Emotion recognition promotes the evaluation and enhancement of Virtual Reality (VR) experiences by providing emotional feedback and enabling advanced personalization. However, facial expressions are rarely used to recognize users' emotions, as Head-Mounted Displays (HMDs) occlude the upper half of the face. To address this issue, we conducted a study with 37 participants who played our novel affective VR game EmojiHeroVR. The collected database, EmoHeVRDB (EmojiHeroVR Database), includes 3,556 labeled facial images of 1,778 reenacted emotions. For each labeled image, we also provide 29 additional frames recorded directly before and after the labeled image to facilitate dynamic Facial Expression Recognition (FER). Additionally, EmoHeVRDB includes data on the activations of 63 facial expressions captured via the Meta Quest Pro VR headset for each frame. Leveraging our database, we conducted a baseline evaluation on the static FER classification task with six basic emotions and neutral using the EfficientNet-B0 architecture. The best model achieved an accuracy of 69.84% on the test set, indicating that FER under HMD occlusion is feasible but significantly more challenging than conventional FER.

The vast majority of cardiovascular diseases may be preventable if early signs and risk factors are detected. Cardiovascular monitoring with body-worn sensor devices like sensor patches allows for the detection of such signs while preserving the freedom and comfort of patients. However, the analysis of the sensor data must be robust, reliable, efficient, and highly accurate. Deep learning methods can automate data interpretation, reducing the workload of clinicians. In this work, we analyze the feasibility of applying deep learning models to the classification of synchronized electrocardiogram (ECG) and phonocardiogram (PCG) recordings on resource-constrained medical edge devices. We propose a convolutional neural network with early fusion of data to solve a binary classification problem. We train and validate our model on the synchronized ECG and PCG recordings from the Physionet Challenge 2016 dataset. Our approach reduces memory footprint and compute cost by three orders of magnitude compared to the state-of-the-art while maintaining competitive accuracy. We demonstrate the applicability of our proposed model on medical edge devices by analyzing energy consumption on a microcontroller and an experimental sensor device setup, confirming that on-device inference can be more energy-efficient than continuous data streaming.

This chapter outlines how search engine technology can be used in online public access library catalogs (OPACs) to help improve users experiences, to identify users intentions, and to indicate how it can be applied in the library context, along with how sophisticated ranking criteria can be applied to the online library catalog. A review of the literature and current OPAC developments form the basis of recommendations on how to improve OPACs. Findings were that the major shortcomings of current OPACs are that they are not sufficiently user-centered and that their results presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0 developments. It is argued that OPAC development should be made search-centered before additional features are applied. While the recommendations on ranking functionality and the use of user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find recommendations for developing better OPACs in this chapter. In short, readers will find a systematic view on how the search engines strengths can be applied to improving libraries online catalogs.

This paper proposes a new approach to perform small-signal stability analysis based on linearization of implicit multilinear models. Multilinear models describe the system dynamics by multilinear functions of state, input, and algebraic variables. Using suitable transformations of variables, they can also represent trigonometric functions, which often occur in power systems modeling. This allows tensor representations of grid-following and grid-forming power converters. This paper introduces small-signal stability analysis of equilibrium points based on implicit multilinear models using generalized eigenvalues. The generalized eigenvalues are computed from linear descriptor models of the linearized implicit multilinear model. The proposed approach is tested using a 3-bus network example, first by comparing time-domain simulations of the implicit multilinear model with those of the nonlinear model, and second by comparing the generalized eigenvalues with those of the linearized nonlinear model. The results show that the decomposed tensor representation of the implicit multilinear model allows for a faster linearization compared to conventional methods in MATLAB Simulink.

Purpose: To compare five major Web search engines (Google, Yahoo, MSN, this http URL, and Seekport) for their retrieval effectiveness, taking into account not only the results but also the results descriptions. Design/Methodology/Approach: The study uses real-life queries. Results are made anonymous and are randomised. Results are judged by the persons posing the original queries. Findings: The two major search engines, Google and Yahoo, perform best, and there are no significant differences between them. Google delivers significantly more relevant result descriptions than any other search engine. This could be one reason for users perceiving this engine as superior. Research Limitations: The study is based on a user model where the user takes into account a certain amount of results rather systematically. This may not be the case in real life. Practical Implications: Implies that search engines should focus on relevant descriptions. Searchers are advised to use other search engines in addition to Google. Originality/Value: This is the first major study comparing results and descriptions systematically and proposes new retrieval measures to take into account results descriptions

Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks that visualize a high-dimensional feature space on a low-dimensional map. While SOMs are an excellent tool for data examination and exploration, they inherently cause a loss of detail. Visualizations of the underlying data do not integrate well and, therefore, fail to provide an overall picture. Consequently, we suggest SOMson, an interactive sonification of the underlying data, as a data augmentation technique. The sonification increases the amount of information provided simultaneously by the SOM. Instead of a user study, we present an interactive online example, so readers can explore SOMson themselves. Its strengths, weaknesses, and prospects are discussed.

