A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks

We propose a nonparametric method for estimating derivative financial asset pricing formulae using learning networks. To demonstrate feasibility, we first simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis functions, multilayer perceptrons, and projection pursuit. To illustrate practical relevance, we also apply our approach to S&P 500 futures options data from 1987 to 1991.

Learning and adaptation in physical heterogeneous teams of robots

In this paper we present a novel approach to assigning roles to robots in a team of physical heterogeneous robots. Its members compete for these roles and get rewards for them. The rewards are used to determine each agent’s preferences and which agents are better adapted to the environment. These aspects are included in the decision making process. Agent interactions are modelled using the concept of an ecosystem in which each robot is a species, resulting in emergent behaviour of the whole set of agents. One of the most important features of this approach is its high adaptability. Unlike some other learning techniques, this approach does not need to start a whole exploitation process when the environment changes. All this is exemplified by means of experiments run on a simulator. In addition, the algorithm developed was applied as applied to several teams of robots in order to analyse the impact of heterogeneity in these systems

Multidimensional Adaptations for Open Learning Management Systems

Our work is focused on alleviating the workload for designers of adaptive courses on the complexity task of authoring adaptive learning designs adjusted to specific user characteristics and the user context. We propose an adaptation platform that consists in a set of intelligent agents where each agent carries out an independent adaptation task. The agents apply machine learning techniques to support the user modelling for the adaptation process

Learning how (not) to fire a gun: combatant training and civilian victimization

What is the relationship between the type of training combatants receive upon recruitment into an armed group and their propensity to abuse civilians in civil war? Does military training or political training prevent or exacerbate the victimization of civilians by armed non-state actors? While the literature on civilian victimization has expanded rapidly, few studies have examined the correlation between abuse of civilians and the modes of training that illegal armed actors receive. Using a simple formal model, we develop hypotheses regarding this connection and argue that while military training should not decrease the probability that a combatant engages in civilian abuse, political training should. We test these hypotheses using a new survey consisting of a representative sample of approximately 1,500 demobilized combatants from the Colombian conflict, which we match with department-level data on civilian casualties. The empirical analysis confirms our hypotheses about the connection between training and civilian abuse and the results are robust to adding a full set of controls both at the department and at the individual level

Dialogue Systems for Language Learning

En este artículo se presenta el caso de Milao, un entorno virtual que ofrece a los estudiantes de idiomas extranjeros la oportunidad de desarrollar y mejorar sus habilidades comunicativas dialogando en escenarios de conversación predefinidos que simulan la interacción con un nativo. Esta tecnología propone una solución a uno de los mayores retos en el aprendizaje de lenguas extranjeras: la falta de oportunidades para poner en práctica la gramática y el vocabulario recién adquiridos. Combinando la investigación sobre la lingüística y el aprendizaje de lenguas con los avances tecnológicos en el campo del Procesamiento del Lenguaje Natural (NPL), particularmente sobre sistemas de diálogo, hemos creado oportunidades en la demanda de los estudiantes a conversar en la lengua que tratan de aprender.

The effectiveness of assessment learning objects produced using pair programming

Pair Programming is a technique from the software development method eXtreme Programming (XP) whereby two programmers work closely together to develop a piece of software. A similar approach has been used to develop a set of Assessment Learning Objects (ALO). Three members of academic staff have developed a set of ALOs for a total of three different modules (two with overlapping content). In each case a pair programming approach was taken to the development of the ALO. In addition to demonstrating the efficiency of this approach in terms of staff time spent developing the ALOs, a statistical analysis of the outcomes for students who made use of the ALOs is used to demonstrate the effectiveness of the ALOs produced via this method.

Becoming skilled: The cultural and corporeal geographies of teaching and learning Thai Yoga massage

This paper explores the process of learning an embodied knowledge using the work of Dreyfus and Deleuze. Although geographers have begun to acknowledge the role of embodied knowledges in social life, there have been few in-depth case studies of how these skills are learned. This paper offers a case study of Thai Yoga massage (TYM), a ‘complementary and alternative therapy’ which is growing in popularity in the United Kingdom. Having outlined the case study, the paper explores the cultural geographies of the formalisation, documentation and contestation of the set of techniques that have come to cohere in the UK as TYM. The paper then interrogates the messy corporeal geographies of learning a skill, and briefly considers how more advanced practitioners experience their skilled practice.

Implicit and explicit processes in a second-language learning task

Two experiments examined the learning of a set of Greek pronunciation rules through explicit and implicit modes of rule presentation. Experiment 1 compared the effectiveness of implicit and explicit modes of presentation in two modalities, visual and auditory. Subjects in the explicit or rule group were presented with the rule set, and those in the implicit or natural group were shown a set of Greek words, composed of letters from the rule set, linked to their pronunciations. Subjects learned the Greek words to criterion and were then given a series of tests which aimed to tap different types of knowledge. The results showed an advantage of explicit study of the rules. In addition, an interaction was found between mode of presentation and modality. Explicit instruction was more effective in the visual than in the auditory modality, whereas there was no modality effect for implicit instruction. Experiment 2 examined a possible reason for the advantage of the rule groups by comparing different combinations of explicit and implicit presentation in the study and learning phases. The results suggested that explicit presentation of the rules is only beneficial when it is followed by practice at applying them.

Developing an engaging virtual action learning programme for SME managers – A UK perspective

In this paper we present results from an EU-funded project with the aim of examining the adaptation of e-learning to meet the needs of managers in different contexts. A set of design considerations is elucidated. These principles were derived from an analysis of five completed projects. This was followed by focus group discussion in the UK to test the principles derived.. These focus group were planned so as to gain greater clarity in the design of e-learning programmes aimed at UK-based SME leaders and managers. This paper starts by looking at the importance of SME management development for the economic wellbeing of the community and goes on to review research into issues in engaging managers in development activities. The results of a review of an earlier experimental programme (ESeN) are presented as it formed part of the process which led to the identification of theoretical design principles then tested in the focus groups. Finally, recommendations are presented for SME e-learning providers as well as areas for further research.

Example uses of a learning object

A self study course for learning to program using the C programming language has been developed. A Learning Object approach was used in the design of the course. One of the benefits of the Learning Object approach is that the learning material can be reused for different purposes. 'Me course developed is designed so that learners can choose the pedagogical approach most suited to their personal learning requirements. For all learning approaches a set of common Assessment Learning Objects (ALOs or tests) have been created. The design of formative assessments with ALOs can be carried out by the Instructional Designer grouping ALOs to correspond to a specific assessment intention. The course is non-credit earning, so there is no summative assessment, all assessment is formative. In this paper examples of ALOs and their uses is presented together with their uses as decided by the Instructional Designer and learner. Personalisation of the formative assessment of skills can be decided by the Instructional Designer or the learner using a repository of pre-designed ALOs. The process of combining ALOs can be carried out manually or in a semi-automated way using metadata that describes the ALO and the skill it is designed to assess.

Dynamic automata for mobile robot learning

Researchers at the University of Reading have developed over many years some simple mobile robots that explore an environment they perceive through simple ultrasonic sensors. Information from these sensors has allowed the robots to learn the simple task of moving around while avoiding dynamic obstacles using a static set of fuzzy automata, the choice of which has been criticised, due to its arbitrary nature. This paper considers how a dynamic set of automata can overcome this criticism. In addition, a new reinforcement learning function is outlined which is both scalable to different numbers and types of sensors. The innovations compare successfully with earlier work.

Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon

Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large-scale interdisciplinary questions. Bringing together a diverse scientific community with a single geographic focus can help increase awareness of research questions both within and among disciplines, and reveal the opportunities that may exist for advancing acquisition of reliable knowledge. This approach could be useful for a variety of globally important scientific questions.

The impact of Group Intelligence software on enquiry-based learning

Despite the increasing use of groupware technologies in education, there is little evidence of their impact, especially within an enquiry-based learning (EBL) context. In this paper, we examine the use of a commercial standard Group Intelligence software called GroupSystems®ThinkTank. To date, ThinkTank has been adopted mainly in the USA and supports teams in generating ideas, categorising, prioritising, voting and multi-criteria decision-making and automatically generates a report at the end of each session. The software was used by students carrying out an EBL project, set by employers, for a full academic year. The criteria for assessing the impact of ThinkTank on student learning were those of creativity, participation, productivity, engagement and understanding. Data was collected throughout the year using a combination of interviews and questionnaires, and written feedback from employers. The overall findings show an increase in levels of productivity and creativity, evidence of a deeper understanding of their work but some variation in attitudes towards participation in the early stages of the project.

eRules: a modular adaptive classification rule learning algorithm for data streams

Advances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values.

Experiences of learning French: a snapshot at Years 11, 12 and 13

This paper considers the attitudes of students in Years 11, 12 and 13 towards French and, in particular, how they view the reasons behind their level of achievement. It reports findings from a small-scale pilot study, conducted in four schools and colleges, involving 83 students in Year 11, 26 in Year 12 and 14 in Year 13. The findings indicate that French is perceived by many Year 11 students to be difficult and uninteresting. These students, furthermore, do not consider that French is of much benefit in terms of their future career. The data suggest that there is a tendency among students in all three year groups to attribute their lack of success in French to their own low ability and to the difficulty of tasks set, which, it is argued, may affect their levels of motivation in a negative way. Few students in the study have any insight into the importance of learning strategies in overcoming difficulties experienced in language learning. Students' attitudes are then discussed in relation to learning strategy training. It is argued that if learners are encouraged to explore the possibility that their achievement in French may be related to the efficacy of the learning strategies they use, rather than to factors such as low ability or task difficulty, their self-concept, motivation and language learning achievements can be enhanced. A brief outline is given of a planned research project which proposes to address these issues further.