This report provides an integrated analytical framework to identify the drivers and barriers to the use of cooperation mechanisms for CSP deployment, empirically identifies those drivers and barriers and ranks them according to the views of different types of stakeholders. Our findings demonstrate the most significant barriers to be the higher costs of CSP compared to other renewables (on an LCOE basis), heterogeneous regulated energy prices and support schemes, resistance to lose sovereignty over energy market and existing interconnections capacities. On the other hand, the most relevant drivers to the use of the cooperation mechanisms for CSP in the future include the dispatchable nature of CSP, new domestic jobs and industrial opportunities, complementarity with PV and policy ambition (renewable energy targets). Read more on this report here.


text:
Q: Json Encoded PHP array has null appended to it At this point in the debugging this is what I am sending to my javascript
  header("Content-Type: application/json");
  //echo json_encode($full_product_list);
  echo json_encode(array());

and here is my ajax call
  jQuery.get( "non public api sorry ")
    .done(function(data) {
      console.log("I got a response")
      console.log(data)
    })
    .fail(function(data) {
      console.log("Error?")
      console.log(data);
  })

It errors everytime and in the data my response string for the empty array is
"[]null"
Entire function being called for reference same thing I get an error and at the end of my json there is "null" appended.
function getAllProducts() {
  $full_product_list = array();
  $loop = new WP_Query( array( 'post_type' => 'product', 'posts_per_page' => -1 ) );
  $pf = new WC_Product_Factory();

  while ( $loop->have_posts() ) : $loop->the_post();
    $post_id = get_the_ID();
    $product = $pf->get_product($post_id);
    $attributes = $product->get_attributes();
    $attrs = array();

    foreach ($attributes as $attr) {
      $name = str_replace("pa_fablab_", "", $attr["name"]);
      $attrs[$name] = $attr["value"];
    }

    $item = array(
      "id" => $post_id,
      "name" => get_the_title(),
      "price" => $product->get_price()
    );

    if (sizeof($attrs) > 0) {
      $full_product_list[] = array_merge($item, $attrs);
    }
  endwhile; wp_reset_query();
  header("Content-Type: application/json");
  //echo json_encode($full_product_list);
  echo json_encode(array());
}

A: Return something from your php page, add die() after to remove the null
header("Content-Type: application/json");
  //echo json_encode($full_product_list);
  echo json_encode(array("message"=>"ok"));
  die();

redpajama_set_name: RedPajamaStackExchange


Padua is one of the oldest cities in Italy and, along with one of the oldest universities in the country, features some relics and tombs, which have made Padua the target of countless pilgrims for centuries. The city gained world fame because of the famous cycle of frescoes by Giotto in the Cappella degli Scrovegni. Although the town often seems to lag behind its two big rivals Venice and Verona, it still offers a wide range of attractions for its visitors. In addition, the region around Padua is a soothing spa paradise and the Euganean hills are excellent for hiking and relaxing.
The famous frescoes by Giotto in the Cappella degli Scrovegni, the grave of St. Luke in the Basilica di Santa Giustina, the relics of St. Anthony in the Basilica di Sant'Antonio, the Caffè Pedrocchi and the Palazzo della Ragione – these are just some of the magnificent sights that await travellers during a visit to Padua. In addition, Padua is one of the oldest cities in Italy and its university is not only the third oldest in the country but also had famous teachers such as Galileo Galilei. ZAINOO offers Padua travellers a wealth of background information for travel planning and ensures that you do not miss the main attractions. All interesting places as well as sightseeing tours of Padua, hotels and accommodation can be found on the following pages.
Four beautiful spa resorts in the immediate vicinity of Padua and in the middle of the Euganean hills together form the biggest spa region of Italy. The towns of Abano Terme, Montegrotto Terme, Battaglia Terme and Galzignano annually help countless rheumatoid and arthritis patients and athletes to ease their pain significantly. These tranquil spas near Padua are reinforced by the green hills and picturesque scenery of the beautiful Euganean hills, which form a wonderful contrast to the frantic pace of the industrial city of Padua.
The ZAINOO online travel guide provides a comprehensive platform to Padua with extensive information and insider tips for your stay in Padua and its surroundings.


"Bob, it's your father. He's been frozen!" said my mother Linda.
"Wait. What? Frozen!" This did not augur well.
"Yes. Some weirdo in a blue leotard and funny sunglasses came up to us at the mall and waved a plastic icicle at him. Now he's frozen. What the hell is going on?" my mother's panic reached me through the phone.
"A villain in blue you say. Don't worry, mother. I know someone who can help. He'll be right there, " I said and promptly hung up the phone.
The Weather Mage! My arch-nemesis had somehow discovered the identity of my parents. I would need to take care of him straight away, but first my father. I stood up and and went to asked my office manager if I could go get lunch. He was intent on whatever was on his computer screen and off he waved me. I headed out the back and walked behind the Chinese place next door. I used my math based magic to don the red and green uniform of Math Wiz.
Scribing the ancient formula for distance traveling with my trusty number two wand into the air before me, I was instantly transported to Billings Park. I found my parents near the fountain with my father frozen in a light blue pose of terror and my mother wrenching her hands in concern.
"What seems to be the problem here, citizens?" I asked assured of my secret identity even with my parents.
"Are you fricking kidding me? My husband is frozen. That's the problem. Are you blind?" my mother seemed a little agitated.
"Never fear. I can save him," I stated as I use my number two wand to scribe the heat transfer formula in the air between me and my father. Beginning with an almost glacial slowness, the color returned to my father and he began to move. With a cracking and a popping of polar icecaps, he freed himself from foul state the Weather Mage had put him.
"What the hell?" apparently my father was a little disoriented.
"If all is well then I must be off to round up the villain who did this dastardly deed," I said.
I then promptly teleported away to begin my search for the Weather Mage.


Reason to celebrate: February 17, 2019 is National My Way Day.
My Way Day has actually been produced in event of that terrific expression "It's my means or the higher means". The past of My Way Day is confusing yet it believed to have actually been encouraged by vital events in the world occurring due to the fact that someone had a strong suggestion and they just weren't worried to go through with it.
On that note, the concept of My Way Day is straightforward-- it is the day where you are encouraged to do whatever you want, nonetheless you desire. Do you elaborate viewing every Bond film straight wearing a onesie and eating delicious chocolate? Go ahead. Exactly how regarding being a bit native mineral adventurous and start to compose that novel you were always thinking about? Definitely fine. My Way Day is your day-- simply make certain that whatever you decide to do, it's legal.


text:
Newark foreclosure crisis brings heartache to some, bargains to others
City employees take deposits from winning bidders at today's auction of city properties.
By David Giambusso/The Star-Ledger
By David Giambusso and Tom De Poto/The Star-Ledger
NEWARK — The city was a tale of two crises Thursday.
On one end of Broad Street, the sing-song voice of an auctioneer echoed through the Terrace Ballroom at Newark Symphony Hall as about 100 hopeful investors bid on 79 foreclosed properties at bargain-basement prices.
Down the street, at City Hall, local leaders and community advocates spoke in somber tones, debating a way out of a foreclosure crisis that continues to ravage city coffers and the pocketbooks of its residents.
"It's as if my life, security for my family, a roof over our heads is a game to them," Newark resident Grace Alexander said, referring to Bank of America, whom she said would not modify her mortgage when she fell behind on payments. "Our impending homelessness is not a game to us."
She is not alone.
NJ Communities United, an advocacy group for low- and moderate-income residents, released a nine-page paper Thursday titled "Newark Homewrecker," detailing how the foreclosure crisis has cost homeowners in the city $1.9 billion in lost property value since the housing bubble burst in 2007.
City Councilmen Darrin Sharif and Ron C. Rice hosted the group at City Hall along with about 40 residents and community activists to hear issues surrounding the crisis.
The report estimated that the nearly 7,000 foreclosures between 2008 and 2012 cost the city $56 million in police and fire department calls, safety inspections, trash removal and maintenance. It anticipates the city could face an additional $74 million in costs as more homes move through the foreclosure pipeline.
"Neither the banks nor the Christie administration wants to deal with the reality of these toxic loans," said Trina Scordo, executive director of NJ Communities United.
She cited the $75 million New Jersey received in federal aid to help distressed homeowners that Gov. Christie rolled into the general treasury to help balance the budget.
Sharif, calling the situation "horrible and horrendous," said he had invited representatives from the Christie administration, Bank of America, Wells Fargo and Chase, but all declined.
New Jersey also received $300 million in 2010 and used it to start the New Jersey Homekeeper program. After a rocky start in which only a small percentage of the money was distributed, Lisa Ryan, a spokeswoman for the Division of Consumer Affairs that oversees the program, said New Jersey Homekeeper awarded 2,515 foreclosure prevention loans totaling more than $100 million by the end of 2012. The average loan is about $40,000.
Down the street, at the auction, Newark residents and out-of-town investors were looking to make the best of a bad situation. Unlike bank foreclosures, the city reclaimed the auctioned properties for unpaid taxes.
Richard Grossklaus, co-founder of Newark's Integrity House and a 27-year resident, was seated in the front row. An abandoned lot next to his home on Martin Luther King Boulevard was up for bid.
"I'm buying from my own personal money," he said. "Others are buying from an investment pool. It would just give me peace of mind to own it. I've adopted it for many years."
Bids on the lot went from about $12,000 to $60,000 in under a minute, but Grossklaus won out. City officials said the auction yielded roughly $6 million. The properties, officials said, will now be returned to the tax rolls and no longer be a drain on city coffers.
Carol Meyers, a researcher at SEIU International and author of the "Newark Homewreckers" report, said the city should do more.
She advocated the use of eminent domain, wherein the city could pay fair market value for homes in which the homeowner is behind on mortgage payments, "and create conditions for new sustainable mortgages at current home values, all while borrowers stay in their homes."
• Report: Foreclosure crisis continues to ravage Newark economy

redpajama_set_name: RedPajamaCommonCrawl


Create your unique bolster pillows by choosing your dimensions, style and fabrics. Our beautiful fabrics are available in a variety of colors and patterns and will provide years of lasting beauty! Choose from hundreds of fabrics to best suit your needs. Our most popular bolster pillow size is 20" x 7". Be sure to verify all your options before adding your custom bolster pillow to your cart. Your pillow will include an invisible zipper.
Our bolster pillows are not recommended for extended outdoor use, as the foam filling will retain water. If using outdoors, we recommend bringing them indoors after each use.
Pillow will have single piping (cording/welting) along the end seams. All piping is cut on the bias, giving it an angled appearance.
Pillow will not have any piping (cording/welting) along the end seams.
Our bolster pillows are measured from end to end. Our most popular bolster width is 20", followed closely by 24".


text:
Q: How to implicitly refer to a variable My code has a lot of this: driver.blahblahblah. Consider the following code sample, taken from http://www.browserstack.com/automate/ruby.
require 'rubygems'
require 'selenium-webdriver'
driver = Selenium::WebDriver.for(:remote,
  :url => "http://USERNAME:ACCESS_KEY@hub.browserstack.com/wd/hub")
driver.navigate.to "http://www.google.com/ncr"
element = driver.find_element(:name, 'q')
element.submit
puts driver.title
driver.quit

How can I make driver implicit? For example there's a method called driver.save_screenshot(). I want to say save_screenshot("a.png") because only the driver variable/object has this method.

A: You can use delegate from ActiveSupport like the example below:
require 'active_support/core_ext/module/delegation'

class MyClass
  delegate :find_element, :save_screenshot, to: :driver

  def foo
    find_element
    save_screenshot
  end

  def driver
    @driver ||= Driver.new
  end
end

class Driver
  def find_element
    puts "find_element"
  end

  def save_screenshot
    puts "save_screenshot"
  end
end

MyClass.new.foo

Or decorate the driver using SimpleDelegator (but I don't recommend that).

A: If you have lots of methods whose receiver is driver, then a way to make the receivers implicit is:
driver.instance_eval do
  method_1...
  method_2...
  ...
end

but notice that this slows down a little. If you are just finding a way to be lazy, then the best way is to make the local variable as short as a single letter, and do not bother making it implicit.
d = .... # instead of `drive`
d.method_1...
d.method_2...
...


text:
YouTube star-turned-boxer Logan Paul fears he spent $3.5m on fake Pokemon cards
Paul spent over $3.5 million on Pokemon cards which had been verified, but after a fan site questioned the authenticity of the cards he will now fly to have them checked
Ben DaviesBoxing and MMA writer
Want the day's sports headlines straight to your inbox? Our FREE newsletter will keep you ahead of the game
Logan Paul is flying to Chicago to have $3.5million worth of Pokemon cards checked for authenticity after a fan site claimed they were fake.
The YouTube star has continued to collect the cards and unveil them on his podcast which currently has over 3.5m subscribers on the streaming platform.
Several Pokemon experts later questioned whether the cards were real after his huge outlay, despite the collection previously being verified by card authentication company BBCE.
Paul has now issued an update as he plans to fly to the company's HQ, tweeting: "Update on this: I'm flying to Chicago this weekend to verify the case with BBCE, the company who insured its authenticity. to be continued…"
Pokemon cards have been a craze for several years now and date back to 1996 when they were first created with the collectible items run alongside the successful TV series and video-game franchise.
Logan Paul is an avid collector of Pokemon cards
YouTube star Paul has leapt into several ventures since his rise to fame began, including taking to the boxing ring.
He fought YouTube rival KSI twice in 2018 and 2019, generating huge revenue due to their popularity and the pay-per-view sales surrounding the events.
Paul has since featured in videos with his rival, burying the hatchet while also announcing the creation of a new energy drink company called "Prime".
A historic boxing exhibition with Floyd Mayweather followed this year which generated huge sums with Pauly earning over £15m after pay-per-view sales.
Before the fight he entered the ring with a £10,000 Pokemon card attached to his neck in a bid to bring him good luck.
Now he has taken a step back from his main YouTube channel in a bid to continue the success of his podcast.
Who do you think Logan Paul should face next in the boxing ring? Let us know in the comments section below
The 26-year-old admitted he was fed up of his content being "demonetised" and wanted to have fun with his career instead.
"Demonetisation, being blacklisted, being shadow-banned, it's really demotivating," Paul explained in a recent interview with the Los Angeles Times.
"When you are yourself, the platform that you're on, because of the advertisers, because of public sentiment, whatever it is, no longer wants to support you."
Paul has not ruled out a return to the ring after the success of his last exhibition with Mayweather but a reported exhibition with heavyweight legend Mike Tyson has yet to materialse.
Nina CarberryNina Carberry's famous horse racing family, relationship with husband Ted Walsh Jr and childrenNina Carberry is the favourite to win this year's Dancing With The Stars on RTE


Loved the main charcter seemed so real and yet the ... ByShellie Blumon December 15, 2014 Format: Paperback A real page turner.... I kept seeing it as a movie! Loved the main charcter seemed so real and yet the paranormal stuff was really entertaining.
A MUST READ! ByKimon March 19, 2015 Format: Paperback | Verified Purchase I enjoyed this entire book. It was very interesting and the scripts kept my attention. I would highly recommend you to read it. Get ready get ready get ready. Looking forward to the movie from this book. May God bless you Ronald Gordon.
Terriffic Paranormal Romance! Janet Dawson Dec.23,2015 ByPen Nameon December 23, 2015 Format: Kindle Edition WOW, this is an awesome book, you just can't put it down. The characters in this story including Ronald Jackson and Penny are very intriguing, they make you cave for more.It's a wonderful paranormal romance novel.


Title: "The Secrets of Skinny Chicks: How to Feel Great in Your Favorite Jeans When It Doesn't Come Naturally"
I recently read a book called "The Secrets of Skinny Chicks: How to Feel Great in Your Favorite Jeans When It Doesn't Come Naturally" by Karen Bridson. I got it out of the library thinking it would be a book about Intuitive Eating -- on the back, it talked of waiting for hunger, and eating just 'til satisfied.
And, sure enough, there were many instances where the women profiled talked of only eating when hungry, and stopping just at satisfied. There was also almost a full chapter that talked of "mindful eating" (same thing as Intuitive Eating -- listening to your body's cues). And, I loved the parts where the author talked about loving your body for its uniqueness, and about loving yourself for YOU. She talked quite a bit about overcoming Emotional eating, too.
** This is STILL a "diet" book! Do not read it with the intention of a "quick fix"! No diet is going to work for you forever... they just don't.


text:
Q: Simple variable test With Bash you can do a simple variable test like this
$ [ $foo ]; echo $?
1

$ foo=bar

$ [ $foo ]; echo $?
0

The expression is true if and only if the argument is not null.

What would be a similar test with C?
Edit
Lets say an int type for argument sake.

A: In general, there is implicit boolean conversion in C. So all of the following will print "bad":
int a = 0;
if (a) {                  // if a is nonzero.
    printf("good");
}
else {
    printf("bad");
}

char* str = NULL;
if (str) {                  // if str is nonzero. (NULL is zero).
    printf("good");
}
else {
    printf("bad");
}


