Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Personality type and self-directed learning

This correlational study was designed to investigate the relationship between self-directed learning and personality type. A sample of 133 graduate and undergraduate education students completed the MBTI and the SDLRS. Two hypotheses were examined: (a) scores on the intuitive scale will account for a significant amount of the variance in the prediction of selfdirected learning readiness and, (b) scores on the introverted scale will account for a significant amount of the variance in self-directed learning readiness. Stepwise multiple regression analyses indicated that psychological type accounts for 28% of the variance in self-directed learning. Support for the first hypothesis was found with 15% of the variance in selfdirected learning accounted for by intuition. The second hypothesis was not supported. Introversion accounted for 13% of the variance but in a negative manner. Results of this study indicate that personality type does influence the ability of the learner to be self-directed in studies. These findings add another dimension for the adult educator to consider when attempting to develop self-directedness in learners.

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

e-learning services provision and management

With the rapid development of information technology, learners demand effective personalised learning support, which imposes a new learning paradigm in learning content management. Standards as well as best practice in industry and research community have taken place to address the paradigm shift. With respect to this trend, it is recognised that finding learning content which meet personal learning requirements remains challenging. This paper describes a model of e-learning services provision which integrates the best practice in e-learning and Web services technology so that learning content management is capable of supporting applications of learning services.

Mobile Assisted Language Learning : A Literature Review

Mobile assisted language learning (MALL) is a subarea of the growing field of mobile learning (mLearning) research which increasingly attracts the attention of scholars. This study provides a systematic review of MALL research within the specific area of second language acquisition during the period 2007 - 2012 in terms of research approaches, methods, theories and models, as well as results in the form of linguistic knowledge and skills. The findings show that studies of mobile technology use in different aspects of language learning support the hypothesis that mobile technology can enhance learners’ second language acquisition. However, most of the reviewed studies are experimental, small-scale, and conducted within a short period of time. There is also a lack of cumulative research; most theories and concepts are used only in one or a few papers. This raises the issue of the reliability of findings over time, across changing technologies, and in terms of scalability. In terms of gained linguistic knowledge and skills, attention is primarily on learners’ vocabulary acquisition, listening and speaking skills, and language acquisition in more general terms.

Characterization of texture in image of skin lesions by support vector machine

Due to the increased incidence of skin cancer, computational methods based on intelligent approaches have been developed to aid dermatologists in the diagnosis of skin lesions. This paper proposes a method to classify texture in images, since it is an important feature for the successfully identification of skin lesions. For this is defined a feature vector, with the fractal dimension of images through the box-counting method (BCM), which is used with a SVM to classify the texture of the lesions in to non-irregular or irregular. With the proposed solution, we could obtain an accuracy of 72.84%. © 2012 AISTI.

Good practice guide in learning and teaching

Once again this publication is produced to celebrate and promote good teaching and learning support and to offer encouragement to those imaginative and innovative staff who continue to wish to challenge students to learn to maximum effect. It is hoped that others will pick up some good ideas from the articles contained in this volume. We have again changed our approach for this 2007/08 edition (our fifth) of the Aston Business School Good Practice Guide. As before, some contributions were selected from those identifying interesting best practice on their Annual Module Reflection Forms in 2006/2007. Brookes? contribution this year is directly from her annual reflection. Other contributors received HELM (Research Centre in Higher Education Learning and Management) small research grants in 2006/2007. Part of the conditions were for them to write an article for this publication. We have also been less tight on the length of the articles this year. Some contributions are, therefore, on the way to being journal articles. HELM will be working with these authors to help develop these for publication. Looking back over the last five years it is brilliant to see how many different people have contributed over the years and, therefore, how much innovative learning and teaching work has been taking place in ABS over this time. In the first edition we were just pleased for people to write a few pages on their teaching. Now things have changed dramatically. The majority of the articles are grounded in empirical research (some funded by HELM small research grants) and Palmer?s article was produced as part of the University?s Postgraduate Certificate in Learning and Teaching. Most encouraging of all, four of this year?s articles have since been developed further and submitted to refereed journals. We await news of publication as we go to press. It is not surprising that how to manage large groups still remains a central theme of the articles, ABS has a large and still growing student body. Essex and Simpson have looked at trying to encourage students to attend taught sessions, on the basis that there is a strong correlation between attendance and higher performance. Their findings are forming the platform of a further study currently being carried out in the Undergraduate Programme. A number of the other articles concentrate on trying to encourage students to engage with study in an innovative way. This is particularly obvious in Shaw?s work. Everyone who has been around campus lately has had evidence that the students on Duncan?s modules have clearly been inspired. I found myself, for example, playing golf in the student dining room as part of this initiative! The articles by Jarzabkowski & Guilietti and Ho involved much larger surveys. This is another first for the Good Practice Guide and marks the first step on what will clearly be larger research efforts for these authors in this area. We look forward to the journal publications which will result from this work. The last articles are the result of HELM?s hosting of the national conference of the Higher Education Academy?s Business, Management, Accounting and Finance (BMAF) Subject Centre Conference in May 2007. Belal and Foster have written about their impressions of the Conference and Andrews has included the paper she gave. The papers on employability and widening participation are the centre of HELM?s current work. In the second volume we mentioned the launch of the School?s Research Centre in Higher Education Learning and Management (HELM). Since then HELM has stimulated a lot of activity across the School (and University) particularly linking research and teaching. A list of the HELM seminars for 2007/2008 is listed as Appendix 1 of this publication. Further details can be obtained from Catherine Foster (c.s.foster@aston.ac.uk), who coordinates the HELM seminars. We have also been working on a list of target journals to guide ABS staff who wish to publish in this area. These are included as Appendix 2 of this publication. May I thank the contributors for taking time out of their busy schedules to write the articles and to Julie Green, the Quality Manager, for putting the varying diverse approaches into a coherent and publishable form and for agreeing to fund the printing of this volume.

