Territorial privacy is an old concept for privacy of the personal space dating back to the 19th century. Despite its former relevance, territorial privacy has been neglected in recent years, while privacy research and legislation mainly focused on the issue of information privacy. However, with the prospect of smart and ubiquitous environments, territorial privacy deserves new attention. Walls, as boundaries between personal and public spaces, will be insufficient to guard territorial privacy when our environments are permeated with numerous computing and sensing devices, that gather and share real-time information about us. Territorial privacy boundaries spanning both the physical and virtual world are required for the demarcation of personal spaces in smart environments. In this paper, we analyze and discuss the issue of territorial privacy in smart environments. We further propose a real-time user-centric observation model to describe multimodal observation channels of multiple physical and virtual observers. The model facilitates the definition of a territorial privacy boundary by separating desired from undesired observers, regardless of whether they are physically present in the user’s private territory or virtually participating in it. Moreover, we outline future research challenges and identify areas of work that require attention in the context of territorial privacy in smart environments.

The privacy dangers of data proliferation on the Web are well-known. Information on the Web has facilitated the deanonymization of anonymous bloggers, the de-sanitization of government records and the identification of individuals based on search engine queries. What has received less attention is Web-mining in support of privacy. In this position paper we argue that the very ability ofWeb data to breach privacy demonstrates its value as a laboratory for the detection of privacy breaches before they happen. In addition, we argue that privacy-invasive services may become privacy-respecting by mining publicly available Web data, with little decrease in performance and efficiency.

Some of the top legal ontologies developed so far include the Functional Ontology for Law [FOLaw] The increasing need for legal information and content (Valente 1995), the Frame-Based Ontology (van Kralingen management caused by the growing amount of 1995), the LRI-Core ontology (Breuker 2004), unstructured (or poorly structured) legal data managed by DOLCE CLO [Core Legal Ontology] (Gangemi et al. legal publishing companies, law firms and public 2003), or the Ontology of Fundamental Concepts (Rubino administrations, or the increasing amount of legal et al. 2006, Sartor 2006) the basis for the LKIF-Core information directly available on the World Wide Web, Ontology (Breuker et al. 2007). Nevertheless, most legal have created an urgent need to construct conceptual ontologies are domain specific ontologies, which represent structures for knowledge representation to share and particular legal domains towards search, indexing and manage intelligently all this information, whilst making reasoning in a specific domain of national or European law human-machine communication and understanding (e.g. the IPRONTO ontology by Delgado et al. 2003, the possible.

Online social lending is the Web 2.0's response to classical bank loans. Borrowers publish credit applications on websites which match them with private investors. We point to a conflict between economic interests and privacy goals in online social lending, empirically analyze the effect of data disclosure on credit conditions, and outline directions towards efficient yet privacy-friendly alternative credit markets.

We propose a methodology for automatic synthesis of embedded control software that accounts for exogenous disturbances. The resulting system is guaranteed, by construction, to satisfy a given specification expressed in linear temporal logic. The embedded control software consists of three components: a goal generator, a trajectory planner, and a continuous controller. We demonstrate the effectiveness of the proposed technique through an example of an autonomous vehicle navigating an urban environment. This example also illustrates that the system is not only robust with respect to exogenous disturbances but also capable of handling violation of the environment assumptions.

A Digital Product Memory provides a digital diary of the complete product life cycle that is embedded in the product itself using smart wireless sensor technology. The data is hereby gathered by recording relevant ambient parameters in digital form. In this paper, we present the architecture and cost-efficient implementation of an autonomous digital product memory that generates and interprets its diary using rule-based reasoning methods. As we assume an open, heterogeneous sensor infrastructure, we rely on standard syntax and semantics provided by the Web Ontology Language OWL. The digital product memory collects and provides data using the OWL fragment OWL2 RL which can be processed with standard rule engines. As rule engine we use CLIPS on embedded hardware and exemplify the application of the digital product memory e.g. for predictive maintenance.

On interactive surfaces, an accurate system calibration is crucial for a precise user interaction. Today, geometric distortions are eliminated by a static calibration. However, this calibration is specific to a user’s posture, and parallax distortions occur if this changes (i.e. if the user moves or if multiple users take turns). Within this paper, we describe an approach to model automatic online re-calibration to cope with changing viewpoints by using Partially Observable Markov Decision Processes (POMDP). Hereby, the viewpoint is stochastically deducted from the precision of user interactions on the surface. To enable the implementation on embedded systems, a small model is defined using states and observations, which are formulated relative to the current assumed viewpoint. We show the structure of a family of models, that can be generated automatically based on the user’s position probability and pointing accuracy.

For autonomous systems such as unmanned aerial vehicles tosuccessfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. In order to make use of diverse reasoning modules in such systems, issues ofintegration such as sensor data flow and information flow between such modules has to be taken into account. The DyKnow framework is a tool with a formal basis that pragmatically deals with many of the architectural issues which arise in such systems. This includes a systematic stream-based method for handling the sense-reasoning gap,caused by the wide difference in abstraction levels between the noisy data generally available from sensors and the symbolic, semantically meaningful information required by many high-level reasoning modules. DyKnow has proven to be quite robust and widely applicable to different aspects of hybrid software architectures forrobotics. In this paper, we describe the DyKnow framework and show how it is integrated and used in unmanned aerial vehicle systems developed in our group. In particular, we focus on issues pertaining to the sense-reasoning gap and the symbol grounding problem and the use of DyKnow as a means of generating semantic structures representing situational awareness for such systems. We also discuss the use of DyKnow in the context of automated planning, in particular execution monitoring.

We present a novel approach to build approximate maps of structured environments utilizing human motion and activity. Our approach uses data recorded with a data suit which is equipped with several IMUs to detect movements of a person and door opening and closing events. In our approach we interpret the movements as motion constraints and door handling events as landmark detections in a graph-based SLAM framework. As we cannot distinguish between individual doors, we employ a multi-hypothesis approach on top of the SLAM system to deal with the high data-association uncertainty. As a result, our approach is able to accurately and robustly recover the trajectory of the person. We additionally take advantage of the fact that people traverse free space and that doors separate rooms to recover the geometric structure of the environment after the graph optimization. We evaluate our approach in several experiments carried out with different users and in environments of different types.

Artificial neural networks (ANNs, or simply NNs) are inspired by biological nervous systems and consist of simple processing units (artificial neurons) that are interconnected by weighted connections. Neural networks can be "trained" to solve problems that are difficult to solve by conventional computer algorithms. This paper presents the development and implementation of a generalized back-propagation multi-layer perceptron (MLP) neural network architecture described in very high speed hardware description language (VHDL). The development of hardware platforms has been complicated by the high hardware cost and quantity of the arithmetic operations required in an online MLP, i.e., one used to solve real-time problems. The challenge is thus to find an architecture that minimizes hardware costs while maximizing performance, accuracy, and parameterization. The paper describes herein a platform that offers a high degree of parameterization while maintaining performance comparable to other hardware based MLP implementations.