Transit, Transition Excavating J641 VUJ.

In July 2006 archaeologists from the University of Bristol and Atkins Heritage embarked on a contemporary archaeology project with a difference. We ‘excavated’ an old (1991) Ford Transit van, used by archaeologists and later by works and maintenance teams at the Ironbridge Museum. The object: to see what can be learnt from a very particular, common and characteristic type of contemporary place; to establish what archaeologists and archaeology can contribute to understanding the way society, and specifically we as archaeologists, use and inhabit these places; and to challenge and critique archaeologies of the contemporary past. In this report we describe our excavation and situate it within a wider debate about research practice in contemporary archaeology

Spartan Daily, May 7, 1956

Volume 43, Issue 129https://scholarworks.sjsu.edu/spartandaily/12337/thumbnail.jp

Machine Learning-based Methods for Driver Identification and Behavior Assessment: Applications for CAN and Floating Car Data

The exponential growth of car generated data, the increased connectivity, and the advances in artificial intelligence (AI), enable novel mobility applications. This dissertation focuses on two use-cases of driving data, namely distraction detection and driver identification (ID). Low and medium-income countries account for 93% of traffic deaths; moreover, a major contributing factor to road crashes is distracted driving. Motivated by this, the first part of this thesis explores the possibility of an easy-to-deploy solution to distracted driving detection. Most of the related work uses sophisticated sensors or cameras, which raises privacy concerns and increases the cost. Therefore a machine learning (ML) approach is proposed that only uses signals from the CAN-bus and the inertial measurement unit (IMU). It is then evaluated against a hand-annotated dataset of 13 drivers and delivers reasonable accuracy. This approach is limited in detecting short-term distractions but demonstrates that a viable solution is possible. In the second part, the focus is on the effective identification of drivers using their driving behavior. The aim is to address the shortcomings of the state-of-the-art methods. First, a driver ID mechanism based on discriminative classifiers is used to find a set of suitable signals and features. It uses five signals from the CAN-bus, with hand-engineered features, which is an improvement from current state-of-the-art that mainly focused on external sensors. The second approach is based on Gaussian mixture models (GMMs), although it uses two signals and fewer features, it shows improved accuracy. In this system, the enrollment of a new driver does not require retraining of the models, which was a limitation in the previous approach. In order to reduce the amount of training data a Triplet network is used to train a deep neural network (DNN) that learns to discriminate drivers. The training of the DNN does not require any driving data from the target set of drivers. The DNN encodes pieces of driving data to an embedding space so that in this space examples of the same driver will appear closer to each other and far from examples of other drivers. This technique reduces the amount of data needed for accurate prediction to under a minute of driving data. These three solutions are validated against a real-world dataset of 57 drivers. Lastly, the possibility of a driver ID system is explored that only uses floating car data (FCD), in particular, GPS data from smartphones. A DNN architecture is then designed that encodes the routes, origin, and destination coordinates as well as various other features computed based on contextual information. The proposed model is then evaluated against a dataset of 678 drivers and shows high accuracy. In a nutshell, this work demonstrates that proper driver ID is achievable. The constraints imposed by the use-case and data availability negatively affect the performance; in such cases, the efficient use of the available data is crucial

Scientific Statistical and Methodology and the Doctrine of Reasonable Doubt in Criminal Law; (With Specific Reference to the Breath Analysis for Blood Alcohol) Empirical Fact or Legal Ficton?

Lawyers pride themselves on being men of reason. After all, they postulate, it is the reasonable man who is enshrined at the apex of the Anglo-American legal system in the adjudication of civil disputes; it is the legally trained mind that proves so finely honed a tool in the area of problem solving in private practice; the rational decisional process is the hallmark of the judicial mind. Where the life or liberty of an individual is in contention this expert sense of reason is brought one step further - the criminal law, with few exceptions, will not countenance a mere preponderance of evidence or balance of probabilities in the establishment of the burden of proof; there must be a greater degree of certitude. This latter goal is attained through the application of the doctrine of reasonable doubt . The parameters with which we need be concerned, at least in the context of the imprecise social organism that constitutes law, are thus obvious. It is something less than absolute certainty. 1 The necessary legal goal will have been attained once any arbiters in the criminal decisional process have reached an abiding conviction to a moral certainty as to the guilt of an accused person

Development and testing of a prototype instrumented bicycle model for the prevention of cyclist accidents

Cycling is an increasingly popular mode of travel in cities owing to the great advantages that it offers in terms of space consumption, health and environmental sustainability, and is therefore favoured and promoted by many city authorities worldwide. However, cycling is also perceived as relatively unsafe, and therefore it has yet to be adopted as a viable alternative to the private car. Rising accident numbers, unfortunately, confirm this perception as reality, with a particular source of hazard (and a significant proportion of collisions) appearing to originate from the interaction of cyclists with Heavy Vehicles (HVs). This paper introduces Cyclist 360° Alert, a novel technological solution aimed at tackling this problem and ultimately improving the safety of cyclists and promoting it to non-riders. Following a thorough review of the trends of cyclist collisions, which sets the motivation of the research, the paper goes on to present the Cyclist 360° Alert system architecture design, and examines possible technologies and techniques that can be employed in the accurate positioning of cyclists and vehicles. It then focuses in particular on the aspect of bicycle tracking, and proposes a localisation approach based on micro-electromechanical systems (MEMS) sensor configurations. Initial experimental results from a set of controlled experiments using a purpose-developed prototype bicycle simulator model, are reported, and conclusions on the applicability of specific sensor configurations are drawn, both in terms of sensor accuracy and reliability in taking sample measurements of motion

Indoor positioning for smartphones without infrastructure and user adaptable

Given that the classic solutions for positioning outdoors, such as GPS (Global Positioning System) or GNSS (Global Navigation Satellite System) do not work indoors, there have been emerging multiple alternatives for Indoor Location. Usually these solutions require extensive and complex installations, which involve high costs. In this thesis we present a robust indoor positioning solution for smartphones that maximizes location accuracy while minimizes the required infrastructure. We have considered two main modes of displacement: walking and in a vehicle. Our solution is robust to different users, allows them to carry the phone in different positions and allows to use the device freely while performing different daily activities, such as walking, driving , going up and down stairs, etc. We achieved that by developing a robust indoor positioning system that combines information from multiple sources such as radio frequency readings and inertial sensors

Bodies Folded in Migrant Crypts:Dis/Ability and the Material Culture of Border-Crossing

This article considers media narratives that suggest that hiding in trucks, buses, and other vehicles to cross borders has, in fact, been a common practice in the context of migration to, and within, Europe. We aim to problematize how the tension between the materiality of bordering practices and human migrants generates a dis/abled subject. In this context, dis/ability may be a cause or consequence of migration, both in physical/material (the folding of bodies in the crypt) and cultural/semiotic terms, and may become a barrier to accessing protection, to entering and/or crossing a country, and to performing mobility in general. Dis/ability and migration have not been associated in the literature. We adopt an analytical symmetry between humans and non-humans, in this case between bodies and crypts. By suggesting an infected, ambivalent, and hybrid approach to the human subject, the body-crypt traveling border challenges the essentialist dichotomies between technology and biology, disability and impairment. The articles and reports upon which we rely were collected through extensive searches of databases/archives of online newspapers and news websites