The popularity of ensemble systems in real-world problems is a natural result of their effectiveness for a range of tasks, where single predictors or classifiers can overfit or provide weak solutions. A primary property in ensemble systems, contributing to their ability to generalize better is a combination of individual performances and diversity among individual learners. This lecture presents effective approaches for the generation of multi-level, multi-component combined predictors, through a grammar driven evolutionary framework. Several grammar schemes are presented for the production of hierarchical and fuzzy rule based ensembles. Candidate architectures are investigated in terms of data resampling, and different training approaches are tested, involving ensemble diversity measures

Dr. Athanasios Tsakonas received his M.Eng in Electrical and Computer Engineering from the National Technical University of Athens and his M.Sc. and Ph.D from University of the Aegean. His Ph.D thesis was "Computational Intelligence in Complex Managerial and Financial Domains - The Evolutionary Neural Logic Network Paradigm". Athanasios has gathered strong experience in the analysis, design and development of specialized computational intelligence systems, with applications in the financial and medical sector. His experience includes participation in European and domestic research projects (such as BOEMIE, SHARE, EUNITE, INFER, etc.), occupation of related research positions in top research centers (such as N.C.S.R. Demokritos) or in the private sector (banks, software development companies, etc.), as well as teaching related courses in universities (Aristotle University of Salonica, Demokritus University of Thrace, etc.). His research interests include computational intelligence, data mining, genetic programming and complex systems. He has published 1 book and more than 45 articles in total, in international scientific journals, conferences, or as book chapters. He is with the Smart Technology Research Centre, Bournemouth University, since January 2011.

Peter Kassler - originally a Theoretical Physicist - has done First Principles Modelling in the Process Industry for the last 25 years. What started as steady state simulation has now moved to online - models to support plant operation on the one side, and dynamic simulators for training on the other. In this talk, Dr Kassler will present his research on Adaptative Metalearning for beginners.

Mr Ivan Jimenez, an experienced application developer and database administrator for 4 years in Spain, and a student in his final year in BSc Computing at BU. In this talk, Mr Jimenez will present his work in documents classification. Individuals and organisations are becoming increasingly interested in seeking opinions from the Web for their decision making. However, determining the general opinion on any topic is an overwhelming task for people due to the large volume of documents on the Web. This seminar will present an investigation on sentiment analysis, which aims to fulfil the need for automated systems capable of classifying opinions expressed in documents. The talk will focus on the application of supervised machine learning approaches to solve this problem, outlining the pros and cons of each method. Finally, the developed solution will be briefly presented, followed by an overview of some challenges encountered during the implementation of the artefact.

Ms Monika Berendsen a highly experience Project Manager and developer in EVONIK industries AG since 1986; where she was responsible for the successful completion of very challenging projects.

All complex networks feature skewed distribution of connections, small degree of separation between vertices (compact architecture), high clustering, presence of communities, motifs, hierarchies and non-trivial temporal evolution. The main focus of the talk will be the last of the enumerated characteristics - dynamics of networked systems. Two approaches that are currently under investigation will be presented - analysis and dynamics of motifs in networked structures and molecolar modelling approach to predict networks' dynamics.

Katarzyna Musial received her M.Sc. degree in Computer Science from the Wroclaw University of Technology, Poland in 2006. In the same year she received her second MSc degree in Software Engineering from the Blekinge Institute of Technology, Sweden. She obtained Ph.D. in 2009 from the Institute of Informatics, Wroclaw University of Technology, Poland. She is a Lecturer in Informatics at the Bournemouth University, UK. She is interested especially in complex social networks and dynamics and evolution of complex networked systems.

I will present an overview of popular ways to achieve a consistent and properly layered software architecture and will discuss pros and cons of different solutions. Then I will discuss what solution was chosen in the Infer project and the reasoning behind that choice. I will also compare Infer architecture with solutions used in other similar tools and explain what the biggest challenges ahead of us are and how we will try to overcome them.

The talk consists of an informal overview of the limitations of statistical learning models for analysing nonlinear time series of empirical data; and strategies to overcome such drawbacks. If there is time, we will briefly discuss an example of the application in multivariate time series of neural recordings while the animal performs a decision-making task.

