Recommender systems have been successfully used in many domains with the help of machine learning algorithms. However, such applications tend to use multi-dimensional user data, which has raised widespread concerns about the breach of users’ privacy. Meanwhile, wearable technologies have enabled users to collect fitness-related data through embedded sensors to monitor their conditions or achieve personalized fitness goals. In this article, we propose a novel privacy-aware personalized fitness recommender system. We introduce a multi-level deep learning framework that learns important features from a large-scale real fitness dataset that is collected from wearable Internet of Things (IoT) devices to derive intelligent fitness recommendations. Unlike most existing approaches, our approach achieves personalization by inferring the fitness characteristics of users from sensory data, minimizing the need for explicitly collecting user identity or biometric information, such as name, age, height, and weight. Our proposed models and algorithms predict (a) personalized exercise distance recommendations to help users to achieve target calories, (b) personalized speed sequence recommendations to adjust exercise speed given the nature of the exercise and the chosen route, and (c) personalized heart rate sequence to guide the user of the potential health status for future exercises. Our experimental evaluation on a real-world Fitbit dataset demonstrated high accuracy in predicting exercise distance, speed sequence, and heart rate sequence compared with similar studies. 1 Furthermore, our approach is novel compared with existing studies, as it does not require collecting and using users’ sensitive information. Thus, it preserves the users’ privacy.

Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the generative adversarial networks (GANs) leans to majority class samples. The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augmentation tool to handle this problem and restore the balance to the data. The former pre-trains the autoencoder weights in an unsupervised manner. However, it is unstable when the images from different categories have similar features. The latter is improved based on BAGAN by facilitating supervised autoencoder training, but the pre-training is biased towards the majority classes. In this work, we propose a novel Conditional Variational Autoencoder with Balanced Pre-training for Generative Adversarial Networks (CAPGAN) as an augmentation tool to generate realistic synthetic images. In particular, we utilize a conditional convolutional variational autoencoder with supervised and balanced pre-training for the GAN initialization and training with gradient penalty. Our proposed method presents a superior performance of other state-of-the-art methods on the highly imbalanced version of MNIST, Fashion-MNIST, CIFAR-10, and two medical imaging datasets. Our method can synthesize high-quality minority samples in terms of Fréchet inception distance, structural similarity index measure and perceptual quality. The source code is available at https://github.com/alibraytee/CAPGAN.

The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.

Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.

Multi-label classification is defined as the problem of identifying the multiple labels or categories of new observations based on labeled training data. Multi-labeled data has several challenges, including class imbalance, label correlation, incomplete multi-label matrices, and noisy and irrelevant features. In this article, we propose an integrated multi-label classification approach with incomplete label space and class imbalance (ML-CIB) for simultaneously training the multi-label classification model and addressing the aforementioned challenges. The model learns a new label matrix and captures new label correlations, because it is difficult to find a complete label vector for each instance in real-world data. We also propose a label regularization to handle the imbalanced multi-labeled issue in the new label, and l 1 regularization norm is incorporated in the objective function to select the relevant sparse features. A multi-label feature selection (ML-CIB-FS) method is presented as a variant of the proposed ML-CIB to show the efficacy of the proposed method in selecting the relevant features. ML-CIB is formulated as a constrained objective function. We use the accelerated proximal gradient method to solve the proposed optimisation problem. Last, extensive experiments are conducted on 19 regular-scale and large-scale imbalanced multi-labeled datasets. The promising results show that our method significantly outperforms the state-of-the-art.

One-class support vector machine (OCSVM) has been widely used in the area of structural health monitoring, where only data from one class (i.e., healthy) are available. Incremental learning of OCSVM is critical for online applications in which huge data streams continuously arrive and the healthy data distribution may vary over time. This article proposes a novel adaptive self-advised online OCSVM that incrementally tunes the kernel parameter and decides whether a model update is required or not. As opposed to existing methods, this novel online algorithm does not rely on any fixed threshold, but it uses the slack variables in the OCSVM to determine which new data points should be included in the training set and trigger a model update. The algorithm also incrementally tunes the kernel parameter of OCSVM automatically based on the spatial locations of the edge and interior samples in the training data with respect to the constructed hyperplane of OCSVM. This new online OCSVM algorithm was extensively evaluated using synthetic data and real data from case studies in structural health monitoring. The results showed that the proposed method significantly improved the classification error rates, was able to assimilate the changes in the positive data distribution over time, and maintained a high damage detection accuracy in all case studies.

Civil infrastructures are key to the flow of people and goods in urban environments. Structural Health Monitoring (SHM) is a condition-based maintenance technology, which provides and predicts actionable information on the current and future states of infrastructures. SHM data are usually multi-way data which are produced by multiple highly correlated sensors. Tensor decomposition allows the learning from such data in temporal, spatial and feature modes at the same time. However, to facilitate a real time response for online learning, incremental tensor update need to be used when new data come in, rather than doing the decomposition in a batch manner. This work proposed a method called onlineCP-ALS to incrementally update tensor component matrices, followed by a self-tuning one-class support vector machine for online damage identification. Moreover, a robust clustering technique was applied on the tensor space for online substructure grouping and anomaly detection. These methods were applied to data from lab-based structures and also data collected from the Sydney Harbour Bridge in Australia. We obtained accurate damage detection accuracies for all these datasets. Damage locations were also captured correctly, and different levels of damage severity were well estimated. Furthermore, the clustering technique was able to detect spatial anomalies, which were associated with sensor and instrumentation issues. Our proposed method was efficient and much faster than the batch approach.