To facilitate the understanding and to quantitatively assess the material transport in fluids, a modern characterisation method has emerged in fluid dynamics in the last decades footed in dynamical systems theory. It allows to examine the most influential material lines which are called Lagrangian Coherent Structures (LCS) and order the material transport into dynamically distinct regions at large scales which resist diffusion or mixing. LCS reveal the robust skeleton of material surfaces and are essential to assess material transport in time-dependent flows quantitatively. Candidates of LCS can be estimated and visualised from finite-time stretching and folding fields by calculating the Finite-Time Lyapunov Exponents (FTLE). In this contribution, we provide an OpenFOAM function object to compute FTLE during CFD simulation. This enables the OpenFOAM community to assess the geometry of the material transport in any flow quantitatively on-the-fly using principally any OpenFOAM flow solver.

Time-Sensitive Networking enhances Ethernet-based In-Vehicle Networks (IVNs) with real-time capabilities. Different traffic shaping algorithms have been proposed for time-critical communication, of which the Asynchronous Traffic Shaper (ATS) is an upcoming candidate. However, recent research has shown that ATS can introduce unbounded latencies when shaping traffic from non-FIFO systems. This impacts the applicability of ATS in IVNs, as these networks often use redundancy mechanisms, i.e. Frame Replication and Elimination for Reliability (FRER), that can cause non-FIFO behavior. In this paper, we approach the problem of accumulated delays from ATS by analyzing the scenarios that generate latency and by devising placement and configuration methods for ATS schedulers to prevent this behavior. We evaluate our approach in a simulation environment and show how it prevents conditions of unbounded delays. In an IVN simulation case study, we demonstrate the occurrence of unbounded latencies in a realistic scenario and validate the effectiveness of our solutions in avoiding them.

The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

In past work, the concept of connectors was introduced: directed tensors with the property that any contraction thereof defines a multipartite quantum Bell inequality, i.e., a linear restriction on measurement probabilities that holds in any multipartite quantum experiment. In this paper we propose the notion of ''tight connectors'', which, if contracted according to some simple rules, result in tight quantum Bell inequalities. By construction, the new inequalities are saturated by tensor network states, whose structure mimics the corresponding network of connectors. Some tight connectors are furthermore ''fully self-testing'', which implies that the quantum Bell inequalities they generate can only be maximized with such a tensor network state and specific measurement operators (modulo local isometries). We provide large analytic families of tight, fully self-testing connectors that generate $N$-partite quantum Bell inequalities of correlator form for which the ratio between the maximum quantum and classical values increases exponentially with $N$.

Searching for medical information is both a common and important activity since it influences decisions people make about their healthcare. Using search engine optimization (SEO), content producers seek to increase the visibility of their content. SEO is more likely to be practiced by commercially motivated content producers such as pharmaceutical companies than by non-commercial providers such as governmental bodies. In this study, we ask whether content quality correlates with the presence or absence of SEO measures on a web page. We conducted a user study in which N = 61 participants comprising laypeople as well as experts in health information assessment evaluated health-related web pages classified as either optimized or non-optimized. The subjects rated the expertise of non-optimized web pages as higher than the expertise of optimized pages, justifying their appraisal by the more competent and reputable appearance of non-optimized pages. In addition, comments about the website operators of the non-optimized pages were exclusively positive, while optimized pages tended to receive positive as well as negative assessments. We found no differences between the ratings of laypeople and experts. Since non-optimized, but high-quality content may be outranked by optimized content of lower quality, trusted sources should be prioritized in rankings.

Purpose: The purpose of this paper is to measure the coverage of Google Scholar for the Library and Information Science (LIS) journal literature as identified by a list of core LIS journals from a study by Schloegl and Petschnig (2005). Methods: We checked every article from 35 major LIS journals from the years 2004 to 2006 for availability in Google Scholar (GS). We also collected information on the type of availability-i.e., whether a certain article was available as a PDF for a fee, as a free PDF, or as a preprint. Results: We found that only some journals are completely indexed by Google Scholar, that the ratio of versions available depends on the type of publisher, and that availability varies a lot from journal to journal. Google Scholar cannot substitute for abstracting and indexing services in that it does not cover the complete literature of the field. However, it can be used in many cases to easily find available full texts of articles already found using another tool. Originality/value: This study differs from other Google Scholar coverage studies in that it takes into account not only whether an article is indexed in GS at all, but also the type of availability.

Automotive softwarization is progressing and future cars are expected to operate a Service-Oriented Architecture on multipurpose compute units, which are interconnected via a high-speed Ethernet backbone. The AUTOSAR architecture foresees a universal middleware called SOME/IP that provides the service primitives, interfaces, and application protocols on top of Ethernet and IP. SOME/IP lacks a robust security architecture, even though security is an essential in future Internet-connected vehicles. In this paper, we augment the SOME/IP service discovery with an authentication and certificate management scheme based on DNSSEC and DANE. We argue that the deployment of well-proven, widely tested standard protocols should serve as an appropriate basis for a robust and reliable security infrastructure in cars. Our solution enables on-demand service authentication in offline scenarios, easy online updates, and remains free of attestation collisions. We evaluate our extension of the common vsomeip stack and find performance values that fully comply with car operations.