Emili Balaguer-Ballester Theoretical Physics, 1997; PhD in Time Series Prediction of Atmospheric Pollutants, 1997-2001; Researcher in Machine Learning in Software Industry, Tissat, Valencia, Spain, 2001-2005; Post-Doc in Auditory Perception Modelling. Center for Computational Neuroscience and Robotics, Univ. of Plymouth, 2005-2008; Project Leader in Neural Ensemble Dynamics during Behaviour. Bernstein Center for Computational Neurosicence, Univ. of Heidelberg, 2008-2012; Lecturer in Bournemouth University. Website: http://www.zi-mannheim.de/emili_balaguer.html

Dr Zulfiqar Khan is director sustainable design research centre, fellow of the Institution of Mechanical Engineers (IMechE), Industrial advisor to the professional review committee of IMechE. He is director (voluntary capacity) Poole Tidal Energy partnership (PTEP). His research interests include mechanics, advanced & nano-materials synthesis, tribology and sustainable design.

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining (PDM). Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has been shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users.

Frederic Stahl completed his degree at the University of Applied Science in Weihenstephan (Germany) in Bioinformatics. He received the academic grade as Diploma Engineer in 2006 and obtained his Ph.D. at the University of Portsmouth in 2010. The title of his Thesis is “Parallel Rule Induction”. After his Ph.D. Frederic continued working as “Senior Research Associate” at the University of Portsmouth until 2012. Frederic is currently working as Lecturer at Bournemouth University. His research interests are in the area of data mining of large and complex datasets; parallel and distributed data mining; data stream mining; data mining in resource constraint environments, machine learning and artificial intelligence.

Peter Kassler - originally a Theoretical Physicist - has done First Principles Modelling in the Process Industry for the last 25 years. What started as steady state simulation has now moved to online - models to support plant operation on the one side, and dynamic simulators for training on the other.
He will spend some time for research in the INFER project, an initiative to create a platform for evolving and robust predictive systems. The presentation will be on learning models and what can be learned from the greatest robust and adaptive show on earth: EVOLUTION.

At the beginning of every soft sensor project design decisions have to be made regarding e.g. sampling rates or measurement delays. This seminar will describe the effect of these decisions on the whole soft sensor project. Incidents, which result in a change of a design decision, are mentioned. The increase of complexity caused by changing one of the decisions is estimated. On the basis of real data from a chemical process different decisions are assessed regarding the accuracy of the soft sensor.

Dr. Stephanie Schwan is a researcher at the Process Technology & Engineering department, Evonik Industries AG, Hanau, Germany. Since 2000 she has been working in the area of Quality Engineering. Currently she is at a secondment at Bournemouth University within the framework of INFER (computational INtelligence platform For Evolving and Robust predictive systems) project.

Traditional machine learning methods have worked with full databases of limited size, but the increment of data stream applications had been necessary to develop new approaches for dealing with unlimited size streams. Furthermore, some problems have changes in the probability distribution of the data along time, known as concept drift, which should be detected and take actions like updating the learned model. This seminar will present an introduction to concept drift problem, evaluation issues and some methods developed during Manuel's master project.

Manuel is a PhD student in Bournemouth University since 2012. After finishing the degree of "Computer Engineering" in the University of Granada (Spain), he worked for a year in "Fundación I+D del Software Libre", an innovation and technology center located in Granada. During 2011, he studied the "Master in Soft Computing and Intelligent System" while he was working in the Department of Computer Science and Artificial Intelligence at the same university.

Similarity-Based Virtual Screening which is based on the Similarity Property Principle by Johnson and Maggiora states that a compound in a database whose activity is unknown, but is structurally similar to a compound known to be active for a specific target is likely to have the same activity as the known (active) compound. Quite often a set of retrieved compounds from a database for a query compound is enriched with activity; however, this is not always the case because “Similarity” is constrained by the original computational representation of the compounds. Over the last few decades there have been attempts in making systems capable of comparing and classifying compounds based on their structural and/ or physiochemical properties, many of which have been successful. What the current available methods fail to consider is that similar compounds may have different roles or, to a lesser extent, that dissimilar compounds may have similar roles. The overall goal of the research at hand is to determine the optimal computational representation of molecular structure, behaviour and activity. That is, a novel computational representation of compunds which does not rely solely on structure as a reference for similarity. The application areas will be in the field of Chemoinformatics and will include bioassay data, QSAR building and general ADMET prediction.

Amir Rafati-Afshar started at Bournemouth University in 2006, he studied BSc (HONS) Computing, he graduated with first grade honours, now he is in his second year of his PhD in Computer Analytics.

