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Computer Sciences

2019

Computer Science: Faculty Publications

Articles 1 - 16 of 16

Full-Text Articles in Physical Sciences and Mathematics

Improving Structure Evaluation Through Automatic Hierarchy Expansion, Brian Mcfee, Katherine M. Kinnaird Nov 2019

Improving Structure Evaluation Through Automatic Hierarchy Expansion, Brian Mcfee, Katherine M. Kinnaird

Computer Science: Faculty Publications

Structural segmentation is the task of partitioning a recording into non-overlapping time intervals, and labeling each segment with an identifying marker such as A, B, or verse. Hierarchical structure annotation expands this idea to allow an annotator to segment a song with multiple levels of granularity. While there has been recent progress in developing evaluation criteria for comparing two hierarchical annotations of the same recording, the existing methods have known deficiencies when dealing with inexact label matchings and sequential label repetition. In this article, we investigate methods for automatically enhancing structural annotations by inferring (and expanding) hierarchical information from the …

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Deformable Part Models For Automatically Georeferencing Historical Map Images, Nicholas Howe, Jerod Weinman, John Gouwar, Aabid Shamji Nov 2019

Deformable Part Models For Automatically Georeferencing Historical Map Images, Nicholas Howe, Jerod Weinman, John Gouwar, Aabid Shamji

Computer Science: Faculty Publications

Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Using an evaluation set of 20 historical maps from …

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Preface To Ieee Vast 2019 Conference Track And Vast Challenge, Remco Chang, Daniel Keim, Ross Maciejewski, Kristin Cook, R. Jordan Crouser Oct 2019

Preface To Ieee Vast 2019 Conference Track And Vast Challenge, Remco Chang, Daniel Keim, Ross Maciejewski, Kristin Cook, R. Jordan Crouser

Computer Science: Faculty Publications

No abstract provided.

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Symmetric Inkball Alignment With Loopy Models, Nicholas Howe, Ji Won Chung Sep 2019

Symmetric Inkball Alignment With Loopy Models, Nicholas Howe, Ji Won Chung

Computer Science: Faculty Publications

Alignment tasks generally seek to establish a spatial correspondence between two versions of a text, for example between a set of manuscript images and their transcript. This paper examines a different form of alignment problem, namely pixel-scale alignment between two renditions of a handwritten word or phrase. Using loopy inkball graph models, the proposed technique finds spatial correspondences between two text images such that similar parts map to each other. The method has applications to word spotting and signature verification, and can provide analytical tools for the study of handwriting variation.

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Graph-Based Offline Signature Verification, Paul Maergner, Nicholas Howe, Kaspar Riesen, Andreas Fischer Jun 2019

Graph-Based Offline Signature Verification, Paul Maergner, Nicholas Howe, Kaspar Riesen, Andreas Fischer

Computer Science: Faculty Publications

Graphs provide a powerful representation formalism that offers great promise to benefit tasks like handwritten signature verification. While most state-of-the-art approaches to signature verification rely on fixed-size representations, graphs are flexible in size and allow modeling local features as well as the global structure of the handwriting. In this article, we present two recent graph-based approaches to offline signature verification: keypoint graphs with approximated graph edit distance and inkball models. We provide a comprehensive description of the methods, propose improvements both in terms of computational time and accuracy, and report experimental results for four benchmark datasets. The proposed methods achieve …

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Efficient Gpu Tree Walks For Effective Distributed N-Body Simulations, Jianqiao Liu, Michael Robson, Thomas Quinn, Milind Kulkarni Jun 2019

Efficient Gpu Tree Walks For Effective Distributed N-Body Simulations, Jianqiao Liu, Michael Robson, Thomas Quinn, Milind Kulkarni

Computer Science: Faculty Publications

N-body problems, such as simulating the motion of stars in a galaxy, are popularly solved using tree codes like Barnes-Hut. ChaNGa is a best-of-breed n-body platform that uses an asymptotically-efficient tree traversal strategy known as a dual-tree walk to quickly determine which bodies need to interact with each other to provide an accurate simulation result. However, this strategy does not work well on GPUs, due to the highly-irregular nature of the dual-tree algorithm. On GPUs, ChaNGa uses a hybrid strategy where the CPU performs the tree walk to determine which bodies interact while the GPU performs the force computation. In …

