DigitalAngewandte InformatikTC Grafenau
Ali Fallah Tehrani, M. Strickert, Diane Ahrens
Class of Monotone Kernelized Classifiers on the basis of the Choquet Integral
Expert Systems, vol. 37, no. First published: 21 January 2020, pp. 1-15
The key property of monotone classifiers is that increasing (decreasing) input values lead to increasing (decreasing) the output value. Preserving monotonicity for a classifier typically requires many constraints to be respected by modeling approaches such as artificial intelligence techniques. The type of constraints strongly depends on the modeling assumptions. Of course, for sophisticated models such conditions might be very complex. In this study we present a new family of kernels that we call it Choquet kernels. Henceforth it allows for employing popular kernel‐based methods such as support vector machines. Instead of a naïve approach with exponential computational complexity we propose an equivalent formulation with quadratic time in the number of attributes. Furthermore, since coefficients derived from kernel solutions are not necessarily monotone in the dual form, different approaches are proposed to monotonize coefficients. Finally experiments illustrate beneficial properties of the Choquet kernels.
DigitalAngewandte InformatikAngewandte Wirtschaftswissenschaften
M. Kretschmann, Andreas Fischer, Benedikt Elser
Extracting Keywords from Publication Abstracts for an Automated Researcher Recommendation System
Digitale Welt, vol. 4, no. 1, pp. 20-25
This paper presents an automated keyword assignment system for scientific abstracts. That system is applied to paper abstracts collected in a local publication database and used to drive a researcher recommendation system. Problems like low data volume and missing keywords are discussed. For remediation, training is performed on an extended data set based on large online publication databases. Additionally a closer look at label imbalance in the dataset is taken. Ten multi-label classification algorithms for assigning keywords from a given catalogue to a scientific abstract are compared. The usage of binary relevance as transformation method with LightGBM as classifier yields the best results. Random oversampling before the training phase additionally increases the F1-Score by around 5-6%.
Beitrag (Sammelband oder Tagungsband)
Katharina Heydn, Marc-Philipp Dietrich, Marcus Barkowsky, Götz Winterfeldt, S. Mammen, A. Nüchter
The Golden Bullet: A Comparative Study for Target Acquisition, Pointing and Shooting
Proceedings of the 2019 11th International Conference on Virtual Worlds and Games for Serious Applications (VS-Games) [4-6 Sept. 2019; Vienna, Austria]
In this study, we evaluate an interaction sequence performed by six modalities consisting of desktop-based (DB) and virtual reality (VR) environments using different input devices. For the given study, we implemented a vertical prototype of a first person shooter (FPS) game scenario, focusing on the genre-defining point-and-shoot mechanic. We introduce measures to evaluate the success of the according interaction sequence (times for target acquisition, pointing, shooting, overall net time, and number of shots) and conduct experiments to record and compare the users' performances. We show that interacting using head-tracking for landscape-rotation is performing similarly to the input of a screen-centered mouse and also yielded shortest times in target acquisition and pointing. Although using head-tracking for target acquisition and pointing was most efficient, subjects rated the modality using head-tracking for target acquisition and a 3DOF Controller for pointing best. Eye-tracking (ET) yields promising results, but calibration issues need to be resolved to enhance reliability and overall user experience.
Beitrag (Sammelband oder Tagungsband)
L. Tiotsop, E. Masala, A. Aldahlooh, G. Wallendael, Marcus Barkowsky
Computing Quality-of-Experience Ranges for Video Quality Estimation
Proceedings of the 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) [5-7 June 2019; Berlin]
Typically, the measurement of the Quality of Experience for video sequences aims at a single value, in most cases the Mean Opinion Score (MOS). Predicting this value using various algorithms has been widely studied. However, deviation from the MOS is often handled as an unpredictable error. The approach in this contribution estimates intervals of video quality instead of the single valued MOS. Well-known video quality estimators are fused together to output a lower and upper border for the expected video quality, on the basis of a model derived from a well-known subjectively annotated dataset. Results on different datasets provide insight on the suitability of the well-known estimators for this particular approach.
A. Aldahlooh, E. Masala, G. Wallendael, P. Lambert, Marcus Barkowsky
Improving relevant subjective testing for validation: Comparing machine learning algorithms for finding similarities in VQA datasets using objective measures
Signal Processing: Image Communication, vol. 74, no. May, pp. 32-41
Subjective quality assessment is a necessary activity to validate objective measures or to assess the performance of innovative video processing technologies. However, designing and performing comprehensive tests requires expertise and a large effort especially for the execution part. In this work we propose a methodology that, given a set of processed video sequences prepared by video quality experts, attempts to reduce the number of subjective tests by selecting a subset with minimum size which is expected to yield the same conclusions of the larger set. To this aim, we combine information coming from different types of objective quality metrics with clustering and machine learning algorithms that perform the actual selection, therefore reducing the required subjective assessment effort while trying to preserve the variety of content and conditions needed to ensure the validity of the conclusions. Experiments are conducted on one of the largest publicly available subjectively annotated video sequence dataset. As performance criterion, we chose the validation criteria for video quality measurement algorithms established by the International Telecommunication Union.
T. Mizdos, Marcus Barkowsky, M. Uhrina, P. Pocta
Linking Bitstream Information to QoE: A Study on Still Images Using HEVC Intra Coding
Advances in Electrical and Electronic Engineering, vol. 17, no. 4, pp. 436-445
The coding tools used in image and video encoders aim at high perceptual quality for low bitrates. Analyzing the results of the encoders in terms of quantization parameter, image partitioning, prediction modes or residuals may provide important insight into the link between those tools and the human perception. As a first step, this contribution analyzes the possibility to transcode reference images of three well-known image databases, i.e. IRCCyN/IVC, LIVE and TID2013, from their original, older formats to HEVC; thus creating a homogeneous database of 327 HEVC encoded images accompanied with bitstream parameters and values obtained from objective and subjective assessments. Secondly, it analyzes some of the HEVC intra coding parameters regarding their influence on the image quality by using machine learning, namely Support Vector Machine - Regression.
Decentralized Anomaly Detection with Unused Computing Power in Avionic and Automotive Applications
Kaspersky Industrial Cyber Security Conference 2019, Sochi, Russia