Good practice guide in learning and teaching

Once again this publication is produced to celebrate and promote good teaching and learning support and to offer encouragement to those imaginative and innovative staff who continue to wish to challenge students to learn to maximum effect. It is hoped that others will pick up some good ideas from the articles contained in this volume. We have again changed our approach for this 2006/07 edition (our fourth) of the Aston Business School Good Practice Guide. As before, some contributions were selected from those identifying interesting best practice on their Annual Module reflection forms in 2005/2006. Other contributors received HELM (Research Centre in Higher Education Learning and Management) small research grants in 2005/2006. Part of the conditions were for them to write an article for this publication. We have also been less tight on the length of the articles this year. Some contributions are, therefore, on the way to being journal articles. HELM will be working with these authors to help develop these for publication. The themes covered in this year?s articles are all central to the issues faced by those providing HE teaching and learning opportunities in the 21st Century. Specifically this is providing support and feedback to students in large classes, embracing new uses of technology to encourage active learning and addressing cultural issues in a diverse student population. Michael Grojean and Yves Guillaume used Blackboard™ to give a more interactive learning experience and improve feedback to students. It would be easy for other staff to adopt this approach. Patrick Tissington and Qin Zhou (HELM small research grant holders) were keen to improve the efficiency of student support, as does Roger McDermott. Celine Chew shares her action learning project, completed as part of the Aston University PG Certificate in Teaching and Learning. Her use of Blackboard™ puts emphasis on the learner having to do something to help them meet the learning outcomes. This is what learning should be like, but many of our students seem used to a more passive learning experience, so much needs to be done on changing expectations and cultures about learning. Regina Herzfeldt also looks at cultures. She was awarded a HELM small research grant and carried out some significant new research on cultural diversity in ABS and what it means for developing teaching methods. Her results fit in with what many of us are experiencing in practice. Gina leaves us with some challenges for the future. Her paper certainly needs to be published. This volume finishes with Stuart Cooper and Matt Davies reflecting on how to keep students busy in lectures and Pavel Albores working with students on podcasting. Pavel?s work, which was the result of another HELM small research grant, will also be prepared for publication as a journal article. The students learnt more from this work that any formal lecture and Pavel will be using the approach again this year. Some staff have been awarded HELM small research grants in 2006/07 and these will be published in the next Good Practice Guide. In the second volume we mentioned the launch of the School?s Research Centre in Higher Education Learning and Management (HELM). Since then HELM has stimulated a lot of activity across the School (and University) particularly linking research and teaching. A list of the HELM seminars for 2006/2007 is listed as Appendix 1 of this publication. Further details can be obtained from Catherine Foster (c.s.foster@aston.ac.uk), who coordinates the HELM seminars. For 2006 and 2005 HELM listed, 20 refereed journal articles, 7 book chapters, 1 published conference papers, 20 conference presentations, two official reports, nine working papers and £71,535 of grant money produced in this research area across the School. I hope that this shows that reflection on learning is alive and well in ABS. We have also been working on a list of target journals to guide ABS staff who wish to publish in this area. These are included as Appendix 2 of this publication. May I thank the contributors for taking time out of their busy schedules to write the articles and to Julie Green, the Quality Manager, for putting the varying diverse approaches into a coherent and publishable form and for agreeing to fund the printing of this volume.

