Supporting shape reinterpretation with eye tracking

It has been argued that reinterpretation is an essential process in design generation and idea exploration. However, computational design tools, such as computer-aided design systems, offer poor support for shape reinterpretation, and as such are not well suited to ideation in conceptual design. One of the key difficulties in implementing computational systems that support shape reinterpretation is the issue of interface – how can a user intuitively guide a system with respect to their interpretation of a designed shape? In this paper, a software prototype is presented that uses an eye tracking interface to support reinterpretation of shapes according to recognised subshapes. The prototype is based on eye tracking studies, and uses gaze data and user input to restructure designed shapes so that they afford manipulation according to users’ interpretations

Ship money during the personal rule of Charles I : politics, ideology and the law 1634-1640.

This thesis focuses on ship money as a key to examining politics, ideology and the law during the Personal Rule of Charles I. The work is divided into five chapters, with an Introduction and a Conclusion. The first chapter traces the origins of ship money, places it in the context of the government's foreign arid domestic concerns, and analyses the first writ of 1634. The second chapter examines the development of national ship money from the Privy Council's perspective of "new counsels", as the great experiment In prerogative taxation and as a key to the relationship between central and local governors. This is followed by discussion of the impact of ship money, emphasising the wide variety of response it evoked and the ways In which this response changed, placing this in turn against a background of debate about the nature of authority In the state. The contemporary accounts for ship money are used as the statistical base to illustrate changing response to the service arid the political implications of this. The fourth chapter is concerned with opposition to ship money, which was shaped by the continued absence of a parliament during the Personal Rule. All of the different forms this opposition took, varying from the court to parish level served to strengthen the importance of law and tradition in English society. It is argued that the experience of ship money substantiated fears that there was a conspiracy to subvert the fundamental laws and religion of England, and contributed significantly to a growth in -political consciousness across the country and down the social scale. The fifth chapter covers the period from the summer of 1639 until the abolition of ship money by the Long Parliament, when politics without parliament collapsed in spite of the efforts of the government to unite the country against the Scots

EdVee: A Visual Diagnostic and Course Design Tool for Constructive Alignment

The adoption of digital technologies in higher education offers new opportunities for student learning but also adds complexity to course design and development processes. Although practice varies across institutions and nations, a common response is that teams of people are now involved in the development and creation of blended and online courses. Members of such teams typically include instructional designers, academics, learning designers and technologists, and production teams, and courses are developed over time, often across separate locations and organisations. These factors are creating a need for tools that support the sharing of design information in distributed course design teams. This paper introduces such a course design tool, EdVee, which supports pedagogical innovation through the sharing and visualisation of constructive alignment of learning outcomes, content, learning, and teaching activities and assessment. Key concepts that underpin EdVee are drawn from systems engineering and product development. We demonstrate its value to course design through two case studies: the design of a new course and the evaluation of an existing course

In-season yield prediction using VARIwise

In-season yield prediction supports improved agronomic management and planning for crop sales and insurance contracts. Yield is currently often estimated using rules of thumb and manual boll counts. Data analytics approaches have been developed using site- and season specific multispectral satellite imagery-based correlations that require significant datasets for wider scale transferability. An alternative approach is to forecast yield using known soil-plant-atmosphere interactions in crop production models and calibrated using available field data. USQ has developed software ‘VARIwise’ to provide yield prediction throughout the season combining these models with: (i) plant parameters extracted from UAV imagery using image analysis; (ii) online soil and weather data; and (iii) on-farm management information. In the 2017/18 and 2018/19 seasons, VARIwise was evaluated at one cotton site in Goondiwindi and 16 sites in Griffith. Management zones in the field monitored using the UAV were identified from vegetation index surveys, yield maps or satellite images. Phantom 4 UAV imagery was collected monthly at each site between January and picking for calibrating the crop model. The sites had varying levels of fruit removal, hail damage and heat stress. In the 2017/18 Griffith trial, the percentage yield prediction errors were 10.2% in January, 6.0% in February, 2.5% in March, and 0.5% at picking, and in the 2018/19 Griffith trial the errors were 18.8% in January, 4.9% in February, 9.5% in March, and 10.1% at picking. In the 2018/19 Goondiwindi trial, the yield prediction percentage errors were 8.7% in February, 5.9% in March, 7.1% in April and 2.6% in May. The prediction errors at Griffith were higher in the 2018/19 season than the 2017/18 season because the sites experienced hail and heat stress that are not currently represented within the VARIwise crop model. The yield predictor will be evaluated in 2019/20 to improve performance under insect and hail damage

Shape exploration in design : formalising and supporting a transformational process

The process of sketching can support the sort of transformational thinking that is seen as essential for the interpretation and reinterpretation of ideas in innovative design. Such transformational thinking, however, is not yet well supported by computer-aided design systems. In this paper, outcomes of experimental investigations into the mechanics of sketching are described, in particular those employed by practising architects and industrial designers as they responded to a series of conceptual design tasks,. Analyses of the experimental data suggest that the interactions of designers with their sketches can be formalised according to a finite number of generalised shape rules. A set of shape rules, formalising the reinterpretation and transformations of shapes, e.g. through deformation or restructuring, are presented. These rules are suggestive of the manipulations that need to be afforded in computational tools intended to support designers in design exploration. Accordingly, the results of the experimental investigations informed the development of a prototype shape synthesis system, and a discussion is presented in which the future requirements of such systems are explored

Personalized pancreatic cancer management : a systematic review of how machine learning is supporting decision-making

