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Best Raytracing Project for CSCI 3090, 2014

Congratulations to Wesley Taylor for the best raytracing project for CSCI 3090 for Winter 2014.  Wesley’s rendering includes the basic requirements, plus reflection, refraction, soft…

Simple Multi-Touch Toolkit

While multi-touch computing becomes more common, there comes a requirement for students to learn how to create software for multi-touch environments.  Although there are many powerful toolkits that exist already, they require a strong programming background and thus become difficult to integrate into fast-paced human-computer interaction (HCI) courses or for non-CS students to use.  Researchers at the University of Ontario Institute of Technology (UOIT) and the University of Waterloo (UW) have developed a toolkit with a simplified API called the Simple Multi-Touch Toolkit (SMT).

Based around the concept of touch-enabled zones, SMT is built as a library for the popular processing platform or as a standalone Java library.  By integrating with Processing’s simplified syntax, SMT makes prototyping multi-touch applications fast and easy.   SMT natively supports TUIO and works on Windows Touch but also provides a multi-touch simulator to support non-touch enabled environments.  While SMT was designed to support students and used in undergraduate HCI courses at both UOIT and UW in experimenting with multi-touch input, SurfNet researchers have also used SMT to develop several applications.

The project’s main website is located here at http://vialab.science.uoit.ca/smt/

Check out our GitHub Repository for source code related to this project.

Publications

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Acknowledgements

Investigating the Semantic Patterns of Passwords

Summary

What is the meaning within a password?  And, how does the meaning in your password relate to security risks?  In our research into the ‘secret language of passwords,’ we have investigated the numerical and textual patterns from a semantic (meaning) point of view.  Where prior research investigated letter and number sequences to expose vulnerable passwords, such as “password123,” our research has delved into the composition of seemingly complex passwords such as “ilovedan1201” or “may101982” and revealed common patterns.  In these cases, the patterns of <I><love><male-name><number> and <month in letters><day in numbers><year after 1980 in numbers> are common patterns which, once learned, can be used to generate password guesses, such as “IloveMike203” and “July022001”.

Using linguistic analysis and interactive visualization techniques, we have investigated the patterns of date-like numbers in passwords, and the meaning and relationships between types of words in passwords.  The resulting analysis guided our creation of a password guessing system (not available to the public!) which on several measures is better than any prior published result.  The exposed vulnerabilities are motivating our ongoing work into new ways to help people create semantically secure passwords. This research contributed to a major story in the New York Times Magazine on the Secret Life of Passwords.

Our research started with the many large password leaks that were made publicly available on the Internet.  In particular, the 32 million passwords from the RockYou website, exposed in 2009.

Our published research was conducted in two phases:

Date and Numbers

We started exploring date patterns, as 24% of the RockYou passwords contain a numeric sequence of at least 4 digits. So we wondered whether or not these sequences are dates, and if so, are there any temporal patterns? Our analyses found that 6% of these passwords (almost 2 million accounts!) contain numbers that match a date. To facilitate exploration of the patterns in the choice of dates, we created an interface that allows one to find the frequency that each day, month, year or decade (back to the year 1900) is referred to, as well as the corresponding passwords. We did not count passwords with numbers that are more likely to be keyboard patterns than dates, such as “111111”. Exploring the data through this interface, we confirmed some predictable patterns, such as the preference for dates that have repeated days and months (e.g., 08/08/1989), but also uncovered hidden ones, such as a consistent preference for the first two days of months, holidays, and a few notorious dates (e.g., Titanic accident) . For a detailed report on this work please read our paper or try our exploratory interface.

Words and Building a Password Grammar

In the second part of this research, we turned our attention to semantic patterns in the choice of words. Employing natural language processing techniques, we broke each password into words and classified the words according to their syntactic (grammar) function and semantic (meaning) content. The result is a rich model representing the syntactic and semantic patterns of a collection of passwords. With this model, we can rank the semantic categories to find that “love” is the most prevalent verb in passwords, “honey” is the most used food-related word, and “monkey” is the most popular animal, for example. Contrary to reported psychology research, we found that many categories related to sexuality and profanity are among the top 100. Our work also brought insight into the relations between concepts; for example, our model shows that a male name is four times more likely to follow the string “ilove” than a female name. Our paper, published in the NDSS Symposium 2014, discusses the security implications of our work. In summary, we show that the security provided by passwords is overestimated by methods that do not account for semantic patterns.

Online Demos

Try the dates visualization yourself!

Try the words visualization yourself! 

Software

Semantic-Guesser

Media Coverage

Our research has also been featured in additional media, including:

We have also been featured on UOIT Homepage, including an article entitled “Heartbleed update: UOIT researchers analyze why consumers use weak passwords“.

Publications

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Acknowledgements

Thanks to undergraduate alumni Jeffrey Hickson and Swapan Lobana who worked as research assistants on this project, and to the funding agencies who supported this work.

vialab @ IEEE VIS 2014

This year four of us are attending IEEE VIS in Paris, France to present work by lab members and collaborators. Lab members are presenting on two papers. DimpVis,…

vialab contributions at IEEE VIS 2016

This year at the IEEE VIS Conference in Baltimore members of the lab will present papers, posters, and workshop contributions! These contributions also represent collaborations with the University…

Abbreviating Text Labels on Demand

A known problem in information visualization labelling is when the text is too long to fit in the label space. There are some commonly known techniques used in order to solve this problem like setting a very small font size. On the other hand, sometimes the font size is so small that the text can be difficult to read. Wrapping sentences, dropping letters and text truncation can also be used. However, there is no research on how these techniques affect the legibility and readability of the visualization. In other words, we don’t know whether or not applying these techniques is the best way to tackle this issue. This thesis describes the design and implementation of a crowdsourced study that uses a recommendation system to narrow down abbreviations created by participants allowing us to efficiently collect and test the data in the same session. The study design also aims to investigate the effect of semantic context on the abbreviation that the participants create and the ability to decode them. Finally, based on the study data analysis we present a new technique to automatically make words as short as they need to be to maintain text legibility and readability.

Based on this project we implemented and made available online an API that allows other programmers to use our abbreviation algorithm in their web applications.

Check out our GitHub Repository for source code related to this project.

For some demos applying our “Abbreviation on Demand” algorithm, and some visualizations of our study data access: http://vialab.science.uoit.ca/abbrVisualization/

Publications

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