The processes in the chemical process industry are often complex. In general a simple controller is applied seperately to each controlled variable. By using the knoledge of the process behavior to predict the future behavior of each controlled varible depending on the current input values, the control results can be strongly improved. These improvements allow the plant management team to achieve e.g. a necessary reduction in energy consumtion or to increase the capacity of the plant without changes in the hardware. The presentation gives an overview to the different types of prediction models and how they are used in advanced process control strategies. Furthermore, the limits of the currently used models are discussed.

Dr. Anna Flemming is a researcher at the Process Technology & Engineering department, Evonik Industries, Hanau, Germany. Since 2008 she has been working at Evonik Industires in the area of Advanced Process Control. Currently she is on a secondment at Bournemouth University within the framework of INFER (computational Intelligence platform For Evolving and Robust predictive Systems) project.

Customer profiles are by definition made up of factual and transactional data. It is often the case that due to reasons such as high cost of data acquisition and/or protection, only the transactional data are available for data mining operations. Transactional data however, tend to be highly sparse and skewed, due to a large proportion of the customers engaging in very few transactions. This can result in a bias in the prediction accuracy of classifiers built using them towards the larger proportion of customers with fewer transactions. This seminar talk presents an investigation of an approach for accurately and confidently grouping and classifying customers on the basis of their transactions. Results from experiments on a highly sparse and skewed real-world transactional data, which show that the proposed approach can be used to identify different groups of customers with a high ROC confidence, is also presented.

Edward Apeh is a PhD Researcher with the SMART Technology Centre, DEC, Bournemouth University.

As reliance on the Internet throughout our everyday lives increases, the amount of information generated by our online behaviour has rapidly expanded. Businesses and organisations are now faced with huge amounts of data containing vast quantities of valuable intelligence. The process of extracting this high-level knowledge is commonly referred to as Web Mining. My study looks at the tasks involved in pre-processing these large web usage log files for the subsequent mining process and explores the use of Complex Event Processing (CEP) as an enabling technology. The use of CEP aims to transform the traditionally batch-processed phase into one performed in an online, "real-time" mode, which would reduce the overall amount of time spent on the web mining process.

I am a postgraduate student currently studying for an MSc in Software Engineering at the university. Prior to commencing my Masters studies I was employed as a web developer for several years. Earlier this year, I was working in Japan as an intern at NTT DATA Corporation, a global systems integrator. At NTT DATA I was working within the R&D department as part of a team investigating an emerging data analysis technology known as Complex Event Processing (CEP). Since my return to the UK I have been working on my Master thesis, which aims to combine CEP techniques with the data pre-processing phase of web usage mining in an effort to reduce the amount time required to conduct the complete mining process.

The application of grammar-driven search systems has been proven effective in a wide area of domains and problem tasks. In this presentation, aspects of the design and incorporation of such grammar-driven approaches in evolutionary data mining algorithms are discussed. The talk presents a state-of-the-art example for genetic programming evolutionary search and discusses further research directions.

Athanasios Tsakonas, PhD, is with Smart Technology Research Centre, BU, since January. He has gathered strong experience in the analysis, design and development of highly specialized computational intelligence systems, for industrial, financial and medical applications. His experience includes numerous participations in European or domestic research projects (such as INFER, BOEMIE, SHARE, EUNITE, etc.), and occupation of related research positions in top research centres (such as N.C.S.R. Demokritos) or in the private sector (banks, software development companies, etc.). In parallel, he has taught contemporary and advanced decision technologies in University departments (Dept. of Financial and Management Eng., University of the Aegean, Dept. of Production and Management Eng., Demokritus University of Thrace). He has published more than 50 articles, in total, in international scientific journals and conferences and he is author of one book.

