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A Comparative Study Of Contents Of E-Government Service Websites Of Middle East And North African (Mena) Countries, Abebe Rorissa, Devendra Potnis, Dawit Demissie 2009 University of Tennessee, Knoxville

A Comparative Study Of Contents Of E-Government Service Websites Of Middle East And North African (Mena) Countries, Abebe Rorissa, Devendra Potnis, Dawit Demissie

Devendra Potnis

No abstract provided.


Teachers Podcast, Kathleen King 2009 University of South Florida

Teachers Podcast, Kathleen King

Kathleen P King

This site and its companion site provide over 200 hrs of on-demand digital audio faculty development (k-12 teachers as primary focus). Dr. Kathy King and Mark Gura have been producing and hosting The teachers Podcast (Fka as podcast for teachers) since 2005. Check out this informative and fun series which has garnered over 5 million downloads thus far.


Dynamic Pickup And Delivery Problems, Gerardo Berbeglia 2009 Melbourne Business School

Dynamic Pickup And Delivery Problems, Gerardo Berbeglia

Gerardo Berbeglia

No abstract provided.


Catalytic Innovation In Microfinance For Inclusive Growth: Insights From Sks Microfinance, Lakshmi Mohan, Devendra Potnis 2009 University of Tennessee, Knoxville

Catalytic Innovation In Microfinance For Inclusive Growth: Insights From Sks Microfinance, Lakshmi Mohan, Devendra Potnis

Devendra Potnis

Microfinance offers a means for reaching the poor who are left out of the formal financial sector. A fundamentally new way is needed to create a scalable and sustainable business model to meet this unmet need: a catalytic innovation. Our study focused on Swayam Krishi Sangam (SKS), an archetype of a catalytic innovator. The insights gained from our 3-year longitudinal study led to the proposed framework for a catalytic innovator encompassing five factors: customer focus on the poor and social entrepreneurship for the social mission. operational innovation, information technology, human capital management for scaling, and financial sustainability.

DOI: 10.1080 ...


Measuring E-Governance As An Innovation In The Public Sector, Devendra Potnis 2009 University of Tennessee, Knoxville

Measuring E-Governance As An Innovation In The Public Sector, Devendra Potnis

Devendra Potnis

Since 2001, the United Nations (UN) and affiliated organizations have measured e-Government initiatives of more than 178 Member States of the UN, by devising “e-Government Readiness Index” (e-GRI) and “e-Participation Index” (e-PI). The UN has published rankings for its Member States in terms of e-GRI and e-PI, through e-Government Readiness Assessments (Surveys). Member States of the UN and digital government research community as well as academicians and practitioners regularly use the e-GRI and e-PI as a point-of-reference; this fact alone signifies the importance of evaluating the existing UN methodologies assessing e-Governance. Since e-Governance is one of the greatest innovations in ...


Association-Based Image Retrieval, Arun D. Kulkarni 2009 University of Texas at Tyler

Association-Based Image Retrieval, Arun D. Kulkarni

Arun Kulkarni

No abstract provided.


Diagnosing Intermittent And Persistent Faults Using Static Bayesian Networks, Ole J. Mengshoel, Brian Ricks 2009 Carnegie Mellon University

Diagnosing Intermittent And Persistent Faults Using Static Bayesian Networks, Ole J. Mengshoel, Brian Ricks

Ole J Mengshoel

Both intermittent and persistent faults may occur in a wide range of systems. We present in this paper the introduction of intermittent fault handling techniques into ProDiagnose, an algorithm that previously only handled persistent faults. We discuss novel algorithmic techniques as well as how our static Bayesian networks help diagnose, in an integrated manner, a range of intermittent and persistent faults. Through experiments with data from the ADAPT electrical power system test bed, generated as part of the Second International Diagnostic Competition (DXC-10), we show that this novel variant of ProDiagnose diagnoses intermittent faults accurately and quickly, while maintaining strong ...


A Sketch-Based Language For Representing Uncertainty In The Locations Of Origin Of Herbarium Specimens, Barry J. Kronenfeld, Andrew Weeks 2009 Eastern Illinois University

A Sketch-Based Language For Representing Uncertainty In The Locations Of Origin Of Herbarium Specimens, Barry J. Kronenfeld, Andrew Weeks

Barry J. Kronenfeld

Uncertainty fields have been suggested as an appropriate model for retrospective georeferencing of herbarium specimens. Previous work has focused only on automated data capture methods, but techniques for manual data specification may be able to harness human spatial cognition skills to quickly interpret complex spatial propositions. This paper develops a formal modeling language by which location uncertainty fields can be derived from manually sketched features. The language consists of low-level specification of critical probability isolines from which a surface can be uniquely derived, and high-level specification of features and predicates from which low-level isolines can be derived. In a case ...


