It looks as if the campaign to #CancelColbert may have been somewhat successful…

On Thursday, CBS announced that Stephen Colbert of Comedy Central’s The Colbert Report will replace the outgoing late-night legend David Letterman in the coveted 11:35 time slot. 

The final episode of The Colbert Report will air at the end of the year.

Colbert began working in improvisational theatre with Second City Chicago and as a correspondent on The Daily Show with Jon Stewart. In 2005, he was promoted to host his own show.

A spin-off of The Daily Show‘s fake-news format, but with a twist, The Colbert Report pushed the bounds of political satire with Stephen’s portrayal of an egotistical, willfully oblivious right-wing pundit that has often hit too close to the mark for Bill O’Reilly’s tastes.

But as host of The Late Show, the public will be introduced to the real Stephen Colbert.

Though his true self has peeked through on a few notable occasions, Colbert has maintained his facade for almost a decade on the air. It remains to be seen what a talk show hosted by the real Colbert would look like.

Let’s hope Colbert doesn’t just spend the rest of his days making small talk with A-List celebrities, like most late night hosts. (And let’s see if he can maintain the impressive attention to science that the Report has become known for.)

Will we be saying goodbye to some of the sharpest political comedy of our time?

We’ll find out in 2015. Until then, we at USDemocrazy will be savoring the final episodes of The Colbert Report. 

Every week we tweet a lot of interesting stuff highlighting great content that we find on the web that can be of interest to web designers.

The best way to keep track of our tweets is simply to follow us on Twitter, however, in case you missed some here’s a quick and useful compilation of the best tweets that we sent out this past week.

Note that this is only a very small selection of the links that we tweeted about, so don’t miss out.

To keep up to date with all the cool links, simply follow us @DesignerDepot

Jerry LoFaro creates amazing illustrations http://depot.ly/vEvge http://ow.ly/i/5ctG2

Graphic Designer Reinterprets The ABCs For Math Nerds http://depot.ly/vDQ6k  via @FastCoDesign

Font Size Idea: px at the Root, rem for Components, em for Text Elements http://depot.ly/vEwQw  via @Real_CSS_Tricks

Interview with Khajag Apelian: “Type Design Is Not Only About Drawing Letters” http://depot.ly/vEwJM

How Tobias Frere-Jones discovered NYC’s lost neighborhood of #type – http://ow.ly/vDPX1  via @BrushLovers

How emoji conquered the world: the story of the smiley face from the man who invented it http://depot.ly/vDOx8  via @verge

“Night Walk in Marseille”, an interactive Google demo that lets you explore the sounds and the soul of the city http://depot.ly/vDOsi

A guide to validating product ideas with quick and simple experiments http://depot.ly/vDNsR  #UX via @smashingmag

How A Drexel Professor Created The World’s Biggest Game Of Tetris http://depot.ly/vBaJh

10 Crucial Lessons From History’s Greatest Graphic Designers: http://depot.ly/vB9z6  via @FastCoDesign

Advanced web typography: Justification & hyphenation http://depot.ly/vB93s  via @elliotjaystocks

10 web apps every web designer should know of http://depot.ly/vyb1Q

Interesting thought: Use a recorder to dictate your blog posts http://depot.ly/vvpSW  /@seanHodge

Comic Neue – a font to write passive aggressive office memos http://comicneue.com

A Game of Social Throne, by HootSuite http://depot.ly/vvoDU

 Want more? No problem! Keep track of all our tweets by following us @DesignerDepot

Smarty Bundle: Crests Logo Templates and Badges – only $15!

Computer Science and Electrical Engineering
University of Maryland, Baltimore County
Ph.D. Dissertation Defense

A Rapidly Deployable Image Classification System Using Feature Views

Adrian Rosebrock

9:00am Friday, 18 April 2014, ITE 346, UMBC

Constructing an image classification system using strong, local invariant descriptors is both time consuming and tedious, requiring much experimentation and parameter tunings to obtain an adequate performing model. Furthermore, training a system in a given domain and then migrating the model to a separate domain will likely yield poor performance. As the recent Boston Marathon attacks demonstrated, large, unstructured image databases from traffic cameras, security systems, law enforcement officials, and citizens can be quickly amassed for authorities to review; however, reviewing each and every image is an expensive undertaking, in terms of both time and human effort. Inherently, reviewing crime scene images is a classification task. For example, authorities may want to know if a given image contains a suspect, a suspicious package, or if there are injured people in the photo. Given an emergency situation, these classifications will be needed as quickly and accurately as possible. In this work we present a rapidly deployable image classification system using “feature views”, where each view consists of a set of weak, global features. These weak global descriptors are computationally simple to extract, intuitive to understand, and require substantially less parameter tuning than their local invariant counterparts. We demonstrate that by combining weak features with ensemble methods we are able to outperform current state-of-the-art methods or achieve comparable accuracy with much less effort and domain knowledge. We then provide both theoretical and empirical justifications for our ensemble framework that can be used to construct rapidly deployable image classification systems called “Ecosembles”.