In recent years, most companies now interact with their customers for businesses purposes through contact or call centers. Substantially, customers now perceive a company through their interaction with Customer Services Representatives (CSR’s) in their centers. The CSR has become the key role and channel in maintaining brand reputation and ensuring customer retention. Explicitly, in a contact center environment Customer and CSR are the two main transactional entities, and business development depends on their interaction. Contact center management routinely adopts numerous processes to enhance centre services by training their CSRs, call recording for quality monitoring, acquiring customer feedback after the call, and assessing similar factors. However, these factors are often inadequate in advancing the customer experience, due to operational scale and being exclusively focused on the telephone as the medium for interaction. Every contact center strives to maximize its value through, improved customer satisfaction, retention and first call resolution; and minimized communication expenditures, for example, call handling time or talk time. Smart call routing can manage these improvements to enhance overall customer experience, leading to sales and maintained quality of service.
The CSR to Customer call-outcome is the critical success factor (CSF) to improvement and optimization. This paper considers a new operational model for achieving significantly improved call-outcomes.
A call outcome in contact center environment is most typically random, like flipping a coin, to tell whether a call achieves a sale or not. This random outcome can be made more certain if predicted and optimized by exploiting personal chemistry as a critical factor. Fortunately contact centers are more controlled environments within which to gain psychographic and demographic insights to gauge customer/CSR chemistry.
We propose that descriptive, predictive and prescriptive analytical techniques can be applied to psychographic and demographic insights; to find the ideal mapping between them. By using those techniques, the model shows a ten to fifteen percent improvement in call-outcomes.

Abbas R. Ali is an Advanced Analytics and Optimization Subject Matter Expert in IBM BAO Center of Competence. Mr. Ali received his B.S. degree in computer science and mathematics from the Institute of Management Sciences and M.S. degree in artificial intelligence and natural language processing from the National University of Computers and Emerging Sciences. He has about 8 years of research and development experience in the field of Artificial Intelligence (AI), Mathematics and Statistics. Mr. Ali is a Chartered Statistician from Royal Statistical Society and Chartered IT Professional from British Computer Society. He can be contacted by email at >

Web analytics is aimed at understanding behavioural patterns of users of various web-based applications or services in e-commerce, mass-media, and entertainment industries. Accurately predicting the probability of desired actions on the web (product purchases, membership registrations, newsletter subscriptions, software downloads, accessing certain information resources, clicking ad banners) in specific circumstances would enable us to achieve better personalization and adaptation to diverse customer needs and preferences. The behavior of users may vary depending on the context (e.g. user activity, location, time, access device, weather, holidays) and potentially within the context. Thus, predictions in web analytics are inherently context sensitive, and therefore, complementing the prediction models with context management mechanisms are expected to make them more specialized and predictive analytics decisions for web applications more accurate. In general, the number of contextual factors that may potentially affect human behaviour on the web is enormous and it is hardly possible to capture all of them with a model simpler than the universe itself. Therefore, one of the key challenges is to construct the mechanisms, which would identify, what the (current) context is and how to integrate it into prediction models. Another challenge is to integrate a mechanism of monitoring the stream of user-related and contextual data over time to signal anomalies and changes in predictive model performance. In this talk I will discuss our approach for taking a broad range of practically relevant issues to address in context-aware predictive analytics (CAPA). We will start with discussing of what is meant by context and contextual features in different research areas, particularly with respect to formulating supervised learning tasks. Then, I will give a few motivating examples that illustrate the potential of integrating different information sources and suggesting that we can do better than simply merging contextual and predictive features for leaning a predictive model. Last but not least I will overview the infrastructure for CAPA research and development allowing straightforward deployment and validation of CAPA within real web-based applications.

Mykola Pechenizkiy is Assistant Professor at the Department of Computer Science, Eindhoven University of Technology, the Netherlands. He received his PhD from the Computer Science and Information Systems department at the University of Jyvaskyla, Finland in 2005. He has broad expertise and research interests in data mining and data-driven intelligence, and its application to various (adaptive) information systems serving industry, commerce, medicine and education. He has co-authored over 60 publications and has been organizing several workshops (HaCDAIS@ECML/PKDD2010, LEMEDS@AIME2011), conferences (IEEE CBMS 2008, BNAIC 2009, EDM 2011) and tutorials (at IEEE CBMS 2010, ECML/PKDD 2010, PAKDD 2011) in these areas. Recently, he has co-edited the Handbook of Educational Data Mining and served as a guest editor of two special issues in Elsevier DKE and AIIM journals. Currently, he takes a leading role in NWO HaCDAIS, STW CAPA, EIT ICT Labs Stress@Work and NL Agency CoDAK projects, information on which can be found at www.win.tue.nl/~mpechen/

We investigate how to partition data to identify hidden contexts in supervised learning tasks. Contexts are artefacts in the data, which do not predict the class label directly. Identifying hidden contexts is considered as data preprocessing task, which can help to build more accurate classifiers, tailored for particular contexts and give an insight into the data structure. We present three techniques to identify hidden contexts, which hide class label information from the input data and partition it using clustering techniques.

