SRI Blog

-By Yanyan Zou and Wei Lu  StatNLP  Research Group Singapore University of Technology and Design Paper Link Abstract We propose Text2Math, a model for semantically parsing text into math expressions. The model can be used to solve different math related problems including arithmetic word problems and equation parsing problems. Unlike previous approaches, we tackle the problem from an endto-end structured prediction perspective where our algorithm aims to predict the complete math expression at once as a tree structure, where minimal manual efforts are involved in the process. Empirical results on benchmark datasets demonstrate the efficacy of our approach. Designing computer algorithms that can automatically solve math word problems is a challenge for the AI research community. Two representative tasks have been proposed and studied recently – solving arithmetic word problems and equation parsing as illustrated in image The former task focuses on mapping the input pa

-By Hadi Abdullah, Muhammad Sajidur Rahman, Washington Garcia, Logan Blue, Kevin Warren, Anurag Swarnim Yadav, Tom Shrimpton and Patrick Traynor University of Florida Paper Link Courtesy Arxiv The telephony network is still the most widely used mode of audio communication on the planet, with billions of phone calls occurring every day within the USA alone. Such a degree of activity makes the telephony network a prime target for mass surveillance by governments. However, hiring individual human listeners to monitor these conversations can not scale. To overcome this bottleneck, governments have used Machine Learning (ML) based Automatic Speech Recognition (ASR) systems and Automatic Voice Identification (AVI) systems to conduct mass surveillance of their populations. Governments accomplish this by employing ASR systems to flag anti-state phone conversations and AVI systems to identify the participants. The ASR systems convert the phone call audio into text. Next, the

- By  Binil Starly, Atin Angrish and Paul Cohen Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA Abstract The digitalization of manufacturing has created opportunities for consumers to customize products that fit their individualized needs which in turn would drive demand for manufacturing services. However, this ‘pull’ based manufacturing system production of extremely low quantity and limitless variety for products is expensive to implement. New emerging technology in design automation driven by data-driven computational design, manufacturing-as-a-service marketplaces and digitally enabled micro-factories holds promise towards democratization of innovation. In this paper, scientific, technology and infrastructure challenges are identified and if solved, the impact of these emerging technologies on product innovation and future factory organization is discussed.  Computing technology has create