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The problem of the development ontology-driven architecture of intellectual software systems

arXiv.org Artificial Intelligence

The paper describes the architecture of the intelligence system for automated design of ontological knowledge bases of domain areas and the software model of the management GUI (Graphical User Interface) subsystem


Machine learning for subgroup discovery under treatment effect

arXiv.org Machine Learning

In many practical tasks it is needed to estimate an effect of treatment on individual level. For example, in medicine it is essential to determine the patients that would benefit from a certain medicament. In marketing, knowing the persons that are likely to buy a new product would reduce the amount of spam. In this chapter, we review the methods to estimate an individual treatment effect from a randomized trial, i.e., an experiment when a part of individuals receives a new treatment, while the others do not. Finally, it is shown that new efficient methods are needed in this domain.


Some considerations on how the human brain must be arranged in order to make its replication in a thinking machine possible

arXiv.org Artificial Intelligence

For the most of my life, I have earned my living as a computer vision professional busy with image processing tasks and problems. In the computer vision community there is a widespread belief that artificial vision systems faithfully replicate human vision abilities or at least very closely mimic them. It was a great surprise to me when one day I have realized that computer and human vision have next to nothing in common. The former is occupied with extensive data processing, carrying out massive pixel-based calculations, while the latter is busy with meaningful information processing, concerned with smart objects-based manipulations. And the gap between the two is insurmountable. To resolve this confusion, I had had to return and revaluate first the vision phenomenon itself, define more carefully what visual information is and how to treat it properly. In this work I have not been, as it is usually accepted, biologically inspired . On the contrary, I have drawn my inspirations from a pure mathematical theory, the Kolmogorov s complexity theory. The results of my work have been already published elsewhere. So the objective of this paper is to try and apply the insights gained in course of this my enterprise to a more general case of information processing in human brain and the challenging issue of human intelligence.


Bulgarians tweeting in Cyrillic confused for Russian bots

BBC News

Cyrillic is an ancient script dating back more than 1,000 years with the earliest form of the alphabet emerging in Bulgaria in the 9th Century. But some Bulgarians have found their Twitter accounts suspended and tweets hidden because they are thought to be the work of Russian bots, as Russian also uses the Cyrillic alphabet. Bulgarian blogger Ilia Temelkov tweeted, in Cyrillic: "Hey Twitter, the Cyrillic alphabet is used by 12 countries in addition to Russia. Temelkov points out he is not a bot and asked Twitter not to shadow ban him (meaning to hide his tweets from public view). Google speaks it, so go translate, okay?


More Effective Ontology Authoring with Test-Driven Development

arXiv.org Artificial Intelligence

Faculty of Computing, Poznan University of Technology, Poland, agnieszka.lawrynowicz@cs.put.poznan.pl Abstract Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent test-first approach that aims to reduce authoring time and increase authoring efficiency. Current TDD testing falls short on coverage of OWL features and possible test outcomes, the rigorous foundation thereof, and evaluations to ascertain its effectiveness. We aim to address these issues in one instantiation of TDD for ontology authoring. We first propose a succinct, logic-based model of TDD testing and present novel TDD algorithms so as to cover also any OWL 2 class expression for the TBox and for the principal ABox assertions, and prove their correctness. The algorithms use methods from the OWL API directly such that reclassification is not necessary for test execution, therewith reducing ontology authoring time. The algorithms were implemented in TDDonto2, a Protégé plugin. TDDonto2 was evaluated on editing efficiency and by users. The editing efficiency study demonstrated that it is faster than a typical ontology authoring interface, especially for medium size and large ontologies. The user evaluation demonstrated that modellers make significantly less errors with TDDonto2 compared to the standard Protégé interface and complete their tasks better using less time. Thus, the results indicate that Test-Driven Development is a promising approach in an ontology development methodology. Keywords:Ontology Engineering, Test-Driven Development, OWL 1. Introduction Ontology engineering is facilitated by methods and methodologies, andtooling support for them. The methodologies are mostly information system-like, high-level directions, such as variants on waterfall and lifecycle development [1, 2], although more recently, notions of Agile development are being ported to the ontology development setting, e.g., [3, 4], including testing in some form [5, 6, 7, 8].