This paper presents an approach for mining agricultural problems that have been accumulated in a textual database over a period of 5 years. The problems, which are accompanied by their solutions, offer a wealth of knowledge that can be used by decision makers, researchers, and farmers alike. However, this wealth of knowledge can not be unlocked without a) representing these problems in a structured format, and b) applying algorithms that can summarize and analyze this information. Towards the achievement of the first goal, a multi-faceted object extraction methodology is presented, and for the achievement of the second, association rules are employed. As a proof of concept, the tool was applied of a set of weed problems. The presented methodology can be modified to work with any help and support textual database where both problems and their solutions are present.

Traffic monitoring systems usually make assumptions about the movement of vehicles, such as that they drive in dedicated lanes, and that those lanes rarely include non-vehicle clutter. Urban settings within developing countries often present extremely chaotic traffic scenarios which make these assumptions unrealistic. We show how a standard approach to traffic monitoring can be made more robust by using probabilistic inference, and in such a way that we bypass the need for vehicle segmentation. Instead of tracking individual vehicles but treat a lane of traffic as a fluid and estimate the rate of flow. Our modelling of uncertainty allows us to accurately monitor traffic flow even in the presence of substantial clutter.

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.

Road traffic accidents (RTAs) are a major public health concern, resulting in an estimated 1.2 million deaths and 50 million injuries worldwide each year. In the developing world, RTAs are among the leading cause of death and injury; Ethiopia in particular experiences the highest rate of such accidents. Thus, methods to reduce accident severity are of great interest to traffic agencies and the public at large. In this work, we applied data mining technologies to link recorded road characteristics to accident severity in Ethiopia, and developed a set of rules that could be used by the Ethiopian Traffic Agency to improve safety.

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.

The gender divide in the access to technology in developing economies makes gender characterization and automatic gender identification two of the most critical needs for improving cell phone-based services. Gender identification has been typically solved using voice or image processing.   However, such techniques cannot be applied to cell phone networks mostly due to privacy concerns. In this paper, we present a study aimed at characterizing and automatically identifying the gender of a cell phone user in a developing economy based on behavioral, social and mobility variables. Our contributions are twofold: (1) understanding the role that gender plays on phone usage, and (2) evaluating common machine learning approaches for gender identification. The analysis was carried out using the encrypted CDRs (Call Detail Records) of approximately 10,000 users from a developing economy, whose gender was known a priori. Our results indicate that behavioral and social variables, including the number of input/output calls and the in degree/out degree of the social network, reveal statistically significant differences between male and female callers. Finally, we propose a new gender identification algorithm that can achieve classification rates of up to 80% when the percentage of predicted instances is reduced.

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.

We explore the prospect of inferring the epicenter and influences of seismic activity from changes in background phone communication activities logged at cell towers. In particular, we explore the perturbations in Rwandan call data invoked by an earthquake in February 2008 centered in the Lac Kivu region of the Democratic Republic of the Congo. Beyond the initial seismic event, we investigate the challenge of assessing the distribution of the persistence of needs over geographic regions, using the persistence of call anomalies after the earthquake as a proxy for lasting influences and the potential need for assistance. We also infer uncertainties in the inferences and consider the prospect of identifying the value of surveying the areas so that surveillance resources can be best triaged.

As historically acknowledged in the Reasoning about Actions and Change community, intuitiveness of a logical domain description cannot be fully automated. Moreover, like any other logical theory, action theories may also evolve, and thus knowledge engineers need revision methods to help in accommodating new incoming information about the behavior of actions in an adequate manner. The present work is about changing action domain descriptions in multimodal logic. Its contribution is threefold: first we revisit the semantics of action theory contraction proposed in previous work, giving more robust operators that express minimal change based on a notion of distance between Kripke-models. Second we give algorithms for syntactical action theory contraction and establish their correctness with respect to our semantics for those action theories that satisfy a principle of modularity investigated in previous work. Since modularity can be ensured for every action theory and, as we show here, needs to be computed at most once during the evolution of a domain description, it does not represent a limitation at all to the method here studied. Finally we state AGM-like postulates for action theory contraction and assess the behavior of our operators with respect to them. Moreover, we also address the revision counterpart of action theory change, showing that it benefits from our semantics for contraction.

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.