Humanitarian response to natural disasters and conflicts can be assisted by satellite image analysis. In a humanitarian context, very specific satellite image analysis tasks must be done accurately and in a timely manner to provide operational support. We present PulseSatellite, a collaborative satellite image analysis tool which leverages neural network models that can be retrained on-the fly and adapted to specific humanitarian contexts and geographies. We present two case studies, in mapping shelters and floods respectively, that illustrate the capabilities of PulseSatellite.

We present our initial proof of concept study towards the development of a low-cost 3-D printed smartphone add-on spectrometer. The study aimed at developing a cheap technology (less than 5 USD) to be used for detection of crop diseases in the field using spectrometry. Previously, we experimented with the problem of disease diagnosis using an off-the-shelf and expensive spectrometer (approximately 1000 USD). However, in real world practice, this off-the-shelf device can not be used by typical users (smallholder farmers). Therefore, the study presents a tool that is cheap and user friendly. We present preliminary results and identify requirements for a future version aiming at an accurate diagnostic technology to be used in the field before disease symptoms are visibly seen by the naked eye. Evaluation shows performance of the tool is better than random however below performance of an industry grade spectrometer.

We compare features for dynamic time warping (DTW) when used to bootstrap keyword spotting (KWS) in an almost zero-resource setting. Such quickly-deployable systems aim to support United Nations (UN) humanitarian relief efforts in parts of Africa with severely under-resourced languages. Our objective is to identify acoustic features that provide acceptable KWS performance in such environments. As supervised resource, we restrict ourselves to a small, easily acquired and independently compiled set of isolated keywords. For feature extraction, a multilingual bottleneck feature (BNF) extractor, trained on well-resourced out-of-domain languages, is integrated with a correspondence autoencoder (CAE) trained on extremely sparse in-domain data. On their own, BNFs and CAE features are shown to achieve a more than 2% absolute performance improvement over baseline MFCCs. However, by using BNFs as input to the CAE, even better performance is achieved, with a more than 11% absolute improvement in ROC AUC over MFCCs and more than twice as many top-10 retrievals for two evaluated languages, English and Luganda. We conclude that integrating BNFs with the CAE allows both large out-of-domain and sparse in-domain resources to be exploited for improved ASR-free keyword spotting.

research-article Designing and Evolving an Electronic Agricultural Marketplace in Uganda Share on Authors: Neil Newman University of British Columbia, Vancouver, BC, Canada University of British Columbia, Vancouver, BC, CanadaView Profile , Lauren Falcao Bergquist University of Chicago, Chicago, IL, USA University of Chicago, Chicago, IL, USAView Profile , Nicole Immorlica Microsoft Research, Cambridge, MA, USA Microsoft Research, Cambridge, MA, USAView Profile , Kevin Leyton-Brown University of British Columbia, Vancouver, BC, Canada University of British Columbia, Vancouver, BC, CanadaView Profile , Brendan Lucier Microsoft Research, Cambridge, MA, USA Microsoft Research, Cambridge, MA, USAView Profile , Craig McIntosh University of California San Diego, San Diego, CA, USA University of California San Diego, San Diego, CA, USAView Profile , John Quinn Makerere Univeresity, Kampala, Uganda Makerere Univeresity, Kampala, UgandaView Profile , Richard Ssekibuule Makerere Univeresity, Kampala, Uganda Makerere Univeresity, Kampala, UgandaView Profile Authors Info & Affiliations COMPASS '18: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable SocietiesJune 2018 Article No.: 14Pages 1–11https://doi.org/10.1145/3209811.3209862Published:20 June 2018 3citation164DownloadsMetricsTotal Citations3Total Downloads164Last 12 Months29Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We use dynamic time warping (DTW) as supervision for training a convolutional neural network (CNN) based keyword spotting system using a small set of spoken isolated keywords. The aim is to allow rapid deployment of a keyword spotting system in a new language to support urgent United Nations (UN) relief programmes in parts of Africa where languages are extremely under-resourced and the development of annotated speech resources is infeasible. First, we use 1920 recorded keywords (40 keyword types, 34 minutes of speech) as exemplars in a DTW-based template matching system and apply it to untranscribed broadcast speech. Then, we use the resulting DTW scores as targets to train a CNN on the same unlabelled speech. In this way we use just 34 minutes of labelled speech, but leverage a large amount of unlabelled data for training. While the resulting CNN keyword spotter cannot match the performance of the DTW-based system, it substantially outperforms a CNN classifier trained only on the keywords, improving the area under the ROC curve from 0.54 to 0.64. Because our CNN system is several orders of magnitude faster at runtime than the DTW system, it represents the most viable keyword spotter on this extremely limited dataset.

