Download JAR files tagged by track with all dependencies

Cristal-ise is a description-driven software platform originally developed to track the construction of the CMS ECAL detector of the LHC at CERN. This is its core library, known as the kernel, which manages business objects called Items. Items are entirely configured from data, called descriptions, held in other Items. Every change of a state in an Item is a consequence of an execution of an activity in that Item's lifecycle, meaning that Cristal-ise applications are completely traceable, even in their design. It also supports extensive versioning of Item description data, giving the system a high level of flexibility.

Shakebug allows you to receive feedback from your beta testers or real users and improve the quality of your application in a simple way. Here users just need to shake their mobile and all the data regarding bugs & crashes can be seen by developers through their log in panel. It also helpful to analyse your users, session, location etc. Addition, you can add events on each action of your application and track it using this Shakebug framework. Sign up for a service at https://www.shakebug.com

scala-tptp-parser is a library for parsing the input languages of the TPTP infrastructure for knowledge representation and reasoning. The package contains a data structure for the abstract syntax tree (AST) of the parsed input as well as the parser for the different language of the TPTP, see http://tptp.org for details. In particular, parser are available for: - THF (TH0/TH1): Monomorphic and polymorphic higher-order logic, - TFF (TF0/TF1): Monomorphic and polymorphic typed first-order logic, including extended TFF (TFX), - FOF: Untyped first-order logic, - TCF: Typed clause-normal form, - CNF: (Untyped) clause-normal form, and - TPI: TPTP Process Instruction language. The parser was initially based on v7.4.0.3 of the TPTP syntax BNF (http://tptp.org/TPTP/SyntaxBNF.html), but is continuously updated to keep track of TPTP language updates.

A collection of filters for manipulating multi-instance data. Includes PropositionalToMultiInstance, MultiInstanceToPropositional, MILESFilter and RELAGGS. For more information see: M.-A. Krogel, S. Wrobel: Facets of Aggregation Approaches to Propositionalization. In: Work-in-Progress Track at the Thirteenth International Conference on Inductive Logic Programming (ILP), 2003. Y. Chen, J. Bi, J.Z. Wang (2006). MILES: Multiple-instance learning via embedded instance selection. IEEE PAMI. 28(12):1931-1947. James Foulds, Eibe Frank: Revisiting multiple-instance learning via embedded instance selection. In: 21st Australasian Joint Conference on Artificial Intelligence, 300-310, 2008.

Sirix is a temporal storage system effectively and efficiently storing snapshots of time varying (currently semi-structured) data taking full advantage of flash based drives as for instance SSDs. We not only provide several ways of navigating the tree-structure of a single revision, we also support navigation in time. Furthermore we provide a novel storage technique called sliding snapshot to circumvent intermitant full dump snapshots to fast track their in-memory reconstruction and thus we avoiding write peaks and having to read long chains of page fragments/increments/deltas. Sirix uses copy-on-write (COW) as well as an append-only storage making it an ideal candidate for flash based drives while not dropping support for erstwhile disks.

The Liberty Audit feature is used to report and track auditable events to ensure the integrity of your system. The Liberty Audit feature introduces an infrastructure which serves two purposes: - Confirming the effectiveness and integrity of the existing configuration - Identifying areas where improvement to the configuration may be needed The Liberty Audit feature has the ability to capture the following auditable events: - Basic authentication - Start and stop of the Audit service - Form login - Client certificate login - Servlet runAs delegation - Form logout - Servlet 3.0 login, logout and authenticate APIs The Liberty Audit feature supports the Cloud Auditing Data Federation (CADF) event model. The CADF model describes a data model and associated schema definitions for an audit event. The feature provides a default implementation, the AuditFileHandler, which emits human-readable audit records to a file-based log. Each audit record is emitted in JSON format.

