Performative digital asset management: To propose a framework and proof of concept model that effectively enables researchers to document, archive and curate their non-traditional research data

This cross disciplinary study was conducted as two research and development projects. The outcome is a multimodal and dynamic chronicle, which incorporates the tracking of spatial, temporal and visual elements of performative practice-led and design-led research journeys. The distilled model provides a strong new approach to demonstrate rigour in non-traditional research outputs including provenance and an 'augmented web of facticity'.

High connectivity in rastrelliger kanagurta: Influence of historical signatures and migratory behaviour inferred from mtDNA Cytochrome b

Phylogeographic patterns and population structure of the pelagic Indian mackerel, Rastrelliger kanagurta were examined in 23 populations collected from the Indonesian-Malaysian Archipelago (IMA) and the West Indian Ocean (WIO). Despite the vast expanse of the IMA and neighbouring seas, no evidence for geographical structure was evident. An indication that R. kanagurta populations across this region are essentially panmictic. This study also revealed that historical isolation was insufficient for R. kanagurta to attain migration drift equilibrium. Two distinct subpopulations were detected between the WIO and the IMA (and adjacent populations); interpopulation genetic variation was high. A plausible explanation for the genetic differentiation observed between the IMA and WIO regions suggest historical isolation as a result of fluctuations in sea levels during the late Pleistocene. This occurrence resulted in the evolution of a phylogeographic break for this species to the north of the Andaman Sea.

Patient safety and the RCA: A document analysis

This research examined the function of Queensland Health's Root Cause Analysis (RCA) to improve patient safety through an investigation of patient harm events where permanent harm and preventable death, Severity Assessment Code 1, were the outcome of healthcare. Unedited and highly legislated RCAs from across Queensland Health public hospitals from 2009, 2010 and 2011 comprised the data. A document analysis revealed the RCAs opposed organisational policy and dominant theoretical directives. If we accept the prevailing assumption that patient harm is a systemic issue, then the RCA is failing to address harm events in healthcare.

Document timespan normalisation and understanding temporality for clinical records search

Previous qualitative research has highlighted that temporality plays an important role in relevance for clinical records search. In this study, an investigation is undertaken to determine the effect that the timespan of events within a patient record has on relevance in a retrieval scenario. In addition, based on the standard practise of document length normalisation, a document timespan normalisation model that specifically accounts for timespans is proposed. Initial analysis revealed that in general relevant patient records tended to cover a longer timespan of events than non-relevant patient records. However, an empirical evaluation using the TREC Medical Records track supports the opposite view that shorter documents (in terms of timespan) are better for retrieval. These findings highlight that the role of temporality in relevance is complex and how to effectively deal with temporality within a retrieval scenario remains an open question.

Semi-supervised document clustering via loci

Document clustering is one of the prominent methods for mining important information from the vast amount of data available on the web. However, document clustering generally suffers from the curse of dimensionality. Providentially in high dimensional space, data points tend to be more concentrated in some areas of clusters. We take advantage of this phenomenon by introducing a novel concept of dynamic cluster representation named as loci. Clusters’ loci are efficiently calculated using documents’ ranking scores generated from a search engine. We propose a fast loci-based semi-supervised document clustering algorithm that uses clusters’ loci instead of conventional centroids for assigning documents to clusters. Empirical analysis on real-world datasets shows that the proposed method produces cluster solutions with promising quality and is substantially faster than several benchmarked centroid-based semi-supervised document clustering methods.

Use of physicochemical signatures to assess the sources of metals in urban road dust

Road deposited dust is a complex mixture of pollutants derived from a wide range of sources. Accurate identification of these sources is seminal for effective source-oriented control measures. A range of techniques such as enrichment factor analysis (EF), principal component analysis (PCA) and hierarchical cluster analysis (HCA) are available for identifying sources of complex mixtures. However, they have multiple deficiencies when applied individually. This study presents an approach for the effective utilisation of EF, PCA and HCA for source identification, so that their specific deficiencies on an individual basis are eliminated. EF analysis confirmed the non-soil origin of metals such as Na, Cu, Cd, Zn, Sn, K, Ca, Sb, Ba, Ti, Ni and Mo providing guidance in the identification of anthropogenic sources. PCA and HCA identified four sources, with soil and asphalt wear in combination being the most prominent sources. Other sources were tyre wear, brake wear and sea salt.

Double-authentication-preventing signatures

Digital signatures are often used by trusted authorities to make unique bindings between a subject and a digital object; for example, certificate authorities certify a public key belongs to a domain name, and time-stamping authorities certify that a certain piece of information existed at a certain time. Traditional digital signature schemes however impose no uniqueness conditions, so a trusted authority could make multiple certifications for the same subject but different objects, be it intentionally, by accident, or following a (legal or illegal) coercion. We propose the notion of a double-authentication-preventing signature, in which a value to be signed is split into two parts: a subject and a message. If a signer ever signs two different messages for the same subject, enough information is revealed to allow anyone to compute valid signatures on behalf of the signer. This double-signature forgeability property discourages signers from misbehaving—a form of self-enforcement—and would give binding authorities like CAs some cryptographic arguments to resist legal coercion. We give a generic construction using a new type of trapdoor functions with extractability properties, which we show can be instantiated using the group of sign-agnostic quadratic residues modulo a Blum integer; we show an additional application of these new extractable trapdoor functions to standard digital signatures.

Parents as co-researchers at home: Using an observational method to document young children’s use of technology

This paper discusses the use of observational video recordings to document young children’s use of technology in their homes. Although observational research practices have been used for decades, often with video-based techniques, the participant group in this study (i.e., very young children) and the setting (i.e., private homes), provide a rich space for exploring the benefits and limitations of qualitative observation. The data gathered in this study point to a number of key decisions and issues that researchers must face in designing observational research, particularly where non-researchers (in this case, parents) act as surrogates for the researcher at the data collection stage. The involvement of parents and children as research videographers in the home resulted in very rich and detailed data about children’s use of technology in their daily lives. However, limitations noted in the dataset (e.g., image quality) provide important guidance for researchers developing projects using similar methods in future. The paper provides recommendations for future observational designs in similar settings and/or with similar participant groups.

Mining topical relevant patterns for multi-document summarization

Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.