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JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

JUNG the Java Universal Network/Graph Framework--is a software library that provides a common and extendible language for the modeling, analysis, and visualization of data that can be represented as a graph or network. It is written in Java, which allows JUNG-based applications to make use of the extensive built-in capabilities of the Java API, as well as those of other existing third-party Java libraries. The JUNG architecture is designed to support a variety of representations of entities and their relations, such as directed and undirected graphs, multi-modal graphs, graphs with parallel edges, and hypergraphs. It provides a mechanism for annotating graphs, entities, and relations with metadata. This facilitates the creation of analytic tools for complex data sets that can examine the relations between entities as well as the metadata attached to each entity and relation. The current distribution of JUNG includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.). JUNG also provides a visualization framework that makes it easy to construct tools for the interactive exploration of network data. Users can use one of the layout algorithms provided, or use the framework to create their own custom layouts. In addition, filtering mechanisms are provided which allow users to focus their attention, or their algorithms, on specific portions of the graph.

The Video SDKs are app development kits provided to enrich apps with video collaboration features to connect customers and communities. Use these SDKs to build apps with highly customized user interfaces along with access to raw video and audio data. Video SDKs are designed to be: * Easy to use: Import libraries, call required functions, and all video conferencing will be handled for you. * Lightweight: Video SDKs are streamlined toolkits with an enormous reduction in size compared to Meeting SDKs with all the power of Zoom's video and audio solutions. * Highly customizable: Raw video and audio data is available to you, allowing your chosen renderer to customize the video experience. Video sessions created by the Video SDKs are launched instantly, bringing a delightful video communication experience to your users with high-quality video and audio. Direct access to raw video and audio data allows improved interaction between users and the app video stream. Imagine a gaming video streaming app with direct interaction between the player and viewers based on in-game events or prompts from the community. Or, imagine an AR streaming platform with direct viewer access to the on-screen video. As with our Meeting SDKs, Video SDKs allow screen-sharing from devices, in-session chat messages, and high-quality video and audio streams similar to Zoom's core capabilities. The Video SDKs enable the following functionality in your app: * Launch a video communication session instantly * Share screen directly from your device * Send instant chat messages during the session * Capture and review raw data locally * Test different rendering schema and enjoy high-quality video and audio streams * Broadcast the video session to third-party live streaming providers To know more, see: https://developers.zoom.us/docs/video-sdk/

The Video SDKs are app development kits provided to enrich apps with video collaboration features to connect customers and communities. Use these SDKs to build apps with highly customized user interfaces along with access to raw video and audio data. Video SDKs are designed to be: * Easy to use: Import libraries, call required functions, and all video conferencing will be handled for you. * Lightweight: Video SDKs are streamlined toolkits with an enormous reduction in size compared to Meeting SDKs with all the power of Zoom's video and audio solutions. * Highly customizable: Raw video and audio data is available to you, allowing your chosen renderer to customize the video experience. Video sessions created by the Video SDKs are launched instantly, bringing a delightful video communication experience to your users with high-quality video and audio. Direct access to raw video and audio data allows improved interaction between users and the app video stream. Imagine a gaming video streaming app with direct interaction between the player and viewers based on in-game events or prompts from the community. Or, imagine an AR streaming platform with direct viewer access to the on-screen video. As with our Meeting SDKs, Video SDKs allow screen-sharing from devices, in-session chat messages, and high-quality video and audio streams similar to Zoom's core capabilities. The Video SDKs enable the following functionality in your app: * Launch a video communication session instantly * Share screen directly from your device * Send instant chat messages during the session * Capture and review raw data locally * Test different rendering schema and enjoy high-quality video and audio streams * Broadcast the video session to third-party live streaming providers To know more, see: https://developers.zoom.us/docs/video-sdk/

