Capping computation time and storage requirements for appearance-based localization with CAT-SLAM

Appearance-based localization is increasingly used for loop closure detection in metric SLAM systems. Since it relies only upon the appearance-based similarity between images from two locations, it can perform loop closure regardless of accumulated metric error. However, the computation time and memory requirements of current appearance-based methods scale linearly not only with the size of the environment but also with the operation time of the platform. These properties impose severe restrictions on longterm autonomy for mobile robots, as loop closure performance will inevitably degrade with increased operation time. We present a set of improvements to the appearance-based SLAM algorithm CAT-SLAM to constrain computation scaling and memory usage with minimal degradation in performance over time. The appearance-based comparison stage is accelerated by exploiting properties of the particle observation update, and nodes in the continuous trajectory map are removed according to minimal information loss criteria. We demonstrate constant time and space loop closure detection in a large urban environment with recall performance exceeding FAB-MAP by a factor of 3 at 100% precision, and investigate the minimum computational and memory requirements for maintaining mapping performance.

A human-simulated immune evolutionary computation approach

Premature convergence to local optimal solutions is one of the main difficulties when using evolutionary algorithms in real-world optimization problems. To prevent premature convergence and degeneration phenomenon, this paper proposes a new optimization computation approach, human-simulated immune evolutionary algorithm (HSIEA). Considering that the premature convergence problem is due to the lack of diversity in the population, the HSIEA employs the clonal selection principle of artificial immune system theory to preserve the diversity of solutions for the search process. Mathematical descriptions and procedures of the HSIEA are given, and four new evolutionary operators are formulated which are clone, variation, recombination, and selection. Two benchmark optimization functions are investigated to demonstrate the effectiveness of the proposed HSIEA.

A framework for mathematics graphical tasks : The influence of the graphic element on student sense making

Graphical tasks have become a prominent aspect of mathematics assessment. From a conceptual stance, the purpose of this study was to better understand the composition of graphical tasks commonly used to assess students’ mathematics understandings. Through an iterative design, the investigation described the sense making of 11–12-year-olds as they decoded mathematics tasks which contained a graphic. An ongoing analysis of two phases of data collection was undertaken as we analysed the extent to which various elements of text, graphics, and symbols influenced student sense making. Specifically, the study outlined the changed behaviour (and performance) of the participants as they solved graphical tasks that had been modified with respect to these elements. We propose a theoretical framework for understanding the composition of a graphical task and identify three specific elements which are dependently and independently related to each other, namely: the graphic; the text; and the symbols. Results indicated that although changes to the graphical tasks were minimal, a change in student success and understanding was most evident when the graphic element was modified. Implications include the need for test designers to carefully consider the graphics embedded within mathematics tasks since the elements within graphical tasks greatly influence student understanding.

An evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles with tactical and kinematic constraints

This paper presents a novel evolutionary computation approach to three-dimensional path planning for unmanned aerial vehicles (UAVs) with tactical and kinematic constraints. A genetic algorithm (GA) is modified and extended for path planning. Two GAs are seeded at the initial and final positions with a common objective to minimise their distance apart under given UAV constraints. This is accomplished by the synchronous optimisation of subsequent control vectors. The proposed evolutionary computation approach is called synchronous genetic algorithm (SGA). The sequence of control vectors generated by the SGA constitutes to a near-optimal path plan. The resulting path plan exhibits no discontinuity when transitioning from curve to straight trajectories. Experiments and results show that the paths generated by the SGA are within 2% of the optimal solution. Such a path planner when implemented on a hardware accelerator, such as field programmable gate array chips, can be used in the UAV as on-board replanner, as well as in ground station systems for assisting in high precision planning and modelling of mission scenarios.

Improving the efficiency of RFID authentication with pre-computation

Security of RFID authentication protocols has received considerable interest recently. However, an important aspect of such protocols that has not received as much attention is the efficiency of their communication. In this paper we investigate the efficiency benefits of pre-computation for time-constrained applications in small to medium RFID networks. We also outline a protocol utilizing this mechanism in order to demonstrate the benefits and drawbacks of using thisapproach. The proposed protocol shows promising results as it is able to offer the security of untraceableprotocols whilst only requiring the time comparable to that of more efficient but traceable protocols.

Basic graphics : an introduction to rudimentary graphic design

These notes were compiled from several authorities to be used for teaching and learning purposes here at QUT, with the focus on first and second year landscape architecture design studio units.

