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Self-Awareness for Autonomous systems

PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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Towards perceptually coherent depth maps in 2D-To-3D conversion 27

Towards perceptually coherent depth maps in 2D-To-3D convers...

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27th Annual Stereoscopic Displays and Applications Conference, SD and A 2016

作者： Brosch, Nicole Schausberger, Tanja Gelautz, Margrit Institute of Software Technology and Interactive Systems Vienna University of Technology Vienna Austria

We propose a semi-Automatic 2D-To-3D conversion algorithm that is embedded in an efficient optimization framework, i.e., cost volume filtering, which assigns pixels to depth values initialized by user-given scribbles. The proposed algorithm is capable of capturing depth changes of objects that move towards or farther away from the camera. We achieve this by determining a rough depth order between objects in each frame, according to the motion observed in the video, and incorporate this depth order into the depth interpolation process. In contrast to previous publications, our algorithm focuses on avoiding conflicts between the generated depth maps and monocular depth cues that are present in the video, i.e., motion-caused occlusions, and thus takes a step towards the generation of perceptually coherent depth maps. We demonstrate the capabilities of our proposed algorithm on synthetic and recorded video data and by comparison with depth ground truth. Experimental evaluations show that we obtain temporally and perceptually coherent 2D-To-3D conversions in which temporal and spatial edges coincide with edges in the corresponding input video. We achieve competitive 2D-To-3D conversion results. Our proposed depth interpolation can clearly improve the conversion results for videos that contain objects which exhibit motion in depth, compared to commonly performed naive depth interpolation techniques. © 2016 Society for Imaging Science and technology.

关键词： Interpolation

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Reverse engineering of production processes based on Markov chains

Reverse engineering of production processes based on Markov ...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Alexandra Mazak Manuel Wimmer Polina Patsuk-Boesch Business Informatics Group (BIG) Institute of Software Technology and Interactive Systems

Understanding and providing knowledge of production processes is crucial for flexible production systems as many decisions are postponed to the operation time. Furthermore, dealing with process improvements requires to have a clear picture about the status of the currently employed process. This becomes even more challenging with the emergence of Cyber-Physical Production systems (CPPS). However, CPPS also provide the opportunity to observe the running processes by using concepts from IoT to producing logs for reflecting the events happening in the system during its execution. Therefore, we propose in this paper a fully automated approach for representing operational logs as models which additionally allows analytical means. In particular, we provide a transformation chain which allows the reverse engineering of Markov chains from event logs. The reverse engineered Markov chains allow to abstract the complexity of run-time information as well as to enable what-if analysis whenever improvements are needed by employing current model-based as well as measurement-based technologies. We demonstrate the approach based on a lab-sized transportation line system.

关键词： Adaptation models Computational modeling Reverse engineering Automation Production systems Data models

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Drum transcription from polyphonic music with recurrent neural networks

Drum transcription from polyphonic music with recurrent neur...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Richard Vogl Matthias Dorfer Peter Knees Dept. of Computational Perception Johannes Kepler University Linz Austria Institute of Software Technology & Interactive Systems Vienna University of Technology Austria

ISBN: (纸本)9781509041183

Automatic drum transcription methods aim at extracting a symbolic representation of notes played by a drum kit in audio recordings. For automatic music analysis, this task is of particular interest as such a transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In this work, an approach to transcribe drums from polyphonic audio signals based on a recurrent neural network is presented. Deep learning techniques like dropout and data augmentation are applied to improve the generalization capabilities of the system. The method is evaluated using established reference datasets consisting of solo drum tracks as well as drums mixed with accompaniment. The results are compared to state-of-the-art approaches on the same datasets. The evaluation reveals that F-measure values higher than state of the art can be achieved using the proposed method.

关键词： Drum transcription neural networks deep learning automatic transcription data augmentation

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software quality: The future of systems- and software development: 8th International Conference, SWQD 2016 vienna, Austria, January 18–21, 2016 Proceedings 8th

Software quality: The future of systems- and software develo...

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8th International Conference on software Quality Days, SWQD 2016

作者： Winkler, Dietmar Biffl, Stefan Bergsmann, Johannes Institute of Software Technology and Interactive Systems Vienna University of Technology Vienna Austria Software Quality Lab GmbH Linz Austria

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Multigroup discrimination based on weighted local projections

arXiv

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arXiv 2017年

作者： Ortner, Thomas Hoffmann, Irene Filzmoser, Peter Zaharieva, Maia Breiteneder, Christian Brodinova, Sarka Institute of Statistics and Mathematical Methods in Economics Institute of Software Technology and Interactive Systems Tu Wien

A novel approach for supervised classification analysis for high dimensional and flat data (more variables than observations) is proposed. We use the information of class-membership of observations to determine groups of observations locally describing the group structure. By projecting the data on the subspace spanned by those groups, local projections are defined based on the projection concepts from Ortner et al. (2017a) and Ortner et al. (2017b). For each local projection a local discriminant analysis (LDA) model is computed using the information within the projection space as well as the distance to the projection space. The models provide information about the quality of separation for each class combination. Based on this information, weights are defined for aggregating the LDA-based posterior probabilities of each subspace to a new overall probability. The same weights are used for classifying new observations. In addition to the provided methodology, implemented in the R-package lop, a method of visualizing the connectivity of groups in high-dimensional spaces is proposed at the basis of the posterior probabilities. A thorough evaluation is performed using three different real-world datasets, underlining the strengths of local projection based classification and the provided visualization methodology. Copyright © 2017, The Authors. All rights reserved.