We have deprecated ZoomVideoSDK maven repository. Since Video SDK features have been modularized, we are deprecating this repository and publishing new releases into the following repositories. See the feature libraries documentation for details. ZoomVideoSDK Core — core Video SDK features: https://mvnrepository.com/artifact/us.zoom.videosdk/zoomvideosdk-core ZoomVideoSDK Annotation — Screen share annotation feature: https://mvnrepository.com/artifact/us.zoom.videosdk/zoomvideosdk-annotation ZoomVideoSDK Videoeffects — Virtual background feature: https://mvnrepository.com/artifact/us.zoom.videosdk/zoomvideosdk-videoeffects The Video SDKs are app development kits provided to enrich apps with video collaboration features to connect customers and communities. Use these SDKs to build apps with highly customized user interfaces along with access to raw video and audio data. Video SDKs are designed to be: * Easy to use: Import libraries, call required functions, and all video conferencing will be handled for you. * Lightweight: Video SDKs are streamlined toolkits with an enormous reduction in size compared to Meeting SDKs with all the power of Zoom's video and audio solutions. * Highly customizable: Raw video and audio data is available to you, allowing your chosen renderer to customize the video experience. Video sessions created by the Video SDKs are launched instantly, bringing a delightful video communication experience to your users with high-quality video and audio. Direct access to raw video and audio data allows improved interaction between users and the app video stream. Imagine a gaming video streaming app with direct interaction between the player and viewers based on in-game events or prompts from the community. Or, imagine an AR streaming platform with direct viewer access to the on-screen video. As with our Meeting SDKs, Video SDKs allow screen-sharing from devices, in-session chat messages, and high-quality video and audio streams similar to Zoom's core capabilities. The Video SDKs enable the following functionality in your app: * Launch a video communication session instantly * Share screen directly from your device * Send instant chat messages during the session * Capture and review raw data locally * Test different rendering schema and enjoy high-quality video and audio streams * Broadcast the video session to third-party live streaming providers To know more, see: https://developers.zoom.us/docs/video-sdk/

Chips-n-Salsa is a Java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. The library includes implementations of several stochastic local search algorithms, including simulated annealing, hill climbers, as well as constructive search algorithms such as stochastic sampling. Chips-n-Salsa now also includes genetic algorithms as well as evolutionary algorithms more generally. The library very extensively supports simulated annealing. It includes several classes for representing solutions to a variety of optimization problems. For example, the library includes a BitVector class that implements vectors of bits, as well as classes for representing solutions to problems where we are searching for an optimal vector of integers or reals. For each of the built-in representations, the library provides the most common mutation operators for generating random neighbors of candidate solutions, as well as common crossover operators for use with evolutionary algorithms. Additionally, the library provides extensive support for permutation optimization problems, including implementations of many different mutation operators for permutations, and utilizing the efficiently implemented Permutation class of the JavaPermutationTools (JPT) library. Chips-n-Salsa is customizable, making extensive use of Java's generic types, enabling using the library to optimize other types of representations beyond what is provided in the library. It is hybridizable, providing support for integrating multiple forms of local search (e.g., using a hill climber on a solution generated by simulated annealing), creating hybrid mutation operators (e.g., local search using multiple mutation operators), as well as support for running more than one type of search for the same problem concurrently using multiple threads as a form of algorithm portfolio. Chips-n-Salsa is iterative, with support for multistart metaheuristics, including implementations of several restart schedules for varying the run lengths across the restarts. It also supports parallel execution of multiple instances of the same, or different, stochastic local search algorithms for an instance of a problem to accelerate the search process. The library supports self-adaptive search in a variety of ways, such as including implementations of adaptive annealing schedules for simulated annealing, such as the Modified Lam schedule, implementations of the simpler annealing schedules but which self-tune the initial temperature and other parameters, and restart schedules that adapt to run length.

