TY - THES
AU - Löken, Nils
ID - 15482
TI - Cryptography for the Crowd — A Study of Cryptographic Schemes with Applications to Crowd Work
ER -
TY - JOUR
AU - Yigitbas, Enes
AU - Jovanovikj, Ivan
AU - Biermeier, Kai
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 15266
JF - International Journal on Software and Systems Modeling (SoSyM)
TI - Integrated Model-driven Development of Self-adaptive User Interfaces (to appear)
ER -
TY - JOUR
AB - We derive a data-driven method for the approximation of the Koopman generator called gEDMD, which can be regarded as a straightforward extension of EDMD (extended dynamic mode decomposition). This approach is applicable to deterministic and stochastic dynamical systems. It can be used for computing eigenvalues, eigenfunctions, and modes of the generator and for system identification. In addition to learning the governing equations of deterministic systems, which then reduces to SINDy (sparse identification of nonlinear dynamics), it is possible to identify the drift and diffusion terms of stochastic differential equations from data. Moreover, we apply gEDMD to derive coarse-grained models of high-dimensional systems, and also to determine efficient model predictive control strategies. We highlight relationships with other methods and demonstrate the efficacy of the proposed methods using several guiding examples and prototypical molecular dynamics problems.
AU - Klus, Stefan
AU - Nüske, Feliks
AU - Peitz, Sebastian
AU - Niemann, Jan-Hendrik
AU - Clementi, Cecilia
AU - Schütte, Christof
ID - 16288
JF - Physica D: Nonlinear Phenomena
SN - 0167-2789
TI - Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
VL - 406
ER -
TY - JOUR
AB - The control of complex systems is of critical importance in many branches of science, engineering, and industry, many of which are governed by nonlinear partial differential equations. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration). However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible. Fortunately, these high- dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required. Advances in machine learning have the potential to revolutionize flow control given its ability to extract principled, low-rank feature spaces characterizing such complex systems.We present a novel deep learning modelpredictive control framework that exploits low-rank features of the flow in order to achieve considerable improvements to control performance. Instead of predicting the entire fluid state, we use a recurrent neural network (RNN) to accurately predict the control relevant quantities of the system, which are then embedded into an MPC framework to construct a feedback loop. In order to lower the data requirements and to improve the prediction accuracy and thus the control performance, incoming sensor data are used to update the RNN online. The results are validated using varying fluid flow examples of increasing complexity.
AU - Bieker, Katharina
AU - Peitz, Sebastian
AU - Brunton, Steven L.
AU - Kutz, J. Nathan
AU - Dellnitz, Michael
ID - 16290
JF - Theoretical and Computational Fluid Dynamics
SN - 0935-4964
TI - Deep model predictive flow control with limited sensor data and online learning
ER -
TY - CONF
AU - Pauck, Felix
AU - Bodden, Eric
AU - Wehrheim, Heike
ED - Felderer, Michael
ED - Hasselbring, Wilhelm
ED - Rabiser, Rick
ED - Jung, Reiner
ID - 16214
T2 - Software Engineering 2020, Fachtagung des GI-Fachbereichs Softwaretechnik, 24.-28. Februar 2020, Innsbruck, Austria
TI - Reproducing Taint-Analysis Results with ReproDroid
VL - {P-300}
ER -
TY - CONF
AB - Network function virtualization (NFV) proposes
to replace physical middleboxes with more flexible virtual
network functions (VNFs). To dynamically adjust to everchanging
traffic demands, VNFs have to be instantiated and
their allocated resources have to be adjusted on demand.
Deciding the amount of allocated resources is non-trivial.
Existing optimization approaches often assume fixed resource
requirements for each VNF instance. However, this can easily
lead to either waste of resources or bad service quality if too
many or too few resources are allocated.
To solve this problem, we train machine learning models
on real VNF data, containing measurements of performance
and resource requirements. For each VNF, the trained models
can then accurately predict the required resources to handle
a certain traffic load. We integrate these machine learning
models into an algorithm for joint VNF scaling and placement
and evaluate their impact on resulting VNF placements. Our
evaluation based on real-world data shows that using suitable
machine learning models effectively avoids over- and underallocation
of resources, leading to up to 12 times lower resource
consumption and better service quality with up to 4.5 times
lower total delay than using standard fixed resource allocation.
