Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (... more Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any fact- or rule-based knowledge source such as an ontology like the UMLS. Moreover, hidden associations between candidate concepts meaningful in the current context, may not exist within a single document, but within the collection, via alternate lexical forms. Therefore, inspired by the recent success of sequence-to-sequence neural models in delivering the state-of-the-art in a wide range of NLP tasks, we develop a novel sequence-to-set framework with neural attention for learning document representations that can effect term transfer within the corpus, for semantically tagging a large collection of documents. We demonstrate that our proposed method can be effective in both a supervised multi-label classification setup for text categorization, as well as in a unique ...
Proceedings of the 24th International Conference on World Wide Web, 2015
With the onset of social media and news aggregators on the Web, the newspaper industry is faced w... more With the onset of social media and news aggregators on the Web, the newspaper industry is faced with a declining subscriber base. In order to retain customers both on-line and in print, it is therefore critical to predict and mitigate customer churn. Newspapers typically have heterogeneous sources of valuable data: circulation data, customer subscription information, news content, and search click log data. An ensemble of predictive models over multiple sources faces unique challenges-ascertaining short-term versus long-term effects of features on churn, and determining mutual information properties across multiple data sources. We present TopChurn, a novel system that uses topic models [5, 29, 24] as a means of extracting dominant features from user complaints and Web data for churn prediction. TopChurn uses a maximum entropy-based approach [21] to identify features that are most indicative of subscribers likely to drop subscription within a specified period of time. We conduct temporal analyses to determine long-term versus short-term effects of status changes on subscriber accounts, included in our temporal models of churn; and topic and sentiment analyses on news and clicklogs, included in our Web models of churn. We then validate our insights via experiments over real data from The Columbus Dispatch, a mainstream daily newspaper, and demonstrate that our churn models significantly outperform baselines for various prediction windows.
2015 IEEE International Conference on Big Data (Big Data), 2015
Big Data is characterized by the five V's - of Volume, Velocity, Variety, Veracity and Value.... more Big Data is characterized by the five V's - of Volume, Velocity, Variety, Veracity and Value. Research on Big Data, that is, the practice of gaining insights from it, challenges the intellectual, process, and computational limits of an enterprise. Leveraging the correct and appropriate toolset requires careful consideration of a large software ecosystem. Powerful algorithms exist, but the exploratory and often ad-hoc nature of analytic demands and a distinct lack of established processes and methodologies make it difficult for Big Data teams to set expectations or even create valid project plans. The exponential growth of data generated exceeds the capacity of humans to process it, and compels us to develop automated computing methods that require significant and expensive computing power in order to scale effectively. In this paper, we characterize data-driven practice and research and explore how we might design effective methods for systematizing such practice and research [19, 22]. Brief case studies are presented in order to ground our conclusions and insights.
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of... more Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-revi...
The Extensible Markup Language [XML], was intended to be a meta-language, when it was initially a... more The Extensible Markup Language [XML], was intended to be a meta-language, when it was initially approved as a Web Standard by the World Wide Web Consortium (W3C), in February of 1998. Since then, it has come a very long way in applicability and popularity and is fast becoming the Standard for Data Interchange over the Web. XML has now formed the foundation for a completely new way of communicating across the Internet. The power of XML to be applied universally to a number of areas lies in the fact that it is a clean standard and provides a very simple way for a document to carry information about itself. In a generic sense, this would imply that XML infuses the information itself with "Intelligence." This feature of XML can be harnessed to lend the flexibility and the information storage and management capability desirable in most Web-based applications. This paper researches and evaluates the tradeoffs, advantages and issues involved in applying XML to the storage and man...
Predicting the quality of a text document is a critical task when presented with the problem of m... more Predicting the quality of a text document is a critical task when presented with the problem of measuring the performance of a document before its release. In this work, we evaluate various features including those extracted from the text content (textual) and those describing higher-level characteristics of the text (meta) features that are not directly available from the text, and show how these features inform prediction of document quality in different ways. Moreover, we also compare our methods on both social user-generated data such as tweets, and scholarly user-generated data such as academic articles, showing how the same features differently influence prediction of quality across these disparate domains.
Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (... more Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any fact- or rule-based knowledge source such as an ontology like the UMLS. Moreover, hidden associations between candidate concepts meaningful in the current context, may not exist within a single document, but within the collection, via alternate lexical forms. Therefore, inspired by the recent success of sequence-to-sequence neural models in delivering the state-of-the-art in a wide range of NLP tasks, we develop a novel sequence-to-set framework with neural attention for learning document representations that can effect term transfer within the corpus, for semantically tagging a large collection of documents. We demonstrate that our proposed method can be effective in both a supervised multi-label classification setup for text categorization, as well as in a unique ...
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in c... more Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature. These may not explicitly refer to entities or canonical concept forms occurring in a fact-based knowledge source, e.g. the UMLS ontology. Moreover, hidden associations between related concepts meaningful in the current context, may not exist within a single document, but across documents in the collection. Predicting semantic concept tags of documents can therefore serve to associate documents related in unseen contexts, or categorize them, in information filtering or retrieval scenarios. Thus, inspired by the success of sequence-to-sequence neural models, we develop a novel sequence-to-set framework with attention, for learning document representations in a unique unsupervised setting, using no human-annotated document labels or external knowledge resources and only corpus-derived term statistics to drive the training. This can effect term transfer within a corpus for semantically tagging a large collection of documents. Our sequence-to-set modeling approach to predict semantic tags , gives to the best of our knowledge, the state-of-theart for both, an unsupervised query expansion (QE) task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25based document retrieval system; and also over the MLTM system baseline (Soleimani and Miller, 2016), for both supervised and semi-supervised multi-label prediction tasks with del.icio.us and Ohsumed datasets. We make our code and data publicly available 1 .
In fact-based information retrieval, stateof-the-art performance is traditionally achieved by kno... more In fact-based information retrieval, stateof-the-art performance is traditionally achieved by knowledge graphs driven by knowledge bases, as they can represent facts about and capture relationships between entities very well. However, in domains such as medical information retrieval, where addressing specific information needs of complex queries may require understanding query intent by capturing novel associations between potentially latent concepts, these systems can fall short. In this work, we develop a novel, completely unsupervised, neural language model-based ranking approach for semantic tagging of documents, using the document to be tagged as a query into the model to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text. For this we extend the word embeddingbased generalized language model (GLM) due to (Ganguly et al., 2015), to employ phrasal embeddings, and use the semantic tags thus obtained for downstream query expansion, both directly and in feedback loop settings. Our method, evaluated using the TREC 2016 clinical decision support challenge dataset, shows statistically significant improvement not only over various baselines that use standard MeSH terms and UMLS concepts for query expansion, but also over baselines using human expert-assigned concept tags for the queries, on top of a standard Okapi BM25-based document retrieval system.
The VisPort application adopts a service-oriented architecture to encapsulate visualization funct... more The VisPort application adopts a service-oriented architecture to encapsulate visualization functionality that supports remote access [1]. Data sets are typically large and multi-dimensional and made available locally to the web services that run visualization jobs. The visualization products – typically images or movies – are viewed in the user’s standard Web browser [1]. NOAA Coastal Sites is a new addition to the suite of services within the VisPort application framework directed towards our Ocean Modeling and Science Education user communities, which experiments with data made available remotely via web services. We implement an n-tiered service-oriented architecture for remote visualization of the Meteorology, Water Levels, Tides and Currents data available from the Center for Operational Oceanographic Products and Services (CO-OPS) at NOAA.
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of... more Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-revi...
The VisPort visualization portal is an experiment in providing Web-based access to visualization ... more The VisPort visualization portal is an experiment in providing Web-based access to visualization functionality from any place and at any time. VisPort adopts a service-oriented architecture to encapsulate visualization functionality and to support remote access. Users employ browser-based client applications to choose data and services, set parameters, and launch visualization jobs. Visualization products -typically images or movies -are viewed in the user's standard Web browser. VisPort emphasizes visualization solutions customized for specific application communities. Finally, VisPort relies heavily on XML, and introduces the notion of visualization informatics -the formalization and specialization of information related to the process and products of visualization.
