While Facebook provides a “like” button to indicate approval of news feed posts, there is no indication that this feature is used to filter news feed content.  Facebook does provide an option to block news feed posts of designated friends, but this is a very coarse grained filter.  More recently, Facebook presented an interesting analysis of news feed posts.  This study identifies post topics that are more likely to be “liked” or commented on by friends as well as identifying post topics that are correlated with high friend  counts.  While the Facebook study analyzes post content, it only compares against metrics inherent to Facebook itself, such as “likes” per post, number of comments per post, age of posters, and friend counts.  The study neither attempts to quantify the news feed topic preferences of Facebook users, nor does it attempt to identify a Facebook user’s best or worst friends based on friends’ news feed posts.

Design

As previously mentioned, the main idea is for each FeedMod user to moderate each news feed post in a manner similar to Slashdot post moderations.  Each news feed post is assigned a moderation which consists of an integer value score and descriptor string.  For this experiment, I chose the following moderations:

Value	Descriptor	Examples
-2	Troll	Rants or politically charged arguments; I really can't stand these, hence the -2
-1	Spam	Advertisements, such as requests to attend an event, donate money, or to come to a friend's place of business to buy something
-1	Redundant	Endless duplicate announcements, typically on holidays and birthdays
-1	Indifferent	Stuff I really don't care about, such as random song lyrics or sports posts about teams that I don't follow
-1	Unintelligible	Something posted out of context, usually by someone with whom I'm out of touch
0	Normal	I assign my posts to this, otherwise I use it rarely
+1	Funny	Self explanatory, but I found myself only using it when I was about to fall out of my chair laughing
+1	Insightful	Anything that gives insight into the highlights of a friend's life or a well formed argument on a topic
+1	Informative	Usually updates on what someone is doing
+1	Inquisitive	Someone posing a question to his or her friends
+2	Interesting	Usually news articles or a post about something I hadn't heard of or thought about before (these posts are the best, hence the +2)

By moderating the status updates of all of my Facebook friends, I am then able to assign a score to each friend based on those moderations.  I can even moderate status update comments made by friends and non-friends alike.  The score, similar to Slashdot’s “karma,” is created by summing the moderation values for all of a friend’s posts.  In this way, I can see the balance of how positive or negative a friend’s posts tend to be.  The advantage of this method is that friends with a large number of good or bad posts will stick out.

Implementation and Facebook API

My original idea was to use the Facebook API to modify the news feed and allow me to moderate friends’ posts.  A month ago when I started this, I had no experience with the Facebook API and did not know if it would even allow me to do such a thing.  While I did achieve what I wanted in being able to moderate friends’ posts, I can say conclusively that the Facebook API is seriously lacking.  I have two major issues with the API: 1) an inability to customize the Facebook website, forcing developers to reinvent the wheel for even the smallest applications, and 2) limited access to information easily accessed on the main Facebook website.

What I really wanted to do was to be able to modify the existing Facebook news feed and insert a moderation select list next to each status update.  When a FeedMod user selected a moderation, the post-moderation pair would be stored in a database.  Unfortunately, Facebook does not allow developers to modify any portion of the Facebook website, including the news feed.  It would be much easier if Facebook allowed this since I wouldn’t have to recreate the entire news feed.  This lack of customization is a serious barrier to developers who have an idea for a simple application that makes use of the existing Facebook website.

The Facebook API does, however, allow you to query status updates and friend information for use on your own website.  What I learned was that apps within Facebook are actually remote websites running within an iframe on the main Facebook website.  Unfortunately, Facebook limits the information you can get: news feed items are restricted to status updates, links, videos, and photos — you cannot retrieve time ordered taggings, newly added friends, profile picture updates, and likes, among other things.

Interface

While I found the Facebook API disappointing, I was able to recreate the Facebook news feed on my own webserver, using the Facebook PHP toolkit.  Finally, with a database backend to store moderations, I was able to produce a customized news feed with the ability to moderate friends’ posts:

News Feed with Moderation Select Lists

To the right of each status update, I included a select list to moderate the post.  I also display the current moderation total for the friend, the friend’s moderated post count, and the percentile rank out of all friends (the friend with the most negative moderation total is 0%, the most positive is 100%).  This way, at a quick glance it is easy to see how many good or bad posts a friend has made to date.

I also created a statistics page, which ranks friends by the number of posts as well as lists of the top ten best and worst friends based on their moderation totals.  I also breakdown the moderation total and display the leaders of each moderation attribute (for example, who has the most “spam” posts).

Evaluation

After finishing the implementation, I then moderated posts for a month and now present an analysis of the results.  During a one month period, I moderated over 2,000 posts with 250 individual posters.  Some other interesting tidbits:

• 67% of my friends posted at least once within this one month period
• 64% of all moderated posters were friends; the remaining are non-friends commenting on a friend’s post
• 93% of all moderated posts were made by friends

Here is a breakdown of all moderated posts by their assigned attributes:

Moderation attributes by percentage of all moderated posts.

From the figure, a clear majority of posts are positive, while 35% are negative.  Out of all negative posts, the “indifferent” and “unintelligible” categories are used the most.  On the whole, I find most posts that I don’t like to be something I don’t care much about (and don’t want to read) or something that I don’t understand (usually it’s something taken out of context), hence the pervasiveness of the “indifferent” and “unintelligible” categories.  The “informative” category dominates the positive posts, followed by “insightful” and “interesting.”  My experience is that a significant number of posts (almost 40% to be exact) are status updates announcing what a particular friend is doing.  While most of these are mundane, I don’t really find them to be bothersome, hence the positive score.

Next, we look at how many posts each Facebook friend created during my one month evaluation:

Most friends post very little, but a few post all the time.

This figure shows the number of posts made by each friend that posted at least once.  A little less than 40% of my friends posted once, while about 3% posted more than 40 times in the past month.  One friend posted almost 120 times, or roughly four times per day!  Clearly, my news feed is dominated by posts from the same handful of friends.

Next, we look at the distribution of moderation scores across all of my Facebook friends:

Most of my friends have slightly positive moderation scores.

This figure shows that most of my Facebook friends (the ones that posted at least once) have a slightly positive moderation total. Since most of my friends do not post very much, the lack of extreme scores makes sense.  A small number of friends have very bad or very good scores — 3% have have a total less than -6 and another 3% have a total score greater than +20.  Those 3% that have the worst scores clearly stand out from the majority of my friends and are definitely good candidates for defriending.  Conversely, the top 3% go a long way in keeping my news feed enjoyable.

Lastly, we look at both post count and moderation total:

Friends that post more tend to be more positive, but there are some outliers.

Here, we see the quantity and quality of posts made by each Facebook friend.  Some friends have identical (post count, moderation total) values, hence the z-axis indicating the number of overlapping friends.  Like the previous two figures, this shows that most friends have slightly positive moderation totals and low post counts.  It also appears as though friends with higher post counts tend to be more positive.  However, there are some friends that buck the trend and have very negative scores for relatively high post counts (as identified previously, these are the ones that should be defriended).

Conclusions and Future Work

After struggling with the limitations of the Facebook API, I was able to implement a moderation system for Facebook news feed posts. This moderation system quantifies the contributions of each friend to the news feed and clearly identifies the friends with very good and very bad posts.  Using this system, it is possible to filter the news feed based on friends’ moderation scores as well as filter the news feed based on previous moderation of similar content.  It is also possible to extend the moderation system across a user’s social network, identifying friends whose posts are well liked by everyone.

Within the past few days, my news feed has been jammed with these “numbers” posts.  For the most part, it’s been people who I haven’t seen or talked to in years posting a status update with a number followed by a random out of context comment that makes no sense to me.  It’s been getting so bad that one of my Facebook friends posted that he would defriend anyone that posted another “numbers” status update.  While these people aren’t trying to sell me Viagra, the effect is the same as email spam: annoyance and frustration.

It’s no surprise that the top reason for defriending is “frequent, unimportant posts.”  Others, as well as myself, are getting frustrated with junk like these “numbers” posts and are on the brink of defriending the perpetrators.  To solve this, I propose FeedMod, a Facebook news feed moderation system to remove the garbage and push the good stuff to the top.

Disclaimer

I have absolutely zero experience with the Facebook API, so I have no idea how much of this is possible.  We’ll see.

How it works

My proposal is to borrow from the Slashdot moderation system.  Facebook already has a “like” button, but it’s inconclusive that by clicking it, similar posts will be given priority in the news feed.  There is also an “x” for each post, but clicking this seems heavy-handed, since it appears to blot out most posts from that particular friend.  Instead, FeedMod participants will moderate each post with a positive, neutral, or negative descriptor.  The results of this moderation is to flush out the good posts and hide the bad ones.

Unlike the Slashdot system, every news feed post can always be moderated by a FeedMod user.  With Slashdot, the goal is to have publicly acceptable posts float to the top, hence the need for randomly selected moderators to avoid gaming the system.  With FeedMod, however, the goal is to have posts acceptable to individual users float to the top, so users should be able to moderate everything in order to provide as much training information as possible.  Perhaps later the moderation can extend across groups of friends so that certain Facebook users can be recognized as having posts liked or disliked by all of their friends.

Label each post as good, bad, or neutral

I would keep all the positive Slashdot post descriptors: informative, insightful, interesting, and funny.  I would keep one neutral descriptor, such as “normal” or “neutral.”  Also, I would modify the negative descriptors from what is used on Slashdot so that they are more appropriate for social networking and individual preferences: out of context, spam, unintelligible, and troll.  Most news feed posts that annoy me either fall into the “unintelligible” or “out of context” categories: posts that make no sense due to poor writing skills or lack of background information since I don’t live every minute of my life with the poster.  Stuff like the “numbers” posts would fall into the “spam” category and posts from friends looking to incite others (usually politics) would get labeled “troll.”

Customized News Feed

As a FeedMod user labels each news feed post with a descriptor, the news feed becomes customized to the user’s preferences.  This is the tricky part, because I’m not sure if the Facebook API will allow me to rip out the text of each post and store it in a database.  If I’m able to do that, I can try to model a user’s feed preferences based on the friends they mod as mostly positive or negative and the likelihood that a new post from a particular friend will be acceptable.  I could go a step further and find keywords or topics that are found in good and bad posts, allowing new posts to be shown or hidden in the feed based on their content.

If all else fails, defriend

I should definitely be able to rank a user’s friends by the number of positive, neutral, and negative posts.  That way, after a few weeks of modding posts in the news feed, a user can have a top 5 and bottom 5 list of the most liked and most offensive friends.  Then, the user can just defriend those bottom 5 and see an increase in the news feed quality.  There can also be rankings by each specific positive or negative descriptor as well as ranking by post volume.

What’s Next?

When time permits, I’ll try to figure out what the Facebook API is capable of and hopefully be able to attach moderation descriptors to each news feed post.  With that, I’ll be able to find the friends whose posts I like the most as well as the least.  Learning preferences will be more difficult, especially if the API has limitations on what can be accessed in a user’s news feed.  Why Facebook hasn’t already implemented a better news feed moderation system is beyond me.  I’m sure they’ve at least considered it.

For well over a year, I’ve imported the RSS feed from my blog to Facebook via the Notes application.  This particular feature has been flaky in the past and now appears to be completely broken despite cries to fix it.  I’m betting my bottom dollar that the poor implementation and maintenance of the blog import feature is deliberate on Facebook’s part: they want you to stay within their walled garden and keep all of your content solely within it.

Before last week, anything I posted on my blog would take up to three days to show up on Facebook.  Occasionally, I would make several posts and they would all show up out of order several days later.  To make posts show up immediately, I had to log in to Facebook and manually update the blog import.  Several days is an eternity for a service that depends on real time information, especially when I get 20 or 30 wall posts every hour.  Why should my blog posts be treated differently than wall posts?  Google has real time search for the entire internet, but Facebook can’t keep up with a handful of RSS feeds for new blog posts?  It’s obvious that Facebook could easily make an import feature that functions in real time and would allow you to import anything from anywhere.  So why haven’t they done this?

Now, however, the Facebook blog import feature appears to be completely broken.  After writing a blog post last week and then trying to manually import it, I got the following obtuse error from Facebook: “The blog/rss url you entered is not valid. “  I got no such errors from Feed Validator.  I also thought it could be because I had upgraded to WordPress 3.0, but a discussion thread revealed that plenty of people with other blogging services were also having trouble importing to Facebook.  After a week and a half, I have a hard time believing that Facebook would allow a bug like this to go ignored without some kind of acknowledgment or fix.  What are they up to?

My guess is that Facebook deliberately broke the blog import feature, thinking that those who used it would just forget about it and start posting to Facebook directly.  If true, it’s quite the subversive attempt to gain even more control over my information.  I’m certainly not going to abandon my blog just because I can’t import it into Facebook.  My guess is that the more restrictions like this that Facebook imposes, the more incentivized people will be to abandon their accounts.  Changing privacy policies at the drop of a hat may not be enough to convince Facebook users to leave, but I’m betting that restricting users’ control of their own data will be the last straw.

In the book, Kurzweil says that soon computers will be everywhere, even in the walls.  Within the sensor network community, I’ve heard similar arguments dozens of times.  The real question is: why do we need computers in the walls?  What is the underlying motivation for having computing intelligence in your clothes, in your desk, or in your dishwasher?  What can sensor networks do to improve your life?  Right now, it’s really hard to say.

