ACM DEV '12Pub Date : 2012-03-11DOI: 10.1145/2160601.2160607
R. Munro, Christopher D. Manning
{"title":"Short message communications: users, topics, and in-language processing","authors":"R. Munro, Christopher D. Manning","doi":"10.1145/2160601.2160607","DOIUrl":"https://doi.org/10.1145/2160601.2160607","url":null,"abstract":"This paper investigates three dimensions of cross-domain analysis for humanitarian information processing: citizen reporting vs organizational reporting; Twitter vs SMS; and English vs non-English communications. Short messages sent during the response to the recent earthquake in Haiti and floods in Pakistan are analyzed. It is clear that SMS and Twitter were used very differently at the time, by different groups of people. SMS was primarily used by individuals on the ground while Twitter was primarily used by the international community. Turning to semi-automated strategies that employ natural language processing, it is found that English-optimal strategies do not carry over to Urdu or Kreyol, especially with regards to subword variation. Looking at machine-learning models that attempt to combine both Twitter and SMS, it is found that the cross-domain prediction accuracy is very poor, but some loss in accuracy can be overcome by learning prior distributions over the sources. It is concluded that there is only limited utility in treating SMS and Twitter as equivalent information sources -- perhaps much less than the relatively large number of recent Twitter-focused papers would indicate.","PeriodicalId":153059,"journal":{"name":"ACM DEV '12","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123938866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ACM DEV '12Pub Date : 2012-03-11DOI: 10.1145/2160601.2160627
N. Amanquah, M. Mzyece
{"title":"Mobile application research and development: the African context","authors":"N. Amanquah, M. Mzyece","doi":"10.1145/2160601.2160627","DOIUrl":"https://doi.org/10.1145/2160601.2160627","url":null,"abstract":"This paper addresses the opportunities and challenges created for application developers and researchers by the growth in the use of mobile phones across Africa","PeriodicalId":153059,"journal":{"name":"ACM DEV '12","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131248003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ACM DEV '12Pub Date : 2012-03-11DOI: 10.1145/2160601.2160624
Rakshit Agrawal, Mridu Atray, S. Sundari
{"title":"Concept to design a hand-held crop management device (CMD) for farmers","authors":"Rakshit Agrawal, Mridu Atray, S. Sundari","doi":"10.1145/2160601.2160624","DOIUrl":"https://doi.org/10.1145/2160601.2160624","url":null,"abstract":"In this publication, the authors present the concept to design a hand-held device for crop management (CMD). The proposed design is of a device capable of providing guidance to farmers with respect to irrigation, fertilization, scheduling of crop management and pest control. CMD incorporates modifications/additions in a low cost mobile phone converting it into a dedicated agriculture support solution. The device takes as input, data regarding the type of soil, annual rainfall, atmospheric humidity, temperature, wind velocity, and daylight period from the Internet. User provides information regarding agricultural techniques practiced directly by him. Soil temperature sensor, pH sensor, and soil water content sensors would be provided in the CMD. The device is appended with a rule based decision making algorithm which would generate statements suggesting measures for improved crop management.","PeriodicalId":153059,"journal":{"name":"ACM DEV '12","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128849628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ACM DEV '12Pub Date : 2012-03-11DOI: 10.1145/2160601.2160605
Kuang Chen, Akshay Kannan, Yoriyasu Yano, J. Hellerstein, Tapan S. Parikh
{"title":"Shreddr: pipelined paper digitization for low-resource organizations","authors":"Kuang Chen, Akshay Kannan, Yoriyasu Yano, J. Hellerstein, Tapan S. Parikh","doi":"10.1145/2160601.2160605","DOIUrl":"https://doi.org/10.1145/2160601.2160605","url":null,"abstract":"For low-resource organizations working in developing regions, infrastructure and capacity for data collection have not kept pace with the increasing demand for accurate and timely data. Despite continued emphasis and investment, many data collection efforts still suffer from delays, inefficiency and difficulties maintaining quality. Data is often still \"stuck\" on paper forms, making it unavailable for decision-makers and operational staff. We apply techniques from computer vision, database systems and machine learning, and leverage new infrastructure -- online workers and mobile connectivity -- to redesign data entry with high data quality. Shreddr delivers self-serve, low-cost and on-demand data entry service allowing low-resource organizations to quickly transform stacks of paper into structured electronic records through a novel combination of optimizations: batch processing and compression techniques from database systems, automatic document processing using computer vision, and value verification through crowd-sourcing. In this paper, we describe Shreddr's design and implementation, and measure system performance with a large-scale evaluation in Mali, where Shreddr was used to enter over a million values from 36,819 pages. Within this case study, we found that Shreddr can significantly decrease the effort and cost of data entry, while maintaining a high level of quality.","PeriodicalId":153059,"journal":{"name":"ACM DEV '12","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125258707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ACM DEV '12Pub Date : 2012-03-11DOI: 10.1145/2160601.2160618
H. Y. Chan, R. Rosenfeld
{"title":"Discriminative pronunciation learning for speech recognition for resource scarce languages","authors":"H. Y. Chan, R. Rosenfeld","doi":"10.1145/2160601.2160618","DOIUrl":"https://doi.org/10.1145/2160601.2160618","url":null,"abstract":"In this paper, we describe a method to create speech recognition capability for small vocabularies in resource-scarce languages. By resource-scarce languages, we mean languages that have a small or economically disadvantaged user base which are typically ignored by the commercial world. We use a high-quality well-trained speech recognizer as our baseline to remove the dependence on large audio data for an accurate acoustic model. Using cross-language phoneme mapping, the baseline recognizer effectively recognizes words in our target language. We automate the generation of pronunciations and generate a set of initial pronunciations for each word in the vocabulary. Next, we remove potential conflicts in word recognition by discriminative training.","PeriodicalId":153059,"journal":{"name":"ACM DEV '12","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124675743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}