In research on blog data, comments are often ignored, What makes a blog post noteworthy? One measure of the and it is easy to see why: comments are very noisy, full popularity or breadth of interest of a blog post is the extent of nonstandard grammar and spelling, usually unedited, often to which readers of the blog are inspired to leave comments cryptic and uninformative, at least to those outside the on the post. In this paper, we study the relationship between blog's community. A few studies have focused on information the text contents of a blog post and the volume of response in comments. Mishe and Glance (2006) showed the it will receive from blog readers. Modeling this relationship value of comments in characterizing the social repercussions has the potential to reveal the interests of a blog's readership of a post, including popularity and controversy. Their largescale community to its authors, readers, advertisers, and scientists user study correlated popularity and comment activity.

We present a novel framework for learning to interpret and generate language using only perceptual context as supervision. We demonstrate its capabilities by developing a system that learns to sportscast simulated robot soccer games in both English and Korean without any language-specific prior knowledge. Training employs only ambiguous supervision consisting of a stream of descriptive textual comments and a sequence of events extracted from the simulation trace. The system simultaneously establishes correspondences between individual comments and the events that they describe while building a translation model that supports both parsing and generation. We also present a novel algorithm for learning which events are worth describing. Human evaluations of the generated commentaries indicate they are of reasonable quality and in some cases even on par with those produced by humans for our limited domain.

LEXSYS, (Legume Expert System) was a project conceived at IITA (International Institute of Tropical Agriculture) Ibadan Nigeria. It was initiated by the COMBS (Collaborative Group on Maize-Based Systems Research in the 1990. It was meant for a general framework for characterizing on-farm testing for technology design for sustainable cereal-based cropping system. LEXSYS is not a true expert system as the name would imply, but simply a user-friendly information system. This work is an attempt to give a formal representation of the existing system and then present areas where intelligent agent can be applied.

Browsing linked open data (LOD) is a promising, yet, often unsatisfactory experience today. User-support for the identification of relevant information within the fast-growing cloud of LOD is limited. This paper presents LENA-TR, a browser for LOD that highlights relevant information with respect to different knowledge aspects hidden in linked data. Its interpretation of faceted navigation facilitates the sense-making and browsing of LOD, solving many of the shortcomings experienced in LOD browsing today.

Food shortages are increasing in many areas of the world. In this paper, we consider the problem of understanding the causal relationships between socioeconomic factors in a developing-world household and their risk of experiencing famine. We analyse the extent to which it is possible to predict famine in a household based on these factors, looking at a data collected from 5404 households in Uganda. To do this we use a set of causal structure learning algorithms, employed as a committee that votes on the causal relationships between the variables. We contrast prediction accuracy of famine based on feature sets suggested by our prior knowledge and by the models we learn.

Telephone-based information access has the potential to deliver a significant positive impact in the developing world. We discuss some of the most important issues that must be addressed in order to realize this potential, including matters related to resource development, automatic speech recognition, text-to-speech systems, and user-interface design. Although our main focus has been on the eleven official languages of South Africa, we believe that many of these same issues will be relevant for the application of speech technology throughout the developing world.

Among many approaches to address the high-level decision making problem for autonomous robots and agents, the robot programming and plan language Golog follows a logic-based deliberative approach, and its successors were successfully deployed in a number of robotics applications over the past ten years. Usually, Golog interpreter are implemented in Prolog, which is not available for our target platform, the bi-ped robot platform Nao. In this paper we sketch our novel prototype implementation of a Golog interpreter in the scripting language Lua. With the example of the elevator domain we discuss how the basic action theory is specified and how we implemented fluent regression or backtracking in Lua. One possible advantage of the availability of a Non-Prolog implementation of Golog could be that Golog becomes available on a larger number of platforms, and also becomes more attractive for roboticists outside the Cognitive Robotics community.

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.

Local information access (LIA) programs tap into existing public health data flows, and present data in simple and useful ways to ground staff. LIAs hold great potential for improving rural health systems in developing regions; benefits include more evidence-based decision making and optimizations at a local scale, as well as improved service delivery and data quality. Our fledgling LIA program in rural Uganda currently provides clinicians with a small set of static data visualizations for discussion. To increase the program’s effectiveness, we want to automatically identify relevant data visualizations. We propose an adaptive tool that learns from local clinicians’ decision-making processes to predict and generate visualizations that show actionable anomalies.

Community health worker programs provide healthcare to those living outside the financial and physical reach of the standard health infrastructure. These programs are particularly prevalent in low resource regions. Frequently such programs involve community health workers making household visits across a significant geographical area. We suggest that this problem can be posed as a formal routing and scheduling problem, and to use techniques developed from solving the travelling salesman problem with time windows. In addition, household visits can generate a series of future follow up visits, a feature not often handled in the combinatorial scheduling and routing literature. We present the basic problem and outline potential research directions.