An information accountability perspective to eHealth security and privacy

Rigid security boundaries hinder the proliferation of eHealth. Through active audit logs, accountable-eHealth systems alleviate privacy concerns and enhance information availability.

Improving spoken term detection using complementary information

This research has made contributions to the area of spoken term detection (STD), defined as the process of finding all occurrences of a specified search term in a large collection of speech segments. The use of visual information in the form of lip movements of the speaker in addition to audio and the use of topic of the speech segments, and the expected frequency of words in the target speech domain, are proposed. By using these complementary information, improvement in the performance of STD has been achieved which enables efficient search of key words in large collection of multimedia documents.

Functional activation during the Rapid Visual Information Processing task in a middle aged cohort: An fMRI study

The Rapid Visual Information Processing (RVIP) task, a serial discrimination task where task performance believed to reflect sustained attention capabilities, is widely used in behavioural research and increasingly in neuroimaging studies. To date, functional neuroimaging research into the RVIP has been undertaken using block analyses, reflecting the sustained processing involved in the task, but not necessarily the transient processes associated with individual trial performance. Furthermore, this research has been limited to young cohorts. This study assessed the behavioural and functional magnetic resonance imaging (fMRI) outcomes of the RVIP task using both block and event-related analyses in a healthy middle aged cohort (mean age = 53.56 years, n = 16). The results show that the version of the RVIP used here is sensitive to changes in attentional demand processes with participants achieving a 43% accuracy hit rate in the experimental task compared with 96% accuracy in the control task. As shown by previous research, the block analysis revealed an increase in activation in a network of frontal, parietal, occipital and cerebellar regions. The event related analysis showed a similar network of activation, seemingly omitting regions involved in the processing of the task (as shown in the block analysis), such as occipital areas and the thalamus, providing an indication of a network of regions involved in correct trial performance. Frontal (superior and inferior frontal gryi), parietal (precuenus, inferior parietal lobe) and cerebellar regions were shown to be active in both the block and event-related analyses, suggesting their importance in sustained attention/vigilance. These networks and the differences between them are discussed in detail, as well as implications for future research in middle aged cohorts.

How information around acute events comes into being on social media?

In this digital age, as social media is emerging as a central site where information is shared and interpreted, it is essential to study information construction issues on social media sites in order to understand how social reality is constructed. While there is a number of studies taking an information-as-objective point of view, this proposed study emphasizes the constructed and interpretive nature of information and explores the processes through which information surrounding acute events comes into being on micro-blogs. In order to conduct this analysis systematically and theoretically, the concept of interpretive communities will be deployed. This research investigates if or not micro-blog based social groups can serve as interpretive communities, and, if so, what role might they play in the construction of information, and the social impacts that may arise. To understand how this process is entangled with the surrounding social, political, technical contexts, cases from both China (focusing on Sina Weibo) and Australia (focusing on Twitter) will be analysed.

Analysis of word embeddings and sequence features for clinical information extraction

This study investigates the use of unsupervised features derived from word embedding approaches and novel sequence representation approaches for improving clinical information extraction systems. Our results corroborate previous findings that indicate that the use of word embeddings significantly improve the effectiveness of concept extraction models; however, we further determine the influence that the corpora used to generate such features have. We also demonstrate the promise of sequence-based unsupervised features for further improving concept extraction.

An experimental analysis of information acquisition in prediction markets

We study which factors in terms of trading environment and trader characteristics determine individual information acquisition in experimental asset markets. Traders with larger endowments, existing inconclusive information, lower risk aversion, and less experience in financial markets tend to acquire more information. Overall, we find that traders overacquire information, so that informed traders on average obtain negative profits net of information costs. Information acquisition and the associated losses do not diminish over time. This overacquisition phenomenon is inconsistent with predictions of rational expectations equilibrium, and we argue it resembles the overdissipation results from the contest literature. We find that more acquired information in the market leads to smaller differences between fundamental asset values and prices. Thus, the overacquisition phenomenon is a novel explanation for the high forecasting accuracy of prediction markets.

Public-private partnerships in information and communication technology for education

Public-private partnerships (PPPs) have generated a lot of interest from governments around the world for leveraging private sector involvement in developing and sustaining public infrastructure and services. Initially, PPPs were favoured by transport, energy, and other large infrastructure-intensive sectors. More recently, the concept has been expanded to include social sectors such as education.