Apache Commons Crypto is a cryptographic library optimized with AES-NI (Advanced Encryption Standard New Instructions). It provides Java API for both cipher level and Java stream level. Developers can use it to implement high performance AES encryption/decryption with the minimum code and effort. Please note that Crypto doesn't implement the cryptographic algorithm such as AES directly. It wraps to OpenSSL or JCE which implement the algorithms. Features -------- 1. Cipher API for low level cryptographic operations. 2. Java stream API (CryptoInputStream/CryptoOutputStream) for high level stream encryption/decryption. 3. Both optimized with high performance AES encryption/decryption. (1400 MB/s - 1700 MB/s throughput in modern Xeon processors). 4. JNI-based implementation to achieve comparable performance to the native C/C++ version based on OpenSsl. 5. Portable across various operating systems (currently only Linux/MacOSX/Windows); Apache Commons Crypto loads the library according to your machine environment (it checks system properties, `os.name` and `os.arch`). 6. Simple usage. Add the commons-crypto-(version).jar file to your classpath. Export restrictions ------------------- This distribution includes cryptographic software. The country in which you currently reside may have restrictions on the import, possession, use, and/or re-export to another country, of encryption software. BEFORE using any encryption software, please check your country's laws, regulations and policies concerning the import, possession, or use, and re-export of encryption software, to see if this is permitted. See <http://www.wassenaar.org/> for more information. The U.S. Government Department of Commerce, Bureau of Industry and Security (BIS), has classified this software as Export Commodity Control Number (ECCN) 5D002.C.1, which includes information security software using or performing cryptographic functions with asymmetric algorithms. The form and manner of this Apache Software Foundation distribution makes it eligible for export under the License Exception ENC Technology Software Unrestricted (TSU) exception (see the BIS Export Administration Regulations, Section 740.13) for both object code and source code. The following provides more details on the included cryptographic software: * Commons Crypto use [Java Cryptography Extension](http://docs.oracle.com/javase/8/docs/technotes/guides/security/crypto/CryptoSpec.html) provided by Java * Commons Crypto link to and use [OpenSSL](https://www.openssl.org/) ciphers

# pact-jvm-consumer-java8 Provides a Java8 lambda based DSL for use with Junit to build consumer tests. # A Lambda DSL for Pact This is an extension for the pact DSL provided by [pact-jvm-consumer](../pact-jvm-consumer). The difference between the default pact DSL and this lambda DSL is, as the name suggests, the usage of lambdas. The use of lambdas makes the code much cleaner. ## Why a new DSL implementation? The lambda DSL solves the following two main issues. Both are visible in the following code sample: ```java new PactDslJsonArray() .array() # open an array .stringValue(&quot;a1&quot;) # choose the method that is valid for arrays .stringValue(&quot;a2&quot;) # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .numberValue(1) # choose the method that is valid for arrays .numberValue(2) # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .object() # now we work with an object .stringValue(&quot;foo&quot;, &quot;Foo&quot;) # choose the method that is valid for objects .closeObject() # close the object and we&apos;re back in the array .closeArray() # close the array ``` ### The existing DSL is quite error-prone Methods may only be called in certain states. For example `object()` may only be called when you&apos;re currently working on an array whereas `object(name)` is only allowed to be called when working on an object. But both of the methods are available. You&apos;ll find out at runtime if you&apos;re using the correct method. Finally, the need for opening and closing objects and arrays makes usage cumbersome. The lambda DSL has no ambiguous methods and there&apos;s no need to close objects and arrays as all the work on such an object is wrapped in a lamda call. ### The existing DSL is hard to read When formatting your source code with an IDE the code becomes hard to read as there&apos;s no indentation possible. Of course, you could do it by hand but we want auto formatting! Auto formatting works great for the new DSL! ```java