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Uwe Aickelin

Publications and source records attributed to Uwe Aickelin.

At least 127 records · Page 7Linked to original sources

Simulating the Dynamics of T Cell Subsets Throughout the Lifetime

It is widely accepted that the immune system undergoes age-related changes correlating with increased disease in the elderly. T cell subsets have been implicated. The aim of this work is firstly to implement and validate a simulation of T regulatory cell (Treg) dynamics throughout the lifetime, based on a model by Baltcheva. We show that our initial simulation produces an inversion between precursor and mature Treys at around 20 years of age, though the output differs significantly from the original laboratory dataset. Secondly, this report discusses development of the model to incorporate new data from a cross-sectional study of healthy blood donors addressing balance between Treys and Th17 cells with novel markers for Treg. The potential for simulation to add insight into immune aging is discussed.

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Comparing Decison Support Tools for Cargo Screening Processes

When planning to change operations at ports there are two key stake holders with very different interests involved in the decision making processes. Port operators are attentive to their standards, a smooth service flow and economic viability while border agencies are concerned about national security. The time taken for security checks often interferes with the compliance to service standards that port operators would like to achieve. Decision support tools as for example Cost-Benefit Analysis or Multi Criteria Analysis are useful helpers to better understand the impact of changes to a system. They allow investigating future scenarios and helping to find solutions that are acceptable for all parties involved in port operations. In this paper we evaluate two different modelling methods, namely scenario analysis and discrete event simulation. These are useful for driving the decision support tools (i.e. they provide the inputs the decision support tools require). Our aims are, on the one hand, to guide the reader through the modelling processes and, on the other hand, to demonstrate what kind of decision support information one can obtain from the different modelling methods presented.

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Investigating Immune System Aging: System Dynamics and Agent-Based Modeling

System dynamics and agent based simulation models can both be used to model and understand interactions of entities within a population. Our modeling work presented here is concerned with understanding the suitability of the different types of simulation for the immune system aging problems and comparing their results. We are trying to answer questions such as: How fit is the immune system given a certain age? Would an immune boost be of therapeutic value, e.g. to improve the effectiveness of a simultaneous vaccination? Understanding the processes of immune system aging and degradation may also help in development of therapies that reverse some of the damages caused thus improving life expectancy. Therefore as a first step our research focuses on T cells; major contributors to immune system functionality. One of the main factors influencing immune system aging is the output rate of naive T cells. Of further interest is the number and phenotypical variety of these cells in an individual, which will be the case study focused on in this paper.

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Scenario Analysis, Decision Trees and Simulation for Cost Benefit Analysis of the Cargo Screening Process

In this paper we present our ideas for conducting a cost benefit analysis by using three different methods: scenario analysis, decision trees and simulation. Then we introduce our case study and examine these methods in a real world situation. We show how these tools can be used and what the results are for each of them. Our aim is to conduct a comparison of these different probabilistic methods of estimating costs for port security risk assessment studies. Methodologically, we are trying to understand the limits of all the tools mentioned above by focusing on rare events.

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Towards modelling cost and risks of infrequent events in the cargo screening process

We introduce a simulation model of the port of Calais with a focus on the operation of immigration controls. Our aim is to compare the cost and benefits of different screening policies. Methodologically, we are trying to understand the limits of discrete event simulation of rare events. When will they become 'too rare' for simulation to give meaningful results?

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The effect of baroque music on the PassPoints graphical password

Graphical passwords have been demonstrated to be the possible alternatives to traditional alphanumeric passwords. However, they still tend to follow predictable patterns that are easier to attack. The crux of the problem is users' memory limitations. Users are the weakest link in password authentication mechanism. It shows that baroque music has positive effects on human memorizing and learning. We introduce baroque music to the PassPoints graphical password scheme and conduct a laboratory study in this paper. Results shown that there is no statistic difference between the music group and the control group without music in short-term recall experiments, both had high recall success rates. But in long-term recall, the music group performed significantly better. We also found that the music group tended to set significantly more complicated passwords, which are usually more resistant to dictionary and other guess attacks. But compared with the control group, the music group took more time to log in both in short-term and long-term tests. Besides, it appears that background music does not work in terms of hotspots.

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Can background baroque music help to improve the memorability of graphical passwords?

