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.

Cellular phones can be used as mobile sensors, continuously logging users’ behavior including movement, communication and proximity to others. While it is well understood that data generated from mobile phones includes a record of phone calls, there are also more sophisticated data types, such as Bluetooth or cell tower proximity logging, which reveal movement patterns and day-to-day human interactions. We explore the possibility of using mobile phone data to compare movement and communication patterns across cultures. The goal of this proof-of-concept study is to quantify behavior in order to compare different populations. We compare our ability to predict future calling behavior and movement patterns from the cellular phone data of subjects in two distinct groups: a set of university students at MIT in the United States and the University of Nairobi in Kenya. In addition, we show how Bluetooth data may be used to estimate the diffusion of an airborne pathogen outbreak in the different populations.

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.

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.

A frequent topic discussed in the Linked Data community, especially when trying to outreach its values, is "What can we do with all this data ?". In this paper, we demonstrate (1) how to measure semantic distance on Linked Data in order to identify relatedness between resources, and (2) how such measures can be used to provide a new kind of self-explanatory recommendations, bringing together Linked Data and Artificial Intelligence principles, and demonstrating how intelligent agents could emerge in the realm of Linked Data.

This paper introduces the IRIS mobile robot project. IRIS is a largely student designed and implemented mobile robot platform created to provide a mechanism for classroom explorations of topics in artificial intelligence, cognitive science, and robotics. It has been designed to be used by students from middle school through college.

Development is fundamentally about understanding people, their motivations, behaviors and reactions. We have two primary means of understanding people — observing what they do, and what they say. As the AI4D community has noted, people's increased use of mobile devices has led to a wealth of new data relevant to these topics. We are on the cusp of developing incredibly powerful tools that can help us understand how human beings migrate, transact and acquire wealth. This could have a large impact on how we determine policies and allocate resources. Most of this analysis has tended to focus on what people do — where they go, who they talk to, what they buy, etc. I argue that what people say is an equally rich source of development data, often containing information that cannot be obtained from people's actions, such as their needs, hopes and aspirations. Voice is the most natural form of communication, especially for people who speak a non-mainstream language, and/or have marginal literacy skills.  These are often exactly those populations who are most disenfranchised, and therefore most need their voices to be heard.

But whereas in the general Rwandan populace males tend Despite the increasing ubiquity of mobile phones in the developing to be much better educated (76.3% of males are literate, but world, remarkably little is known about the structure only 64.7% of females), among mobile phone users it is the and demographics of the mobile phone market. While a women who achieve higher levels of education: the median few qualitative studies have detailed social norms of phone woman completes secondary school, while the median man use in specific communities (Donner 2007; Burrell 2009), does not (t 4.79). Table 1 shows a few statistics on asset and a handful of quantitative researchers have begun to analyze ownership, with associated sampling error.

One of the most significant challenges in achieving digital privacy is incorporating privacy policy directly in computer systems. While rule systems have long existed, translating privacy laws, regulations, policies, and contracts into processor amenable forms is slow and difficult because the legal text is scattered, run-on, and unstructured, antithetical to the lean and logical forms of computer science. We are using and developing intermediate isomorphic forms as a Rosetta Stone-like tool to accelerate the translation process and in hopes of providing support to future domain-specific Natural Language Processing technology. This report describes our experience, thoughts about how to improve the form, and discoveries about the form and logic of the legal text that will affect the successful development of a rules tool to implement real-world complex privacy policies.

As educators, we have high hopes for Electronic Tangibles (ETs), we expect ETs to: Interest more students in the study of computing Broaden students' views of computing Invite non-majors to learn something about the computing Attract students to computer science as a major Help students learn about particular ETs Attract students to our classes by incorporating a flashy ET in the course material Improve student understanding of some difficult topics Maintain student interest throughout the class However some important questions arise: Can we and should we extend these benefits throughout the K-20 curriculum? And if we can't, are we guilty of bait-and-switch?