Deep Learning Methods for Human Activity Recognition using Wearables

Wearable sensors provide an infrastructure-less multi-modal sensing method. Current trends point to a pervasive integration of wearables into our lives with these devices providing the basis for wellness and healthcare applications across rehabilitation, caring for a growing older population, and improving human performance. Fundamental to these applications is our ability to automatically and accurately recognise human activities from often tiny sensors embedded in wearables. In this dissertation, we consider the problem of human activity recognition (HAR) using multi-channel time-series data captured by wearable sensors. Our collective know-how regarding the solution of HAR problems with wearables has progressed immensely through the use of deep learning paradigms. Nevertheless, this field still faces unique methodological challenges. As such, this dissertation focuses on developing end-to-end deep learning frameworks to promote HAR application opportunities using wearable sensor technologies and to mitigate specific associated challenges. In our efforts, the investigated problems cover a diverse range of HAR challenges and spans from fully supervised to unsupervised problem domains. In order to enhance automatic feature extraction from multi-channel time-series data for HAR, the problem of learning enriched and highly discriminative activity feature representations with deep neural networks is considered. Accordingly, novel end-to-end network elements are designed which: (a) exploit the latent relationships between multi-channel sensor modalities and specific activities, (b) employ effective regularisation through data-agnostic augmentation for multi-modal sensor data streams, and (c) incorporate optimization objectives to encourage minimal intra-class representation differences, while maximising inter-class differences to achieve more discriminative features. In order to promote new opportunities in HAR with emerging battery-less sensing platforms, the problem of learning from irregularly sampled and temporally sparse readings captured by passive sensing modalities is considered. For the first time, an efficient set-based deep learning framework is developed to address the problem. This framework is able to learn directly from the generated data, bypassing the need for the conventional interpolation pre-processing stage. In order to address the multi-class window problem and create potential solutions for the challenging task of concurrent human activity recognition, the problem of enabling simultaneous prediction of multiple activities for sensory segments is considered. As such, the flexibility provided by the emerging set learning concepts is further leveraged to introduce a novel formulation of HAR. This formulation treats HAR as a set prediction problem and elegantly caters for segments carrying sensor data from multiple activities. To address this set prediction problem, a unified deep HAR architecture is designed that: (a) incorporates a set objective to learn mappings from raw input sensory segments to target activity sets, and (b) precedes the supervised learning phase with unsupervised parameter pre-training to exploit unlabelled data for better generalisation performance. In order to leverage the easily accessible unlabelled activity data-streams to serve downstream classification tasks, the problem of unsupervised representation learning from multi-channel time-series data is considered. For the first time, a novel recurrent generative adversarial (GAN) framework is developed that explores the GAN’s latent feature space to extract highly discriminating activity features in an unsupervised fashion. The superiority of the learned representations is substantiated by their ability to outperform the de facto unsupervised approaches based on autoencoder frameworks. At the same time, they rival the recognition performance of fully supervised trained models on downstream classification benchmarks. In recognition of the scarcity of large-scale annotated sensor datasets and the tediousness of collecting additional labelled data in this domain, the hitherto unexplored problem of end-to-end clustering of human activities from unlabelled wearable data is considered. To address this problem, a first study is presented for the purpose of developing a stand-alone deep learning paradigm to discover semantically meaningful clusters of human actions. In particular, the paradigm is intended to: (a) leverage the inherently sequential nature of sensory data, (b) exploit self-supervision from reconstruction and future prediction tasks, and (c) incorporate clustering-oriented objectives to promote the formation of highly discriminative activity clusters. The systematic investigations in this study create new opportunities for HAR to learn human activities using unlabelled data that can be conveniently and cheaply collected from wearables.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Old Keys May Not Open New Doors: The Necessity of Agility in Cybersecurity Policymaking

The volatile and dynamic nature of cyberspace has raised concerns over security and organisations are trying to make policies to protect their digital assets. However, policymaking in this field is still using traditional methods, which are slow and incompatible with the pace of change in the environment. Thus, it is vital to increase the speed of policy development in an agile and flexible manner. The question is, what does agility mean here and why is it important for organisations? To answer these questions, this study uses a systematic literature review approach and investigates 42 selected papers. By analysing the selected papers, a definition of cybersecurity policymaking agility is provided, and its importance in combating new cyberthreats is discussed. Building on and extending the organisational agility, policymaking and cybersecurity management research streams, the findings of this study propose new research opportunities for future studies

