A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Extreme learning machines for virtual metrology and etch rate prediction

Virtual metrology (VM) aims to predict metrology values using sensor data from production equipment and physical metrology values of preceding samples. VM is a promising technology for the semiconductor manufacturing industry as it can reduce the frequency of in-line metrology operations and provide supportive information for other operations such as fault detection, predictive maintenance and run-to-run control. Methods with minimal user intervention are required to perform VM in a real-time industrial process. In this paper we propose extreme learning machines (ELM) as a competitive alternative to popular methods like lasso and ridge regression for developing VM models. In addition, we propose a new way to choose the hidden layer weights of ELMs that leads to an improvement in its prediction performance.

Privacy-Protected Facial Biometric Verification via Fuzzy Forest Learning

Although visual surveillance has emerged as an effective technolody for public security, privacy has become an issue of great concern in the transmission and distribution of surveillance videos. For example, personal facial images should not be browsed without permission. To cope with this issue, face image scrambling has emerged as a simple solution for privacyrelated applications. Consequently, online facial biometric verification needs to be carried out in the scrambled domain thus bringing a new challenge to face classification. In this paper, we investigate face verification issues in the scrambled domain and propose a novel scheme to handle this challenge. In our proposed method, to make feature extraction from scrambled face images robust, a biased random subspace sampling scheme is applied to construct fuzzy decision trees from randomly selected features, and fuzzy forest decision using fuzzy memberships is then obtained from combining all fuzzy tree decisions. In our experiment, we first estimated the optimal parameters for the construction of the random forest, and then applied the optimized model to the benchmark tests using three publically available face datasets. The experimental results validated that our proposed scheme can robustly cope with the challenging tests in the scrambled domain, and achieved an improved accuracy over all tests, making our method a promising candidate for the emerging privacy-related facial biometric applications.

A realistic evaluation of Early Warning Systems and acute care training in Northern Ireland: findings and recommendations

Statement of purpose The purpose of this concurrent session is to present the main findings and recommendations from a five year study evaluating the implementation of Early Warning Systems (EWS) and the Acute Life-threatening Events: Recognition and Treatment (ALERT) course in Northern Ireland. The presentation will provide delegates with an understanding of those factors that enable and constrain successful implementation of EWS and ALERT in practice in order to provide an impetus for change. Methods The research design was a multiple case study approach of four wards in two hospitals in Northern Ireland. It followed the principles of realist evaluation research which allowed empirical data to be gathered to test and refine RRS programme theory [1]. The stages included identifying the programme theories underpinning EWS and ALERT, generating hypotheses, gathering empirical evidence and refining the programme theories. This approach used a variety of mixed methods including individual and focus group interviews, observation and documentary analysis of EWS compliance data and ALERT training records. A within and across case comparison facilitated the development of mid-range theories from the research evidence. Results The official RRS theories developed from the realist synthesis were critically evaluated and compared with the study findings to develop a mid-range theory to explain what works, for whom in what circumstances. The findings of what works suggests that clinical experience, established working relationships, flexible implementation of protocols, ongoing experiential learning, empowerment and pre-emptive management are key to the success of EWS and ALERT implementation. Each concept is presented as ‘context, mechanism and outcome configurations’ to provide an understanding of how the context impacts on individual reasoning or behaviour to produce certain outcomes. Conclusion These findings highlight the combination of factors that can improve the implementation and sustainability of EWS and ALERT and in light of this evidence several recommendations are made to provide policymakers with guidance and direction for future policy development. References: 1. Pawson R and Tilley N. (1997) Realistic Evaluation. Sage Publications; London Type of submission: Concurrent session Source of funding: Sandra Ryan Fellowship funded by the School of Nursing & Midwifery, Queen’s University of Belfast

Early warning systems: problems, pragmatics and potential

Symposium Chair: Dr Jennifer McGaughey

Title: Early Warning Systems: problems, pragmatics and potential

Early Warning Systems (EWS) provide a mechanism for staff to recognise, refer and manage deteriorating patients on general hospital wards. Implementation of EWS in practice has required considerable change in the delivery of critical care across hospitals. Drawing their experience of these changes the authors will demonstrate the problems and potential of using EWS to improve patient outcomes.