Another researcher in the sensor network community likened sensor network research ideas as “hammers looking for nails.”  That is, solutions are invented before the problems are defined.  With emerging technologies, its hard to say immediately what their benefits are.  An interesting quote from a guy in the augmented reality community depicts how a new technology can sound cool but has little practical value:

“The first movies that ran could show anything, like an elephant in the zoo,” Meier said. “100 years ago it wasn’t about the elephant, it was ‘that thing is moving!’ Eventually it became more about the content…”

I would say that the current state of sensor networks is much like the above movie analogy.  Computing in the walls sounds cool, but what can small, networked devices in your walls do that really matters?  Few research papers offer any practical applications, since most sensor network papers focus on small fundamental problems: radio interference and MAC protocols, routing issues, and primitive event detection with sensors.  The few applications that exist in research papers aren’t exciting: activity recognition with body sensor networks (e.g. sitting, standing, walking), vehicle detection and tracking, and human health monitoring.

A few of these sensor network applications have made it into the mainstream media.  Lightweight health monitoring sensors may sound boring, but could provide motivation to stay in shape when your daily living habits are compared online with your peers.  Another project aims to put health monitoring sensors in soldiers’ underwear, with the aim that the underwear can release drugs and treat wounds.  One idea from IBM promises to have a computer at road intersections notify cars to turn off their engines and save fuel at lights.  Lastly, the smart home concept promises to network household appliances together to notify users of energy usage and to reduce energy consumption during peak time periods.

Whether the above ideas are really exciting or constitute the limits of sensor network capability is another matter.  I’m hoping that some really cool stuff will emerge in the future.  My bet is on participatory sensing and peer to peer collaboration with mobile phone-based systems, but like most people, I don’t have any specifics.

Out of 746,369 fans, the Facebook API randomly selected my fiance as one of the 10 lucky fans to have his or her name, picture, profile link, and fan status broadcasted to the world on a website other than Facebook itself.  Or maybe it wasn’t random, but reloading the page selects a new set of 10 fans, and I don’t think I’ve ever looked at one of these before and noticed someone I knew.  How many of these 746,369 fans are even aware that by “liking” PostSecret, this information can be accessible to anyone on the internet and not just from Facebook?  Unclear and ever changing privacy policies are just one of several significant problems with the shift to trusting third parties for nearly all of your data.

Everyone is becoming increasingly reliant on social networking sites, cloud computing, webmail, software as a service, and content distribution platforms to handle their data.  Fewer and fewer people are storing their data on their own computer and instead are forking it over to third parties in droves.  Entrusting a third party with your data raises three chief concerns: reliability, privacy, and freedom.  First, how can you be sure a third party web service will always provide you access to your data?  Next, how can you be sure your data is protected by a third party according to its privacy policy?  Lastly, can do do as you please with the data you have submitted to a third party?  The answers to these questions are dubious at best, but there are some steps anyone can take to minimize the problems and regain control of your information.

Reliability

Gmail seems to have a lot more problems than it used to.  There’s been several occasions in the last year where I could not log in to retrieve my mail because of some server-side problem.  Furthermore, I’ve been getting that yellow “still working…” bar flashing across the top of the Gmail page quite a bit, making my mail experience all the more frustrating.  It makes me wonder, what incentive does Google have to ensure the integrity of your mail?  It’s a free service, so if Gmail goes down, it isn’t as if they are going to lose revenue due to paying customers pulling out.  Indirectly, ad revenue will decrease, but there is no direct motivation for Google to provide you, the user, with your email 100 percent of the time.

Without direct control over my email, I’m more or less living in fear of a sudden and long lasting outage, unable to read, send, or receive email.  What if Google, intentionally or not, decides to block access to my account?  A similar incident occurred to one Google Groups user who was locked out for three years until finally getting in touch with a customer service rep from Google after upgrading his account to a paid service.  With a direct incentive to provide reliable service, Google was able to quickly fix the problem in this case.   As with most web service models, users are not the customer, advertisers are, so you get what you pay for.

Privacy

When users submit data to a third party on the internet, privacy is anything but assured.  As evidenced by the latest Facebook scandal, privacy policies mean nothing and are changed at will.  Information thought to be private is sold to advertisers or suddenly exposed to the internet as was done with Facebook users’ likes and fan affiliations.  What would happen if Gmail decided to open users’ email accounts for all to read?  Users don’t have much clout to stop such changes from happening, especially without providing a monetary incentive for third parties to keep their word.   Incidents such as these show that third parties care little about users and their wishes as to how their data should be handled.  Users themselves are probably the only ones that can be trusted with their own data, for third parties see user data only as a commodity that can be bought and sold.

