Cryptographic techniques for personal communication systems security

The advent of personal communication systems within the last decade has depended upon the utilization of advanced digital schemes for source and channel coding and for modulation. The inherent digital nature of the communications processing has allowed the convenient incorporation of cryptographic techniques to implement security in these communications systems. There are various security requirements, of both the service provider and the mobile subscriber, which may be provided for in a personal communications system. Such security provisions include the privacy of user data, the authentication of communicating parties, the provision for data integrity, and the provision for both location confidentiality and party anonymity. This thesis is concerned with an investigation of the private-key and public-key cryptographic techniques pertinent to the security requirements of personal communication systems and an analysis of the security provisions of Second-Generation personal communication systems is presented. Particular attention has been paid to the properties of the cryptographic protocols which have been employed in current Second-Generation systems. It has been found that certain security-related protocols implemented in the Second-Generation systems have specific weaknesses. A theoretical evaluation of these protocols has been performed using formal analysis techniques and certain assumptions made during the development of the systems are shown to contribute to the security weaknesses. Various attack scenarios which exploit these protocol weaknesses are presented. The Fiat-Sharmir zero-knowledge cryptosystem is presented as an example of how asymmetric algorithm cryptography may be employed as part of an improved security solution. Various modifications to this cryptosystem have been evaluated and their critical parameters are shown to be capable of being optimized to suit a particular applications. The implementation of such a system using current smart card technology has been evaluated.

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.

GEO Label:user and producer perspectives on a label for geospatial data

One of the aims of the Science and Technology Committee (STC) of the Group on Earth Observations (GEO) was to establish a GEO Label- a label to certify geospatial datasets and their quality. As proposed, the GEO Label will be used as a value indicator for geospatial data and datasets accessible through the Global Earth Observation System of Systems (GEOSS). It is suggested that the development of such a label will significantly improve user recognition of the quality of geospatial datasets and that its use will help promote trust in datasets that carry the established GEO Label. Furthermore, the GEO Label is seen as an incentive to data providers. At the moment GEOSS contains a large amount of data and is constantly growing. Taking this into account, a GEO Label could assist in searching by providing users with visual cues of dataset quality and possibly relevance; a GEO Label could effectively stand as a decision support mechanism for dataset selection. Currently our project - GeoViQua, - together with EGIDA and ID-03 is undertaking research to define and evaluate the concept of a GEO Label. The development and evaluation process will be carried out in three phases. In phase I we have conducted an online survey (GEO Label Questionnaire) to identify the initial user and producer views on a GEO Label or its potential role. In phase II we will conduct a further study presenting some GEO Label examples that will be based on Phase I. We will elicit feedback on these examples under controlled conditions. In phase III we will create physical prototypes which will be used in a human subject study. The most successful prototypes will then be put forward as potential GEO Label options. At the moment we are in phase I, where we developed an online questionnaire to collect the initial GEO Label requirements and to identify the role that a GEO Label should serve from the user and producer standpoint. The GEO Label Questionnaire consists of generic questions to identify whether users and producers believe a GEO Label is relevant to geospatial data; whether they want a single "one-for-all" label or separate labels that will serve a particular role; the function that would be most relevant for a GEO Label to carry; and the functionality that users and producers would like to see from common rating and review systems they use. To distribute the questionnaire, relevant user and expert groups were contacted at meetings or by email. At this stage we successfully collected over 80 valid responses from geospatial data users and producers. This communication will provide a comprehensive analysis of the survey results, indicating to what extent the users surveyed in Phase I value a GEO Label, and suggesting in what directions a GEO Label may develop. Potential GEO Label examples based on the results of the survey will be presented for use in Phase II.

Spatio-temporal techniques for user identification by means of GPS mobility data

One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of “significant places”, thus making it possible to identify a user from his/her mobility data. In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.

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.

Semi-supervised learning of hierarchical latent trait models for data visualisation

An interactive hierarchical Generative Topographic Mapping (HGTM) ¸iteH_GTM has been developed to visualise complex data sets. In this paper, we build a more general visualisation system by extending the HGTM visualisation system in 3 directions: bf (1) We generalize HGTM to noise models from the exponential family of distributions. The basic building block is the Latent Trait Model (LTM) developed in ¸iteKaban_pami. bf (2) We give the user a choice of initializing the child plots of the current plot in either em interactive, or em automatic mode. In the interactive mode the user interactively selects ``regions of interest'' as in ¸iteH_GTM, whereas in the automatic mode an unsupervised minimum message length (MML)-driven construction of a mixture of LTMs is employed. bf (3) We derive general formulas for magnification factors in latent trait models. Magnification factors are a useful tool to improve our understanding of the visualisation plots, since they can highlight the boundaries between data clusters. The unsupervised construction is particularly useful when high-level plots are covered with dense clusters of highly overlapping data projections, making it difficult to use the interactive mode. Such a situation often arises when visualizing large data sets. We illustrate our approach on a toy example and apply our system to three more complex real data sets.