This review critically analyzes how machine learning is being utilized to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, electronic searches of MEDLINE, Embase, PubMed and Cochrane Database were undertaken. Studies were assessed using the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies (CHARMS) checklist. In total 89,959 citations were retrieved. Six studies met the inclusion criteria. Three studies were Markov decision-analysis models comparing neoadjuvant therapy versus upfront surgery. Three studies predicted survival time using Bayesian modeling (n = 1), Artificial Neural Network (n = 1), and one study explored machine learning algorithms including: Bayesian Network, decision trees, nearest neighbor, and Artificial Neural Networks. The main methodological issues identified were: limited data sources which limits generalizability and potentiates bias, lack of external validation, and the need for transparency in methods of internal validation, consecutive sampling, and selection of candidate predictors. The future direction of research relies on expanding our view of the multidisciplinary team to include professionals from computing and data science with algorithms developed in conjunction with clinicians and viewed as aids, not replacement, to traditional clinical decision making

Vpliv športne identitete vrhunskih atletov na kognitivno ocenjevanje in soočanje z neuspešnostjo v športu

Previous studies have demonstrated that strong and exclusive athletic identity is a risk factor for adjustment difficulties following major sport career transitions (e.g., Cecić Erpič, Wylleman, & Zupančič, 2004; Grove, Lavallee, & Gordon, 1997). However, research investigating the influence of athletic identity on adjustment to negative events that athletes encounter more routinely is scant. This study adopted a stress perspective (Lazarus & Folkman, 1984) and qualitative method to examine the influence of athletic identity on athletes' appraisal and coping responses to underperforming. Three male and three female UK international track athletes provided accounts of their experiences of underperforming in semi-structured interviews. Athletic identity was established with the Athletic Identity Measurement Scale (AIMS; Brewer, Van Raalte, & Linder, 1993), in addition to qualitative data. Case studies were constructed and cross-case comparisons revealed that athletes with strong and exclusive athletic identity appraised underperforming as a threat to their self-identities, experienced intense emotional disturbance and implemented emotionfocused and avoidance coping. These findings suggest that the risks of over-identification with the athlete role are more widespread than is currently recognized and highlight the need for intervention programs that encourage athletes to invest in non-sport sources of identification

EdVee: A Visual Diagnostic and Course Design Tool for Constructive Alignment

The adoption of digital technologies in higher education offers new opportunities for student learning but also adds complexity to course design and development processes. Although practice varies across institutions and nations, a common response is that teams of people are now involved in the development and creation of blended and online courses. Members of such teams typically include instructional designers, academics, learning designers and technologists, and production teams, and courses are developed over time, often across separate locations and organisations. These factors are creating a need for tools that support the sharing of design information in distributed course design teams. This paper introduces such a course design tool, EdVee, which supports pedagogical innovation through the sharing and visualisation of constructive alignment of learning outcomes, content, learning, and teaching activities and assessment. Key concepts that underpin EdVee are drawn from systems engineering and product development. We demonstrate its value to course design through two case studies: the design of a new course and the evaluation of an existing course

Interpretation of geometric shapes – an eye movement study

This paper describes the first in a series of studies which seek to explore the correlation of eye movements with interpretation of geometric shapes. These studies are intended to inform the development of an eye tracking interface for computational tools to support and enhance the fluid interaction required in creative design. A common criticism of computational design tools is that they do not enable manipulation of designed shapes according to all perceived features. Instead the manipulations afforded are limited by formal structures of shapes. This research examines the potential for eye movement data to be used to recognise and make available for manipulation the perceived features in shapes. The objective of this first study is to analyse eye movement data with the intention of recognising moments in which an interpretation of shape is made. Results suggest that duration of fixation and distance between successive fixations prove to be consistent indicators of shape interpretation

Personalized prognostic bayesian network for pancreatic cancer : delivering personalized pancreatic cancer management throughout the patient journey

Background and Objectives: The aim of this study is to create the first Personalized Prognostic Bayesian Network for Pancreatic Cancer (PPBN-PC) to provide personalized predictions of 3-year or more survival time post resection. PPBN-PC’s ability to handle the dynamic nature of the care processes, with predictions evolving as more information becomes available, was assessed at the pre and post-operative stage of the patient journey. Materials and Methods: Parent nodes were identified from PubMed survival analysis studies (n=48691) and included: tumour factors, patient factors, tumour markers, inflammatory markers, neoadjuvant therapy, pathology and adjuvant therapy. Variables underwent a two-stage weighting process to summarise both the weight of the evidence against conflicting findings and a normalized weighting process placing each variable’s weighting within the entirety of the existing body of evidence. Priors for the model were calculated using the normalized weight for each variable as the weighted mean of the TNormal distribution for the corresponding parent node. Results: The PPBN-PC was validated against a dataset of 365 patients who presented to a tertiary referral centre with potentially resectable pancreatic cancer. Model performance measured by Area Under the Curve (AUC) ranged from 0.94 (P-value 0.002; 95% CI 0.859-1.000) for 0 missing data points to AUC 0.74 (P-value 0.000; 95% CI 0.660-0.809) accepting more than 4 missing data points in the validation dataset, for accuracy of pre-operative predictions. PPBN-PC performance for prognostic updating based on post-operatively available information ranged from AUC 0.97 (P-value 0.000; 95% CI 0.908-1.000) for 0 missing data points in pre and post-operative validation dataset to AUC 0.75 (P-value 0.000; 95% CI 0.655-0.838) accepting more than 4 missing data points in the pre and up to and including 2 missing data points in the post-operative validation dataset. The latter was the only point at which AUC fell below 0.80. Validated against every other combination of missing pre and post-operative data points PPBN-PC maintained an AUC greater than 0.8 (range 0.97-0.80) with P-value consistently below 0.001. Conclusion: This marks an important step towards achieving the delivery of precision medicine, as the next step will be to incorporated genomic data into the model hence combining genetic, pathology and clinical data, creating a vehicle to deliver personalized precision medicine