JSONXML project is library used to parse/format tree-like object structures in most popular text formats: XML and JSON. For parsing it accepts "java.io.Reader" and return java object. For formatting it accepts java object and "java.io.Writer". Object is generally structure that contains Map and/or List elements. Map is ordered set of named items. List is set of unnamed items. Reflection may be used to convert objects into set of maps/lists and vice versa. JSON parser is implemented explicitly. XML parser is based on SAX parser and applies only certain rules for result. Library is designed to allow various entry points for variable decisions depending on end use needs. 1. Formats - formats are used to enable locale-specific parsing/formatting of numbers and dates. 2. ReflectiveBuilder - enables reflection. Default implementation uses getters/setters only. 3. ObjectsRegistry - used to keep track of parsed or formatted objects and allow resolvable references in formatted (text) form.

GroupDocs.Metadata for Java is a class library to process metadata associated with various Document, Image, CAD, Audio, Video and Archive formats. Key Features: - Covers most popular metadata standards: XMP, EXIF, IPTC, Image Resource Blocks, ID3, document properties - Covers most popular document formats: Microsoft Word, Microsoft Excel, Microsoft PowerPoint, PDF, Microsoft OneNote, Microsoft Visio, Open Document Format - Covers most popular image formats: BMP, GIF, DjVu, JPEG, PNG, TIFF, PSD, WebP, WMF, EMF, DICOM - Covers most popular email formats: Outlook Message, Email Message - Covers most popular audio formats: Mp3, WAV - Covers most popular video formats: Avi, Mov - Create, modify and remove metadata associated with supported document and image formats with a few lines of code - Manage EXIF metadata in Jpeg and TIFF formats - Manage XMP metadata in image and PDF formats - Manage Image Resource blocks in image formats - Manage audio metadata: ID3 tag (ID3v1, ID3v2), Lyrics3 tag, APE - Utilities to inspect and clean hidden metadata in document formats - Utilities to Search and Compare all metadata - Utilities to Export metadata to Excel/Csv - Metadata cleaner utility - MIME type detection - Read track changes. Accept or reject track changes - Read EXIF maker-notes: Sony, Nikon, Canon, Panasonic For more details on the library, please visit GroupDocs website at: https://products.groupdocs.com/metadata/Java Note: The library comes up with some limitations in the evaluation mode. In order to test full features of GroupDocs.Metadata for Java library, please request a free 30-day temporary license.

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!

pact-jvm-consumer-specs2 ======================== ## Specs2 Bindings for the pact-jvm library ## Dependency In the root folder of your project in build.sbt add the line: ```scala libraryDependencies += "au.com.dius.pact.consumer" %% "specs2" % "4.0.1" ``` or if you are using Gradle: ```groovy dependencies { testCompile "au.com.dius.pact.consumer:specs2_2.13:4.0.1" } ``` __*Note:*__ `PactSpec` requires spec2 3.x. Also, for spray users there's an incompatibility between specs2 v3.x and spray. Follow these instructions to resolve that problem: https://groups.google.com/forum/#!msg/spray-user/2T6SBp4OJeI/AJlnJuAKPRsJ ## Usage To author a test, mix `PactSpec` into your spec First we define a service client called `ConsumerService`. In our example this is a simple wrapper for `dispatch`, an HTTP client. The source code can be found in the test folder alongside the `ExamplePactSpec`. Here is a simple example: ``` import au.com.dius.pact.consumer.PactSpec class ExamplePactSpec extends Specification with PactSpec { val consumer = "My Consumer" val provider = "My Provider" override def is = uponReceiving("a request for foo") .matching(path = "/foo") .willRespondWith(body = "{}") .withConsumerTest { providerConfig => Await.result(ConsumerService(providerConfig.url).simpleGet("/foo"), Duration(1000, MILLISECONDS)) must beEqualTo(200, Some("{}")) } } ``` This spec will be run along with the rest of your specs2 unit tests and will output your pact json to ``` /target/pacts/<Consumer>_<Provider>.json ``` # Forcing pact files to be overwritten (3.6.5+) By default, when the pact file is written, it will be merged with any existing pact file. To force the file to be overwritten, set the Java system property `pact.writer.overwrite` to `true`. # Test Analytics We are tracking anonymous analytics to gather important usage statistics like JVM version and operating system. To disable tracking, set the 'pact_do_not_track' system property or environment variable to 'true'.