Emerging infectious diseases are critical issues of public health and the economic and social sta... more Emerging infectious diseases are critical issues of public health and the economic and social stability of nations. As demonstrated by the international response to the severe acute respiratory syndrome (SARS) and influenza A, rapid genomic sequencing is a crucial tool to understand diseases that occur at the interface of human and animal populations. However, our ability to make sense of sequence data lags behind our ability to acquire the data. The potential of sequence data on pathogens is not fully realized until raw data are translated into public health intelligence. Sequencing technologies have become highly mechanized. If the political will for data sharing remains strong, the frontier for progress in emerging infectious diseases will be in analysis of sequence data and translation of results into better public health science and policy. For example, applying analytical tools such as Supramap (http://supramap.osu.edu) to genomic data for pathogens, public health scientists can track specific mutations in pathogens that confer the ability to infect humans or resist drugs. The results produced by the Supramap application are compelling visualizations of pathogen lineages and features mapped into geographic information systems that can be used to test hypotheses and to follow the spread of diseases across geography and hosts and communicate the results to a wide audience.
Distance Education has entered a new era, where it is now possible for courseware to be delivered... more Distance Education has entered a new era, where it is now possible for courseware to be delivered online in a very effective, efficient and appealing manner. At the Institute for Advanced Education in GeoSpatial Science (IAESG), a project funded by NASA, it is our objective to develop a repository of dynamic online coursework in the field of Geospatial Information Technology. These courses are intended to enhance the traditional university learning through visually rich content, and also through closely emulating the concept of an Expert in a virtual environment. It thus becomes imperative to address a number of pedagogical issues in the design of such a dynamic web-based course delivery system. Traditional education generally involves two main entities: an Expert, and some form of reference material such as a textbook. The Expert imparts knowledge interactively in the real world, and is in some sense the more 'active' or 'intelligent' agent, whereas the textbook is just reference since it has limited scope, and hence is the 'passive' or 'static' agent. Thus the pedagogical issue that needs the most immediate attention, while dealing with learner-centered online course delivery is the issue of how to emulate the real-world Expert in a virtual learning environment. We realize that the role, the Expert plays in knowledge transfer is multi-faceted. One of the roles of the Export is to use traditional testing methodologies like quizzes and exercise to evaluate the performance of a learner. In an online environment, the best way to achieve this continous evaluation is to get constant feedback on the performance and learning style of the student, via 'Student Status Monitoring' (SSM) tool. The research we present focuses on this pivotal aspect of effective online learning.
Distance Education has entered a new era, where it is now possible for courseware to be delivered... more Distance Education has entered a new era, where it is now possible for courseware to be delivered online in a very effective, efficient and appealing manner. At the Institute for Advanced Education in GeoSpatial Science (IAESG), a project funded by NASA, it is our objective to develop a repository of dynamic online coursework in the field of Geospatial Information Technology. These courses are intended to enhance the traditional university learning through visually rich content, and also through closely emulating the concept of an Expert in a virtual environment. It thus becomes imperative to address a number of pedagogical issues in the design of such a dynamic web-based course delivery system. Traditional education generally involves two main entities: an Expert, and some form of reference material such as a textbook. The Expert imparts knowledge interactively in the real world, and is in some sense the more 'active' or 'intelligent' agent, whereas the textbook is just reference since it has limited scope, and hence is the 'passive' or 'static' agent. Thus the pedagogical issue that needs the most immediate attention, while dealing with learner-centered online course delivery is the issue of how to emulate the real-world Expert in a virtual learning environment. We realize that the role, the Expert plays in knowledge transfer is multi-faceted. One of the roles of the Export is to use traditional testing methodologies like quizzes and exercise to evaluate the performance of a learner. In an online environment, the best way to achieve this continous evaluation is to get constant feedback on the performance and learning style of the student, via 'Student Status Monitoring' (SSM) tool. The research we present focuses on this pivotal aspect of effective online learning.
Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (... more Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any fact- or rule-based knowledge source such as an ontology like the UMLS. Moreover, hidden associations between candidate concepts meaningful in the current context, may not exist within a single document, but within the collection, via alternate lexical forms. Therefore, inspired by the recent success of sequence-to-sequence neural models in delivering the state-of-the-art in a wide range of NLP tasks, we develop a novel sequence-to-set framework with neural attention for learning document representations that can effect term transfer within the corpus, for semantically tagging a large collection of documents. We demonstrate that our proposed method can be effective in both a supervised multi-label classification setup for text categorization, as well as in a unique ...