Policy Helix and Antecedents of Cybersecurity Policymaking Agility

The cyber threat landscape is constantly changing, and organisations need to stay current with the dynamism of their internal and external environment. One important aspect is to be agile in cybersecurity policymaking (CSPM) to identify signals, devise proper policies, and mitigate risks. However, the literature in this aspect is still understudied, and this paper strives to fill this gap by investigating the notion of agility in cybersecurity policymaking and identifying its antecedents. The paper investigates the importance of agility as a means to counter emerging threats, contributing actionable insights and best practices to the ongoing discourse on cybersecurity policymaking. The findings emphasise the vital role of agility in pursuing cyber resilience and encourage policymakers and stakeholders to embrace this principle. Ultimately, this study deepens the understanding of the agile policymaking process and introduces asset management, vulnerability management, cyber risk management, and robust awareness processes as the antecedents of CSPM agility. The findings can provide insights for both the theory and practice of IS research by introducing the concept of agility in CSPM and identifying its antecedents

Online) An Open Access

ABSTRACT This study aims, is description of exhausting and organizational commitment universities, by separating of full-time and tuition-fee masters of physical education, comparing these two groups, even their relation between organizational commitment and burnout in an analyzed way for each group, comparing the balance of variants in both groups. This research is a kind of description one, and done as arena. the statistical community is included all masters (full-time and tuition-fee), statistical model is equal to statistical community. For assigning burnout, the questionnaire of maslach burnout (1996), for assigning organization commitment the questionnaire of organizational commitment of meer Allen (1991) and personal questionnaires are used. The results showed that the abundance and intensity of devoid personal purities of full-time masters, had a meaning full relation to feeling commitment. There was a significant difference between Abundance and intensity of devoid personal parting of tuition fee had a meaning full relation to feeling commitment and their manner. Between feeling commitment and abundance of feeling deprecation, and feeling commitment and abundance of devoid personal purities, at both groups, there is a meaningful difference that organizational commitment in full-time masters is more than tuition-fee ones. Only the feeling commitment can be a factor for burnout

Synergistic effect of hydrophilic nanoparticles and anionic surfactant on the stability and viscoelastic properties of oil in water (o/w) emulations; Application for enhanced oil recovery (EOR)

With the rapidly increased global energy demand, great attention has been focused on utilizing nanotechnology and particularly nanofluids in enhanced oil recovery (EOR) to produce more oil from low-productivity oil reservoirs. Nanofluid flooding has introduced as one of the promising methods for enhanced oil recovery using environment-friendly nanoparticles (NPs) to be as an innovative-alternative for chemical methods of EOR. This work investigates the synergistic effects of anionic surfactant and hydrophilic silica nanoparticles on the stability and the mechanical behavior of oil in water (O/W) emulsions for their application in EOR. To achieve this, an extensive series of experiments were conducted at a wide range of temperatures (23 – 70 °C) and ambient pressure to systematically evaluate the stability and the viscoelastic properties of the oil in water (O/W) emulsion with the presence of hydrophilic silica nanoparticles and an anionic surfactant. In this context, the initial oil to water volume ratio was 25:75. Sodium dodecylsulfate (SDS) was used as the anionic surfactant and n-decane was used as model oil. A wide concentration ranges of NPs (0.01 – 0.2 wt%) and surfactant (0.1 – 0.3 wt%) were used to formulate different emulsions. For stability measurements, a dynamic light scattering and zetasizer were used to measure the particle size distribution and zeta potential respectively. Creaming and phase behaviors were also investigated. The viscoelastic measurements were conducted using Discovery Hybrid Rheometer. Results show that in the presence of surfactant, and NPs mitigates the coalescence of dispersed oil droplets giving high promises in EOR applications. Further, over the tested range of temperatures, the viscosity of O/W emulsion remains stable which indicates thermal stability. Despite studies examining the use of nanoparticle-surfactant combination in sub-surface applications, no reported data is currently available, to the best of our knowledge, about the potential synergistic effect of this combination on the stability and viscoelastic properties of O/W emulsion. This study gives the first insight on nanoparticle-surfactant synergistic effect on oil in water (O/W) emulsion for EOR applications

Evaluation of the Effect of Varying the Angle of Asphaltic Concrete Core on the Behavior of the Meijaran Rockfill Dam

The use of asphaltic concrete cores for sealing embankments and rockfill dams is very important. The self-healing properties of bitumen, simple construction in cold and rainy conditions compared to clay cores, good flexibility and connection with embankment materials are the essential characteristics of asphaltic concrete. The main concern regarding the use of asphaltic concrete cores in Iran is mainly the performance of these dams under seismic loads. The evaluations of the performance of these types of dams in other countries show that asphaltic concrete cores perform satisfactorily in the static state, but in earthquake conditions, the situation may be different. In this paper, the static and seismic behavior of the Meijaran dam in Iran, Mazandaran, is evaluated for three core angles of 90◦, 60◦ and 45◦ . This evaluation was conducted at the end of the impounding stage and after applying seismic loads using FLAC 2D software and Mohr–Coulomb consitutive models. The results were matched with the ICOLD recommendation to use angled cores in dams with asphaltic cores and showed that the dam performs better with angled cores. Finally, for the Meijaran dam, the results from the dynamic analysis are compared with the results from the centrifuge test