Open Source Software

Team Members: Martin Porcheron, Joel Fischer, Michel Valstar

Open Source Code URL: https://github.com/MixedRealityLab/nottreal/

Programming Language: Python

Type of Software License: MIT License

Project Summary:  NottReal is an interactive tool to support Wizard of Oz studies by simulating a Voice User Interface, including both optional audio and visual output. Its main features include: 1) tabbed lists of pre-scripted messages, 2) entry for custom messages, 3) currently queued messages, 4) previously sent messages, 5) previously filled slots, and 6) options to log events and send messages with a loading animation (see Figure 1).

DOI: https://doi.org/10.1145/3405755.3406168

Source of Funding/Acknowledgments: This work was supported by the Engineering and Physical Sciences Research Council [grant number EP/N014243/1] and the Department for International Development.

Team Members: Zack Ellerby, Josie McCulloch, John Young, Christian Wagner, Oliver Miles

Open Source Code URL: https://github.com/decsys/decsys

Type of Software License: AGPL-3.0-only

Project Summary:

DECSYS is a newly developed open-source software tool, which enables the creation and administration of digital surveys that elicit both conventional and interval-valued responses. DECSYS incorporates a range of features, and is designed to maximise versatility for experimenters and usability for participants. Surveys can be conducted either locally or online, and results easily exported.

References:

C. Wagner, S. Miller, J. M. Garibaldi, D. T. Anderson and T. C. Havens, "From Interval-Valued Data to General Type-2 Fuzzy Sets," in IEEE Transactions on Fuzzy Systems, vol. 23, no. 2, pp. 248-269, April 2015, doi: 10.1109/TFUZZ.2014.2310734.

Z. Ellerby, J. McCulloch, J. Young and C. Wagner, "DECSYS – Discrete and Ellipse-based response Capture SYStem," 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019, pp. 1-6. DOI: 10.1109/FUZZ-IEEE.2019.8858996

Z. Ellerby, O. Miles, J. McCulloch and C. Wagner, "Insights from interval-valued ratings of consumer products—a DECSYS appraisal," 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020, pp. 1-8. DOI: 10.1109/FUZZ48607.2020.9177634

Z. Ellerby, C. Wagner, S. Broomell, “Capturing Richer Information—On Establishing the Validity of an Interval-Valued Survey Response Mode”, Accepted to Behavior Research Methods, 2021.

Source of Funding/Acknowledgments (optional): The development of the software has been supported in part by the National Cyber Security Centre (NCSC) and the UK EPSRC grant EP/P011918/1: Leveraging the Multi-Stakeholder Nature of Cyber Security 

Team Members: Josie McCulloch

Open Source Code URL: https://bitbucket.org/JosieMcCulloch/syfsel/src/master/

Programming Language: Python

Type of Software License: GPL-3

Project Summary:

SyFSeL is a free open-source library that automatically generates synthetic fuzzy sets aimed to for use in empirically testing methods developed for fuzzy sets. SyFSeL generates as many sets as desired, with specified membership function type (normal, bi-modal or multi-modal) and fuzzy set type (type-1 or type-2) to enable users to emulate real data. Fuzzy sets are stored in csv format so users can easily import the generated sets into their own fuzzy systems software and SyFSeL can also create graphical plots of the generated sets.

References: J. McCulloch, "SyFSeL: Generating Synthetic Fuzzy Sets Made Simple," 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018, pp. 1-6.

DOI: 10.1109/FUZZ-IEEE.2018.8491549

Source of Funding/Acknowledgments (optional): The development of the software has been supported in part by the UK EPSRC grant EP/P011918/1: Leveraging the Multi-Stakeholder Nature of Cyber Security

A python-based toolkit for automatically generating and analysing data-driven fuzzy sets

Team Members: Josie McCulloch

Open Source Code URL: https://bitbucket.org/JosieMcCulloch/fuzzycreator/src/master/

Programming Language: Python

Type of Software License (optional): GPL-3

Project Summary:

fuzzycreator is a free, open-source, python based toolkit that facilitates the automatic generation of type-1 and type-2 fuzzy sets from data, and their analysis through measures such as similarity and distance.

References: J. McCulloch, "Fuzzycreator: A python-based toolkit for automatically generating and analysing data-driven fuzzy sets," 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017, pp. 1-6.

DOI: 10.1109/FUZZ-IEEE.2017.8015445

Source of Funding/Acknowledgments (optional): The development of the software has been supported in part by the UK EPSRC grant EP/P011918/1: Leveraging the Multi-Stakeholder Nature of Cyber Security

Team Members: Christian Wagner

Open Source Code URL: http://juzzy.wagnerweb.net/

Programming Language:Java

Type of Software License: BSD-3

Project Summary:

Juzzy is a free, open-source, Java based library for the design and implementation of type-1, interval and general type-2 set and system based applications.

References: C. Wagner, "Juzzy - A Java based toolkit for Type-2 Fuzzy Logic," 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), 2013, pp. 45-52

DOI: 10.1109/T2FZZ.2013.6613298.

An online toolkit for the design, implementation, execution and sharing of Type-1 and Type-2 fuzzy logic systems

Team Members: Christian Wagner

Open Source Code URL: http://juzzy.wagnerweb.net/

Programming Language: Java

Project Summary:

JuzzyOnline is a browser-based toolkit for the design and execution of type-1, interval and general type-2 fuzzy logic systems. JuzzyOnline includes features for generating figures for all types of fuzzy sets.

