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.

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.

Diabetic retinopathy, an eye disorder caused by diabetes, is the primary cause of blindness in America and over 99% of cases in India. India and China currently account for over 90 million diabetic patients and are on the verge of an explosion of diabetic populations. This may result in an unprecedented number of persons becoming blind unless diabetic retinopathy can be detected early. Aravind Eye Hospitals is the largest eye care facility in the world, handling over 2 million patients per year. The hospital is on a massive drive throughout southern India to detect diabetic retinopathy at an early stage. To that end, a group of 10-15 physicians are responsible for manually diagnosing over 2 million retinal images per year to detect diabetic retinopathy. While the task is extremely laborious, a large fraction of cases turn out to be normal indicating that much of this time is spent diagnosing completely normal cases. This paper describes our early experiences working with Aravind Eye Hospitals to develop an automated system to detect diabetic retinopathy from retinal images. The automated diabetic retinopathy problem is a hard computer vision problem whose goal is to detect features of retinopathy, such as hemorrhages and exudates, in retinal color fundus images. We describe our initial efforts towards building such a system using a range of computer vision techniques and discuss the potential impact on early detection of diabetic retinopathy.

With the introduction of low-cost wireless communication many new applications have been made possible; applications where systems can collaboratively learn and get wiser without human supervision. One potential application is automated monitoring for fault isolation in mobile mechatronic systems such as commercial vehicles. The paper proposes an agent design that is based on uploading software agents to a fleet of mechatronic systems. Each agent searches for interesting state representations of a system and reports them to a central server application. The states from the fleet of systems can then be used to form a consensus from which it can be possible to detect deviations and even locating a fault.

In this paper we describe the production and consumption of linked data in the scenario of the Italian news agency ANSA portal. The goal of the use-case is to provide viewers of a news item with background information and links to related news articles contained on the portal. This information enrichment process is entity-based: ANSA news archive is analyzed using Name Entity Recognition, and each detected entity is annotated with a unique identifier. These identifiers are obtained using the Entity Name Server developed within the scope of the OKKAM European project. Subsequently the news are published on the portal using RDFa and linked to a semantic search engine that provides background information harvested from sources such as DBpedia and links to additional news sources. The presented project has the potential to contribute to Linked Data by creating and publishing a large quantity of entities and assertions about them coming from the ANSA news archive.

Over the last several years, the market for speech technology has seen significant developments (Pieraccini and Huerta We learned some lessons which we use as guidelines 2005) and powerful commercial off-the-shelf solutions for in the development of multimodal dialogue systems where speech recognition (ASR) or speech synthesis (TTS). Further users can combine speech and gestures when using multiple application scenarios, more diverse and dynamic information interaction devices. In earlier projects (Wahlster 2003; Reithinger sources, and more complex prototype systems need et al. 2005) we integrated different sub-components to be addressed in the context of QA. Dialogue-based QA allows to multimodal interaction systems. Other lessons served as a user to pose questions in natural speech, followed by guidelines in the development of semantic dialogue systems answers presented in a concise form (Sonntag et al. 2007).

We have adapted an expert system of medical diagnosis for use by low to mid-level health workers in remote and rural locations. Key to the successful deployment of this expert system is the rapid adaptation of the database and clinical interface for use in specific regions and by varying user skill.

How is shape represented during spatial tasks such as mental rotation? This research investigated the format of mental representations of 3-D shapes during mental rotation. Specifically, we tested the extent to which visual information, such as color, is represented during mental rotation using methods ranging from reaction time studies, verbal protocol analysis, and eyetracking. Another set of studies examined whether people use piecemeal or holistic strategies to rotate complex objects. Results show that individuals with good rotation ability do not represent color during mental rotation and rotate whole shapes; whereas poor rotators do represent color and rotate individual pieces of the shape using piecemeal strategies. This work contributes to theories about cognitive shape processing by showing that different information processing strategies may be one cause of individual differences in mentally rotation performance.

The friend of a friend (FOAF) vocabulary is widely used on the Web to describe individual people and their properties. Since FOAF does not require a unique ID for a person, it is not clear when two FOAF agents should be linked as co-referent, i.e., denote the same person in the world. One approach is to use the presence of inverse functional properties (e.g., foaf:mbox) as evidence that two individuals are the same. Another applies heuristics based on the string similarity of values of FOAF properties such as name and school as evidence for or against co-reference. Performance is limited, however, by many factors: non-semantic string matching, noise, changes in the world, and the lack of more sophisticated graph analytics. We describe a supervised machine learning approach that uses features defined over pairs of FOAF individuals to produce a classifier for identifying co-referent FOAF instances. We present initial results using data collected from Swoogle and other sources and describe plans for additional analysis.