Indre Zliobaite is a lecturer in computational intelligence at the Bournemouth University. Her research interests include adaptive learning, detecting and handling changes (concept drift) in an online learning, data streams.

The talk will address evaluation of online classifiers that are designed to adapt to changes in data distribution over time (concept drift). A standard procedure to evaluate such classifiers is the test-then-train, which iteratively uses the incoming instances for testing and then for updating a classifier. Such learning risks to overfit, since a dataset is processed only once in a fixed sequential order while every output of the classifier depends on the instances seen so far. The problem is particularly serious when several classifiers are compared, since the same test set arranged in a different order may indicate a different winner. To reduce this risk we propose to run multiple tests with permuted data. The proposed procedure allows us to assess robustness of classifiers when changes happen unexpectedly.

Indre Zliobaite has recently joined Bournemouth University as a lecturer in computational intelligence. Her research interests include adaptive learning, detecting and handling changes (concept drift) in an online learning, data streams.

Ludmila Kuncheva is currently a Professor at the School of Computer Science, Bangor University, UK. Her interests include pattern recognition and classification, machine learning, classifier combination and fMRI data analysis.

Wrapped in mysticism and superstition in the past, "mind reading" is now raising new scientific horizons beside ethical debates. Functional magnetic resonance imaging (fMRI) is currently the most advanced technology at the disposal of cognitive neuroscience. It measures blood oxygenation level-dependent (BOLD) signal and tries to discover how mental states are mapped onto patterns of neural activity. Feature selection and classification of fMRI data is still a formidable analytic challenge even for the state-of-the-art pattern recognition and machine learning. This talk will explain the main difficulties and approaches in fMRI data analysis. We will look at how classifier ensembles can be used for this problem. Results from an experiment will be presented, which favour the Random Oracle ensembles and Random Subspace ensembles for fMRI classification.

Trevor Martin is Professor of Artificial Intelligence at the University of Bristol. Since 2001 he has been 80% funded by BT as a Senior Research Fellow, researching soft computing in intelligent information management including areas such as the semantic web, soft concept hierarchies and user modelling.

We have recently seen a step change in the volume of data, the range of data formats and access modes plus a huge increase in the speed at which many data sources are updated. Consequently, information systems can no longer be represented by monolithic, rigorously defined and centralised data models. In many cases, this means that the data relevant to a decision is scattered over multiple, poorly structured, locations such as personal file stores, networked databases, various web pages, etc. Human intelligence is relatively good at working with incomplete knowledge - memorably summarised as the "known knowns, the known unknowns and the unknown unknowns". This talk will look at the strengths and weaknesses of Computational Intelligence in dealing with the problems of the "unknown" and its use in developing tools that can support today’s knowledge workers.

Dr. Pamela Abbott is a lecturer and researcher in the area of Information Systems Management within the Department of Information Systems and Computing, Brunel University, West London.

In achieving success in global sourcing arrangements, the role of a cultural liaison, boundary spanner or transnational intermediary is frequently highlighted as being critical. In this paper, we argue that concepts like "boundary spanning" have been limited in theorizing the complexities of cross-cultural collaborations in offshore outsourcing processes. This paper presents an alternative framework of "creolization" that combines and further extends theoretical understandings of these processes. We investigated 13 companies through 26 in-depth, semi-structured interviews in Xi'an Software Park, an emerging Chinese software and services outsourcing hub. A grounded analysis of the data revealed four conceptual groupings for the practices undertaken at these companies, labeled as boundary spanning, mixed identity, network expansion and cultural hybridity. We posit that the process of creolization supports these practices and furthermore provides a unique basis for strategies positioning cross-cultural work from a supplier's perspective.

Mark Girolami holds a Chair in Statistics at University College London (UCL), Department of Statistical Science. He is also an honorary professor in the Department of Computer Science at UCL, is Director of the Centre for Computational Statistics and Machine Learning at UCL, a Fellow of the Institute of Engineering & Technology, and an EPSRC Advanced Research Fellow.

