Learning sentiment classification model from labeled features

We propose a novel framework where an initial classifier is learned by incorporating prior information extracted from an existing sentiment lexicon. Preferences on expectations of sentiment labels of those lexicon words are expressed using generalized expectation criteria. Documents classified with high confidence are then used as pseudo-labeled examples for automatical domain-specific feature acquisition. The word-class distributions of such self-learned features are estimated from the pseudo-labeled examples and are used to train another classifier by constraining the model's predictions on unlabeled instances. Experiments on both the movie review data and the multi-domain sentiment dataset show that our approach attains comparable or better performance than exiting weakly-supervised sentiment classification methods despite using no labeled documents.

Transdisciplinarity and reflective practice in sustainable development learning

Using various extracts from the reflective commentaries of MSc students, this article explores how transdisciplinarity and reflective practice operate in the programme. It shows how learners managed the uncertainties of sustainable development through regular critical and evaluative reflections. Students were able to apprehend the several worlds making up the sustainable development project and their own personal learning journey through the various competing, complementary and occasionally contradictory perspectives, modes of learning, sources of knowledge and information. One conceptual device facilitating this process was offering an understanding of sustainable development as constituting a ‘dialogue of values’, an approach that effectively invites students to square the metaphorical circle - i.e. broadly reconciling (ecological) sustainability with (economic) development.

Supply chain learning needs:towards a port community perspective

The traditional role of ports in the wider supply chain context is currently being subject to a process of radical review. In broad terms, the traditional model is being replaced by a model which focuses on higher value and more knowledge intensive activities. This trend requires a change in the way in which new knowledge and skills are developed by staff in companies of all kinds within port communities. Traditional models need to be re-evaluated to reflect the increasing importance of knowledge and skills acquisition, particularly in relation to the supply chain management (SCM) concept and the evolving role of information and communications technology (ICT) in improving supply chain capability. This paper describes the case of NITL’s Foundation Certificate Programme (FCP) learning programme with specific reference to its use in addressing some of current shortcomings related to supply chain knowledge and skills in port communities. The FCP rationale is based on the need to move from traditional approaches of supply chain organisation where the various links in the chain were measured and managed in isolation from each other and thus tended to operate at cross purposes, towards more cooperative and integrated approaches.

Putting supply chain learning theory into practice:lessons from an Irish case

The logistics service market is currently going through a fundamental transition. The development of closer relationships with customers and the continuous adaptation of products and services, represent potentially successful approaches to the development of improved competitive capability. To this end knowledge resources and learning processes increasingly represent key elements within the evolving framework of the 3PL business. This paper describes the case of NITL’s Foundation Certificate Programme (FCP) learning programme with specific reference to its use in addressing some of current shortcomings related to supply chain knowledge and skills in the Irish third party logistics (3PL) industry. The FCP rationale is based on the need to move from traditional approaches of supply chain organisation where the various links in the chain were measured and managed in isolation from each other and thus tended to operate at cross purposes, towards more integrated approaches.

Evaluating the impact of a web-based resource on student learning

This paper reports on an experiment of using a publisher provided web-based resource to make available a series of optional practice quizzes and other supplementary material to all students taking a first year introductory microeconomics module. The empirical analysis evaluates the impact these supplementary resources had on student learning. First, we investigate which students decided to make use of the resources. Then, we analyse the impact this decision has on their subsequent performance in the examination at the end of the module. The results show that, even after taking into account the possibility of self-selection bias, using the web-based resource had a significant positive effect on student learning.

Architecting self-aware software systems

Contemporary software systems are becoming increasingly large, heterogeneous, and decentralised. They operate in dynamic environments and their architectures exhibit complex trade-offs across dimensions of goals, time, and interaction, which emerges internally from the systems and externally from their environment. This gives rise to the vision of self-aware architecture, where design decisions and execution strategies for these concerns are dynamically analysed and seamlessly managed at run-time. Drawing on the concept of self-awareness from psychology, this paper extends the foundation of software architecture styles for self-adaptive systems to arrive at a new principled approach for architecting self-aware systems. We demonstrate the added value and applicability of the approach in the context of service provisioning to cloud-reliant service-based applications.

