Issues in learning an ontology from text

Ontology construction for any domain is a labour intensive and complex process. Any methodology that can reduce the cost and increase efficiency has the potential to make a major impact in the life sciences. This paper describes an experiment in ontology construction from text for the animal behaviour domain. Our objective was to see how much could be done in a simple and relatively rapid manner using a corpus of journal papers. We used a sequence of pre-existing text processing steps, and here describe the different choices made to clean the input, to derive a set of terms and to structure those terms in a number of hierarchies. We describe some of the challenges, especially that of focusing the ontology appropriately given a starting point of a heterogeneous corpus. Results - Using mainly automated techniques, we were able to construct an 18055 term ontology-like structure with 73% recall of animal behaviour terms, but a precision of only 26%. We were able to clean unwanted terms from the nascent ontology using lexico-syntactic patterns that tested the validity of term inclusion within the ontology. We used the same technique to test for subsumption relationships between the remaining terms to add structure to the initially broad and shallow structure we generated. All outputs are available at http://thirlmere.aston.ac.uk/~kiffer/animalbehaviour/ webcite. Conclusion - We present a systematic method for the initial steps of ontology or structured vocabulary construction for scientific domains that requires limited human effort and can make a contribution both to ontology learning and maintenance. The method is useful both for the exploration of a scientific domain and as a stepping stone towards formally rigourous ontologies. The filtering of recognised terms from a heterogeneous corpus to focus upon those that are the topic of the ontology is identified to be one of the main challenges for research in ontology learning.

Evolution, recurrency and kernels in learning to model inflation

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. We use non-linear, artificial intelligence techniques, namely, recurrent neural networks, evolution strategies and kernel methods in our forecasting experiment. In the experiment, these three methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation. There is evidence in the literature that evolutionary methods can be used to evolve kernels hence our future work should combine the evolutionary and kernel methods to get the benefits of both.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Improving scheduling techniques in heterogeneous systems with dynamic, on-line optimisations

Computational performance increasingly depends on parallelism, and many systems rely on heterogeneous resources such as GPUs and FPGAs to accelerate computationally intensive applications. However, implementations for such heterogeneous systems are often hand-crafted and optimised to one computation scenario, and it can be challenging to maintain high performance when application parameters change. In this paper, we demonstrate that machine learning can help to dynamically choose parameters for task scheduling and load-balancing based on changing characteristics of the incoming workload. We use a financial option pricing application as a case study. We propose a simulation of processing financial tasks on a heterogeneous system with GPUs and FPGAs, and show how dynamic, on-line optimisations could improve such a system. We compare on-line and batch processing algorithms, and we also consider cases with no dynamic optimisations.

Teaching Strategies and Ontologies for E-Learning

* The work is partially suported by Russian Foundation for Basic Studies (grant 02-01-00466).

Using Reengineering Techniques for Distance Study Course Improvement

The paper analyses the reengineering concept as it comes from software engineering and management fields. We combine two viewpoints and apply them to solve a problem of reengineering of a distance study system, in general, and the unit of learning, in particular. We propose a framework for reengineering of unit of learning, based on general model of software reengineering, and present a case study, in which we describe, how one topic of distance study course was reengineered, considering triple consistency principle and requirements for computer science. The proposed framework contributes to increasing quality, effectiveness and systematization of delivering distance studies.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

Game-based Approach in e-Learning

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

A case study of adults in college who developed an experiential learning portfolio

