Creating a legal framework for copyright management of open access within the Australian academic and research sector

This chapter analyses the copyright law framework needed to ensure open access to outputs of the Australian academic and research sector such as journal articles and theses. It overviews the new knowledge landscape, the principles of copyright law, the concept of open access to knowledge, the recently developed open content models of copyright licensing and the challenges faced in providing greater access to knowledge and research outputs.

Towards coordinated multirobot missions for lunar sample collection in an unknown environment

In this paper, the recent results of the space project IMPERA are presented. The goal of IMPERA is the development of a multirobot planning and plan execution architecture with a focus on a lunar sample collection scenario in an unknown environment. We describe the implementation and verification of different modules that are integrated into a distributed system architecture. The modules include a mission planning approach for a multirobot system and modules for task and skill execution within a lunar use-case scenario. The skills needed for the test scenario include cooperative exploration and mapping strategies for an unknown environment, the localization and classification of sample containers using a novel approach of semantic perception, and the skill of transporting sample containers to a collection point using a mobile manipulation robot. Additionally, we present our approach of a reliable communication framework that can deal with communication loss during the mission. Several modules are tested within several experiments in the domain of planning and plan execution, communication, coordinated exploration, perception, and object transportation. An overall system integration is tested on a mission scenario experiment using three robots.

Reframe QUT's evaluation framework : a case study of planning, policy and positioning leading to educational best practice

Every university in Australia has a set of policies that guide the institution in its educational practices, however, the policies are often developed in isolation to each other. Now imagine a space where policies are evidence-based, refined annually, cohesively interrelated, and meet stakeholders’ needs. Is this happenstance or the result of good planning? Culturally, Queensland University of Technology (QUT) is a risk-averse institution that takes pride in its financial solvency and is always keen to know “how are we going?” With a twenty-year history of annual reporting that assures the quality of course performance through multiple lines of evidence, QUT’s Learning and Teaching Unit went one step further and strategically aligned a suite of policies that take into consideration the needs of their stakeholders, collaborate with other areas across the institution and use multiple lines of evidence to inform curriculum decision-making. In QUT’s experience, strategic planning can lead to policy that is designed to meet stakeholders’ needs, not manage them; where decision-making is supported by evidence, not rhetoric; where all feedback is incorporated, not ignored; and where policies are cohesively interrelated, not isolated. While many may call this ‘policy nirvana’, QUT has positioned itself to demonstrate good educational practice through Reframe, its evaluation framework. In this case, best practice was achieved through the application of a theory of change and a design-led logic model that allows for transition to other institutions with different cultural specificity. The evaluation approach follows Seldin’s (2003) notion to offer depth and breadth to the evaluation framework along with Berk’s (2005) concept of multiple lines of evidence. In summary, this paper offers university executives, academics, planning and quality staff an opportunity to understand the critical steps that lead to strategic planning and design of evidence-based educational policy that positions a university for best practice in learning and teaching.

A framework for qualitative analysis of focus group data in information systems

Focus groups are a popular qualitative research method for information systems researchers. However, compared with the abundance of research articles and handbooks on planning and conducting focus groups, surprisingly, there is little research on how to analyse focus group data. Moreover, those few articles that specifically address focus group analysis are all in fields other than information systems, and offer little specific guidance for information systems researchers. Further, even the studies that exist in other fields do not provide a systematic and integrated procedure to analyse both focus group ‘content’ and ‘interaction’ data. As the focus group is a valuable method to answer the research questions of many IS studies (in the business, government and society contexts), we believe that more attention should be paid to this method in the IS research. This paper offers a systematic and integrated procedure for qualitative focus group data analysis in information systems research.

Internet-wide scanning taxonomy and framework

Industrial control systems (ICS) have been moving from dedicated communications to switched and routed corporate networks, making it probable that these devices are being exposed to the Internet. Many ICS have been designed with poor or little security features, making them vulnerable to potential attack. Recently, several tools have been developed that can scan the internet, including ZMap, Masscan and Shodan. However, little in-depth analysis has been done to compare these Internet-wide scanning techniques, and few Internet-wide scans have been conducted targeting ICS and protocols. In this paper we present a Taxonomy of Internet-wide scanning with a comparison of three popular network scanning tools, and a framework for conducting Internet-wide scans.

A framework for analysing creative activity within puzzle-game play experiences

This thesis is an analyzing creative processes that can be fostered through computer gaming. Outcomes from the research build on our knowledge of how computer games foster creative thinking. The research proposes guidelines that build upon our understanding of the relationship between the creative processes that players undertake during a game and the components of the game that allow these processes to occur. These guidelines may be used in the game design process to better facilitate creative gameplay activity. A significant research contribution is the ability to create games that facilitate creative thinking through engaging interactions with technology.

The effect of autonomy transparency in human-robot interactions : a preliminary study on operator cognitive workload and situation awareness in multiple heterogeneous UAV management

The autonomous capabilities in collaborative unmanned aircraft systems are growing rapidly. Without appropriate transparency, the effectiveness of the future multiple Unmanned Aerial Vehicle (UAV) management paradigm will be significantly limited by the human agent’s cognitive abilities; where the operator’s CognitiveWorkload (CW) and Situation Awareness (SA) will present as disproportionate. This proposes a challenge in evaluating the impact of robot autonomous capability feedback, allowing the human agent greater transparency into the robot’s autonomous status - in a supervisory role. This paper presents; the motivation, aim, related works, experiment theory, methodology, results and discussions, and the future work succeeding this preliminary study. The results in this paper illustrates that, with a greater transparency of a UAV’s autonomous capability, an overall improvement in the subjects’ cognitive abilities was evident, that is, with a confidence of 95%, the test subjects’ mean CW was demonstrated to have a statistically significant reduction, while their mean SA was demonstrated to have a significant increase.

Locally weighted learning methods for non-rigid robot control

This thesis develops a novel approach to robot control that learns to account for a robot's dynamic complexities while executing various control tasks using inspiration from biological sensorimotor control and machine learning. A robot that can learn its own control system can account for complex situations and adapt to changes in control conditions to maximise its performance and reliability in the real world. This research has developed two novel learning methods, with the aim of solving issues with learning control of non-rigid robots that incorporate additional dynamic complexities. The new learning control system was evaluated on a real three degree-of-freedom elastic joint robot arm with a number of experiments: initially validating the learning method and testing its ability to generalise to new tasks, then evaluating the system during a learning control task requiring continuous online model adaptation.

Improving robot vision models for object detection through interaction

We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.

Teleoperation of a 7 DOF humanoid robot arm using human arm accelerations and EMG signals

We present our work on tele-operating a complex humanoid robot with the help of bio-signals collected from the operator. The frameworks (for robot vision, collision avoidance and machine learning), developed in our lab, allow for a safe interaction with the environment, when combined. This even works with noisy control signals, such as, the operator’s hand acceleration and their electromyography (EMG) signals. These bio-signals are used to execute equivalent actions (such as, reaching and grasping of objects) on the 7 DOF arm.