978 resultados para automated online-coaching
Resumo:
There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.
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The amount of financial loss from online fraud suffered by people in Western Australia has almost halved, dropping from A$16.8 million in 2014 to A$9.8 million for 2015, according to a statement this January from the state’s Attorney General and Minister for Commerce, Michael Mischin. In addition, the minister noted that losses from relationship and dating fraud have fallen by 55%, to A$4.9 million lost last year. These are both impressive claims, and at face value, there is truth to the statistics. Both assertions are based on data received by WA’s Scamnet, which is the public interface between consumer protection and citizens. While it is good to see a reduction in the number of losses overall, particularly to relationship and dating fraud, it is highly unlikely that the statistics tell the full story.
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On a first reading of this article, one is struck by the complexity of language the authors use. Those of us who write in the social sciences and cultural studies are torn between the tenets of parsimony as present in the discourses of the natural sciences and the dangers of undertheorising our work. One assumes that there is a point that would be a happy medium. Finding that point often seems to be elusive. That said, I find the idea of linguistic sophistication to be appealing and in this sense the authors here have attempted to introduce new language into the world of sports coaching research through a process of reconceptualisation.
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Understanding the work of Senior and Assistant Coaches in the AFL is important to better develop the next generation of performance coaches. Hence the focus of this research was to examine the knowledge, competences and learning of senior and assistant coaches in the Australian Football League. Specifically, the research sought to understand the ways in which Senior and Assistant Coaches in the AFL have come to know their “craft” with the particular aim of enhancing future coaching practice. Performance coaching is generally regarded as a cognitive activity and therefore “getting inside the heads” of AFL coaches will assist in our understanding of the complex coaching work in which they are engaged. In-depth interviews provided coaches an opportunity to reflect on their practices and how they learned their craft. Fundamental to this research was an understanding that the AFL and each club within the league be regarded as learning organizations and workplaces where learning takes place. Moreover the process of mentoring is regarded as a central learning process and a significant factor contributing to improved professional coaching practice. This applied research aims to inform coach development in Australian football, the annual review process (quality of performance) of employed coaches, and the recruitment of coaches in the AFL. Improving the quality of coaching in the AFL will, in turn, improve the performance of the players and teams, and subsequently enhance the continued development and sustainability of the game.
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This paper presents a novel crop detection system applied to the challenging task of field sweet pepper (capsicum) detection. The field-grown sweet pepper crop presents several challenges for robotic systems such as the high degree of occlusion and the fact that the crop can have a similar colour to the background (green on green). To overcome these issues, we propose a two-stage system that performs per-pixel segmentation followed by region detection. The output of the segmentation is used to search for highly probable regions and declares these to be sweet pepper. We propose the novel use of the local binary pattern (LBP) to perform crop segmentation. This feature improves the accuracy of crop segmentation from an AUC of 0.10, for previously proposed features, to 0.56. Using the LBP feature as the basis for our two-stage algorithm, we are able to detect 69.2% of field grown sweet peppers in three sites. This is an impressive result given that the average detection accuracy of people viewing the same colour imagery is 66.8%.
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Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.
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Because the worldwide demand for sperm donors is much higher than the actual supply available through fertility clinics, an informal online market has emerged for sperm donation. Very little empirical evidence exists, however, on this newly formed market and even less on the characteristics that lead to donor success. This article therefore explores the determinants of online sperm donors’ selection success, which leads to the production of offspring via informal donation. We find that donor age and income play a significant role in donor success as measured by the number of times selected, even though there is no requirement for ongoing paternal investment. Donors with less extroverted and lively personality traits who are more intellectual, shy and systematic are more successful in realizing offspring via informal donation. These results contribute to both the economic literature on human behaviour and on large-scale decision-making.
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The application of computer-aided inspection integrated with the coordinate measuring machine and laser scanners to inspect manufactured aircraft parts using robust registration of two-point datasets is a subject of active research in computational metrology. This paper presents a novel approach to automated inspection by matching shapes based on the modified iterative closest point (ICP) method to define a criterion for the acceptance or rejection of a part. This procedure improves upon existing methods by doing away with the following, viz., the need for constructing either a tessellated or smooth representation of the inspected part and requirements for an a priori knowledge of approximate registration and correspondence between the points representing the computer-aided design datasets and the part to be inspected. In addition, this procedure establishes a better measure for error between the two matched datasets. The use of localized region-based triangulation is proposed for tracking the error. The approach described improves the convergence of the ICP technique with a dramatic decrease in computational effort. Experimental results obtained by implementing this proposed approach using both synthetic and practical data show that the present method is efficient and robust. This method thereby validates the algorithm, and the examples demonstrate its potential to be used in engineering applications.
