Situation inference and context recognition for intelligent mobile sensing applications

The usage of smart devices is an integral element in our daily life. With the richness of data streaming from sensors embedded in these smart devices, the applications of ubiquitous computing are limitless for future intelligent systems. Situation inference is a non-trivial issue in the domain of ubiquitous computing research due to the challenges of mobile sensing in unrestricted environments. There are various advantages to having robust and intelligent situation inference from data streamed by mobile sensors. For instance, we would be able to gain a deeper understanding of human behaviours in certain situations via a mobile sensing paradigm. It can then be used to recommend resources or actions for enhanced cognitive augmentation, such as improved productivity and better human decision making. Sensor data can be streamed continuously from heterogeneous sources with different frequencies in a pervasive sensing environment (e.g., smart home). It is difficult and time-consuming to build a model that is capable of recognising multiple activities. These activities can be performed simultaneously with different granularities. We investigate the separability aspect of multiple activities in time-series data and develop OPTWIN as a technique to determine the optimal time window size to be used in a segmentation process. As a result, this novel technique reduces need for sensitivity analysis, which is an inherently time consuming task. To achieve an effective outcome, OPTWIN leverages multi-objective optimisation by minimising the impurity (the number of overlapped windows of human activity labels on one label space over time series data) while maximising class separability. The next issue is to effectively model and recognise multiple activities based on the user's contexts. Hence, an intelligent system should address the problem of multi-activity and context recognition prior to the situation inference process in mobile sensing applications. The performance of simultaneous recognition of human activities and contexts can be easily affected by the choices of modelling approaches to build an intelligent model. We investigate the associations of these activities and contexts at multiple levels of mobile sensing perspectives to reveal the dependency property in multi-context recognition problem. We design a Mobile Context Recognition System, which incorporates a Context-based Activity Recognition (CBAR) modelling approach to produce effective outcome from both multi-stage and multi-target inference processes to recognise human activities and their contexts simultaneously. Upon our empirical evaluation on real-world datasets, the CBAR modelling approach has significantly improved the overall accuracy of simultaneous inference on transportation mode and human activity of mobile users. The accuracy of activity and context recognition can also be influenced progressively by how reliable user annotations are. Essentially, reliable user annotation is required for activity and context recognition. These annotations are usually acquired during data capture in the world. We research the needs of reducing user burden effectively during mobile sensor data collection, through experience sampling of these annotations in-the-wild. To this end, we design CoAct-nnotate --- a technique that aims to improve the sampling of human activities and contexts by providing accurate annotation prediction and facilitates interactive user feedback acquisition for ubiquitous sensing. CoAct-nnotate incorporates a novel multi-view multi-instance learning mechanism to perform more accurate annotation prediction. It also includes a progressive learning process (i.e., model retraining based on co-training and active learning) to improve its predictive performance over time. Moving beyond context recognition of mobile users, human activities can be related to essential tasks that the users perform in daily life. Conversely, the boundaries between the types of tasks are inherently difficult to establish, as they can be defined differently from the individuals' perspectives. Consequently, we investigate the implication of contextual signals for user tasks in mobile sensing applications. To define the boundary of tasks and hence recognise them, we incorporate such situation inference process (i.e., task recognition) into the proposed Intelligent Task Recognition (ITR) framework to learn users' Cyber-Physical-Social activities from their mobile sensing data. By recognising the engaged tasks accurately at a given time via mobile sensing, an intelligent system can then offer proactive supports to its user to progress and complete their tasks. Finally, for robust and effective learning of mobile sensing data from heterogeneous sources (e.g., Internet-of-Things in a mobile crowdsensing scenario), we investigate the utility of sensor data in provisioning their storage and design QDaS --- an application agnostic framework for quality-driven data summarisation. This allows an effective data summarisation by performing density-based clustering on multivariate time series data from a selected source (i.e., data provider). Thus, the source selection process is determined by the measure of data quality. Nevertheless, this framework allows intelligent systems to retain comparable predictive results by its effective learning on the compact representations of mobile sensing data, while having a higher space saving ratio. This thesis contains novel contributions in terms of the techniques that can be employed for mobile situation inference and context recognition, especially in the domain of ubiquitous computing and intelligent assistive technologies. This research implements and extends the capabilities of machine learning techniques to solve real-world problems on multi-context recognition, mobile data summarisation and situation inference from mobile sensing. We firmly believe that the contributions in this research will help the future study to move forward in building more intelligent systems and applications

A review of data mining techniques for research in online shopping behaviour through frequent navigation paths

Knowing how consumers navigate online shopping web sites enables retailers to not only better design their sites for navigation but also place buying recommendations at strategic points and personalise the flow of content. Frequent navigation paths can be derived from browsing histories or clickstreams with sequence-oriented data mining techniques. In this working paper, we highlight, with examples, the relevance of frequent navigation paths to online shopping behaviour research and review some relevant data mining techniques

Leveraging ChatGPT for Power System Programming Tasks

The rapid digitalization of power systems has led to a significant increase in coding tasks for power engineers. This research article explores how ChatGPT, an advanced AI language model, can assist power engineers and researchers in a range of coding tasks. From simple to complex, we present three case studies to illustrate the benefits of ChatGPT in various coding scenarios. For routine tasks such as daily unit commitment, ChatGPT can increase efficiency by directly generating batch number of codes and reducing repetitive programming and debugging time for power engineers. For complex problems such as decentralized optimization of mul-ti-vector energy systems, ChatGPT can reduce the learning cost of power engineers on problem formulation and the choice of numerical solvers. For new problems without readily avaliable solutions such as ultra-fast unit commitment, ChatGPT can organize technology roadmap, gen-erate data and develop model and code. Furthermore, this paper discuss generic prompt ap-proaches for different tasks in power systems, providing insights for power engineers and re-searchers seeking to harness ChatGPT in terms of auto coding, new knowledge learning and new problem solving. The findings demonstrate the potential of ChatGPT as a powerful tool in the domain of power system engineering

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Data Mining

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process. This book presents recent theoretical and practical advances in the field of data mining. It discusses a number of data mining methods, including classification, clustering, and association rule mining. This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment

EXPLORATION OF LIGNIN-BASED SUPERABSORBENT POLYMERS (HYDROGELS) FOR SOIL WATER MANAGEMENT AND AS A CARRIER FOR DELIVERING RHIZOBIUM SPP.

Superabsorbent polymers (hydrogels) as soil amendments may improve soil hydraulic properties and act as carrier materials beneficial to soil microorganisms. Researchers have mostly explored synthetic hydrogels which may not be environmentally sustainable. This dissertation focused on the development and application of lignin-based hydrogels as sustainable soil amendments. This dissertation also explores the development of pedotransfer transfer functions (PTFs) for predicting saturated hydraulic conductivity using statistical and machine learning methods with a publicly available large data set. A lignin-based hydrogel was synthesized, and its impact on soil water retention was determined in silt loam and loamy fine sand soils. Hydrogel treatment significantly increased water retention at saturation/near saturation by 0.12 cm3 cm-3 and at field capacity by 0.08 cm3 cm-3 for silt loam soil compared to a control treatment with no added lignin hydrogel. Hydrogel application significantly increased water retention at -3 cm to -15,000 cm soil water pressure head by 0.01 - 0.03 cm3 cm-3 for the loamy fine sand soil. Calculations demonstrated that at a 1% (w/w) concentration or lower, lignin-based hydrogels in silt loam and loamy fine sand soils would not increase plant available soil water storage. The incorporation of lignin-hydrogels significantly decreased saturated hydraulic conductivity. In unsaturated conditions, application of the lignin-based hydrogel at 0.1 and 0.3% (w/w) increased hydraulic conductivity. New pedotransfer functions (PTFs) for predicting saturated hydraulic conductivity were developed using machine learning (ML) and a large public database. Random forest regression and gradient boosted regression both gave the best performances with R2 =0.71 and RMSE = 0.47 cm h-1 on the validation data set. The concentration of lignin-alginate hydrogel added to Rhizobial cell culture did not affect cell survival. All treatments of wet bioencapsulated beads achieved a similar yield of 97%, however, the presence of starch in the lignin-alginate beads increased the survival of Rhizobium cells

Analysis of oxygenation and other risk factors of retinopathy of prematurity in preterm babies