The talk proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined on the Riemann manifold to resolve the shortcomings of existing Monte Carlo algorithms when sampling from target densities that may be high dimensional and exhibit strong correlations. The methods provide fully automated adaptation mechanisms that circumvent the costly pilot runs that are required to tune proposal densities for Metropolis-Hastings or indeed Hamiltonian Monte Carlo and Metropolis adjusted Langevin algorithms. This allows for highly efficient sampling even in very high dimensions where different scalings may be required for the transient and stationary phases of the Markov chain. The methodology proposed exploits the Riemann geometry of the parameter space of statistical models and thus automatically adapts to the local structure when simulating paths across this manifold, providing highly efficient convergence and exploration of the target density. The performance of these Riemann manifold Monte Carlo methods is rigorously assessed by performing inference on logistic regression models, mixture models, log-Gaussian Cox point processes, stochastic volatility models and Bayesian estimation of dynamic systems described by non-linear differential equations. Substantial improvements in the time-normalized effective sample size are reported when compared with alternative sampling approaches.

Dr. Stephanie Schwan is a researcher at the Process Technology & Engineering department, Evonik Industries, Hanau, Germany. Since 2000 she has been working in the area of Quality Engineering. Currently she is at the secondment at Bournemouth University within the framework of INFER (computational Intelligence platform For Evolving and Robust predictive Systems) project.

Stephanie will talk about the challenges in the development and maintenance of soft sensors at Evonik Industries. The research conducted during Stephanie’s three month long secondment will focuse on the identification, development and research of existing and new pre-processing and predictive techniques driven specifically by the process industry requirements though applicable in the general platform context. She will present the topics she wants to focus on as well as the expected outcomes of her secondment.

Rapid development in computer and sensor technology not only used for highly specialised applications but widespread and pervasive across a wide range of business and industry has facilitated easy capture and storage of immense amounts of data. Examples of such data collection include medical history data in health care, financial data in banking, point of sale data in retail, plant monitoring data based on instant availability of various sensor readings in various industries, or airborne hyperspectral imaging data in natural resources identification to mention only a few. However, with an increasing computer power available at affordable prices and the availability of vast amount of data there is an increasing need for robust methods and systems, which can take advantage of all available information.

In essence there is a need for intelligent and smart adaptive methods but do they really exist? Are there any existing intelligent techniques which are more suitable for certain type of problems than others? How do we select those methods and can we be sure that the method of choice is the best for solving our problem? Do we need a combination of methods and if so then how to best combine them for different purposes? Are there any generic frameworks and requirements which would be highly desirable for solving data intensive and unstationary problems? All these questions and many others have been the focus of research vigorously pursued in many disciplines and some of them will be discussed in the talk and have been addressed in greater detail in our recently compiled book with the same title: "Do Smart Adaptive Systems Exist?".

One of the more promising approaches to constructing smart adaptive systems is based on intelligent technologies including artificial neural networks, fuzzy systems, methods from machine learning, parts of learning theory and evolutionary computing which have been especially successful in applications where input-output data can be collected but the underlying physical model is unknown. The incorporation of intelligent technologies has been used in the conception and design of complex systems in which analytical and expert systems techniques are used in combination. Viewed from a much broader perspective, the above mentioned intelligent technologies are constituents of a very active research area known under the names of soft computing, computational intelligence or hybrid intelligent systems.

However, hybrid soft computing frameworks are relatively young, even comparing to the individual constituent technologies, and a lot of research is required to understand their strengths and weaknesses. Nevertheless hybridization and combination of intelligent technologies within a flexible open framework seem to be the most promising direction in achieving the truly smart and adaptive systems today.

Despite all the challenges it is unquestionable that smart adaptive intelligent systems and intelligent technology have started to have a huge impact on our everyday life and many applications can already be found in various commercially available products as illustrated in the recent report compiled by one of the world's leading think tank advanced technology organisations and very suggestively titled: "Get smart: How intelligent technology will enhance our world".

All of the above will be covered in this talk and illustrations provided on the basis of on going collaborative research projects and successful completed applications of various intelligent technologies and highly flexible predictive systems in telecommunication, process and airline industries with such large companies as British Telecommunication plc, Lufthansa Systems GmbH and Evonik-Degussa GmbH.

"Selling the right product to the right customer at the right time for the right price to maximise profits" - this is what revenue management is all about. It has become a mainstream business practice with applications in many key industries, like hospitality and transportation industry.

Airline carriers were the first companies to apply revenue management in the 1970s and remain to be one of the most active users until today. This talk will introduce revenue management in general, describing its components and implementation using different examples and scenarios before looking at airline industry in particular. A special focus will be put on forecasting as a critical factor for the success of a revenue management system.

