The Experts below are selected from a list of 807 Experts worldwide ranked by ideXlab platform

Andrew Tam - One of the best experts on this subject based on the ideXlab platform.

  • Indirect Text Entry Using One or Two Keys
    ACM Press, 2015
    Co-Authors: Melanie Baljko, Andrew Tam
    Abstract:

    This paper introduces a new descriptive model for indi-rect text composition facilities that is based on the notion of a Containment Hierarchy. This paper also demonstrates a novel, computer-aided technique for the design of indirect text selection interfaces — one in which Huffman coding is used for the derivation of the Containment Hierarchy. This approach guarantees the derivation of optimal Containment hierarchies, insofar as mean encoding length. This paper de-scribes an empirical study of two two-key indirect text entry variants and compares them to one another and to the pre-dictive model. The intended application of these techniques is the design of improved indirect text entry facilities for the users of AAC systems. Categories and Subject Descriptor

  • ASSETS - Indirect text entry using one or two keys
    Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility - Assets '06, 2006
    Co-Authors: Melanie Baljko, Andrew Tam
    Abstract:

    This paper introduces a new descriptive model for indirect text composition facilities that is based on the notion of a Containment Hierarchy. This paper also demonstrates a novel, computer-aided technique for the design of indirect text selection interfaces -- one in which Huffman coding is used for the derivation of the Containment Hierarchy. This approach guarantees the derivation of optimal Containment hierarchies, insofar as mean encoding length. This paper describes an empirical study of two two-key indirect text entry variants and compares them to one another and to the predictive model. The intended application of these techniques is the design of improved indirect text entry facilities for the users of AAC systems.

Melanie Baljko - One of the best experts on this subject based on the ideXlab platform.

  • Indirect Text Entry Using One or Two Keys
    ACM Press, 2015
    Co-Authors: Melanie Baljko, Andrew Tam
    Abstract:

    This paper introduces a new descriptive model for indi-rect text composition facilities that is based on the notion of a Containment Hierarchy. This paper also demonstrates a novel, computer-aided technique for the design of indirect text selection interfaces — one in which Huffman coding is used for the derivation of the Containment Hierarchy. This approach guarantees the derivation of optimal Containment hierarchies, insofar as mean encoding length. This paper de-scribes an empirical study of two two-key indirect text entry variants and compares them to one another and to the pre-dictive model. The intended application of these techniques is the design of improved indirect text entry facilities for the users of AAC systems. Categories and Subject Descriptor

  • ASSETS - Indirect text entry using one or two keys
    Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility - Assets '06, 2006
    Co-Authors: Melanie Baljko, Andrew Tam
    Abstract:

    This paper introduces a new descriptive model for indirect text composition facilities that is based on the notion of a Containment Hierarchy. This paper also demonstrates a novel, computer-aided technique for the design of indirect text selection interfaces -- one in which Huffman coding is used for the derivation of the Containment Hierarchy. This approach guarantees the derivation of optimal Containment hierarchies, insofar as mean encoding length. This paper describes an empirical study of two two-key indirect text entry variants and compares them to one another and to the predictive model. The intended application of these techniques is the design of improved indirect text entry facilities for the users of AAC systems.

James Geller - One of the best experts on this subject based on the ideXlab platform.

  • Efficient Transitive Closure Reasoning in a Combined Class/Part/Containment Hierarchy
    Knowledge and Information Systems, 2002
    Co-Authors: Yugyung Lee, James Geller
    Abstract:

    Class hierarchies form the backbone of many implemented knowledge representation and reasoning systems. They are used for inheritance, classification and transitive closure reasoning. Part hierarchies are also important in artificial intelligence. Other hierarchies, e.g. Containment hierarchies, have received less attention in artificial intelligence. This paper presents an architecture and an implementation of a Hierarchy reasoner that integrates a class Hierarchy, a part Hierarchy, and a Containment Hierarchy into one structure. In order to make an implemented reasoner useful, it needs to operate at least at speeds comparable to human reasoning. As real-world hierarchies are always large, special techniques need to be used to achieve this. We have developed a set of parallel algorithms and a data representation called maximally reduced tree cover for that purpose. The maximally reduced tree cover is an improvement of a materialized transitive closure representation which has appeared in the literature. Our experiments with a medical vocabulary show that transitive closure reasoning for combined class/part/Containment hierarchies in near constant time is possible for a fixed hardware configuration.

  • efficient transitive closure reasoning in a combined class part Containment Hierarchy
    Knowledge and Information Systems, 2002
    Co-Authors: Yugyung Lee, James Geller
    Abstract:

    Class hierarchies form the backbone of many implemented knowledge representation and reasoning systems. They are used for inheritance, classification and transitive closure reasoning. Part hierarchies are also important in artificial intelligence. Other hierarchies, e.g. Containment hierarchies, have received less attention in artificial intelligence. This paper presents an architecture and an implementation of a Hierarchy reasoner that integrates a class Hierarchy, a part Hierarchy, and a Containment Hierarchy into one structure. In order to make an implemented reasoner useful, it needs to operate at least at speeds comparable to human reasoning. As real-world hierarchies are always large, special techniques need to be used to achieve this. We have developed a set of parallel algorithms and a data representation called maximally reduced tree cover for that purpose. The maximally reduced tree cover is an improvement of a materialized transitive closure representation which has appeared in the literature. Our experiments with a medical vocabulary show that transitive closure reasoning for combined class/part/Containment hierarchies in near constant time is possible for a fixed hardware configuration.