Maintaining adequate and stable blood oxygen level is important for preterm babies to avoid the risk of brain, lung and retinal injury such as retinopathy of prematurity (ROP). However, wide disparities in policies and practices of oxygenation in preterm babies exist among neonatal care providers as it is still unclear which best method of monitoring and what features of oxygen measurements are important to clinician’s interpretations for assessing preterm babies at risk of developing severe ROP or unstable health condition. This thesis consists of two projects: NZ-ROP that examines multiple factors of severe ROP including summary statistics (mean, standard deviation (SD), coefficient of variation (CV) and desaturation) for oxygen saturation (OS) features in very extreme preterm babies, and NZ-LP that investigates the efficacy of some of these statistics for health monitoring of late preterm babies. The OS data in NZ-ROP were recorded using modified oximeters that have offsets and inherent software artefact, both of which mask the actual saturation for certain OS ranges and may complicate the choice of methods in the analyses. Therefore, novel algorithms involving linear and quadratic interpolations are developed, implemented on the New Zealand data, and validated using the data of a UK preterm baby, as recorded from offsets and non-offsets oximeters. For all data sets, the algorithms produced saturation distributions that were very close to those obtained from the non-offset oximeter. The algorithms perform within the recommended standards of commercial oximeters currently used in the clinical practice. ROP is a multifactorial disease, with oxygenation fluctuations as one of the key contributors. The all-subsets logistic regression, robust and generalised additive statistical modelling, along with a model averaging approach, are applied in NZ-ROP to determine the relationship of variability and level of OS with severe ROP, and the extent of contribution of various clinical predictors to the severity of this eye disease. Desaturation, as a measure of OS variability, has the strongest association with severe ROP among all OS statistics, in particular, the risk of severe ROP is almost three times higher in babies that exhibit greater occurrences of desaturation episodes. Additionally, this study identifies longer periods of ventilation support, frequent desaturation events, extreme prematurity and low birth weight as the most important factors that substantially exacerbate the severity of ROP, and therefore signify babies’ underlying condition of being severely ill. Persistent cardiorespiratory instabilities prior to hospital discharge may expose preterm babies to a greater risk of neuro-developmental impairments. In NZ-LP, the statistical summaries of mean, SD and CV are computed from the OS measurements of healthy stable and unstable babies, and the performance of these statistics in detecting the unstable babies is evaluated using an extremeness index for outlying data and a hierarchical clustering technique. With SD and CV, the clinically unstable babies were very well separated from the group of stable babies, wherein, the separation was even more apparent with the use of CV. These suggest that measures of variability could be better than saturation level for highlighting babies’ underlying instability due to immature physiological systems, but the combination of variability and level through the CV are believed to be even better. Identification and summarisation of useful OS features quantitatively hold great promise for improved monitoring of oxygenation instability and diagnosis of severe ROP for preterm babies

A meta-architecture analysis for a coevolved system-of-systems

Modern engineered systems are becoming increasingly complex. This is driven in part by an increase in the use of systems-of-systems and network-centric concepts to improve system performance. The growth of systems-of-systems allows stakeholders to achieve improved performance, but also presents new challenges due to increased complexity. These challenges include managing the integration of asynchronously developed systems and assessing SoS performance in uncertain environments. Many modern systems-of-systems must adapt to operating environment changes to maintain or improve performance. Coevolution is the result of the system and the environment adapting to changes in each other to obtain a performance advantage. The complexity that engineered systems-of-systems exhibit poses challenges to traditional systems engineering approaches. Systems engineers are presented with the problem of understanding how these systems can be designed or adapted given these challenges. Understanding how the environment influences system-of-systems performance allows systems engineers to target the right set of capabilities when adapting the system for improved performance. This research explores coevolution in a counter-trafficking system-of-systems and develops an approach to demonstrate its impacts. The approach implements a trade study using swing weights to demonstrate the influence of coevolution on stakeholder value, develops a novel future architecture to address degraded capabilities, and demonstrates the impact of the environment on system performance using simulation. The results provide systems engineers with a way to assess the impacts of coevolution on the system-of-systems, identify those capabilities most affected, and explore alternative meta-architectures to improve system-of-systems performance in new environments --Abstract, page iii