Graphical passwords have been proposed as an alternative to alphanumeric passwords with their advantages in usability and security. However, they still tend to follow predictable patterns that are easier for attackers to exploit, probably due to users' memory limitations. Various literatures show that baroque music has positive effects on human learning and memorizing. To alleviate users' memory burden, we investigate the novel idea of introducing baroque music to graphical password schemes (specifically DAS, PassPoints and Story) and conduct a laboratory study to see whether it is helpful. In a ten minutes short-term recall, we found that participants in all conditions had high recall success rates that were not statistically different from each other. After one week, the music group coped PassPoints passwords significantly better than the group without music. But there was no statistical difference between two groups in recalling DAS passwords or Story passwords. Further more, we found that the music group tended to set significantly more complicated PassPoints passwords but less complicated DAS passwords.

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A New Graphical Password Scheme Resistant to Shoulder-Surfing

Shoulder-surfing is a known risk where an attacker can capture a password by direct observation or by recording the authentication session. Due to the visual interface, this problem has become exacerbated in graphical passwords. There have been some graphical schemes resistant or immune to shoulder-surfing, but they have significant usability drawbacks, usually in the time and effort to log in. In this paper, we propose and evaluate a new shoulder-surfing resistant scheme which has a desirable usability for PDAs. Our inspiration comes from the drawing input method in DAS and the association mnemonics in Story for sequence retrieval. The new scheme requires users to draw a curve across their password images orderly rather than click directly on them. The drawing input trick along with the complementary measures, such as erasing the drawing trace, displaying degraded images, and starting and ending with randomly designated images provide a good resistance to shouldersurfing. A preliminary user study showed that users were able to enter their passwords accurately and to remember them over time.

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An audio CAPTCHA to distinguish humans from computers

CAPTCHAs are employed as a security measure to differentiate human users from bots. A new sound-based CAPTCHA is proposed in this paper, which exploits the gaps between human voice and synthetic voice rather than relays on the auditory perception of human. The user is required to read out a given sentence, which is selected randomly from a specified book. The generated audio file will be analyzed automatically to judge whether the user is a human or not. In this paper, the design of the new CAPTCHA, the analysis of the audio files, and the choice of the audio frame window function are described in detail. And also, some experiments are conducted to fix the critical threshold and the coefficients of three indicators to ensure the security. The proposed audio CAPTCHA is proved accessible to users. The user study has shown that the human success rate reaches approximately 97% and the pass rate of attack software using Microsoft SDK 5.1 is only 4%. The experiments also indicated that it could be solved by most human users in less than 14 seconds and the average time is only 7.8 seconds.

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Defining a Simulation Strategy for Cancer Immunocompetence

Although there are various types of cancer treatments, none of these currently take into account the effect of ageing of the immune system and hence altered responses to cancer. Recent studies have shown that in vitro stimulation of T cells can help in the treatment of patients. There are many factors that have to be considered when simulating an organism's immunocompetence. Our particular interest lies in the study of loss of immunocompetence with age. We are trying to answer questions such as: Given a certain age of a patient, how fit is their immune system to fight cancer? Would an immune boost improve the effectiveness of a cancer treatment given the patient's immune phenotype and age? We believe that understanding the processes of immune system ageing and degradation through computer simulation may help in answering these questions. Specifically, we have decided to look at the change in numbers of naive T cells with age, as they play a important role in responses to cancer and anti-tumour vaccination. In this work we present an agent-based simulation model to understand the interactions which influence the naive T cell populations over time. Our agent model is based on existing mathematical system dynamic model, but in comparisons offers better scope for customisation and detailed analysis. We believe that the results obtained can in future help with the modelling of T cell populations inside tumours.

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The Dendritic Cell Algorithm for Intrusion Detection

As one of the solutions to intrusion detection problems, Artificial Immune Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is no one archetypal AIS, instead there are four major paradigms. Among them, the Dendritic Cell Algorithm (DCA) has produced promising results in various applications. The aim of this chapter is to demonstrate the potential for the DCA as a suitable candidate for intrusion detection problems. We review some of the commonly used AIS paradigms for intrusion detection problems and demonstrate the advantages of one particular algorithm, the DCA. In order to clearly describe the algorithm, the background to its development and a formal definition are given. In addition, improvements to the original DCA are presented and their implications are discussed, including previous work done on an online analysis component with segmentation and ongoing work on automated data preprocessing. Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.

