scholarly journals Exporting Type Information for IP Flow Information Export (IPFIX) Information Elements

2009 ◽  
Author(s):  
E. Boschi ◽  
B. Trammell ◽  
L. Mark ◽  
T. Zseby
Keyword(s):  
Flow Information ◽  
Type Information

scholarly journals Decoding stimuli (tool-hand) and viewpoint invariant grasp-type information

Cortex ◽  
2021 ◽  
Vol 139 ◽  
pp. 152-165
Author(s):  
Fredrik Bergström ◽  
Moritz Wurm ◽  
Daniela Valério ◽  
Angelika Lingnau ◽  
Jorge Almeida
Keyword(s):  
Type Information ◽  
Grasp Type

scholarly journals Two-Pass Technique for Clone Detection and Type Classification Using Tree-Based Convolution Neural Network

2021 ◽  
Vol 11 (14) ◽  
pp. 6613
Author(s):  
Young-Bin Jo ◽  
Jihyun Lee ◽  
Cheol-Jung Yoo
Keyword(s):  
Neural Network ◽  
Average Rate ◽  
Convolution Neural Network ◽  
Clone Detection ◽  
Code Clones ◽  
Classification Technique ◽  
Development Costs ◽  
Code Quality ◽  
Type Information ◽  
Type Classification

Appropriate reliance on code clones significantly reduces development costs and hastens the development process. Reckless cloning, in contrast, reduces code quality and ultimately adds costs and time. To avoid this scenario, many researchers have proposed methods for clone detection and refactoring. The developed techniques, however, are only reliably capable of detecting clones that are either entirely identical or that only use modified identifiers, and do not provide clone-type information. This paper proposes a two-pass clone classification technique that uses a tree-based convolution neural network (TBCNN) to detect multiple clone types, including clones that are not wholly identical or to which only small changes have been made, and automatically classify them by type. Our method was validated with BigCloneBench, a well-known and wildly used dataset of cloned code. Our experimental results validate that our technique detected clones with an average rate of 96% recall and precision, and classified clones with an average rate of 78% recall and precision.

scholarly journals Classifying Forest Type in the National Forest Inventory Context with Airborne Hyperspectral and Lidar Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1863
Author(s):  
Caileigh Shoot ◽  
Hans-Erik Andersen ◽  
L. Monika Moskal ◽  
Chad Babcock ◽  
Bruce D. Cook ◽  
...  
Keyword(s):  
Random Forest ◽  
Forest Inventory ◽  
Forest Type ◽  
Vegetation Indices ◽  
National Forest Inventory ◽  
Classification Algorithm ◽  
Lidar Data ◽  
National Forest ◽  
Interior Alaska ◽  
Type Information

Forest structure and composition regulate a range of ecosystem services, including biodiversity, water and nutrient cycling, and wood volume for resource extraction. Forest type is an important metric measured in the US Forest Service Forest Inventory and Analysis (FIA) program, the national forest inventory of the USA. Forest type information can be used to quantify carbon and other forest resources within specific domains to support ecological analysis and forest management decisions, such as managing for disease and pests. In this study, we developed a methodology that uses a combination of airborne hyperspectral and lidar data to map FIA-defined forest type between sparsely sampled FIA plot data collected in interior Alaska. To determine the best classification algorithm and remote sensing data for this task, five classification algorithms were tested with six different combinations of raw hyperspectral data, hyperspectral vegetation indices, and lidar-derived canopy and topography metrics. Models were trained using forest type information from 632 FIA subplots collected in interior Alaska. Of the thirty model and input combinations tested, the random forest classification algorithm with hyperspectral vegetation indices and lidar-derived topography and canopy height metrics had the highest accuracy (78% overall accuracy). This study supports random forest as a powerful classifier for natural resource data. It also demonstrates the benefits from combining both structural (lidar) and spectral (imagery) data for forest type classification.

scholarly journals James Hamilton McLean: The master of the Gastropoda

Zoosymposia ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 14-43
Author(s):  
LINDSEY T. GROVES ◽  
DANIEL L. GEIGER ◽  
JANN E. VENDETTI ◽  
EUGENE V. COAN
Keyword(s):  
Los Angeles ◽  
Natural History ◽  
Los Angeles County ◽  
Natural History Museum ◽  
Current Status ◽  
History Museum ◽  
Type Information

A biography of the late James H. McLean, former Curator of Malacology at the Natural History Museum of Los Angeles County is provided. It is complemented with a full bibliography and list of 344 taxa named by him and co-authors (with type information and current status), as well as 40 patronyms.

The use of data type information in an interactive database environment

1981 ◽  
Vol 11 (2) ◽  
pp. 104-106
Author(s):  
Peter Buneman ◽  
Ira Winston
Keyword(s):  
Data Type ◽  
Use Of Data ◽  
Type Information
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