AU - Schneider, Stefan Balthasar
AU - Satheeschandran, Narayanan Puthenpurayil
AU - Peuster, Manuel
AU - Karl, Holger
ID - 16219
T2 - IEEE Conference on Network Softwarization (NetSoft)
TI - Machine Learning for Dynamic Resource Allocation in Network Function Virtualization
ER -
TY - JOUR
AU - Bellman, K.
AU - Dutt, N.
AU - Esterle, L.
AU - Herkersdorf, A.
AU - Jantsch, A.
AU - Landauer, C.
AU - R. Lewis, P.
AU - Platzner, Marco
AU - TaheriNejad, N.
AU - Tammemäe, K.
ID - 15836
JF - ACM Transactions on Cyber-Physical Systems
TI - Self-aware Cyber-Physical Systems
VL - Accepted for Publication
ER -
TY - CONF
AU - Krauter, Stefan
AU - Zhang, L.
ID - 16858
T2 - Proceedings of the 14 th International Renewable Energy Storage Conference, Düsseldorf (Deutschland), 10.–12. März 2020 (verschoben auf 16.–18. März 2021)
TI - Probability of Correct Decision–Making at Triggering of Load-Shifting Intended for low CO 2 -intensity and low EEX trading price via simple Grid Frequency Monitoring
ER -
TY - CONF
AU - Krumme, Matthias
AU - Webersen, Manuel
AU - Claes, Leander
AU - Webersen, Yvonne
ID - 13943
T2 - Fortschritte der Akustik - DAGA 2020
TI - Analoge Klangsynthese zur Vermittlung von Grundkenntnissen der Signalverarbeitung an Studierende nicht-technischer Fachrichtungen
ER -
TY - JOUR
AU - Liebendörfer, Michael
AU - Göller, Robin
AU - Biehler, Rolf
AU - Hochmuth, Reinhard
AU - Kortemeyer, Jörg
AU - Ostsieker, Laura
AU - Rode, Jana
AU - Schaper, Niclas
ID - 16961
JF - Journal für Mathematik-Didaktik
SN - 0173-5322
TI - LimSt – Ein Fragebogen zur Erhebung von Lernstrategien im mathematikhaltigen Studium
ER -
TY - CONF
AU - Dreiling, Dmitrij
AU - Itner, Dominik Thor
AU - Feldmann, Nadine
AU - Gravenkamp, Hauke
AU - Henning, Bernd
ID - 17089
TI - Increasing the sensitivity in the determination of material parameters by using arbitrary loads in ultrasonic transmission measurements
ER -
TY - CONF
AU - Weidmann, Nils
AU - Anjorin, Anthony
ID - 17084
SN - 0302-9743
T2 - Fundamental Approaches to Software Engineering
TI - Schema Compliant Consistency Management via Triple Graph Grammars and Integer Linear Programming
ER -
TY - CONF
AU - Krings, Sarah Claudia
AU - Yigitbas, Enes
AU - Jovanovikj, Ivan
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 16790
SN - 978-1-4503-7984-7/20/06
T2 - Proceedings of the 12th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2020)
TI - Development Framework for Context-Aware Augmented Reality Applications
ER -
TY - CHAP
AU - Jazayeri, Bahar
AU - Schwichtenberg, Simon
AU - Küster, Jochen
AU - Zimmermann, Olaf
AU - Engels, Gregor
ID - 17337
SN - 0302-9743
T2 - Advanced Information Systems Engineering
TI - Modeling and Analyzing Architectural Diversity of Open Platforms
ER -
TY - CONF
AB - We consider a natural extension to the metric uncapacitated Facility Location Problem (FLP) in which requests ask for different commodities out of a finite set \( S \) of commodities.
Ravi and Sinha (SODA 2004) introduced the model as the \emph{Multi-Commodity Facility Location Problem} (MFLP) and considered it an offline optimization problem.
The model itself is similar to the FLP: i.e., requests are located at points of a finite metric space and the task of an algorithm is to construct facilities and assign requests to facilities while minimizing the construction cost and the sum over all assignment distances.
In addition, requests and facilities are heterogeneous; they request or offer multiple commodities out of $S$.
A request has to be connected to a set of facilities jointly offering the commodities demanded by it.
In comparison to the FLP, an algorithm has to decide not only if and where to place facilities, but also which commodities to offer at each.
