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This artifact provides the implementation of the L* learning algorithm described in the paper "Learning Regular Sets from Queries and Counterexamples" (https://doi.org/10.1016/0890-5401(87)90052-6) by Dana Angluin including variations and optimizations thereof such as the versions based on "On the Learnability of Infinitary Regular Sets" (https://dx.doi.org/10.1006/inco.1995.1070) by Oded Maler and Amir Pnueli or "Inference of finite automata using homing sequences" (http://dx.doi.org/10.1006/inco.1993.1021) by Ronald L. Rivest and Robert E. Schapire.

This artifact provides the implementation of the model checker presented in the paper "M3C: Modal Meta Model Checking" (https://dx.doi.org/10.1007/978-3-030-00244-2_15) by Bernhard Steffen and Alnis Murtovi. The paper is based on "Model Checking for Context-Free Processes" (https://link.springer.com/chapter/10.1007/BFb0084787) by Olaf Burkart and Bernhard Steffen. Note that this implementation requires a runtime dependency to a specific ADDLib backend (see https://add-lib.scce.info/), which is not explicitly included in this artifact due to packaging reasons.

ISO 13616-1:2007 specifies the elements of an international bank account number (IBAN) used to facilitate the processing of data internationally in data interchange, in financial environments as well as within and between other industries. The IBAN is designed for automated processing, but can also be used conveniently in other media interchange when appropriate (e.g. paper document exchange, etc.). ISO 13616-1:2007 does not specify internal procedures, file organization techniques, storage media, languages, etc. to be used in its implementation, nor is it designed to facilitate the routing of messages within a network. It is applicable to the textual data which might be conveyed through a system (network). This project provides a general purpose ISO 13616-1:2007 compatible IBAN Java class.

This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999. This classifier should produce similar results to one created by passing the base learner to Bagging, which is in turn passed to a CostSensitiveClassifier operating on minimum expected cost. The difference is that MetaCost produces a single cost-sensitive classifier of the base learner, giving the benefits of fast classification and interpretable output (if the base learner itself is interpretable). This implementation uses all bagging iterations when reclassifying training data (the MetaCost paper reports a marginal improvement when only those iterations containing each training instance are used in reclassifying that instance).

A NSGA-II implementation using Java. This implementation of NSGA-II algorithm is in pure reference to the original published paper. This is not an effort to convert the originally implemented C code in Java. The original C code by the authors has not be referred to while writing this implementation. This is a fully customizable implementation of the NSGA-II algorithm, made as generic as possible. This documentation assumes you have basic understanding of the NSGA-II algorithm. Apart from the core concepts of the algorithm, everything else in this package can be implemented as per the user's choice and plugged into the algorithm dynamically. Since NSGA-II is more like a set of protocols to follow as an algorithm rather than a concrete implementation of every aspect, this package has been re-written from scratch keeping complete customizability in mind. Apart from the core concepts of the algorithm, everything is considered to be a plugin external to the algorithm that can be implemented by the user and dynamically plugged into the algorithm during runtime as needed. This opens up the possibility of the package to be used simply as a PoC or be converted into something much more complex according to the users needs.

A bottom-up rewrite machine is a compiler construction tool that is often used in the compiler's back end to convert a tree-structured representation of a program into machine code -- or, in Java's case, bytecode. JBurg can also be used as a general-purpose dynamic programming engine. JBurg is descended from iburg-class BURGs, described in Fraser, Hanson, and Proebsting's paper, "Engineering a Simple, Efficient Code Generator Generator." JBurg brings similar O(N) minimum-cost tree rewriting capabilities to Java, and also allows the programmer to specify transitions between non-terminal states, that are significantly more powerful than iburg's transitive closures: JBurg transformation rules allow the transformation to inject additional program logic, which makes a JBurg specification more like a grammar than like a list of pattern-matching rules.

The boilerpipe library provides algorithms to detect and remove the surplus "clutter" (boilerplate, templates) around the main textual content of a web page. The library already provides specific strategies for common tasks (for example: news article extraction) and may also be easily extended for individual problem settings. Extracting content is very fast (milliseconds), just needs the input document (no global or site-level information required) and is usually quite accurate. Boilerpipe is a Java library written by Christian Kohlschütter. It is released under the Apache License 2.0. The algorithms used by the library are based on (and extending) some concepts of the paper "Boilerplate Detection using Shallow Text Features" by Christian Kohlschütter et al., presented at WSDM 2010 -- The Third ACM International Conference on Web Search and Data Mining New York City, NY USA.

BEhavioural Agents Simple Testing Tool - BEAST Tool The aim of this project is the development of a system which allows Behavior Driven Development (BDD) in Multi-Agent Systems (MAS), to make testing practices more accessible and intuitive to everybody. In one hand, in order to let tests be writable by newcomers and experts alike, system must allow the redaction of tests in plain text, because client does not need to have knowledge of our code. This plain text will be traduced to software later. The definition of test will be realized with the terminology Given-When-Then, which allows trace an easy guide of the behavior of a given scenario when something happened. In the other hand, due to the complexity of MAS, making unit testing of an agent that needs the interaction with others is almost impossible until the whole system is finished. This implies to leave testing issues to the end of the project, generating big troubles in case of malfunction. Consequently, its necessary to carry out a tool to allow the creation of mock agents and to perform tests during the whole development process. Therefore another objective of our systems is to include a mocking tool which permits testing continuously. Definitively, our tool allows the testing of any MAS in the development process, increasing its modularity and decreasing its elaboration and testing cost. These tests will be written in plain text so that anyone would be able to understand them. For further reading, a paper published in ITMAS2012 workshop can be found in: http://scholar.google.es/citations?view_op=view_citation&hl=es&user=mT3KgXUAAAAJ&citation_for_view=mT3KgXUAAAAJ:Tyk-4Ss8FVUC