N-best error simulation for training spoken dialogue systems

A recent trend in spoken dialogue research is the use of reinforcement learning to train dialogue systems in a simulated environment. Past researchers have shown that the types of errors that are simulated can have a significant effect on simulated dialogue performance. Since modern systems typically receive an N-best list of possible user utterances, it is important to be able to simulate a full N-best list of hypotheses. This paper presents a new method for simulating such errors based on logistic regression, as well as a new method for simulating the structure of N-best lists of semantics and their probabilities, based on the Dirichlet distribution. Off-line evaluations show that the new Dirichlet model results in a much closer match to the receiver operating characteristics (ROC) of the live data. Experiments also show that the logistic model gives confusions that are closer to the type of confusions observed in live situations. The hope is that these new error models will be able to improve the resulting performance of trained dialogue systems. © 2012 IEEE.

Training and assignment of multi-skilled workers for implementing seru production systems

Confronted with high variety and low volume market demands, many companies, especially the Japanese electronics manufacturing companies, have reconfigured their conveyor assembly lines and adopted seru production systems. Seru production system is a new type of work-cell-based manufacturing system. A lot of successful practices and experience show that seru production system can gain considerable flexibility of job shop and high efficiency of conveyor assembly line. In implementing seru production, the multi-skilled worker is the most important precondition, and some issues about multi-skilled workers are central and foremost. In this paper, we investigate the training and assignment problem of workers when a conveyor assembly line is entirely reconfigured into several serus. We formulate a mathematical model with double objectives which aim to minimize the total training cost and to balance the total processing times among multi-skilled workers in each seru. To obtain the satisfied task-to-worker training plan and worker-to-seru assignment plan, a three-stage heuristic algorithm with nine steps is developed to solve this mathematical model. Then, several computational cases are taken and computed by MATLAB programming. The computation and analysis results validate the performances of the proposed mathematical model and heuristic algorithm. © 2013 Springer-Verlag London.

Impact of Residual Transmit RF Impairments on Training-Based MIMO Systems

Radio-frequency (RF) impairments, which intimately exist in wireless communication systems, can severely limit the performance of multiple-input-multiple-output (MIMO) systems. Although we can resort to compensation schemes to mitigate some of these impairments, a certain amount of residual impairments always persists. In this paper, we consider a training-based point-to-point MIMO system with residual transmit RF impairments (RTRI) using spatial multiplexing transmission. Specifically, we derive a new linear channel estimator for the proposed model, and show that RTRI create an estimation error floor in the high signal-to-noise ratio (SNR) regime. Moreover, we derive closed-form expressions for the signal-to-noise-plus-interference ratio (SINR) distributions, along with analytical expressions for the ergodic achievable rates of zero-forcing, maximum ratio combining, and minimum mean-squared error receivers, respectively. In addition, we optimize the ergodic achievable rates with respect to the training sequence length and demonstrate that finite dimensional systems with RTRI generally require more training at high SNRs than those with ideal hardware. Finally, we extend our analysis to large-scale MIMO configurations, and derive deterministic equivalents of the ergodic achievable rates. It is shown that, by deploying large receive antenna arrays, the extra training requirements due to RTRI can be eliminated. In fact, with a sufficiently large number of receive antennas, systems with RTRI may even need less training than systems with ideal hardware.

A realistic evaluation of track and trigger systems and acute care training for early recognition and treatment of deteriorating ward-based patients: research protocol.