To the best of our knowledge we are the first to study the problem in its online variant in which requests, their positions and their commodities are not known beforehand but revealed over time.
We present results regarding the competitive ratio.
On the one hand, we show that heterogeneity influences the competitive ratio by developing a lower bound on the competitive ratio for any randomized online algorithm of \( \Omega ( \sqrt{|S|} + \frac{\log n}{\log \log n} ) \) that already holds for simple line metrics.
Here, \( n \) is the number of requests.
On the other side, we establish a deterministic \( \mathcal{O}(\sqrt{|S|} \cdot \log n) \)-competitive algorithm and a randomized \( \mathcal{O}(\sqrt{|S|} \cdot \frac{\log n}{\log \log n} ) \)-competitive algorithm.
Further, we show that when considering a more special class of cost functions for the construction cost of a facility, the competitive ratio decreases given by our deterministic algorithm depending on the function.
AU - Castenow, Jannik
AU - Feldkord, Björn
AU - Knollmann, Till
AU - Malatyali, Manuel
AU - Meyer auf der Heide, Friedhelm
ID - 17370
KW - Online Multi-Commodity Facility Location
KW - Competitive Ratio
KW - Online Optimization
KW - Facility Location Problem
SN - 9781450369350
T2 - Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures
TI - The Online Multi-Commodity Facility Location Problem
ER -
TY - CONF
AU - Hardes, Tobias
AU - Sommer, Christoph
ID - 17399
SN - 9781728145716
T2 - 2019 IEEE Vehicular Networking Conference (VNC)
TI - Towards Heterogeneous Communication Strategies for Urban Platooning at Intersections
ER -
TY - CONF
AU - Razzaghi Kouchaksaraei, Hadi
AU - Prasad Shivarpatna Venkatesh, Ashwin
AU - Churi, Amey
AU - Illian, Marvin
AU - Karl, Holger
ID - 16726
T2 - European Conference on Networks and Communications (EUCNC 2020)
TI - Dynamic Provisioning of Network Services on Heterogeneous Resources
ER -
TY - CONF
AU - Tornede, Alexander
AU - Wever, Marcel Dominik
AU - Hüllermeier, Eyke
ID - 17407
T2 - Discovery Science
TI - Extreme Algorithm Selection with Dyadic Feature Representation
ER -
TY - GEN
AB - Software verification has recently made enormous progress due to the
development of novel verification methods and the speed-up of supporting
technologies like SMT solving. To keep software verification tools up to date
with these advances, tool developers keep on integrating newly designed methods
into their tools, almost exclusively by re-implementing the method within their
own framework. While this allows for a conceptual re-use of methods, it
requires novel implementations for every new technique.
In this paper, we employ cooperative verification in order to avoid
reimplementation and enable usage of novel tools as black-box components in
verification. Specifically, cooperation is employed for the core ingredient of
software verification which is invariant generation. Finding an adequate loop
invariant is key to the success of a verification run. Our framework named
CoVerCIG allows a master verification tool to delegate the task of invariant
generation to one or several specialized helper invariant generators. Their
results are then utilized within the verification run of the master verifier,
allowing in particular for crosschecking the validity of the invariant. We
experimentally evaluate our framework on an instance with two masters and three
different invariant generators using a number of benchmarks from SV-COMP 2020.
The experiments show that the use of CoVerCIG can increase the number of
correctly verified tasks without increasing the used resources
AU - Haltermann, Jan Frederik
AU - Wehrheim, Heike
ID - 17825
T2 - arXiv:2008.04551
TI - Cooperative Verification via Collective Invariant Generation
ER -
TY - JOUR
AB - Multi-objective optimization is an active field of research that has many applications. Owing to its success and because decision-making processes are becoming more and more complex, there is a recent trend for incorporating many objectives into such problems. The challenge with such problems, however, is that the dimensions of the solution sets—the so-called Pareto sets and fronts—grow with the number of objectives. It is thus no longer possible to compute or to approximate the entire solution set of a given problem that contains many (e.g. more than three) objectives. On the other hand, the computation of single solutions (e.g. via scalarization methods) leads to unsatisfying results in many cases, even if user preferences are incorporated. In this article, the Pareto Explorer tool is presented—a global/local exploration tool for the treatment of many-objective optimization problems (MaOPs). In the first step, a solution of the problem is computed via a global search algorithm that ideally already includes user preferences. In the second step, a local search along the Pareto set/front of the given MaOP is performed in user specified directions. For this, several continuation-like procedures are proposed that can incorporate preferences defined in decision, objective, or in weight space. The applicability and usefulness of Pareto Explorer is demonstrated on benchmark problems as well as on an application from industrial laundry design.
AU - Schütze, Oliver
AU - Cuate, Oliver
AU - Martín, Adanay
AU - Peitz, Sebastian
AU - Dellnitz, Michael
ID - 10596
IS - 5
JF - Engineering Optimization
SN - 0305-215X
TI - Pareto Explorer: a global/local exploration tool for many-objective optimization problems
VL - 52
ER -
TY - CHAP
AU - Yigitbas, Enes
AU - Jovanovikj, Ivan
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 15267
T2 - Handling Security, Usability, User Experience and Reliability in User-Centered Development Processes - IFIP WG 13.2/13.5
TI - On the Development of Context-aware Augmented Reality Applications (to appear)
ER -
TY - CHAP
AB - In the development of model predictive controllers for PDE-constrained problems, the use of reduced order models is essential to enable real-time applicability. Besides local linearization approaches, proper orthogonal decomposition (POD) has been most widely used in the past in order to derive such models. Due to the huge advances concerning both theory as well as the numerical approximation, a very promising alternative based on the Koopman operator has recently emerged. In this chapter, we present two control strategies for model predictive control of nonlinear PDEs using data-efficient approximations of the Koopman operator. In the first one, the dynamic control system is replaced by a small number of autonomous systems with different yet constant inputs. The control problem is consequently transformed into a switching problem. In the second approach, a bilinear surrogate model is obtained via a convex combination of these autonomous systems. Using a recent convergence result for extended dynamic mode decomposition (EDMD), convergence of the reduced objective function can be shown. We study the properties of these two strategies with respect to solution quality, data requirements, and complexity of the resulting optimization problem using the 1-dimensional Burgers equation and the 2-dimensional Navier–Stokes equations as examples. Finally, an extension for online adaptivity is presented.
AU - Peitz, Sebastian
AU - Klus, Stefan
ID - 16289
SN - 0170-8643
T2 - Lecture Notes in Control and Information Sciences
TI - Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator
VL - 484
ER -
TY - GEN
AB - In recent years, the success of the Koopman operator in dynamical systems
analysis has also fueled the development of Koopman operator-based control
frameworks. In order to preserve the relatively low data requirements for an
approximation via Dynamic Mode Decomposition, a quantization approach was
recently proposed in [Peitz & Klus, Automatica 106, 2019]. This way, control
of nonlinear dynamical systems can be realized by means of switched systems
techniques, using only a finite set of autonomous Koopman operator-based
reduced models. These individual systems can be approximated very efficiently
from data. The main idea is to transform a control system into a set of
autonomous systems for which the optimal switching sequence has to be computed.
In this article, we extend these results to continuous control inputs using
relaxation. This way, we combine the advantages of the data efficiency of
approximating a finite set of autonomous systems with continuous controls. We
show that when using the Koopman generator, this relaxation --- realized by
linear interpolation between two operators --- does not introduce any error for
control affine systems. This allows us to control high-dimensional nonlinear
systems using bilinear, low-dimensional surrogate models. The efficiency of the
proposed approach is demonstrated using several examples with increasing
complexity, from the Duffing oscillator to the chaotic fluidic pinball.
AU - Peitz, Sebastian
AU - Otto, Samuel E.
AU - Rowley, Clarence W.
ID - 16309
T2 - arXiv:2003.07094
TI - Data-Driven Model Predictive Control using Interpolated Koopman Generators
ER -
TY - JOUR
AU - Jovanovikj, Ivan
AU - Yigitbas, Enes
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 16570
JF - Softwaretechnik-Trends, Proceedings of the 22st Workshop Software-Reengineering & Evolution (WSRE) & 11h Workshop Design for Future (DFF)
TI - Challenges in Model-Driven Development of Multi-Platform Augmented Reality Applications (to appear)
ER -
TY - CONF
AU - Zafeiropoulos, A.
AU - Fotopoulou, E.
AU - Peuster, Manuel
AU - Schneider, Stefan Balthasar
AU - Gouvas, P.
AU - Behnke, D.
AU - Müller, M.
AU - Bök, P.
AU - Trakadas, P.
AU - Karkazis, P.