We consider the extraction of information from broadcast radio speech in Uganda for the purposes of informing relief and development programmes by the United Nations. Although Internet penetration in Uganda is low, mobile phones are ubiquitous and have made radio a vibrant medium for interactive public discussion. Vulnerable groups make use of radio to discuss issues related to, for example, agriculture, health, governance and gender by means of phone-in or text-in talk shows. We have compiled corpora and developed a radio-browsing system for Ugandan English and for two indigenous languages, Luganda and Acholi. The systems employ automatic speech recognisers using HMM/GMM, SGMM and DNN/HMM acoustic models as keyword spotters. We present the first results indicating promising performance of the radio-browsing system.

We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, a critical bottleneck of the naive approach, can be remarkably mitigated. We also give the dual formulation of the optimization problem, which further reduces the computation cost for large-scale Markov networks. Through experiments, we demonstrate the usefulness of our method.

Modelling the density of an infectious disease in space and time is a task generally carried out separately from the diagnosis of that disease in individuals. These two inference problems are complementary, however: diagnosis of disease can be done more accurately if prior information from a spatial risk model is employed, and in turn a disease density model can benefit from the incorporation of rich symptomatic information rather than simple counts of presumed cases of infection. We propose a unifying framework for both of these tasks, and illustrate it with the case of malaria. To do this we first introduce a state space model of malaria spread, and secondly a computer vision based system for detecting plasmodium in microscopical blood smear images, which can be run on location-aware mobile devices. We demonstrate the tractability of combining both elements and the improvement in accuracy this brings about.

Information about the spread of crop disease is vital in developing countries, and as a result the governments of such countries devote scarce resources to gathering such data. Unfortunately, current surveys tend to be slow and expensive, and hence also tend to gather insufficient quantities of data. In this work we describe three general methods for improving the use of survey resources by performing data collection with mobile devices and by directing survey progress through the application of AI techniques. First, we describe a spatial disease density model based on Gaussian process ordinal regression, which offers a better representation of the disease level distribution, as compared to the statistical approaches typically applied. Second, we show how this model can be used to dynamically route survey teams to obtain the most valuable survey possible given a fixed budget. Third, we demonstrate that the diagnosis of plant disease can be automated using images taken by a camera phone, enabling data collection by survey workers with only basic training. We have applied our methods to the specific challenge of viral cassava disease monitoring in Uganda, for which we have implemented a real-time mobile survey system that will soon see practical use.

We discuss the use of divergences in dissimilarity-based classification. Divergences can be employed whenever vectorial data consists of non-negative, potentially normalized features. This is, for instance, the case in spectral data or histograms. In particular, we introduce and study divergence based learning vector quantization (DLVQ). We derive cost function based DLVQ schemes for the family of γ‐divergences which includes the well-known Kullback–Leibler divergence and the so-called Cauchy–Schwarz divergence as special cases. The corresponding training schemes are applied to two different real world data sets. The first one, a benchmark data set (Wisconsin Breast Cancer) is available in the public domain. In the second problem, color histograms of leaf images are used to detect the presence of cassava mosaic disease in cassava plants. We compare the use of standard Euclidean distances with DLVQ for different parameter settings. We show that DLVQ can yield superior classification accuracies and Receiver Operating Characteristics.

Condition monitoring often involves the analysis of systems with hidden factors that switch between different modes of operation in some way. Given a sequence of observations, the task is to infer the filtering distribution of the switch setting at each time step. In this paper, we present factorial switching linear dynamical systems as a general framework for handling such problems. We show how domain knowledge and learning can be successfully combined in this framework, and introduce a new factor (the "X-factor") for dealing with unmodeled variation. We demonstrate the flexibility of this type of model by applying it to the problem of monitoring the condition of a premature baby receiving intensive care. The state of health of a baby cannot be observed directly, but different underlying factors are associated with particular patterns of physiological measurements and artifacts. We have explicit knowledge of common factors and use the X-factor to model novel patterns which are clinically significant but have unknown cause. Experimental results are given which show the developed methods to be effective on typical intensive care unit monitoring data.

We consider the problem of modifying the noise properties of a channel in order to make the source as indecipherable as possible given the output. Applications include jamming communications, maintaining confidentiality near spoken conversations and masking noise pollution. We present results as to how this can be done efficiently, assuming that we have a Gaussian channel and a constraint on the power of the noise. We go on to consider the case in which there is a positive signal which we want to remain coherent, as well as a negative signal which we wish to confound. We also discuss the application of the theory to acoustic signals, where we consider aspects of the human auditory system.

The observed physiological dynamics of an infant receiving intensive care are affected by many possible factors, including interventions to the baby, the operation of the monitoring equipment and the state of health. The Factorial Switching Kalman Filter can be used to infer the presence of such factors from a sequence of observations, and to estimate the true values where these observations have been corrupted. We apply this model to clinical time series data and show it to be effective in identifying a number of artifactual and physiological patterns.