The worst case imaginable is a third party with an ax to grind against one of its users, deliberately ignoring its privacy policies to turn users’ private information against them.  Unfortunately, this happens quite frequently.  In the Duke Lacrosse incident, an unnamed source, most likely a Duke employee with administrative access to students’ email accounts voluntarily turned over to police an inflammatory email written by one of the lacrosse team members.  If a university cannot be trusted with keeping student emails private, than how can anyone expect a free web service to keep its word?  Even Mark Zuckerberg himself has been accused of accessing private information stored by Facebook users.

Freedom

While privacy explores the misuse of user data by third parties, another aspect defines the lack of control users have over data they submit to third parties: freedom.  While most social networking sites, webmail, and other web services assert that any user data stored on their services is still owned by the user, this seems to be anything but true.  Handing over data to a third party effectively gives that third party complete control over how that data can be used.  Anyone who has ever attempted to completely erase their Facebook profile can attest to how little control they have over their own information: it’s nearly impossible.  Ever tried to export from Facebook your status updates, wall posts, news stories, and photos?  That’s impossible, too.

Along with Facebook, the iTunes/Apple content distribution system also provides an excellent case study of how third parties effectively have complete control over user data.  As users invest more time, money, and more of their own content into such systems, the harder it is to switch to a competing system or quit altogether.  Like Facebook, the Apple/iTunes model prevents data portability and forces users to stick with Apple products even if better products exist.  Since users cannot move their purchased apps from one platform to another, they are forced to stick  with Apple.  To return to the Facebook analogy, Diaspora is billed as a completely open source and distributed social networking system.  This social networking service could be argued as being “better” than Facebook, but lock-in will keep users on Facebook.  There’s no way for Facebook users to export their friend lists, photos, and wall posts to Diaspora, so everyone will just stay on Facebook.

Any Solutions?

There aren’t really any clear cut solutions to this increasing dependence on third party web services.  The best move is to decrease reliability on such third parties.  I’ve tried running my own mail server, but outgoing email is blocked by my ISP in fear that I’m a spam relay.  Downloading mail with a client is an option so at least I have copies on my own computer of everything I’ve sent and received in case Gmail were to go down.  With respect to privacy, I’ve pretty much assumed that everything I post on the internet will be available to everyone, regardless of privacy controls.  Anything I don’t want out there, I don’t post.  To maintain at least some control over my own data, I host my own blog and ensure that I have a copy of everything I write and submit to sites like Facebook, Google Docs, and Dropbox.  My guess is that as the internet evolves, and especially with the new and annoying “cloud computing” buzzword, this reliance on third parties is only going to become greater.

For most of the time since its introduction, the suggestion feature did what it was supposed to:  recommend me people that I knew that I wasn’t Facebook friends with.  The suggestion feature occasionally listed someone I knew well, but usually listed people I knew of but was not good friends with.  Most suggestions were pretty understandable: people I had a few classes with or people that were track/cross country team members after I had exhausted my eligibility.  A few people in the computer science department were thrown in there as well.  For awhile it also recommended family members way out on some distant fork of my family tree.

Suddenly, within the past week or two, the friend suggestions have become very weird.  Now I don’t even recognize the names of the people it suggests.  Currently, the suggestions feature recommends 27 items: 2 groups and 25 people.  Of the 25 people:

• I know 0 (again, I don’t even recognize any of the names)
• 1 is male; the other 24 are female (do I not have enough female friends?)
• 13 have a hometown or current city in Virginia
• 2 have Virginia Beach listed as their hometown or current city
• 1 is on a collegiate sports team
• 23 have an affiliation with William and Mary
• 19 are undergraduates at William and Mary
• 12 have friends in common

Obviously, the intent is to try to match me with other people with the same geographic location, school, and interests/activities, but it isn’t working.  The school and geographic regions may be appropriate, but it isn’t enough.  The gender and undergraduate distributions are a bit odd, and only half have any friends in common.  I know there are plenty of better matches than the ones currently suggested to me, as evidenced by the suggestions made over the past months.  I do remember that a week or so ago, my profile suddenly changed after some kind of update with the way interests, work, and school items are handled.  The recommendation changes may have something to do with that update if some of those items are no longer considered in the suggestions algorithm.

