The segmentation of the liver, lesions, and vessels from pre-operative CT scans is of major importance in hepatic surgery planning. However, large databases with reference segmentations for these regions of interest remain unavailable, a challenge often encountered in medical image segmentation. In this work, we propose the FuSe loss, a novel loss function for multi-task learning on datasets with partial annotations. By employing the nnU-Net’s 3D self-configuring pipeline to calibrate and train a deep network for the joint segmentation of the liver, lesions, and vessels, we show how the FuSe loss allows to learn from the differently annotated IRCAD and LiTS datasets, improving the overall baseline segmentation performance. With the FuSe loss, the dice scores reached up to 95.9%, 70.6% and 60.0% for the liver, lesions, and vessels respectively.

We present fuzzy boundaries, a method of segmenting text whereby we consider a population of boundaries to be in a fuzzy-state. Furthermore, we aim to use this method to present a multifaceted segmentation approach that is applicable across various domains that require the use of text segmentation: Text summerisation, Rhetorical Stricture Theory, sentence-based segmentation, paragraph-based segmentation and search-algorithms that require topic-based querying. The work first outlines the subject domain together with previous work surrounding the topic of text segmentation. We move to discussing the rationale, exploring our motivations for such a method. The model and its composition is then described next along side the direction taken in the implementation of the model. We move to the results and performance of our method concluding finally with a discussion on the benefits and justifications for our propositions.

Sentiment Analysis is a strand of Natural Language Processing that deals with the emotional polarity a given piece of text has. To gain this understanding from just a string of words, we must first consider a suitable way to break down the text to further classify what each part means. This can be done in a plethora of ways, which mostly stem from the understanding of a classifier. We believe that there is a large amount of information stored in structure-based features within the text, for instance, where the writer may place negation-terms not, neither and how this affects the overall polarity. Similarly, how words in sentences or sections, remote to the current-analysed section, may affect the polarity of said section. A combination of features from both a conventional and a structure-based understanding may also provide us with a larger accuracy in polarity. Therefore, this paper aims to explain both conventional sentiment analysis methods with structure-based methods as well as their practices, advantages and disadvantages concluding with how sentiment analysis can move forward with the appropriation of hybrid methods (methods involving motifs, practices and understandings) from conventional and structure-based methods, for classification.

Cloud computing is gaining increasing interest as an enabling technology for improved productivity, efficiency and cost reduction. It also has the potential to add value to reliability and scalability of organisation’s systems, which would allow organisations to better focus on their core business and strategies. This research paper aimed at exploring the perceived potential for value creation from cloud computing in Australian Regional Governments. Using the Technology-Organization-Environment (TOE) framework, the Diffusion of Innovation (DOI) theory and Desires Framework (DF), a conceptual research model is devised. The key identified factors that are likely to influence adoption of cloud computing are identified along with the implications to current policies, anticipated benefits and challenges of adoption of cloud computing. The research employed a qualitative approach involving 21 in-depth interviews with Australian local councils’ IT managers. The findings of this research are mapped to the research model factors, thereby presenting and validating a number of value-adding concepts that are expected to contribute to investment decisions on cloud computing adoption.

Due to a demand for computing of huge proportions and cheap in cost, cloud computing is now a favoured model. This type of technology has been utilized by some government organizations to address the demands and requirements of their citizen. However, constraints exist with the use of this model impacting on service provision and use of this technology. This paper will explore the disparate challenges and issues that impacting on the use of cloud computing in Australian local councils. 480 IT staff from 47 local government councils was investigated. The findings will aid authorities to analyse the effectiveness of endorsing cloud computing and develop their understanding of the problematic components this may entail. This research comprises a series of findings on cloud computing with specific focus in this paper on different challenges and issues surrounding the adoption of this technology by Australian local councils.

The terminology of cloud computing defines a network, which shares applications, processing power and large systems among many other computers. It can be a foundation for media channels and other applications. Cloud computing provides great opportunity for each practicing individuals to connect and extend their local computing capabilities over the global processing power. Advanced means of communication are one of the products of such computing. Cloud computing may function as a single-function application, a platform on which these applications can work, a group of services that provide numerous advantageous computing resources, and the potential to save a great quantity of data. The incorporation of cloud computing and the media processing is therefore quit natural selection for both of the areas, and therefore comes out the media cloud.

Technology plays an important role in helping organizations control quality and costs, and take advantage of opportunities in a highly competitive and increasingly complex business environment. Cloud computing offers greater access to computing power, storage, software, and remote data centres thro ugh the web. This research aims to confirm the factors to be considered for cloud computing adoption in Australian regional municipal governments. The research involved data from interviews with IT managers from selected regional municipal governments, and survey data from 480 IT staff across 47 regional municipal governments. The major factors to be considered for the adoption of cloud computing in regional municipal governments were identified as Internet connectivity, Internet speed, availability, reliability, data storage location, security, data sovereignty, cost, integration, data backup, provider dependability, employees’ knowledge, and transportability. The findings of this research may help managers increase their awareness about factors to be considered when regional municipal governments planning to adopt cloud computing.

Cloud computing is seen as an increasingly important enabler for improving productivity, efficiency and cost reduction. This research aims to identify factors that are perceived likely to influence the adoption of cloud computing for Australian municipal governments. The research model draws from the technology-organization-environment (TOE) framework and the diffusion of innovation (DOI) model. The research employed in-depth interviews of Australian councils' IT managers. The main factors that were identified as playing a significant role in Australian local council's adoption of cloud services were: relative advantage, compatibility, cost, technology readiness, competitive pressure.

We present NOAM, an integrated platform for the monitoring and analysis of news media content. NOAM is the data management system behind various applications and scientific studies aiming at modelling the mediasphere. The system is also intended to address the need in the AI community for platforms where various AI technologies are integrated and deployed in the real world. It combines a relational database (DB) with state of the art AI technologies, including data mining, machine learning and natural language processing. These technologies are organised in a robust, distributed architecture of collaborating modules, that are used to populate and annotate the DB. NOAM manages tens of millions of news items in multiple languages, automatically annotating them in order to enable queries based on their semantic properties. The system also includes a unified user interface for interacting with its various modules.