The first paper (Dr Jennifer McGaughey: Early Warning Systems: what works?) reviews the research evidence regarding the factors that support or constrain the implementation of Early Warning System (EWS) in practice. These findings explain those processes which impact on the successful achievement of patient outcomes. In order to improve detection and standardise practice National EWS have been implemented in the United Kingdom. The second paper (Catherine Plowright: The implementation of the National EWS in a District General Hospital) focuses on the process of implementing and auditing a National EWS. This process improvement is essential to contribute to future collaborative research and collection of robust datasets to improve patient safety as recommended by the Royal College of Physicians (RCP 2012). To successfully implement NEWS in practice requires strategic planning and staff education. The practical issues of training staff is discussed in the third paper. This paper (Collette Laws-Chapman: Simulation as a modality to embed the use of Early Warning Systems) focuses on using simulation and structured debrief to enhance learning in the early recognition and management of deteriorating patients. This session emphasises the importance of cognitive and social skills developed alongside practical skills in the simulated setting.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Learning Bayesian Networks with Thousands of Variables

We present a method for learning Bayesian networks from data sets containing thousands of variables without the need for structure constraints. Our approach is made of two parts. The first is a novel algorithm that effectively explores the space of possible parent sets of a node. It guides the exploration towards the most promising parent sets on the basis of an approximated score function that is computed in constant time. The second part is an improvement of an existing ordering-based algorithm for structure optimization. The new algorithm provably achieves a higher score compared to its original formulation. Our novel approach consistently outperforms the state of the art on very large data sets.

Improving E-Assessment Systems With A focus On Adaptive Feedback And Marking

Institutions involved in the provision of tertiary education across Europe are feeling the pinch. European universities, and other higher education (HE) institutions, must operate in a climate where the pressure of government spending cuts (Garben, 2012) is in stark juxtaposition to the EU’s strategy to drive forward and maintain a growth of student numbers in the sector (eurostat, 2015).

In order to remain competitive, universities and HE institutions are making ever-greater use of electronic assessment (E-Assessment) systems (Chatzigavriil et all, 2015; Ferrell, 2012). These systems are attractive primarily because they offer a cost-effect and scalable approach for assessment. In addition to scalability, they also offer reliability, consistency and impartiality; furthermore, from the perspective of a student they are most popular because they can offer instant feedback (Walet, 2012).

There are disadvantages, though.

First, feedback is often returned to a student immediately on competition of their assessment. While it is possible to disable the instant feedback option (this is often the case during an end of semester exam period when assessment scores must be can be ratified before release), however, this option tends to be a global ‘all on’ or ‘all off’ configuration option which is controlled centrally rather than configurable on a per-assessment basis.

If a formative in-term assessment is to be taken by multiple groups of
students, each at different times, this restriction means that answers to each question will be disclosed to the first group of students undertaking the assessment. As soon as the answers are released “into the wild” the academic integrity of the assessment is lost for subsequent student groups.

Second, the style of feedback provided to a student for each question is often limited to a simple ‘correct’ or ‘incorrect’ indicator. While this type of feedback has its place, it often does not provide a student with enough insight to improve their understanding of a topic that they did not answer correctly.

Most E-Assessment systems boast a wide range of question types including Multiple Choice, Multiple Response, Free Text Entry/Text Matching and Numerical questions. The design of these types of questions is often quite restrictive and formulaic, which has a knock-on effect on the quality of feedback that can be provided in each case.

Multiple Choice Questions (MCQs) are most prevalent as they are the most prescriptive and therefore most the straightforward to mark consistently. They are also the most amenable question types, which allow easy provision of meaningful, relevant feedback to each possible outcome chosen.
Text matching questions tend to be more problematic due to their free text entry nature. Common misspellings or case-sensitivity errors can often be accounted for by the software but they are by no means fool proof, as it is very difficult to predict in advance the range of possible variations on an answer that would be considered worthy of marks by a manual marker of a paper based equivalent of the same question.

