Towards event analysis in time-series data: Asynchronous probabilistic models and learning from partial labels

In this thesis, we contribute in two main directions: modeling asynchronous time-series data and learning from partial labelled data. We first propose novel probabilistic frameworks to improve flexibility and expressiveness of current approaches in modeling complex real-world asynchronous event sequence data. Second, we present a scalable approach to end-to-end learn a deep multi-label classifier with partial labels. To evaluate the effectiveness of our proposed frameworks, we focus on visual recognition application, however, our proposed frameworks are generic and can be used in modeling general settings of learning event sequences, and learning multi-label classifiers from partial labels. Visual recognition is a fundamental piece for achieving machine intelligence, and has a wide range of applications such as human activity analysis, autonomous driving, surveillance and security, health-care monitoring, etc. With a wide range of experiments, we show that our proposed approaches help to build more powerful and effective visual recognition frameworks

Machine Learning and Natural Language Processing in Stock Prediction

In this thesis, we first study the two ill-posed natural language processing tasks related to stock prediction, i.e. stock movement prediction and financial document-level event extraction. While implementing stock prediction and event extraction, we encountered difficulties that could be resolved by utilizing out-of-distribution detection. Consequently, we presented a new approach for out-of-distribution detection, which is the third focus of this thesis. First, we systematically build a platform to study the NLP-aided stock auto-trading algorithms. Our platform is characterized by three features: (1) We provide financial news for each specific stock. (2) We provide various stock factors for each stock. (3) We evaluate performance from more financial-relevant metrics. Such a design allows us to develop and evaluate NLP-aided stock auto-trading algorithms in a more realistic setting. We also propose a system to automatically learn a good feature representation from various input information. The key to our algorithm is a method called semantic role labelling Pooling (SRLP), which leverages Semantic Role Labeling (SRL) to create a compact representation of each news paragraph. Based on SRLP, we further incorporate other stock factors to make the stock movement prediction. In addition, we propose a self-supervised learning strategy based on SRLP to enhance the out-of-distribution generalization performance of our system. Through our experimental study, we show that the proposed method achieves better performance and outperforms all strong baselines’ annualized rate of return as well as the maximum drawdown in back-testing. Second, we propose a generative solution for document-level event extraction that takes into account recent developments in generative event extraction, which have been successful at the sentence level but have not yet been explored for document-level extraction. Our proposed solution includes an encoding scheme to capture entity-to-document level information and a decoding scheme that takes into account all relevant contexts. Extensive experimental results demonstrate that our generative-based solution can perform as well as state-of-theart methods that use specialized structures for document event extraction. This allows our method to serve as an easy-to-use and strong baseline for future research in this area. Finally, we propose a new unsupervised OOD detection model that separates, extracts, and learns the semantic role labelling guided fine-grained local feature representation from different sentence arguments and the full sentence using a margin-based contrastive loss. Then we demonstrate the benefit of applying a self-supervised approach to enhance such global-local feature learning by predicting the SRL extracted role. We conduct our experiments and achieve state-of-the-art performance on out-of-distribution benchmarks.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Motivation and quality management in academic library and information services.

As management fashions go, few have been more pervasive than Quality Management Systems (QMS) like Total Quality Management (TQM) and BS EN ISO 9000 (ISO 9000). Their prominence was fuelled by a mixture of ideological and economic considerations as, by the early to mid-1990s, many organisations were keen to indicate that they were active participants of the `quality revolution'. The exponential growth of interest in QMS was reflected in the library literature although only a small percentage of academic library and information services (LIS) subscribed to the systems. The thesis examines the relationship between QMS and motivation in such organisations. It ventures beyond the benign vision of the `quality gurus' by critically considering the relevance QMS might have for understanding contemporary developments within the organisation and management of academic LIS. The investigation determined that the quality of implementation is a key factor. In addition to senior management commitment, staff are motivated to QMS if there are accompanying changes in communication and training. The more successful LIS were those that did not treat staff as if they were barriers to change, but involved them in the process of implementation. While there were many stated improvements it was discovered that many of the `new' practices within the QMS LIS were not dissimilar to many of the initiatives in their non-QMS LIS counterparts. The investigator identified factors that also limit QMS as a framework for motivation and posits that the crux of the problem can be traced to the concept of `quality' itself. As a self-evident good, workers become morally bound to quality, which enhances their own exploitation. There was evidence that managers can use this legitimating device to quell resistance, via peer pressure, and instil cultural homogeneity

