Dear Manoj,

In order to save you some time, I will let you know up front what is missing from my blog count.

I have done every single blog with the EXCEPTION OF THE LATEST 5 that were conference papers. I did, however, do all of the book blogs because I actually read those.

I do NOT have a blog for HCI Remixed because I didn't know if that was required or not.

Good luck with all that grading!

Chris

Opening Skinner's Box

This book detailed ten of the more famous psychological experiments conducted in the 1900s. Each chapter is devoted to one such experiment, and each one had a profound effect on future research and our lives today (even if we don't realize it). The opening chapter is about B.F. Skinner and his mouse boxes, wherein he used positive reinforcement to train mice to push levers (that's Skinner in the first picture). The author, Lauren Slater, did her best to get background information on each of the experimenters, sometimes going after family members and old research partners. It is this extra information that makes the book so enjoyable. I actually think that Slater is a bit crazy, but her personal insight brings character and feeling to what I would normally view as dispassionate scientific research. In some cases, such as with Bruce Alexander's Rat Park, Slater even attempted to carry out her own related research. In this case, she did a bunch of drugs to try and get herself addicted... which didn't work out for her (luckily).

Lauren Slater doing something with her hands on stage

The one thing that I found strange about this book was the fact that each chapter moves through a cyclical loop of introspection and explanation. She starts with musings and opinions, moves to facts, and then returns to musings. It's almost as if Slater trails off with her own thoughts about the experiments and forgets that she is relating them to the reader. Each of the ten experiments even related to CHI in some way! It's important to consider the psychological effects of computing instead of just the technical ones.

Anyway, it's a great book! It reads like a fictional story instead of a look at experiments.

The Inmates Part 2

After ripping on designers and programmers for the first half of his book, Alan Cooper offers a shred of hope. Well... basically he promotes his own company... but that's beside the point. Cooper has three basic points to make in the last few chapters: create personas, set goals, and use scenarios. These three parts are closely related, as scenarios are run where personas try and accomplish their goals.

• Personas - detailed potential customers created by the designers. Each persona should accurately reflect a certain demographic of customers. The designers should focus on meeting the needs of set personas instead of those of everyone. It is better for 10% of people to love your product than for 100% of hate it.
• Goals - set things that customers want to accomplish. Goals are not tasks. Tasks are steps that must be undertaken to meet end goals. Creating realistic goals for your personas to carry out can help you define what your product should do.
• Scenarios - situations where goals are needed to be met by personas. Again, creating reasonable scenarios can help you design a product that is both functional and helpful. If in the course of running a scenario your persona cannot accomplish their goals, then you should modify your product.

That's pretty much it. In the last chapters, he goes over these key points and gives examples of how his company has used them. So if you want to be a good programmer, you should either hire them or follow suit. It seems that today we as computer scientists have these design tactics ingrained into us. When designing projects, we normally sit around and make up scenarios to describe our thoughts and ideas. It's hard to image that ten years ago, programmers didn't follow these concepts.

Image Recognition for Intelligent Interfaces

Summary:
Professor Trevor Darrell from UC Berkeley was the invited speaker at the 2008 IUI conference. As such, his paper is just an abstract. Seriously, as you read this sentence you might be reading the same amount as what is in his abstract anyway. He mentions that new advances in image recognition have made image-based interfaces a viable alternative to current interfaces that try to analyze physical objects. He then goes into the various parts of the problem he will discuss.

Discussion:
That's about it. I'm assuming that he then started talking about his abstract. Thank you, Manoj, for the shortest paper ever.

Video Object Annotation, Navigation, and Composition

*NOTE: I have not been keeping track of who I leave comments on, but I have comments out there. I'm writing the blogs, reading others, and attending lectures where I can ask questions. Comments seem a little redundant!

Authors:
Dan B Goldman and David Salesin (Adobe Systems, Inc.). Chris Gonterman, Brian Curless, Steven M Seitz (University of Washington)

Summary:
Objects in a video are... objects in a video. Characters and props can be objects, cars and animals, etc. Most video editing software is concerned with timelines and frames, even though objects are what people are more concerned with. Being able to tag an object and have it tracked across frames would greatly speed up the video editing process (no splicing together stills to get your point across), and that's just what the authors of this paper are working on. They focus on the annotation, navigation, and composition of videos in an object-focused way. To accomplish these tasks, videos are preprocessed and low-level motion tracking is employed to determine what objects are in the video.

