Nearly 100 million families across the world rely on cotton farming for their livelihood. Cotton is particularly vulnerable to pest attacks, leading to overuse of pesticides, lost income for farmers, and in some cases farmer suicides. We address this problem by presenting a new solution for pesticide management that uses deep learning, smartphone cameras, inexpensive pest traps, existing digital pipelines, and agricultural extension-worker programs. Although generic, the platform is specifically designed to assist smallholder farmers in the developing world. In addition to outlining the solution, we consider the set of unique constraints this context places on it: data diversity, annotation challenges, shortcomings with traditional evaluation metrics, computing on low-resource devices, and deployment through intermediaries. This paper summarizes key lessons learned while developing and deploying the proposed solution. Such lessons may be applicable to other teams interested in building AI solutions for global development.

If search engines are ever to index all of the spoken content in the world, they will need to handle hundreds of languages for which no automatic speech recognition systems exist. Zero-resource spoken term discovery, in which repeated content is detected in some acoustic representation, offers a potentially useful source of indexing features. This paper describes a text-based simulation of a zero-resource spoken term discovery system that allows any information retrieval test collection to be used as a basis for early development of information retrieval techniques. It is proposed that these techniques can be later applied to actual zero-resource spoken term discovery results.

Typical search engines for spoken content begin with some form of language-specific audio processing such as phonetic word recognition. Many languages, however, lack the language tuned preprocessing tools that are needed to create indexing terms for speech. One approach in such cases is to rely on repetition, detected using acoustic features, to find terms that might be worth indexing. Experiments have shown that this approach yields term sets that might be sufficient for some applications in both spoken term detection and ranked retrieval experiments. Such approaches currently work only with spoken queries, however, and only when the searcher is able to speak in a manner similar to that of the speakers in the collection. This demonstration paper proposes Vapor Engine, a new tool for selectively transcribing repeated terms that can be automatically detected from spoken content in any language. These transcribed terms could then be matched to queries formulated using written terms. Vapor Engine is early in development: it currently supports only single-term queries and has not yet having been formally evaluated. This paper introduces the interface and summarizes the challenges it seeks to address.

Designers of IVR systems often shy away from using speech prompts; preferring, where they can, to use keypad input. Part of the reason is that speech processing is expensive and often error prone. This work attempts to address this problem by offering guidelines for prompt design based on field experiments. It is shown, specifically, that accuracy can be influenced by prompt examples, depending on the nature of the information requested.

Jerome White, Douglas Oard, Aren Jansen, Jiaul Paik, Rashmi Sankepally. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2015.

The development of a new test collection is described in which the task is to search naturally occurring spoken content using naturally occurring spoken queries. To support research on speech retrieval for low-resource settings, the collection includes terms learned by zero-resource term discovery techniques. Use of a new tool designed for exploration of spoken collections provides some additional insight into characteristics of the collection.

Building search engines that can respond to spoken queries with spoken content requires that the system not just be able to find useful responses, but also that it know when it has heard enough about what the user wants to be able to do so.This paper describes a simulation study with queries spoken by non-native speakers that suggests that indicates that finding relevant content is often possible within a half minute, and that combining features based on automatically recognized words with features designed for automated prediction of query difficulty can serve as a useful basis for predicting when that useful content has been found.

We present observations from various user studies that have helped to guide the design and deployment of a voice-based employment platform. Specifically, we present findings as a function of demographics, considering employers and candidates across two regions of Karnataka. Our results are of use to those building interfaces to address rural unemployment in particular, and those looking to understand the employment landscape of rural India in general. Our main contributions are motivating the need for such a platform in this context, and offering insight into the engineering of job matches.

Abstract This paper describes a methodology for developing and verifying a class of distributed systems in which the state space may be discrete or continuous. Our focus is on systems where changes are local in that a small number of components change state while the remainder of the system is unchanged. A proof methodology is developed that ensures global properties, such as invariants and convergence, by guaranteeing local properties within subsystems. This methodology is used to prove the correctness of concrete examples. We present a PVS library of theorems and proofs that can be used to reduce the work required to develop and verify programs in this class. A transformation of these libraries to Java is also outlined.

In the absence of generic programming abstractions for dynamic data in most enterprise programming environments, individual applications treat data streams as a special case requiring custom programming. With the growing number of live data sources such as RSS feeds, messaging and presence servers, multimedia streams, and sensor data. a general-purpose client-server programming model is needed to easily incorporate live data into applications. In this paper, we present Live Data Views, a programming abstraction that represents live data as a time-windowed view over a set of data streams. Live Data Views allow applications to create and retrieve stateful abstractions of dynamic data sources in a uniform manner, via the application of intra- and inter- stream operators. We provide details of our model and evaluate a proof-of-concept Live Data Views implementation to monitor traffic conditions on a highway. We also provide the preliminary design of a J2EE-based implementation, and outline some of the research challenges raised by this abstraction in a distributed computing environment.