Numerical questions are similarly restricted. An answer can be checked for accuracy or whether it is within a certain range of the correct answer, but unless it is a special purpose-built mathematical E-Assessment system the system is unlikely to have computational capability and so cannot, for example, account for “method marks” which are commonly awarded in paper-based marking.

From a pedagogical perspective, the importance of providing useful formative feedback to students at a point in their learning when they can benefit from the feedback and put it to use must not be understated (Grieve et all, 2015; Ferrell, 2012).

In this work, we propose a number of software-based solutions, which will overcome the limitations and inflexibilities of existing E-Assessment systems.

An evaluation of the Socrative (personal response system) app for increasing student engagement and learning in an undergraduate psychology curriculum.

Personal response systems using hardware such as 'clickers' have been around for some time, however their use is often restricted to multiple choice questions (MCQs) and they are therefore used as a summative assessment tool for the individual student. More recent innovations such as 'Socrative' have removed the need for specialist hardware, instead utilising web-based technology and devices common to students, such as smartphones, tablets and laptops. While improving the potential for use in larger classrooms, this also creates the opportunity to pose more engaging open-response questions to students who can 'text in' their thoughts on questions posed in class. This poster will present two applications of the Socrative system in an undergraduate psychology curriculum which aimed to encourage interactive engagement with course content using real-time student responses and lecturer feedback. Data is currently being collected and result will be presented at the conference.
The first application used Socrative to pose MCQs at the end of two modules (a level one Statistics module and level two Individual Differences Psychology module, class size N≈100), with the intention of helping students assess their knowledge of the course. They were asked to rate their self-perceived knowledge of the course on a five-point Likert scale before and after completing the MCQs, as well as their views on the value of the revision session and any issues that had with using the app. The online MCQs remained open between the lecture and the exam, allowing students to revisit the questions at any time during their revision.
This poster will present data regarding the usefulness of the revision MCQs, the metacognitive effect of the MCQs on student's judgements of learning (pre vs post MCQ testing), as well as student engagement with the MCQs between the revision session and the examination. Student opinions on the use of the Socrative system in class will also be discussed.
The second application used Socrative to facilitate a flipped classroom lecture on a level two 'Conceptual Issues in Psychology' module, class size N≈100). The content of this module requires students to think critically about historical and contemporary conceptual issues in psychology and the philosophy of science. Students traditionally struggle with this module due to the emphasis on critical thinking skills, rather than simply the retention of concrete knowledge. To prepare students for the written examination, a flipped classroom lecture was held at the end of the semester. Students were asked to revise their knowledge of a particular area of Psychology by assigned reading, and were told that the flipped lecture would involve them thinking critically about the conceptual issues found in this area. They were informed that questions would be posed by the lecturer in class, and that they would be asked to post their thoughts using the Socrative app for a class discussion. The level of preparation students engaged in for the flipped lecture was measured, as well as qualitative opinions on the usefulness of the session. This poster will discuss the level of student engagement with the flipped lecture, both in terms of preparation for the lecture, and engagement with questions posed during the lecture, as well as the lecturer's experience in facilitating the flipped classroom using the Socrative platform.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Parallel implementation of a learning algorithm for multilayer perceptrons using transputers

In this paper the parallelization of a new learning algorithm for multilayer perceptrons, specifically targeted for nonlinear function approximation purposes, is discussed. Each major step of the algorithm is parallelized, a special emphasis being put in the most computationally intensive task, a least-squares solution of linear systems of equations.

A new formulation of the learning problem for a neural network controller

In this paper we consider the learning problem for a class of multilayer perceptrons which is practically relevant in control systems applications. By reformulating this problem, a new criterion is developed, which reduces the number of iterations required for the learning phase.

Estratégia de ensino Project Based Learning no desenvolvimento de aplicações Web no Curso Profissional Técnico de Gestão e Programação de Sistemas Informáticos

Relatório da Prática de Ensino Supervisionada, Mestrado em Ensino de Informática, Universidade de Lisboa, Instituto de Educação, 2014

Machine learning techniques for music information retrieval

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015