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The Source Analyst library is a powerful tool designed to streamline and expedite the tracking of traffic sources for mobile applications. This versatile library is aptly named "Source Analyst" and is an invaluable asset for app developers and marketers seeking to gain deeper insights into the performance of their advertising campaigns. With just one simple function call, Source Analyst empowers you to efficiently investigate the effectiveness of various advertising sources. Key Features: Effortless Tracking: Source Analyst simplifies the complex task of tracking the origins of traffic for your mobile app. No need for convoluted setups or extensive coding – one function is all it takes. Comprehensive Insights: Gain a comprehensive understanding of where your app's users are coming from. Whether it's through social media, search engines, referral links, or other channels, Source Analyst provides you with clear data on traffic sources. Performance Evaluation: Evaluate the performance of your advertising campaigns with precision. Discover which sources are driving the most valuable users to your app, helping you optimize your marketing efforts effectively. Time-Saving: Say goodbye to hours spent on manual data collection and analysis. Source Analyst automates the tracking process, freeing up your time to focus on making data-driven decisions. Customization: Tailor Source Analyst to your specific needs. Customize the library to track the metrics that matter most to your app's success. Real-time Data: Access real-time data, ensuring that you always have up-to-date insights into your traffic sources. Integration-Friendly: Seamlessly integrate Source Analyst into your existing mobile app infrastructure, whether you're developing for Android or iOS. User-Friendly: Source Analyst is designed with user-friendliness in mind. Its intuitive interface and straightforward documentation make it accessible to developers of all levels of expertise. How It Works: Using Source Analyst is as easy as calling a single function within your code. Simply integrate the library into your app, and you can begin tracking traffic sources immediately. From there, Source Analyst compiles and presents the data in a clear and organized manner, allowing you to make data-driven decisions with ease. In a world where understanding the origins of your app's traffic is essential for marketing success, Source Analyst is the go-to solution. Say goodbye to the complexity of tracking sources and embrace the simplicity and effectiveness of Source Analyst for your mobile app. Harness the power of Source Analyst and unlock a new level of insight into your app's performance today!

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

Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classification and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more. Currently Mahout supports mainly four use cases: Recommendation mining takes users' behavior and from that tries to find items users might like. Clustering takes e.g. text documents and groups them into groups of topically related documents. Classification learns from existing categorized documents what documents of a specific category look like and is able to assign unlabelled documents to the (hopefully) correct category. Frequent itemset mining takes a set of item groups (terms in a query session, shopping cart content) and identifies, which individual items usually appear together.

# Pact Spring/JUnit5 Support This module extends the base [Pact JUnit5 module](../pact-jvm-provider-junit5). See that for more details. For writing Spring Pact verification tests with JUnit 5, there is an JUnit 5 Invocation Context Provider that you can use with the `@TestTemplate` annotation. This will generate a test for each interaction found for the pact files for the provider. To use it, add the `@Provider` and `@ExtendWith(SpringExtension.class)` and one of the pact source annotations to your test class (as per a JUnit 5 test), then add a method annotated with `@TestTemplate` and `@ExtendWith(PactVerificationSpringProvider.class)` that takes a `PactVerificationContext` parameter. You will need to call `verifyInteraction()` on the context parameter in your test template method. For example: ```java @ExtendWith(SpringExtension.class) @SpringBootTest(webEnvironment = SpringBootTest.WebEnvironment.DEFINED_PORT) @Provider(&quot;Animal Profile Service&quot;) @PactBroker public class ContractVerificationTest { @TestTemplate @ExtendWith(PactVerificationSpringProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } } ``` You will now be able to setup all the required properties using the Spring context, e.g. creating an application YAML file in the test resources: ```yaml pactbroker: host: your.broker.host auth: username: broker-user password: broker.password ``` You can also run pact tests against `MockMvc` without need to spin up the whole application context which takes time and often requires more additional setup (e.g. database). In order to run lightweight tests just use `@WebMvcTest` from Spring and `MockMvcTestTarget` as a test target before each test. For example: ```java @WebMvcTest @Provider(&quot;myAwesomeService&quot;) @PactBroker class ContractVerificationTest { @Autowired private MockMvc mockMvc; @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } @BeforeEach void before(PactVerificationContext context) { context.setTarget(new MockMvcTestTarget(mockMvc)); } } ``` You can also use `MockMvcTestTarget` for tests without spring context by providing the controllers manually. For example: ```java @Provider(&quot;myAwesomeService&quot;) @PactFolder(&quot;pacts&quot;) class MockMvcTestTargetStandaloneMockMvcTestJava { @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } @BeforeEach void before(PactVerificationContext context) { MockMvcTestTarget testTarget = new MockMvcTestTarget(); testTarget.setControllers(new DataResource()); context.setTarget(testTarget); } @RestController static class DataResource { @GetMapping(&quot;/data&quot;) @ResponseStatus(HttpStatus.NO_CONTENT) void getData(@RequestParam(&quot;ticketId&quot;) String ticketId) { } } } ``` **Important:** Since `@WebMvcTest` starts only Spring MVC components you can&apos;t use `PactVerificationSpringProvider` and need to fallback to `PactVerificationInvocationContextProvider`

