2017
Most regular expression matching engines have operators and features to enhance the succinctness of classical regular expressions, such as interval quantifiers and regular lookahead. In addition, matching engines in for example Perl, Java, Ruby and .NET, also provide operators, such as atomic operators, that constrain the backtracking behavior of the engine. The most common use is to prevent needless backtracking, but the operators will often also change the language accepted. As such it is essential to develop a theoretical sound basis for the matching semantics of regular expressions with atomic operators. We here establish that atomic operators preserve regularity, but are exponentially more succinct for some languages. Further we investigate the state complexity of deterministic and non-deterministic finite automata accepting the language corresponding to a regular expression with atomic operators, and show that emptiness testing is PSPACE-complete.
@{175,
author = {Martin Berglund and Brink van der Merwe and Bruce Watson and N. Weideman},
title = {On the Semantics of Atomic Subgroups in Practical Regular Expressions},
abstract = {Most regular expression matching engines have operators and features to enhance the succinctness of classical regular expressions, such as interval quantifiers and regular lookahead. In addition, matching engines in for example Perl, Java, Ruby and .NET, also provide operators, such as atomic operators, that constrain the backtracking behavior of the engine. The most common use is to prevent needless backtracking, but the operators will often also change the language accepted. As such it is essential to develop a theoretical sound basis for the matching semantics of regular expressions with atomic operators. We here establish that atomic operators preserve regularity, but are exponentially more succinct for some languages. Further we investigate the state complexity of deterministic and non-deterministic finite automata accepting the language corresponding to a regular expression with atomic operators, and show that emptiness testing is PSPACE-complete.},
year = {2017},
journal = {Implementation and Application of Automata, 22nd International Conference, CIAA 2017},
pages = {14-26},
month = {27/06-30/06},
publisher = {Springer},
address = {Marne-la-Vallee, France},
isbn = {978-3-319-60133-5},
url = {http://www.springer.com/978-3-319-60133-5},
}
Context: version control repositories contain a wealth of implicit information that can be used to answer many questions about a project’s development process. However, this information is not directly accessible in the repositories and must be extracted and visualized. Objective: the main objective of this work is to develop a flexible and generic interactive visualization engine called ConceptCloud that supports exploratory search in version control repositories. Method: ConceptCloud is a flexible, interactive browser for SVN and Git repositories. Its main novelty is the combination of an intuitive tag cloud visualization with an underlying concept lattice that provides a formal structure for navigation. ConceptCloud supports concurrent navigation in multiple linked but individually customizable tag clouds, which allows for multi-faceted repository browsing, and scriptable construction of unique visualizations. Results: we describe the mathematical foundations and implementation of our approach and use ConceptCloud to quickly gain insight into the team structure and development process of three projects. We perform a user study to determine the usability of ConceptCloud. We show that untrained participants are able to answer historical questions about a software project better using ConceptCloud than using a linear list of commits. Conclusion: ConceptCloud can be used to answer many difficult questions such as “What has happened in this project while I was away?” and “Which developers collaborate?”. Tag clouds generated from our approach provide a visualization in which version control data can be aggregated and explored interactively.
@article{174,
author = {Bernd Fischer and M. Esterhuizen and G.J. Greene},
title = {Visualizing and Exploring Software Version Control Repositories using Interactive Tag Clouds over Formal Concept Lattices},
abstract = {Context: version control repositories contain a wealth of implicit information that can be used to answer many questions about a project’s development process. However, this information is not directly accessible in the repositories and must be extracted and visualized.
Objective: the main objective of this work is to develop a flexible and generic interactive visualization engine called ConceptCloud that supports exploratory search in version control repositories.
Method: ConceptCloud is a flexible, interactive browser for SVN and Git repositories. Its main novelty is the combination of an intuitive tag cloud visualization with an underlying concept lattice that provides a formal structure for navigation. ConceptCloud supports concurrent navigation in multiple linked but individually customizable tag clouds, which allows for multi-faceted repository browsing, and scriptable construction of unique visualizations.