Good practice guide in learning and teaching

Once again this publication is produced to celebrate and promote good teaching and learning support and to offer encouragement to those imaginative and innovative staff who continue to wish to challenge students to learn to maximum effect. It is hoped that others will pick up some good ideas from the articles contained in this volume. We had changed our editorial approach in drawing together the articles for this 2005/6 edition (our third) of the ABS Good Practice Guide. Firstly we have expanded our contributors beyond ABS academics. This year?s articles have also been written by staff from other areas of the University, a PhD student, a post-doctoral researcher and staff working in learning support. We see this as an acknowledgement that the learning environment involves a range of people in the process of student support. We have also expanded the maximum length of the articles from two to five pages, in order to allow greater reflection on the issues. The themes of the papers cluster around issues relating to diversity (widening participation and internationalisation of the student body), imaginative use of new technology (electronic reading on BlackboardTM ) and reflective practitioners, (reflection on rigour and relevance; on how best to train students in research ethics, relevance in the curriculum and the creativity of the teaching process) Discussion of efforts to train the HE teachers of the future looks forward to the next academic year when the Higher Education Academy?s professional standards will be introduced across the sector. In the last volume we mentioned the launch of the School?s Research Centre in Higher Education Learning and Management (HELM). Since then HELM has stimulated a lot of activity across the School (and University) particularly linking research and teaching. A list of the HELM seminars is listed as an appendix to this publication. Further details can be obtained from Catherine Foster (c.s.foster@aston.ac.uk) who coordinates the HELM seminars. HELM has also won its first independent grant from the EU Leonardo programme to look at the effect of business education on employment. In its annual report to the ABS Research Committee HELM listed for 2004 and 2005, 11 refereed journal articles, 4 book chapters, 3 published conference papers, 18 conference papers, one official reports and £72,500 of grant money produced in this research area across the School. I hope that this shows that reflection on learning is live and well in ABS. May I thank the contributors for taking time out of their busy schedules to write the articles and to Julie Green, the Quality Manager, for putting our diverse approaches into a coherent and publishable form.

IT and learning in Higher Education

Educational institutions are under pressure to provide high quality education to large numbers of students very efficiently. The efficiency target combined with the large numbers generally militates against providing students with a great deal of personal or small group tutorial contact with academic staff. As a result of this, students often develop their learning criteria as a group activity, being guided by comparisons one with another rather than the formal assessments made of their submitted work. IT systems and the World Wide Web are increasingly employed to amplify the resources of academic departments although their emphasis tends to be with course administration rather than learning support. The ready availability of information on the World Wide Web and the ease with which is may be incorporated into essays can lead students to develop a limited view of learning as the process of finding, editing and linking information. This paper examines a module design strategy for tackling these issues, based on developments in modules where practical knowledge is a significant element of the learning objectives. Attempts to make effective use of IT support in these modules will be reviewed as a contribution to the development of an IT for learning strategy currently being undertaken in the author’s Institution.

The impact of a family home-learning program on levels of parental/caregiver efficacy

This study sought to determine if participation in a home education learning program would impact the perceived levels of parental self-efficacy of parents/caregivers who participate in the completion of home-learning assignments and increase their levels of home-learning involvement practices. Also, the study examined the relationship between the parental involvement practice of completing interactive home-learning assignments and the reading comprehension achievement of first grade students. A total of 146 students and their parents/caregivers representing a convenience sample of eight first grade classes participated in the study. Four classes (n=74) were selected as the experimental group and four classes (n=72) served as the control group. There were 72 girls in the sample and 74 boys and the median age was 6 years 6 months. The study employed a quasi-experimental research design utilizing eight existing first grade classes. It examined the effects of a home-learning support intervention program on the perceived efficacy levels of the participating parents/caregivers, as measured by the Parent Perceptions of Parent Efficacy Scale (Hoover-Dempsey, Bassler, & Brissie, 1992) administered on a pre/post basis. The amount and type of parent involvement in the completion of home assignments was determined by means of a locally developed instrument, the H.E.L.P. Parent Involvement Home-learning Scale, administered on a pre/post basis. Student achievement in reading comprehension was measured via the reading subtest of the Brigance, CIB-S pre and post. The elementary students and their parents/caregivers participated in an interactive home-learning intervention program for 12 weeks that required parent/caregiver assistance. Results revealed the experimental group of parents/caregivers had a significant increase in their levels of perceived self-efficacy, p<.001, from the pre to post, and also had significantly increased levels of parental involvement in seven home-learning activities, p<.001, than the control group parents/caregivers. The experimental group students demonstrated significantly higher reading levels than the control group students, p<.001. This study provided evidence that interactive home-learning activities improved the levels of parental self-efficacy and parental involvement in home-learning activities, and improved the reading comprehension of the experimental group in comparison to the control.