array.object((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); # an attribute o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); # an attribute o.object(&quot;tar&quot;, (tarObject) -&gt; { # an attribute with a nested object tarObject.stringValue(&quot;a&quot;, &quot;A&quot;); # attribute of the nested object tarObject.stringValue(&quot;b&quot;, &quot;B&quot;); # attribute of the nested object }) }); ``` ## Installation ### Maven ``` &lt;dependency&gt; &lt;groupId&gt;au.com.dius&lt;/groupId&gt; &lt;artifactId&gt;pact-jvm-consumer-java8_2.12&lt;/artifactId&gt; &lt;version&gt;${pact.version}&lt;/version&gt; &lt;/dependency&gt; ``` ## Usage Start with a static import of `LambdaDsl`. This class contains factory methods for the lambda dsl extension. When you come accross the `body()` method of `PactDslWithProvider` builder start using the new extensions. The call to `LambdaDsl` replaces the call to instance `new PactDslJsonArray()` and `new PactDslJsonBody()` of the pact library. ```java io.pactfoundation.consumer.dsl.LambdaDsl.* ``` ### Response body as json array ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonArray; ... PactDslWithProvider builder = ... builder.given(&quot;some state&quot;) .uponReceiving(&quot;a request&quot;) .path(&quot;/my-app/my-service&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(newJsonArray((a) -&gt; { a.stringValue(&quot;a1&quot;); a.stringValue(&quot;a2&quot;); }).build()); ``` ### Response body as json object ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonBody; ... PactDslWithProvider builder = ... builder.given(&quot;some state&quot;) .uponReceiving(&quot;a request&quot;) .path(&quot;/my-app/my-service&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(newJsonBody((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); }).build()); ``` ### Examples #### Simple Json object When creating simple json structures the difference between the two approaches isn&apos;t big. ##### JSON ```json { &quot;bar&quot;: &quot;Bar&quot;, &quot;foo&quot;: &quot;Foo&quot; } ``` ##### Pact DSL ```java new PactDslJsonBody() .stringValue(&quot;foo&quot;, &quot;Foo&quot;) .stringValue(&quot;bar&quot;, &quot;Bar&quot;) ``` ##### Lambda DSL ```java newJsonBody((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); }).build(); ``` #### An array of arrays When we come to more complex constructs with arrays and nested objects the beauty of lambdas become visible! ##### JSON ```json [ [&quot;a1&quot;, &quot;a2&quot;], [1, 2], [{&quot;foo&quot;: &quot;Foo&quot;}] ] ``` ##### Pact DSL ```java new PactDslJsonArray() .array() .stringValue(&quot;a1&quot;) .stringValue(&quot;a2&quot;) .closeArray() .array() .numberValue(1) .numberValue(2) .closeArray() .array() .object() .stringValue(&quot;foo&quot;, &quot;Foo&quot;) .closeObject() .closeArray(); ``` ##### Lambda DSL ```java newJsonArray((rootArray) -&gt; { rootArray.array((a) -&gt; a.stringValue(&quot;a1&quot;).stringValue(&quot;a2&quot;)); rootArray.array((a) -&gt; a.numberValue(1).numberValue(2)); rootArray.array((a) -&gt; a.object((o) -&gt; o.stringValue(&quot;foo&quot;, &quot;Foo&quot;))); }).build(); ``` `object` is a reserved word in Kotlin. To allow using the DSL without escaping, a Kotlin extension `newObject` is available: ```kotlin newJsonArray { rootArray -&gt; rootArray.array { a -&gt; a.stringValue(&quot;a1&quot;).stringValue(&quot;a2&quot;) } rootArray.array { a -&gt; a.numberValue(1).numberValue(2) } rootArray.array { a -&gt; a.newObject { o -&gt; o.stringValue(&quot;foo&quot;, &quot;Foo&quot;) } } }.build(); ```

# pact-jvm-consumer-java8 Provides a Java8 lambda based DSL for use with Junit to build consumer tests. # A Lambda DSL for Pact This is an extension for the pact DSL provided by [pact-jvm-consumer](../pact-jvm-consumer). The difference between the default pact DSL and this lambda DSL is, as the name suggests, the usage of lambdas. The use of lambdas makes the code much cleaner. ## Why a new DSL implementation? The lambda DSL solves the following two main issues. Both are visible in the following code sample: ```java new PactDslJsonArray() .array() # open an array .stringValue(&quot;a1&quot;) # choose the method that is valid for arrays .stringValue(&quot;a2&quot;) # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .numberValue(1) # choose the method that is valid for arrays .numberValue(2) # choose the method that is valid for arrays .closeArray() # close the array .array() # open an array .object() # now we work with an object .stringValue(&quot;foo&quot;, &quot;Foo&quot;) # choose the method that is valid for objects .closeObject() # close the object and we&apos;re back in the array .closeArray() # close the array ``` ### The existing DSL is quite error-prone Methods may only be called in certain states. For example `object()` may only be called when you&apos;re currently working on an array whereas `object(name)` is only allowed to be called when working on an object. But both of the methods are available. You&apos;ll find out at runtime if you&apos;re using the correct method. Finally, the need for opening and closing objects and arrays makes usage cumbersome. The lambda DSL has no ambiguous methods and there&apos;s no need to close objects and arrays as all the work on such an object is wrapped in a lamda call. ### The existing DSL is hard to read When formatting your source code with an IDE the code becomes hard to read as there&apos;s no indentation possible. Of course, you could do it by hand but we want auto formatting! Auto formatting works great for the new DSL! ```java array.object((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); # an attribute o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); # an attribute o.object(&quot;tar&quot;, (tarObject) -&gt; { # an attribute with a nested object tarObject.stringValue(&quot;a&quot;, &quot;A&quot;); # attribute of the nested object tarObject.stringValue(&quot;b&quot;, &quot;B&quot;); # attribute of the nested object }) }); ``` ## Installation ### Maven ``` &lt;dependency&gt; &lt;groupId&gt;au.com.dius&lt;/groupId&gt; &lt;artifactId&gt;pact-jvm-consumer-java8_2.12&lt;/artifactId&gt; &lt;version&gt;${pact.version}&lt;/version&gt; &lt;/dependency&gt; ``` ## Usage Start with a static import of `LambdaDsl`. This class contains factory methods for the lambda dsl extension. When you come accross the `body()` method of `PactDslWithProvider` builder start using the new extensions. The call to `LambdaDsl` replaces the call to instance `new PactDslJsonArray()` and `new PactDslJsonBody()` of the pact library. ```java io.pactfoundation.consumer.dsl.LambdaDsl.* ``` ### Response body as json array ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonArray; ... PactDslWithProvider builder = ... builder.given(&quot;some state&quot;) .uponReceiving(&quot;a request&quot;) .path(&quot;/my-app/my-service&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(newJsonArray((a) -&gt; { a.stringValue(&quot;a1&quot;); a.stringValue(&quot;a2&quot;); }).build()); ``` ### Response body as json object ```java import static io.pactfoundation.consumer.dsl.LambdaDsl.newJsonBody; ... PactDslWithProvider builder = ... builder.given(&quot;some state&quot;) .uponReceiving(&quot;a request&quot;) .path(&quot;/my-app/my-service&quot;) .method(&quot;GET&quot;) .willRespondWith() .status(200) .body(newJsonBody((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); }).build()); ``` ### Examples #### Simple Json object When creating simple json structures the difference between the two approaches isn&apos;t big. ##### JSON ```json { &quot;bar&quot;: &quot;Bar&quot;, &quot;foo&quot;: &quot;Foo&quot; } ``` ##### Pact DSL ```java new PactDslJsonBody() .stringValue(&quot;foo&quot;, &quot;Foo&quot;) .stringValue(&quot;bar&quot;, &quot;Bar&quot;) ``` ##### Lambda DSL ```java newJsonBody((o) -&gt; { o.stringValue(&quot;foo&quot;, &quot;Foo&quot;); o.stringValue(&quot;bar&quot;, &quot;Bar&quot;); }).build(); ``` #### An array of arrays When we come to more complex constructs with arrays and nested objects the beauty of lambdas become visible! ##### JSON ```json [ [&quot;a1&quot;, &quot;a2&quot;], [1, 2], [{&quot;foo&quot;: &quot;Foo&quot;}] ] ``` ##### Pact DSL ```java new PactDslJsonArray() .array() .stringValue(&quot;a1&quot;) .stringValue(&quot;a2&quot;) .closeArray() .array() .numberValue(1) .numberValue(2) .closeArray() .array() .object() .stringValue(&quot;foo&quot;, &quot;Foo&quot;) .closeObject() .closeArray(); ``` ##### Lambda DSL ```java newJsonArray((rootArray) -&gt; { rootArray.array((a) -&gt; a.stringValue(&quot;a1&quot;).stringValue(&quot;a2&quot;)); rootArray.array((a) -&gt; a.numberValue(1).numberValue(2)); rootArray.array((a) -&gt; a.object((o) -&gt; o.stringValue(&quot;foo&quot;, &quot;Foo&quot;))); }).build(); ``` ##### Kotlin Lambda DSL ```kotlin newJsonArray { newArray { stringValue(&quot;a1&quot;) stringValue(&quot;a2&quot;) } newArray { numberValue(1) numberValue(2) } newArray { newObject { stringValue(&quot;foo&quot;, &quot;Foo&quot;) } } } ```