Automatically assessing driving behaviour against traffic rules is a challenging task for improving the safety of Automated Vehicles (AVs). There are no AV specific traffic rules against which AV behaviour can be assessed. Moreover current traffic rules can be imprecisely expressed and are sometimes conflicting making it hard to validate AV driving behaviour. Therefore, in this paper, we propose a Defeasible Deontic Logic (DDL) based driving behaviour assessment methodology for AVs. DDL is used to effectively handle rule exceptions and resolve conflicts in rule norms. A data-driven experiment is conducted to prove the effectiveness of the proposed methodology.

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Abstract. Privacy is an important component of freedom and plays a key role in protecting fundamental human rights. It is becoming increasingly difficult to ignore the fact that without appropriate levels of privacy, a person's rights are diminished. Users want to protect their privacy -particularly in "privacy invasive" areas such as social networks. However, Social Network users seldom know how to protect their own privacy through online mechanisms. What is required is an emerging concept that provides users legitimate control over their own personal information, whilst preserving and maintaining the advantages of engaging with online services such as Social Networks. This paper reviews "Privacy by Design (PbD)" and shows how it applies to diverse privacy areas. Such an approach will move towards mitigating many of the privacy issues in online information systems and can be a potential pathway for protecting users' personal information. The research has also posed many questions in need of further investigation for different open source distributed Social Networks. Findings from this research will lead to a novel distributed architecture that provides more transparent and accountable privacy for the users of online information systems.

Extracting and formalising legal norms from legal documents is a time-consuming and complex procedure. Therefore, the automatic methods that can accelerate this process are in high demand. In this paper, we address two major questions related to this problem: (i) what are the challenges in formalising legal documents into a machine understandable formalism? (ii) to what extent can the data-driven state-of-the-art approaches developed in the Natural Language Processing (NLP) community be used to automate the normative mining process. The results of our experiments indicate that NLP technologies such as relation extraction and semantic parsing are promising research avenues to advance research in this area.

The use of social networking has exploded, with millions of people using various web-and mobile-based services around the world. This increase in social networking use has led to user anxiety related to privacy and the unauthorised exposure of personal information. Large-scale sharing in virtual spaces means that researchers, designers and developers now need to re-consider the issues and challenges of maintaining privacy when using social networking services. This paper provides a comprehensive survey of the current state-of-the-art privacy in social networks for both desktop and mobile uses and devices from various architectural vantage points. The survey will assist researchers and analysts in academia and industry to move towards mitigating many of the privacy issues in social networks.

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