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.

Managing and learning with multiple models: Objectives and optimization algorithms

The quality of environmental decisions should be gauged according to managers' objectives. Management objectives generally seek to maximize quantifiable measures of system benefit, for instance population growth rate. Reaching these goals often requires a certain degree of learning about the system. Learning can occur by using management action in combination with a monitoring system. Furthermore, actions can be chosen strategically to obtain specific kinds of information. Formal decision making tools can choose actions to favor such learning in two ways: implicitly via the optimization algorithm that is used when there is a management objective (for instance, when using adaptive management), or explicitly by quantifying knowledge and using it as the fundamental project objective, an approach new to conservation.This paper outlines three conservation project objectives - a pure management objective, a pure learning objective, and an objective that is a weighted mixture of these two. We use eight optimization algorithms to choose actions that meet project objectives and illustrate them in a simulated conservation project. The algorithms provide a taxonomy of decision making tools in conservation management when there is uncertainty surrounding competing models of system function. The algorithms build upon each other such that their differences are highlighted and practitioners may see where their decision making tools can be improved. © 2010 Elsevier Ltd.

Understanding expectations with multiple controllers in an augmented reality videogame

Player experiences and expectations are connected. The presumptions players have about how they control their gameplay interactions may shape the way they play and perceive videogames. A successfully engaging player experience might rest on the way controllers meet players' expectations. We studied player interaction with novel controllers on the Sony PlayStation Wonderbook, an augmented reality (AR) gaming system. Our goal was to understand player expectations regarding game controllers in AR game design. Based on this preliminary study, we propose several interaction guidelines for hybrid input from both augmented reality and physical game controllers

A framework for identification of similarities between multiple algorithms

This thesis in software engineering presents a novel automated framework to identify similar operations utilized by multiple algorithms for solving related computing problems. It provides a new effective solution to perform multi-application based algorithm analysis, employing fundamentally light-weight static analysis techniques compared to the state-of-art approaches. Significant performance improvements are achieved across the objective algorithms through enhancing the efficiency of the identified similar operations, targeting discrete application domains.

Interactive relationships among multiple dimensions of professional commitment: Implications for stress outcomes in lawyers

The purpose of this study was to examine the main and interactive effects of four dimensions of professional commitment on strain (i.e., depression, anxiety, perceived health status, and job dissatisfaction) for a sample of 176 law professionals. The study utilized a two-wave design in which professional commitment and strain were measured at Time 1 and strain was measured again at Time 2 (T2), 2 months later. A significant two-way interaction indicated that high affective commitment was related to less T2 job dissatisfaction only for lawyers with low accumulated costs. A significant four-way interaction indicated that high affective professional commitment was only related to fewer symptoms of T2 anxiety for lawyers with high normative professional commitment and both low limited alternatives and accumulated costs. A similar pattern of results emerged in regard to T2 perceived health status. The theoretical and practical implications of these results for career counselors are discussed.

Inventory of pedagogy and practice: Confirmatory factor analysis of multiple facets of teaching

Student perceptions of teaching have often been used in tertiary education for evaluation purposes. However, there is a paucity of research on the validity, reliability, and applicability of instruments that cover a wide range of student perceptions of pedagogies and practices in high school settings for descriptive purposes. The study attempts to validate an inventory of pedagogy and practice (IPP) that provides researchers and practitioners with a psychometrically sound instrument that covers the most salient factors related to teaching. Using a sample of students (N = 1515) from 39 schools in Singapore, 14 factors about teaching in English lessons from the students’ perspective were tested with confirmatory factor analysis (classroom task goal, structure and clarity, curiosity and interest, positive class climate, feedback, questioning, quality homework, review of students’ work, conventional teaching, exam preparation, behaviour management, maximizing learning time, student-centred pedagogy, and subject domain teaching). Two external criterion factors were used to further test the IPP factor structure. The inventory will enable teachers to understand more about their teaching and researchers to examine how teaching may be related to learning outcomes.

Multiple perspectives on quality of life for residents with dementia in long term care facilities: Protocol for a comprehensive Australian study

Background Dementia is a chronic illness without cure or effective treatment, which results in declining mental and physical function and assistance from others to manage activities of daily living. Many people with dementia live in long term care facilities, yet research into their quality of life (QoL) was rare until the last decade. Previous studies failed to incorporate important variables related to the facility and care provision or to look closely at the daily lives of residents. This paper presents a protocol for a comprehensive, multi-perspective assessment of QoL of residents with dementia living in long term care in Australia. A secondary aim is investigating the effectiveness of self-report instruments for measuring QoL. Methods The study utilizes a descriptive, mixed methods design to examine how facility, care staff, and resident factors impact QoL. Over 500 residents with dementia from a stratified, random sample of 53 facilities are being recruited. A sub-sample of 12 residents is also taking part in qualitative interviews and observations. Conclusions This national study will provide a broad understanding of factors underlying QoL for residents with dementia in long term care. The present study uses a similar methodology to the US-based Collaborative Studies of Long Term Care (CS-LTC) Dementia Care Study, applying it to the Australian setting.