This single-case study provides a description and explanation of selected adult students' perspectives on the impact that the development of an experiential learning portfolio had on their understanding of their professional and personal lives. The conceptual framework that undergirded the study included theoretical and empirical studies on adult learning, experiential learning, and the academic quality of nontraditional degree programs with a portfolio component. The study employed qualitative data collection techniques of individual interviews, document review, field notes, and researcher journal. A purposive sample of 8 adult students who completed portfolios as a component of their undergraduate degrees participated in the study. The 4 male and 4 female students who were interviewed represented 4 ethnic/racial groups and ranged in age from 32 to 55 years. Each student's portfolio was read prior to the interview to frame the semi-structured interview questions in light of written portfolio documents. ^ Students were interviewed twice over a 3-month period. The study lasted 8 months from data collection to final presentation of the findings. The data from interview transcriptions and student portfolios were analyzed, categorized, coded, and sorted into 4 major themes and 2 additional themes and submitted to interpretive analysis. ^ Participants' attitudes, perceptions, and opinions of their learning from the portfolio development experience were presented in the findings, which were illustrated through the use of excerpts from interview responses and individual portfolios. The participants displayed a positive reaction to the learning they acquired from the portfolio development process, regardless of their initial concerns about the challenges of creating a portfolio. Concerns were replaced by a greater recognition and understanding of their previous professional and personal accomplishments and their ability to reach future goals. Other key findings included (a) a better understanding of the role work played in their learning and development, (b) a deeper recognition of the impact of mentors and role models throughout their lives, (c) an increase in writing and organizational competencies, and (d) a sense of self-discovery and personal empowerment. ^

A study of learners' perceived value and satisfaction for implied effectiveness of online learning systems

Online learning systems (OLS) have become center stage for corporations and educational institutions as a competitive tool in the knowledge economy. The satisfaction construct has received extensive coverage in information systems literature as an indicator of effectiveness but has been criticized for lack of validity; yet, the value construct has been largely ignored, although it has a long history in psychology, sociology, and behavioral science. The purpose of this dissertation is to investigate the value and satisfaction constructs in the context of OLS, and their perceived by learners relationship for implied effectiveness of OLS. ^ First, a qualitative phase is employed to gather OLS values from learners' focus groups, followed by a pilot phase to refine a proposed instrument, and a main phase to validate the survey. Responses were received from 75 students in four focus groups, 141 in the pilot, and 207 the main survey. Extensive data cleaning and exploratory factor analysis were done to identify factors of learners' perceived value and satisfaction of OLS. Then, Value-Satisfaction grids and the Learners' Value Index of Satisfaction (LeVIS) were developed as benchmarking tools of OLS. Moreover, Multicriteria Decision Analysis (MCDA) techniques were employed to impute value from satisfaction scores in order to reduce survey response time. ^ The results provided four satisfaction and four value factors with high reliability (Cronbach's α). Moreover, value and satisfaction were found to have low linear and nonlinear correlations, indicating that they are two distinct uncorrelated constructs. This is consistent with the literature. Value-Satisfaction grids and the LeVIS index indicated relatively high effectiveness for technology and support characteristics, relatively low effectiveness for professor's characteristics, while course and learner characteristics indicated average effectiveness. ^ The main contributions of this study include identifying, defining, and articulating the relationship between value and satisfaction constructs as assessment of users' implied IS effectiveness, as well as assessing the accuracy of MCDA procedures to predict value scores, thus reducing by half the survey questionnaire size. ^

The state of the nations:current and future developments in technology enhanced learning

Right across Europe technology is playing a vital part in enhancing learning for an increasingly diverse population of learners. Learning is increasingly flexible, social and mobile and supported by high quality multi-media resources. Institutional VLEs are seeing a shift towards open source products and these core systems are supplemented by a range of social and collaborative learning tools based on web 2.0 technologies. Learners undertaking field studies and those in the workplace are coming to expect that these off-campus experiences will also be technology-rich whether supported by institutional or user-owned devices. As well as keeping European businesses competitive, learning is seen as a means of increasing social mobility and supporting an agenda of social justice. For a number of years the EUNIS E-Learning Task Force (ELTF) has conducted snapshot surveys of e-learning across member institutions, collected case studies of good practice in e-learning see (Hayes, et al., 2009) in references, supported a group looking at the future of e-learning, and showcased the best of innovation in its e-learning Award. Now for the first time the ELTF membership has come together to undertake an analysis of developments in the member states and to assess what this might mean for the future. The group applied the techniques of World Café conversation and Scenario Thinking to develop its thoughts. The analysis is unashamedly qualitative and draws on expertise from leading universities across eight of the EUNIS member states. What emerges is interesting in terms of the common trends in developments in all of the nations and similarities in hopes and concerns about the future development of learning.

