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.

A key challenge for policymakers in many developing countries is to decide which intervention or collection of interventions works best to improve learning outcomes in their schools. Our aim is to develop a causal model that explains student learning outcomes in terms of observable characteristics as well as conditions and processes difficult to observe directly. We start with a theoretical model based on the results of previous research, direct experience and experts’ knowledge in the field. This model is then refined through application of supervised learning methods to available data sets. Once calibrated with local data in a country, the model estimates the probability that a given intervention would affect learning outcomes.

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.

There is a wealth of information on the Web about any number of topics. Many communities in developing regions are often interested in information relating to specific topics. For example, health workers are interested in specific medical information regarding epidemic diseases in their region while teachers and students are interested in educational information relating to their curriculum. This paper presents the design of Contextual Information Portals, searchable information portals that contain a vertical slice of the Web about arbitrary topics tailored to a specific context. Contextual portals are particularly useful for communities that lack Internet or Web access or in regions with very poor network connectivity. This paper outlines the design space for constructing contextual information portals and describes the key technical challenges involved. We have implemented a proof-of-concept of our ideas, and performed an initial evaluation on a variety of topics relating to epidemiology, agriculture, and education.

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.

Human trafficking is a multi-dimensional problem for which we have incomplete data, limited knowledge of the exploiters, and no understanding of the dynamics of the process. It is a problem that requires a larger, more complete database, understanding of key actors and their interactions in a dynamic environment. These methods exist in the areas of Data Mining, Machine Learning, Network Analysis, and Multi-agent systems. Using these methods, it is possible to create a model which is unique to detecting and preventing human trafficking. These methods can give applicable and successful solutions for different components of the problem of human trafficking. The goal is to build an intelligent system to enable collaboration and analysis, to identify and profile victims, traffickers, buyers, and exploiters, to predict human trafficking patterns, and to disrupt and destabilize human trafficking networks. In this paper, I will outline how some of these methods may be able to help analyze and model the dynamic phenomenon of human trafficking. The purpose is to see whether, using intelligent systems and appropriate collaboration and analysis tools, optimized intervention strategies can be created to profile victims and traffickers as well as impact, dissolve, and disrupt the human trafficking network in such a way that the network is unable to recover.

Some manufacturers provide benchmarks for individual sensors but we found no benchmarks that take a holistic view of the overall screening procedures and no benchmarks that take operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. Our aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right commodity-threat combination in order to maximise detection rates. In this paper we present our ideas for developing such a system and highlight the research challenges we have identified. Then we introduce our first case study and report on the progress we have made so far. Keywords: port security, cargo screening, modelling and simulation, decision support, detection rate matrix 1. INTRODUCTION The primary goal of cargo screening at sea ports and air ports is to detect human stowaways, conventional, nuclear, chemical and radiological weapons and other potential threats. This is an extremely difficult task due to the sheer volume of cargo being moved through ports between countries. For example in sea freight, 200 million containers are moved through 220 ports around the globe every year; this is 90% of all non bulk sea cargo (Dorndorf, Herbers, Panascia, and Zimmermann 2007). Little is known about the efficiency of current cargo screening processes as few benchmarks exist against which they could be measured (e.g.

Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trials are carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance.

The analysis of system calls is one method employed by anomaly detection systems to recognise malicious code execution. Similarities can be drawn between this process and the behaviour of certain cells belonging to the human immune system, and can be applied to construct an artificial immune system. A recently developed hypothesis in immunology, the Danger Theory, states that our immune system responds to the presence of intruders through sensing molecules belonging to those invaders, plus signals generated by the host indicating danger and damage. We propose the incorporation of this concept into a responsive intrusion detection system, where behavioural information of the system and running processes is combined with information regarding individual system calls.

In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study. We had to determine an efficient implementation of management policy in the store's fitting room using DES and ABS. Overall, we have found that both simulation models were a good representation of the real system when modelling human reactive behaviour.