Proceedings of the 24th International Conference on World Wide Web, 2015
With the onset of social media and news aggregators on the Web, the newspaper industry is faced w... more With the onset of social media and news aggregators on the Web, the newspaper industry is faced with a declining subscriber base. In order to retain customers both on-line and in print, it is therefore critical to predict and mitigate customer churn. Newspapers typically have heterogeneous sources of valuable data: circulation data, customer subscription information, news content, and search click log data. An ensemble of predictive models over multiple sources faces unique challenges-ascertaining short-term versus long-term effects of features on churn, and determining mutual information properties across multiple data sources. We present TopChurn, a novel system that uses topic models [5, 29, 24] as a means of extracting dominant features from user complaints and Web data for churn prediction. TopChurn uses a maximum entropy-based approach [21] to identify features that are most indicative of subscribers likely to drop subscription within a specified period of time. We conduct temporal analyses to determine long-term versus short-term effects of status changes on subscriber accounts, included in our temporal models of churn; and topic and sentiment analyses on news and clicklogs, included in our Web models of churn. We then validate our insights via experiments over real data from The Columbus Dispatch, a mainstream daily newspaper, and demonstrate that our churn models significantly outperform baselines for various prediction windows.
2015 IEEE International Conference on Big Data (Big Data), 2015
Big Data is characterized by the five V's - of Volume, Velocity, Variety, Veracity and Value.... more Big Data is characterized by the five V's - of Volume, Velocity, Variety, Veracity and Value. Research on Big Data, that is, the practice of gaining insights from it, challenges the intellectual, process, and computational limits of an enterprise. Leveraging the correct and appropriate toolset requires careful consideration of a large software ecosystem. Powerful algorithms exist, but the exploratory and often ad-hoc nature of analytic demands and a distinct lack of established processes and methodologies make it difficult for Big Data teams to set expectations or even create valid project plans. The exponential growth of data generated exceeds the capacity of humans to process it, and compels us to develop automated computing methods that require significant and expensive computing power in order to scale effectively. In this paper, we characterize data-driven practice and research and explore how we might design effective methods for systematizing such practice and research [19, 22]. Brief case studies are presented in order to ground our conclusions and insights.
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of... more Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-revi...
The Extensible Markup Language [XML], was intended to be a meta-language, when it was initially a... more The Extensible Markup Language [XML], was intended to be a meta-language, when it was initially approved as a Web Standard by the World Wide Web Consortium (W3C), in February of 1998. Since then, it has come a very long way in applicability and popularity and is fast becoming the Standard for Data Interchange over the Web. XML has now formed the foundation for a completely new way of communicating across the Internet. The power of XML to be applied universally to a number of areas lies in the fact that it is a clean standard and provides a very simple way for a document to carry information about itself. In a generic sense, this would imply that XML infuses the information itself with "Intelligence." This feature of XML can be harnessed to lend the flexibility and the information storage and management capability desirable in most Web-based applications. This paper researches and evaluates the tradeoffs, advantages and issues involved in applying XML to the storage and man...
Predicting the quality of a text document is a critical task when presented with the problem of m... more Predicting the quality of a text document is a critical task when presented with the problem of measuring the performance of a document before its release. In this work, we evaluate various features including those extracted from the text content (textual) and those describing higher-level characteristics of the text (meta) features that are not directly available from the text, and show how these features inform prediction of document quality in different ways. Moreover, we also compare our methods on both social user-generated data such as tweets, and scholarly user-generated data such as academic articles, showing how the same features differently influence prediction of quality across these disparate domains.
Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (... more Novel contexts may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature, that may not explicitly refer to entities or canonical concept forms occurring in any fact- or rule-based knowledge source such as an ontology like the UMLS. Moreover, hidden associations between candidate concepts meaningful in the current context, may not exist within a single document, but within the collection, via alternate lexical forms. Therefore, inspired by the recent success of sequence-to-sequence neural models in delivering the state-of-the-art in a wide range of NLP tasks, we develop a novel sequence-to-set framework with neural attention for learning document representations that can effect term transfer within the corpus, for semantically tagging a large collection of documents. We demonstrate that our proposed method can be effective in both a supervised multi-label classification setup for text categorization, as well as in a unique ...
Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in c... more Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature. These may not explicitly refer to entities or canonical concept forms occurring in a fact-based knowledge source, e.g. the UMLS ontology. Moreover, hidden associations between related concepts meaningful in the current context, may not exist within a single document, but across documents in the collection. Predicting semantic concept tags of documents can therefore serve to associate documents related in unseen contexts, or categorize them, in information filtering or retrieval scenarios. Thus, inspired by the success of sequence-to-sequence neural models, we develop a novel sequence-to-set framework with attention, for learning document representations in a unique unsupervised setting, using no human-annotated document labels or external knowledge resources and only corpus-derived term statistics to drive the training. This can effect term transfer within a corpus for semantically tagging a large collection of documents. Our sequence-to-set modeling approach to predict semantic tags , gives to the best of our knowledge, the state-of-theart for both, an unsupervised query expansion (QE) task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25based document retrieval system; and also over the MLTM system baseline (Soleimani and Miller, 2016), for both supervised and semi-supervised multi-label prediction tasks with del.icio.us and Ohsumed datasets. We make our code and data publicly available 1 .
In fact-based information retrieval, stateof-the-art performance is traditionally achieved by kno... more In fact-based information retrieval, stateof-the-art performance is traditionally achieved by knowledge graphs driven by knowledge bases, as they can represent facts about and capture relationships between entities very well. However, in domains such as medical information retrieval, where addressing specific information needs of complex queries may require understanding query intent by capturing novel associations between potentially latent concepts, these systems can fall short. In this work, we develop a novel, completely unsupervised, neural language model-based ranking approach for semantic tagging of documents, using the document to be tagged as a query into the model to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text. For this we extend the word embeddingbased generalized language model (GLM) due to (Ganguly et al., 2015), to employ phrasal embeddings, and use the semantic tags thus obtained for downstream query expansion, both directly and in feedback loop settings. Our method, evaluated using the TREC 2016 clinical decision support challenge dataset, shows statistically significant improvement not only over various baselines that use standard MeSH terms and UMLS concepts for query expansion, but also over baselines using human expert-assigned concept tags for the queries, on top of a standard Okapi BM25-based document retrieval system.
The VisPort application adopts a service-oriented architecture to encapsulate visualization funct... more The VisPort application adopts a service-oriented architecture to encapsulate visualization functionality that supports remote access [1]. Data sets are typically large and multi-dimensional and made available locally to the web services that run visualization jobs. The visualization products – typically images or movies – are viewed in the user’s standard Web browser [1]. NOAA Coastal Sites is a new addition to the suite of services within the VisPort application framework directed towards our Ocean Modeling and Science Education user communities, which experiments with data made available remotely via web services. We implement an n-tiered service-oriented architecture for remote visualization of the Meteorology, Water Levels, Tides and Currents data available from the Center for Operational Oceanographic Products and Services (CO-OPS) at NOAA.
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of... more Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences. Thus they have the potential to automatically answer specific queries about products, and to address the problems of answer starvation and answer augmentation on associated consumer Q & A forums, by providing good answer alternatives. In this work, we explore several recently successful neural approaches to modeling sentence pairs, that could better learn the relationship between questions and ground truth answers, and thus help infer reviews that can best answer a question or augment a given answer. In particular, we hypothesize that our adversarial domain adaptation-based approach, due to its ability to additionally learn domain-invariant features from a large number of unlabeled, unpaired question-revi...
The VisPort visualization portal is an experiment in providing Web-based access to visualization ... more The VisPort visualization portal is an experiment in providing Web-based access to visualization functionality from any place and at any time. VisPort adopts a service-oriented architecture to encapsulate visualization functionality and to support remote access. Users employ browser-based client applications to choose data and services, set parameters, and launch visualization jobs. Visualization products -typically images or movies -are viewed in the user's standard Web browser. VisPort emphasizes visualization solutions customized for specific application communities. Finally, VisPort relies heavily on XML, and introduces the notion of visualization informatics -the formalization and specialization of information related to the process and products of visualization.
