30,865 research outputs found
A Change Support Model for Distributed Collaborative Work
Distributed collaborative software development tends to make artifacts and
decisions inconsistent and uncertain. We try to solve this problem by providing
an information repository to reflect the state of works precisely, by managing
the states of artifacts/products made through collaborative work, and the
states of decisions made through communications. In this paper, we propose
models and a tool to construct the artifact-related part of the information
repository, and explain the way to use the repository to resolve
inconsistencies caused by concurrent changes of artifacts. We first show the
model and the tool to generate the dependency relationships among UML model
elements as content of the information repository. Next, we present the model
and the method to generate change support workflows from the information
repository. These workflows give us the way to efficiently modify the
change-related artifacts for each change request. Finally, we define
inconsistency patterns that enable us to be aware of the possibility of
inconsistency occurrences. By combining this mechanism with version control
systems, we can make changes safely. Our models and tool are useful in the
maintenance phase to perform changes safely and efficiently.Comment: 10 pages, 13 figures, 4 table
Building with Drones: Accurate 3D Facade Reconstruction using MAVs
Automatic reconstruction of 3D models from images using multi-view
Structure-from-Motion methods has been one of the most fruitful outcomes of
computer vision. These advances combined with the growing popularity of Micro
Aerial Vehicles as an autonomous imaging platform, have made 3D vision tools
ubiquitous for large number of Architecture, Engineering and Construction
applications among audiences, mostly unskilled in computer vision. However, to
obtain high-resolution and accurate reconstructions from a large-scale object
using SfM, there are many critical constraints on the quality of image data,
which often become sources of inaccuracy as the current 3D reconstruction
pipelines do not facilitate the users to determine the fidelity of input data
during the image acquisition. In this paper, we present and advocate a
closed-loop interactive approach that performs incremental reconstruction in
real-time and gives users an online feedback about the quality parameters like
Ground Sampling Distance (GSD), image redundancy, etc on a surface mesh. We
also propose a novel multi-scale camera network design to prevent scene drift
caused by incremental map building, and release the first multi-scale image
sequence dataset as a benchmark. Further, we evaluate our system on real
outdoor scenes, and show that our interactive pipeline combined with a
multi-scale camera network approach provides compelling accuracy in multi-view
reconstruction tasks when compared against the state-of-the-art methods.Comment: 8 Pages, 2015 IEEE International Conference on Robotics and
Automation (ICRA '15), Seattle, WA, US
Biologically Inspired Approaches to Automated Feature Extraction and Target Recognition
Ongoing research at Boston University has produced computational models of biological vision and learning that embody a growing corpus of scientific data and predictions. Vision models perform long-range grouping and figure/ground segmentation, and memory models create attentionally controlled recognition codes that intrinsically cornbine botton-up activation and top-down learned expectations. These two streams of research form the foundation of novel dynamically integrated systems for image understanding. Simulations using multispectral images illustrate road completion across occlusions in a cluttered scene and information fusion from incorrect labels that are simultaneously inconsistent and correct. The CNS Vision and Technology Labs (cns.bu.edulvisionlab and cns.bu.edu/techlab) are further integrating science and technology through analysis, testing, and development of cognitive and neural models for large-scale applications, complemented by software specification and code distribution.Air Force Office of Scientific Research (F40620-01-1-0423); National Geographic-Intelligence Agency (NMA 201-001-1-2016); National Science Foundation (SBE-0354378; BCS-0235298); Office of Naval Research (N00014-01-1-0624); National Geospatial-Intelligence Agency and the National Society of Siegfried Martens (NMA 501-03-1-2030, DGE-0221680); Department of Homeland Security graduate fellowshi
Reason Maintenance - Conceptual Framework
This paper describes the conceptual framework for reason maintenance developed as part of
WP2
Reason Maintenance - State of the Art
This paper describes state of the art in reason maintenance with a focus on its future usage in the KiWi project. To give a bigger picture of the field, it also mentions closely related issues such as non-monotonic logic and paraconsistency. The paper is organized as follows: first, two motivating scenarios referring to semantic wikis are presented which are then used to introduce the different reason maintenance techniques
Interviewing suspects: examining the association between skills, questioning, evidence disclosure, and interview outcomes
The interviewing of suspects is an important element in the investigation of crime. However, studies concerning actual performance of investigators when undertaking such interviews remain sparse. Nevertheless, in England and Wales, since the introduction of a prescribed framework over 20 years ago, field studies have generally shown an improvement in interviewing performance, notwithstanding ongoing concerns largely relating to the more demanding aspects (such as building/maintaining rapport, intermittent summarising and the logical development of topics). Using a sample of 70 real-life interviews, the present study examined questioning and various evidence disclosure strategies (which have also been found demanding), examining their relationships between interview skills and interview outcomes. It was found that when evidence was disclosed gradually (but revealed later), interviews were generally both more skilled and involved the gaining of comprehensive accounts, whereas when evidence was disclosed either early or very late, interviews were found to be both less skilled and less likely to involve this outcome. These findings contribute towards an increased research base for the prescribed framework
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