Particle Swarm Optimisation of Spoken Dialogue System Strategies

International audienceDialogue management optimisation has been cast into a plan- ning under uncertainty problem for long. Some methods such as Reinforcement Learning (RL) are now part of the state of the art. Whatever the solving method, strong assumptions are made about the dialogue system properties. For instance, RL assumes that the dialogue state space is Markovian. Such con- straints may involve important engineering work. This paper introduces a more general approach, based on fewer modelling assumptions. A Black Box Optimisation (BBO) method and more precisely a Particle Swarm Optimisation (PSO) is used to solve the control problem. In addition, PSO allows taking ad- vantage of the parallel aspect of the problem of optimising a system online with many users calling at the same time. Some preliminary results are presented

The doctoral research abstracts Vol:1 2012 / Institute of Graduate Studies, UiTM

Foreword: Congratulations to Institute of Graduate Studies on the 1st issue of The Doctoral Research Abstracts. This inaugural issue consists of 40 abstracts from our PhD graduands receiving their scrolls in the UiTM’s 76th Convocation. This convocation is very significant especially for UiTM since we are celebrating the success of 40 PhD graduands from 12 of the university’s 25 faculties – the largest number ever conferred at any one time. To the 40 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and by persevering right till the very end. Let it remain in your thoughts and hearts that knowledge is Godgiven, and for those of us who have some to spare, never fear to share with those around us, and never be sparing in serving the community and the country, in the name of the Almighty. Dato’ Prof Ir Dr Sahol Hamid Bin Abu Bakar , FASc Vice Chancellor Universiti Teknologi MAR

Lightweight Adaptation of Classifiers to Users and Contexts: Trends of the Emerging Domain

Intelligent computer applications need to adapt their behaviour to contexts and users, but conventional classifier adaptation methods require long data collection and/or training times. Therefore classifier adaptation is often performed as follows: at design time application developers define typical usage contexts and provide reasoning models for each of these contexts, and then at runtime an appropriate model is selected from available ones. Typically, definition of usage contexts and reasoning models heavily relies on domain knowledge. However, in practice many applications are used in so diverse situations that no developer can predict them all and collect for each situation adequate training and test databases. Such applications have to adapt to a new user or unknown context at runtime just from interaction with the user, preferably in fairly lightweight ways, that is, requiring limited user effort to collect training data and limited time of performing the adaptation. This paper analyses adaptation trends in several emerging domains and outlines promising ideas, proposed for making multimodal classifiers user-specific and context-specific without significant user efforts, detailed domain knowledge, and/or complete retraining of the classifiers. Based on this analysis, this paper identifies important application characteristics and presents guidelines to consider these characteristics in adaptation design

Speaking while listening: Language processing in speech shadowing and translation

Contains fulltext : 233349.pdf (Publisher’s version ) (Open Access)Radboud University, 25 mei 2021Promotores : Meyer, A.S., Roelofs, A.P.A.199 p

Critical analysis for big data studies in construction: significant gaps in knowledge

Purpose The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry. Design/methodology/approach The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature. Findings A significant rise in construction big data research is identified with an increasing trend in number of yearly articles. The main themes discussed were big data as a concept, big data analytical methods/techniques, big data opportunities – challenges and big data application. The paper emphasises “the implication of big data in to overall sustainability” as a gap that needs to be addressed. These implications are categorised as social, economic and environmental aspects. Research limitations/implications The SLR is carried out for construction technology and management research for the time period of 2007–2017 in Scopus and emerald databases only. Practical implications The paper enables practitioners to explore the key themes discussed around big data research as well as the practical applicability of big data techniques. The advances in existing big data research inform practitioners the current social, economic and environmental implications of big data which would ultimately help them to incorporate into their strategies to pursue competitive advantage. Identification of knowledge gaps helps keep the academic research move forward for a continuously evolving body of knowledge. The suggested new research avenues will inform future researchers for potential trending and untouched areas for research. Social implications Identification of knowledge gaps helps keep the academic research move forward for continuous improvement while learning. The continuously evolving body of knowledge is an asset to the society in terms of revealing the truth about emerging technologies. Originality/value There is currently no comprehensive review that addresses social, economic and environmental implications of big data in construction literature. Through this paper, these gaps are identified and filled in an understandable way. This paper establishes these gaps as key issues to consider for the continuous future improvement of big data research in the context of the construction industry

A deep learning approach to predict and optimise energy in fish processing industries

The fish processing sector is experiencing increased pressure to reduce its energy consumption and carbon footprint as a response to (a) an increasingly stringent energy regulatory landscape, (b) rising fuel prices, and (c) the incentives to improve social and environmental performance. In this paper, a standalone forecasting computational platform is developed to optimise energy usage and reduce energy costs. This short-term forecasting model is achieved using an artificial neural network (ANN) to predict power and temperature at thirty-minute intervals in two cold rooms of a fish processing plant. The proposed ANN function is optimised by genetic algorithms (GA) with simulated annealing algorithms (SA) to model the relationships between future temperature and power and the system variables affecting them. To assess the accuracy of the proposed method, extensive experiments were conducted using real-world data sets. The results of the experiments indicate that the proposed ANN model performs with higher accuracy than (a) the long short-term memory (LSTM) as an artificial recurrent neural network (RNN) architecture, (b) peephole-LSTM, and (c) the gated recurrent unit (GRU). This paper finds that using GA & SA algorithms; ANN parameters can be optimised. The RMSE obtained by the ANN compared with the second-ranked method GRU was consequently 16% and 4% less for the predicted temperature and power. The results in one particular site demonstrate energy cost savings in the range of 15%–18% after applying the forecast-optimiser approach. The proposed prediction model is used in a fish processing plant for energy management and is scalable to other sites

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics