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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.

This module provides access to the set of utility classes required to interact with an instance of an AppWorks Gateway from an AppWorks Service. It covers AppWorks dedicated service deployment API, which is a limited subset of the full AppWorks admin API. We currently provide the facility for services to listen to service life-cycle events such as 'service installed', 'service upgraded' and 'service uninstalled'. We also provide the facility for centralised configuration setting management. As AppWorks services are edited from the AppWork Gateway administration console, we provide the ability to respond to updates made here, and also expose creation and update end points for these settings.

A plugin for the Apache Felix Webconsole. When installed, this bundle will add a tab on the Web Console that provides a graphic overview of the OSGi framework, including services, bundles, and components. Colors are used to encode the different states. Bundles are shown to be started, stopped, starting, resolved, and installed. Services are shown to be used, looked for, or registered in the air. Components can be seen to be operating or not satisfied. Most objects have a tooltip with extra information and all objects can be clicked upon. Hovering over a bundle or service shows only the conenections to/from the component. The display updates every 5 seconds to provide a continuous view on the operations of the framework it runs on.

OpenCL4Java is a thin Java wrapper around OpenCL's C API. It uses JNA as its interop layer library, which means it works on all of the (many) JNA-supported platforms (see http://jna.dev.java.net/). It is autogenerated by JNAerator (http://jnaerator.googlecode.com/), so updates to newer OpenCL specs are a matter of seconds. Note that OpenCL4Java is used by JavaCL, an Object-Oriented API that presents OpenCL in a much more practical, powerful and idiomatic way to Java. For more info, please visit http://code.google.com/p/nativelibs4java/wiki/OpenCL.

RBFNetwork implements a normalized Gaussian radial basisbasis function network. It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class. RBFRegressor implements radial basis function networks for regression, trained in a fully supervised manner using WEKA's Optimization class by minimizing squared error with the BFGS method. It is possible to use conjugate gradient descent rather than BFGS updates, which is faster for cases with many parameters, and to use normalized basis functions instead of unnormalized ones.

OpenCL4Java is a thin Java wrapper around OpenCL's C API. It uses JNA as its interop layer library, which means it works on all of the (many) JNA-supported platforms (see http://jna.dev.java.net/). It is autogenerated by JNAerator (http://jnaerator.googlecode.com/), so updates to newer OpenCL specs are a matter of seconds. Note that OpenCL4Java is used by JavaCL, an Object-Oriented API that presents OpenCL in a much more practical, powerful and idiomatic way to Java. For more info, please visit http://code.google.com/p/nativelibs4java/wiki/OpenCL.

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.

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.