text:
Tipo Newsletters e Flashes Insights Livros Artigos
Américo Oliveira Fragoso Benedita Gonçalves Paulo Pinheiro Rodrigo Esteves de Oliveira Pedro Ferreirinha Tiago Marreiros Moreira Francisco Cabral Matos Manuel Protásio Paulo Olavo Cunha Frederico Gonçalves Pereira Carla Gonçalves Borges João Velez de Lima Ana Luís de Sousa Helena Vaz Pinto Sofia Bobone Hugo Moredo Santos João Afonso Fialho José Miguel Oliveira Joaquim Pedro Lampreia Miguel C. Reis José Pedro Fazenda Martins Magda Cocco Margarida Couto Inês Antas de Barros Tiago Bessa Nuno Ruiz Paulo de Barros Baptista Pedro Simões Coelho Ricardo Bordalo Junqueiro Rui Andrade Samuel Fernandes de Almeida Sofia Ribeiro Branco Tiago Correia Moreira Vanda Cascão Francisca Paulouro Tiago Piló Conceição Gamito Teresa Pitôrra Ângela Viana Beatriz Assunção Ribeiro David Paula Iakovina Kindylidi Inês Perestrello Isabel Ornelas Joana Lobato Heitor João Riscado Rapoula Lisa Ventura Lopes Manuel Gouveia Pereira Marco Caldeira Maria Folque Marília Frias Orlando Vogler Guiné Pedro Fontes Pedro Pires Fernandes Rui Gordete Almeida Tiago Cochofel de Azevedo Inês Pires Santos
País Angola Cabo Verde Guiné Equatorial Moçambique São Tomé e Príncipe Timor-Leste
Ano 2022 2021 2020 2019 2018 2017
Mês Janeiro Fevereiro Abril Junho Julho Agosto Setembro Outubro Novembro Dezembro
Highlights Media Eventos Podcast Publicações
27 de Julho de 2022, VdA
Space Brief - Sustainability
The long-term sustainability of outer space
For the space sector to continue for the years to come as the same bustling innovative sector enabling growth and transformational changes to life on earth, it is necessary to ensure that space activities are carried out in a sustainable manner.
To that effect, the UN COPUOS formulated the Guidelines for the Long-Term Sustainability of Outer Space ("LTSs"), voluntary guidelines with the aim to preserve the "ability to maintain the conduct of space activities indefinitely into the future in a manner that realizes the objectives of equitable access to the benefits of the exploration and use of outer space for peaceful purposes, in order to meet the needs of the present generations while preserving the outer space environment for future generations". Among others, LTSs include (i) the adoption, revision and amendment, as necessary, of national regulatory frameworks for outer space activities, (ii) the supervision of national space activities, (iii) ensuring the equitable, rational and efficient use of the radio frequency spectrum and the various orbital regions used by satellites, (iv) developing space weather models and tools and collect established practices on the mitigation of space weather effects, (v) sharing experience related to the long-term sustainability of outer space activities and develop new procedures, as appropriate, for information exchange, and (vi) investigating and considering new measures to manage the space debris population in the long term.
The importance of space sustainability has also been reinforced by, among others, the leaders of the G7, which committed themselves to "the safe and sustainable use of space to support humanity's ambitions now and in the future" and welcomed the LTSs, while calling "on others to join us in implementing these guidelines". More importantly, they acknowledged "the importance of developing common standards, best practices and guidelines related to sustainable space operations alongside the need for a collaborative approach for space traffic management and co-ordination".
The importance of addressing the risks arising from the growth of space activities is further stressed by WEF's 2022 Global Risks Report, whilst the UNCUOPUS's 2022 Report on the implementation of the LTSs in the European Space Agency again notes that States and international intergovernmental organizations are encouraged to voluntarily take measures to ensure that the LTS guidelines are implemented to the greatest extent feasible and practicable.
The EU Space Surveillance and Tracking (EU SST), on its turn, has joined the Net Zero Space Declaration, which promotes sustainable space activities and the protection of Earth's orbital environment. Note, as well, the 2022 MoU signed between the European Union Agency for the Space Programme (EUSPA) and the UN Office for Outer Space Affairs (UNOOSA), which "builds on the common objectives regarding international cooperation in space activities and the use of space technology, data and services for achieving sustainable development".
At the national level, we are also witnessing LTS being filtered down to national space legislations and regulations and licensing decisions, notably as technical standards, thus influencing the way in which space activities are carried out. This is understandable as the sustainability of space activities, as described in the LTS, deals also with the need for national regulatory frameworks to address new developments and new space activities so as to avoid legal gaps.
For instance, in the UK, regulatory changes have been pursued with a view to implement the LTS as reported to the UNOOSA, which saw, among others, the signature of an agreement with UNOOSA to promote sustainability. Additionally, the British government announced a series of measures in June 2022 to drive space sustainability, including a regulatory review to incentivise sustainable practices and the development of a new Space Sustainability Standard.
In Portugal, the existing legislation – which was enacted prior to the LTS – already includes, as licensing conditions, that the space activity provides appropriate safeguards against damage to outer space and guarantees the minimization of space debris, to the greatest possible extent, according to international principles and obligations (with the operator being required to submit security plans and a space debris mitigation plan in the context of the licensing procedure).
Finally, sustainability of space activities also needs to be considered in the context of space defence activities. To that effect, the US Space Force has confirmed on September 2021 that, when preparing response options to attacks in space, the sustainability of those options is factored into the decision. A step further, the UK is pushing for a UN resolution acknowledging the need for international norms and principles on reasonable behaviour in space.
In light of the foregoing, operators of space activities need to prepare their business models with LTS embedded therein or may risk breaching applicable law and/or losing access to financing (public and private). Aligning space activities with LTS may require the development of guidelines on LTS matters, as well as of reporting criteria and practices, in the operator's structure. In addition, legal challenges will have also to be addressed, such as with relation to the sharing of information between stakeholders (notably when sharing commercially sensitive information), the reformulation of the operator's corporate structure to address capacity building and safety requirements being imposed by LTS, and/or compliance with regulations and best practices on sustainability throughout the supply chain of operators.
Sustainability: Space's next frontier
Insights Relacionados
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Space Traffic Management Insights, 2022-07-27
The role of space data and services in responding to ESG concerns Insights, 2022-07-27
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What's the best DAB radio available for less than £100? This is a difficult question to answer, as there are lots of great digital radios on the market, and the one that suits you might not be ideal for everyone else.
We've researched the market and picked out 5 of the best digital radios available for under £100. They're all unique and have different features, designs and prices, so there should be something here for everyone.
Please note, prices can fluctuate, so we can't guarantee that all of these radios will still be available for less than £100 when you read this article.
The Majority Oakington is one of the most popular digital radios in the UK at the moment, and it's not hard to see why. This radio offers a very impressive list of features and has a very reasonable price tag. We think it's the best DAB radio under £100 for most people.
As you can see, the Oakington is very versatile and offers a radio, CD player, alarm clock and Bluetooth speaker in one package. The build quality isn't as high as it could be, but it performs well for the price. Read our full review of the Majority Oakington here.
The Roberts Play is one of our favourite portable DAB radios. It doesn't offer the same range of features as the Majority Huntingdon, but it's a great compact radio for the kitchen or listening to around the house and in the garden.
One of the most impressive things about the Roberts Play is that it has a built-in battery charger that charges AA batteries. This is much more convenient than having to recharge batteries separately, and is cheaper than buying a bespoke battery pack.
This radio doesn't have any advanced features, but it's very easy to use and has good reception. It's a reliable radio from a reputable brand, so if you just need a straightforward portable DAB radio it's a great choice. Read our full review of the Roberts Play here.
If you want something distinctive and eye-catching, this radio from British brand VQ could be a good choice. The VQ Retro Mini is a stylish FM/DAB/DAB+ radio with Bluetooth that can be powered by the mains or 4 AA batteries.
This particular version of the VQ Retro Mini comes in a choice of Emma Bridgewater floral designs: Polka Dot, Rose & Bee or Wallflower.
This radio's features include an aux input, a headphone jack and a USB port for charging devices. There's also a dual alarm clock and a countdown timer which could be useful in the kitchen.
You pay a bit of a premium for the Retro Mini's design, but this radio still offers good value for money. Read our full review of the VQ Retro Mini here.
Pure was a pioneering brand in the early days of DAB radio, and their early Evoke radios set the standard in terms of both sound quality and design. The Evoke H2 is the cheapest radio in Pure's latest line of Evoke radios released in November 2016.
The Evoke H2 is a dependable and great-looking radio that would be perfect for the kitchen. It has a nice wood-effect finish and has a vertical design so it won't take up much space on a kitchen countertop.
This radio doesn't have very advanced features, but it's one of the best-sounding radios you can get for under £100. It features a clock, alarm and kitchen timer, which are useful if you use it in the kitchen or the bedroom. Read our full review of the Evoke H2 here.
Unlike most portable DAB radios, the AZATOM Multiplex has a good amount of bass, giving it a warm sound. Another nice thing about this radio is that you can adjust the bass and treble to get the sound you want.
This radio offers great value for money and could be a good alternative to the Majority Oakington if you want something more compact and portable and can live without a CD player. Read our full review of the AZATOM Multiplex here.
Most of the radios reviewed here have quite modern designs, but they vary greatly in style. The Pure Evoke H2 has a minimalistic Scandinavian-style design with a wood-effect finish, while the Majority Huntingdon is bold and black with sharper edges.
Do you want a portable radio?
Many of the radios reviewed here need to be plugged into the mains, while a few take batteries and can be carried around the house or into the garden. The Roberts Play is a small and compact portable radio that you can easily carry around, while the Majority Huntingdon is bigger and can't be used with batteries, so it's more suitable to keeping in the living room.
Do you want a radio with Bluetooth?
Bluetooth-enabled radios let you stream music or podcasts from your smartphone or laptop. This is great if you have music downloaded on your devices. It also means you can use the radio as a Bluetooth speaker while streaming music from Spotify, YouTube or anywhere else online.
Radios with Bluetooth usually cost a bit more, so if you won't use this feature you can probably save a bit of money by opting for a radio without Bluetooth.
All the radios reviewed here should be available for between £50 and £100 (though prices change quite often). The Majority Huntingdon probably offers the best value for money in terms of the number of features, but if you don't need so many features you can opt for something cheaper instead.
Do you want a DAB alarm clock radio?
Not all of these radios offer an alarm, and some aren't the right size for keeping on your bedside table. If you're looking to use your radio as an alarm, it's best to get one that's designed for this purpose, like the Pure Siesta Rise which has a large clock display.
If you still have a large CD collection, having a CD player built into your digital radio would be an obvious bonus. The Majority Huntingdon is the only radio on this list with a CD player. For other people, it would be better to get a Bluetooth radio to stream music from their smartphone.
If we had to pick just one of these radios to recommend it would be the Majority Oakington as it offers great value for money and offers a lot of features for the price.
For some more recommendations, check out this article: Best DAB Radio.
Does DAB Radio Work in the USA?


David Coady as Benji at Tassajara.
An informal gathering to mark the one-year anniversary of David Coady's death will be held on Sunday, July 22 at 5pm.
Anyone who knew David is welcome to attend.
Abbot Myogen Steve Stücky emailed members of the City Center sangha to say he'd be at Tassajara during the gathering. At Tassajara, they'll be holding an anniversary memorial service on the 21st and an Oban ceremony the evening of the 22nd.
Please feel free to bring food, beverage, music, stories, photos, or other memorabilia that remind you of David.
I apologize for not including Green Gulch Farm as he lived there for a time too.
I reentered the Zen Center community in 2009, moving into City Center where I stayed for one year, and then to Tassajara. I remember David Coady as a lively, spirited fellow who possessed the most remarkable sense of humor. On those occasions when we worked together in the City Center kitchen, the tone was always fun and unpredictable.
My first Summer at Tassajara, I was caught up in the hubbub of guest season, and doing my best to adapt to life in the valley. On a day off, when David was visiting, he brought me to the beautiful tree platform on the overlook trail. In my time at Tassajara, I spent many a peaceful hour there.
My last memory of David – I was washing pots in the kitchen, standing at the sink. Outside the window, along came David, thumbing his nose at me. I never got around to thanking him for that.


text:
package com.pm.commons;

import android.app.Activity;
import android.os.Bundle;
import android.util.Log;
import android.view.View;
import android.view.Window;

public class BaseActiviy extends Activity {

	@Override
	protected void onCreate(Bundle savedInstanceState) {
		super.onCreate(savedInstanceState);
		requestWindowFeature(Window.FEATURE_NO_TITLE);
		AppManager.getAppManager().addActivity(this);
	}
	protected void _initView() {
	}

	@Override
	protected void onStart() {
		super.onStart();
		log("onStart");
	}

	@Override
	protected void onStop() {
		super.onStop();
		log("onStop");
	}

	@Override
	protected void onDestroy() {
		super.onDestroy();
		AppManager.getAppManager().finishActivity(this);
		log("onDestory");
	}

	private void log(String message) {
		if (DEBUG) {
			Log.v("TrackActiivy", ":" + message + ": " + this.getClass().getSimpleName());
		}
	}

	public void onBackButtonClicked(View v) {
		onBackPressed();
	}

	private static final boolean DEBUG = true;
}

redpajama_set_name: RedPajamaGithub


Thousands of protesters joined the Women's March in Washington, D.C. and its many nationwide sister marches on Jan. 21. Sophomore Sophie Gillard discusses why she marched.
Election Day hype has one last hurrah as students geared up with political slogans and outfits.
In this audio Q&A, YPO board members talk about which candidates and policies they support in the presidential election.
Republican students have taken action on the national election, volunteering with the likes of Marco Rubio and Ted Cruz.


