Alexa is Amazon’s voice service and the brain behind tens of millions of devices like the Echo family of devices including
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The Alexa Skills Kit (ASK) is a collection of self-service APIs, tools, documentation, and code samples that makes it fast and easy for you to add skills to Alexa. ASK enables designers, developers, and brands to build engaging skills and reach customers through tens of millions of Alexa-enabled devices. With ASK, you can leverage Amazon’s knowledge and pioneering work in the field of voice design.
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NGSIv2 implements a pagination mechanism (which by the way is very similar to the one implemented in NGSIv1) in order to help clients to retrieve large sets of resources. This mechanism works for all listing operations in the API (e.g.
The mechanism is based on three URI parameters:
limit , in order to specify the maximum number of elements (default is 20, maximun allowed is 1000).limit
offset , in order to skip a given number of elements at the beginning (default is 0)offset
), if activated then a
header is added to the response, with a count of total elements.
By default, results are returned ordered by increasing creation time. In the case of entities query, this can be changed with the
Let's illustrate with an example: a given client cannot process more than 100 results in a single response and the query includes a total of 322 results. The client could do the following (only the URL is included, for the sake of completeness).
Note that if the request uses an offset beyond the total number of results, an empty list is returned, as shown below:
In order to help clients organize query and discovery requests with a large number of responses (for example, think of how costly could be returning a query matching 1,000,000 results in a single HTTP response to a queryContext request), queryContext (and related convenience operations ) and discoverContextAvailability (and related convenience operations ) allow pagination. The mechanism is based on three URI parameters:
limit , in order to specify the maximum number of entities or context registrations (for queryContext and discoverContextAvailability respectively) (default is 20, maximun allowed is 1000).
details (allowed values are “on” and “off”, default is “off”), in order to get a global errorCode for the response including a count of total elements (in the case of using “on”). Note that using details set to “on” slightly breaks NGSI standard, which states that global errorCode must be used only in the case of general error with the request. However, we think it is very useful for a client to know in advance how many results in total the query has (and if you want to keep strict with NGSI, you can simply ignore the details parameter :)
Sprout is a transport protocol for interactive applications that desire high throughput and low delay.
In evaluations on traces from commercial LTE and 3G networks, Sprout achieved 2–4 times the throughput of Skype, Apple Facetime, and Google Hangout, while reducing self-inflicted delay by 7–9 times .2–4 times 7–9 times
— Keith Winstein, Anirudh Sivaraman, and Hari Balakrishnan
Research paper » Tech video » Source code »
Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks , in the proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 2013), Lombard, Ill., April 2013.
Slides and a public summary are available on the open-access conference proceedings site .
The talk was given April 5, 2013.
The source code to Sprout (evaluated in the research paper) is available on GitHub.
Source code to Sprout Tunnel (allows TCP to be tunneled within Sprout) available on GitHub
Our "explicit-state" MPEG-2 implementation includes an encoder API (based on mpeg2enc) and a decoder API (built around libmpeg2) .
Our code for saturating wireless networks and then replaying those traces on a hardware gateway is also available on GitHub.
Our implementation of the "CoDel" AQM algorithm is available in a different repository.
The page describes a
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port of our trace driven emulator, cellsim, used as part of a problem set in MIT's graduate networking class (6.829).
Cellular network traces were collected while driving in Boston, Mass., and surrounding municipalities. We simultaneously saturated the uplink and downlink of an Android smartphone tethered to a Linux laptop and measured the times when packets arrived across each link. Uplink datagrams were sent to a server at MIT, generally less than 50 ms RTT from the carrier's first/last hop.
The code for the saturatr can be found on GitHub.
The Verizon LTE and 1xEV-DO (3G) traces used a Samsung Galaxy Nexus smartphone running Android Ice Cream Sandwich. The ATT trace used a Samsung Galaxy S3 smartphone. The T-Mobile trace used a Nexus S smartphone running Android Jelly Bean.
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