NorCom Information Technology

Abstract: A method and device for performing the method steps is described for identifying objects in a movie. When an observer watches a movie, he may mark a displayed object that he is interested in. The marked object is then identified using various criteria, and stored information according to the marked object is then presented to the observer on the screen of the observer's displaying device.

Abstract: If intelligent algorithms are the rockets of self-driving cars, then the data that is used to train and validate these algorithms can be considered the fuel on which these cars are running. Indeed, developing algorithms for self-driving cars requires many iterations on large sets of data which are collected under various real-case driving scenarios, and this is especially so as these algorithms increasingly incorporate deep neural nets.

Abstract: We propose an approach how to develop and integrate usability metrics in (multimodal) dialog systems and user interfaces with Model Driven Software Development (MDSD). It enables the developer to start early with simple metrics and allows him to refine them during the ongoing work. Additionally, we show how the resulting metrics can be used to estimate the usability in advance, so that the developer may care about critical parts of the application before testing it.

Abstract: One of the key issues in automated theorem proving is the search for optimal proof strategies Since there is not one uniform strategy which works optimally on all proof tasks, one is faced with the difficult problem of selecting a good strategy for a given task Strategy parallelism is a way of circumventing this strategy selection problem However, the problem of selecting the parallel strategies and distributing the available resources remains Therefore we have developed a method for strategy evaluation and selection based on training data We present a theorem prover system which has been automated with respect to the entire process of theorem prover application including automatic data generation, automatic schedule selection and classical automated theorem proving In the theorem prover e-SETHEO, we present an implementation of such a system that, for the first time, can handle the necessary problem domain adaption fully automatically and which is an improvement of the prover which solved the largest number of problems in the MIX division of the CADE-16 ATP competition This is followed by some experimental data produced with this system We address the problem of test set extraction and give an assessment of our work as well as a lookout to future research issues