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Bgp Hijacking Classification, Shinyoung Cho, Romain Fontugne, Kenjiro Cho, Alberto Dainotti, Phillipa Gill Jun 2019

Bgp Hijacking Classification, Shinyoung Cho, Romain Fontugne, Kenjiro Cho, Alberto Dainotti, Phillipa Gill

Computer Science: Faculty Publications

Recent reports show that BGP hijacking has increased substantially. BGP hijacking allows malicious ASes to obtain IP prefixes for spamming as well as intercepting or blackholing traffic. While systems to prevent hijacks are hard to deploy and require the cooperation of many other organizations, techniques to detect hijacks have been a popular area of study. In this paper, we classify detected hijack events in order to document BGP detectors output and understand the nature of reported events. We introduce four categories of BGP hijack: typos, prepending mistakes, origin changes, and forged AS paths. We leverage AS hegemony-a measure of dependency …

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Support For User Generated Evolutions Of Goal Models, Boyue Caroline Hu, Alicia M. Grubb May 2019

Support For User Generated Evolutions Of Goal Models, Boyue Caroline Hu, Alicia M. Grubb

Computer Science: Faculty Publications

Goal models are used in early phase requirements engineering to elicit stakeholders' intentions, analyze dependencies, and help stakeholders make trade-off decisions about the project and its interaction with the environment. The Evolving Intentions framework extended goal model analysis to evaluate how models change over time, by creating simulation paths showing possible evolutions of the model. More recently, we extended this analysis to allow users to explore states along the path and generate their own simulation paths. However, this approach is limited by users' ability to comprehend the state space, which grows exponentially with the size of the model. In this …

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Crowdsourcing Image Schemas, Dagmar Gromann, Jamie C. Macbeth Jan 2019

Crowdsourcing Image Schemas, Dagmar Gromann, Jamie C. Macbeth

Computer Science: Faculty Publications

With their potential to map experiental structures from the sensorimotor to the abstract cognitive realm, image schemas are believed to provide an embodied grounding to our cognitive conceptual system, including natural language. Few empirical studies have evaluated humans’ intuitive understanding of image schemas or the coherence of image-schematic annotations of natural language. In this paper we present the results of a human-subjects study in which 100 participants annotate 12 simple English sentences with one or more image schemas. We find that human subjects recruited from a crowdsourcing platform can understand image schema descriptions and use them to perform annotations of …

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Auxetic Regions In Large Deformations Of Periodic Frameworks, Ciprian S. Borcea, Ileana Streinu Jan 2019

Auxetic Regions In Large Deformations Of Periodic Frameworks, Ciprian S. Borcea, Ileana Streinu

Computer Science: Faculty Publications

In materials science, auxetic behavior refers to lateral widening upon stretching. We investigate the problem of finding domains of auxeticity in global deformation spaces of periodic frameworks. Case studies include planar periodic mechanisms constructed from quadrilaterals with diagonals as periods and other frameworks with two vertex orbits. We relate several geometric and kinematic descriptions.

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Data Usage In Mir: History & Future Recommendations, Wenqin Chen, Jessica Keast, Jordan Moody, Corinne Moriarty, Felicia Villalobos, Virtue Winter, Xueqi Zhang, Xuanqi Lyu, Elizabeth Freeman, Jessie Wang, Sherry Cai, Katherine M. Kinnaird Jan 2019

Data Usage In Mir: History & Future Recommendations, Wenqin Chen, Jessica Keast, Jordan Moody, Corinne Moriarty, Felicia Villalobos, Virtue Winter, Xueqi Zhang, Xuanqi Lyu, Elizabeth Freeman, Jessie Wang, Sherry Cai, Katherine M. Kinnaird

Computer Science: Faculty Publications

The MIR community faces unique challenges in terms of data access, due in large part to country-specific copyright laws. As a result, there is an emerging divide in the MIR research community between labs that have access to music through large companies with abundant funds, and independent labs at smaller institutions who do not have such expansive access. This paper explores how independent researchers have worked to overcome limitations of access to music data without contributing to the crisis of reproducibility. Acknowledging that there is no single solution for every data access problem that smaller labs face, we propose a …

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Towards Modeling Conceptual Dependency Primitives With Image Schema Logic, Jamie C. Macbeth, Dagmar Gromann Jan 2019