My guess is that Facebook uses something like k-NN or Bayesian classification to figure out the friend/group/fan page suggestions.  Given a common set of feature vectors for each profile, Facebook may determine the k closest matches, where k=27 in this case.   It may even use the profile features of my current friends to figure out potential friends.  Nevertheless, it seems their algorithm needs improvement.

I’ve found many of these infographics to be packed with spelling and grammatical errors.  For example, try to find the error in this visualization of U.S. debt holders.  With enormous font sizes and few words, any spelling or grammatical error really stands out.  Such easily identifiable problems make me question the integrity of the statistics (and implicit arguments) these infographics present.  Where did the data come from and how reliable are the sources?  Many infographics do not provide references, so how am I to know that it isn’t just some ten year old kid making this stuff up?  What if multiple sources produce conflicting results?  In such cases, it’s almost guaranteed that the infographic creator just picked the result/data that best furthered his or her argument.  Lastly, what information is not presented?  When reading an infographic, I always wonder if I am seeing the whole picture.  With so little information actually presented, I have no doubt that most of these infographics leave out plenty, especially stuff that hurts the creator’s argument.

I admit that infographics pique my interest in a subject to which I haven’t given much thought.  However, with minimal content and questionable integrity, they may be no more than chartjunk.

A holistic computing solution

Everyone splits their computing time between a whole pile of devices: cell phones, media players, laptops, netbooks, desktops, and gaming consoles.  Even typical cable TV boxes have a hard drive and user interface.  The purposes and capabilities of such devices is becoming increasingly diverse and will probably continue to do so in the short term.  However, it’s becoming a real pain to use a standalone device for a different task.   Carry a cell phone for voice and text messages.  Carry a laptop or netbook for working on the go.  Carry a media player to listen to music.  When at home, many people switch from a laptop to a separate desktop PC.  It would be great to see some consolidation in order to reduce frustration with dragging around multiple devices and learning the quirks of each.

Since most people aren’t running weather prediction simulations, a single small mobile device would be sufficient for most people’s computing needs.  Ideally, something the size of a cell phone would combine the functionalities of a phone, camera, media player, laptop/netbook PC, and even desktop PC.  No longer would people with multiple devices need to synchronize information or become familiar with multiple interfaces.  A single device would provide a user with most of the computing power and capability that he or she would need without the hassle of dragging around a separate phone, media player, and laptop.  The device would be designed such that it could easily support and interface with different user input and output methods.

Improved user interfaces

Improved interfaces in mobile devices would make for a better user experience and improve productivity.  With multiple gadgets to haul around, each comes with a different interface with varying levels of usability.  For the most part, the smaller the device, the worse the user experience becomes.  A desktop computer with a keyboard, mouse, and giant monitor provides a solid experience for most, with both ease of input and output.  A netbook, however, may satisfy a user’s computing needs, but may cramp usability and productivity with its tiny screen and uncomfortable keyboard.  Even worse, web browsing and writing emails or text messages on some cell phones can be nearly impossible.  Personally, my experience with multi-touch phones has been horrible, since most of the time the phone selects something other than what I intended.  Rethinking and improving the physical and software interfaces would permit a shift to a cell phone-sized holistic computing device.

Improvements to both physical and software interfaces would provide huge benefits for the end user.  With respect to physical interfaces, improving multi-touch surfaces would be a big step in the right direction.  Input methods that are simple and accurate would make phone calls, text messaging, and web browsing on mobile devices much more enjoyable.   Output methods should extend beyond a tiny three inch screen, such as a wearable HUD or projector similar to that used in MIT’s SixthSense.  With regards to software interfaces, most cell phone operating systems provide horrible user interfaces which make the simplest tasks a real pain.  Most of these software designs inherit from heavyweight PC interfaces where input is made easy with keyboard and mouse.  Mobile developers should focus on increasing usability by making tasks require the fewest amount of user inputs possible.

Behavior and activity recognition

Computing systems of tomorrow could predict a user’s intentions and act upon these predictions.  With the introduction of accelerometers, GPS receivers, light sensors, cameras, and microphones in cell phones, plenty of research has provided ways to recognize user behavior and activities.  Such research can help provide an augmented reality for users, pointing out suggestions as to what a user could do based on his or her surroundings, current activity and learned preferences and behavior.  For example, a user traveling in an unfamiliar city could get instant suggestions as to where to eat when he or she normally takes a meal, with a mobile device providing directions to restaraunts that match the kinds of foods the user normally eats.  A HUD would allow the device to paint a path directly on the streets to take without the user staring down at his or her phone.  A device could automatically perform Internet searches and return data relevant to what a user is doing, whether it be retrieving a weather report before a user heads to the beach or providing real-time flight delay information as a user drives to the airport.  Such intelligent systems could interact with the physical world and turn on the lights or adjust the temperature at a user’s home before he or she arrives.    Also, behavioral and activity recognition would eliminate the need for user-generated Twitter and Facebook posts, performing automatic updates whenever a user changes activities or does something unusual.