Goldplat plc, the AIM listed African gold producer, announces that, further to the statement made in the Interim Results dated 30 March 2015, the operating loss for the year ended 30 June 2015 is now expected to be materially the same as the operating loss for the half year ended 31 December 2014.
The Company expects to announce full year results for the year ended 30 June 2015 on 28 September 2015.
Gerard Kisbey-Green, Goldplat's CEO, commented "The substantial improvements in trading have not flowed through as rapidly as we had hoped. In particular, resumption of operations and improvements in Ghana were not realised during the period with significant value still in the pipeline at the year-end.
On a positive note, management is budgeting for an overall operating profit in the year ending 30 June 2016 and believe that capital projects will be funded internally with peak funding requirements being financed by non-equity capital during the period."


text:
\section{Introduction}

Website privacy policies are difficult to read and people struggle to understand the content.
Recent studies~\cite{Sadeh:2013} have raised concerns over their opaqueness, which poses a considerable challenge to both Internet users and policy regulators.
Nowadays, consumers supply their personal information to online websites in exchange for personalized services;
they are surrounded by smart gadgets such as voice assistants and surveillance cameras, which constantly monitor their activities in the home and work environments.
Without clearly specifying how users' information will be collected, used and shared, there is a substantial risk of information misuse, including undesired advertisements and privacy breaches.
Especially with recent high-profile cases involving Facebook and Cambridge Analytica, the public is becoming more aware and concerned with how their information is handled.

Privacy policies are binding agreements between companies and users that stipulate how companies collect, use, and share users' personal information.
They are lengthy and difficult to read.
Bhatia et al.~\shortcite{Bhatia:2016} suggested two possible causes for this.
First, privacy policies must be \emph{comprehensive} in order to cover a variety of uses (e.g., in-store and online purchases).
Second, the policies have to be \emph{accurate} to all data practices and systems.
Clearly, it would be difficult for a company's legal counsel to anticipate all future needs.
They need to resort to vague language to describe the content, causing it to be difficult to read and compromising the effectiveness of privacy policies.

\begin{table}[t]
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{footnotesize}
\begin{fontppl}
\begin{tabular}{|l|p{2.5in}|}
\hline
S1 & We \textcolor{red}{\textbf{\emph{may}}} use the \textcolor{red}{\textbf{\emph{information}}} automatically collected from your computer or \textcolor{red}{\textbf{\emph{other devices}}} for the following uses... (Vagueness: 3.8)\\
\hline
\hline
S2 & In addition, in \textcolor{red}{\textbf{\emph{some}}} cases the Sites can deliver content based on your current location if you choose to enable that feature.\\ & (Vagueness: 2.25)\\
\hline
\hline
S3 & Our Sites and Services \textcolor{red}{\textbf{\emph{may}}}, \textcolor{red}{\textbf{\emph{from time to time}}}, provide links to sites operated by \textcolor{red}{\textbf{\emph{third parties}}}. (Vagueness: 3.2)\\
\hline
\hline
S4 & To customize and serve advertising and \textcolor{red}{\textbf{\emph{other}}} marketing communications that \textcolor{red}{\textbf{\emph{may}}} be visible to you on our Sites and Services or \textcolor{red}{\textbf{\emph{elsewhere}}} on the internet. (Vagueness: 4)\\
\hline
\hline
S5 & This includes your credit card number, income level, or \textcolor{red}{\textbf{\emph{any other}}} information that would \textcolor{red}{\textbf{\emph{normally}}} be considered confidential.\\ & (Vagueness: 3)\\

\hline
\end{tabular}
\end{fontppl}
\end{footnotesize}
\caption{Example human-annotated vague words and sentences. Vague words are \emph{italicized}. Averaged sentence vagueness is given in the parentheses. Higher score is more vague.
}
\label{tab:example}
\vspace{-0.2in}
\end{table}

In this paper, we present the first study on automatic detection of vague content in website privacy policies.
We construct a sizable corpus containing word- and sentence-level human annotations of vagueness for privacy policy documents.
The corpus contains a total of 133K words and 4.5K sentences.
Our methods for automatically detecting vague words and sentences are based on deep neural networks, which have demonstrated impressive recent success.
Specifically, we investigate context-aware and context-agnostic models for predicting word vagueness, where feature representations of words are built with and without considering their surrounding words.
By this, we seek to verify the hypothesis that vagueness is an intrinsic property of words and has little to do with context.
To understand sentence vagueness, we explore auxiliary-classifier generative adversarial networks (AC-GAN, Odena et al., 2018\nocite{Odena:2018}).
The model has performed strongly on vision tasks (e.g., image synthesis), however, whether it can be adapted to handle text data has not been thoroughly investigated.
We train the AC-GAN model to discriminate between real/fake privacy policy sentences while simultaneously classifying sentences exhibiting different levels of vagueness, including ``clear,'' ``somewhat clear,'' ``vague,'' and ``extremely vague,'' thus improving the model's generalization capabilities.
The detected vague words and sentences can assist users in browsing privacy policy documents, and privacy regulators in assessing the clarity of privacy policy practices.
Our research contributions include the following:
\begin{itemize}[topsep=3pt,itemsep=-1pt,leftmargin=*]

\item we present the first study on automatic detection of vague content in privacy policies.
Vague content compromises the usability of privacy policies and there is an urgent need to identify and resolve vagueness;

\item we construct a sizable text corpus including human annotations for 133K words and 4.5K sentences of privacy policy texts.
The data\footnote{\url{https://loganlebanoff.github.io/data/vagueness_data.tar.gz}} is available publicly to advance research on language vagueness; and

\item we investigate both context-aware and context-agnostic methods for predicting vague words. We also explore the auxiliary-classifier generative adversarial networks for characterizing sentence vagueness. This is the first study leveraging deep neural networks for detecting vague content in privacy policies.

\end{itemize}

\section{Related Work}

Privacy policies are often verbose, difficult to read, and perceived as ineffective \cite{Mcdonald:2008}.
In particular, vague language in these documents hurts understanding. \emph{``A term is regarded as vague if it admits borderline cases, where speakers are reluctant to say either the term definitely applies or definitely does not apply,''} a definition of vagueness quoted from~\cite{Deemter:2010}.
Legal scholars and language philosophers strive to understand vagueness from a theoretical perspective~\cite{Keefe:2000,Shapiro:2006}.
The ``sorites paradox'' describes the phenomenon of vagueness~\cite{Keefe:2000}.
It states that small changes in the object do not affect the applicability of a vague term.
For example, a room can remain ``bright'' even if the light is dimmed little by little until it is entirely extinguished, thus creating a paradox.
Hyde~\shortcite{Hyde:2014} further suggests that vagueness is a feature pertaining to multiple syntactic categories.
Nouns, adjectives and adverbs (e.g., ``child'', ``tall'', ``many'') are all susceptible to reasoning.
These studies often focus on linguistic case studies but not on developing resources for automatic detection of vagueness.

Recent years have seen a growing interest in using natural language processing techniques to improve the effectiveness of website privacy policies.
Sadeh et al.~\shortcite{Sadeh:2013} describe a Usable Privacy Policy Project that seeks to semi-automate the extraction of salient details from privacy policies.
Other studies include crowdsourcing privacy policy annotations and categorizing data practices~\cite{Ammar:2012,Massey:2013,Wilson:2016:WWW,Wilson:2016:ACL}, grouping text segments related to certain policy issues~\cite{Liu:2014:COLING,Ramanath:2014:ACL}, summarizing terms of services~\cite{Braun:2017}, identifying user opt-out choices~\cite{Sathyendra:2017}, and many others.
These studies emphasize the ``too long to read'' issue of privacy policies but leave behind the ``difficult to understand'' aspect, such as identifying and eliminating vague content.

The work of~\cite{Liu:2016:PLT} is close to ours.
The authors attempt to learn vector representations of words in privacy policies using deep neural networks, where the vectors encode not only semantic/syntactic aspects but also vagueness of words.
The model is later fed to an interactive visualization tool~\cite{Strobelt:2016} to test its ability to discover related vague terms.
While promising, their approach is not fully automatic, and the feasibility of detecting vague words and sentences in an automatic manner is still left untested.

In this work we conduct the first study to automatically detect vague content from privacy policies.
We ask human annotators to label vague words and sentences and train supervised classifiers to do the same.
Classifying vague words is a challenging task, because vagueness is an understudied  property and it spans multiple syntactic categories (e.g., ``usually,'' ``personal data,'' ``necessary'').
Neural network classifiers such as CNN and LSTM have demonstrated prior success on text classification tasks~\cite{Zhang:2015}, but whether they can be utilized to identify vague terms is not well understood.

For sentence classification, we investigate auxiliary classifier generative adversarial networks (AC-GAN, Odena et al., 2018\nocite{Odena:2018}).
GANs have seen growing popularity in recent years~\cite{Mirza:2014,Yu:2016,Li:2017:GAN,Gu:2018,Cai:2018}.
AC-GAN is a variant of GAN that generates word sequences using class-conditional probabilities.
E.g., it generates ``fake'' privacy policy sentences exhibiting different degrees of vagueness (e.g., ``clear,'' ``vague,'' ``extremely vague'').
AC-GAN nicely combines real (human-annotated) and fake (synthetic) privacy policy sentences in a discriminative framework to improve the model's generalization capabilities.
This can be equated to a semi-supervised learning paradigm through augmentation of the dataset with generated sentences.
Data augmentation is particularly valuable for vagueness detection, which generally has small expensive datasets.
We perform a full analysis on AC-GAN and compare it to state-of-the-art systems.

