The ink and stylus tablets discovered at the Roman Fort of Vindolanda have provided a unique resource for scholars of ancient history. However, the stylus tablets in particular have proved extremely difficult to read. The aim of this thesis is to explore the extent to which techniques from Artificial Intelligence can be used to develop a system that could aid historians in reading the stylus texts. This system would utilise image processing techniques that have been developed in Engineering Science to analyse the stylus tablets, whilst incorporating knowledge elicited from experts working on the texts, to propagate possible suggestions of the text contained within the tablets. This thesis reports on what appears to be the first system developed to aid experts in the process of reading an ancient document. There has been little previous research carried out to see how papyrologists actually carry out their task. This thesis studies closely how experts working with primary sources, such as the Vindolanda Texts, operate. Using Knowledge Elicitation Techniques, a model is proposed for how they read a text. Information regarding the letter forms and language used at Vindolanda is collated, A corpus of annotated images is built up, to provide a data set regarding the letter forms used in the ink and stylus texts. In order to relate this information to the work done on image processing, a stochastic Minimum Description Length (MDL) architecture is adopted, and adapted, to form the basis of a system that can propagate interpretations of the Vindolanda texts. In doing so a system is constructed that can read in image data and output textual interpretations of the writing that appears on the documents. It is demonstrated that knowledge elicitation techniques can be used to capture and mobilise expert information. The process of reading ancient, and ambiguous texts, is made explicit. It is also shown that MDL can be used as a basis to build large systems that reason about complex information effectively. This research presents the first stages towards developing a cognitive visual system that can propagate realistic interpretations from image data, and so aid the papyrologists in their task.

Connectionism, a new technique for modeling cognitive processes, has been presented by its supporters as a revolutionary advance that will soon replace conventional artificial intelligence (AI) research based on the serial computer. In this thesis, I identify three 'gambits' with which critics attempt to undermine connectionist claims, and show that use of these gambits depends on the status of the respondent's own discipline. I argue that in cases where the respondent's discipline has an accepted identity, for example biology and psychology, they take contradictory stances on the issue of continuity between their discipline and connectionism. By contrast, responses from supporters of AI, which has an uncertain status, insist on a continuous relationship between connectionism and AI. To account for this, I suggest that claims made by both supporters and critics of connectionism, which those actors would regard as purely cognitive, are tacitly structured by Kuhn's model of scientific change. As certain claims which the actors would describe as purely cognitive can be accounted for by the presence in common scholarly parlance of a particular philosophical model of scientific change, I conclude that in the confrontation between connectionism and conventional AI there exists a complex relationship between social and cognitive processes. / Master of Science

Indianapolis / E-scooters are ubiquitous and their number keeps escalating, increasing their interactions with other vehicles on the road. E-scooter riders have an atypical behavior that varies enormously from other vulnerable road users, creating new challenges for vehicle active safety systems and automated driving functionalities. The detection of e-scooter riders by other vehicles is the first step in taking care of the risks. This research presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark dataset for e-scooter riders in natural environments. An efficient system pipeline built using two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2, performs detection of these vulnerable e-scooter riders.

E-scooters are ubiquitous and their number keeps escalating, increasing their interactions with other vehicles on the road. E-scooter riders have an atypical behavior that varies enormously from other vulnerable road users, creating new challenges for vehicle active safety systems and automated driving functionalities. The detection of e-scooter riders by other vehicles is the first step in taking care of the risks. This research presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians and a benchmark dataset for e-scooter riders in natural environments. An efficient system pipeline built using two existing state-of-the-art convolutional neural networks (CNN), You Only Look Once (YOLOv3) and MobileNetV2, performs detection of these vulnerable e-scooter riders.<br>

<p>  </p> <p>In the field of Engineering Analysis and Simulation, part simplification is often used to reduce the computational time and requirements of finite element solvers. Reducing the complexity of the model through simplification introduces error into the analysis, the amount of which depends on the engineering scenario, CAD model, and method of simplification. Expert Analysts utilize their experience and understanding to mitigate the error in analysis through intelligent simplification method selection, however, there is no formalized system of selection. Artificial Intelligence, specifically through the use of Machine Learning algorithms, has been explored as a method of capturing and automating upon this informal knowledge. One existing method which found success only explored Computational Fluid Dynamics simulations without validating the method on other kinds of engineering analysis cases. This study attempts to validate this a priori method on a new situation and directly compare the results between studies. To accomplish this, a new CAD Assembly model database was generated of over 300 simplified and non-simplified examples. Afterwards, the models were subjected to a Structural Analysis simulation, where analysis data could be generated and stored. Finally, a Regression Neural Network was utilized to create Machine Learning models to predict analysis result errors. This study examines the question of how minimal a neural network architecture will be able to make predictions with a comparable accuracy to that of the previous studies.   </p>