Aim The aim of the study is to evaluate factors that enable or constrain the implementation and service delivery of early warnings systems or acute care training in practice. Background To date there is limited evidence to support the effectiveness of acute care initiatives (early warning systems, acute care training, outreach) in reducing the number of adverse events (cardiac arrest, death, unanticipated Intensive Care admission) through increased recognition and management of deteriorating ward based patients in hospital [1-3]. The reasons posited are that previous research primarily focused on measuring patient outcomes following the implementation of an intervention or programme without considering the social factors (the organisation, the people, external influences) which may have affected the process of implementation and hence measured end-points. Further research which considers the social processes is required in order to understand why a programme works, or does not work, in particular circumstances [4]. Method The design is a multiple case study approach of four general wards in two acute hospitals where Early Warning Systems (EWS) and Acute Life-threatening Events Recognition and Treatment (ALERT) course have been implemented. Various methods are being used to collect data about individual capacities, interpersonal relationships and institutional balance and infrastructures in order to understand the intended and unintended process outcomes of implementing EWS and ALERT in practice. This information will be gathered from individual and focus group interviews with key participants (ALERT facilitators, nursing and medical ALERT instructors, ward managers, doctors, ward nurses and health care assistants from each hospital); non-participant observation of ward organisation and structure; audit of patients' EWS charts and audit of the medical notes of patients who deteriorated during the study period to ascertain whether ALERT principles were followed. Discussion & progress to date This study commenced in January 2007. Ethical approval has been granted and data collection is ongoing with interviews being conducted with key stakeholders. The findings from this study will provide evidence for policy-makers to make informed decisions regarding the direction for strategic and service planning of acute care services to improve the level of care provided to acutely ill patients in hospital. References 1. Esmonde L, McDonnell A, Ball C, Waskett C, Morgan R, Rashidain A et al. Investigating the effectiveness of Critical Care Outreach Services: A systematic review. Intensive Care Medicine 2006; 32: 1713-1721 2. McGaughey J, Alderdice F, Fowler R, Kapila A, Mayhew A, Moutray M. Outreach and Early Warning Systems for the prevention of Intensive Care admission and death of critically ill patients on general hospital wards. Cochrane Database of Systematic Reviews 2007, Issue 3. www.thecochranelibrary.com 3. Winters BD, Pham JC, Hunt EA, Guallar E, Berenholtz S, Pronovost PJ (2007) Rapid Response Systems: A systematic review. Critical Care Medicine 2007; 35 (5): 1238-43 4. Pawson R and Tilley N. Realistic Evaluation. London; Sage: 1997

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

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.

Training multilayer perceptrons for control systems applications - a comparison of different approaches

Multilayer perceptrons (MLPs) (1) are the most common artificial neural networks employed in a large field of applications. In control and signal processing applications, MLPs are mainly used as nonlinear mapping approximators. The most common training algorithm used with MLPs is the error back-propagation (BP) alg. (1).

Training control centers' operators in incident diagnosis and power restoration using intelligent tutoring systems

The activity of Control Center operators is important to guarantee the effective performance of Power Systems. Operators’ actions are crucial to deal with incidents, especially severe faults like blackouts. In this paper, we present an Intelligent Tutoring approach for training Portuguese Control Center operators in tasks like incident analysis and diagnosis, and service restoration of Power Systems. Intelligent Tutoring System (ITS) approach is used in the training of the operators, having into account context awareness and the unobtrusive integration in the working environment. Several Artificial Intelligence techniques were criteriously used and combined together to obtain an effective Intelligent Tutoring environment, namely Multiagent Systems, Neural Networks, Constraint-based Modeling, Intelligent Planning, Knowledge Representation, Expert Systems, User Modeling, and Intelligent User Interfaces.

Cyber-ambient intelligent training of operators in power systems control centres

Cyber-Physical Systems and Ambient Intelligence are two of the most important and emerging paradigms of our days. The introduction of renewable sources gave origin to a completely different dimension of the distribution generation problem. On the other hand, Electricity Markets introduced a different dimension in the complexity, the economic dimension. Our goal is to study how to proceed with the Intelligent Training of Operators in Power Systems Control Centres, considering the new reality of Renewable Sources, Distributed Generation, and Electricity Markets, under the emerging paradigms of Cyber-Physical Systems and Ambient Intelligence. We propose Intelligent Tutoring Systems as the approach to deal with the intelligent training of operators in these new circumstances.

Testing Delivery Systems in Transnational virtual learning: the vocational management training for the european tourism industry (VocMat) case study

This article discusses the lessons learned from developing and delivering the Vocational Management Training for the European Tourism Industry (VocMat) online training programme, which was aimed at providing flexible, online distance learning for the European tourism industry. The programme was designed to address managers ‘need for flexible, senior management level training which they could access at a time and place which fitted in with their working and non-work commitments. The authors present two main approaches to using the Virtual Learning Environment, the feedback from the participants, and the implications of online Technology in extending tourism training opportunities

Superimposed channel training for two-way MIMO relay systems

Two-way relaying systems are known to be capable of providing higher spectral efficiency compared with one-way relaying systems. However, the channel estimation problem for two-way relaying systems becomes more complicated. In this paper, we propose a superimposed channel training scheme for two-way MIMO relay communication systems, where the individual channel information for users-relay and relay-users links are estimated. The optimal structure of the source and relay training sequences are derived when the mean-squared error (MSE) of channel estimation is minimized. We also optimize the power allocation between the source and relay training sequences to improve the performance of the algorithm. Numerical examples are shown to demonstrate the performance of the proposed channel training algorithm.