\section{The Corpus}

\begin{table}[t]
\setlength{\tabcolsep}{3pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{footnotesize}
\begin{fontppl}
\begin{tabular}{lr|lr}
\bottomrule[0.3mm]
\textbf{Vague Term} & \textbf{Freq.} & \textbf{Vague Term} & \textbf{Freq.}\\
\hline
may & 1,575 & other information & 30\\
personal information & 465 & non-personal info. & 30\\
information & 302 & sometimes & 27 \\
other & 261 & reasonably & 26\\
some & 214 & appropriate & 25\\
certain & 205 & necessary & 24\\
third parties & 183 & certain information & 23\\
third party & 134 & typically & 22\\
personally iden. info. & 88 & affiliates & 21\\
time to time & 75 & reasonable & 20\\
most & 54 & non-personal & 19\\
generally & 52 & personally iden. & 18\\
personal data & 52 & such as & 18\\
third-party & 49 & usually & 17\\
others & 41 & personal & 16\\
general & 39 & may be & 15\\
many & 37 & content & 14\\
various & 36 & otherwise & 14\\
might & 35 & periodically & 14\\
services & 33 & similar & 14\\
\toprule[0.3mm]
\end{tabular}
\end{fontppl}
\end{footnotesize}
\caption{The most frequent vague terms identified by human annotators and their frequencies in our corpus. ``iden.'' and ``info.'' are shorthand for ``identifiable'' and ``information.''
}
\label{tab:vague_terms}
\vspace{-0.15in}
\end{table}

Annotating vague words and sentences is a nontrivial task.
We describe our effort to select privacy policy sentences for annotation, recruit qualified workers, and design annotation guidelines.

We select 100 website privacy policies from the collection gathered by Liu et al.~\shortcite{Liu:2014:COLING}.
The documents are quite lengthy, containing on average 2.3K words.
More importantly, most content is not vague.
To obtain a more balanced corpus, a filtering step is used to select only sentences that have a moderate-to-high chance of containing vague content.
Fortunately, Bhatia et al.~\shortcite{Bhatia:2016} provide a list of 40 cue words for vagueness, manually compiled by policy experts.
We therefore retain only sentences containing one of the cue words for further annotation.
A brief examination shows that most of the sentences removed from the corpus are indeed clear.
Even with this bias, the resulting corpus still contains a small portion of clear sentences (See Figure~\ref{fig:plot_vague}).
The reason is that a cue word can be used in a way that is not vague.
For example, in the sentence ``Users \textit{may} post to our website,'' the word \emph{may} indicates permission but not possibility, and therefore the sentence is not vague.

Reidenberg et al.~\shortcite{Reidenberg:2015} discuss attempts to use crowdsourced workers as a cost-effective alternative to policy experts for annotating privacy policies.
In this study, we hire crowd workers from the Amazon Mechanical Turk platform.
To recruit quality workers, we require them to reside in the U.S. and be proficient in English; they are skilled workers maintaining a task success rate of 90\% or above.
We provide example labelled vague terms obtained from the case studies described in Bhatia et al.~\shortcite{Bhatia:2016} to reduce discrepancies among workers.
The annotators are then asked to use their best judgment to perform the task.

Given a privacy policy sentence, the annotators are instructed to identify all vague terms\footnote{We use ``term'' to denote either a single word or a phrase.} and assign a score of vagueness to the sentence.
A vague term is limited to be 5 words or less (e.g., ``including but not limited to'').
We use this rule to prevent annotators from tagging an entire sentence/clause as vague.
A slider is provided in the interface to allow annotators to select a vagueness score for the sentence: 1 is extremely clear and 5 is extremely vague.
We design a human intelligence task (HIT) to include 5 privacy policy sentences and a worker is rewarded \$0.05 for completing the task.
Five human workers are recruited to perform each task.

\begin{figure}
\centering
\includegraphics[width=3.1in]{plot_vague}
\caption{(Left) Percentage of sentences containing different numbers of vague words. (Right) Perc. of sentences with different levels of vagueness. 1 is clear, 5 is extremely vague.}
\label{fig:plot_vague}
\vspace{-0.1in}
\end{figure}

We obtain annotations for 133K words and 4.5K sentences.
The average sentence vagueness score is 2.4$\pm$0.9.
As of inter-annotator agreement, we find that
47.2\% of the sentences have their vagueness scores agreed by 3 or more annotators;
12.5\% of the sentence vagueness scores are agreed by 4 or more annotators.
Furthermore, the annotators are not required to select vague words if they believe the sentences are clear.
We remove vague words selected by a single annotator. Among the rest, 46.1\% of the words are selected by 3 or more annotators;
18.5\% of the words are selected by 4 or more annotators.
These results suggest that, although annotating vague terms and sentences is considered challenging, our annotators can reach a reasonable degree of agreement.\footnote{We choose not to calculate a kappa statistic, because labelling vague words/sentences is not a clear-cut classification task and it is difficult to apply kappa to this setting.}
We present example vague terms in Table~\ref{tab:vague_terms}.
Note that we obtain a total of 1,124 unique vague terms, which go well beyond the 40 cue words used for sentence preselection.
Figure~\ref{fig:plot_vague} shows more statistics on sentence vagueness, including
(i) the percentages of sentences containing different numbers of vague words, and (ii) the percentages of sentences whose vagueness scores fall in different ranges.

\section{Word Vagueness}

We seek to test an important hypothesis related to word vagueness.
We conjecture that vagueness is an intrinsic property of words; whether a word is vague or not has little to do with its context words.
To verify this hypothesis, we build context-aware and context-agnostic models to classify each word in a privacy policy sentence as either vague or non-vague.
The ground-truth labels are obtained by consolidating human annotations (see Table~\ref{tab:word_labels} for an example).
A word is labelled 1 if it is selected by two or more annotators, otherwise 0.
We describe details of the two classifiers below.

\vspace{0.05in}
\noindent\textbf{Context-aware classifier.}
It builds feature representations of words based on the surrounding context words.
Given its strong performance, we construct a bidirectional LSTM~\cite{Hochreiter:1997} for this purpose.
A word is replaced by its word2vec embedding~\cite{Mikolov:2013} before it is fed to the model.
For each time step, we concatenate the hidden states obtained from the forward and backward passes and use it as input to a feedforward layer with sigmoid activation to predict if a word is vague or non-vague.
Because single words consist of the majority of the human-annotated vague terms, we choose to use binary word labels instead of a BIO scheme~\cite{Chiu:2016} for sequence tagging.
Figure~\ref{fig:word_classifiers} shows the architecture.

\vspace{0.05in}
\noindent\textbf{Context-agnostic classifier.}
It uses intrinsic feature representations of words without considering the context.
Specifically, we represent a word using its word2vec embedding, then feed it to a feedforward layer with sigmoid activation to obtain the prediction (Figure~\ref{fig:word_classifiers}).
We train the classifier using a list of unique words obtained from the training data; a word is considered positive if it has a ground truth label of 1 in any sentence, otherwise negative.
Note that the ratio of positive/negative unique words in our corpus is 1068/3176=0.34.
At test time, we apply the binary classifier to each word of the test set.
A word is assigned the same label regardless of which sentence it appears in.
We adopt this setting to ensure the context-aware and context-agnostic results are comparable.

\begin{table}[t]
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{scriptsize}
\begin{fontppl}
\begin{tabular}{|p{2.9in}|}
\hline
\textbf{Sent:} This includes your credit card number , income level , or any other information that would normally be considered confidential .\\
\hline
\hline
\textbf{Annotator 1:} any, other, normally\\
\textbf{Annotator 2:} any other information\\
\textbf{Annotator 3:} normally, confidential, any other\\
\hline
\hline
\textbf{Ground Truth Labels:} [This]$_0$ [includes]$_0$ [your]$_0$ [credit]$_0$ [card]$_0$ [number]$_0$ , [income]$_0$ [level]$_0$ , [or]$_0$ \textcolor{black}{\textbf{\emph{[any]}}}$_{1}$ \textcolor{black}{\textbf{\emph{[other]}}}$_{1}$ [information]$_{0}$ [that]$_{0}$ [would]$_{0}$ \textcolor{black}{\textbf{\emph{[normally]}}}$_{1}$ [be]$_{0}$ [considered]$_{0}$ [confidential]$_{0}$ .\\
\hline
\end{tabular}
\end{fontppl}
\end{scriptsize}
\caption{Ground truth labels are obtained by consolidating human-annotated vague terms; ``any,'' ``other,'' ``normally'' are labelled 1 because they are selected by 2 or more annotators.
}
\label{tab:word_labels}
\vspace{-0.1in}
\end{table}

\begin{figure}
\centering
\includegraphics[width=2.5in]{word_classifiers}
\caption{(Left) Context-aware word classifier implemented as a bidirectional LSTM. (Right) Context-agnostic classifier.  ``V'' and ``N'' are shorthands for ``vague'' and ``non-vague.''
}
\label{fig:word_classifiers}
\vspace{-0.2in}
\end{figure}

\vspace{-0.05in}
\section{Sentence Vagueness}

We next investigate how vagueness is manifested in privacy policy sentences.
Our goal is to assign a label to each sentence indicating its level of vagueness.
We derive ground truth sentence labels by averaging over vagueness scores assigned by human annotators, and further discretizing the scores into four buckets: [1,2), [2,3), [3,4), [4,5], respectively corresponding to ``clear,'' ``somewhat clear,'' ``vague,'' and ``extremely vague'' categories.
The sentences in the four buckets respectively consist of 26.9\%, 50.8\%, 20.5\%, and 1.8\% of the total annotated sentences.
We choose to predict discrete labels instead of continuous scores because labels are more informative to human readers. E.g., a label of ``extremely vague'' is more likely to trigger user alerts than a score of 4.2.