References: C. Wagner, M. Pierfitt and J. McCulloch, "Juzzy online: An online toolkit for the design, implementation, execution and sharing of Type-1 and Type-2 fuzzy logic systems," 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014, pp. 2321-2328

DOI: 10.1109/FUZZ-IEEE.2014.6891548.

Team Members: Chao Chen, Jon M. Garibaldi, Tajul Razak

Open Source Code URL: https://CRAN.R-project.org/package=FuzzyR

Programming Language: R

Type of Software License: GPL-2 | GPL-3

Project Summary:

FuzzyR is a free, open-source fuzzy logic toolbox for the R programming language. Whilst keeping existing functionalities of the previous toolboxes (e.g. FuzzyToolkitUoN), the main extension of the FuzzyR toolbox is the capability to optimise type-1 and interval type-2 fuzzy inference systems based on an extended ANFIS architecture. An accuracy function is also added to provide performance indicators, featuring eight alternative accuracy measures, including a new measure UMBRAE. In addition, graphical user interfaces have been provided so that the properties of a fuzzy inference system can be visualised and manipulated. ​In the latest release we have made an extension of the toolbox for non-singleton fuzzy logic systems.

References:

C. Chen, T. R. Razak, and J. M. Garibaldi, “FuzzyR: An Extended Fuzzy Logic Toolbox for the R Programming Language,” in Proceedings IEEE International Conference on Fuzzy Systems, 2020, pp. 1–8.

C. Chen, Y. Zhao, C. Wagner, D. Pekaslan and J. M. Garibaldi, “An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems” Accepted to IEEE International Conference on Fuzzy Systems, 2021

DOI: 10.1109/FUZZ48607.2020.9177780

Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19

Team Members:  Min Chen, Rita Borgo, Radu Jianu, Benjamin Bach, Euan Freeman, Hui Fang, Franck Patrick Vidal, Jonathan C. Roberts, Thomas Torsney-Weir, Alfie Abdul-Rahman, Richard Reeve, Louise Matthews, Jason Antony Dykes, Aidan David Slingsby, Daniel William Archambault, Panagiotis Ritsos, Robert S. Laramee, Joseph David Wood, Cagatay Turkay, Kai Xu

Open Source Code URL:  https://sites.google.com/view/rampvis/teams

Project Summary:

RAMP VIS is a group of volunteers specialised in Data Visualization and Visual Analytics, who answered a call to support the modelling scientists and epidemiologists in the Scottish COVID-19 Response Consortium (SCRC).  Most complex models in the literature take decades and sometimes centuries to develop.  Many are built on a substantial amount of post-hoc evidence and analysis, while many are still being improved today. The success can usually be attributed to a collective effort by generations of modelling scientists in data collection, observation, and analysis; hypothesis formulation; and model development, validation, deployment, monitoring, and improvement. In combating COVID-19, such a collective effort in decades has to be compressed into a period of weeks and months.

References:

RAMPVIS: Towards a New Methodology for Developing Visualisation Capabilities for Large-scale Emergency Responses, M. Chen, A. Abdul-Rahman, D. Archambault, J. Dykes, A. Slingsby, P. D. Ritsos, T. Torsney-Weir, C. Turkay, B. Bach, A. Brett, H. Fang, R. Jianu, S. Khan, R. S. Laramee, P. H. Nguyen, R. Reeve, J. C. Roberts, F. Vidal, Q. Wang, J. Wood, K. Xu

ArXiv, 2020, arXiv:2012.04757v1

Source of Funding/Acknowledgments:

RAMP VIS: Making Visual Analytics an Integral Part of the Technological Infrastructure for Combating COVID-19 by Min Chen (Principal Investigator) et al, funded by the Engineering and Physical Sciences Resource Council (EPSRC EP/V054236/1), UK, 2021, £430,497.00

https://sites.google.com/view/rampvis/

Team Members: Qiru Wang, Robert S Laramee, Damon Berridge

Open Source Code URL:  https://github.com/thevisgroup/EHealthVis

Project Summary:

This project will develop free, open source, novel visual analytics and visualisation software to study the UK's rapidly increasing volume of big healthcare data. By opening up and exposing the healthcare data analysts to visual and graphical depictions of the enormous collection of the UK's healthcare data including EHR data, a whole new digital world of health understanding, exploration, analysis, comparison and engagement is possible. This healthcare adventure coincides with a rapid rise in interest to improve the collective health of the UK.

References:

Chao Tong, Richard Roberts, Robert S Laramee, Daniel Thayer, Damon Berridge, Cartographic Treemaps for the Visualization of Public Healthcare Data, The Computer Graphics and Visual Computing (CGVC) Conference 2017, 14-15 September 2017, Manchester, UK ( PDF file, supplementary PDF, video, https://doi.org/10.2312/cgvc.20171276 )

Source of Funding/Acknowledgments:

Bringing Healthcare Data to Life by Robert S. Laramee (Principal Investigator), Damon Berridge (Co-Investigator), funded by the Engineering and Physical Sciences Resource Council (EPSRC EP/S010238/1), UK, 2019-2022, £441,485.97

https://gow.epsrc.ukri.org/NGBOViewGrant.aspx?GrantRef=EP/S010238/1