46 research outputs found
On the use of text classification methods for text summarisation
This thesis describes research work undertaken in the fields of text and questionnaire mining. More specifically, the research work is directed at the use of text classification techniques for the purpose of summarising the free text part of questionnaires. In this thesis text summarisation is conceived of as a form of text classification in that the classes assigned to text documents can be viewed as an indication (summarisation) of the main ideas of the original free text but in a coherent and reduced form. The reason for considering this type of summary is because summarising unstructured free text, such as that found in questionnaires, is not deemed to be effective using conventional text summarisation techniques. Four approaches are described in the context of the classification summarisation of free text from different sources, focused on the free text part of questionnaires. The first approach considers the use of standard classification techniques for text summarisation and was motivated by the desire to establish a benchmark with which the more specialised summarisation classification techniques presented later in this thesis could be compared. The second approach, called Classifier Generation Using Secondary Data (CGUSD), addresses the case when the available data is not considered sufficient for training purposes (or possibly because no data is available at all). The third approach, called Semi-Automated Rule Summarisation Extraction Tool (SARSET), presents a semi-automated classification technique to support document summarisation classification in which there is more involvement by the domain experts in the classifier generation process, the idea was that this might serve to produce more effective summaries. The fourth is a hierarchical summarisation classification approach which assumes that text summarisation can be achieved using a classification approach whereby several class labels can be associated with documents which then constitute the summarisation. For evaluation purposes three types of text were considered: (i) questionnaire free text, (ii) text from medical abstracts and (iii) text from news stories
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IoT-based Activities of Daily Living for Abnormal Behaviour Detection: Privacy Issues and Potential Countermeasures
Activities of Daily Living (ADL) systems have been playing an important role in assessing and monitoring the quality of life of elderly people for many years. With the recent advancement and integration of Internet of Things (IoT) devices within the ADL systems, the number and quality of services offered has increased significantly. One of these vital services is abnormal behaviour detection based on the data collected from IoT devices within smart homes. However, the IoT data collected could have enormous privacy implications on smart home users if the data is not handled properly. We address this issue by analysing a generic ADL system for abnormal behaviour detection, including its entities and their interactions. We highlight three major privacy issues: (i) identity privacy, (ii) data confidentiality, and (iii) metadata data leakage. These issues are particularly relevant to ADL systems and we propose potential countermeasures to tackle them. Finally, we sketch a privacy-preserving version of a state-of-the-art ADL system to demonstrate the effectiveness of our proposed countermeasures, before suggesting future research directions
Modelling Activities of Daily Living Using Local Interpretable Model-Agnostic Explanation Algorithm
The use of Artificial Intelligence (AI) in healthcare, particularly in recognising anomalous behaviour during Activities of Daily Living (ADLs), is useful for supporting independent living. Transparency and interpretability of ADLs can play a vital role in decision-making processes, particularly in healthcare sectors. This work intends to offer additional information to AI-based prediction of ADLs through the use of Local Interpretable Model-agnostic Explanations (LIME). In this study, 5,125 low resolution thermal images gleaned from ADLs in a laboratory environment which mimics a smart home were clustered and analysed using Data Mining software and AI algorithms respectively. Results indicated that LIME presented saliency maps of ADLs in diverse scenarios such as ‘Making Tea’ and ‘Sitting Down’ to consume it. Further work will seek to fine-tune the models for better accurac
Probabilistic Analysis of Temporal and Sequential Aspects of Activities of Daily Living for Abnormal Behaviour Detection
This paper presents a probabilistic approach for the identification of abnormal behaviour in Activities of Daily Living (ADLs) from dense sensor data collected from 30 participants. The ADLs considered are related to preparing and drinking (i) tea, and (ii) coffee. Abnormal behaviour identified in the context of these activities can be an indicator of a progressive health problem or the occurrence of a hazardous incident. The approach presented considers the temporal and sequential aspects of the actions that are part of each ADL and that vary between participants. The average and standard deviation for the duration and number of steps of each activity are calculated to define the average time and steps and a range within which a behaviour could be considered as normal for each stage and activity. The Cumulative Distribution Function (CDF) is used to obtain the probabilities of abnormal behaviours related to the early and late completion of activities and stages within an activity in terms of time and steps. Analysis shows that CDF can provide precise and reliable results regarding the presence of abnormal behaviour in stages and activities that last over a minute or consist of many steps. Finally, this approach could be used to train machine learning algorithms for abnormal behaviour detection.status: publishe
BECA: A Blockchain-Based Edge Computing Architecture for Internet of Things Systems
The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage