STORM - a novel information fusion and cluster interpretation technique

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert knowledge is frequently a matter of combining multiple data sources from disparate hypothetical spaces. In cases where such spaces belong to different data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data for a specific set of problems. We show that our method provides a better visual understanding of one hypothetical space with the help of data from another hypothetical space. We believe that our model has implications for the field of exploratory data analysis and knowledge discovery.

Capital structure, risk and asymmetric information : theory and evidence

This paper proposes a principal-agent model between banks and firms with risk and asymmetric information. A mixed form of finance to firms is assumed. The capital structure of firms is a relevant cause for the final aggregate level of investment in the economy. In the model analyzed, there may be a separating equilibrium, which is not economically efficient, because aggregate investments fall short of the first-best level. Based on European firm-level data, an empirical model is presented which validates the result of the relevance of the capital structure of firms. The relative magnitude of equity in the capital structure makes a real difference to the profits obtained by firms in the economy.

The Hungarian Revolution of 1956 at Sixty Years, Liberal Reform, and the Search for a Useable Past

The story of the 1956 Hungarian Revolution at sixty years remains contested. The current center-right government led by Prime Minister Viktor Orbán at once embraces the Revolution and yet at the same time trumpets the failure of the liberal states of the West. Hungarians are encouraged to view the authoritarian politics of Vladmir Putin as a successful model worthy of emulation. In this light the liberal state envisioned by many of the revolutionaries, let alone the liberal state expected by the European Union stands in contrast with one of the principal tenets of the ruling FIDESz/Christian Democrat (KDNP) coalition. At the same time, the current yearning for an illiberal state accords with a strand of desire more akin to those who supported Cardinal Mindszenty during the Revolution and by extension his sympathy for the authoritarian regime of Miklós Horthy.

Layered Information Modeling and a Knowledge Organization Paradigm: Inherent Classification and the Design of Interoperable Systems

In knowledge technology work, as expressed by the scope of this conference, there are a number of communities, each uncovering new methods, theories, and practices. The Library and Information Science (LIS) community is one such community. This community, through tradition and innovation, theories and practice, organizes knowledge and develops knowledge technologies formed by iterative research hewn to the values of equal access and discovery for all. The Information Modeling community is another contributor to knowledge technologies. It concerns itself with the construction of symbolic models that capture the meaning of information and organize it in ways that are computer-based, but human understandable. A recent paper that examines certain assumptions in information modeling builds a bridge between these two communities, offering a forum for a discussion on common aims from a common perspective. In a June 2000 article, Parsons and Wand separate classes from instances in information modeling in order to free instances from what they call the “tyranny” of classes. They attribute a number of problems in information modeling to inherent classification – or the disregard for the fact that instances can be conceptualized independent of any class assignment. By faceting instances from classes, Parsons and Wand strike a sonorous chord with classification theory as understood in LIS. In the practice community and in the publications of LIS, faceted classification has shifted the paradigm of knowledge organization theory in the twentieth century. Here, with the proposal of inherent classification and the resulting layered information modeling, a clear line joins both the LIS classification theory community and the information modeling community. Both communities have their eyes turned toward networked resource discovery, and with this conceptual conjunction a new paradigmatic conversation can take place. Parsons and Wand propose that the layered information model can facilitate schema integration, schema evolution, and interoperability. These three spheres in information modeling have their own connotation, but are not distant from the aims of classification research in LIS. In this new conceptual conjunction, established by Parsons and Ward, information modeling through the layered information model, can expand the horizons of classification theory beyond LIS, promoting a cross-fertilization of ideas on the interoperability of subject access tools like classification schemes, thesauri, taxonomies, and ontologies. This paper examines the common ground between the layered information model and faceted classification, establishing a vocabulary and outlining some common principles. It then turns to the issue of schema and the horizons of conventional classification and the differences between Information Modeling and Library and Information Science. Finally, a framework is proposed that deploys an interpretation of the layered information modeling approach in a knowledge technologies context. In order to design subject access systems that will integrate, evolve and interoperate in a networked environment, knowledge organization specialists must consider a semantic class independence like Parsons and Wand propose for information modeling.

A survey of the meat goat industry in Queensland and New South Wales. 1. General property information, goat and pasture management.

This study aimed to survey farmers knowledge and practices on the management of pastures, stocking rates and markets of meat goat-producing enterprises within New South Wales and Queensland, Australia. An interview-based questionnaire was conducted on properties that derived a significant proportion of their income from goats. The survey covered 31 landholders with a total land area of 567 177 ha and a reported total of 160 010 goats. A total of 55% (17/31) of producers were involved in both opportunistic harvesting and commercial goat operations, and 45% (14/31) were specialised seedstock producers. Goats were the most important livestock enterprise on 55% (17/31) of surveyed properties. Stocking rate varied considerably (0.3?9.3 goats/ha) within and across surveyed properties and was found to be negatively associated with property size and positively associated with rainfall. Overall, 81% (25/31) of producers reported that the purpose of running goats on their properties was to target international markets. Producers also cited the importance of targeting markets as a way to increase profitability. Fifty-three percent of producers were located over 600 km from a processing plant and the high cost of freight can limit the continuity of goats supplied to abattoirs. Fencing was an important issue for goat farmers, with many producers acknowledging this could potentially add to capital costs associated with better goat management and production. Producers in the pastoral regions appear to have a low investment in pasture development and opportunistic goat harvesting appears to be an important source of income.

Metadata aggregation : a critical component of research infrastructure for the future

The development of research data management infrastructure and services and making research data more discoverable and accessible to the research community is a key priority at the national, state and individual university level. This paper will discuss and reflect upon a collaborative project between Griffith University and the Queensland University of Technology to commission a Metadata Hub or Metadata Aggregation service based upon open source software components. It will describe the role that metadata aggregation services play in modern research infrastructure and argue that this role is a critical one.

Cultural impact on e-service use in Saudi Arabia : results from questionnaire

This paper reports the results of a mixed method approach to answer: what are the cultural values that impact on e-service use in Saudi Arabia? Cultural theories, dimensions, and models previously identified in the literature, in addition to individual interviews and focus groups, test the current identified uncovered elements of Saudi culture. This paper will firstly, introduce the importance of culture and define the aspects of Saudi culture. It will then describe the method used and present the questionnaire findings. All of the tested hypotheses were found consistent with their predicted outcomes except hypotheses 4 and 8 were partially consistent. It is evidenced that consideration of the impact of the cultural values will mainly contribute to the enhancement of social and organisational aspects of e-society research and practices, by deeply understanding them as of the influntials to e-service implementation.

Cultural impact on e-service use in Saudi Arabia : the need for interaction with other humans

This paper reports the results of a mixed method approach to answer: To what extent do cultural values impact on e-service use in Saudi Arabia, and if so how? This paper will firstly, introduce the importance of culture and define the aspects of Saudi culture with focus on our scope: the fear of a lack of Interaction with other Humans. It will then describe the method used and present the qualitative and quantitative findings related to the need for Interactions with other Humans. Much of the written literature about human interaction aims at Information Systems design or design improvement. Yet, this is different to what is being investigated in this study. One of the factors this study will consider is the perceived lack of interaction with other humans or the anxiety people may feel in missing the physical interaction with other people by fully moving business interaction to the virtual world. The review of the literature indicates that the impact of such factor on Information and Communication Technologies (ICT) use has not been studied. This research aims to cover this gap by investigating to what extent the fear of a lack of Interaction with other Humans, as one of Saudi Arabia’s cultural values, impacts on e-service use in Saudi Arabia. The tested hypothesis was found consistent with its predicted outcome: the fear of a lack of Interaction with other Humans is a negative predictor of intention to use e-services in Saudi Arabia. It is evidenced that consideration of the impact of the cultural values will mainly contribute to the enhancement of ICTs implementation and use.

Consensus σ70 promoter prediction using hadoop

MapReduce frameworks such as Hadoop are well suited to handling large sets of data which can be processed separately and independently, with canonical applications in information retrieval and sales record analysis. Rapid advances in sequencing technology have ensured an explosion in the availability of genomic data, with a consequent rise in the importance of large scale comparative genomics, often involving operations and data relationships which deviate from the classical Map Reduce structure. This work examines the application of Hadoop to patterns of this nature, using as our focus a wellestablished workflow for identifying promoters - binding sites for regulatory proteins - Across multiple gene regions and organisms, coupled with the unifying step of assembling these results into a consensus sequence. Our approach demonstrates the utility of Hadoop for problems of this nature, showing how the tyranny of the "dominant decomposition" can be at least partially overcome. It also demonstrates how load balance and the granularity of parallelism can be optimized by pre-processing that splits and reorganizes input files, allowing a wide range of related problems to be brought under the same computational umbrella.