The anti-Stoner excitations are a spin-flips in which, effectively, an electron is promoted from a minority to a majority spin state, i.e., complementary to Stoner excitations and spin-waves. Since their spectral power is negligible in strong itinerant ferromagnets and they are identically absent in the ferromagnetic Heisenberg model, their properties and role in correlating electrons were hardly investigated so far. On the other hand, they are present in weak ferromagnets, fcc Ni being a prominent example, and both types of spin-flips (down-to-up and up-to-down) must be treated on the equal footing in systems with the degenerate spin up and down bands, in particular antiferromagnets in which case we choose CrSb as a model system. For these two materials we evaluate the strength of the effective interaction between the quasiparticles and the gas of virtual spin-flip excitations. To this end, we compute the corresponding self-energy taking advantage of our novel efficient \textit{ab initio} numerical scheme. We find that in Ni the band-structure renormalization due to the anti-Stoner processes is weaker than the one due to Stoner-type magnons in the majority spin channel but the two become comparable in the minority one. The effect can be traced back primarily to the spectral strength of the respective spin excitations and the densities of the final available quasiparticle states in the scattering process. For the antiferromagnet, the situation is more complex and we observe that the electron-magnon interaction is sensitive not only to these densities of states but critically to the spatial shapes of the coupling magnonic modes as well.

We study theoretically the influence of the temperature and disorder on the spin wave spectrum of the magnonic crystal Fe$_{1-c}$Co$_{c}$. Our formalism is based on the analysis of a Heisenberg Hamiltonian by means of the wave vector and frequency dependent transverse magnetic susceptibility. The exchange integrals entering the model are obtained from the \emph{ab initio} magnetic force theorem. The coherent potential approximation is employed to treat the disorder and random phase approximation in order to account for the softening of the magnon spectrum at finite temperatures. The alloy turns out to exhibit many advantageous properties for spintronic applications. Apart from high Curie temperature, its magnonic bandgap remains stable at elevated temperatures and is largely unaffected by the disorder. We pay particular attention to the attenuation of magnons introduced by the alloying. The damping turns out to be a non-monotonic function of the impurity concentration due to the non-trivial evolution of the value of exchange integrals with the Co concentration. The disorder induced damping of magnons is estimated to be much smaller than their Landau damping.

Inter-connected sensors and actuators have scaled down to small embedded devices such as wearables, and at the same time meet a massive deployment at the Internet edge: the Internet of Things (IoT). Many of these IoT devices run on low-power batteries and are forced to operate on very constrained resources, namely slow CPUs, tiny memories, and low-power radios. Establishing a network infrastructure that is energy efficient, wireless, and still covers a wide area is a larger challenge in this regime. LoRa is a low complexity long range radio technology, which tries to meet these this http URL LoRaWAN a network model for widespread deployment has been established, which enjoys open public LoRaWAN dissemination such as with the infrastructure of TheThingsNetwork. In this paper, we report about our experiences with developing and deploying LoRa-based smart city applications as part of the MONICA project in Hamburg. Our contributions are twofold. First, we describe the design and implementation of end-to-end IoT applications based on the friendly IoT operating system RIOT. Second, we report on measurements and evaluations of our large field trials during several public events in the city of Hamburg. Our results show that LoRaWAN provides a suitable communication layer for a variety of Smart City use-cases and IoT applications, but also identifies its limitations and weaknesses.

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and -- as versatile platforms to host Network Anomaly Detection Systems (NADSs) -- for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs.

When musicians perform in an ensemble, synchronizing to a mutual pace is the foundation of their musical interaction. Clock generators, e.g., metronomes, or drum machines, might assist such synchronization, but these means, in general, will also distort this natural, self-organized, inter-human synchronization process. In this work, the synchronization of musicians to an external rhythm is modeled using the Impulse Pattern Formulation (IPF), an analytical modeling approach for synergetic systems motivated by research on musical instruments. Nonlinear coupling of system components is described as the interaction of individually propagating and exponentially damped impulse trains. The derived model is systematically examined by analyzing its behavior when coupled to numerical designed and carefully controlled rhythmical beat sequences. The results are evaluated by comparison in the light of other publications on tapping. Finally, the IPF model can be applied to analyze the personal rhythmical signature of specific musicians or to replace drum machines and click tracks with more musical and creative solutions.

This representative study of German search engine users (N=1,000) focuses on the ability of users to distinguish between organic results and advertisements on Google results pages. We combine questions about Google's business with task-based studies in which users were asked to distinguish between ads and organic results in screenshots of results pages. We find that only a small percentage of users is able to reliably distinguish between ads and organic results, and that user knowledge of Google's business model is very limited. We conclude that ads are insufficiently labelled as such, and that many users may click on ads assuming that they are selecting organic results.