Better inter-device collaboration

Figuring out how to get cell phone pictures off the phone and onto a computer can be a monumental task.  It’s even more enjoyable to get a projector to correctly display a presentation on a laptop.  Nearly every slideshow presentation I’ve witnessed, in classes and in conferences requires each presenter to wage war with his or her laptop and the projector to get the presentation to display properly.  Improving inter-device communication would make everyday computing more seamless and a lot less frustrating.   For example, a slideshow presentation could be loaded on a mobile device and a user could walk into a room with a projector, with the mobile device automatically connecting wirelessly to the projector and displaying the presentation.  There would be no cables to plug in, no display settings to modify, and no buttons and inputs to fidget with on the projector.  Synchronizing and moving data between different devices stands to gain significant improvement, for people are constantly upgrading their cell phones and laptops as well as sharing their data with others.

Longer design lifetimes

I’m guessing I’m not the only one with a pile of old hardware that’s worn out from too much use or discarded due to obsolescence.  More robust devices with longer expected lifetimes would reduce the pile of useless junk in the closet.  This would be especially helpful for mobile devices, which often wind up in mud puddles, toilets, or under someone’s steel-toed boot.  Modular construction would allow for periodic upgrades without throwing away the whole device.  An effort towards longer lifetimes and upgradeability would also significantly cut down on the amount of toxic e-waste.

Longer battery life

Most cell phones don’t last past  a couple days of standby or two hours of talk time.  Most laptops don’t make it past three or four hours.  With an increased focus towards mobile and ubiquitous computing, improvements in battery technology, power savings, and battery recharging would do wonders.  Apple has a new battery design in its latest laptops that double battery life, but more strides in this direction are needed.  Efforts on power-conscious radio communication and CPU utilization will contribute to power savings through better software.  Lastly, harnessing available energy sources such as motion, body heat, and the sun will also allow mobile devices to run unplugged for longer periods.

In general, these issues are what I think mobile computing needs in order to really take off.  The current experience is haphazard and lacking, requiring a mobile user to carry multiple devices with poor interfaces and short battery life.  With future applications, mobile devices will do a lot more than allow phone calls, web browsing, and text messaging.  They will further the integration of the cyber and the physical world, helping a user interact with the environment and the Internet in ways currently unimaginable.

The most visible privacy issues have been raised with the introduction of social networking: Facebook’s Beacon being one of the most infamous, with Facebook secretly collecting your online activities to generate targeted advertisements.  More recently, software designed to control and monitor children’s internet usage was revealed to actually transmit all internet activity, including instant messages, back to the companies that designed the programs.  The list of abuses and potential abuses goes on and on and will only get worse.  The introduction of cloud computing and the shift to thin clients mean that third parties are handling more and more personal data. The more opportunities others are given to handle our personal information, preferences, photos, browsing habits, and documents, the more opportunities exist for the abuse of that control.

Social networking and the monitoring of internet habits is only the tip of the iceberg.  The social networking concept is quickly moving beyond the confines of the Internet and integrating itself with the physical world.  The concepts of participatory sensing, body sensor networks, and smart homes/offices will see the introduction of internet-linked sensors placed everywhere.  Cameras and microphones will soon be on every street corner.  Temperature, humidity, and other energy monitoring sensors will be commonplace in every home, all linked to the internet.  Even now, most cell phones come equipped with GPS, accelerometers, and microphones, allowing for activity recognition and localization.  This “information saturation” will allow any developer to design an application that makes all kinds of weird discoveries: with body sensor networks and smart phones, rush hour traffic can be monitored in real time, local nightlife hotspots can be easily discovered, and suggestions can be made on how to save energy based on water and electricity usage in your home.

While the integration of the internet into the real world sounds cool, imagine what someone could do if your sensing information got into the wrong hands.  Would you really want your health insurance company to know your heart rate or blood pressure at any time of day?  Would you really want everyone to know that you waste the most water out of everyone who lives on your block?  Would you want a crazed stalker to know where you were at any moment or to learn your daily activities or routines?