AU - Karl, Holger
ID - 16222
T2 - IEEE Conference on Network Softwarization (NetSoft)
TI - Benchmarking and Profiling 5G Verticals' Applications: An Industrial IoT Use Case
ER -
TY - JOUR
AB - CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-theart ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.
AU - Kühne, Thomas
AU - Iannuzzi, Marcella
AU - Ben, Mauro Del
AU - Rybkin, Vladimir V.
AU - Seewald, Patrick
AU - Stein, Frederick
AU - Laino, Teodoro
AU - Khaliullin, Rustam Z.
AU - Schütt, Ole
AU - Schiffmann, Florian
AU - Golze, Dorothea
AU - Wilhelm, Jan
AU - Chulkov, Sergey
AU - Mohammad Hossein Bani-Hashemian, Mohammad Hossein Bani-Hashemian
AU - Weber, Valéry
AU - Borstnik, Urban
AU - Taillefumier, Mathieu
AU - Jakobovits, Alice Shoshana
AU - Lazzaro, Alfio
AU - Pabst, Hans
AU - Müller, Tiziano
AU - Schade, Robert
AU - Guidon, Manuel
AU - Andermatt, Samuel
AU - Holmberg, Nico
AU - Schenter, Gregory K.
AU - Hehn, Anna
AU - Bussy, Augustin
AU - Belleflamme, Fabian
AU - Tabacchi, Gloria
AU - Glöß, Andreas
AU - Lass, Michael
AU - Bethune, Iain
AU - Mundy, Christopher J.
AU - Plessl, Christian
AU - Watkins, Matt
AU - VandeVondele, Joost
AU - Krack, Matthias
AU - Hutter, Jürg
ID - 16277
IS - 19
JF - The Journal of Chemical Physics
TI - CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations
VL - 152
ER -
TY - CONF
AU - Claes, Leander
AU - Baumhögger, Elmar
AU - Rüther, Torben
AU - Gierse, Jan
AU - Tröster, Thomas
AU - Henning, Bernd
ID - 15490
T2 - Fortschritte der Akustik - DAGA 2020
TI - Reduction of systematic measurement deviation in acoustic absorption measurement systems
ER -
TY - CONF
AU - Al-Khatib, Khalid
AU - Hou, Yufang
AU - Wachsmuth, Henning
AU - Jochim, Charles
AU - Bonin, Francesca
AU - Stein, Benno
ID - 15820
T2 - Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020)
TI - End-to-End Argumentation Knowledge Graph Construction
ER -
TY - JOUR
AB - Radiation tolerance in FPGAs is an important field of research particularly for reliable computation in electronics used in aerospace and satellite missions. The motivation behind this research is the degradation of reliability in FPGA hardware due to single-event effects caused by radiation particles. Redundancy is a commonly used technique to enhance the fault-tolerance capability of radiation-sensitive applications. However, redundancy comes with an overhead in terms of excessive area consumption, latency, and power dissipation. Moreover, the redundant circuit implementations vary in structure and resource usage with the redundancy insertion algorithms as well as number of used redundant stages. The radiation environment varies during the operation time span of the mission depending on the orbit and space weather conditions. Therefore, the overheads due to redundancy should also be optimized at run-time with respect to the current radiation level. In this paper, we propose a technique called Dynamic Reliability Management (DRM) that utilizes the radiation data, interprets it, selects a suitable redundancy level, and performs the run-time reconfiguration, thus varying the reliability levels of the target computation modules. DRM is composed of two parts. The design-time tool flow of DRM generates a library of various redundant implementations of the circuit with different magnitudes of performance factors. The run-time tool flow, while utilizing the radiation/error-rate data, selects a required redundancy level and reconfigures the computation module with the corresponding redundant implementation. Both parts of DRM have been verified by experimentation on various benchmarks. The most significant finding we have from this experimentation is that the performance can be scaled multiple times by using partial reconfiguration feature of DRM, e.g., 7.7 and 3.7 times better performance results obtained for our data sorter and matrix multiplier case studies compared with static reliability management techniques. Therefore, DRM allows for maintaining a suitable trade-off between computation reliability and performance overhead during run-time of an application.
