Sheir Yarkoni, Elena Raponi, Thomas Bäck, Sebastian Schmitt,
"Quantum Annealing for Industry Applications: Introduction and Review",
Report on Progress in Physics, 2022.
Abstract
Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial
optimization problems. In recent years, advances in quantum technologies have enabled the development
of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for
programmable use. Specifically, quantum annealing processors produced by D-Wave Systems have been
studied and tested extensively in both ...
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Karsten Kreutz and Julian Eggert,
"Robust Car Following Models require Explicit Reaction Times",
Intelligent Transportation Systems Conference (ITSC) 2022, 2022.
Abstract
Car following models like the Intelligent Driver
Model (IDM) describe the longitudinal behavior of an ego-car
depending on a leading vehicle. Especially the IDM has been
applied within a broad range of mobility-related tasks like the
analysis of traffic phenomena, microscopic traffic simulations
as well as for single vehicle behavior prediction. Although car
following models can be formulated with explicit delays (e.g. in
form of system re...
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Yasuyuki Shimizu, Nivesh Dommaraju, Mariusz Bujny, Stefan Menzel, Markus Olhofer, Fabian Duddeck,
"Deformation Clustering Methods for Topologically Optimized Structures under Crash Load based on Displacement Time Series ",
15th World Congress on Computational Mechanics 2022, 2022.
Abstract
Multi-objective topology optimization has been receiving more and more attention in structural design recently. It attempts to maximize several performance objectives by redistributing the material in a design space for a given set of boundary conditions and constraints, yielding many Pareto-optimal solutions. However, the high number of solutions makes it difficult to identify preferred designs. Therefore, an automated way of summarizing solutio...
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Jörg Deigmöller, Pavel Smirnov, Chao Wang, Julian Eggert, Johane Takeuchi,
"Situational Question Answering using Memory Nets",
International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2022.
Abstract
In the recent years, there have been quite a number of publications and benchmarks on Embodied Question Answering (EQA). This domain tackles the problem of asking questions to an agent that navigates in a simulated environment. Those questions usually refer to physical objects embedded in the environment. The challenge here is to build an end-to-end system that learns the mapping of a question to a sequence of actions, to finally generate an app...
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Thomas Schmitt,
"Multi-Objective Building Energy Management Optimization with Model Predictive Control",
TU Darmstadt, 2022.
Abstract
Today’s goals for the reduction of CO 2 emissions are significantly impacting both the civil and the industrial sector. The increasing share of renewable energy sources leads to more volatile and challenging conditions for power consumption. The building sector is responsible for approximately a third of both CO 2 emissions and energy consumption in Germany. At the same
time, it offers the potential to adapt to the changing conditions by the i...
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Meike Kühne, Patricia Wollstadt, Tim Schrills, Thomas Franke,
"Preregistration: Psychological factors underlying the perception of cooperation in a smart-charging scenario",
The Open Science Framework (OSF.io), 2022.
Abstract
It is often assumed to be beneficial for human-machine interaction (HMI) systems to behave cooperatively in interaction with a human user. In particular, it is assumed that cooperation in HMI leads to increased user satisfaction and task success. Despite a high interest in cooperation as a design paradigm for HMI, it remains unclear which psychological factors determine whether a machine is perceived as cooperative in an HMI context. The present ...
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Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff, Thomas Bäck,
"A Systematic Approach to Analyze the Computational Cost of Robustness in Model-Assisted Robust Optimization",
International Conference on Parallel Problem Solving from Nature (PPSN), 2022.
Abstract
Real-world optimization scenarios under uncertainty and noise are typically handled with robust optimization techniques, which reformulate the original optimization problem into a robust counterpart, e.g., by taking an average of the function values over different perturbations to a specific input. Solving the robust counterpart instead of the original problem can significantly increase the associated computational cost, which is often overlooked...
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Zhenpeng Shi, Nikolay Matyunin, Kalman Graffi, David Starobinski,
"Uncovering Product Vulnerabilities with Threat Knowledge Graph",
IEEE Secure Development Conference, pp. 84-90, 2022.
Abstract
Threat modeling and security assessment rely on public information on products, vulnerabilities and weaknesses. So far, databases in these categories have rarely been analyzed in combination. Yet, doing so could help predict unreported vulnerabilities and identify common threat patterns. In this paper, we propose a methodology for producing and optimizing a knowledge graph that aggregates knowledge from common threat databases (CPE, CVE, and CWE)...
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Manuel Dietrich and Thomas Weisswange,
"Exploring Privacy Implications for Domestic Robot Mediators",
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022) - Workshop on Robot Trust for Symbiotic Societies (RTSS 2022), 2022.
Abstract
To become part of our everyday social environment, robots will need to be developed in a way to gain an appropriate level of trust of humans, both users and bystanders. One important aspect influencing trust is the handling of privacy. In this paper, we explore privacy challenges of social robots when acting in the role of mediators in human-human interactions within domestic assistance scenarios. We approach this topic by reviewing privacy resea...
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Tapabrata Ray, Hemant Singh, Kamrul Rahi, Tobias Rodemann, Markus Olhofer,
"Towards identification of solutions of interest for multi-objective problems considering both objective and variable space information",
Applied Soft Computing, vol. 119, 2022.
Abstract
In practical multi/many-objective optimization problems, a decision maker (DM) is often only interested in a handful of solutions of interest (SOI) instead of a large collection of trade-off solutions spanning the entire Pareto Front (PF). Optimization algorithms capable of searching for SOIs have so far considered selection measures based on objective space only, such as reflex angle, bend angle, expected marginal utility, etc. Depending on the ...
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