Complex symbolic sequence clustering and multiple classifiers for predictive process monitoring

This paper addresses the following predictive business process monitoring problem: Given the execution trace of an ongoing case,and given a set of traces of historical (completed) cases, predict the most likely outcome of the ongoing case. In this context, a trace refers to a sequence of events with corresponding payloads, where a payload consists of a set of attribute-value pairs. Meanwhile, an outcome refers to a label associated to completed cases, like, for example, a label indicating that a given case completed “on time” (with respect to a given desired duration) or “late”, or a label indicating that a given case led to a customer complaint or not. The paper tackles this problem via a two-phased approach. In the first phase, prefixes of historical cases are encoded using complex symbolic sequences and clustered. In the second phase, a classifier is built for each of the clusters. To predict the outcome of an ongoing case at runtime given its (uncompleted) trace, we select the closest cluster(s) to the trace in question and apply the respective classifier(s), taking into account the Euclidean distance of the trace from the center of the clusters. We consider two families of clustering algorithms – hierarchical clustering and k-medoids – and use random forests for classification. The approach was evaluated on four real-life datasets.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

Integrating and evaluating neural word embeddings in information retrieval

Recent advances in neural language models have contributed new methods for learning distributed vector representations of words (also called word embeddings). Two such methods are the continuous bag-of-words model and the skipgram model. These methods have been shown to produce embeddings that capture higher order relationships between words that are highly effective in natural language processing tasks involving the use of word similarity and word analogy. Despite these promising results, there has been little analysis of the use of these word embeddings for retrieval. Motivated by these observations, in this paper, we set out to determine how these word embeddings can be used within a retrieval model and what the benefit might be. To this aim, we use neural word embeddings within the well known translation language model for information retrieval. This language model captures implicit semantic relations between the words in queries and those in relevant documents, thus producing more accurate estimations of document relevance. The word embeddings used to estimate neural language models produce translations that differ from previous translation language model approaches; differences that deliver improvements in retrieval effectiveness. The models are robust to choices made in building word embeddings and, even more so, our results show that embeddings do not even need to be produced from the same corpus being used for retrieval.

Information-movement Coupling as a Hallmark of Sport Expertise

Expert interceptive actions are grounded in both perceptual judgment and movement control, yet research has largely focused on the role of anticipation. More recently, the emergence of ecological psychology has provided movement scientists with opportunities to develop further understanding of the processes underpinning the development of expert information-movement couplings. In this chapter we discuss key research that has enhanced our understanding of perceptual learning with specific focus on the concepts of education of attention and calibration. We conclude by discussing the practical implications of this research in the study of expertise highlighting the need for future research using sporting tasks.

Toward a design theory for green information systems

We report on ongoing research to develop a design theory for classes of information systems that allow for work practices that exhibit a minimal harmful impact on the natural environment. We call such information systems Green IS. In this paper we describe the building blocks of our Green IS design theory, which develops prescriptions for information systems that allow for: (1) belief formation, action formation and outcome measurement relating to (2) environmentally sustainable work practices and environmentally sustainable decisions on (3) a macro or micro level. For each element, we specify structural features, symbolic expressions, user abilities and goals required for the affordances to emerge. We also provide a set of testable propositions derived from our design theory and declare two principles of implementation.

Integrated Information and Communication Technology Strategies for Competitive Higher Education in Asia and the Pacific

Developing countries in Asia and the Pacific are rapidly reaching middle income economic status. Their competitive advantage is shifting from labor-intensive industries and natural resource-based economies to knowledge-based economies that innovate and create new products and services. Early adoption of information and communication technology (ICT) can allow countries to leapfrog over the traditional development pathway into production of knowledge-based products and services. Since higher education institutions (HEIs) are considered a primary engine of economic growth, adoption of ICT is imperative for securing competitive advantage. ICT is thought to be one of the fastest growing industries and is frequently heralded as a transforming influence on higher education systems globally and, consequently, is enhancing the competitive advantage of countries. It is increasingly becoming evident that an institution-wide ICT strategy covering all evolving functions of competitive HEIs is necessary. Such a system may be designed as an integrated platform but implemented in phases.

Impact of online privacy concerns and brand reputation on consumer willingness to provide personal information

The aim of this research was to identify the role of brand reputation in encouraging consumer willingness to provide personal data online, for the benefits of personalisation. This study extends on Malhotra, Kim and Agarwal’s (2004) Internet Users Information Privacy Concerns Model, and uses the theoretical underpinning of Social Contract Theory to assess how brand reputation moderates the relationship between trusting beliefs and perceived value (Privacy Calculus framework) with willingness to give personal information. The research is highly relevant as most privacy research undertaken to date focuses on consumer related concerns. Very little research exists examining the role of brand reputation and online privacy. Practical implications of this research include gaining knowledge as to how to minimise online privacy concerns; improve brand reputation; and provide insight on how to reduce consumer resistance to the collection of personal information and encourage consumer opt-in.

Integration of clinical information systems in Saudi Arabian health care contexts: A grounded theory exploration

This research examined the implementation of clinical information system technology in a large Saudi Arabian health care organisation. The research was underpinned by symbolic interactionism and grounded theory methods informed data collection and analysis. Observations, a review of policy documents and 38 interviews with registered nurses produced in-depth data. Analysis generated three abstracted concepts that explained how imported technology increased practice and health care complexity rather than enhance quality patient care. The core category, Disseminating Change, also depicted a hierarchical and patriarchal culture that shaped the implementation process at the levels of government, organisation and the individual.