Data visualization with simultaneous feature selection

Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of the visualization model. The approach not only provides a better projection by modeling irrelevant features with a separate noise model but also gives feature saliency values which help the user to assess the significance of each feature. We compare the quality of projection obtained using the new approach with the projections from traditional GTM and self-organizing maps (SOM) algorithms. The results obtained on a synthetic and a real-life chemoinformatics dataset demonstrate that the proposed approach successfully identifies feature significance and provides coherent (compact) projections. © 2006 IEEE.

Introduction to the theory and application of data envelopment analysis:a foundation text with integrated software

The book aims to introduce the reader to DEA in the most accessible manner possible. It is specifically aimed at those who have had no prior exposure to DEA and wish to learn its essentials, how it works, its key uses, and the mechanics of using it. The latter will include using DEA software. Students on degree or training courses will find the book especially helpful. The same is true of practitioners engaging in comparative efficiency assessments and performance management within their organisation. Examples are used throughout the book to help the reader consolidate the concepts covered. Table of content: List of Tables. List of Figures. Preface. Abbreviations. 1. Introduction to Performance Measurement. 2. Definitions of Efficiency and Related Measures. 3. Data Envelopment Analysis Under Constant Returns to Scale: Basic Principles. 4. Data Envelopment Analysis under Constant Returns to Scale: General Models. 5. Using Data Envelopment Analysis in Practice. 6. Data Envelopment Analysis under Variable Returns to Scale. 7. Assessing Policy Effectiveness and Productivity Change Using DEA. 8. Incorporating Value Judgements in DEA Assessments. 9. Extensions to Basic DEA Models. 10. A Limited User Guide for Warwick DEA Software. Author Index. Topic Index. References.

Updating finite element models using measured vibration data

The modelling of mechanical structures using finite element analysis has become an indispensable stage in the design of new components and products. Once the theoretical design has been optimised a prototype may be constructed and tested. What can the engineer do if the measured and theoretically predicted vibration characteristics of the structure are significantly different? This thesis considers the problems of changing the parameters of the finite element model to improve the correlation between a physical structure and its mathematical model. Two new methods are introduced to perform the systematic parameter updating. The first uses the measured modal model to derive the parameter values with the minimum variance. The user must provide estimates for the variance of the theoretical parameter values and the measured data. Previous authors using similar methods have assumed that the estimated parameters and measured modal properties are statistically independent. This will generally be the case during the first iteration but will not be the case subsequently. The second method updates the parameters directly from the frequency response functions. The order of the finite element model of the structure is reduced as a function of the unknown parameters. A method related to a weighted equation error algorithm is used to update the parameters. After each iteration the weighting changes so that on convergence the output error is minimised. The suggested methods are extensively tested using simulated data. An H frame is then used to demonstrate the algorithms on a physical structure.

Packet voice/data in local broadcast networks

A local area network that can support both voice and data packets offers economic advantages due to the use of only a single network for both types of traffic, greater flexibility to changing user demands, and it also enables efficient use to be made of the transmission capacity. The latter aspect is very important in local broadcast networks where the capacity is a scarce resource, for example mobile radio. This research has examined two types of local broadcast network, these being the Ethernet-type bus local area network and a mobile radio network with a central base station. With such contention networks, medium access control (MAC) protocols are required to gain access to the channel. MAC protocols must provide efficient scheduling on the channel between the distributed population of stations who want to transmit. No access scheme can exceed the performance of a single server queue, due to the spatial distribution of the stations. Stations cannot in general form a queue without using part of the channel capacity to exchange protocol information. In this research, several medium access protocols have been examined and developed in order to increase the channel throughput compared to existing protocols. However, the established performance measures of average packet time delay and throughput cannot adequately characterise protocol performance for packet voice. Rather, the percentage of bits delivered within a given time bound becomes the relevant performance measure. Performance evaluation of the protocols has been examined using discrete event simulation and in some cases also by mathematical modelling. All the protocols use either implicit or explicit reservation schemes, with their efficiency dependent on the fact that many voice packets are generated periodically within a talkspurt. Two of the protocols are based on the existing 'Reservation Virtual Time CSMA/CD' protocol, which forms a distributed queue through implicit reservations. This protocol has been improved firstly by utilising two channels, a packet transmission channel and a packet contention channel. Packet contention is then performed in parallel with a packet transmission to increase throughput. The second protocol uses variable length packets to reduce the contention time between transmissions on a single channel. A third protocol developed, is based on contention for explicit reservations. Once a station has achieved a reservation, it maintains this effective queue position for the remainder of the talkspurt and transmits after it has sensed the transmission from the preceeding station within the queue. In the mobile radio environment, adaptions to the protocols were necessary in order that their operation was robust to signal fading. This was achieved through centralised control at a base station, unlike the local area network versions where the control was distributed at the stations. The results show an improvement in throughput compared to some previous protocols. Further work includes subjective testing to validate the protocols' effectiveness.

