The correct recognition of behaviours based on sensor observations in a smart home is a challenging problem; the sensor observations themselves can be noisy, and the pattern activity seen for a behaviour is rarely identical for different occurrences of the behaviour. For this reason, probabilistic methods such as Hidden Markov Models are preferred over symbolic reasoning approaches. However, these models do not deal well with interleaved behaviours, nor do they allow small variations in behaviour to be detected as abnormal, although this might be useful for the smart home, since changes in ingrained habit could be early signs of illness. We propose methods for using Allen's temporal relations in order to solve these problems, and demonstrate how they can be used to recognise the interleaving of different behaviours, as well as to reason about behaviours that are frequently seen together, and therefore form a behavioural pattern or habit. In this way we have been able to extend our behaviour recognition system to recognise unusual presentations of behaviours.

Internal conflict, in which a character is torn by opposing motivations, is central to drama. Actors portray such conflict in part by mimicking involuntary behaviors that occur as a result of such conflicts. In this paper, we examine the role of timing – pauses and hesitation, in particular – in internal conflict. We argue that virtual actors can be made more expressive if we can emulate the underlying structures of inhibition and conflict detection believed to operate in the human system. We discuss work in progress on this problem that uses the Twig procedural animation system.

The objective of our research effort is to enable robots to engage in complex collaborative tasks with human-robot interaction. To function as a reliable assistant or teammate, the robot must be able to adapt to the actions of its human partner and respond to temporal variations in its own and its partner's actions. Dynamic plan execution algorithms provide a fast and robust method of executing collaborative multi-robot tasks in the presence of temporal uncertainty. However, current state of the art algorithms, rely on hand-crafted plans, providing no means of generating plans for new tasks. In this paper, we outline our approach for learning a model of collaborative robot behavior by observing human-human interaction of the target task. Through statistical analysis of the recorded human behavior we extract patterns of common behavior, and use the resulting model to learn a temporal plan. The result is a learning framework that automatically produces temporal plans for use with dynamic planning that model human collaborative behavior and produce human-like behavior in the robot. In this paper, we present our current progress in the development of this learning framework.

The present privacy legislation continue to be drafted on the basis of the Strasburg Convention of 1981. The mere fact that present privacy laws are based on principles drafted 29 years ago, when the web did not exist, shows that privacy legislation need to make a quantum leap to be in line with the realities of to-day’s real life operating environment. If the status quo is kept, the law and its application shall face serious (and sometimes insurmountable) obstacles to its implementation, making compliance costly for private business, at the same time jeopardizing effectiveness of privacy protection for individuals. A new set of rules should be drafted and established, addressing the changed environment of information and communication technology, in order to allow free flow of information at the same time assuring due protection of personal data.

Most social networks have implemented extensive and complex controls in order to battle the host of privacy concerns that initially plagued their online communities. These controls have taken the form of a-priori access control, which allow users to construct barriers preventing unwanted users from viewing their personal information. However, in cases in which the access restriction mechanisms are bypassed or when the access restrictions are met but the data is later misused, this system leaves users unprotected. Our framework, Respect My Privacy, proposes an alternative approach to the protection of privacy. Our strategy is similar to how legal and social rules work in our societies where the vast majority of these rules are not enforced perfectly or automatically, yet most of us follow the majority of the rules because social systems built up over thousands of years encourage us to do so and often make compliance easier than violation. Our project aims to support similar functionality in social networks. Instead of focusing on enforcing privacy policies through restricted access, we focus on helping users conform to existing policies by making them aware of the usage restrictions associated with the data. The framework has two main functions - generating privacy or usage control policies for social networks, and visualizing these policies while exploring social networks. We have implemented this functionality across three platforms: Facebook, OpenSocial and Tabulator, a Semantic Web browser. These applications enable users to specify privacy preferences for their data and then display this privacy-annotated data prominently enabling other users to easily recognize and conform to these preferences.

Given the prevalence of data leaks, organizations appreciate the importance of implementing privacy policies to protect sensitive data. The growing field of Data Loss Prevention (DLP) offers tools to enforce such policies for both data stored within an organization and data being shared outside of an organization (e.g. through email). While the DLP community has given much attention to the problem of enforcing data privacy policies in a comprehensive manner, little has been done to support the development of such policies. We present a small user study demonstrating that developing such policies is also a very challenging problem. In our study, users were asked to evaluate various expressive file names for sensitivity; that it, they were asked to consider how broadly they were willing to share those filenames both inside and outside their place of employment. The study indicates that users interpret their employer’s privacy concerns in differing ways, resulting in complex, personalized privacy policies at the user end. These results suggest that it may be difficult for users to form a coherent organization-level privacy policy and that the results of a DLP-based enforcement of such policies (e.g. quarantined emails) may be confusing for many users in the organization.

While there is a plethora of mechanisms to ensure lawful access to privacy-protected data, additional research is required in order to reassure individuals that their personal data is being used for the purpose that they consented to. This is particularly important in the context of new data mining approaches, as used, for instance, in biomedical research and commercial data mining. We argue for the use of computational workflows to ensure and enforce appropriate use of sensitive personal data. Computational workflows describe in a declarative manner the data processing steps and the expected results of complex data analysis processes such as data mining (Gil et al. 2007b; Taylor et al. 2006). We see workflows as an artifact that captures, among other things, how data is being used and for what purpose. Existing frameworks for computational workflows need to be extended to incorporate privacy policies that can govern the use of data.

Functional or end user requirements are the tasks that the system - Protection and control of consolidated data under development is expected to perform. However, nonfunctional - Data retrieval requirements are the qualities that the system is - Equitable treatment of users to adhere to. Functional requirements are not as difficult - Data retention and disposal to tackle, as it is easier to test their implementation in the - User monitoring and protection against unauthorized system under development. Security and privacy requirements monitoring are considered nonfunctional requirements, although in many instances they do have functionality. To identify Several laws and regulations provide a set of guidelines privacy risks early in the design process, privacy requirements that can be used to assess privacy risks. For example, engineering is used (Chiasera et al. 2008). However, the Health Insurance Portability and Accountability Act unlike security requirements engineering, little attention is (HIPAA) addresses privacy concerns of health information paid to privacy requirements engineering, thus it is less mature systems by enforcing data exchange standards.

In this paper, we introduce a method for learning and adapting cooperative control strategies in real-time stochastic domains. Our framework is an instance of the intelligent cooperative control architecture (iCCA). The agent starts by following the "safe" plan calculated by the planning module and incrementally adapting the policy to maximize rewards. Actor-critic and consensus-based bundle algorithm (CBBA) were employed as the building blocks of the iCCA framework. We demonstrate the performance of our approach by simulating limited fuel unmanned aerial vehicles aiming for stochastic targets. The integrated framework boosted the optimality of the solution by 10 percent compared to running each of the modules individually.

This paper addresses the use of unmanned aircraft systems to provide embedded reasoning for atmospheric science. In particular, a specific form of heterogeneous unmanned aircraft system (UAS) is introduced. This UAS is comprised of two classes of aircraft with significantly different, though complementary, attributes: miniature daughterships that provide improved flexibility and spatio-temporal diversity of sensed data and larger motherships that carry and deploy the daughterships while facilitating coordination through increased mobility, computation, and communication. Current efforts designing unmanned aircraft for in situ sensing are described as well as future architectures for embedded reasoning by autonomous systems within complex atmospheric phenomena.