Jsgen is a Java Source Generation Framework: That means, it should be a valuable tool, if you intend to write a custom generator for Java sources. As such, it is the successor of a previous framework, called JaxMeJS (http://jaxme.sourceforge.net/JaxMeJS/docs/index.html). The predecessor came into being as a standalone project. It was incorporated into the bigger JaxMe project, when the latter was adopted by the Apache Webservices project. And it was buried as part of the bigger project, when the latter was moved to the Apache Attic (http://svn.apache.org/repos/asf/webservices/archive/jaxme/). That was fine for quite some time, because the latest released version (JaxMeJS 0.5.2) did its job quite well. Over the years, however, the Java language has evolved, and the lack of support for features like Generics, or Annotations, became a burden. Hence the Successor: Jsgen picks up, where JaxMeJS ended. It is, however, a complete rewrite with several additional features, that the author considers to be important for modern Java applications: 1. It supports Generics. 2. It supports Annotations. 3. The builder pattern has been adopted. Almost all important classes are implemented as builders. This should make writing the actual source generators much more concise, and maintainable, than it used to be before. 4. The code style is configurable. Code styles allow you to concentrate on the actual work. The resulting Jave source will look nicely formatted, anyways. As of this writing, you can select between two builtin code styles: - The default code style is basically the authors personal free style, roughly comparable to the default code style of the Eclipse Java IDE. - As an alternative, there is also a Maven code style, which is widely used in the Open Source communities. Compared to the default style, it is less concise, if not even a bit verbose. On the other hand, it is widely adopted by projects in the vicinity of {{{https://maven.apache.org}Apache Maven}}. 5. Import lists are created, and sorted, automatically.

HockeySDK-Android implements support for using HockeyApp in your Android application. The following features are currently supported: Collect crash reports:If your app crashes, a crash log is written to the device's storage. If the user starts the app again, they will be asked asked to submit the crash report to HockeyApp. This works for both beta and live apps, i.e. those submitted to Google Play or other app stores. Crash logs contain viable information for you to help resolve the issue. Furthermore, you as a developer can add additional information to the report as well. Update Alpha/Beta apps: The app will check with HockeyApp if a new version for your alpha/beta build is available. If yes, it will show a dialog to users and let them see the release notes, the version history and start the installation process right away. You can even force the installation of certain updates. User Metrics: Understand user behavior to improve your app. Track usage through daily and monthly active users. Monitor crash impacted users. Measure customer engagement through session count. Add custom tracking calls to learn which features your users are actually using. This feature requires a minimum API level of 14 (Android 4.x Ice Cream Sandwich). Feedback: Besides crash reports, collecting feedback from your users from within your app is a great option to help with improving your app. You act on and answer feedback directly from the HockeyApp backend. Authenticate: Identify and authenticate users against your registered testers with the HockeyApp backend.