AU - Anwer, Jahanzeb
AU - Meisner, Sebastian
AU - Platzner, Marco
ID - 17092
JF - International Journal of Reconfigurable Computing
SN - 1687-7195
TI - Dynamic Reliability Management for FPGA-Based Systems
ER -
TY - CONF
AU - Kiesel, Johannes
AU - Lang, Kevin
AU - Wachsmuth, Henning
AU - Hornecker, Eva
AU - Stein, Benno
ID - 15825
T2 - Proceedings of the 2020 ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2020)
TI - Investigating Expectations for Voice-based and Conversational Argument Search on the Web
ER -
TY - CONF
AU - Jovanovikj, Ivan
AU - Yigitbas, Enes
AU - Sauer, Stefan
AU - Engels, Gregor
ID - 15604
SN - 978-989-758-400-8
T2 - Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development - Volume 1: MODELSWARD
TI - Concept-based Co-Migration of Test Cases
ER -
TY - CHAP
AU - Moritzer, Elmar
AU - Hüttner, Matthias
AU - Henning, Bernd
AU - Webersen, Manuel
ED - Hopmann, Christian
ED - Dahlmann, Rainer
ID - 17352
SN - 9783662608081
T2 - Advances in Polymer Processing 2020
TI - The Influence of Hydrothermal Aging on the Material Properties of Continuous Fiber-Reinforced Thermoplastics and its Non-Destructive Characterization
ER -
TY - CHAP
AB - Diagrammatisches Schlie{\ss}en wird im Zusammenhang mit dem Lernen von Mathmematik und ihrer Symbolsprache als wesentliche Theorie der Wissenskonstruktion diskutiert. Dabei wird h{\"{a}}ufig davon ausgegangen, dass die Wissenskonstruktion im Sinne diagrammatischen Schlie{\ss}ens erfolgt. Deskriptive Rekonstruktionen diagrammatischen Schlie{\ss}ens bei Lernenden stellen jedoch ein Desiderat der mathematikdidaktischen Forschung dar. Der vorliegende Beitrag befasst sich mit der Fragestellung, wie sich diagrammatisches Schlie{\ss}en bei Lernenden rekonstruieren l{\"{a}}sst. Als m{\"{o}}gliche Werkzeuge f{\"{u}}r eine solche Rekonstruktion werden Toulmins Argumentationsschema und Vergnauds Schema-Begriff exemplarisch angewandt, um das diagrammatische Schlie{\ss}en eines Sch{\"{u}}lerpaars beim Einstieg in die Subtraktion negativer Zahlen zu rekonstruieren. Abschlie{\ss}end wird die tats{\"{a}}chliche Eignung der beiden Ans{\"{a}}tze zur Rekonstruktion diagrammatischen Schlie{\ss}ens diskutiert.
AU - Schumacher, Jan
AU - Rezat, Sebastian
ED - Kadunz, Gert
ID - 13108
T2 - Zeichen und Sprache im Mathematikunterricht
TI - Rekonstruktion diagrammatischen Schließens beim Erlernen der Subtraktion negativer Zahlen. Vergleich zweier methodischer Zugänge
ER -
TY - JOUR
AU - Ho, Nam
AU - Kaufmann, Paul
AU - Platzner, Marco
ID - 17369
JF - International Journal of Hybrid intelligent Systems
TI - Evolution of Application-Specific Cache Mappings
ER -
TY - CONF
AU - Castenow, Jannik
AU - Kling, Peter
AU - Knollmann, Till
AU - Meyer auf der Heide, Friedhelm
ID - 17371
SN - 9781450369350
T2 - Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures
TI - A Discrete and Continuous Study of the Max-Chain-Formation Problem: Slow Down to Speed up
ER -
TY - CONF
AU - Hanselle, Jonas Manuel
AU - Tornede, Alexander
AU - Wever, Marcel Dominik
AU - Hüllermeier, Eyke
ID - 17408
T2 - KI 2020: Advances in Artificial Intelligence
TI - Hybrid Ranking and Regression for Algorithm Selection
ER -
TY - GEN
AB - Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data.
While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.
These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.
In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.
AU - Heid, Stefan Helmut
AU - Wever, Marcel Dominik
AU - Hüllermeier, Eyke
ID - 17605
T2 - Journal of Data Mining and Digital Humanities
TI - Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
ER -
TY - CONF
AU - Haeb-Umbach, Reinhold
ED - Böck, Ronald
ED - Siegert, Ingo
ED - Wendemuth, Andreas
ID - 17763
KW - Poster
SN - 978-3-959081-93-1
T2 - Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020
TI - Sprachtechnologien für Digitale Assistenten
ER -
TY - JOUR
AB - In this work we present a set-oriented path following method for the computation of relative global
attractors of parameter-dependent dynamical systems. We start with an initial approximation of the
relative global attractor for a fixed parameter λ0 computed by a set-oriented subdivision method.
