1,490,929 research outputs found
Distensibility of Deformable Aortic Replicas Assessed by an Integrated In-Vitro and In-Silico Approach - DATA
No full textRaw data of the numerical simulation and video analysis of the paper "Distensibility of Deformable Aortic Replicas Assessed by an Integrated In-Vitro and In-Silico Approach"
An Ensemble-Based Decision Tree Approach for Educational Data Mining
No full textNowadays, data mining and machine learning techniques are applied to a variety of different topics (e. g., healthcare and disease, security, decision support, sentiment analysis, education, etc.). Educational data mining investigates the performance of students and gives solutions to enhance the quality of education. The aim of this study is to use different data mining and machine learning algorithms on actual data sets related to students. To this end, we apply two decision tree methods. The methods can create several simple and understandable rules . Moreover, the performance of a decision tree is optimized by using an ensemble technique named Rotation Forest algorithm. Our findings indicate that the Rotation Forest algorithm can enhance the performance of decision trees in terms of different metrics. In addition, we found that the size of tree generated by decision trees ensemble were bigger than simple ones. This means that the proposed methodology can reveal more information concerning simple rules
Constructing Survey Data. An Interactional Approach
No full textEngaging and informative, this book provides students and researchers with a pragmatic, new perspective on the process of collecting survey data. By proposing a post-positivist, interviewee-centred approach, it improves the quality and impact of survey data by emphasising the interaction between interviewer and interviewee. Extending the conventional methodology with contributions from linguistics, anthropology, cognitive studies and ethnomethodology, Gobo and Mauceri analyse the answering process in structured interviews built around questionnaires. The following key areas are explored in detail: An historical overview of survey research The process of preparing the survey and designing data collection The methods of detecting bias and improving data quality The strategies for combining quantitative and qualitative approaches The survey within global and local contexts. Incorporating the work of experts in interpersonal and intercultural relations, this book offers readers an intriguing critical perspective on survey research.Engaging and informative, this book provides students and researchers with a pragmatic, new perspective on the process of collecting survey data. By proposing a post-positivist, interviewee-centred approach, it improves the quality and impact of survey data by emphasising the interaction between interviewer and interviewee. Extending the conventional methodology with contributions from linguistics, anthropology, cognitive studies and ethnomethodology, Gobo and Mauceri analyse the answering process in structured interviews built around questionnaires. The following key areas are explored in detail: An historical overview of survey research The process of preparing the survey and designing data collection The methods of detecting bias and improving data quality The strategies for combining quantitative and qualitative approaches The survey within global and local contexts. Incorporating the work of experts in interpersonal and intercultural relations, this book offers readers an intrigu
Author Co-Citation Analysis (ACA): a powerful tool for representing implicit knowledge of scholar knowledge workers
No full textIn the last decade, knowledge has emerged as one of the most important and valuable organizational assets. Gradually this importance caused to emergence of new discipline entitled ―knowledge management‖. However one of the major challenges of knowledge management is conversion implicit or tacit knowledge to explicit knowledge. Thus Making knowledge visible so that it can be better accessed, discussed, valued or generally managed is a long-standing objective in knowledge management. Accordingly in this paper author co- citation analysis (ACA) will be proposed as an efficient technique of knowledge visualization in academia (Scholar knowledge workers)
Towards Linked Research Data: An Institutional Approach
No full textWiljes C, Jahn N, Lier F, et al. Towards Linked Research Data: An Institutional Approach. In: García Castro A, Lange C, Lord P, Stevens R, eds. 3rd Workshop on Semantic Publishing (SePublica). CEUR Workshop Proceedings. Aachen; 2013: 27-38.For Open Science to be widely adopted, a strong institutional support for scientists will be essential. Bielefeld University and the associated Center of Excellence Cognitive Interaction Technology (CITEC) have developed a platform that enables researchers to manage their publications and the underlying research data in an easy and efficient way. Following a Linked Data approach we integrate this data into a unified linked data store and interlink it with additional data sources from inside the university and outside sources like DBpedia. Based on the existing platform, a concrete case study from the domain of biology is implemented that releases optical motion tracking data of stick insect locomotion. We investigate the cost and usefulness of such a detailed, domain-specific semantic enrichment in order to evaluate whether this approach might be considered for large-scale deployment
An expert data-driven air combat maneuver model learning approach
No full textThis paper considers the problem of a learning air combat maneuver model when an expert pilot’s trajectories are given. Most studies of imitation learning require large amount of data for training and have to interact with real environments, even under uncertain dynamics of enemy aircraft. Thus, we propose a new approach to solve this problem by: (i) training an internal model that can represent future states and imitate the maneuvering of an expert using MDN-RNN and a controller and (ii) generating expert-like trajectories via a dreaming process, which imagines an engagement situation in a hypothetical environment model. This approach does not require interaction with the real environment nor a reward function for training. We demonstrate the similarity between the expert trajectory and the trajectory reconstructed by the proposed model
Learning from medical data streams: an introduction
No full textClinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference
Big Data, Big Libraries, Big Problems?: the 2014 LibTech Anti-talk?
No full textThe desire to create automatons is a familiar theme in human history, and during the age of the Enlightenment mechanical automatons became not only an “emblem of the cosmos”, but a symbol of man’s confidence that he would unlock nature’s greatest mysteries and fully harness her power. And yet only a century later, automatons had begun to represent human repression and servitude, a theme later picked up by writers of science fiction. Man’s confidence undeterred, the endgame of the modern scientific and technological mindset, or MSTM, seems to be increasingly coming into view with the rise of “information technology” in general and “Big data” in particular. Along with those who wield them, these can be seen as functioning together as a “mechanical muse” of sorts – surprisingly alluring – and, like a physical automaton can serve as a symbol – a microcosm – of what the MSTM sees (at the very least in practice) as the cosmic machine, our “final frontier”. And yet, individuals who unreflectively participate in these things – giving themselves over to them and seeking the powers afforded by the technology apart from technology’s rightful purposes – in fact yield to the same pragmatism and reductionism those wielding them are captive to. Thus, they ultimately nullify themselves philosophically, politically, and economically – their value increasingly being only the data concerning their persons, and its perceived usefulness. Likewise libraries, the time-honored place of, and symbol for, the intellectual flowering of the individual, will, insofar as they spurn the classical liberal arts (with the idea that things are intrinsically good, and in the case of humans, special as well) in favor of the alluring embrace of MSTM-driven “information technology” and Big data - unwittingly contribute to their irrelevance and demise as they find themselves increasingly less needed, valued, wanted. Likewise for the liberal arts as a whole, and in fact history itself, if the acid of a “science” untethered from what is, in fact, good (intrinsically), continues to gain strengt
Optimizing the potential of research data through an integrated data management approach: Considering research method, data life cycle, big data and linked data in an eresearch example in australian rock art
No full textThis paper looks at the current state of e-research strategies in rock art on the example of the Global Rock Art Database, global and Australian e-research communities. It examines current practice, attitudes and requirements for discipline specific research methods in an integrated data management cycle approach. Analysing qualitative and quantitative data collected between 2012 and 2018 through conversations, consultations, a cross-sectional questionnaire and a longitudinal study of the Rock Art Database, the paper compares it’s findings to previous interdisciplinary studies within e-research environments. The resulting data illustrates current practice and trends in rock art within an e-research context and aims to inform future best practice towards integrated data models digitally connecting international research data.Full Tex
Connecting dynamic vegetation models to data - an inverse perspective
No full textDynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice
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