\subsection{Auxiliary-Classifer GAN}

Predicting vague sentences is a nontrivial task due to the complexity and richness of natural language.
We propose to tackle this problem by exploring the auxiliary classifier generative adversarial networks (AC-GAN, Odena et al., 2018\nocite{Odena:2018}).
We choose GAN because of its ability to combine text generation and classification in a unified framework~\cite{Yu:2016,Li:2017:GAN,Gu:2018}.
Privacy policy sentences are particularly suited for text generation because the policy language is restricted and a text generator can effectively learn the patterns. AC-GAN has a great potential to make use of both human-annotated data and ``fake'' augmented data for classification.
The system architecture is presented in Figure~\ref{fig:acgan}.
The generator learns to generate ``fake'' privacy policy sentences and sentences exhibiting different levels of vagueness using class conditional probabilities (hence the name auxiliary-classifer GAN).
The discriminator learns to discriminate among real/fake sentences as well as sentences of different levels of vagueness.
They are jointly trained using a heuristic, non-saturating game loss.
In the following we present the model details.

\begin{figure}
\centering
\includegraphics[width=2.9in]{acgan}
\caption{System architecture for AC-GAN. (Left) The Generator generates plausible privacy policy sentences ($\mbox{X\_fake}$).
The Discriminator must learn to differentiate between real and fake sentences as well as predicting the vagueness category ($\mbox{C}$) of the sentences.
(Right) RNN-generator. A vocabulary distribution is generated for each step. Gumbel-softmax is applied to the distributions to calculate weighted embeddings to be used by the Discriminator (arrows pointing up).
Argmax is applied to the distributions to retrieve embeddings to be passed to the next step (arrows pointing down).
}
\label{fig:acgan}
\vspace{-0.1in}
\end{figure}

\subsection{Sentence Generator}

The generator focuses on generating ``fake'' samples that resemble privacy policy sentences of a given vagueness category.
This is denoted by $P(X|C)$, where $X = \{x_t\}_{t=1}^{T}$ is a sequence of words and $C \in \{1,2,3,4\}$ is a vagueness category.
A vagueness category is randomly sampled in the generation process, and the generator attempts to generate a sentence of that vagueness level.
A typical RNN text generator unrolls the sequence $X$ one word at a time until an end-of-sentence symbol (EOS) is reached.
At time step $t$, it samples a word $x_t$ from a vocabulary-sized vector of probability estimates $P(x_t)$:
\begin{align*}
& x_t \sim P(x_t) = \mbox{softmax}(\mathbf{a}_t),
\numberthis\label{equ:p_x}\\
& \mathbf{a}_t = \mathbf{W}\mathbf{h}_t + \mathbf{b},
\numberthis\label{equ:a_t}\\
& \mathbf{h}_t = f_{RNN}(\mathbf{h}_{t-1}, x_{t-1}),
\numberthis\label{equ:h_t}
\end{align*}
where $\mathbf{a}_t$ is a vector of activation values and $\mathbf{h}_t$ is the $t$-th RNN hidden state.
We train a neural text generator, implemented as Long Short-Term Memory networks~\cite{Hochreiter:1997}, on a large collection of privacy policy sentences using cross-entropy loss.
While generating natural language sentences is successfully tackled by recurrent neural networks, the generated sentences are not necessarily vague.
Training the generator only on vague sentences is impractical because there is a limited number of annotated sentences.
In this paper we introduce a new way of defining class conditional probabilities:
\begin{align*}
x_t \sim P(x_t|C) = \mbox{softmax}(\mathbf{a}_t + \lambda_C \mathbf{v}),
\numberthis\label{equ:x_t_vague}
\end{align*}
where $\mathbf{v}$ is a vocabulary-sized, trainable vector indicating how likely a vocabulary word is vague.
$\lambda_C$ is a coefficient for vagueness category $C$.
The underlying assumption is that a ``clear'' sentence is less likely to contain vague words ($\lambda_C$ is negative), whereas an ``extremely vague'' sentence tends to contain many vague words ($\lambda_C$ is positive).

Finally, the generated ``fake'' sentences, together with ``real'' sentences labelled by human annotators, are fed to the discriminator for training a classifier discriminating between real/fake sentences and sentences of different vagueness levels.
Nevertheless, there remains a critical issue with the current system:
we cannot backpropagate through discrete samples $X$.
As a result, the generator parameters cannot be properly updated using backpropagation.
To circumvent this issue, we attempt the reparameterization trick with Gumbel-Softmax relaxation~\cite{Gu:2018}.

\vspace{0.05in}
\noindent\textbf{Straight-Through Gumbel-Softmax.}
Two competing issues exist in the RNN generator.
First, the discriminator requires a continuous form for each generated word to keep the entire model differentiable.
Second, the generator requires a discrete choice for each word to generate a sentence, rather than propagating "partial words" through the sequence.
To solve this problem, the softmax distribution of each word is sent to the discriminator, while the argmax over the distribution is sent to the next time step of the generator.
This system is referred to as Straight-Through (ST) Gumbel.

We explain the process of calculating the softmax distribution to send to the discriminator.
To simulate the random-sampling process, the approach applies reparameterization to shift randomness from sampling a discrete variable $x_t$ (Eq.~(\ref{equ:x_t_vague})) to sampling a continuous noise vector $\mathbf{z}_t$ following the Gumbel distribution (Eq.~(\ref{equ:gumbel})).
The noise vector is added to the activation $\mathbf{a}_t + \lambda_C \mathbf{v}$ to compute the argmax (Eq.~(\ref{equ:x_t_argmax})).
To simulate the argmax operation, a temperature parameter $\tau$ is applied to softmax (Eq.~(\ref{equ:x_t_approx})), where small values of $\tau$ greatly skew the distribution, causing it to peak at the largest value, while still remaining differentiable.
Similar reparameterization is also used for variational auto-encoders~\cite{Kingma:2014}.
\begin{align*}
& \mathbf{z}_t \sim \mbox{Gumbel}(z)
\numberthis\label{equ:gumbel}
\end{align*}
\begin{align*}
& x_t = \mbox{argmax}(\mathbf{a}_t + \mathbf{z}_t + \lambda_C \mathbf{v})
\numberthis\label{equ:x_t_argmax}\\
&P(x_t|C) = \mbox{softmax}(\frac{\mathbf{a}_t + \mathbf{z}_t + \lambda_C \mathbf{v}}{\tau})
\numberthis\label{equ:x_t_approx}
\end{align*}

The generator requires a discrete word to propagate to the next time step of the RNN.
The word with the maximum activation value is chosen as shown in (Eq.~(\ref{equ:x_t_argmax})).
An illustration of ST Gumbel is presented in Figure~\ref{fig:acgan}.

\subsection{Sentence Discriminator}

A sentence discriminator learns to perform two tasks simultaneously.
Given a privacy policy sentence $X$, it predicts a probability distribution over its sources, denoted by $P(S|X)$, where $S$ = $\{\mbox{real, fake}\}$; and a probability distribution over its level of vagueness, denoted by $P(C|X)$, $C$ = \{clear, somewhat clear, vague, extremely vague\}.
The learning objective for the discriminator is to maximize the log-likelihood of making correct predictions on both tasks, denoted by $L_C + L_S$, where $L_C$ and $L_S$ are defined in Eq.~(\ref{equ:L_C}) and (\ref{equ:L_S}).
\begin{align*}
L_C &= \mathop{\mathbb{E}}[\log P(C=c | X_{real+fake})]
\numberthis\label{equ:L_C}\\
L_S &= \mathop{\mathbb{E}}[\log P(S=real | X_{real})] \\
& + \mathop{\mathbb{E}}[\log P(S=fake | X_{fake})]
\numberthis\label{equ:L_S}
\end{align*}

The ground truth vagueness labels $C$ for real sentences are annotated by human annotators.
For fake sentences the labels are randomly sampled in the generation process;
and conditioned on the sampled vagueness labels, fake sentences are generated using $P(x_t|C)$ (Eq.~(\ref{equ:x_t_approx})).
\begin{align*}
L_C' &= \mathop{\mathbb{E}}[\log P(C=c | X_{fake})]
\numberthis\label{equ:L_C_gen}\\
L_S' & = \mathop{\mathbb{E}}[\log P(S=real | X_{fake})]
\numberthis\label{equ:L_S_gen}
\end{align*}

The \emph{generator} is trained to maximize $L_C' + L_S'$ as illustrated in Eq.~(\ref{equ:L_C_gen}-\ref{equ:L_S_gen}).
Intuitively, the generator is rewarded (or punished) only based on the ``fake'' samples it produces.
It is rewarded by generating sentences correctly exhibiting different levels of vagueness, denoted by ($L_C'$).
It is also rewarded by generating sentences that look ``real'' and cannot be easily distinguished by the discriminator ($L_S'$).
Eq.~(\ref{equ:L_S_gen}) corresponds to a heuristic, non-saturating game loss that mitigates gradient saturation~\cite{Goodfellow:2016}.

We experiment with two variants of the discriminator, implemented respectively using the convolutional neural networks (CNN)~\cite{Zhang:2015} and LSTM~\cite{Hochreiter:1997}.
In both cases, the discriminator assigns a source and a vagueness label to each sentence.
The CNN discriminator scans through each sentence using using a sliding window and apply a number of filters to each window.
A max pooling over the sequence is performed to create a feature map for the sentence.
This feature map is treated as the sentence representation.
It is fed to two separate dense layers with softmax activation to predict $P(C|X)$ and $P(S|X)$ respectively.
In contrast, the LSTM discriminator runs a forward pass through the sentence and uses the last hidden state as the sentence representation.
Similarly, this representation is fed to two dense layers used to predict $P(C|X)$ and $P(S|X)$.
Both methods produce probability estimations using a shared sentence representation.
Given the scarcity of labelled sentences, this multitask setting is expected to improve the model's generalization capabilities.

\begin{table}[t]
\setlength{\tabcolsep}{9.5pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{small}
\begin{tabular}{|l|ccc|}
\hline
& \multicolumn{3}{c|}{\textbf{Word-Level}}\\
\textbf{System} & \textbf{P (\%)} & \textbf{R (\%)} & \textbf{F (\%)} \\
\hline
\hline
Context-Agnostic & 11.30 & \textbf{78.15} & 19.71\\
Context-Aware & \textbf{68.39} & 53.57 & \textbf{60.08}\\
\hline
\end{tabular}
\end{small}
\caption{Results of detecting vague words in privacy policies using context-aware and context-agnostic classifiers.
}
\label{tab:results_word}
\vspace{-0.1in}
\end{table}

\section{Experiments}

We conduct experiments on the annotated corpus using a 5-fold cross validation; 10\% of the training data in each fold are reserved for validation.
In the following sections we present details of experimental settings and report results on detecting vague words and sentences in privacy policy texts.