Privacy and security is going to be an increasing concern as sensor networks become more commonplace and integrated with the internet.  While I was at UVA two weeks ago, Prof. John Stankovic mentioned that security and privacy in sensor networks is a huge problem and unfortunately, little is being done in this area.

A recent article by a UCLA student illustrated some of the problems with participatory sensing and presented some general solutions.  Disclaimer:  as part of the lead-in on her article, she says: “the developers I work with might say [my research area] is about telling them what they should be doing—which I must admit is the goal of this article.”  When someone says they know better than you, it’s time to run, not walk, to the nearest exit.  That said, I do agree with some of the things she says.  The author argues that a general framework should be designed for all participatory sensing applications that allow for user privacy management.  The frameworks should allow a user to easily understand how the systems work and how to control the release to the outside world of personal sensor data and inferences.  Ultimately, the author argues that data generated by a sensor network that you own is yours to control and distribute.  Third parties must respect the wishes of the content generators.

The author doesn’t go into fine-grained detail about how to ensure the preservation of an end user’s privacy wishes.  I could imagine some kind of CA could help verify that those third parties which access a user’s content/sensor data are who they say they are.  Some kind of feedback mechanism could allow a user to see where his or her sensor data went and how it was used.  If a third party abused a user’s data, the CA certificate could be revoked, effectively tarnishing the reputation of that third party.  Most likely, encryption would have to be introduced to ensure nobody but the permitted third parties could access a user’s data.  Unfortunately, encryption is very energy and bandwidth heavy for low power wireless sensors.

We all know the wrong way to go about privacy with the experiences of Facebook and its tacked-on privacy measures.  Initially, Facebook provided little privacy controls with no framework at all to allow a user to control access to his or her information.  Even now, the privacy controls for Facebook are hard to access and even harder to understand what they do.  A user has no fine-grained control over adjust who can access what content and exactly how your information is being shared with third party applications and advertisers.  A comprehensive and user-oriented privacy framework installed from the ground up would help stop the mishaps that are common with social networking.  Such a framework would come into its own with the increase in participatory sensing.

On that note, it’s time to get a few cell phones and start a participatory sensing application of my own to provide motivation for some research problems.  The possibilities are limitless and I can throw privacy to the wind (for now).  Unfortunately, as the author of the ACM article mentioned: researchers and developers don’t think about privacy, they think about research problems and cool applications.  In a small-scale research environment, the information is mine to control since I control the sensors, the application, and the release of any data or inferences from the data.  But, if anything were to be released into the wild and become popular, a framework for end user privacy control would be indispensable.  I certainly don’t want some nutcase knowing when and where I’m sleeping.

The interface is great since I just have to fire up a web browser and cookies automatically log me in.  There’s no messing around with a software mp3 player and picking out the correct format so the player can interpret the stream.  Sound quality is okay, but it seems that some tracks are better than others.

Nearly all the reviews I read said that Pandora was excellent in picking out songs they liked.  I don’t entirely agree with this.  In the web-based interface, you name an artist or song you like and Pandora plays music based on particular musical qualities of the artist or song.  It seems that for me, when I name an artist or song in my existing mp3 archive, one of three things happens, each with about equal probability:

1.  Pandora plays a song I’ve already got in my mp3 collection.  Many times it isn’t even the same artist of the initial artist/song I specified, which makes it kind of weird.  It would appear as though the preferences/data mining engine is almost too good, but this definitely isn’t always the case.

2.  Pandora plays something that seems almost completely orthogonal to what I specified.  For example, I put in Bruce Springsteen and it played something from Megadeth with lots of screaming and out of control percussion.  I’m not sure how this relationship was determined, but it definitely didn’t give me what I wanted.

3.  I actually hear something new that sounds similar to the artist/song I put in.  In these cases, there definitely seems to be a relationship between the original song or artist I specified and what Pandora plays.  This is the case I would prefer.

Pandora definitely doesn’t live up to all the hype — its preference/similarity determination seems to be either spot on or way off.  Most of the time it seems to work, but I would rather hear something new than something I’ve already got in my mp3 collection (of course Pandora doesn’t know this).  That’s one of the reasons I’ve been listening to Radio Paradise — they play all kinds of stuff I’ve never heard before and a wide enough variety to keep me coming back.  One of the big features of Last.fm is that it can determine your preferences from your existing collection, so I may have to give that a try to see the differences.  It seems that like most of these “Web 2.0″ apps, the data mining and social aggregation algorithms sort of work, but not enough to be really cool.