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Microvolunteering is bite-size volunteering with no commitment to repeat and minimum formality, involving short and specific actions. Online microvolunteering occurs through an internet-connected device. University students' online microvolunteering decisions were investigated using an extended theory of planned behavior (TPB) comprising attitudes and normative and control perceptions, with the additional variables of moral norm and group norm. Participants (N = 303) completed the main TPB questionnaire and 1-month follow-up survey (N = 171) assessing engagement in online microvolunteering. Results generally supported standard and additional TPB constructs predicting intention. Intention predicted behavior. The findings suggest an important role for attitudes and moral considerations in understanding what influences this increasingly popular form of online activity.
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We propose a novel, language-neutral approach for searching online handwritten text using Frechet distance. Online handwritten data, which is available as a time series (x,y,t), is treated as representing a parameterized curve in two-dimensions and the problem of searching online handwritten text is posed as a problem of matching two curves in a two-dimensional Euclidean space. Frechet distance is a natural measure for matching curves. The main contribution of this paper is the formulation of a variant of Frechet distance that can be used for retrieving words even when only a prefix of the word is given as query. Extensive experiments on UNIPEN dataset(1) consisting of over 16,000 words written by 7 users show that our method outperforms the state-of-the-art DTW method. Experiments were also conducted on a Multilingual dataset, generated on a PDA, with encouraging results. Our approach can be used to implement useful, exciting features like auto-completion of handwriting in PDAs.
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How young women engage in physical violence with other young women is an issue that raises specific concerns in both criminological literature and theories. Current theoretical explanations construct young women’s violence in one of two ways: young women are not physically violent at all, and adhere to an accepted performance of hegemonic femininity; or young women reject accepted performances of hegemonic femininity in favour of a masculine gendered performance to engage in violence successfully. This article draws on qualitative and quantitative data obtained from a structured observation and thematic analysis of 60 online videos featuring young women’s violent altercations. It argues that, contrary to this dichotomous construction, there appears to be a third way young women are performing violence, underpinned by masculine characteristics of aggression but upholding a hegemonic feminine gender performance. In making this argument, this article demonstrates that a more complex exploration and conceptualisation of young women’s violence, away from gendered constructs, is required for greater understanding of the issue.
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The legality of the operation of Google’s search engine, and its liability as an Internet intermediary, has been tested in various jurisdictions on various grounds. In Australia, there was an ultimately unsuccessful case against Google under the Australian Consumer Law relating to how it presents results from its search engine. Despite this failed claim, several complex issues were not adequately addressed in the case including whether Google sufficiently distinguishes between the different parts of its search results page, so as not to mislead or deceive consumers. This article seeks to address this question of consumer confusion by drawing on empirical survey evidence of Australian consumers’ understanding of Google’s search results layout. This evidence, the first of its kind in Australia, indicates some level of consumer confusion. The implications for future legal proceedings in against Google in Australia and in other jurisdictions are discussed.
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Background The leading causes of morbidity and mortality for people in high-income countries living with HIV are now non-AIDS malignancies, cardiovascular disease and other non-communicable diseases associated with ageing. This protocol describes the trial of HealthMap, a model of care for people with HIV (PWHIV) that includes use of an interactive shared health record and self-management support. The aims of the HealthMap trial are to evaluate engagement of PWHIV and healthcare providers with the model, and its effectiveness for reducing coronary heart disease risk, enhancing self-management, and improving mental health and quality of life of PWHIV. Methods/Design The study is a two-arm cluster randomised trial involving HIV clinical sites in several states in Australia. Doctors will be randomised to the HealthMap model (immediate arm) or to proceed with usual care (deferred arm). People with HIV whose doctors are randomised to the immediate arm receive 1) new opportunities to discuss their health status and goals with their HIV doctor using a HealthMap shared health record; 2) access to their own health record from home; 3) access to health coaching delivered by telephone and online; and 4) access to a peer moderated online group chat programme. Data will be collected from participating PWHIV (n = 710) at baseline, 6 months, and 12 months and from participating doctors (n = 60) at baseline and 12 months. The control arm will be offered the HealthMap intervention at the end of the trial. The primary study outcomes, measured at 12 months, are 1) 10-year risk of non-fatal acute myocardial infarction or coronary heart disease death as estimated by a Framingham Heart Study risk equation; and 2) Positive and Active Engagement in Life Scale from the Health Education Impact Questionnaire (heiQ). Discussion The study will determine the viability and utility of a novel technology-supported model of care for maintaining the health and wellbeing of people with HIV. If shown to be effective, the HealthMap model may provide a generalisable, scalable and sustainable system for supporting the care needs of people with HIV, addressing issues of equity of access. Trial registration Universal Trial Number (UTN) U111111506489; ClinicalTrial.gov Id NCT02178930 submitted 29 June 2014
Resumo:
This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.