Welcome to TiddlyWiki created by Jeremy Ruston; Copyright © 2004-2007 Jeremy Ruston, Copyright © 2007-2011 UnaMesa Association
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To get started with this blank [[TiddlyWiki]], you'll need to modify the following tiddlers:
* [[SiteTitle]] & [[SiteSubtitle]]: The title and subtitle of the site, as shown above (after saving, they will also appear in the browser title bar)
* [[MainMenu]]: The menu (usually on the left)
* [[DefaultTiddlers]]: Contains the names of the tiddlers that you want to appear when the TiddlyWiki is opened
You'll also need to enter your username for signing your edits: <<option txtUserName>>
These [[InterfaceOptions]] for customising [[TiddlyWiki]] are saved in your browser
Your username for signing your edits. Write it as a [[WikiWord]] (eg [[JoeBloggs]])
<<option txtUserName>>
<<option chkSaveBackups>> [[SaveBackups]]
<<option chkAutoSave>> [[AutoSave]]
<<option chkRegExpSearch>> [[RegExpSearch]]
<<option chkCaseSensitiveSearch>> [[CaseSensitiveSearch]]
<<option chkAnimate>> [[EnableAnimations]]
----
Also see [[AdvancedOptions]]
To get started with this blank [[TiddlyWiki]], you'll need to modify the following tiddlers:
* [[SiteTitle]] & [[SiteSubtitle]]: The title and subtitle of the site, as shown above (after saving, they will also appear in the browser title bar)
* [[MainMenu]]: The menu (usually on the left)
* [[DefaultTiddlers]]: Contains the names of the tiddlers that you want to appear when the TiddlyWiki is opened
You'll also need to enter your username for signing your edits: <<option txtUserName>>
[[Trajectory Data]]
[[Synthetic Time Series]]
[[References]]
[1] S. Kumar, H. T. Nguyen, and E. Suzuki,
"Understanding the Behaviour of Reactive Robots in a Patrol Task by Analysing Their Trajectories,"
In Procs. of the 2010 IEEE/WIC/ACM Intl. Conf. on Web Intelligence and Intelligent Agent Technology, Vol. 2, pp.56-63, 2010.
URL:http://www.i.kyushu-u.ac.jp/~suzuki/jstanr.htm
[2] A. Kouno, S. Takano, and E. Suzuki,
"Constructing Low-cost Swarm Robots that March in Column Formation,"
In Procs. of the 7th Intl. Conf. on Swarm Intelligence (ANTS'10), pp.556-557 2010.
URL:http://www.i.kyushu-u.ac.jp/~suzuki/jstanr.htm
[3] B. Kaluza, V. Mirchevska, E. Dovgan, M. Lustrek, and M. Gams,
"An Agent-based Approach to Care in Independent Living,"
In Ambient Intelligence, Lecture Notes in Computer Science, vol. 6439, pp.177-186, 2010.
URL:http://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity
[4] Ben H. Williams, Marc Toussaint, and Amos J. Storkey.
"A Primitive-based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data."
In Proceedings of the 20th international joint conference on Artifical intelligence (IJCAI'07), pp.1119-1124. 2007.
URL:http://archive.ics.uci.edu/ml/datasets/Character+Trajectories
[5] N. Saito.
"Local Feature Extraction and Its Applications using a Library of Bases."
"""PhD""" thesis, Yale University, New Haven, CT, USA, 1994.
[6] T. Pham, D. and B. Chan, A.
"Control Chart Pattern Recognition using a New Type of Self-organizing Neural Network."
Journal of Systems and Control Engineering, 212(2):115-127, March 1998.
Time Series and Trajectory Datasets
!"""Cylinder-Bell-Funnel""" Function [[[5]|References]]
*Format: Mathematica Package/Matlab
*The Mathematica Package below generates a training/test split instance
[[SyntheticTimeSeriesData.m|http://anis.dept.eng.gunma-u.ac.jp/~datasets/SyntheticTimeSeriesData.m]]
*Instruction
** Load the package by {{{<<SyntheticTimeSeriesData`}}} and execute {{{CBFData[]}}} in Mathematica
** Training/test data/labels are stored in the local directory in separate files.
!Control Chart [[[6]|References]]
*Format: Mathematica Package/Matlab
*The Mathematica Package below formats a benchmark instance made public at [[UCR|http://www.cs.ucr.edu/~eamonn/time_series_data/]]
[[SyntheticTimeSeriesData.m|http://anis.dept.eng.gunma-u.ac.jp/~datasets/SyntheticTimeSeriesData.m]]
*Instruction
** Load the package by {{{<<SyntheticTimeSeriesData`}}} and execute {{{ControlChartData[]}}} in Mathematica
** Training/test data/labels are stored in the local directory in separate files.
!Pivot Behavior Trajectory [[[1]|References]]
Format: Matlab
*Datasets are prepared for 3-fold cross-validation, processed from 3 trajectories of an agent.