关键词： Probability

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Local projections for high-dimensional outlier detection

arXiv

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arXiv 2017年

作者： Ortner, Thomas Filzmoser, Peter Zaharieva, Maia Brodinova, Sarka Breiteneder, Christian TU Wien Institute of Statistics and Mathematical Methods in Economics TU Wien Institute of Software Technology and Interactive Systems

In this paper, we propose a novel approach for outlier detection, called local projections, which is based on concepts of Local Outlier Factor (LOF) (Breunig et al., 2000) and RobPCA (Hubert et al., 2005). By using aspects of both methods, our algorithm is robust towards noise variables and is capable of performing outlier detection in multi-group situations. We are further not reliant on a specific underlying data distribution. For each observation of a dataset, we identify a local group of dense nearby observations, which we call a core, based on a modification of the k-nearest neighbours algorithm. By projecting the dataset onto the space spanned by those observations, two aspects are revealed. First, we can analyze the distance from an observation to the center of the core within the projection space in order to provide a measure of quality of description of the observation by the projection. Second, we consider the distance of the observation to the projection space in order to assess the suitability of the core for describing the outlyingness of the observation. These novel interpretations lead to a univariate measure of outlyingness based on aggregations over all local projections, which outperforms LOF and RobPCA as well as other popular methods like PCOut (Filzmoser et al., 2008) and subspace-based outlier detection (Kriegel et al., 2009) in our simulation setups. Experiments in the context of real-word applications employing datasets of various dimensionality demonstrate the advantages of local projections. Copyright © 2017, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Major variants of the SIS architecture pattern for collective intelligence systems 16

Major variants of the SIS architecture pattern for collectiv...

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21st European Conference on Pattern Languages of Programs, EuroPLoP 2016

作者： Musil, Angelika Musil, Juergen Biffl, Stefan Institute of Software Technology and Interactive Systems TU Wien Favoritenstrasse 9/188 Vienna1040 Austria

ISBN: (纸本)9781450340748

Collective Intelligence systems (CIS), such as social networking services, wikis, and media sharing platforms, access and harness the collective knowledge of connected people by providing a web-based environment to share, distribute, and retrieve topic- specific information in an efficient way. In order to design well-tailored CIS, software architects need a complete understanding about (1) architectural principles that all kinds of CIS have in common, and (2) system variants in the field. Thus to provide consolidated systematic knowledge of architectural commonalities and variations in the CIS domain, we present in this work five major pattern variants of CIS. We investigated a number of CIS in the field with focus on a detailed survey of existing variants among key architecture-significant principles based on previously identified basic concepts, principles, and characteristics of software architectures that all CIS have in common. The variants are identified along two dimensions with respect to the relationship between the key elements of artifacts and actor records across and within two layers of the system. © 2016 ACM.

关键词： software architecture

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On marrying model-driven engineering and process mining: A case study in execution-based model profiling 6

On marrying model-driven engineering and process mining: A c...

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6th International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2016

作者： Mazak, Alexandra Wimmer, Manuel Institute of Software Technology and Interactive Systems TU Wien Favoritenstrasse 9-11 Vienna1040 Austria

In model-driven engineering (MDE), models are mostly used in prescriptive ways for system engineering. While prescriptive models are indeed an important ingredient to realize a system, for later phases in the systems' lifecycles additional model types are beneficial to use. Unfortunately, current MDE approaches mostly neglect the information upstream in terms of descriptive models from operations to design, which would be highly needed to improve systems continuously. To tackle this limitation, we propose execution-based model profiling as a continuous process to improve prescriptive models at design-time through runtime information by incorporating knowledge in form of profiled metadata from event logs generated during the execution of a code model. For this purpose we combine techniques of process mining (PM) with runtime models of MDE. In the course of a case study, we implement a preliminary prototype of our framework based on a traffic light system example which shows the feasibility and benefits of our execution-based model profiling approach. © 2016, CEUR-WS. All rights reserved.

关键词： Data mining

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Machine learning with dual process models 5

Machine learning with dual process models

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5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016

作者： Klauninger, Bert Unger, Martin Eidenberger, Horst Institute for Interactive Media Systems Vienna University of Technology Vienna Austria

ISBN: (纸本)9789897581731

Similarity measurement processes are a core part of most machine learning algorithms. Traditional approaches focus on either taxonomic or thematic thinking. Psychological research suggests that a combination of both is needed to model human-like similarity perception adequately. Such a combination is called a Similarity Dual Process Model (DPM). This paper describes how to construct DPMs as a linear combination of existing measures of similarity and distance. We use generalisation functions to convert distance into similarity. DPMs are similar to kernel functions. Thus, they can be integrated into any machine learning algorithm that uses kernel ***, not all DPMs that can be formulated work equally well. Therefore we test classification performance in a real-world task: the detection of pedestrians in images. We assume that DPMs are only viable if they yield better classifiers than their constituting parts. In our experiments, we found DPM kernels that matched the performance of conventional ones for our data set. Eventually, we provide a construction kit to build such kernels to encourage further experiments in other application domains of machine learning. © Copyright 2016 by SCITEPRESS - Science and technology Publications, Lda. All rights reserved.

关键词： Learning algorithms

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