Posters

Action Method URI
List Posters GET /poster
Get Poster GET /poster/member/{id}
Create Poster POST /poster
Update Poster PUT /poster/member/{id}
Delete Poster DELETE /poster/member/{id}

List Posters

Parameter Details Default
poster_page Page number of records 1
limit Number of records to return 50

Request

GET /poster

View Sample Response

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Get Poster

Parameter Details Default
id ID of the poster 1

Request

GET /poster/member/{id}

View Sample Response

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Create Poster

You can create posters via a POST request using the poster parameters specified below. Upon successful completion of a POST request you will receive a 200 http response code. To update an existing poster please see the Update Poster section below.

Request

POST /poster

Response

The response body will be the same format as the sample response below but for the newly created poster instead of an array of posters.
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Update Poster

You can update a poster via a PUT request using the poster parameters specified below. All fields included in an update request will be updated to the given value. Upon successful completion of a PUT request you will receive a 200 http response code.
To specify the poster to update please refer to Identifying Objects.

Request

PUT /poster/member/{id}

Response

The response body will be the same format as the sample response below but for the updated poster instead of an array of posters.
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Delete Poster

You can delete an poster via a DELETE request. To specify the poster to delete please refer to Identifying Objects.
Upon successful completion of a DELETE request you will receive a 200 http response code.

Request

DELETE /poster/delete/{id}

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Poster Parameters

* Poster descriptions must be well-formed XHTML.

Parameter Example Details
poster[item_attributes][display_value] Title of the poster Required
poster[item_attributes][article_attributes][content] Description of the poster Optional, XHTML*
poster[company_attributes][name] Institution, example: University of Denver Optional
poster[location_name] Location, example: Room 7 Optional
poster[group_names] Groups, example: Monday AM Session, Tuesday PM Session Optional, comma-separated
poster[event_client_ids] Events, example: [{:client_id => ‘123’, :api_auth_id => 1}, {:client_id => ‘1234’, :api_auth_id => 2}] Optional, provide an array of event client ids and their respective api_auth_ids. Note: api_auth_ids are optional.
client_id 1122334455 Optional

Tags

Tags are a comma-separated list of words and phrases that are used when users search for posters.

Parameter Example Details
poster[item_attributes][tags_list] social networking, conferences, communities Optional

Sample JSON Response

[
   {
      "id":1,
      "institution":"Future Technologies, Inc.",
      "title":"CAPTCHA Challenge Tradeoffs:  Familiarity of Strings versus Degradation of Images",
      "updated_on":"2014-05-01T21:46:17.000000Z",
      "content":"

It is a well documented fact that, for human readers, familiar text is more legible than unfamiliar text. nCurrent-generation computer vision systems also are able to exploit some kinds of prior knowledge of linguistic context: nfor example, many OCR systems can use known lexica (word-lists, such as of commonly occurring English words) to disambiguate ninterpretations. It is interesting that human readers can exploit various degrees of familiarity: for example, nstrings of characters which, while not found in dictionar- ies, are similar to spelled words: e.g. u201cpronounceableu201d strings, nor strings made up of frequently occurring character n-grams. In contrast to this, computer vision technologies for exploitingnsuch poorly characterized constraints (absent an explicit, complete lex- icon) are not yet well developed. This gap in ability nmay allow us to design stronger CAPTCHAs. We measure the familiarity of challenge strings generated by four methodsn(described by Bentley and Mallows) and we use the ScatterType CAPTCHA to degrade challenge images. nWe report the results of a human legibility trial which supports the hypothesis that more familiar strings are nindeed more legible in CAPTCHAs. Our measurements may enable engineering CAPTCHAs with a more uniform distribution of ndifficulty by balancing image degradations against familiarity.

n", "client_ids":[ "1234" ], "icon_id":11, "location":{ "id":375, "name":"Room 7" }, "tags":[ { "id":812993611, "tag_name":"Biometrics" }, { "id":649428831, "tag_name":"Biometrics>Face" }, { "id":952568152, "tag_name":"Biometrics>Face>Algorithms" } ], "event_ids":[ 445748296, 778788682 ], "groups":[ { "id":1, "name":"Monday AM Session", "color":"#FF3300", "position":1 } ] } ]

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