Optimization of complex QoS-aware service compositions

In Service-oriented Architectures, business processes can be realized by composing loosely coupled services. The problem of QoS-aware service composition is widely recognized in the literature. Existing approaches on computing an optimal solution to this problem tackle structured business processes, i.e., business processes which are composed of XOR-block, AND-block, and repeat loop orchestration components. As of yet, OR-block and unstructured orchestration components have not been sufficiently considered in the context of QoS-aware service composition. The work at hand addresses this shortcoming. An approach for computing an optimal solution to the service composition problem is proposed considering the structured orchestration components, such as AND/XOR/OR-block and repeat loop, as well as unstructured orchestration components.

Impacts of vessel capacity reduction programmes on efficiency in fisheries : the case of Australia’s multispecies northern prawn fishery

Capacity reduction programmes, in the form of buybacks or decommissioning, have had relatively widespread application in fisheries in the US, Europe and Australia. A common criticism of such programmes is that they remove the least efficient vessels first, resulting in an increase in average efficiency of the remaining fleet, which tends to increase the effective fishing power of the remaining fleet. In this paper, the effects of a buyback programme on average technical efficiency in Australia’s Northern Prawn Fishery are examined using a multi-output production function approach with an explicit inefficiency model. As expected, the results indicate that average efficiency of the remaining vessels was generally greater than that of the removed vessels. Further, there was some evidence of an increase in average scale efficiency in the fleet as the remaining vessels were closer, on average, to the optimal scale. Key factors affecting technical efficiency included company structure and the number of vessels fishing. In regard to fleet size, our model suggests positive externalities associated with more boats fishing at any point in time (due to information sharing and reduced search costs), but also negative externalities due to crowding, with the latter effect dominating the former. Hence, the buyback resulted in a net increase in the individual efficiency of the remaining vessels due to reduced crowding, as well as raising average efficiency through removal of less efficient vessels.

eHealth-as-a-Service (eHaaS) : towards universal stakeholder engagement

With the introduction of the Personally Controlled Health Record (PCEHR), the Australian public is being asked to accept greater responsibility for their healthcare by taking an active role in the management of personal health information. Although well designed, constructed and intentioned, policy and privacy concerns have resulted in an eHealth model that may impact future health sharing requirements. Hence, as a case study for a consumer eHealth initative in the Australian context, eHealth-as-a-Service (eHaaS) serves as a disruptive step in in the aggregation and transformation of health information for use as real-world knowledge. The strategic value of extending the community Health Record Bank (HRB) model lies in the ability to automatically draw on a multitude of relevant data repositories and sources to create a single source of the truth and to engage market forces to create financial sustainability. The opportunity to transform the beleaguered Australian PCEHR into a realisable and sustainable technology consumption model for patient safety is explored. Moreover, the current clerical focus of healthcare practitioners acting in the role of de facto record keepers is renegotiated to establish a shared knowledge creation landscape of action for safer patient interventions. To achieve this potential however requires a platform that will facilitate efficient and trusted unification of all health information available in real-time across the continuum of care. eHaaS provides a sustainable environment and encouragement to realise this potential.

A pilot study on affective classification of facial images for emerging news topics

The proliferation of news reports published in online websites and news information sharing among social media users necessitates effective techniques for analysing the image, text and video data related to news topics. This paper presents the first study to classify affective facial images on emerging news topics. The proposed system dynamically monitors and selects the current hot (of great interest) news topics with strong affective interestingness using textual keywords in news articles and social media discussions. Images from the selected hot topics are extracted and classified into three categorized emotions, positive, neutral and negative, based on facial expressions of subjects in the images. Performance evaluations on two facial image datasets collected from real-world resources demonstrate the applicability and effectiveness of the proposed system in affective classification of facial images in news reports. Facial expression shows high consistency with the affective textual content in news reports for positive emotion, while only low correlation has been observed for neutral and negative. The system can be directly used for applications, such as assisting editors in choosing photos with a proper affective semantic for a certain topic during news report preparation.