National, inter-institutional, graduate modules – blended learning approaches for PhD education in Ireland

This paper is a case study that describes the design and delivery of national PhD lectures with 40 PhD candidates in Digital Arts and Humanities in Ireland simultaneously to four remote locations, in Trinity College Dublin, in University College Cork, in NUI Maynooth and NUI Galway. Blended learning approaches were utilized to augment traditional teaching practices combining: face-to-face engagement, video-conferencing to multiple sites, social media lecture delivery support – a live blog and micro blogging, shared, open student web presence online. Techniques for creating an effective, active learning environment were discerned via a range of learning options offered to students through student surveys after semester one. Students rejected the traditional lecture format, even through the novel delivery method via video link to a number of national academic institutions was employed. Students also rejected the use of a moderated forum as a means of creating engagement across the various institutions involved. Students preferred a mix of approaches for this online national engagement. The paper discusses successful methods used to promote interactive teaching and learning. These included Peer to peer learning, Workshop style delivery, Social media. The lecture became a national, synchronous workshop. The paper describes how allowing students to have a voice in the virtual classroom they become animated and engaged in an open culture of shared experience and scholarship, create networks beyond their institutions, and across disciplinary boundaries. We offer an analysis of our experiences to assist other educators in their course design, with a particular emphasis on social media engagement.

Next generation e-learning platform: virtual laboratory simulations

Bioscience subjects require a significant amount of training in laboratory techniques to produce highly skilled science graduates. Many techniques which are currently used in diagnostic, research and industrial laboratories require expensive equipment for single users; examples of which include next generation sequencing, quantitative PCR, mass spectrometry and other analytical techniques. The cost of the machines, reagents and limited access frequently preclude undergraduate students from using such cutting edge techniques. In addition to cost and availability, the time taken for analytical runs on equipment such as High Performance Liquid Chromatography (HPLC) does not necessarily fit with the limitations of timetabling. Understanding the theory underlying these techniques without the accompanying practical classes can be unexciting for students. One alternative from wet laboratory provision is to use virtual simulations of such practical which enable students to see the machines and interact with them to generate data. The Faculty of Science and Technology at the University of Westminster has provided all second and third year undergraduate students with iPads so that these students all have access to a mobile device to assist with learning. We have purchased licences from Labster to access a range of virtual laboratory simulations. These virtual laboratories are fully equipped and require student responses to multiple answer questions in order to progress through the experiment. In a pilot study to look at the feasibility of the Labster virtual laboratory simulations with the iPad devices; second year Biological Science students (n=36) worked through the Labster HPLC simulation on iPads. The virtual HPLC simulation enabled students to optimise the conditions for the separation of drugs. Answers to Multiple choice questions were necessary to progress through the simulation, these focussed on the underlying principles of the HPLC technique. Following the virtual laboratory simulation students went to a real HPLC in the analytical suite in order to separate of asprin, caffeine and paracetamol. In a survey 100% of students (n=36) in this cohort agreed that the Labster virtual simulation had helped them to understand HPLC. In free text responses one student commented that "The terminology is very clear and I enjoyed using Labster very much”. One member of staff commented that “there was a very good knowledge interaction with the virtual practical”.

A Proposed Framework for Simulation Based Learning of Inheritance

Different types of serious games have been used in elucidating computer science areas such as computer games, mobile games, Lego-based games, virtual worlds and webbased games. Different evaluation techniques have been conducted like questionnaires, interviews, discussions and tests. Simulation have been widely used in computer science as a motivational and interactive learning tool. This paper aims to evaluate the possibility of successful implementation of simulation in computer programming modules. A framework is proposed to measure the impact of serious games on enhancing students understanding of key computer science concepts. Experiments will be held on the EEECS of Queen’s University Belfast students to test the framework and attain results.

A Biocultural Examination of Student Learning Behaviors in Large Undergraduate Lectures

Thesis (Ph.D.)--University of Washington, 2016-08