Assessing the impulse control of violent offenders using computer-based cognitive performance tasks

The topic of this thesis is impulsivity. The meaning and measurement of impulse control is explored, with a particular focus on forensic settings. Impulsivity is central to many areas of psychology; it is one of the most common diagnostic criteria of mental disorders and is fundamental to the understanding of forensic personalities. Despite this widespread importance there is little agreement as to the definition or structure of impulsivity, and its measurement is fraught with difficulty owing to a reliance on self-report methods. This research aims to address this problem by investigating the viability of using simple computerised cognitive performance tasks as complementary components of a multi-method assessment strategy for impulse control. Ultimately, the usefulness of this measurement strategy for a forensic sample is assessed. Impulsivity is found to be a multifaceted construct comprised of a constellation of distinct sub-dimensions. Computerised cognitive performance tasks are valid and reliable measures that can assess impulsivity at a neuronal level. Self-report and performance task methods assess distinct components of impulse control and, for the optimal assessment of impulse control, a multi-method battery of self-report and performance task measures is advocated. Such a battery is shown to have demonstrated utility in a forensic sample, and recommendations for forensic assessment in the Irish context are discussed.

Hazard and early warning analysis based on domain specific modeling technologies

An aim of proactive risk management strategies is the timely identification of safety related risks. One way to achieve this is by deploying early warning systems. Early warning systems aim to provide useful information on the presence of potential threats to the system, the level of vulnerability of a system, or both of these, in a timely manner. This information can then be used to take proactive safety measures. The United Nation’s has recommended that any early warning system need to have four essential elements, which are the risk knowledge element, a monitoring and warning service, dissemination and communication and a response capability. This research deals with the risk knowledge element of an early warning system. The risk knowledge element of an early warning system contains models of possible accident scenarios. These accident scenarios are created by using hazard analysis techniques, which are categorised as traditional and contemporary. The assumption in traditional hazard analysis techniques is that accidents are occurred due to a sequence of events, whereas, the assumption of contemporary hazard analysis techniques is that safety is an emergent property of complex systems. The problem is that there is no availability of a software editor which can be used by analysts to create models of accident scenarios based on contemporary hazard analysis techniques and generate computer code that represent the models at the same time. This research aims to enhance the process of generating computer code based on graphical models that associate early warning signs and causal factors to a hazard, based on contemporary hazard analyses techniques. For this purpose, the thesis investigates the use of Domain Specific Modeling (DSM) technologies. The contributions of this thesis is the design and development of a set of three graphical Domain Specific Modeling languages (DSML)s, that when combined together, provide all of the necessary constructs that will enable safety experts and practitioners to conduct hazard and early warning analysis based on a contemporary hazard analysis approach. The languages represent those elements and relations necessary to define accident scenarios and their associated early warning signs. The three DSMLs were incorporated in to a prototype software editor that enables safety scientists and practitioners to create and edit hazard and early warning analysis models in a usable manner and as a result to generate executable code automatically. This research proves that the DSM technologies can be used to develop a set of three DSMLs which can allow user to conduct hazard and early warning analysis in more usable manner. Furthermore, the three DSMLs and their dedicated editor, which are presented in this thesis, may provide a significant enhancement to the process of creating the risk knowledge element of computer based early warning systems.

Surveillance during pregnancy: methods and response rates from a hospital based pilot study of the Pregnancy Risk Assessment Monitoring System in Ireland

Background: Many European countries including Ireland lack high quality, on-going, population based estimates of maternal behaviours and experiences during pregnancy. PRAMS is a CDC surveillance program which was established in the United States in 1987 to generate high quality, population based data to reduce infant mortality rates and improve maternal and infant health. PRAMS is the only on-going population based surveillance system of maternal behaviours and experiences that occur before, during and after pregnancy worldwide.Methods: The objective of this study was to adapt, test and evaluate a modified CDC PRAMS methodology in Ireland. The birth certificate file which is the standard approach to sampling for PRAMS in the United States was not available for the PRAMS Ireland study. Consequently, delivery record books for the period between 3 and 5 months before the study start date at a large urban obstetric hospital [8,900 births per year] were used to randomly sample 124 women. Name, address, maternal age, infant sex, gestational age at delivery, delivery method, APGAR score and birth weight were manually extracted from records. Stillbirths and early neonatal deaths were excluded using APGAR scores and hospital records. Women were sent a letter of invitation to participate including option to opt out, followed by a modified PRAMS survey, a reminder letter and a final survey.Results: The response rate for the pilot was 67%. Two per cent of women refused the survey, 7% opted out of the study and 24% did not respond. Survey items were at least 88% complete for all 82 respondents. Prevalence estimates of socially undesirable behaviours such as alcohol consumption during pregnancy were high [>50%] and comparable with international estimates.Conclusion: PRAMS is a feasible and valid method of collecting information on maternal experiences and behaviours during pregnancy in Ireland. PRAMS may offer a potential solution to data deficits in maternal health behaviour indicators in Ireland with further work. This study is important to researchers in Europe and elsewhere who may be interested in new ways of tailoring an established CDC methodology to their unique settings to resolve data deficits in maternal health.