By using previously obtained approximations of the parameter-dependent relative global attractor
we can track it with respect to a one-dimensional parameter λ > λ0 without restarting the whole
subdivision procedure. We illustrate the feasibility of the set-oriented path following method by
exploring the dynamics in low-dimensional models for shear flows during the transition to turbulence
and of large-scale atmospheric regime changes .
AU - Gerlach, Raphael
AU - Ziessler, Adrian
AU - Eckhardt, Bruno
AU - Dellnitz, Michael
ID - 16710
JF - SIAM Journal on Applied Dynamical Systems
SN - 1536-0040
TI - A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors
ER -
TY - GEN
AU - Warner, Daniel
ID - 15770
TI - On the complexity of local transformations in SDN overlays
ER -
TY - CONF
AU - Chen, Wei-Fan
AU - Syed, Shahbaz
AU - Stein, Benno
AU - Hagen, Matthias
AU - Potthast, Martin
ID - 15826
T2 - Proceedings of the the Web Conference 2020
TI - Abstractive Snippet Generation
ER -
TY - CONF
AB - In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.
AU - Wever, Marcel Dominik
AU - Tornede, Alexander
AU - Mohr, Felix
AU - Hüllermeier, Eyke
ID - 15629
TI - LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification
ER -
TY - THES
AU - Feldkord, Björn
ID - 15631
TI - Mobile Resource Allocation
ER -
TY - JOUR
AU - Claes, Leander
AU - Steidl, Carolin
AU - Hetkämper, Tim
AU - Henning, Bernd
ID - 15489
JF - arXiv.org
TI - Estimation of acoustic wave non-linearity in ultrasonic measurement systems
ER -
TY - JOUR
AB - Currently, the coexistence of multiple users and devices challenges the network's ability to reliably connect them. This article proposes a novel communication architecture that satisfies the requirements of fifth-generation (5G) mobile network applications. In particular, this architecture extends and combines ultra-dense networking (UDN), multi-access edge computing (MEC), and virtual infrastructure manager (VIM) concepts to provide a flexible network of moving radio access (RA) nodes, flying or moving to areas where users and devices struggle for connectivity and data rate. Furthermore, advances in radio communications and non-orthogonal multiple access (NOMA), virtualization technologies and energy-awareness mechanisms are integrated towards a mobile UDN that not only allows RA nodes to follow the user but also enables the virtualized network functions (VNFs) to adapt to user mobility by migrating from one node to another. Performance evaluation shows that the underlying network improves connectivity of users and devices through the flexible deployment of moving RA nodes and the use of NOMA.
AU - Nomikos, Nikolaos
AU - Michailidis, Emmanouel T.
AU - Trakadas, Panagiotis
AU - Vouyioukas, Demosthenes
AU - Karl, Holger
AU - Martrat, Josep
AU - Zahariadis, Theodore
AU - Papadopoulos, Konstantinos
AU - Voliotis, Stamatis
ID - 16278
JF - Vehicular Communications
SN - 2214-2096
TI - A UAV-based moving 5G RAN for massive connectivity of mobile users and IoT devices
ER -
TY - JOUR
AB - Assigning bands of the wireless spectrum as resources to users is a common problem in wireless networks. Typically, frequency bands were assumed to be available in a stable manner. Nevertheless, in recent scenarios where wireless networks may be deployed in unknown environments, spectrum competition is considered, making it uncertain whether a frequency band is available at all or at what quality. To fully exploit such resources with uncertain availability, the multi-armed bandit (MAB) method, a representative online learning technique, has been applied to design spectrum scheduling algorithms. This article surveys such proposals. We describe the following three aspects: how to model spectrum scheduling problems within the MAB framework, what the main thread is following which prevalent algorithms are designed, and how to evaluate algorithm performance and complexity. We also give some promising directions for future research in related fields.
