Abstract Strongly walk regular graphs (SWRGs or s-SWRGs) form a natural generalization of strongly regular graphs (SRGs) where paths of length 2 are replaced by paths of length s. They can be constructed as coset graphs of the duals of projective three-weight codes whose weights satisfy a certain equation. We provide classifications of the feasible parameters of these codes in the binary and ternary case for medium size code lengths. For the binary case, the divisibility of the weights of these codes is investigated and several general results are shown. It is known that an s-SWRG has at most 4 distinct eigenvalues $$k> \theta _1> \theta _2 > \theta _3$$ k > θ 1 > θ 2 > θ 3 , and that the triple $$(\theta _1, \theta _2, \theta _3)$$ ( θ 1 , θ 2 , θ 3 ) satisfies a certain homogeneous polynomial equation of degree $$s - 2$$ s - 2 (Van Dam, Omidi, 2013). This equation defines a plane algebraic curve; we use methods from algorithmic arithmetic geometry to show that for $$s = 5$$ s = 5 and $$s = 7$$ s = 7 , there are only the obvious solutions, and we conjecture this to remain true for all (odd) $$s \ge 9$$ s ≥ 9 .

We propose a new technique for visual analytics and annotation of long-term pervasive eye tracking data for which a combined analysis of gaze and egocentric video is necessary. Our approach enables two important tasks for such data for hour-long videos from individual participants: (1) efficient annotation and (2) direct interpretation of the results. Exemplary time spans can be selected by the user and are then used as a query that initiates a fuzzy search of similar time spans based on gaze and video features. In an iterative refinement loop, the query interface then provides suggestions for the importance of individual features to improve the search results. A multi-layered timeline visualization shows an overview of annotated time spans. We demonstrate the efficiency of our approach for analyzing activities in about seven hours of video in a case study and discuss feedback on our approach from novices and experts performing the annotation task.

Automated Production Systems (aPS) are highly complex, long-living mechatronic systems, which are usually programmed according to the standard IEC 61131-3. Control soft-ware development in aPS has to cope with challenges that sharply differ from classical high-level language programming, such as the assurance of maintainability for up to five decades or hard real-time requirements, e.g., to prevent damage to humans in case of a fault. In computer science, object-oriented programming is an established means to increase software reusability and maintainability. However, this approach is seldom used in IEC 61131-3-compliant control software, although the object-oriented extension of the standard (OO-IEC) has been available since 2013. Companies using OO-IEC generally benefit from a drastic reduction in the complexity of their control software. However, certain constellations of OO-IEC elements could lead to runtime problems, which is a crucial problem regarding the hard real-time requirements of aPS to guarantee safety. Therefore, this paper introduces two metrics to detect runtime-critical control software parts and optimization potentials without executing the code or performing time measurements. Using an industrial example, the metrics are evaluated with PLC experts of two companies, who confirmed the reliability and the advantages of the metrics.

Since two years there is a recent trend in optical flow estimation to improve the results of state-of-the-art variational methods by applying additional filtering steps such as median filters, bilateral filters, and non-local techniques. So far, however, the application of such filters has been restricted to two-frame optical flow methods. In this paper, we go beyond this two-frame case and investigate the usefulness of such filtering steps for multi-frame optical flow estimation. Thereby we consider both the application to single flow fields as well as the filtering of the entire spatio-temporal flow volume. In this context, we propose the use of a joint trilateral filter that processes all flow fields simultaneously while imposing consistency of joint flow structures at the same time. Evaluations on the Middlebury benchmark clearly demonstrate the success of our filtering strategy. Achieving rank 3, our method yields state-of-the art results and significantly outperforms the baseline method providing considerably sharper results.

Semantic recognition and annotation of unqiue enities and their relations is a key in understanding the essence contained in large text corpora. It typically requires a combination of efficient automatic methods and manual verification. Usually, both parts are seen as consecutive steps. In this demo we present MIKE, a user interface enabling the integration of user feedback into an iterative extraction process. We show how an extraction system can directly learn from such integrated user supervision. In general, this setup allows for stepwise training of the extraction system to a particular domain, while using user feedback early in the iterative extraction process improves extraction quality and reduces the overall human effort needed.