Real-Time Hand Shape Classification

The problem of hand shape classification is challenging since a hand is characterized by a large number of degrees of freedom. Numerous shape descriptors have been proposed and applied over the years to estimate and classify hand poses in reasonable time. In this paper we discuss our parallel framework for real-time hand shape classification applicable in real-time applications. We show how the number of gallery images influences the classification accuracy and execution time of the parallel algorithm. We present the speedup and efficiency analyses that prove the efficacy of the parallel implementation. Noteworthy, different methods can be used at each step of our parallel framework. Here, we combine the shape contexts with the appearance-based techniques to enhance the robustness of the algorithm and to increase the classification score. An extensive experimental study proves the superiority of the proposed approach over existing state-of-the-art methods.Comment: 11 page

A NEW APPROACH TO THE RULE-BASED SYSTEMS DESIGN AND IMPLEMENTATION PROCESS

The paper discusses selected problems encountered in practical rule-based systems (RBS) design and implementation. To solve them XTT, a new visual knowledge representation is introduced. Then a complete, integrated RBS design, implementation and analysis methodology is presented. This methodology is supported by a visual CASE tool called Mirella.The main goal is to move the design procedure to a more abstract, logical level, where knowledge specification is based on use of abstract rule representation. The design specification is automatically translated into Prolog code, so the designer can focus on logical specification of safety and reliability. On the other hand, system formal aspects are automatically verified on-line during the design, so that its verifiable characteristics are preserved

Local Universal Rule-based Explanations

Explainable artificial intelligence (XAI) is one of the most intensively developed are of AI in recent years. It is also one of the most fragmented one with multiple methods that focus on different aspects of explanations. This makes difficult to obtain the full spectrum of explanation at once in a compact and consistent way. To address this issue, we present Local Universal Explainer (LUX) that is a rule-based explainer which can generate factual, counterfactual and visual explanations. It is based on a modified version of decision tree algorithms that allows for oblique splits and integration with feature importance XAI methods such as SHAP or LIME. It does not use data generation in opposite to other algorithms, but is focused on selecting local concepts in a form of high-density clusters of real data that have the highest impact on forming the decision boundary of the explained model. We tested our method on real and synthetic datasets and compared it with state-of-the-art rule-based explainers such as LORE, EXPLAN and Anchor. Our method outperforms currently existing approaches in terms of simplicity, global fidelity and representativeness

at the 14th Conference of the Spanish Association for Artificial Intelligence (CAEPIA 2011)

Technical Report TR-2011/1, Department of Languages and Computation. University of Almeria November 2011. Joaquín Cañadas, Grzegorz J. Nalepa, Joachim Baumeister (Editors)The seventh workshop on Knowledge Engineering and Software Engineering (KESE7) was held at the Conference of the Spanish Association for Artificial Intelligence (CAEPIA-2011) in La Laguna (Tenerife), Spain, and brought together researchers and practitioners from both fields of software engineering and artificial intelligence. The intention was to give ample space for exchanging latest research results as well as knowledge about practical experience.University of Almería, Almería, Spain. AGH University of Science and Technology, Kraków, Poland. University of Würzburg, Würzburg, Germany