This talk deals with the current development and challenges on the field of soft sensing. Soft sensors are predictive models applied in the process industry. Here, the models can either replace traditional hardware sensors or provide additional information about critical process characteristics such as the quality of the process product. First soft sensors appeared more than two decades ago and since then there was a steady development in this area. Despite this fact, there are still many challenges that prohibited a real break through, which would lead to a widespread application of soft sensors. The reasons for this fact and possible solutions will be discussed during this talk.

Soft sensors are widely used to estimate process variables that are difficult to measure online. However, the predictive accuracy gradually decreases with changes in the state of chemical plants. Regression models can be updated, but if the model is updated with abnormal data, the predictive ability deteriorates. Therefore, we have proposed a new fault detection and classification method using independent component analysis (ICA) and support vector machine (SVM). This method, named ICA-SVM, was applied to the soft sensor in order to increase fault detection ability and predictive accuracy. We could comprehend the state of a plant by using the ICA-SVM model and estimate the objective variable by the regression model, updating it appropriately. In addition, we have proposed a method to estimate the relationships between applicability domains and the accuracy of prediction of soft sensor models quantitatively. The larger the distances to models(DMs), the lower the estimated accuracy of prediction. Hence, the model between DMs and accuracy can separate variations in process variables and y analyzer fault. The proposed methods were applied to industrial plant data and were found to exhibit higher predictive performance and fault detection ability than traditional methods.

The drug-development process is both time-consuming and expensive: it takes an average of 15 years and $800 million to bring a drug to the market. The process of discovering a new drug for a particular disease usually involves High-Throughput Screening (HTS), a mixture of robotics, control software, liquid-handlers and optical readers - it is an expensive and specialist process. Virtual screening is the computational or in silico screening of chemical compounds and complements the HTS process. It can utilise several computational techniques depending on the amount and type of information available about the compounds and the disease target. This talk will give an overview of the computational techniques that can be used to aid the drug-development process and describes the differing methods used for molecular structure data representation.

It is today a traditional exercise to view the end purpose of computer graphics techniques as photorealism, which can be defined as the generation of synthetic images that cannot be distinguished from reality. After decades of research striving for this, and given appropriate resources in hardware, modern renderers can now produce results very close to photographic images. Improved efficiency for this, as well as further advances, is still possible but at the same time there is an increasing amount of research focusing not on approximation of the real world but on the eventual purpose of the depiction and also all of the communicative aspects this can convey, thus influencing a variety of important factors. These can vary from low-level perceptual processes and emotional responses to cognitive workloads and information interpretation. This talk will focus on the evaluation of non-photorealistic rendering styles in the application area of mobile pedestrian navigation, with particular attention to usability. The methodology includes self-reported measures and task-based experiments. Additionally, there will be a brief discussion in the potential use of novel modalities that could be employed for future work in the area such as eye-tracking and BCIs (brainwave computer interfaces).

: The aim of the lecture is to present the interplay between complex network theory and the modern technology-based networks. The Internet, Semantic Web, service networks will be discussed in this context, then synergies between them and biologic, evolutionary and other emergentr natural networks will be shown. The networks will be also discussed as a result of collective actions of independent subjects which form the network to achieve their targets. The models of agent networks, cell automata networks along with dynamic network phenomena (synchronization, reaching consensus, opinion formation will be presented. In the end - the consequences for the future development of technology-based networks will be discussed.

Mashups are a relatively new approach, to combine data from different sources to create valuable information, principally for data aggregation applications. This utilises the potential of the internet and related technologies, to allow users to process tasks collaboratively, and form communities among those with similar interests. We present currently available mashup platforms and key issues to extend data-orentied mashups into process-oriented mashups.

Estimation of the generalisation ability of a classification or regression model is an important issue in the machine learning world, as it indicates expected performance on previously unseen data and is also used for model selection. Currently used generalisation error estimation procedures like cross--validation (CV) or bootstrap are stochastic and thus require multiple repetitions in order to produce reliable results, which is computationally expensive if not prohibitive. The discussed correntropy-based Density Preserving Sampling procedure (DPS) eliminates the need for repeating the error estimation procedure by dividing the available data into subsets, which are guaranteed to be representative of the input dataset. This allows to produce low variance error estimates with accuracy comparable to 10 times repeated cross-validation at a fraction of computations required by CV, which has been investigated using a set of publicly available benchmark datasets and standard classifiers.