The origin of a large (>3km) maar volcano by coalescence of multiple shallow craters: Lake Purrumbete maar, southeastern Australia

Lake Purrumbete maar is located in the intraplate, monogenetic Newer Volcanics Province in southeastern Australia. The extremely large crater of 3000. m in diameter formed on an intersection of two fault lines and comprises at least three coalesced vents. The evolution of these vents is controlled by the interaction of the tectonic setting and the properties of both hard and soft rock aquifers. Lithics in the maar deposits originate from country rock formations less than 300. m deep, indicating that the large size of the crater cannot only be the result of the downwards migration of the explosion foci in a single vent. Vertical crater walls and primary inward dipping beds evidence that the original size of the crater has been largely preserved. Detailed mapping of the facies distributions, the direction of transport of base surges and pyroclastic flows, and the distribution of ballistic block fields, form the basis for the reconstruction of the complex eruption history,which is characterised by alternations of the eruption style between relatively dry and wet phreatomagmatic conditions, and migration of the vent location along tectonic structures. Three temporally separated eruption phases are recognised, each starting at the same crater located directly at the intersection of two local fault lines. Activity then moved quickly to different locations. A significant volcanic hiatus between two of the three phases shows that the magmatic system was reactivated. The enlargement of especially the main crater by both lateral and vertical growth led to the interception of the individual craters and the formation of the large circular crater. Lake Purrumbete maar is an excellent example of how complicated the evolution of large, seemingly simple, circular maar volcanoes can be, and raises the question if these systems are actually monogenetic.

Engaging a multiple-track approach to building capacity for 21st Century engineering, opportunities and challenges for rapid curriculum renewal

‘Complexity’ is a term that is increasingly prevalent in conversations about building capacity for 21st Century professional engineers. Society is grappling with the urgent and challenging reality of accommodating seven billion people, meeting needs and innovating lifestyle improvements in ways that do not destroy atmospheric, biological and oceanic systems critical to life. Over the last two decades in particular, engineering educators have been active in attempting to build capacity amongst professionals to deliver ‘sustainable development’ in this rapidly changing global context. However curriculum literature clearly points to a lack of significant progress, with efforts best described as ad hoc and highly varied. Given the limited timeframes for action to curb environmental degradation proposed by scientists and intergovernmental agencies, the authors of this paper propose it is imperative that curriculum renewal towards education for sustainable development proceeds rapidly, systemically, and in a transformational manner. Within this context, the paper discusses the need to consider a multiple track approach to building capacity for 21st Century engineering, including priorities and timeframes for undergraduate and postgraduate curriculum renewal. The paper begins with a contextual discussion of the term complexity and how it relates to life in the 21st Century. The authors then present a whole of system approach for planning and implementing rapid curriculum renewal that addresses the critical roles of several generations of engineering professionals over the next three decades. The paper concludes with observations regarding engaging with this approach in the context of emerging accreditation requirements and existing curriculum renewal frameworks.

Pull the plug or take the plunge: Multiple opportunities and the speed of venturing decisions in the Australian mining industry

Effectively capturing opportunities requires rapid decision-making. We investigate the speed of opportunity evaluation decisions by focusing on firms' venture termination and venture advancement decisions. Experience, standard operating procedures, and confidence allow firms to make opportunity evaluation decisions faster; we propose that a firm's attentional orientation, as reflected in its project portfolio, limits the number of domains in which these speed-enhancing mechanisms can be developed. Hence firms' decision speed is likely to vary between different types of decisions. Using unique data on 3,269 mineral exploration ventures in the Australian mining industry, we find that firms with a higher degree of attention toward earlier-stage exploration activities are quicker to abandon potential opportunities in early development but slower to do so later, and that such firms are also slower to advance on potential opportunities at all stages compared to firms that focus their attention differently. Market dynamism moderates these relationships, but only with regard to initial evaluation decisions. Our study extends research on decision speed by showing that firms are not necessarily fast or slow regarding all the decisions they make, and by offering an opportunity evaluation framework that recognizes that decision makers can, in fact often do, pursue multiple potential opportunities simultaneously.