# Pact Junit 5 Extension ## Overview For writing Pact verification tests with JUnit 5, there is an JUnit 5 Invocation Context Provider that you can use with the `@TestTemplate` annotation. This will generate a test for each interaction found for the pact files for the provider. To use it, add the `@Provider` and one of the pact source annotations to your test class (as per a JUnit 4 test), then add a method annotated with `@TestTemplate` and `@ExtendWith(PactVerificationInvocationContextProvider.class)` that takes a `PactVerificationContext` parameter. You will need to call `verifyInteraction()` on the context parameter in your test template method. For example: ```java @Provider(&quot;myAwesomeService&quot;) @PactFolder(&quot;pacts&quot;) public class ContractVerificationTest { @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void pactVerificationTestTemplate(PactVerificationContext context) { context.verifyInteraction(); } } ``` For details on the provider and pact source annotations, refer to the [Pact junit runner](../pact-jvm-provider-junit/README.md) docs. ## Test target You can set the test target (the object that defines the target of the test, which should point to your provider) on the `PactVerificationContext`, but you need to do this in a before test method (annotated with `@BeforeEach`). There are three different test targets you can use: `HttpTestTarget`, `HttpsTestTarget` and `AmpqTestTarget`. For example: ```java @BeforeEach void before(PactVerificationContext context) { context.setTarget(HttpTestTarget.fromUrl(new URL(myProviderUrl))); // or something like // context.setTarget(new HttpTestTarget(&quot;localhost&quot;, myProviderPort, &quot;/&quot;)); } ``` ## Provider State Methods Provider State Methods work in the same way as with JUnit 4 tests, refer to the [Pact junit runner](../pact-jvm-provider-junit/README.md) docs. ## Modifying the requests before they are sent **Important Note:** You should only use this feature for things that can not be persisted in the pact file. By modifying the request, you are potentially modifying the contract from the consumer tests! Sometimes you may need to add things to the requests that can&apos;t be persisted in a pact file. Examples of these would be authentication tokens, which have a small life span. The Http and Https test targets support injecting the request that will executed into the test template method. You can then add things to the request before calling the `verifyInteraction()` method. For example to add a header: ```java @TestTemplate @ExtendWith(PactVerificationInvocationContextProvider.class) void testTemplate(PactVerificationContext context, HttpRequest request) { // This will add a header to the request request.addHeader(&quot;X-Auth-Token&quot;, &quot;1234&quot;); context.verifyInteraction(); } ``` ## Objects that can be injected into the test methods You can inject the following objects into your test methods (just like the `PactVerificationContext`). They will be null if injected before the supported phase. | Object | Can be injected from phase | Description | | ------ | --------------- | ----------- | | PactVerificationContext | @BeforeEach | The context to use to execute the interaction test | | Pact | any | The Pact model for the test | | Interaction | any | The Interaction model for the test | | HttpRequest | @TestTemplate | The request that is going to be executed (only for HTTP and HTTPS targets) | | ProviderVerifier | @TestTemplate | The verifier instance that is used to verify the interaction |

The OODT grid services (product and profile services) use CORBA or RMI as their underlying network transport. However, limitations of CORBA and RMI make them inappropriate for large-scale deployments. For one, both are procedural mechanisms, providing a remote interface that resembles a method call. This makes streaming of data from a service impossible, because there are limitations to the sizes of data structures that can be passed over a remote method call. Instead, repeated calls must be made to retrieve each block of a product, making transfer speeds horribly slow compared to HTTP or FTP. (Block-based retrieval of profiles was never implemented, resulting in out of memory conditions for large profile results, which is another problem.) Second, both CORBA and RMI rely on a central name registry. The registry makes an object independent of its network location, enabling a client to call it by name (looking up its last known location in the registry). However, this requires that server objects be able to make outbound network calls to the registry (through any outbound firewall), and that the registry accept those registrations (through any inbound firewall). This required administrative action at institutions hosting server objects and at the institution hosting the registry. Often, these firewall exceptions would change without notice as system adminstrators changed at each location (apparently firewall exceptions are poorly documented everywhere). Further, in the two major deployments of OODT (PDS and EDRN), server objects have almost never moved, nullifying any benefit of the registry. This project, OODT Web Grid Services, avoids the prolems of CORBA and RMI by using HTTP as the transport mechanism for products and profiles. Further, it provides a password-protected mechanism to add new sets of product and profile query handlers, enabling seamless activation of additional capabilities.