Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Active node supporting context-aware vertical handover in pervasive computing environment with redundant positioning

A major requirement for pervasive systems is to integrate context-awareness to support heterogeneous networks and device technologies and at the same time support application adaptations to suit user activities. However, current infrastructures for pervasive systems are based on centralized architectures which are focused on context support for service adaptations in response to changes in the computing environment or user mobility. In this paper, we propose a hierarchical architecture based on active nodes, which maximizes the computational capabilities of various nodes within the pervasive computing environment, while efficiently gathering and evaluating context information from the user's working environment. The migratable active node architecture employs various decision making processes for evaluating a rich set of context information in order to dynamically allocate active nodes in the working environment, perform application adaptations and predict user mobility. The active node also utilizes the Redundant Positioning System to accurately manage user's mobility. This paper demonstrates the active node capabilities through context-aware vertical handover applications.

Information geometric measurements of generalisation

Neural networks can be regarded as statistical models, and can be analysed in a Bayesian framework. Generalisation is measured by the performance on independent test data drawn from the same distribution as the training data. Such performance can be quantified by the posterior average of the information divergence between the true and the model distributions. Averaging over the Bayesian posterior guarantees internal coherence; Using information divergence guarantees invariance with respect to representation. The theory generalises the least mean squares theory for linear Gaussian models to general problems of statistical estimation. The main results are: (1)~the ideal optimal estimate is always given by average over the posterior; (2)~the optimal estimate within a computational model is given by the projection of the ideal estimate to the model. This incidentally shows some currently popular methods dealing with hyperpriors are in general unnecessary and misleading. The extension of information divergence to positive normalisable measures reveals a remarkable relation between the dlt dual affine geometry of statistical manifolds and the geometry of the dual pair of Banach spaces Ld and Ldd. It therefore offers conceptual simplification to information geometry. The general conclusion on the issue of evaluating neural network learning rules and other statistical inference methods is that such evaluations are only meaningful under three assumptions: The prior P(p), describing the environment of all the problems; the divergence Dd, specifying the requirement of the task; and the model Q, specifying available computing resources.

Data visualization during the early stages of drug discovery

Multidimensional compound optimization is a new paradigm in the drug discovery process, yielding efficiencies during early stages and reducing attrition in the later stages of drug development. The success of this strategy relies heavily on understanding this multidimensional data and extracting useful information from it. This paper demonstrates how principled visualization algorithms can be used to understand and explore a large data set created in the early stages of drug discovery. The experiments presented are performed on a real-world data set comprising biological activity data and some whole-molecular physicochemical properties. Data visualization is a popular way of presenting complex data in a simpler form. We have applied powerful principled visualization methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), to help the domain experts (screening scientists, chemists, biologists, etc.) understand and draw meaningful decisions. We also benchmark these principled methods against relatively better known visualization approaches, principal component analysis (PCA), Sammon's mapping, and self-organizing maps (SOMs), to demonstrate their enhanced power to help the user visualize the large multidimensional data sets one has to deal with during the early stages of the drug discovery process. The results reported clearly show that the GTM and HGTM algorithms allow the user to cluster active compounds for different targets and understand them better than the benchmarks. An interactive software tool supporting these visualization algorithms was provided to the domain experts. The tool facilitates the domain experts by exploration of the projection obtained from the visualization algorithms providing facilities such as parallel coordinate plots, magnification factors, directional curvatures, and integration with industry standard software. © 2006 American Chemical Society.

DVMS 1.5: A user manual (the data visualization and modeling system)

The data available during the drug discovery process is vast in amount and diverse in nature. To gain useful information from such data, an effective visualisation tool is required. To provide better visualisation facilities to the domain experts (screening scientist, biologist, chemist, etc.),we developed a software which is based on recently developed principled visualisation algorithms such as Generative Topographic Mapping (GTM) and Hierarchical Generative Topographic Mapping (HGTM). The software also supports conventional visualisation techniques such as Principal Component Analysis, NeuroScale, PhiVis, and Locally Linear Embedding (LLE). The software also provides global and local regression facilities . It supports regression algorithms such as Multilayer Perceptron (MLP), Radial Basis Functions network (RBF), Generalised Linear Models (GLM), Mixture of Experts (MoE), and newly developed Guided Mixture of Experts (GME). This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install & use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

miniDVMS v1.8 : A user manual(The data visualization and modeling system)

Today, the data available to tackle many scientific challenges is vast in quantity and diverse in nature. The exploration of heterogeneous information spaces requires suitable mining algorithms as well as effective visual interfaces. miniDVMS v1.8 provides a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualisation domain. The advantage of this interface is that the user is directly involved in the data mining process. Principled projection methods, such as generative topographic mapping (GTM) and hierarchical GTM (HGTM), are integrated with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, and user interaction facilities, to provide this integrated visual data mining framework. The software also supports conventional visualisation techniques such as principal component analysis (PCA), Neuroscale, and PhiVis. This user manual gives an overview of the purpose of the software tool, highlights some of the issues to be taken care while creating a new model, and provides information about how to install and use the tool. The user manual does not require the readers to have familiarity with the algorithms it implements. Basic computing skills are enough to operate the software.