Emerging infectious diseases are critical issues of public health and the economic and social sta... more Emerging infectious diseases are critical issues of public health and the economic and social stability of nations. As demonstrated by the international response to the severe acute respiratory syndrome (SARS) and influenza A, rapid genomic sequencing is a crucial tool to understand diseases that occur at the interface of human and animal populations. However, our ability to make sense of sequence data lags behind our ability to acquire the data. The potential of sequence data on pathogens is not fully realized until raw data are translated into public health intelligence. Sequencing technologies have become highly mechanized. If the political will for data sharing remains strong, the frontier for progress in emerging infectious diseases will be in analysis of sequence data and translation of results into better public health science and policy. For example, applying analytical tools such as Supramap (http://supramap.osu.edu) to genomic data for pathogens, public health scientists can track specific mutations in pathogens that confer the ability to infect humans or resist drugs. The results produced by the Supramap application are compelling visualizations of pathogen lineages and features mapped into geographic information systems that can be used to test hypotheses and to follow the spread of diseases across geography and hosts and communicate the results to a wide audience.
Distance Education has entered a new era, where it is now possible for courseware to be delivered... more Distance Education has entered a new era, where it is now possible for courseware to be delivered online in a very effective, efficient and appealing manner. At the Institute for Advanced Education in GeoSpatial Science (IAESG), a project funded by NASA, it is our objective to develop a repository of dynamic online coursework in the field of Geospatial Information Technology. These courses are intended to enhance the traditional university learning through visually rich content, and also through closely emulating the concept of an Expert in a virtual environment. It thus becomes imperative to address a number of pedagogical issues in the design of such a dynamic web-based course delivery system. Traditional education generally involves two main entities: an Expert, and some form of reference material such as a textbook. The Expert imparts knowledge interactively in the real world, and is in some sense the more 'active' or 'intelligent' agent, whereas the textbook is just reference since it has limited scope, and hence is the 'passive' or 'static' agent. Thus the pedagogical issue that needs the most immediate attention, while dealing with learner-centered online course delivery is the issue of how to emulate the real-world Expert in a virtual learning environment. We realize that the role, the Expert plays in knowledge transfer is multi-faceted. One of the roles of the Export is to use traditional testing methodologies like quizzes and exercise to evaluate the performance of a learner. In an online environment, the best way to achieve this continous evaluation is to get constant feedback on the performance and learning style of the student, via 'Student Status Monitoring' (SSM) tool. The research we present focuses on this pivotal aspect of effective online learning.
Distance Education has entered a new era, where it is now possible for courseware to be delivered... more Distance Education has entered a new era, where it is now possible for courseware to be delivered online in a very effective, efficient and appealing manner. At the Institute for Advanced Education in GeoSpatial Science (IAESG), a project funded by NASA, it is our objective to develop a repository of dynamic online coursework in the field of Geospatial Information Technology. These courses are intended to enhance the traditional university learning through visually rich content, and also through closely emulating the concept of an Expert in a virtual environment. It thus becomes imperative to address a number of pedagogical issues in the design of such a dynamic web-based course delivery system. Traditional education generally involves two main entities: an Expert, and some form of reference material such as a textbook. The Expert imparts knowledge interactively in the real world, and is in some sense the more 'active' or 'intelligent' agent, whereas the textbook is just reference since it has limited scope, and hence is the 'passive' or 'static' agent. Thus the pedagogical issue that needs the most immediate attention, while dealing with learner-centered online course delivery is the issue of how to emulate the real-world Expert in a virtual learning environment. We realize that the role, the Expert plays in knowledge transfer is multi-faceted. One of the roles of the Export is to use traditional testing methodologies like quizzes and exercise to evaluate the performance of a learner. In an online environment, the best way to achieve this continous evaluation is to get constant feedback on the performance and learning style of the student, via 'Student Status Monitoring' (SSM) tool. The research we present focuses on this pivotal aspect of effective online learning.
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Papers by Manirupa Das