Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Mosfeq Rashid

dblp:85/5475 · DBLP profile ↗
← Back
5ranked-venue papers
0as first author
0since 2021 · last 1997
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3Databases, data management, data science and information retrieval · 2

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
2 papers
Data integration and cleaning · 100%
Artificial intelligence
4 papers
Image recognition and object detection · 41% Robot navigation and mapping · 29% Multi-agent systems · 17%

Topics — the 7 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning › semantic integration
ontology-based data integration
0.021997
The InfoSleuth Project · SIGMOD Conference 1997
InfoSleuth: Semantic Integration of Information in Open and Dynamic Environments (Experience Paper) · SIGMOD Conference 1997
Data integration and cleaning
semantic integration
0.021997
The InfoSleuth Project · SIGMOD Conference 1997
InfoSleuth: Semantic Integration of Information in Open and Dynamic Environments (Experience Paper) · SIGMOD Conference 1997
Computer vision › Image recognition and object detection
character recognition
0.021992
Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters · NIPS 1992
Recognizing Overlapping Hand-Printed Characters by Centered-Object Integrated Segmentation and Recognition · NIPS 1991
Robotics › Robot navigation and mapping
active vision
0.011992
Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters · NIPS 1992
Computer vision › Image recognition and object detection › character recognition
handwritten character recognition
0.011992
Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters · NIPS 1992
Robotics › Robot navigation and mapping › active vision
saccade control
0.011992
Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters · NIPS 1992
Machine learning › Learning paradigms › multi-task learning
joint segmentation and recognition
0.011991
Recognizing Overlapping Hand-Printed Characters by Centered-Object Integrated Segmentation and Recognition · NIPS 1991

Methods — techniques the papers use, named apart from their topics

domain ontology · 0.0agent technology · 0.0neural network training · 0.0centered-object segmentation · 0.0
YearPublicationVenuePosition
1997 InfoSleuth: Semantic Integration of Information in Open and Dynamic Environments (Experience Paper)
abstract
The goal of the InfoSleuth project at MCC is to exploit and synthesize new technologies into a unified system that retrieves and processes information in an ever-changing network of information sources. InfoSleuth has its roots in the Carnot project at MCC, which specialized in integrating heterogeneous information bases. However, recent emerging technologies such as internetworking and the World Wide Web have significantly expanded the types, availability, and volume of data available to an information management system. Furthermore, in these new environments, there is no formal control over the registration of new information sources, and applications tend to be developed without complete knowledge of the resources that will be available when they are run. Federated database projects such as Carnot that do static data integration do not scale up and do not cope well with this ever-changing environment. On the other hand, recent Web technologies, based on keyword search engines, are scalable but, unlike federated databases, are incapable of accessing information based on concepts. In this experience paper, we describe the architecture, design, and implementation of a working version of InfoSleuth. We show how InfoSleuth integrates new technological developments such as agent technology, domain ontologies, brokerage, and internet computing, in support of mediated interoperation of data and services in a dynamic and open environment. We demonstrate the use of information brokering and domain ontologies as key elements for scalability.
Roberto J. Bayardo, William Bohrer, Richard S. Brice, Andrzej Cichocki, Jerry Fowler, Abdelsalam Helal, Vipul Kashyap, Tomasz Ksiezyk, Gale Martin, Marian H. Nodine, Mosfeq Rashid, Marek Rusinkiewicz, Ray Shea, C. Unnikrishnan, Amy Unruh, Darrell Woelk
SIGMOD Conference11
1997 The InfoSleuth Project
abstract
Article Free Access Share on The InfoSleuth Project Authors: R. J. Bayardo Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , W. Bohrer Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Brice Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Cichocki Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , J. Fowler Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Halal Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , V. Kashyap Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , T. Ksiezyk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , G. Martin Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Nodine Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rashid Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rusinkiewicz Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Shea Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , C. Unnikrishnan Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Unruh Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , D. Woelk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile Authors Info & Claims SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of dataJune 1997 Pages 543–545https://doi.org/10.1145/253260.253401Online:01 June 1997Publication History 7citation339DownloadsMetricsTotal Citations7Total Downloads339Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Roberto J. Bayardo, William Bohrer, Richard S. Brice, Andrzej Cichocki, Jerry Fowler, Abdelsalam Helal, Vipul Kashyap, Tomasz Ksiezyk, Gale Martin, Marian H. Nodine, Mosfeq Rashid, Marek Rusinkiewicz, Ray Shea, C. Unnikrishnan, Amy Unruh, Darrell Woelk
SIGMOD Conference11
1993 Integrated Segmentation and Recognition Through Exhaustive Scans or Learned Saccadic Jumps
abstract
This paper advances two approaches to integrating handwritten character segmentation and recognition within one system, where the underlying function is learned by a backpropagation neural network. Integrated segmentation and recognition is necessary when characters overlap or touch, or when an individual character is broken up. The first approach exhaustively scans a field of characters, effectively creating a possible segmentation at each scan point. A neural net is trained to both identify when its input window is centered over a character, and if it is, to classify the character. This approach is similar to most recently advanced approaches to integrating segmentation and recognition, and has the common flaw of generating too many possible segmentations to be truly efficient. The second approach overcomes this weakness without reducing accuracy by training a neural network to mimic the ballistic and corrective saccades (eye movements) of human vision. A single neural net learns to jump from character to character, making corrective jumps when necessary, and to classify the centered character when properly fixated. The significant aspect of this system is that the neural net learns to both control what is in its input window as well as to recognize what is in the window. High accuracy results are reported for a standard database of handprinted digits for both approaches.
Gale Martin, Mosfeq Rashid, James A. Pittman
Int. J. Pattern Recognit. Artif. Intell.2
1992 Learning to See Where and What: Training a Net to Make Saccades and Recognize Handwritten Characters
Gale Martin, Mosfeq Rashid, James A. Pittman
NIPS2
1991 Recognizing Overlapping Hand-Printed Characters by Centered-Object Integrated Segmentation and Recognition
Gale Martin, Mosfeq Rashid
NIPS2