Finally, we recognize the fact that image datasets give us the relatively unique opportunity to extract multiple feature representations through the use of various descriptors. In situations where the original dataset is not available for further feature extraction or in cases where multiple feature views are ambiguous (such as predicting income based on geographical location and census data) the Ecosemble method cannot be applied. In order to extend Ecosembles to arbitrary datasets of diverse modalities, we introduce artificial feature views using kernel approximations. These artificial feature views are constructed from a single representation of the data, alleviating the need to explicitly extract multiple feature views. We then apply artificial feature views to a diverse range of non-image classification datasets to demonstrate our method is applicable to multiple modalities, while still outperforming current state-of-the-art methods.

Committee: Drs. Tim Oates (chair), Jesus Caban, Tim Finin, Charles Nicholas, Jian Chen

Computer Science and Electrical Engineering
University of Maryland, Baltimore County
Ph.D. Dissertation Defense

A Rapidly Deployable Image Classification System Using Feature Views

Adrian Rosebrock

9:00am Friday, 18 April 2014, ITE 346, UMBC

Constructing an image classification system using strong, local invariant descriptors is both time consuming and tedious, requiring much experimentation and parameter tunings to obtain an adequate performing model. Furthermore, training a system in a given domain and then migrating the model to a separate domain will likely yield poor performance. As the recent Boston Marathon attacks demonstrated, large, unstructured image databases from traffic cameras, security systems, law enforcement officials, and citizens can be quickly amassed for authorities to review; however, reviewing each and every image is an expensive undertaking, in terms of both time and human effort. Inherently, reviewing crime scene images is a classification task. For example, authorities may want to know if a given image contains a suspect, a suspicious package, or if there are injured people in the photo. Given an emergency situation, these classifications will be needed as quickly and accurately as possible. In this work we present a rapidly deployable image classification system using “feature views”, where each view consists of a set of weak, global features. These weak global descriptors are computationally simple to extract, intuitive to understand, and require substantially less parameter tuning than their local invariant counterparts. We demonstrate that by combining weak features with ensemble methods we are able to outperform current state-of-the-art methods or achieve comparable accuracy with much less effort and domain knowledge. We then provide both theoretical and empirical justifications for our ensemble framework that can be used to construct rapidly deployable image classification systems called “Ecosembles”.

Finally, we recognize the fact that image datasets give us the relatively unique opportunity to extract multiple feature representations through the use of various descriptors. In situations where the original dataset is not available for further feature extraction or in cases where multiple feature views are ambiguous (such as predicting income based on geographical location and census data) the Ecosemble method cannot be applied. In order to extend Ecosembles to arbitrary datasets of diverse modalities, we introduce artificial feature views using kernel approximations. These artificial feature views are constructed from a single representation of the data, alleviating the need to explicitly extract multiple feature views. We then apply artificial feature views to a diverse range of non-image classification datasets to demonstrate our method is applicable to multiple modalities, while still outperforming current state-of-the-art methods.

Committee: Drs. Tim Oates (chair), Jesus Caban, Tim Finin, Charles Nicholas, Jian Chen

This March, while most college students were enjoying freedom from the classroom or even sunny days on the beach, a number of UMBC students instead chose to spend their week off from school in service learning experiences through Alternative Spring Break (ASB).

These week-long service trips introduce students to the broad range of challenges our society faces and teach them how they can help change things for the better. Thanks to donors like you, our students were volunteering and serving for projects and organizations like: 

• The Gesundheit Institute
•  Through the Eyes of a Refugee
• The ARC of Carroll County
• Will Hope for Change
• A Better Impact

Your gifts ensured that students could take advantage of these life-changing education opportunities, so thank you! They truly will carry these memories and lessons throughout their lives.

“My ASB experience was truly memorable. I loved seeing that small actions can make an impact and I had the best time working with other students in the program,” says student Erin Wells who went on the A Better Impact trip. “I have grown so much from just one week, thank you for this opportunity.”

See more photos from the Alternative Spring Break trips!