Dynamical frustration of protein's environment at the nanoseconds time scale

A 21-residue peptide in explicit water has been simulated using classical molecular dynamics. The system's trajectory has been analysed with a novel approach that quantifies the process of how atom's environment trajectories are explored. The approach is based on the measure of Statistical Complexity that extracts complete dynamical information from the signal. The introduced characteristic quantifies the system's dynamics at the nanoseconds time scale. It has been found that the peptide exhibits nanoseconds long periods that significantly differ in the rates of the exploration of the dynamically allowed configurations of the environment. During these periods the rates remain the same but different from other periods and from the rate for water. Periods of dynamical frustration are detected when only limited routes in the space of possible trajectories of the surrounding atoms are realised.

Business strategy and the environment in a traditional manufacturing sector

This research is concerned with the relationship between business strategy and the environment within traditional sectors. It has sought to learn more about the strategic environmental attitudes of SMEs compared with large companies operating under the same market conditions. The sector studied is the ceramics industry (including tableware & ornamental-ware, sanitary ware & tiles, bricks, industrial & advanced ceramics and refractories) in the UK and France. Unlike the automotive, oil, chemical, steel or metal processing sectors, this industry is one of the few industrial sectors which has rarely been considered. The information on this sector was gathered by interviewing people responsible for environmental issues. The actual programme of valid interviews represents approximately a quarter of the UK and French ceramics industry which is large enough to enable a quantitative analysis and significant and non-biased conclusions. As a whole, all companies surveyed agreed that the ceramics activity impacts on the environment, and that they are increasingly affected both by environmental legislation, and by various non-legislative pressures. Approaches to the environmental agenda differ significantly among large and small companies. Smaller companies feel particularly pressed both by the financial costs and management time required to meet complex and changing legislation. The results of this survey also suggest that the ceramics industry sees environmental issues in terms of increased costs rather than new business opportunities. This is due principally to fears of import substitution from countries with lower environmental standards. Finally, replies indicate that generally there is a low level of awareness of the current legislative framework, suggesting a need to shift from a regulatory approach to a more self-regulated approach which encourages companies to be more proactive

An assessment of the learning benefits of using a web-based learning environment when teaching accounting

The impact and use of information and communication technology on learning outcomes for accounting students is not well understood. This study investigates the impact of design features of Blackboard 1 used as aWeb-based Learning Environment (WBLE) in teaching undergraduate accounting students. Specifically, this investigation reports on a number of Blackboard design features (e.g. delivery of lecture notes, announcements, online assessment and model answers) used to deliver learning materials regarded as necessary to enhance learning outcomes. Responses from 369 on-campus students provided data to develop a regression model that seeks to explain enhanced participation and mental effort. The final regression shows that student satisfaction with the use of a WBLE is associated with five design features or variables. These include usefulness and availability of lecture notes, online assessment, model answers, and online chat.

Women in management in the new economic environment:the case of India

Liberalization of the Indian economy has created considerable employment opportunities for those, including women, who possess marketable skills and talent. Historically, women in India have not enjoyed a good status in workplace settings whether in managerial or operative roles. This traditional positioning of women has restricted the intensity of their efforts towards realizing the benefits of the globalisation process. An attempt has been made in this contribution to highlight the important issues relating to women in management in the Indian context. The messages from a review of the literature are analysed. Research evidence from various sources is presented to highlight the dynamics of developments in the status of Indian women managers. The contribution discusses the main aspects of the historical, socio-cultural and economic factors influencing women managers: issues concerning gender-based stereotypes; the main barriers to women's movement to top managerial positions; the impact of developments in information technology (IT) on women managers; and the way forward. Results from two research projects are also presented. The analysis has important messages for practitioners and contributes to women's studies and management in the Indian context. © 2005 Taylor & Francis Ltd.

Design and development of an agent-based information system for process improvement

To meet changing needs of customers and to survive in the increasingly globalised and competitive environment, it is necessary for companies to equip themselves with intelligent tools, thereby enabling managerial levels to use the tactical decision in a better way. However, the implementation of an intelligent system is always a challenge in Small- and Medium-sized Enterprises (SMEs). Therefore, a new and simple approach with 'process rethinking' ability is proposed to generate ongoing process improvements over time. In this paper, a roadmap of the development of an agent-based information system is described. A case example has also been provided to show how the system can assist non-specialists, for example, managers and engineers to make right decisions for a continual process improvement. Copyright © 2006 Inderscience Enterprises Ltd.