GlassesViewer: Open-source software for viewing and analyzing data from the Tobii Pro Glasses 2 eye tracker

AbstractWe present GlassesViewer, open-source software for viewing and analyzing eye-tracking data of the Tobii Pro Glasses 2 head-mounted eye tracker as well as the scene and eye videos and other data streams (pupil size, gyroscope, accelerometer, and TTL input) that this headset can record. The software provides the following functionality written in MATLAB: (1) a graphical interface for navigating the study- and recording structure produced by the Tobii Glasses 2; (2) functionality to unpack, parse, and synchronize the various data and video streams comprising a Glasses 2 recording; and (3) a graphical interface for viewing the Glasses 2’s gaze direction, pupil size, gyroscope and accelerometer time-series data, along with the recorded scene and eye camera videos. In this latter interface, segments of data can furthermore be labeled through user-provided event classification algorithms or by means of manual annotation. Lastly, the toolbox provides integration with the GazeCode tool by Benjamins et al. (2018), enabling a completely open-source workflow for analyzing Tobii Pro Glasses 2 recordings.

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Understanding Open Source Software Evolution Using Fuzzy Data Mining Algorithm for Time Series Data

Advances in Fuzzy Systems ◽

10.1155/2016/1479692 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Munish Saini ◽

Sandeep Mehmi ◽

Kuljit Kaur Chahal

Keyword(s):

Data Mining ◽

Time Series ◽

Open Source ◽

Open Source Software ◽

Time Series Data ◽

Series Data ◽

Software Project ◽

Data Mining Algorithm ◽

Fuzzy Data ◽

Mining Algorithm

Source code management systems (such as Concurrent Versions System (CVS), Subversion, and git) record changes to code repositories of open source software projects. This study explores a fuzzy data mining algorithm for time series data to generate the association rules for evaluating the existing trend and regularity in the evolution of open source software project. The idea to choose fuzzy data mining algorithm for time series data is due to the stochastic nature of the open source software development process. Commit activity of an open source project indicates the activeness of its development community. An active development community is a strong contributor to the success of an open source project. Therefore commit activity analysis along with the trend and regularity analysis for commit activity of open source software project acts as an important indicator to the project managers and analyst regarding the evolutionary prospects of the project in the future.

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Evolution of the Linux Credits file: Methodological challenges and reference data for Open Source research (originally published in Volume 9, Number 6, June 2004)

First Monday ◽

10.5210/fm.v0i0.1480 ◽

2005 ◽

Author(s):

Ilkka Tuomi

Keyword(s):

Open Source ◽

Open Source Software ◽

Time Series Data ◽

Reference Data ◽

Series Data ◽

Automatic Extraction ◽

Policy Recommendations ◽

The Core ◽

Methodological Challenges ◽

Geographical Expansion

This paper presents time–series data that can be extracted from the Linux Credits files and discusses methodological challenges of automatic extraction of research data from open source files. The extracted data is used to describe the geographical expansion of the core Linux developer community. The paper also comments on attempts to use the Linux Credits data to derive policy recommendations for open source software.

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IDENTIFIKASI PERUBAHAN POLA CURAH HUJAN MELALUI PERIODOGRAM STANDAR

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2017.9.1.2859 ◽

2017 ◽

Vol 9 (1) ◽

pp. 103

Author(s):

Gumgum Darmawan ◽

Budhi Handoko ◽

Zulhanif Zulhanif

Keyword(s):

Time Series ◽

Open Source ◽

Open Source Software ◽

Time Series Data ◽

Seasonal Pattern ◽

Series Data ◽

West Java ◽

Periodogram Analysis

Rainfall is time series data that has seasonal pattern, usually period 12. The pattern of rainfall seasonal itself often changes. In this paper, the pattern of rainfall will identify by Periodogram Analysis. We use Time series data from one of city in west Java province. By this analysis, it is proved that the pattern of rainfall has been change. Computation itself, we use macro of Open Source Software R (OSSR).

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