Automated 3D mapping & shape analysis of the lateral ventricles via fluid registration of multiple surface-based atlases

We developed and validated a new method to create automated 3D parametric surface models of the lateral ventricles, designed for monitoring degenerative disease effects in clinical neuroscience studies and drug trials. First we used a set of parameterized surfaces to represent the ventricles in a manually labeled set of 9 subjects' MRIs (atlases). We fluidly registered each of these atlases and mesh models to a set of MRIs from 12 Alzheimer's disease (AD) patients and 14 matched healthy elderly subjects, and we averaged the resulting meshes for each of these images. Validation experiments on expert segmentations showed that (1) the Hausdorff labeling error rapidly decreased, and (2) the power to detect disease-related alterations monotonically improved as the number of atlases, N, was increased from 1 to 9. We then combined the segmentations with a radial mapping approach to localize ventricular shape differences in patients. In surface-based statistical maps, we detected more widespread and intense anatomical deficits as we increased the number of atlases, and we formulated a statistical stopping criterion to determine the optimal value of N. Anterior horn anomalies in Alzheimer's patients were only detected with the multi-atlas segmentation, which clearly outperformed the standard single-atlas approach.

Labeling white matter tracts in hardi by fusing multiple tract atlases with applications to genetics

Accurate identification of white matter structures and segmentation of fibers into tracts is important in neuroimaging and has many potential applications. Even so, it is not trivial because whole brain tractography generates hundreds of thousands of streamlines that include many false positive fibers. We developed and tested an automatic tract labeling algorithm to segment anatomically meaningful tracts from diffusion weighted images. Our multi-atlas method incorporates information from multiple hand-labeled fiber tract atlases. In validations, we showed that the method outperformed the standard ROI-based labeling using a deformable, parcellated atlas. Finally, we show a high-throughput application of the method to genetic population studies. We use the sub-voxel diffusion information from fibers in the clustered tracts based on 105-gradient HARDI scans of 86 young normal twins. The whole workflow shows promise for larger population studies in the future.

Automatic population HARDI white matter tract clustering by label fusion of multiple tract atlases

Automatic labeling of white matter fibres in diffusion-weighted brain MRI is vital for comparing brain integrity and connectivity across populations, but is challenging. Whole brain tractography generates a vast set of fibres throughout the brain, but it is hard to cluster them into anatomically meaningful tracts, due to wide individual variations in the trajectory and shape of white matter pathways. We propose a novel automatic tract labeling algorithm that fuses information from tractography and multiple hand-labeled fibre tract atlases. As streamline tractography can generate a large number of false positive fibres, we developed a top-down approach to extract tracts consistent with known anatomy, based on a distance metric to multiple hand-labeled atlases. Clustering results from different atlases were fused, using a multi-stage fusion scheme. Our "label fusion" method reliably extracted the major tracts from 105-gradient HARDI scans of 100 young normal adults. © 2012 Springer-Verlag.

Predicting white matter integrity from multiple common genetic variants

Several common genetic variants have recently been discovered that appear to influence white matter microstructure, as measured by diffusion tensor imaging (DTI). Each genetic variant explains only a small proportion of the variance in brain microstructure, so we set out to explore their combined effect on the white matter integrity of the corpus callosum. We measured six common candidate single-nucleotide polymorphisms (SNPs) in the COMT, NTRK1, BDNF, ErbB4, CLU, and HFE genes, and investigated their individual and aggregate effects on white matter structure in 395 healthy adult twins and siblings (age: 20-30 years). All subjects were scanned with 4-tesla 94-direction high angular resolution diffusion imaging. When combined using mixed-effects linear regression, a joint model based on five of the candidate SNPs (COMT, NTRK1, ErbB4, CLU, and HFE) explained ∼ 6% of the variance in the average fractional anisotropy (FA) of the corpus callosum. This predictive model had detectable effects on FA at 82% of the corpus callosum voxels, including the genu, body, and splenium. Predicting the brain's fiber microstructure from genotypes may ultimately help in early risk assessment, and eventually, in personalized treatment for neuropsychiatric disorders in which brain integrity and connectivity are affected.

Multiple stages classification of Alzheimer’s disease based on structural brain networks using generalized low rank approximations (GLRAM)

To classify each stage for a progressing disease such as Alzheimer’s disease is a key issue for the disease prevention and treatment. In this study, we derived structural brain networks from diffusion-weighted MRI using whole-brain tractography since there is growing interest in relating connectivity measures to clinical, cognitive, and genetic data. Relatively little work has usedmachine learning to make inferences about variations in brain networks in the progression of the Alzheimer’s disease. Here we developed a framework to utilize generalized low rank approximations of matrices (GLRAM) and modified linear discrimination analysis for unsupervised feature learning and classification of connectivity matrices. We apply the methods to brain networks derived from DWI scans of 41 people with Alzheimer’s disease, 73 people with EMCI, 38 people with LMCI, 47 elderly healthy controls and 221 young healthy controls. Our results show that this new framework can significantly improve classification accuracy when combining multiple datasets; this suggests the value of using data beyond the classification task at hand to model variations in brain connectivity.