Bayesian computation via empirical likelihood

Approximate Bayesian computation has become an essential tool for the analysis of complex stochastic models when the likelihood function is numerically unavailable. However, the well-established statistical method of empirical likelihood provides another route to such settings that bypasses simulations from the model and the choices of the approximate Bayesian computation parameters (summary statistics, distance, tolerance), while being convergent in the number of observations. Furthermore, bypassing model simulations may lead to significant time savings in complex models, for instance those found in population genetics. The Bayesian computation with empirical likelihood algorithm we develop in this paper also provides an evaluation of its own performance through an associated effective sample size. The method is illustrated using several examples, including estimation of standard distributions, time series, and population genetics models.

Embedding graphic organisers in the teaching and learning of family and consumer sciences (home economics) in Kuwait

Educational reforms currently being enacted in Kuwaiti Family and Consumer Sciences (FCS) in response to contemporary demands for increased student-centred teaching and learning are challenging for FCS teachers due to their limited experience with student-centred learning tools such as Graphic Organisers (GOs). To adopt these reforms, Kuwaiti teachers require a better understanding of and competency in promoting cognitive learning processes that will maximise student-centred learning approaches. This study followed the experiences of four Grade 6 FCS Kuwaiti teachers as they undertook a Professional Development (PD) program specifically designed to advance their understanding of the use of GOs and then as they implemented what they had learned in their Grade 6 FCS classroom. The PD program developed for this study was informed by Nasseh.s competency PD model as well as Piaget and Ausubel.s cognitive theories. This model enabled an assessment and evaluation of the development of the teachers. competencies as an outcome of the PD program in terms of the adoption of GOs, in particular, and their capacity to use GOs to engage students in personalised, in-depth, learning through critical thinking and understanding. The research revealed that the PD program was influential in reforming the teachers. learning, understanding of and competency in, cognitive and visual theories of learning, so that they facilitated student-centred teaching and learning processes that enabled students to adopt and adapt GOs in constructivist learning. The implementation of five GOs - Flow Chart, Concept Maps, K-W-L Chart, Fishbone Diagram and Venn Diagram - as learning tools in classrooms was investigated to find if changes in pedagogical approach for supporting conceptual learning through cognitive information processing would reduce the cognitive work load of students and produce better learning approaches. The study as evidenced by the participant teachers. responses and classroom observations, showed a marked increase in student interest, participation, critical thought, problem solving skills, as a result of using GOs, compared to using traditional teaching and learning methods. A theoretical model was developed from the study based on the premise that teachers. knowledge of the subject, pedagogy and student learning precede the implementation of student-centred learning reform, that it plays an important role in the implementation of student-centred learning and that it brings about a change in teaching practice. The model affirmed that observed change in teaching-practice included aspects of teachers. beliefs, as well as confidence and effect on workplace and on student learning, including engagement, understanding, critical thinking and problem solving. The model assumed that change in teaching practice is inseparable from teachers. lifelong PD needs related to knowledge, understanding, skills and competency. These findings produced a set of preliminary guidelines for establishing student-centred constructivist strategies in Kuwaiti education while retaining Kuwait.s cultural uniqueness.

Children's understanding of graphic representations of quantitative data

Children are encountering more and more graphic representations of data in their learning and everyday life. Much of this data occurs in quantitative forms as different forms of measurement are incorporated into the graphics during their construction. In their formal education, children are required to learn to use a range of these quantitative representations in subjects across the school curriculum. Previous research that focuses on the use of information processing and traditional approaches to cognitive psychology concludes that the development of an understanding of such representations of data is a complex process. An alternative approach is to investigate the experiences of children as they interact with graphic representations of quantitative data in their own life-worlds. This paper demonstrates how a phenomenographic approach may be used to reveal the qualitatively different ways in which children in Australian primary and secondary education understand the phenomenon of graphic representations of quantitative data. Seven variations of the children’s understanding were revealed. These have been described interpretively in the article and confirmed through the words of the children. A detailed outcome space demonstrates how these seven variations are structurally related.

Writing the visible page : a multimodal approach to graphic devices in literary fiction

This practice-led project has two outcomes: a collection of short stories titled 'Corkscrew Section', and an exegesis. The short stories combine written narrative with visual elements such as images and typographic devices, while the exegesis analyses the function of these graphic devices within adult literary fiction. My creative writing explores a variety of genres and literary styles, but almost all of the stories are concerned with fusing verbal and visual modes of communication. The exegesis adopts the interpretive paradigm of multimodal stylistics, which aims to analyse graphic devices with the same level of detail as linguistic analysis. Within this framework, the exegesis compares and extends previous studies to develop a systematic method for analysing how the interactions between language, images and typography create meaning within multimodal literature.