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.

Learning to be different:heterogeneity and efficiency in distributed smart camera networks

In this paper we study the self-organising behaviour of smart camera networks which use market-based handover of object tracking responsibilities to achieve an efficient allocation of objects to cameras. Specifically, we compare previously known homogeneous configurations, when all cameras use the same marketing strategy, with heterogeneous configurations, when each camera makes use of its own, possibly different marketing strategy. Our first contribution is to establish that such heterogeneity of marketing strategies can lead to system wide outcomes which are Pareto superior when compared to those possible in homogeneous configurations. However, since the particular configuration required to lead to Pareto efficiency in a given scenario will not be known in advance, our second contribution is to show how online learning of marketing strategies at the individual camera level can lead to high performing heterogeneous configurations from the system point of view, extending the Pareto front when compared to the homogeneous case. Our third contribution is to show that in many cases, the dynamic behaviour resulting from online learning leads to global outcomes which extend the Pareto front even when compared to static heterogeneous configurations. Our evaluation considers results obtained from an open source simulation package as well as data from a network of real cameras. © 2013 IEEE.

Towards e+Learning or How to Increase the Learning into e-Learning

It is discussed some changes in the traditional e-learning notion on the point of view of R. Koper’s question 'where is the learning in e-learning?’. We put a focus on the conception of learning as a management process and present the project Bulgarian Educational Site (BEST) – a possible answer to Koper’s question. The BEST is a virtual learning environment, based on the following principles: learning is a goal-directed and didactics-managed process; learners may define their own learning objectives, monitor and regulate the learning process; collaborative e-learning is more effective; etc. The BEST is based on two famous e-learning systems (Moodle, LAMS) and Plovdiv e-University (versions 1.0 and 2.0). The paper brings up a mater about the new ‘electronic’ pedagogy and proposes an approach for pedagogical modeling and interpretation of e-learning applied in the BEST.

An eMathTeacher Tool for Active Learning Fleury’s Algorithm

An eMathTeacher [Sánchez-Torrubia 2007a] is an eLearning on line self assessment tool that help students to active learning math algorithms by themselves, correcting their mistakes and providing them with clues to find the right solution. The tool presented in this paper is an example of this new concept on Computer Aided Instruction (CAI) resources and has been implemented as a Java applet and designed as an auxiliary instrument for both classroom teaching and individual practicing of Fleury’s algorithm. This tool, included within a set of eMathTeacher tools, has been designed as educational complement of Graph Algorithm active learning for first course students. Its characteristics of visualization, simplicity and interactivity, make this tutorial a great value pedagogical instrument.

String Measure Applied to String Self-Organizing Maps and Networks of Evolutionary Processors

* Supported by projects CCG08-UAM TIC-4425-2009 and TEC2007-68065-C03-02

Training Primary School Teachers to use E-Learning Technologies – A Pilot Study

In this paper we present a blended learning scenario for training of students in master program “ICT in primary school” carried out in South-West University “Neofit Rilski”. Our approach is based on “face to face” lectures and seminars, SCORM compatible e-learning content with a lot of simulation demonstrations, trainings and self assessment, group problem based learning. Also we discuss the results of the course and attitude of the participants in the course towards used methods and possibilities of application of e-learning in primary schools.

On a Virtual Learning Environment

Education in the Information Society is based on asynchronism in time and space, interactivity and virtual restructuring of the educational space. One way to implement such a model of training is web-based - use of the WWW as a virtual environment to access educational materials or to organize the learning process. This work presents a virtual learning environment (VLE) developed for students and made up of modules of dynamically changing content implemented by authorized users. The aim is, through advanced technology for e-learning, testing and self-testing to stimulate students’ activity to focus their potential on the acquisition of the necessary knowledge, skills and competences. The VLE was developed under the Human Resources Development Operational Programme.