AU - Li, Feng
AU - Yu, Dongxiao
AU - Yang, Huan
AU - Yu, Jiguo
AU - Karl, Holger
AU - Cheng, Xiuzhen
ID - 16280
JF - IEEE Wireless Communications
SN - 1536-1284
TI - Multi-Armed-Bandit-Based Spectrum Scheduling Algorithms in Wireless Networks: A Survey
ER -
TY - GEN
AB - In real-world problems, uncertainties (e.g., errors in the measurement,
precision errors) often lead to poor performance of numerical algorithms when
not explicitly taken into account. This is also the case for control problems,
where optimal solutions can degrade in quality or even become infeasible. Thus,
there is the need to design methods that can handle uncertainty. In this work,
we consider nonlinear multi-objective optimal control problems with uncertainty
on the initial conditions, and in particular their incorporation into a
feedback loop via model predictive control (MPC). In multi-objective optimal
control, an optimal compromise between multiple conflicting criteria has to be
found. For such problems, not much has been reported in terms of uncertainties.
To address this problem class, we design an offline/online framework to compute
an approximation of efficient control strategies. This approach is closely
related to explicit MPC for nonlinear systems, where the potentially expensive
optimization problem is solved in an offline phase in order to enable fast
solutions in the online phase. In order to reduce the numerical cost of the
offline phase, we exploit symmetries in the control problems. Furthermore, in
order to ensure optimality of the solutions, we include an additional online
optimization step, which is considerably cheaper than the original
multi-objective optimization problem. We test our framework on a car
maneuvering problem where safety and speed are the objectives. The
multi-objective framework allows for online adaptations of the desired
objective. Alternatively, an automatic scalarizing procedure yields very
efficient feedback controls. Our results show that the method is capable of
designing driving strategies that deal better with uncertainties in the initial
conditions, which translates into potentially safer and faster driving
strategies.
AU - Hernández Castellanos, Carlos Ignacio
AU - Ober-Blöbaum, Sina
AU - Peitz, Sebastian
ID - 16297
T2 - arXiv:2002.06006
TI - Explicit Multi-objective Model Predictive Control for Nonlinear Systems Under Uncertainty
ER -
TY - CONF
AU - Krauter, Stefan
AU - Zhang, L.
ID - 16855
T2 - Tagungsband des 35. Symposiums für Photovoltaische Solarenergie, Kloster Banz, Bad Staffelstein (Deutschland)
TI - Eignung der Netzfrequenz als Instrument der Entscheidungsfindung zur Auslösung von Lastverschiebungen bei niedrigen spezifischen CO 2 -Emsissionen und EEX- Handelspreisen
ER -
TY - GEN
AB - In this article, we present an efficient descent method for locally Lipschitz
continuous multiobjective optimization problems (MOPs). The method is realized
by combining a theoretical result regarding the computation of descent
directions for nonsmooth MOPs with a practical method to approximate the
subdifferentials of the objective functions. We show convergence to points
which satisfy a necessary condition for Pareto optimality. Using a set of test
problems, we compare our method to the multiobjective proximal bundle method by
M\"akel\"a. The results indicate that our method is competitive while being
easier to implement. While the number of objective function evaluations is
larger, the overall number of subgradient evaluations is lower. Finally, we
show that our method can be combined with a subdivision algorithm to compute
entire Pareto sets of nonsmooth MOPs.
AU - Gebken, Bennet
AU - Peitz, Sebastian
ID - 16867
T2 - arXiv:2004.11578
TI - An efficient descent method for locally Lipschitz multiobjective optimization problems
ER -
TY - GEN
AB - Electronic structure calculations based on density-functional theory (DFT)
represent a significant part of today's HPC workloads and pose high demands on
high-performance computing resources. To perform these quantum-mechanical DFT
calculations on complex large-scale systems, so-called linear scaling methods
instead of conventional cubic scaling methods are required. In this work, we
take up the idea of the submatrix method and apply it to the DFT computations
in the software package CP2K. For that purpose, we transform the underlying
numeric operations on distributed, large, sparse matrices into computations on
local, much smaller and nearly dense matrices. This allows us to exploit the
full floating-point performance of modern CPUs and to make use of dedicated
accelerator hardware, where performance has been limited by memory bandwidth
before. We demonstrate both functionality and performance of our implementation
and show how it can be accelerated with GPUs and FPGAs.
AU - Lass, Michael
AU - Schade, Robert
AU - Kühne, Thomas
AU - Plessl, Christian
ID - 16898
TI - A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CP2K
ER -