\subsection{Parameter Settings}
The Xavier scheme~\cite{Glorot:2010} is used for parameter initialization.
For the context-aware word classifier, the bidirectional LSTM has 512 hidden units.
For AC-GAN, the CNN discriminator uses convolutional filters of size $\{3, 4, 5\}$ and 128 filters for each size.
The LSTM generator and discriminator both have 512 hidden units.
The generator is further pretrained on 82K privacy policy sentences using a 10K vocabulary.
The coefficient $\lambda_C$ is set to $\{-1, 0, 1, 2\}$ respectively for `clear,' `somewhat clear,' `vague,' and `extremely vague' categories.
$\mathbf{v}$ is initialized as a binary vector, where an entry is set to 1 if it is one of the 40 cue words for vagueness~\cite{Bhatia:2016}.
Word embeddings are initialized to their word2vec embeddings and are made trainable during the entire training process.

\subsection{Predicting Vague Words}

We compare {context-aware} with {context-agnostic} classifiers on detecting vague words in privacy policy text.
The goal is to test an important hypothesis: that vagueness is an intrinsic property of words, thus a word being vague has little to do with its context.
Results are presented in Table~\ref{tab:results_word}.

\begin{table}[t]
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{footnotesize}
\begin{fontppl}
\begin{tabular}{|l|p{2.5in}|}
\hline
S1 & ... while we use \textcolor{orange}{\textbf{[reasonable]$_{tp}$}} \textcolor{blue}{\textbf{[efforts]$_{fn}$}} to protect your PII, we can not guarantee its absolute security.\\
\hline
\hline
S2 & We use \textcolor{blue}{\textbf{[third-party]$_{fn}$}} advertising companies to serve \textcolor{orange}{\textbf{[some]$_{tp}$}} of the ads when you visit our web site. \\
\hline
\hline
S3 & The \textcolor{blue}{\textbf{[information]$_{fn}$}} we obtain from \textcolor{blue}{\textbf{[those services]$_{fn}$}} \textcolor{magenta}{\textbf{[often depends]$_{fp}$}} on your settings or their privacy policies, so be sure to check what those are. \\
\hline
\hline
S4 & In the event of an insolvency, bankruptcy or receivership, \textcolor{orange}{\textbf{[personal data may]$_{tp}$}} also be transferred as a business asset. \\
\hline
\end{tabular}
\end{fontppl}
\end{footnotesize}
\caption{Examples of detected vague words in privacy policies.
$[\cdot]_{tp}$ denotes true positive, $[\cdot]_{fp}$ is false positive, $[\cdot]_{fn}$ is false negative. All unmarked words are true negatives.
}
\label{tab:example_word}
\end{table}

\begin{table}[t]
\setlength{\tabcolsep}{6pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{small}
\begin{tabular}{|lr|lr|}
\hline
\multicolumn{2}{|c|}{\textbf{False Alarms}} & \multicolumn{2}{c|}{\textbf{Misses}}\\
\textbf{POS  Tag} & \textbf{Perc. (\%)} & \textbf{POS Tag} & \textbf{Perc. (\%)} \\
\hline
\hline
Adjective & \textbf{37.19} & Noun & \textbf{47.64}\\
Noun & 35.24 & Adjective & 25.07 \\
Verb & 20.53 & Verb & 13.31 \\
Adverb & 4.63 & Adverb & 5.62 \\
Determiner & 1.72 & Determiner & 2.79 \\
\hline
\end{tabular}
\end{small}
\caption{The most frequent part-of-speech (POS) tags appeared in false alarms and misses of detected vague words.
}
\label{tab:stat_pos}
\vspace{-0.1in}
\end{table}

Interestingly, context-agnostic classifier yields a high recall score (78.15\%) despite it ignoring context.
This result indicates word vagueness can be encoded in distributed word embeddings.
However, the low precision (11.30\%) suggests that context is important for fine-grained analysis.
While it is possible for experts to create a comprehensive list of vague terms for assessing privacy policies, extra effort is required to verify the tagged vague terms.
Using a context-aware classifier produces more balanced results, improving the F-score from 19.71\% to 60.08\%.
This indicates that the initial hypothesis is incorrect; rather, context is necessary for detecting vague words.

In Table~\ref{tab:example_word}, we present examples of detected vague words.
The nouns have caught our attention.
The classifier misses several of these, including ``efforts,'' ``information,'' ``services,'' perhaps because there is no clear definition for these terminologies.
In Table~\ref{tab:stat_pos}, we found nouns consist of 47.64\% of all the miss-detected vague words, while adjectives consist of 37.19\% of the false alarms.
There is also an interesting phenomenon.
In S3, ``Information'' and ``those services'' are considered more vague by humans than ``often depends.''
However, if those terms are removed from the sentence, yielding ``The [..] we obtain from [..] often depends on your settings or their privacy policies.'' In this case, the vagueness of ``often depends'' become more prominent and is captured by our system.
It suggests that the degree of vagueness may be relative, depending on if other terms in the sentence are more vague.

\subsection{Predicting Vague Sentences}

In Table~\ref{tab:results_sentence} we present results on classifying privacy policy sentences into four categories: clear, somewhat clear, vague, and extremely vague.
We compare AC-GAN with three baselines: CNN and LSTM trained on human-annotated sentences, and a majority baseline
that assigns the most frequent label to all test sentences.
We observe that the AC-GAN models (using CNN discriminator) perform strongly, surpassing all baseline approaches.
CNN shows strong performance, yielding an F-score of 50.92\%.
A similar effect has been demonstrated on other sentence classification tasks, where CNN outperforms LSTM and logistic regression classifiers~\cite{Kim:2014,Zhang:2015}.
We report results of AC-GAN using the CNN discriminator.
Comparing ``Full Model'' with ``Vagueness Only,'' we found that allowing the AC-GAN to only discriminate sentences of different levels of vagueness, but not real/fake sentences, yields better results.
We conjecture this is because training GAN models, especially with a multitask learning objective, can be unstable and more effort is required to balance the two objectives ($L_S$ and $L_C$).
Example sentences generated by AC-GAN are presented in Table~\ref{tab:example_sentence}.

\begin{table}[t]
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{small}
\begin{tabular}{|l|ccc|}
\hline
& \multicolumn{3}{c|}{\textbf{Sentence-Level}}\\
\textbf{System} & \textbf{P (\%)} & \textbf{R (\%)} & \textbf{F (\%)} \\
\hline
\hline
Baseline (Majority) & 25.77 & 50.77 & 34.19\\
LSTM & 47.79 & 50.06 & 47.88\\
CNN & 49.66 & 52.51 & 50.18\\
\hline
\hline
AC-GAN (Full Model) & 51.00 & 53.50 & 50.42\\
AC-GAN (Vagueness Only) & \textbf{52.90} & \textbf{54.64} & \textbf{52.34}\\
\hline
\end{tabular}
\end{small}
\caption{Results on classifying vague sentences.
}
\label{tab:results_sentence}
\end{table}

\begin{table}[t]
\setlength{\tabcolsep}{4.5pt}
\renewcommand{\arraystretch}{1.1}
\centering
\begin{small}
\begin{tabular}{|l|llll|}
\hline
\% (Freq) & {Clear} & {SomeC} & {Vague} & {ExtrV}\\
\hline
\hline
Clear & \textbf{39.4} (477) & 59.8 (723) & 0.7 (8) & 0.2 (2)\\
\hline
SomeC & 12.4 (284) & \textbf{85.2} (1945) & 2.4 (54) & 0.0 (1)\\
\hline
Vague & 3.4 (31) & 89.6 (828) & \textbf{7.0} (65) & 0.0 (0)\\
\hline
ExtrV & 1.2 (1) & 88.9 (72) & 9.9 (8) & \textbf{0.0} (0)\\
\hline
\end{tabular}
\end{small}
\caption{Confusion matrix for sentence classification. The decimal values are the percentage of system-identified sentences that were placed in the specified vagueness class. For example: the item in (row 1, col 2) conveys that 59.8\% of sentences (absolute count is 723) identified by the system as "clear" were actually "somewhat clear" according to humans.
}
\label{tab:confusion_matrix}
\end{table}

\begin{figure}[t]
\centering
\includegraphics[width=3in]{plot_roc}
\caption{ROC curves for classifying vague words (left) and sentences (right).
}
\label{fig:plot_roc}
\vspace{-0.2in}
\end{figure}

Figure~\ref{fig:plot_roc} shows the ROC curves of the four vagueness classes.
Because the dataset is imbalanced, the ROC curves are more informative than F-scores.
The ``clear'' and ``somewhat clear'' classes yield promising AUC scores of 0.71 and 0.78 respectively.
The ``vague'' and ``extremely vague'' classes are more challenging.
They are also the minority classes, consisting of 20.5\% and 1.8\% of the annotated data.
Confusion matrix in Table~\ref{tab:confusion_matrix} reveals that the majority of the sentences are tagged as ``somewhat clear,'' while 7.0\% of the vague sentences are tagged as vague.
It suggests more annotated data may be helpful to enable the classifier to distinguish ``vague'' and ``extremely vague'' sentences.
Interestingly, we found there is little correlation between the sentence vagueness score and sentence length (Pearson correlation $r$=0.18, $p<$0.001) while there is a relatively strong correlation ($r$=0.57, $p<$0.001) between sentence vagueness and the number of vague words in it. This finding verifies our hypothesis that vague words seem to increase the perceived sentence vagueness.