A Snapshot Of Results Of The Survey Study On Rfid Technology, S. F. Wamba 2009 University of Wollongong

A Snapshot Of Results Of The Survey Study On Rfid Technology, S. F. Wamba

Dr Samuel Fosso Wamba

No abstract provided.


Enhancing The Precision Of Content Analysis In Content Adaptation Using Entropy-Based Fuzzy Reasoning, Rick C.S. Chen, Stephen J.H. Yang, Jia Zhang 2009 Carnegie Mellon University, Silicon Valley

Enhancing The Precision Of Content Analysis In Content Adaptation Using Entropy-Based Fuzzy Reasoning, Rick C.S. Chen, Stephen J.H. Yang, Jia Zhang

Jia Zhang

No abstract provided.


Cortical Underconnectivity Coupled With Preserved Visuospatial Cognition In Autism: Evidence From An Fmri Study Of An Embedded Figures Task, Saudamini Damarla, Timothy A. Keller, Rajesh K. Kana, Vladimir L. Cherkassky, Diane L. Williams, Nancy J. Minshew, Marcel Adam Just 2009 Carnegie Mellon University

Cortical Underconnectivity Coupled With Preserved Visuospatial Cognition In Autism: Evidence From An Fmri Study Of An Embedded Figures Task, Saudamini Damarla, Timothy A. Keller, Rajesh K. Kana, Vladimir L. Cherkassky, Diane L. Williams, Nancy J. Minshew, Marcel Adam Just

Marcel Adam Just

No abstract provided.


Nearest Neighbor Based Collection Ocr, Pramod Sankar K., C. V. Jawahar, R. Manmatha 2009 University of Massachusetts - Amherst

Nearest Neighbor Based Collection Ocr, Pramod Sankar K., C. V. Jawahar, R. Manmatha

R. Manmatha

Conventional optical character recognition (OCR) systems operate on individual characters and words, and do not normally exploit document or collection context. We describe a Collection OCR which takes advantage of the fact that multiple examples of the same word (often in the same font) may occur in a document or collection. The idea here is that an OCR or a reCAPTCHA like process generates a partial set of recognized words. In the second stage, a nearest neighbor algorithm compares the remaining word-images to those already recognized and propagates labels from the nearest neighbors. It is shown that by using an ...


Improving State-Of-The-Art Ocr Through High-Precision Document-Specific Modeling, Andrew Kae, Gary B. Huang, Carl Doersch, Erik G. Learned-Miller 2009 University of Massachusetts - Amherst

Improving State-Of-The-Art Ocr Through High-Precision Document-Specific Modeling, Andrew Kae, Gary B. Huang, Carl Doersch, Erik G. Learned-Miller

Andrew Kae

Optical character recognition (OCR) remains a difficult problem for noisy documents or documents not scanned at high resolution. Many current approaches rely on stored font models that are vulnerable to cases in which the docu- ment is noisy or is written in a font dissimilar to the stored fonts. We address these problems by learning character models directly from the document itself, rather than using pre-stored font models. This method has had some success in the past, but we are able to achieve substantial improve- ment in error reduction through a novel method for creating nearly error-free document-specifictraining data and ...


Information Cost Tradeoffs For Augmented Index And Streaming Language Recognition, Amit Chakrabarti, Graham Cormode, Ranganath Kondapally, Andrew McGregor 2009 University of Massachusetts - Amherst

Information Cost Tradeoffs For Augmented Index And Streaming Language Recognition, Amit Chakrabarti, Graham Cormode, Ranganath Kondapally, Andrew Mcgregor

Andrew McGregor

This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of AUGMENTED-INDEX. In contrast to analogous results for INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the significant technical challenge that protocols for AUGMENTED-INDEX may violate the “rectangle property” due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC, 2010] that asked about the multi-pass complexity of recognizing Dyck languages. This results in a natural separation between the standard ...


The Genomics Education Partnership: Successful Integration Of Research Into Laboratory Classes At A Diverse Group Of Undergraduate Institutions, Elizabeth Shoop, et al 2009 Macalester College

The Genomics Education Partnership: Successful Integration Of Research Into Laboratory Classes At A Diverse Group Of Undergraduate Institutions, Elizabeth Shoop, Et Al

Elizabeth Shoop

No abstract provided.


