empirical thresholding technique to handle imperfect models for falls and non-falls. We also develop a new cost model based on severity of falls to provide an operational range of utilities. We present results on three activity recognition datasets, and show how the results may generalize to the difficult problem of fall detection in the real world. Under the condition when falls occur sporadically and rarely in the test set, the results show that (a) knowing the difference in the cost between a reported fall and a false alarm is useful, (b) as the cost of false alarm gets bigger this becomes more significant, and (c) the difference in the cost of between a reported and non-reported fall is not that useful