Results: we describe the mathematical foundations and implementation of our approach and use ConceptCloud to quickly gain insight into the team structure and development process of three projects. We perform a user study to determine the usability of ConceptCloud. We show that untrained participants are able to answer historical questions about a software project better using ConceptCloud than using a linear list of commits.
Conclusion: ConceptCloud can be used to answer many difficult questions such as “What has happened in this project while I was away?” and “Which developers collaborate?”. Tag clouds generated from our approach provide a visualization in which version control data can be aggregated and explored interactively.},
year = {2017},
journal = {Elsevier},
volume = {87},
pages = {223-241},
issue = {2017},
url = {https://www.sciencedirect.com/science/article/pii/S0950584916304050?via%3Dihub},
}
Acquiring an overview of an unfamiliar discipline and exploring relevant papers and journals is often a laborious task for researchers. In this paper we show how exploratory search can be supported on a large collection of academic papers to allow users to answer complex scientometric questions which traditional retrieval approaches do not support optimally. We use our ConceptCloud browser, which makes use of a combination of concept lattices and tag clouds, to visually present academic publication data (specifically, the ACM Digital Library) in a browsable format that facilitates exploratory search. We augment this dataset with semantic categories, obtained through automatic keyphrase extraction from papers’ titles and abstracts, in order to provide the user with uniform keyphrases of the underlying data collection. We use the citations and references of papers to provide additional mechanisms for exploring relevant research by presenting aggregated reference and citation data not only for a single paper but also across topics, authors and journals, which is novel in our approach. We conduct a user study to evaluate our approach in which we asked 34 participants, from different academic backgrounds with varying degrees of research experience, to answer a variety of scientometric questions using our ConceptCloud browser. Participants were able to answer complex scientometric questions using our ConceptCloud browser with a mean correctness of 73%, with the user’s prior research experience having no statistically significant effect on the results.
@article{173,
author = {Bernd Fischer and M. Dunaiski and G.J. Greene},
title = {Exploratory Search of Academic Publication and Citation Data using Interactive Tag Cloud Visualizations},
abstract = {Acquiring an overview of an unfamiliar discipline and exploring relevant papers and journals is often a laborious task for researchers. In this paper we show how exploratory search can be supported on a large collection of academic papers to allow users to answer complex scientometric questions which traditional retrieval approaches do not support optimally. We use our ConceptCloud browser, which makes use of a combination of concept lattices and tag clouds, to visually present academic publication data (specifically, the ACM Digital Library) in a browsable format that facilitates exploratory search. We augment this dataset with semantic categories, obtained through automatic keyphrase extraction from papers’ titles and abstracts, in order to provide the user with uniform keyphrases of the underlying data collection. We use the citations and references of papers to provide additional mechanisms for exploring relevant research by presenting aggregated reference and citation data not only for a single paper but also across topics, authors and journals, which is novel in our approach. We conduct a user study to evaluate our approach in which we asked 34 participants, from different academic backgrounds with varying degrees of research experience, to answer a variety of scientometric questions using our ConceptCloud browser. Participants were able to answer complex scientometric questions using our ConceptCloud browser with a mean correctness of 73%, with the user’s prior research experience having no statistically significant effect on the results.},
year = {2017},
journal = {Scientometrics (Springer)},
volume = {110},
pages = {1539-1571},
issue = {3},
address = {Netherlands},
isbn = {0138-9130},
url = {https://link.springer.com/article/10.1007%2Fs11192-016-2236-3},
}
The description logic dSROIQ is a decidable extension of SROIQ that supports defeasible reasoning in the KLM tradition. It features a parameterised preference order on binary relations in a domain of interpretation, which allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions. In this paper, we address an important limitation both in dSROIQ and in other defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept subsumption to a single preference order on objects. We do this by inducing preference orders on objects from preference orders on roles, and use these to relativise defeasible subsumption. This yields a notion of contextualised defeasible subsumption, with contexts described by roles.