What makes effective advocacy? An exploration of public health nutrition advocacy in England

Background: This research explores aspects of the widely discussed gap between the state of public health nutrition in England and the appropriateness of government and commercial responses. Specifically, it explores the role of NGO advocacy in altering and improving this situation. Recognising that NGOs conduct their advocacy to protect the public good and shape public health nutrition policy, this research analyses the role and effectiveness of NGO advocacy in shifting and shaping the dynamics of food and nutrition policy, and how it can be measured. Drawing on interdisciplinary political theories and existing models of advocacy, the research applies an academic lens to some of the practical challenges and issues faced by NGO advocates. Aim: The aim of this research was to understand the role and effectiveness of NGO advocacy in public health nutrition, a previously under-researched area. The research focuses on England, a country with an active NGO presence, and explored the different types of advocacy and the different ways in which advocacy success is, and could be, measured. The research has relevance for the academic literature, for our understanding of advocacy effectiveness in public health nutrition and, not least, for improving the way NGOs engage with policy and change. Research questions: Three research questions were posed: 1. What conditions support or hinder NGO advocacy in public health nutrition? 2. What advocacy tactics are adopted by NGOs working in public health nutrition? 3. How can the effectiveness of public health nutrition advocacy be measured? Methods: Qualitative research methods were used, primarily drawing on the experiences in England from three implemented public health nutrition policies (FOP labelling, marketing restrictions and salt reduction) and four NGOs working on public health nutrition. A combination of methods, including semi-structured interviews (N=29) and document analysis of grey literature from campaigning groups and government agencies, were used to answer each research question to aid triangulation and validation of the findings. Main findings: Advocacy was found to be a complex mix of positions and strategies, with no single model utilised by NGOs. There is a range of ‘advocacies’ which mean different things, at different times, in different contexts and to different people. Advocacy was found to be intrinsically linked to external conditions, and these conditions often dictate the most appropriate approach and likelihood of success. The research demonstrated different ways in which advocacy can react to, shape and navigate these conditions as part of efforts to achieve change. As a result of the conditions, advocacy was found to be rarely straightforward or predictable, adding a layer of complexity for measuring advocacy. A number of short- and long-term indicators for measuring advocacy were identified and, drawing on the overall findings, it is suggested that in order to get the most value from an advocacy monitoring framework, advocacy should always be contextualised within the wider conditions, and a range of different indicators should be used to reflect these different conditions. The thesis ends by reflecting on the practical implications of the research and proposing a practical framework for monitoring advocacy. Implications: The results of this research provide insights into public health nutrition advocacy and the complexity and dynamics within which it operates. The vital role of the conditions highlights the need to contextualise any assessment of advocacy and thus utilise measures across a range of domains. Progress was found to result from both abrupt and incremental change and as such advocacy which is fluid and dynamic will be necessary in order to react, shape and navigate different conditions. While needing further refinement and research, the monitoring framework presented in this thesis is valuable as a means to identify appropriate advocacy tactics and measures, while being underpinned by the conditions at play. The measuring challenge for advocacy remains, but the proposed framework developed can aid future monitoring and refinement of advocacy and thus help improve the ability of NGOs to support improvements to public health nutrition

The Great Highland Bagpipe in the Eastern United States :inception, development, and perpetuation

This study is an analysis of the inception, development, and perpetuation of the Great Highland Bagpipe (GHB) in the United States, and in particular examines the culture and community of competitive bagpiping. With a focus on the Eastern United States, the study traces the inception and development of bagpiping through three distinct eras. In the first two eras, the GHB enjoyed increasing degrees of popularity among various populations in the United States, before its presence declined almost to the point of extinction. The study then proceeds to the third era, still in progress, exploring the present state of competitive bagpiping in the Eastern United States including an in-depth examination of the Eastern United States Pipe Band Association (EUSPBA). Obstacles to the growth of bagpiping in the EUSPBA are considered, revealing growth trends, as well as attitudes toward and awareness of growth issues. Student perceptions and motivations are analyzed, followed by an examination of teacher attitudes. Specific teaching methods are compared and analyzed. Finally, learning environments, categorized as formal, non-formal, and informal, are described and examined