Optimizing Semantic Coherence In Topic Models, D. Mimno, H. Wallach, E. Talley, M. Leenders, Andrew McCallum 2009 University of Massachusetts - Amherst

Optimizing Semantic Coherence In Topic Models, D. Mimno, H. Wallach, E. Talley, M. Leenders, Andrew Mccallum

Andrew McCallum

Large organizations often face the critical challenge of sharing information and maintaining connections between disparate subunits. Tools for automated analysis of document collections, such as topic models, can provide an important means for communication. The value of topic modeling is in its ability to discover interpretable, coherent themes from unstructured document sets, yet it is not unusual to find semantic mismatches that substantially reduce user confidence. In this paper, we first present an expert-driven topic annotation study, undertaken in order to obtain an annotated set of baseline topics and their distinguishing characteristics. We then present a metric for detecting poor-quality ...


Resource-Bounded Information Extraction: Acquiring Missing Feature Values On Demand, Pallika Kanani, Andrew McCallum, Shaohan Hu 2009 University of Massachusetts - Amherst

Resource-Bounded Information Extraction: Acquiring Missing Feature Values On Demand, Pallika Kanani, Andrew Mccallum, Shaohan Hu

Andrew McCallum

We present a general framework for the task of extracting specific information ``on demand'' from a large corpus such as the Web under resource-constraints. Given a database with missing or uncertain information, the proposed system automatically formulates queries, issues them to a search interface, selects a subset of the documents, extracts the required information from them, and fills the missing values in the original database. We also exploit inherent dependency within the data to obtain useful information with fewer computational resources. We build such a system in the citation database domain that extracts the missing publication years using limited resources ...


Rollout Sampling Policy Iteration For Decentralized Pomdps, Feng Wu, Shlomo Zilberstein, Xiaoping Chen 2009 University of Massachusetts - Amherst

Rollout Sampling Policy Iteration For Decentralized Pomdps, Feng Wu, Shlomo Zilberstein, Xiaoping Chen

Shlomo Zilberstein

We present decentralized rollout sampling policy iteration (DecRSPI)--a new algorithm for multiagent decision problems formalized as DEC-POMDPs. DecRSPI is designed to improve scalability and tackle problems that lack an explicit model. The algorithm uses Monte-Carlo methods to generate a sample of reachable belief states. Then it computes a joint policy for each belief state based on the rollout estimations. A new policy representation allows us to represent solutions compactly. The key benefits of the algorithm are its linear time complexity over the number of agents, its bounded memory usage and good solution quality. It can solve larger problems that ...


Improving State-Of-The-Art Ocr Through High-Precision Document-Specific Modeling, Andrew Kae, Gary B. Huang, Carl Doersch, Erik G. Learned-Miller 2009 University of Massachusetts - Amherst

Improving State-Of-The-Art Ocr Through High-Precision Document-Specific Modeling, Andrew Kae, Gary B. Huang, Carl Doersch, Erik G. Learned-Miller

Erik G Learned-Miller

Optical character recognition (OCR) remains a difficult problem for noisy documents or documents not scanned at high resolution. Many current approaches rely on stored font models that are vulnerable to cases in which the docu- ment is noisy or is written in a font dissimilar to the stored fonts. We address these problems by learning character models directly from the document itself, rather than using pre-stored font models. This method has had some success in the past, but we are able to achieve substantial improve- ment in error reduction through a novel method for creating nearly error-free document-specifictraining data and ...


Constructing Skill Trees For Reinforcement Learning Agents From Demonstration Trajectories, George Konidaris, Scott Kuindersma, Andrew Barto, Roderic Grupen 2009 University of Massachusetts - Amherst

Constructing Skill Trees For Reinforcement Learning Agents From Demonstration Trajectories, George Konidaris, Scott Kuindersma, Andrew Barto, Roderic Grupen

Roderic Grupen

We introduce CST, an algorithm for constructing skill trees from demonstration trajectories in continuous reinforcement learning domains. CST uses a change-point detection method to segment each trajectory into a skill chain by detecting a change of appropriate abstraction, or that a segment is too complex to model as a single skill. The skill chains from each trajectory are then merged to form a skill tree. We demonstrate that CST constructs an appropriate skill tree that can be further refined through learning in a challenging continuous domain, and that it can be used to segment demonstration trajectories on a mobile manipulator ...


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