@{169,
author = {Katarina Britz and Ivan Varzinczak},
title = {Context-based defeasible subsumption for dSROIQ},
abstract = {The description logic dSROIQ is a decidable extension of SROIQ that supports defeasible reasoning in the KLM tradition. It features a parameterised preference order on binary relations in a domain of interpretation, which allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions. In this paper, we address an important limitation both in dSROIQ and in other defeasible extensions of description logics, namely the restriction in the semantics of defeasible concept subsumption to a single preference order on objects. We do this by inducing preference orders on objects from preference orders on roles, and use these to relativise defeasible subsumption. This yields a notion of contextualised defeasible subsumption, with contexts described by roles.},
year = {2017},
journal = {13th International Symposium on Commonsense Reasoning},
month = {06/11-08/11},
}
We present a decidable extension of the Description Logic SROIQ that supports defeasible reasoning in the KLM tradition, and extends it through the introduction of defeasible roles. The semantics of the resulting DL dSROIQ extends the classical semantics with a parameterised preference order on binary relations in a domain of interpretation. This allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions. Reasoning over dSROIQ ontologies is made possible by a translation of entailment to concept satisfiability relative to an RBox only. A tableau algorithm then decides on consistency of dSROIQ-concepts in the preferential semantics.
@misc{168,
author = {Katarina Britz and Ivan Varzinczak},
title = {Towards defeasible SROIQ},
abstract = {We present a decidable extension of the Description Logic SROIQ that supports defeasible reasoning in the KLM tradition, and extends it through the introduction of defeasible roles. The semantics of the resulting DL dSROIQ extends the classical semantics with a parameterised preference order on binary relations in a domain of interpretation. This allows for the use of defeasible roles in complex concepts, as well as in defeasible concept and role subsumption, and in defeasible role assertions. Reasoning over dSROIQ ontologies is made possible by a translation of entailment to concept satisfiability relative to an RBox only. A tableau algorithm then decides on consistency of dSROIQ-concepts in the preferential semantics.},
year = {2017},
isbn = {ISSN 1613-0073},
url = {http://ceur-ws.org/Vol-1879/},
}
No Abstract
@article{152,
author = {Steve Kroon and A. Heavens and Y. Fantaye and E. Sellentin and H. Eggers and Z. Hosenie and A. Mootoovaloo},
title = {No evidence for extensions to the standard cosmological model},
abstract = {No Abstract},
year = {2017},
journal = {Physical Review Letters},
volume = {119},
pages = {101301-101305},
issue = {2017},
publisher = {American Physical Society},
url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.119.101301},
}
Reinforcement Learning (RL) is a powerful technique to develop intelligent agents in the field of Artificial Intelligence (AI). This paper proposes a new RL algorithm called the Temporal-Difference value iteration algorithm with state-value functions and presents applications of this algorithm to the decision-making problems challenged in the RoboCup Small Size League (SSL) domain. Six scenarios were defined to develop shooting skills for an SSL soccer robot in various situations using the proposed algorithm. Furthermore, an Artificial Neural Network (ANN) model, namely Multi-Layer Perceptron (MLP) was used as a function approximator in each application. The experimental results showed that the proposed RL algorithm had effectively trained the RL agent to acquire good shooting skills. The RL agent showed good performance under specified experimental conditions.
