Hans Christian with the state of web printing. All the old classics are still important, like display: none; on irrelevant stuff (like navigation) and using pseudo elements to display the URL's of links. But there are new kids on the block like @page which helps you style individual printed pages and control printing orientation for certain elements, -webkit-print-color-adjust: exact; for printing background colors/images, printing crop marks, and preventing widows.

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Printing the Web is a post from CSS-Tricks

The results of the 2013 Semantic Textual Similarity task (STS) are out. We were happy to find that our system did very well on the core task, placing first out of the 35 participating teams. The three runs we submitted were ranked first, second and third in the overall summary score.

Congratulations are in order for Lushan Han and Abhay Kashyap, the two UMBC doctoral students whose research and hard work produced a very effective system.

The STS task

The STS core task is to take two sentences and to return a score between 0 and 5 representing how similar the sentences are, with a larger number meaning a higher similarity. Compared with word similarity, the definition of sentence similarity tends to be more difficult and different people may have different views.

The STS task provides a reasonable and interesting definition. More importantly, the Pearson correlation scores are about 0.90 [1] for human raters using Amazon Mechanical Turk on the 2012 STS gold standard datasets, almost same to inter-rater agreement level, 0.9026 [2], on the well-known Miller-Charles word similarity dataset. This shows that human raters largely agree on the definitions used in the scale.

• 5: The sentences are completely equivalent, as they mean the same thing, e.g., “The bird is bathing in the sink” and “Birdie is washing itself in the water basin”.
• 4: The sentences are mostly equivalent, but some unimportant details differ, e.g., “In May 2010, the troops attempted to invade Kabul” and “The US army invaded Kabul on May 7th last year, 2010”.
• 3: The sentences are roughly equivalent, but some important information differs/missing, e.g., “John said he is considered a witness but not a suspect.” and “‘He is not a suspect anymore.’ John said.”
• 2: The sentences are not equivalent, but share some details, e.g., “They flew out of the nest in groups” and “They flew into the nest together”.
• 1: The sentences are not equivalent, but are on the same topic, e.g., “The woman is playing the violin” and “The young lady enjoys listening to the guitar”.
• 0: The sentences are on different topics, e.g., “John went horse back riding at dawn with a whole group of friends” and “Sunrise at dawn is a magnificent view to take in if you wake up early enough for it”.

The STS datasets

There were 86 runs submitted from more than 35 teams. Each team could submit up to three runs over sentence pairs drawn from four datasets, which included the following.

• Headlines (750 pairs): a collection of pairs of headlines mined from several news sources by European Media Monitor using the RSS feed, e.g., “Syrian rebels move command from Turkey to Syria” and “Free Syrian Army moves headquarters from Turkey to Syria”.
  
• SMT (750 pairs): a collection with sentence pairs the DARPA GALE program, where one sentence is the output of a machine translation system and the other is a reference translation provided by a human, for example, “The statement, which appeared on a website used by Islamists, said that Al-Qaeda fighters in Islamic Maghreb had attacked three army centers in the town of Yakouren in Tizi-Ouzo” and the sentence “the pronouncement released that the mujaheddin of al qaeda in islamic maghreb countries attacked 3 stations of the apostates in city of aekorn in tizi ouzou , which was posted upon the web page used by islamists”.
• OnWN (561 pairs): a collection of sentence pairs describing word senses, one from OntoNotes and another from WordNet, e.g., “the act of advocating or promoting something” and “the act of choosing or selecting”.
• FNWN (189 pairs): a collection of pairs of sentences describing word senses, one from FrameNet and another from WordNet, for example: “there exist a number of different possible events that may happen in the future. in most cases, there is an agent involved who has to consider which of the possible events will or should occur. a salient_entity which is deeply involved in the event may also be mentioned” and “doing as one pleases or chooses;”.

Our three systems

We used a different system for each of our allowed runs, PairingWords, Galactus and Saiyan. While they shared a lot of the same infrastructure, each used a different mix of ideas and features.

• ParingWords was built using hybrid word similarity features derived from LSA and WordNet. It used a simple algorithm to pair words/phrases in two sentences and compute the average of word similarity of the resulting pairs. It imposes penalties on words that are not matched with the words weighted by their PoS and log frequency. No training data is used. An online demonstration system is available to experiment with the underlying word similarity model used by this approach.
• Galactus used unigrams, bigrams, trigrams and skip bigrams derived from the two sentences and paired them with the highest similarity based on exact string match, corpus and Wordnet based similarity metrics. These, along with contrast scores derived from antonym pairs, were used as features to train a support vector regression model to predict the similarity scores.
• Saiyan was a fine tuned version of galactus which used domain specific features and training data to train a support vector regression model to predict the similarity scores. (Scores for FNWN was directly used from the PairingWords run.)