\begin{table}[h]
\setlength{\tabcolsep}{4pt}
\renewcommand{\arraystretch}{1.2}
\centering
\begin{footnotesize}
\begin{fontppl}
\begin{tabular}{|l|p{2.4in}|}
\hline
Clear & Our commitment to travian games uses paid services or send an order online.\\
\cline{2-2}
& To learn how important anonymization it, we provide a separate medicare.\\
\hline
\hline
SomeC & Slate use certain cookies and offers.\\
\cline{2-2}
& Visitors who apply us an credit card may sign up.\\
\hline
\hline
Vague & There may take certain incidents various offerings found on various topics; some or all individual has used.\\
\cline{2-2}
& You may modify certain edit or otherwise delete certain features or a similar id will no longer.\\
\hline
\hline
ExtrV & Also, some apps may offer contests, sweepstakes, games or some community where necessary.\\
\cline{2-2}
& If necessary, buying or clarify certain links, certain features of our site may place or some or some features may offer stack or unauthorized access some some functionality.\\
\hline
\end{tabular}
\end{fontppl}
\end{footnotesize}
\caption{Plausible sentences generated by AC-GAN. They exhibit different levels of vagueness. ``SomeC'' and ``ExtrV'' are shorthands for ``somewhat clear'' and ``extremely vague.''
}
\label{tab:example_sentence}
\vspace{-0.1in}
\end{table}

\vspace{0.05in}
\noindent\textbf{Lessons learned.}
We summarize some lessons learned from annotating and detecting vague content in privacy policies, useful for policy regulators, users and website operators. In general, privacy policies are suggested to:
\begin{itemize}[topsep=3pt,itemsep=-1pt,leftmargin=*]
\item provide clear definitions for key concepts.
Lacking definition is a major source of confusion for the unfamiliar reader. Example concepts include personally identifiable information, personal (non-personal) information, third parties, service providers, subsidiaries, etc.

\item suppress the use of vague words. There are on average 2.5 vague words per sentence in our corpus. The more vague words, the more likely the sentence is perceived as vague ($r$ = 0.57);

\item use sentences with simple syntactic structure to ease understanding. A sophisticated sentence, with vague terms in it, e.g., ``You may request deletion of your personal data by us, but please note that we may be required (by law \emph{or otherwise}) to keep this information and not delete it...'' appears especially confusing to readers.

\end{itemize}

\section{Conclusion}

In this paper we present the first empirical study on automatic detection of vague content in privacy policies.
We create a sizable text corpus including human annotations of vague words and sentences.
We further investigate the feasibility of predicting vague words and sentences using deep neural networks.
Specifically we investigate context-agnostic and context-aware models for  detecting vague words, and AC-GAN for detecting vague sentences.
Our results suggest that a supervised paradigm for vagueness detection provides a promising avenue for identifying vague content and improving the usability of privacy policies.

\section*{Acknowledgments}
We thank the anonymous reviewers for their valuable comments and  suggestions, which help to improve this paper.
This work is in part supported by an ORC In-House Research Grant awarded to Liu.

\section{Supplemental Material}
\label{sec:supplemental}

\begin{table}[h]
\setlength{\tabcolsep}{5pt}
\renewcommand{\arraystretch}{1.2}
\centering
\begin{footnotesize}
\begin{fontppl}
\begin{tabular}{|l|p{2.5in}|}
\hline
Clear & Our commitment to travian games uses paid services or send an order online.\\
\cline{2-2}
& To learn how important anonymization it, we provide a separate medicare.\\
\hline
\hline
SomeC & Slate use certain cookies and offers.\\
\cline{2-2}
& Visitors who apply us an credit card may sign up.\\
\hline
\hline
Vague & There may take certain incidents various offerings found on various topics; some or all individual has used.\\
\cline{2-2}
& You may modify certain edit or otherwise delete certain features or a similar id will no longer.\\
\hline
\hline
ExtrV & Also, some apps may offer contests, sweepstakes, games or some community where necessary.\\
\cline{2-2}
& If necessary, buying or clarify certain links, certain features of our site may place or some or some features may offer stack or unauthorized access some some functionality.\\
\hline
\end{tabular}
\end{fontppl}
\end{footnotesize}
\caption{Example sentences generated by AC-GAN exhibiting different levels of vagueness.
``SomeC'' and ``ExtrV'' are shorthands for ``somewhat clear'' and ``extremely vague.''
The generated sentences are typical outputs of an RNN-based sentence generator.
They look plausible but may make little sense to humans.
The problem may be related to the fact that the generator was trained on limited in-domain data.
}
\label{tab:example_sentence}
\end{table}

\end{document}

redpajama_set_name: RedPajamaArXiv


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How HP Lost its "Way"
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So even the pope can't execute strategy. A recent article in The Wall Street Journal, The Papacy After Benedict said "an intellectually strong Pope such as Benedict can set worthy goals and articulate them with force and eloquence. But it comes to naught if the Vatican is either incapable of following through to execute the Pope's intentions, or even undermines them."
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text:
Chasing Time by ellymelly
TV » Doctor Who Rated: K+, English, Adventure & Mystery, 11th Doctor, River Song/Melody P. III, Words: 14k+, Favs: 13, Follows: 39, Published: 9/30/2012 Updated: 1/28/2013}
57 Chapter 12: Remains of Dying Stars
Author's Note: Hey everyone, this is the final chapter. Thank you all for reading and please leave a review if you have a moment. If anyone has any requests for fic I'm looking to write another adventure story and am happy to take suggestions! MUCH LOVE *ellymelly*
REMAINS OF DYING STARS
Eight years old, Melody Pond perched on the edge of the Tardis, feet dangling over oblivion with the universe stretching out in every direction. In front of her was a soft, pink star – burning all alone with no planets for company. The Tarids was fuelling her engines, mining a crack in time that arched around its puffed out, dying body for a boost.
"What's up, Pond?" the Doctor appeared behind her. He sat down and tucked himself into the small space between her and the door.
The little girl shrugged, hugging her knees up to her chest. Dirty blond hair fell over her shoulders in a crazy mess.
"How about a story, hmm? You love stories."
Melody perked up a bit, glancing up at her Doctor. "What kind of a story?"
"Oh..." he sighed theatrically, leaning against the door frame. "A good one – full of explorers and timey-wimey stuff."
They were in the ancient past. Though plentiful, the stars around them were either too dim or exceedingly bright – ready to burst open like great big water balloons at any moment and thrust out their stardust into the night. This is when the cracks had first started to appear... From the very beginning the universe was broken.
"See this star?" The Doctor pointed in front of them. There were incredible arches of pink plasma spewing out – arching over and plummeting back down into the corona. "It's getting very old and just like the Tardis, it is starting to run out of fuel. When some stars run out of fuel they swell up really really big – like this one then poof! All of its gas just flies off into space."
Melody was impressed but a little frown crossed her features. "That's sad..."
"No, no, no..." the Doctor insisted. "Not sad. Time is a great big line-y thing with loops in it." Melody was frowning at him again. "Okay – not really but lots of things like stars and civilisations go in cycles. This star will create another one with planets and moons."
"People?"
"The next star after that will create more planets – with people. And," he perked up. "Did I mention they were really pretty when they go boom?"
The Doctor picked her up and moved her back inside the Tardis. He closed the door and revved the engines for a few minutes, flying her another billion and a half years into the future. When they opened the door again they found themselves amongst the dust of the previous star and in its place, the healthy white glow of its child. Tiny, rocky planets hummed busily around it, some of them worryingly close to crashing into each other.
The Time crack to the right of the star seemed wider – the Tardis's engines replying in kind with a healthy surge of power.
"It's pretty," Melody whispered, smiling again. "Where are we?"
"A very very special place – a lost solar system. No one can find it after the next few cycles well," he amended. "An ancient race once did – Vesparians – but it's just a story. Probably not even true."
"Did the Vesparians die too?" Melody held the Doctor's hand as he led her back to the console.
"No one knows... just like this place, Time sort of – lost them."
The Doctor went back to the door. He looked up at the darkness in the voids between stars and wondered – then closed it.


text:
Q: Regex regular expression I'm programming a calculator in C# to begin with that.
I will separate a string in two variables= Nb1 and Nb2. I looked on the web for examples and I found something :
var numAlpha = new Regex("(?<Alpha>[a-zA-Z]*)(?<Numeric>[0-9]*)");
var match = numAlpha.Match("codename123");
var alpha = match.Groups["Alpha"].Value; // Alpha = codename
var num = match.Groups["Numeric"].Value; // Numeric = 123

I fails to only adapt for the numbers : " 121165468746*1132" or "4586/6953"
Nb1 =121165468746 || 4586
Nb2 =1132 || 6953
Can you help me ? I'm going crazy  :-)

A: var numAlpha = new Regex("(?<NumOne>[0-9]+)(?<Operator>[^0-9])(?<NumTwo>[0-9]+)");
var match = numAlpha.Match("121165468746*1132");
var nb1 = match.Groups["NumOne"].Value; // nb1 = 121165468746
var nb2 = match.Groups["NumTwo"].Value; // nb2 = 1132
var op = match.Groups["Operator"].Value; // op = *

It looks like what you're trying to do is match some pair of integers separated by an operator.  The above regex uses named groups (?<GroupName> ... ), and two simple regular expressions to achieve that.  [0-9]+ will match one or more digits, and [^0-9] will match any one non-digit character, which here is being assumed to be the operator.
If this isn't what you were looking for, leave a comment and I'll try to help you out.  In the mean time, some reading material:

*

*Regular Expressions Page, with plenty of tutorials and references

*Javascript Regular Expressions Tester with Syntax Highlighting

*Java RegEx Tester.  More powerful, fewer frills.

A: Try to use this pattern for your Regex: it assumes that there are at least two numbers with one or more digits, separated by one or more non-digit characters (in case the operator is not only one character). The groups are called n1 and n2.
^(?<n1>\d+)[^\d]+?(?<n2>\d+)$

A: Use the following to match numbers with predefined 4 basic operations Multiply, Subtract, Add, Divide. You can add more operators to the "op" expressions as per your need.
Regex rg = new Regex(@"(?<num1>[0-9]+)(?<op>[\*\-\+\\])(?<num2>[0-9]+)");