@article{151,
author = {Steve Kroon and M. Yoon and J. Bekker},
title = {New reinforcement learning algorithm for robot soccer},
abstract = {Reinforcement Learning (RL) is a powerful technique to develop intelligent agents in the field of Artificial Intelligence (AI). This paper proposes a new RL algorithm called the Temporal-Difference value iteration algorithm with state-value functions and presents applications of this algorithm to the decision-making problems challenged in the RoboCup Small Size League (SSL) domain. Six scenarios were defined to develop shooting skills for an SSL soccer robot in various situations using the proposed algorithm. Furthermore, an Artificial Neural Network (ANN) model, namely Multi-Layer Perceptron (MLP) was used as a function approximator in each application. The experimental results showed that the proposed RL algorithm had effectively trained the RL agent to acquire good shooting skills. The RL agent showed good performance under specified experimental conditions.},
year = {2017},
journal = {Orion},
volume = {33},
pages = {1-20},
issue = {1},
publisher = {Operations Research Society of South Africa (ORSSA)},
address = {South Africa},
isbn = {2224-0004 (online)},
url = {http://orion.journals.ac.za/pub/article/view/542},
}
2016
No Abstract
@phdthesis{207,
author = {L. van Niekerk and Bruce Watson},
title = {The Development and Evaluation of an Electronic Serious Game Aimed at the Education of Core Programming Skills},
abstract = {No Abstract},
year = {2016},
volume = {MA},
url = {http://hdl.handle.net/10019.1/100119},
}
No Abstract
@misc{149,
author = {Henriette Harmse and Katarina Britz and Aurona Gerber},
title = {Armstrong Relations for Ontology Design and Evaluation},
abstract = {No Abstract},
year = {2016},
url = {http://ceur-ws.org/Vol-1577/},
}
No Abstract
@misc{146,
author = {Brink van der Merwe},
title = {The Output Size Problem for String-to-Tree Transducers},
abstract = {No Abstract},
year = {2016},
url = {http://www.ims.uni-stuttgart.de/events/TTATT2016},
}
No Abstract
@{145,
author = {Brink van der Merwe},
title = {Analyzing Matching Time Behavior of Backtracking Regular Expression Matchers by Using Ambiguity of NFA.},
abstract = {No Abstract},
year = {2016},
journal = {21st International Conference on Implementation and Application of Automata},
pages = {322-334},
month = {19/07-22/07},
}
No Abstract
@{143,
author = {Steve Kroon and PB Le Roux and Willem Bester},
title = {DSaaS: A cloud Service for Persistent Data Structures},
abstract = {No Abstract},
year = {2016},
journal = {CLOSER, 6th International Conference on Cloud Computing and Services Science},
pages = {37-48},
month = {23/04-25/04},
address = {Portugal},
isbn = {978-989-758-182-3},
}
Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standard convolutional network which produces single pixel outputs, as well as Fully Convolutional Networks (FCN) for patch prediction. It was shown that the custom image processing pipeline, which achieved a worm segmentation accuracy of 94%, was outperformed by all of the architectures considered, with the best being 97.3% achieved by a FCN with a single downsampling layer. These results demonstrate the promise of employing convolutional neural network architectures as an alternative to ad-hoc image processing pipelines on optical microscopy images of C. elegans.
@{142,
author = {Hennie de Villiers and Stiaan Wiehman},
title = {Semantic Segmentation of Bioimages Using Convolutional Neural Networks},
abstract = {Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standard convolutional network which produces single pixel outputs, as well as Fully Convolutional Networks (FCN) for patch prediction. It was shown that the custom image processing pipeline, which achieved a worm segmentation accuracy of 94%, was outperformed by all of the architectures considered, with the best being 97.3% achieved by a FCN with a single downsampling layer. These results demonstrate the promise of employing convolutional neural network architectures as an alternative to ad-hoc image processing pipelines on optical microscopy images of C. elegans.},
year = {2016},
journal = {2016 International Joint Conference on Neural Networks (IJCNN)},
pages = {624-631},
month = {24/07-29/07},
address = {Singapore},
isbn = {978-1-5090-0620-5},
}
No Abstract
@{141,
author = {G.J. Greene and Bernd Fischer},
title = {CVExplorer: Identifying Candidate Developers by Mining and Exploring Their Open Source Contributions},
abstract = {No Abstract},
year = {2016},
journal = {Automated Software Engineering},
pages = {804-809},
month = {03/09-07/09},
isbn = {978-1-4503-3845-5},
}
No Abstract
@misc{138,
author = {G.J. Greene and Bernd Fischer},
title = {Single-Focus Broadening Navigation in Concept Lattices},
abstract = {No Abstract},
year = {2016},
url = {https://cla2016.hse.ru/data/2016/07/24/1119022942/CDUD2016.pdf#page=39},
}