Jochen L. Leidner - One of the best experts on this subject based on the ideXlab platform.

  • Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling
    arXiv: Information Retrieval, 2019
    Co-Authors: Daniel Pfeifer, Jochen L. Leidner
    Abstract:

    We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. It is governed by a simple generative model, where the likelihood to generate the training documents via topics is optimized. The algorithm starts with one-word topics and joins two topics at every step. It therefore generates a solution for every desired number of topics ranging between the size of the training vocabulary and one. The process represents an agglomerative clustering that corresponds to a binary tree of topics. A resulting tree may act as a Containment Hierarchy, typically with more general topics towards the root of tree and more specific topics towards the leaves. Topic Grouper is not governed by a background distribution such as the Dirichlet and avoids hyper parameter optimizations. We show that Topic Grouper has reasonable predictive power and also a reasonable theoretical and practical complexity. Topic Grouper can deal well with stop words and function words and tends to push them into their own topics. Also, it can handle topic distributions, where some topics are more frequent than others. We present typical examples of computed topics from evaluation datasets, where topics appear conclusive and coherent. In this context, the fact that each word belongs to exactly one topic is not a major limitation; in some scenarios this can even be a genuine advantage, e.g.~a related shopping basket analysis may aid in optimizing groupings of articles in sales catalogs.

  • ECIR (1) - Topic Grouper: An Agglomerative Clustering Approach to Topic Modeling
    Lecture Notes in Computer Science, 2019
    Co-Authors: Daniel Pfeifer, Jochen L. Leidner
    Abstract:

    We introduce Topic Grouper as a complementary approach in the field of probabilistic topic modeling. Topic Grouper creates a disjunctive partitioning of the training vocabulary in a stepwise manner such that resulting partitions represent topics. Topic generation is based on a simple probabilistic model and agglomerative clustering, where clusters are formed as sets of words from the vocabulary. The resulting binary tree of topics may act as a Containment Hierarchy typically with more general topics towards the root of tree and more specific topics towards the leaves. As opposed to other topic modeling approaches, Topic Grouper avoids the need for hyper parameter optimizations.

Sriram Swaminarayan - One of the best experts on this subject based on the ideXlab platform.

  • Feature Extraction, Anomaly, and Change Detection on WorldView 2 Imagery by Hierarchical Image Segmentation – a study
    2015
    Co-Authors: Lakshman Prasad, James Theiler, Matthew Fair, Sriram Swaminarayan
    Abstract:

    We study spatio-spectral feature extraction and image-adaptive anomaly and change detection on 8-band WorldView 2 imagery using a hierarchical polygonal image segmentation scheme. Features are represented as polygons with spectral and structural attributes, along with neighborhood structure and Containment Hierarchy for contextual feature identification. Further, the hierarchical segmentation provides multiple, coarse-scale, sub-backgrounds representing relatively uniform regions, which localize and simplify the spectral distribution of an image. This paves the way for facilitating anomaly and change detection when restricted to the contexts of these backgrounds. For example, forestry, urban areas, and agricultural land have very different spatio-spectral characteristics and their joint contribution to the image statistics can result in a complex distribution against which detecting anomalies could in general be a challenging problem. Our segmentation scheme provides sub-regions in the later stages of the Hierarchy that correspond to homogeneous areas of an image while at the same time allowing inclusion of distinctive small features embedded in these regions. The exclusion of other image areas by focusing on these sub-backgrounds helps discover these outliers more easily with simpler methods of discrimination. By selecting appropriate bands in WorldView2 imagery, the above approach can be used to achieve fine spatio-spectral control in searching and characterizing features, anomalies, and changes of interest. The anomalies and changes are als

  • Feature extraction, anomaly, and change detection on WorldView-2 imagery by hierarchical image segmentation: a study
    Algorithms and Technologies for Multispectral Hyperspectral and Ultraspectral Imagery XVIII, 2012
    Co-Authors: Lakshman Prasad, James Theiler, Matthew Fair, Sriram Swaminarayan
    Abstract:

    We study spatio-spectral feature extraction and image-adaptive anomaly and change detection on 8-band WorldView 2 imagery using a hierarchical polygonal image segmentation scheme. Features are represented as polygons with spectral and structural attributes, along with neighborhood structure and Containment Hierarchy for contextual feature identification. Further, the hierarchical segmentation provides multiple, coarse-scale, sub-backgrounds representing relatively uniform regions, which localize and simplify the spectral distribution of an image. This paves the way for facilitating anomaly and change detection when restricted to the contexts of these backgrounds. For example, forestry, urban areas, and agricultural land have very different spatio-spectral characteristics and their joint contribution to the image statistics can result in a complex distribution against which detecting anomalies could in general be a challenging problem. Our segmentation scheme provides sub-regions in the later stages of the Hierarchy that correspond to homogeneous areas of an image while at the same time allowing inclusion of distinctive small features embedded in these regions. The exclusion of other image areas by focusing on these sub-backgrounds helps discover these outliers more easily with simpler methods of discrimination. By selecting appropriate bands in WorldView2 imagery, the above approach can be used to achieve fine spatio-spectral control in searching and characterizing features, anomalies, and changes of interest. The anomalies and changes are also polygons, which have spectral and structural attributes associated with them, allowing further characterization in the larger context of the image. The segmentation and feature detections can be used as multiple layers in a Geospatial Information System (GIS) for annotating imagery.