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Laser ultrasound was used to determine dispersion curves of surface acoustic waves on a Si (001) surface covered by AlScN films with a scandium content between 0 and 41%. By including off-symmetry directions for wavevectors, all five independent elastic constants of the film were extracted from the measurements. Results for their dependence on the Sc content are presented and compared to corresponding data in the literature, obtained by alternative experimental methods or by ab-initio calculations.
Diffracted waves carry high resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. Because of the low signal-to-noise ratio of diffracted waves, it is challenging to preserve them during processing and to identify them in the final data. It is, therefore, a traditional approach to pick manually the diffractions. However, such task is tedious and often prohibitive, thus, current attention is given to domain adaptation. Those methods aim to transfer knowledge from a labeled domain to train the model, and then infer on the real unlabeled data. In this regard, it is common practice to create a synthetic labeled training dataset, followed by testing on unlabeled real data. Unfortunately, such procedure may fail due to the existing gap between the synthetic and the real distribution since quite often synthetic data oversimplifies the problem, and consequently the transfer learning becomes a hard and non-trivial procedure. Furthermore, deep neural networks are characterized by their high sensitivity towards cross-domain distribution shift. In this work, we present deep learning model that builds a bridge between both distributions creating a semi-synthetic datatset that fills in the gap between synthetic and real domains. More specifically, our proposal is a feed-forward, fully convolutional neural network for imageto-image translation that allows to insert synthetic diffractions while preserving the original reflection signal. A series of experiments validate that our approach produces convincing seismic data containing the desired synthetic diffractions.
We propose in this work to solve privacy preserving set relations performed by a third party in an outsourced configuration. We argue that solving the disjointness relation based on Bloom filters is a new contribution in particular by having another layer of privacy on the sets cardinality. We propose to compose the set relations in a slightly different way by applying a keyed hash function. Besides discussing the correctness of the set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. We are in particular interested in how having bits overlapping in the Bloom filters impacts the privacy level of our approach. Finally, we present our results with real-world parameters in two concrete scenarios.
This paper describes a comparative study of two tactile systems supporting navigation for persons with little or no visual and auditory perception. The efficacy of a tactile head-mounted device (HMD) was compared to that of a wearable device, a tactile belt. A study with twenty participants showed that the participants took significantly less time to complete a course when navigating with the HMD, as compared to the belt.
To reach customers by dialog marketing campaigns is more and more difficult. This is a common problem of companies and marketing agencies worldwide: information overload, multi-channel-communication and a confusing variety of offers make it hard to gain the attention of the target group. The contribution of this paper is four-fold: we provide an overview of the current state of print dialog marketing activities and trends (I). Based on this corpus we identify the main key performance indicators of dialog marketing customer interaction (II). A qualitative user experience study identifies the customer wishes and needs, focusing on lottery offers for senior citizens (III). Finally, we evaluate the success of two different dialog marketing campaigns with 20,000 clients and compare the key performance indicators of the original hands-on experience-based print mailings with user experience tested and optimized mailings (IV).
Im Rahmen des Forschungsprojekts Professional UX entwickelt die Hochschule Offenburg gemeinsam mit dem Softwarehaus Dr. Hornecker in Freiburg eine innovative Systemlösung, die es ermöglicht, anhand von Mimik, Stimme und Blickverlauf beim Nutzer entstehende Emotionen bei der Nutzung interaktiver Anwendungen zu erfassen und zu interpretieren. Ziel der Untersuchung ist es, Indikatoren zu identifizieren, die eine exakte Zuordnung von wahrgenommenen Reizen zu den jeweils ausgelösten Emotionen erlauben. Sobald negative Emotionen wie Ärger oder Unsicherheit auftreten, kann dieser erfasst und im Nachgang der jeweils irritierende Reiz eliminiert werden. Das Projektteam hat einen ersten Prototyp für die Professional UX Systemlösung in Form von Hard- und Software entwickelt, mit dem es möglich ist, UX-Messungen während der User Interaktion durchzuführen und automatisiert mithilfe von KI auswerten zu lassen.
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.
This paper presents a novel low-jitter interface between a low-cost integrated IEEE802.11 chip and a FPGA. It is designed to be part of system hardware for ultra-precise synchronization between wireless stations. On physical level, it uses Wi-Fi chip coexistence signal lines and UART frame encoding. On its basis, we propose an efficient communication protocol providing precise timestamping of incoming frames and internal diagnostic mechanisms for detecting communication faults. Meanwhile it is simple enough to be implemented both in low-cost FPGA and commodity IEEE802.11 chip firmware. The results of computer simulation shows that developed FPGA implementation of the proposed protocol can precisely timestamp incoming frames as well as detect most of communication errors even in conditions of high interference. The probability of undetected errors was investigated. The results of this analysis are significant for the development of novel wireless synchronization hardware.
The recent successes and wide spread application of compute intensive machine learning and data analytics methods have been boosting the usage of the Python programming language on HPC systems. While Python provides many advantages for the users, it has not been designed with a focus on multiuser environments or parallel programming - making it quite challenging to maintain stable and secure Python workflows on a HPC system. In this paper, we analyze the key problems induced by the usage of Python on HPC clusters and sketch appropriate workarounds for efficiently maintaining multi-user Python software environments, securing and restricting resources of Python jobs and containing Python processes, while focusing on Deep Learning applications running on GPU clusters.
OVVL (the Open Weakness and Vulnerability Modeller) is a tool and methodology to support threat modeling in the early stages of the secure software development lifecycle. We provide an overview of OVVL (https://ovvl.org), its data model and browser-based UI. We equally provide a discussion of initial experiments on how identified threats in the design phase can be aligned with later activities in the software lifecycle (issue management and security testing).