Towards Modeling Conceptual Dependency Primitives With Image Schema Logic, Jamie C. Macbeth, Dagmar Gromann

Computer Science: Faculty Publications

Conceptual Dependency (CD) primitives and Image Schemas (IS) share a common goal of grounding symbols of natural language in a representation that allows for automated semantic interpretation. Both seek to establish a connection between high-level conceptualizations in natural language and abstract cognitive building blocks. Some previous approaches have established a CD-IS correspondence. In this paper, we build on this correspondence in order to apply a logic designed for image schemas to selected CD primitives with the goal of formally taking account of the CD inventory. The logic draws from Region Connection Calculus (RCC-8), Qualitative Trajectory Calculus (QTC), Cardinal Directions and …

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Details Of Deformable Part Models For Automatically Georeferencing Historical Map Images, Nicholas Howe, Jerod Weinman, John Gouwar, Aabid Shamji Jan 2019

Details Of Deformable Part Models For Automatically Georeferencing Historical Map Images, Nicholas Howe, Jerod Weinman, John Gouwar, Aabid Shamji

Computer Science: Faculty Publications

Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Us- ing an evaluation set of 20 historical maps …

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Using The Vast Challenge In Undergraduate Cs Research, Christopher P. Andrews, R. Jordan Crouser Jan 2019

Using The Vast Challenge In Undergraduate Cs Research, Christopher P. Andrews, R. Jordan Crouser

Computer Science: Faculty Publications

The Visual Analytics Science and Technology (VAST) Challenge is a yearly competition designed to push forward visual analytics research through synthetic, yet realistic analytic tasks. In this paper, we discuss the challenges and the successes we have experienced incorporating the VAST Challenge and associated datasets into undergraduate research programs at two liberal arts colleges. We advocate for increased undergraduate participation in this and similar competitions, arguing they afford unique opportunities for positive development in early researchers.

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Linguistic Variation And Anomalies In Comparisons Of Human And Machine-Generated Image Captions, Minyue Dai, Sandra Grandic, Jamie C. Macbeth Jan 2019

Linguistic Variation And Anomalies In Comparisons Of Human And Machine-Generated Image Captions, Minyue Dai, Sandra Grandic, Jamie C. Macbeth

Computer Science: Faculty Publications

Describing the content of a visual image is a fundamental ability of human vision and language systems. Over the past several years, researchers have published on major improvements on image captioning, largely due to the development of deep learning systems trained on large data sets of images and human-written captions. However, these systems have major limitations, and their development has been narrowly focused on improving scores on relatively simple “bag-of-words” metrics. Very little work has examined the overall complex patterns of the language produced by image-captioning systems and how it compares to captions written by humans. In this paper, we …

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Reports Of The Aaai 2019 Spring Symposium Series, Ioana Baldini, Clark Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani H. Gilpin, Knut Hinkelmann, Dylan Holmes, Takashi Kido, Murat Kocaoglu, William F. Lawless, Alessio Lomuscio, Jamie C. Macbeth, Andreas Martin, Ranjeev Mittu, Evan Patterson, Donald Sofge, Prasad Tadepalli, Keiki Takadama, Shomir Wilson Jan 2019

Reports Of The Aaai 2019 Spring Symposium Series, Ioana Baldini, Clark Barrett, Antonio Chella, Carlos Cinelli, David Gamez, Leilani H. Gilpin, Knut Hinkelmann, Dylan Holmes, Takashi Kido, Murat Kocaoglu, William F. Lawless, Alessio Lomuscio, Jamie C. Macbeth, Andreas Martin, Ranjeev Mittu, Evan Patterson, Donald Sofge, Prasad Tadepalli, Keiki Takadama, Shomir Wilson

Computer Science: Faculty Publications

Applications of machine learning combined with AI algorithms have propelled unprecedented economic disruptions across diverse fields in industry, military, medicine, finance, and others. With the forecast for even larger impacts, the present economic impact of machine learning is estimated in the trillions of dollars. But as autonomous machines become ubiquitous, recent problems have surfaced. Early on, and again in 2018, Judea Pearl warned AI scientists they must "build machines that make sense of what goes on in their environment," a warning still unheeded that may impede future development. For example, self-driving vehicles often rely on sparse data; self-driving cars have …

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