*Each fold consists of training/validation set of subsequences of velocity norm
*Each subsequence is a 18-dimensional vector.
*The zip files contain training/validation sets named in the format "cv?{trn/vld}.mat"
[[Data|http://www.rs.tus.ac.jp/ando/datasets/data_tall.zip]] [[Label|http://www.rs.tus.ac.jp/ando/datasets/label_tall.zip]]
!Interactive Behavior Trajectory [[[2]|References]]
Format: Matlab (Zipped)
Datasets """PX"""1-3 are processed respectively from an independent set of agent trajectories.
Each dataset has been prepared for a 5-fold cross-validation.
Each fold consists of training/validation set of 3528/882 subsequences of velocity norm (real-value vectors).
Each zip files contain training/validation sets named in the format "cv?{trn/vld}.mat"
*"""PX"""1 [[Data|http://www.rs.tus.ac.jp/ando/datasets/data_px1.zip]] [[Label|http://www.rs.tus.ac.jp/ando/datasets/label_px1.zip]]
*"""PX"""2 [[Data|http://www.rs.tus.ac.jp/ando/datasets/data_px2.zip]] [[Label|http://www.rs.tus.ac.jp/ando/datasets/label_px2.zip]]
*"""PX"""3 [[Data|http://http://www.rs.tus.ac.jp/ando/datasets/data_px3.zip]] [[Label|http://www.rs.tus.ac.jp/ando/datasets/label_px3.zip]]
!Sensor Tag Data [[[3]|References]]
Format: Mathematica Package/Matlab
*Datasets S1-4 are processed from trajectories of [[Localization Data for Person Activity Data Set|http://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity]] from [[UCI machine learning repository|http://archive.ics.uci.edu/ml/index.html]]
*The Mathematica Package below generates 3-fold cross-validation datasets
[[SensorTagDataCV.m|http://www.rs.tus.ac.jp/ando/datasets/SensorTagDataCV.m]]
*Instructions
** Load the package by {{{<<SensorTagDataCV`}}} and execute {{{SensorTagDataCV[url]}}} in Mathematica
**url is the path to the mat file. It can also be a local path to a downloaded file
**current link is "http://archive.ics.uci.edu/ml/machine-learning-databases/00196/ConfLongDemo_JSI.txt"
* Files are generated to the current directory
**Each file contains a training/validation set and named in the format "data_st?cv?{trn/vld}.mat"
!Handwriting Data [[[4]|References]]
Format: Mathematica Package/Matlab
*Datasets H1-3 are processed from trajectories of [[Character Trajectories Data Set|http://archive.ics.uci.edu/ml/datasets/Character+Trajectories]] from [[UCI machine learning repository|http://archive.ics.uci.edu/ml/index.html]]
* The Mathematica package below generates 3-fold cross-validation datasets
[[HandwritingDataCV.m|http://www.rs.tus.ac.jp/ando/datasets/HandwritingDataCV.m]]
*Instructions
** Load the package by {{{<<HandwritingDataCV`}}} and execute {{{HandwritingDataCV[url]}}} in Mathematica
**url is the path to the mat file. It can also be a local path to a downloaded file
**current link is 'http://ftp.ics.uci.edu/pub/machine-learning-databases/character-trajectories/mixoutALL_shifted.mat'
* Files are generated to the current directory
**Each file contains a training/validation set and named in the format "data_hw?cv?{trn/vld}.mat"
This site provides codes for reproducing the datasets used in the following paper
*Shin Ando and Einoshin Suzuki, """"Time-Sensitive""" Classification of Behavioral Data," In Proceedings of 2013 SIAM International Conference on Data Mining pp.458-466 (DOI:[[10.1137/1.9781611972832.51|http://dx.doi.org/10.1137/1.9781611972832.51]])