The results

Here’s how our three runs ranked (out of 86) on each of the four different data sets and on the overall task (mean).

Over the next two weeks we will write a short system paper for the *SEM 2013, the Second Joint Conference on Lexical and Computational Semantics.

[1] Eneko Agirre, Daniel Cer, Mona Diab and Gonzalez-Agirre Aitor. 2012. SemEval-2012 task 6: A pilot on semantic textual similarity. In Proc. 6th Int. Workshop on Semantic Evaluation (SemEval 2012), in conjunction with the First Joint Conf. on Lexical and Computational Semantics (*SEM 2012)., Montreal,Canada.

[2] P. Resnik, “Using information content to evaluate semantic similarity in a taxonomy,” in Proc. 14th Int. Joint Conf. on Artificial Intelligence, 1995.

When viewers decide what’s on TV, who wins?

In a salvo against Netflix, which has gained attention for its original series “House of Cards,” Amazon announced today that it would be producing a pilot for a TV series derived from “Zombieland,” the hit 2009 horror-comedy. What’s most interesting about Amazon’s announcement, though, is the revelation that it will only be ordering a full season of the series depending on customer feedback.

I've always wanted to get this going since seeing this Lennart Schoors post, but this Lee Mallabone tutorial finally pushed me to do it. The debug settings are specific to Rails, but if you're a CodeKit users you probably just do this.

In the past I've thought: I don't need no line number mapping! My SCSS is so organized and modular I can always jump right to what I need to edit. But I could smell defensiveness and stale attitude as I thought it, so here's to being open minded and giving ourselves better tools.

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The Showcase will be held on Friday, April 12, 2013, from 4-6 p.m. Proposals for presentations are now being accepted from CWIT and T-SITE scholars, affiliates, and other students in the College of Engineering and Information Technology (COEIT).

The Center for Women in Technology (CWIT) celebrates scholarship, engagement, and leadership excellence in information technology and engineering students within the COEIT at UMBC. Proposals are now being accepted for presentations from all students in the CWIT community and the college. Sessions will be completely student-run and presentations will be directed to a peer audience. Inspiration, innovation, and creativity in presentations are encouraged.

Please find the call for proposals and application attached. Applications are due by Monday, March 25, and those selected will be notified by Wednesday, March 27, in order to have time to prepare the presentations. Direct questions and send completed proposals to Cindy Greenwood, Assistant Director - Cyber Scholars Program, at cindyg@umbc.eduor drop applications off to Cindy's office (ITE 449) or the CWIT office (ITE 452). 

 
We look forward to reading your proposals!

I remember when the behavior for placeholder="" text on inputs and textareas made that subtle functionality change in WebKit where the text would remain in place until you started typing rather than disappear on focus of the empty input. I think it may have been first on iOS actually, but now both stable Chrome and Safari do this.

I also remember thinking it was a smart change. Of course the placeholder is no substitute for a real explaining what the input is for. But if the placeholder is providing a valuable tip related to how best to fill out that field, it's shame to disappear so quickly.

However, Jeremy Keith points out some research by Markus Earnst that suggests leaving the placeholder in place breaks some expectations and causes problems:

It seems that a relevant number of users do not even try to start typing as long as the placeholder text remains visible.

Granted, the research was a bit limited in scope, but it makes sense.

Jeremey provides this CSS solution if you want the old behavior back in WebKit based browsers:

[placeholder]:focus::-webkit-input-placeholder {
      color: transparent;
    }

This is me chiming in with a possible best-of-both worlds middle ground:

[placeholder]:focus::-webkit-input-placeholder {
      transition: opacity 0.5s 0.5s ease; 
      opacity: 0;
    }

The second time value in that transition means "wait half a second before firing off this transition." I suspect that some people click or tab to inputs to get ready to enter information before they are cognitively ready to pay attention to that input yet. But once the focus takes place, then they start paying attention. If we give them a half a second where the placeholder is still visible and another half a second to fade it away, that is likely enough for them to understand the hint that the placeholder was giving them.

The event timeline being:

1. User clicks (or taps, or tabs to, or whatever) the input
2. Their eyes move to the input and their mind starts paying attention to it
3. They comprehend the placeholder hint
4. Placeholder goes away

A demo of that and another variation where the placeholder slides away toward the label:

Check out this Pen!

For the record, I'm not saying this is absolutely better. There is no hard science here. I'm just proposing an alternate UI experience that seems to make sense to me.

Are there any other solutions we could explore?

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Hang On Placeholders is a post from CSS-Tricks

Startup Sherpa’s predictive intelligence offers valuable insights when and where you need them.

Google Now, an app for Android smartphones that serves up useful information such as flight details when it thinks you need it, is getting some competition from a former Googler.