Sampling Strategies for Tackling Imbalanced Data in Human Activity Recognition

Human activity recognition (HAR) using wearable sensors is a topic that is being actively researched in machine learning. Smart, sensor-embedded devices, such as smartphones, fitness trackers, or smart watches that collect detailed data on movement, are widely available now. HAR may be applied in areas such as healthcare, physiotherapy, and fitness to assist users of these smart devices in their daily lives. However, one of the main challenges facing HAR, particularly when it is used in supervised learning, is how balanced data may be obtained for algorithm optimisation and testing. Because users engage in some activities more than others, e.g. walking more than running, HAR datasets are typically imbalanced. The lack of dataset representation from minority classes, therefore, hinders the ability of HAR classifiers to sufficiently capture new instances of those activities. Inspired by the concept of data fusion, this thesis will introduce three new hybrid sampling methods. Thus, the diversity of the synthesised samples will be enhanced by combining output from separate sampling methods into three hybrid approaches. The advantage of the hybrid method is that it provides diverse synthetic data that can increase the size of the training data from different sampling approaches. This leads to improvements in the generalisation of a learning activity recognition model. The first strategy, known as the (DBM), combines synthetic minority oversampling techniques (SMOTE) with Random_SMOTE, both of which are built around the k-nearest neighbours algorithm. The second technique, called the noise detection-based method (NDBM), combines Tomek links (SMOTE_Tomeklinks) and the modified synthetic minority oversampling technique (MSMOTE). The third approach, titled the cluster-based method (CBM), combines cluster-based synthetic oversampling (CBSO) and the proximity weighted synthetic oversampling technique (ProWSyn). The performance of the proposed hybrid methods is compared with existing methods using accelerometer data from three commonly used benchmark datasets. The results show that the DBM, NDBM and CBM can significantly reduce the impact of class imbalance and enhance F1 scores of the multilayer perceptron (MLP) by as much as 9 % to 20 % compared with their constituent sampling methods. Also, the Friedman statistical significance test was conducted to compare the effect of the different sampling methods. The test results confirm that the CBM is more effective than the other sampling approaches. This thesis also introduces a method based on the Wasserstein generative adversarial network (WGAN) for generating different types of data on human activity. The WGAN is more stable to train than a generative adversarial network (GAN) and this is due to the use of a stable metric, namely Wasserstein distance, to compare the similarity between the real data distribution with the generated data distribution. WGAN is a deep learning approach, and in contrast to the six existing sampling methods referred to previously, it can operate on raw sensor data as convolutional and recurrent layers can act as feature extractors. WGAN is used to generate raw sensor data to overcome the limitations of the traditional machine learning-based sampling methods that can only operate on extracted features. The synthetic data that is produced by WGAN is then used to oversample the imbalanced training data. This thesis demonstrates that this approach significantly enhances the learning ability of the convolutional neural network(CNN) by as much as 5 % to 6 % from imbalanced human activity datasets. This thesis concludes that the proposed sampling methods based on traditional machine learning are efficient when human activity training data is imbalanced and small. These methods are less complex to implement, require less human activity training data to produce synthetic data and fewer computational resources than the WGAN approach. The proposed WGAN method is effective at producing raw sensor data when a large quantity of human activity training data is available. Additionally, it is time-consuming to optimise the hyperparameters related to the WGAN architecture, which significantly impacts the performance of the method

Intrusion detection based on bidirectional long short-term memory with attention mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score

Deep Learning-based Driver Behavior Modeling and Analysis

Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have increased drastically over the years. In the past decades, there has been a remarkable push towards designing and developing new driver assistance systems with much better recognition and prediction capabilities. Equipped with various sensory systems, these Advanced Driver Assistance Systems (ADAS) are able to accurately perceive information on road conditions, predict traffic situations, estimate driving risks, and provide drivers with imminent warnings and visual assistance. In this thesis, we focus on two main aspects of driver behavior modeling in the design of new generation of ADAS. We first aim at improving the generalization ability of driver distraction recognition systems to diverse driving scenarios using the latest tools of machine learning and connectionist modeling, namely deep learning. To this end, we collect a large dataset of images on various driving situations of drivers from the Internet. Then we introduce Generative Adversarial Networks (GANs) as a data augmentation tool to enhance detection accuracy. A novel driver monitoring system is also introduced. This monitoring system combines multi-information resources, including a driver distraction recognition system, to assess the danger levels of driving situations. Moreover, this thesis proposes a multi-modal system for distraction recognition under various lighting conditions and presents a new Convolutional Neural Network (CNN) architecture, which can operate real-time on a resources-limited computational platform. The new CNN is built upon a novel network bottleneck of Depthwise Separable Convolution layers. The second part of this thesis focuses on driver maneuver prediction, which infers the direction a driver will turn to before a green traffic light is on and predicts accurately whether or not he/she will change the current driving lane. Here, a new method to label driving maneuver records is proposed, by which driving feature sequences for the training of prediction systems are more closely related to their labels. To this end, a new prediction system, which is based on Quasi-Recurrent Neural Networks, is introduced. In addition, and as an application of maneuver prediction, a novel driving proficiency assessment method is proposed. This method exploits the generalization abilities of different maneuver prediction systems to estimate drivers' driving abilities, and it demonstrates several advantages against existing assessment methods. In conjunction with the theoretical contribution, a series of comprehensive experiments are conducted, and the proposed methods are assessed against state-of-the-art works. The analysis of experimental results shows the improvement of results as compared with existing techniques

On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p

TASKED: Transformer-based Adversarial learning for human activity recognition using wearable sensors via Self-KnowledgE Distillation

Wearable sensor-based human activity recognition (HAR) has emerged as a principal research area and is utilized in a variety of applications. Recently, deep learning-based methods have achieved significant improvement in the HAR field with the development of human-computer interaction applications. However, they are limited to operating in a local neighborhood in the process of a standard convolution neural network, and correlations between different sensors on body positions are ignored. In addition, they still face significant challenging problems with performance degradation due to large gaps in the distribution of training and test data, and behavioral differences between subjects. In this work, we propose a novel Transformer-based Adversarial learning framework for human activity recognition using wearable sensors via Self-KnowledgE Distillation (TASKED), that accounts for individual sensor orientations and spatial and temporal features. The proposed method is capable of learning cross-domain embedding feature representations from multiple subjects datasets using adversarial learning and the maximum mean discrepancy (MMD) regularization to align the data distribution over multiple domains. In the proposed method, we adopt the teacher-free self-knowledge distillation to improve the stability of the training procedure and the performance of human activity recognition. Experimental results show that TASKED not only outperforms state-of-the-art methods on the four real-world public HAR datasets (alone or combined) but also improves the subject generalization effectively.Comment: 17 pages, 5 figures, Submitted to Knowledge-Based Systems, Elsevier. arXiv admin note: substantial text overlap with arXiv:2110.1216