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Evaluating Different Cost-Benefit Analysis Methods for Port Security Operations

Service industries, such as ports, are attentive to their standards, a smooth service flow and economic viability. Cost benefit analysis has proven itself as a useful tool to support this type of decision making; it has been used by businesses and governmental agencies for many years. In this book chapter we demonstrate different modelling methods that are used for estimating input factors required for conducting cost benefit analysis based on a single case study. These methods are: scenario analysis, decision trees, Monte-Carlo simulation modelling and discrete event simulation modelling. Our aims are, on the one hand, to guide the analyst through the modelling processes and, on the other hand, to demonstrate what additional decision support information can be obtained from applying each of these modelling methods.

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Investigating the effectiveness of Variance Reduction Techniques in Manufacturing, Call Center and Cross-docking Discrete Event Simulation Models

Variance reduction techniques have been shown by others in the past to be a useful tool to reduce variance in Simulation studies. However, their application and success in the past has been mainly domain specific, with relatively little guidelines as to their general applicability, in particular for novices in this area. To facilitate their use, this study aims to investigate the robustness of individual techniques across a set of scenarios from different domains. Experimental results show that Control Variates is the only technique which achieves a reduction in variance across all domains. Furthermore, applied individually, Antithetic Variates and Control Variates perform particularly well in the Cross-docking scenarios, which was previously unknown.

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Real-world Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms

In mobile robotics, a solid test for adaptation is the ability of a control system to function not only in a diverse number of physical environments, but also on a number of different robotic platforms. This paper demonstrates that a set of behaviours evolved in simulation on a miniature robot (epuck) can be transferred to a much larger-scale platform (Pioneer), both in simulation and in the real world. The chosen architecture uses artificial evolution of epuck behaviours to obtain a genetic sequence, which is then employed to seed an idiotypic, artificial immune system (AIS) on the Pioneers. Despite numerous hardware and software differences between the platforms, navigation and target-finding experiments show that the evolved behaviours transfer very well to the larger robot when the idiotypic AIS technique is used. In contrast, transferability is poor when reinforcement learning alone is used, which validates the adaptability of the chosen architecture.

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Motif Detection Inspired by Immune Memory (JORS)

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify variable length unknown motifs which repeat within time series data. The algorithm searches from a neutral perspective that is independent of the data being analysed and the underlying motifs. In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets. The algorithm is able to identify a population of meaningful motifs in both cases, and the value of these motifs is discussed.

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Modelling Electricity Consumption in Office Buildings: An Agent Based Approach

In this paper, we develop an agent-based model which integrates four important elements, i.e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, to simulate the electricity consumption in office buildings. Based on a case study, we use this model to test the effectiveness of different electricity management strategies, and solve practical office electricity consumption problems. This paper theoretically contributes to an integration of the four elements involved in the complex organisational issue of office electricity consumption, and practically contributes to an application of an agent-based approach for office building electricity consumption study.

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Privileged Information for Data Clustering

Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case of unsupervised learning, or the input and output space X x Y in the case of supervised and semi-supervised learning. In the last number of years the relaxation of this assumption has been explored and the importance of incorporation of additional information within machine learning algorithms became more apparent. Traditionally such fusion of information was the domain of semi-supervised learning. More recently the inclusion of knowledge from separate hypothetical spaces has been proposed by Vapnik as part of the supervised setting. In this work we are interested in exploring Vapnik's idea of master-class learning and the associated learning using privileged information, however within the unsupervised setting. Adoption of the advanced supervised learning paradigm for the unsupervised setting instigates investigation into the difference between privileged and technical data. By means of our proposed aRi-MAX method stability of the KMeans algorithm is improved and identification of the best clustering solution is achieved on an artificial dataset. Subsequently an information theoretic dot product based algorithm called P-Dot is proposed. This method has the ability to utilize a wide variety of clustering techniques, individually or in combination, while fusing privileged and technical data for improved clustering. Application of the P-Dot method to the task of digit recognition confirms our findings in a real-world scenario.

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Validation of a Microsimulation of the Port of Dover

Modelling and simulating the traffic of heavily used but secure environments such as seaports and airports is of increasing importance. Errors made when simulating these environments can have long standing economic, social and environmental implications. This paper discusses issues and problems that may arise when designing a simulation strategy. Data for the Port is presented, methods for lightweight vehicle assessment that can be used to calibrate and validate simulations are also discussed along with a diagnosis of overcalibration issues. We show that decisions about where the intelligence lies in a system has important repercussions for the reliability of system statistics. Finally, conclusions are drawn about how microsimulations can be moved forward as a robust planning tool for the 21st century.

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