Data transmission by frequency-hopped multilevel frequency shift keying

Spread spectrum systems make use of radio frequency bandwidths which far exceed the minimum bandwidth necessary to transmit the basic message information.These systems are designed to provide satisfactory communication of the message information under difficult transmission conditions. Frequency-hopped multilevel frequency shift keying (FH-MFSK) is one of the many techniques used in spread spectrum systems. It is a combination of frequency hopping and time hopping. In this system many users share a common frequency band using code division multiplexing. Each user is assigned an address and the message is modulated into the address. The receiver, knowing the address, decodes the received signal and extracts the message. This technique is suggested for digital mobile telephony. This thesis is concerned with an investigation of the possibility of utilising FH-MFSK for data transmission corrupted by additive white gaussian noise (A.W.G.N.). Work related to FH-MFSK has so far been mostly confined to its validity, and its performance in the presence of A.W.G.N. has not been reported before. An experimental system was therefore constructed which utilised combined hardware and software and operated under the supervision of a microprocessor system. The experimental system was used to develop an error-rate model for the system under investigation. The performance of FH-MFSK for data transmission was established in the presence of A.W.G.N. and with deleted and delayed sample effects. Its capability for multiuser applications was determined theoretically. The results show that FH-MFSK is a suitable technique for data transmission in the presence of A.W.G.N.

Spatially clustered associations in health related geospatial data

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources an dWeb services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial ‘mashups’ to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and ‘correlation’ of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.

Visual data mining: integrating machine learning with information visualization

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. Most existing systems concentrate either on mining algorithms or on visualization techniques. Though visual methods developed in information visualization have been helpful, for improved understanding of a complex large high-dimensional dataset, there is a need for an effective projection of such a dataset onto a lower-dimension (2D or 3D) manifold. This paper introduces a flexible visual data mining framework which combines advanced projection algorithms developed in the machine learning domain and visual techniques developed in the information visualization domain. The framework follows Shneiderman’s mantra to provide an effective user interface. The advantage of such an interface is that the user is directly involved in the data mining process. We integrate principled projection methods, such as Generative Topographic Mapping (GTM) and Hierarchical GTM (HGTM), with powerful visual techniques, such as magnification factors, directional curvatures, parallel coordinates, billboarding, and user interaction facilities, to provide an integrated visual data mining framework. Results on a real life high-dimensional dataset from the chemoinformatics domain are also reported and discussed. Projection results of GTM are analytically compared with the projection results from other traditional projection methods, and it is also shown that the HGTM algorithm provides additional value for large datasets. The computational complexity of these algorithms is discussed to demonstrate their suitability for the visual data mining framework.

Automatic processing, quality assurance and serving of real-time weather data over lightweight protocols

Recent advances in technology have produced a significant increase in the availability of free sensor data over the Internet. With affordable weather monitoring stations now available to individual meteorology enthusiasts a reservoir of real time data such as temperature, rainfall and wind speed can now be obtained for most of the United States and Europe. Despite the abundance of available data, obtaining useable information about the weather in your local neighbourhood requires complex processing that poses several challenges. This paper discusses a collection of technologies and applications that harvest, refine and process this data, culminating in information that has been tailored toward the user. In this case we are particularly interested in allowing a user to make direct queries about the weather at any location, even when this is not directly instrumented, using interpolation methods. We also consider how the uncertainty that the interpolation introduces can then be communicated to the user of the system, using UncertML, a developing standard for uncertainty representation.