The colored dots are computed to track probable objects

Annotation deals with adding graphics (such as text bubbles, highlights, outlines, etc.) to moving objects. Uses include sports broadcasting, surveillance videos, and post-production notation for professionals. The five annotations that they implemented were graffiti, scribbles, speech balloons, path arrows, and hyperlinks.

For navigation, the new system allows a user to select an object and drag the mouse to a new location on the screen. Once released, the video will move to a time when that object is close to that release point, thus computing video cuts for the user. The system visualizes ranges of motion for an object by placing a "starburst widget" on it which uses vectors to indicate the length and direction of motion that the object undergoes forward and backward in time.

Video-to-still composition is all about splicing together images from the video to create a single composition. The authors use a drag-and-drop system to move selected objects forward or backward through frames until the object is where it is wanted. All other objects in the frame remain frozen in place until they are directly selected and subsequently manipulated. In this way, a composite image can be created that has each object exactly where the user wants it to be.

The black car was pulled forward in time to appear to be right in front of the white one (affirmative action!)

Discussion:
Awesome stuff... except it takes 5 minutes PER FRAME to preprocess the video! That's an epic amount of time (and that's for 720 x 480 resolution). If they can speed that up, then they are golden. You should check out the paper for yourself!

Scratch Input: Creating Large, Inexpensive, Unpowered and Mobile Finger Input Surfaces

Authors:
Chris Harrison, Scott E. Hudson (Carnegie Mellon University)

Summary:
Scratch Input is a sensor that detects the sound of a fingernail being dragged across different surfaces. The reason for such a sensor is to allow for the addition of a finger input device (gesture recognition). The device (which fits into a modified stethoscope) is small enough to be added to mobile devices. The sensor can be placed onto any solid surface and detect the unique high frequency of scratching (listed as 3000Hz or more). In this paper, the authors also go over some examples of when Scratch Input could be useful. One of which comes from the case that a cell phone is equipped with the device and is resting on a table. When an incoming call is received, the user performs a certain gesture on the tabletop and the phone takes the call on speakerphone. Another example involved placing the device on a wall and using different gestures on said wall to manipulate the playback of music. The authors found that, while testing on tables during a user study, people were able to accurately perform a set of six gestures with an average accuracy of 89.5%. They concluded that their product is both accurate and easy to use.

Some gestures

Discussion:
These guys seem to like making cheap little gadgets (my previous blog was over another such product). I wonder what it is that drives them to do this kind of research and development? Anyway, just like their last paper, this seems like a cool idea. Being super super lazy and just scratching or tapping on the wall or a table to get stuff done would rock! If I want my computer to start torrenting episodes of Archer, all I have to do is sketch out a big A. If I want my cell phone to call Dominos, but I don't want to have to reach over and pick it up, I can draw a D and then yell my order at the phone. It's every lazy person's dream!

Lightweight Material Detection for Placement-Aware Mobile Computing

Authors:
Chris Harrison, Scott E. Hudson (Carnegie Mellon University)

Summary:
Placement-awareness would allow mobile devices to take certain actions without being explicitly told to do so (the authors give the example of a cell phone silencing itself while its owner is at work). In this paper, both cell phones and laptops are used to demonstrate the potential of a new sensor that observes its surroundings to determine the placement of its operating device. A user could map certain materials to locations, and the multispectral sensor could then predict its location by comparison (figure 1).

Figure 1- This picture is humongous.

After giving some examples of use, the authors discuss how the sensor works and what it is made of. To combat situations where no light reaches the bottom of the device, the sensor is equipped with different LEDs that can be reflected off of the resting surface. It does its detection in seconds, and costs less than a dollar to manufacture. With the help of a naive Bayes classifier, the sensor learns which materials correspond to which locations within 86.6% accuracy (which, they say, is much better than anything else on the market).

Discussion:
This sensor has potential, but it definitely needs to have an override added to it. If for some reason the sensor thinks that you're in a location that you aren't then you're going to get pwned. But saving energy, as they discussed in the paper, is a definite plus. I see a company catching on and then charging beastly fees for this $1 sensor (that's the way the world goes round aka Microsoft).