Can Machine Learning beat Physics at Modeling Car Crashes?

This study aimed to look at a traditional method used for measuring the severity and principle direction of force of a car crash and see if it could be improved on using machine learning models. The data used was publicly available from the NHTSA database and included descriptions of the vehicle, test and sensors as well as the accelerometer data over the period of the crashes. The models built were SVM classifiers and multinomial regression models. Although the SVM and Regression models were built successfully and gave higher levels of accuracy than the momentum models in terms of the severity, the traditional momentum model’s severity results were not statistically significant and it was therefore impossible to say the SVM classifier was an improvement using the same data. The principle direction of force was improved on using both a multi-level SVM classifier and a multinomial regression and the results were statistically significant

Attacks on the mind and the legal limits of the seduction industry

This chapter explores consciously manipulated sexual ‘consent’ from legal, psychological, and philosophical perspectives. As a prism through which to explore these issues, the paper looks at the lucrative ‘seduction industry’, with a particular focus on courses which purport to teach single men how to ‘programme’ women in the pursuit of sexual ‘consent’. Sitting between the extremes of consent by hypnosis, and consent by charm, we ask whether (and how) the law should engage with such activity, and what, if anything, this phenomenon tells us about the nature of consent. We analyse techniques of programmed consent over three parts. In Part A we discuss attacks on the mind in general terms, exploring the extent to which the law protects against mental manipulations. In Part B we focus on the seduction industry, and the detail of the claims made about their techniques. Finally, in Part C, we discuss how the current law (in the sexual context at least) could be used to protect victims’ mental integrity, whether the techniques attempted for programming consent are successful or not

Towards a Framework for Modelling Multimedia Conferencing Calls in the Next Generation Network

This paper is concerned with the creation of a multiparty multimedia conferencing application which can be used in Next Generation Networks. It begins by suggesting ways in which conferencing can be modeled with a focus on separating signaling and media transfer functionality. Enabling technologies which could support the modeling framework derived and which are compatible with Next Generation Network (NGN) principles are reviewed. Finally, a design and implementation for a simple multimedia conferencing application are described

GlobalCom

The objective of this developed application is to provide a company with a means of communicating with its employees no matter where they are physically located and what communication resources they may have at any particular time. The core focus of this application is to provide a Unified Messaging System using synchronous and asynchronous forms

Developing Real-Time Multimedia Conferencing Services Using Java and SIP

This paper examines Java\u27s suitability in creating real-time multimedia communications-based applications in Next Generation Networks (NGNs). We investigate some of the current enabling technologies provided by the Java plaiform which are concerned with the rapid development of realtime communications-based products and services. In particular, we look at creating a multiparty conferencing and collaboration service using the Session Initiation Protocol (SIP) and the JAIN Framework and present an approach which models multiparty conferencing applications by separating signaling and media transfer functionality. We map our model through the design stage to an implementation in Java. This paper is based on real experiences derived from work on an applied research project which is concerned with the development of a collaborative system which allows multiple distributed scientists to graphically analyse a common set of data obtained from mobile sensors in a virtual conference environment. Potential applications areas include education, emergency response services, gaming and any general collaborative application