VAMP - a vision based sensor network for health care hygiene

Adequate hand-washing has been shown to be a critical activity in preventing the transmission of infections such as MRSA in health-care environments. Hand-washing guidelines published by various health-care related institutions recommend a technique incorporating six hand-washing poses that ensure all areas of the hands are thoroughly cleaned. In this paper, an embedded wireless vision system (VAMP) capable of accurately monitoring hand-washing quality is presented. The VAMP system hardware consists of a low resolution CMOS image sensor and FPGA processor which are integrated with a microcontroller and ZigBee standard wireless transceiver to create a wireless sensor network (WSN) based vision system that can be retargeted at a variety of health care applications. The device captures and processes images locally in real-time, determines if hand-washing procedures have been correctly undertaken and then passes the resulting high-level data over a low-bandwidth wireless link. The paper outlines the hardware and software mechanisms of the VAMP system and illustrates that it offers an easy to integrate sensor solution to adequately monitor and improve hand hygiene quality. Future work to develop a miniaturized, low cost system capable of being integrated into everyday products is also discussed.

Hardware design of cryptographic accelerators

With the rapid growth of the Internet and digital communications, the volume of sensitive electronic transactions being transferred and stored over and on insecure media has increased dramatically in recent years. The growing demand for cryptographic systems to secure this data, across a multitude of platforms, ranging from large servers to small mobile devices and smart cards, has necessitated research into low cost, flexible and secure solutions. As constraints on architectures such as area, speed and power become key factors in choosing a cryptosystem, methods for speeding up the development and evaluation process are necessary. This thesis investigates flexible hardware architectures for the main components of a cryptographic system. Dedicated hardware accelerators can provide significant performance improvements when compared to implementations on general purpose processors. Each of the designs proposed are analysed in terms of speed, area, power, energy and efficiency. Field Programmable Gate Arrays (FPGAs) are chosen as the development platform due to their fast development time and reconfigurable nature. Firstly, a reconfigurable architecture for performing elliptic curve point scalar multiplication on an FPGA is presented. Elliptic curve cryptography is one such method to secure data, offering similar security levels to traditional systems, such as RSA, but with smaller key sizes, translating into lower memory and bandwidth requirements. The architecture is implemented using different underlying algorithms and coordinates for dedicated Double-and-Add algorithms, twisted Edwards algorithms and SPA secure algorithms, and its power consumption and energy on an FPGA measured. Hardware implementation results for these new algorithms are compared against their software counterparts and the best choices for minimum area-time and area-energy circuits are then identified and examined for larger key and field sizes. Secondly, implementation methods for another component of a cryptographic system, namely hash functions, developed in the recently concluded SHA-3 hash competition are presented. Various designs from the three rounds of the NIST run competition are implemented on FPGA along with an interface to allow fair comparison of the different hash functions when operating in a standardised and constrained environment. Different methods of implementation for the designs and their subsequent performance is examined in terms of throughput, area and energy costs using various constraint metrics. Comparing many different implementation methods and algorithms is nontrivial. Another aim of this thesis is the development of generic interfaces used both to reduce implementation and test time and also to enable fair baseline comparisons of different algorithms when operating in a standardised and constrained environment. Finally, a hardware-software co-design cryptographic architecture is presented. This architecture is capable of supporting multiple types of cryptographic algorithms and is described through an application for performing public key cryptography, namely the Elliptic Curve Digital Signature Algorithm (ECDSA). This architecture makes use of the elliptic curve architecture and the hash functions described previously. These components, along with a random number generator, provide hardware acceleration for a Microblaze based cryptographic system. The trade-off in terms of performance for flexibility is discussed using dedicated software, and hardware-software co-design implementations of the elliptic curve point scalar multiplication block. Results are then presented in terms of the overall cryptographic system.

Learning to drive: from hazard detection to hazard handling

Hazard perception has been found to correlate with crash involvement, and has thus been suggested as the most likely source of any skill gap between novice and experienced drivers. The most commonly used method for measuring hazard perception is to evaluate the perception-reaction time to filmed traffic events. It can be argued that this method lacks ecological validity and may be of limited value in predicting the actions drivers’ will take to hazards encountered. The first two studies of this thesis compare novice and experienced drivers’ performance on a hazard detection test, requiring discrete button press responses, with their behaviour in a more dynamic driving environment, requiring hazard handling ability. Results indicate that the hazard handling test is more successful at identifying experience-related differences in response time to hazards. Hazard detection test scores were strongly related to performance on a driver theory test, implying that traditional hazard perception tests may be focusing more on declarative knowledge of driving than on the procedural knowledge required to successfully avoid hazards while driving. One in five Irish drivers crash within a year of passing their driving test. This suggests that the current driver training system does not fully prepare drivers for the dangers they will encounter. Thus, the third and fourth studies in this thesis focus on the development of two simulator-based training regimes. In the third study participants receive intensive training on the molar elements of driving i.e. speed and distance evaluation. The fourth study focuses on training higher order situation awareness skills, including perception, comprehension and projection. Results indicate significant improvement in aspects of speed, distance and situation awareness across training days. However, neither training programme leads to significant improvements in hazard handling performance, highlighting the difficulties of applying learning to situations not previously encountered.