Information communication technologies (ICTs) an knowledge sharing : the case of professional accountants in Malaysia

Based on the knowledge sharing model by Nonaka (1994), this study examines the relative efficacy of various Information Communication Technologies (ICTs) applications in facilitating sharing of explicit and tacit knowledge among professional accountants in Malaysia. The results of this study indicate that ICTs, generally, facilitate all modes of knowledge sharing. Best-Practice Repositories are effective for sharing of both explicit and tacit knowledge, while internet/e-mail facilities are effective for tacit knowledge sharing. Data warehousing /mining, on the other hand, is effective in facilitating self learning through tacit-to-tacit mode and explicit-to-explicit mode. ICT facilities used mainly for office administration are ineffective for knowledge sharing purpose. The implications of the findings are
discussed.

Cloud-linked and campus-linked students’ perceptions of collaborative learning and design based learning in engineering

The aim of this paper is to analyse and present cloud- link as well as campus-linked students’ perceptions of collaborative learning and design based learning in engineering. Project oriented design based learning (PODBL) is a learning and teaching approach, where students learn through design activities while being driven by project(s). PODBL enhances cloud-linked and campus-linked students’ ability to acquire career essential skills that fulfill future industry needs. A paper-based survey is used to recognise a cohort of students' experience of collaborative learning and design based learning in engineering. The paper-based survey was given to 30 students from an engineering discipline. The quantitative analysis of the survey results shows that more than 50% of the students view collaborative learning to have a large benefit in design-based learning.

Subsuming methods: finding new optimisation opportunities in object-oriented software

The majority of existing application profiling techniques ag- gregate and report performance costs by method or call- ing context. Modern large-scale object-oriented applications consist of thousands of methods with complex calling pat- terns. Consequently, when profiled, their performance costs tend to be thinly distributed across many thousands of loca- tions with few easily identifiable optimisation opportunities. However experienced performance engineers know that there are repeated patterns of method calls in the execution of an application that are induced by the libraries, design patterns and coding idioms used in the software. Automati- cally identifying and aggregating costs over these patterns of method calls allows us to identify opportunities to improve performance based on optimising these patterns. We have developed an analysis technique that is able to identify the entry point methods, which we call subsuming methods, of such patterns. Our ofiine analysis runs over previously collected runtime performance data structured in a calling context tree, such as produced by a large number of existing commercial and open source profilers. We have evaluated our approach on the DaCapo bench- mark suite, showing that our analysis significantly reduces the size and complexity of the runtime performance data set, facilitating its comprehension and interpretation. We also demonstrate, with a collection of case studies, that our analysis identifies new optimisation opportunities that can lead to significant performance improvements (from 20% to over 50% improvement in our case studies).

Automated analysis of performance and energy consumption for cloud applications

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected finegrained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.

Generating reusable visual notations using model transformation

Visual notations are a key aspect of visual languages. They provide a direct mapping between the intended information and set of graphical symbols. Visual notations are most often implemented using the low level syntax of programming languages which is time consuming, error prone, difficult to maintain and hardly human-centric. In this paper we describe an alternative approach to generating visual notations using byexample model transformations. In our new approach, a semantic mapping between model and view is implemented using model transformations. The notations resulting from this approach can be reused by mapping varieties of input data to their model and can be composed into different visualisations. Our approach is implemented in the CONVErT framework and has been applied to many visualisation examples. Two case studies for visualising statistical charts and visualisation of traffic data are presented in this paper. A detailed user study of our approach for reusing notations and generating visualisations has been provided that shows good reusability and general acceptance of the novel approach.

Software engineering for multi-tenancy computing challenges and implications

Multi-tenancy is a cloud computing phenomenon. Multiple instances of an application occupy and share resources from a large pool, allowing different users to have their own version of the same application running and coexisting on the same hardware but in isolated virtual spaces. In this position paper we survey the current landscape of multi-tenancy, laying out the challenges and complexity of software engineering where multi-tenancy issues are involved. Multitenancy allows cloud service providers to better utilise computing resources, supporting the development of more exible services to customers based on economy of scale, reducing overheads and infrastructural costs. Nevertheless, there are major challenges in migration from single tenant applications to multi-tenancy. These have not been fully explored in research or practice to date. In particular, the reengineering effort of multi-tenancy in Software-as-a-Service cloud applications requires many complex and important aspects that should be taken into consideration, such as security, scalability, scheduling, data isolation, etc. Our study emphasizes scheduling policies and cloud provisioning and deployment with regards to multi-tenancy issues. We employ CloudSim and MapReduce in our experiments to simulate and analyse multi-tenancy models, scenarios, performance, scalability, scheduling and reliability on cloud platforms.

Towards anomalous diffusion sources detection in a large network

Witnessing the wide spread of malicious information in large networks, we develop an efficient method to detect anomalous diffusion sources and thus protect networks from security and privacy attacks. To date, most existing work on diffusion sources detection are based on the assumption that network snapshots that reflect information diffusion can be obtained continuously. However, obtaining snapshots of an entire network needs to deploy detectors on all network nodes and thus is very expensive. Alternatively, in this article, we study the diffusion sources locating problem by learning from information diffusion data collected from only a small subset of network nodes. Specifically, we present a new regression learning model that can detect anomalous diffusion sources by jointly solving five challenges, that is, unknown number of source nodes, few activated detectors, unknown initial propagation time, uncertain propagation path and uncertain propagation time delay. We theoretically analyze the strength of the model and derive performance bounds. We empirically test and compare the model using both synthetic and real-world networks to demonstrate its performance.

Library learning spaces in the digital age

Students describe the Library as being central to their learning, offering focus and inspiration, enabling access to information and technologies, and collaboration with peers. Deakin University Library’s building redevelopment program has been integral to the Library’s re-imagined value proposition for students learning in the digital age. The introduction of new generation library and learning spaces strengthens the University’s offer to students for a brilliant education where you are and where you want to go through premium cloud and located learning experiences that are personal, engaging and relevant.

The Library’s building projects are distinctive in terms of location and the built environment, as well as the characteristics of the university campus communities. Each progressive project has brought new aspirations and challenges. Through joint research with Deakin University’s School of Architecture and Built Environment, the Library has developed a quality framework for planning and assessing library and learning spaces.

This paper will discuss the research findings to date on the quality framework and the need to continually review and assess indicators of quality in a highly dynamic digital environment. The Library’s experiences in introducing high-end multimedia provide some insights into planning for and delivering enduring value.

The next steps in exploring the question of how library spaces assist students in achieving their learning goals are introduced.

Digital TV in Australia and the USA: a cross-impact analysis of the adoption and diffusion of digital TV in Australia and the United States

The paper examines the adoption and diffusion of Digital Television (DTV) in Australia and the United States, identifying historical, technical, regulatory, marketing, and other commonalities and differences that appear to be most significant to its adoption, as both countries have experienced a 'sluggish' diffusion and adoption of DTV so far. Using library research and borrowing the cross-impact matrix method from futures research, the authors develop J J events related to the various influences and groups of stakeholders that had shaped the policy making and adoption of DTV. We then carry out a comparative analysis between the two countries to make evident their impacts, strengths, and directions of influence. The authors suggest that the implementation of DTV in these two developed countries appears to be nearly identical. Even though Australian and US broadcasting models are fundamentally different, the diffusion process for DTV is primarily affected by the nature of digital technology and globalisation, two trends that may be diminishing the import of the nation-state in the technology adoption process. The paper concludes that these broader economic and technical events may have greater import to DTV's successful diffusion than do traditional, cultural, and nationalistic factors suggested in earlier comparative broadcast studies.