Composite SaaS resource management in cloud computing using evolutionary computation

Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered through the Cloud is Software as a Service or SaaS. SaaS can be delivered in a composite form, consisting of a set of application and data components that work together to deliver higher-level functional software. SaaS is receiving substantial attention today from both software providers and users. It is also predicted to has positive future markets by analyst firms. This raises new challenges for SaaS providers managing SaaS, especially in large-scale data centres like Cloud. One of the challenges is providing management of Cloud resources for SaaS which guarantees maintaining SaaS performance while optimising resources use. Extensive research on the resource optimisation of Cloud service has not yet addressed the challenges of managing resources for composite SaaS. This research addresses this gap by focusing on three new problems of composite SaaS: placement, clustering and scalability. The overall aim is to develop efficient and scalable mechanisms that facilitate the delivery of high performance composite SaaS for users while optimising the resources used. All three problems are characterised as highly constrained, large-scaled and complex combinatorial optimisation problems. Therefore, evolutionary algorithms are adopted as the main technique in solving these problems. The first research problem refers to how a composite SaaS is placed onto Cloud servers to optimise its performance while satisfying the SaaS resource and response time constraints. Existing research on this problem often ignores the dependencies between components and considers placement of a homogenous type of component only. A precise problem formulation of composite SaaS placement problem is presented. A classical genetic algorithm and two versions of cooperative co-evolutionary algorithms are designed to now manage the placement of heterogeneous types of SaaS components together with their dependencies, requirements and constraints. Experimental results demonstrate the efficiency and scalability of these new algorithms. In the second problem, SaaS components are assumed to be already running on Cloud virtual machines (VMs). However, due to the environment of a Cloud, the current placement may need to be modified. Existing techniques focused mostly at the infrastructure level instead of the application level. This research addressed the problem at the application level by clustering suitable components to VMs to optimise the resource used and to maintain the SaaS performance. Two versions of grouping genetic algorithms (GGAs) are designed to cater for the structural group of a composite SaaS. The first GGA used a repair-based method while the second used a penalty-based method to handle the problem constraints. The experimental results confirmed that the GGAs always produced a better reconfiguration placement plan compared with a common heuristic for clustering problems. The third research problem deals with the replication or deletion of SaaS instances in coping with the SaaS workload. To determine a scaling plan that can minimise the resource used and maintain the SaaS performance is a critical task. Additionally, the problem consists of constraints and interdependency between components, making solutions even more difficult to find. A hybrid genetic algorithm (HGA) was developed to solve this problem by exploring the problem search space through its genetic operators and fitness function to determine the SaaS scaling plan. The HGA also uses the problem's domain knowledge to ensure that the solutions meet the problem's constraints and achieve its objectives. The experimental results demonstrated that the HGA constantly outperform a heuristic algorithm by achieving a low-cost scaling and placement plan. This research has identified three significant new problems for composite SaaS in Cloud. Various types of evolutionary algorithms have also been developed in addressing the problems where these contribute to the evolutionary computation field. The algorithms provide solutions for efficient resource management of composite SaaS in Cloud that resulted to a low total cost of ownership for users while guaranteeing the SaaS performance.

Sensationalizing death? Graphic disaster images in the tabloid and broadsheet press

Debates over the extent of graphic imagery of death in newspapers often suffer from generalized assertions that are based on inadequate or incomplete empirical evidence. Newspapers are believed to display death in very graphic ways, with particularly the tabloid press assumedly leading a race to the bottom. This article reports the results of a study of tabloid and broadsheet images of death from the 2010 Haiti earthquake in eight Western European and North American countries. It shows that, far from omnipresent, graphic images of death are relatively rare. While tabloids overall display a larger percentage of graphic images, this was not the case everywhere, with particularly the UK, Canada and the US displaying strong similarities between tabloids and broadsheets. In Austria, Germany, Norway and Switzerland, on the other hand, there were distinct differences between the two types. The article argues that different extents of tabloidization may account for these differences.

Graphic death in the news media : present or absent?

The debate over the absence or presence of death in public discourse has dominated death studies for some time. While the argument that death had been removed from public discourse and only existed in the private realm dominated at first, in recent years scholars have come to accept that death has moved back into public discourse. An important aspect has been the role played by the mass media. However, there has been little empirical research as to what level of death is actually visible, for example in terms of photographs. To this end, this paper examines how two German and two Australian newspapers cover death in terms of graphic photographs. By examining the number and types of photographs published during a 2-month timeframe, as well as through in-depth interviews with journalists, this paper argues that visible death is still largely absent from public discourse. Importantly, there exist differences as to what level of graphic death is acceptable between individual newspapers, as well as countries, supporting the argument that the absence/presence of death dichotomy needs to be viewed in a much more complex light.

Discussion of : constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation

This is a discussion of the journal article: "Construcing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation". The article and discussion have appeared in the Journal of the Royal Statistical Society: Series B (Statistical Methodology).