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This article describes a contactless fiber-optic position sensor. It comprises a Polymer Optical Fiber that is grinded to form a D-shaped cross-section with an exposed fiber core. This sensor has two photodiodes at both fiber ends to measure the emitted light intensity. Light is coupled using a red LED at the side face into the exposed core of the fiber at a defined position. The position of the LED at the length of the fiber is measured by calculating the optical power quotient measured by both photodiodes. To test this sensor, Polymer Optical Fibers with different side surface roughness are produced and qualified. Measurements show that the optical power quotient is reproducible and nearly linear over the length of the fiber. It is also seen that the fiber attenuation increases when grinding the fiber side-face with rougher sandpaper. Position measurements show an absolute position error of this sensor in the range of a few millimeters. Microscope images show surface defects along the polished side face of the fiber that are expected to lead to a nonuniform attenuation along the fiber and cause the position errors. Overall, it is proven that this sensor principle works as a contactless low-cost position sensor for short distances with an absolute position standard deviation error lower than 1 mm.
Radar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation.
This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture.
Radio access network based positioning and ranging technologies become more and more important for autonomous driving. Positioning on the 5G Sidelink (SL) is discussed in Release 18 (2022-2024) of the Third Generation Partnership Project. For accurate timing-based ranging, a high temporal and spatial resolution is fundamental. The bandwidth for 5G SL positioning is limited due to sharing the existing SL resources with communication. Therefore, we send the positioning reference signal on two separate Component Carriers, i.e. we use a fragmented spectrum. We introduce a two step algorithm for estimating the time of arrival using the inflection point method. We proved a fragmented spectrum can achieve the same accuracy as a continuous spectrum.
At least 80% of modern and postmodern poems exhibit neither rhyme nor metrical schemes such as iamb or trochee. However, does this mean that they are free of any rhythmical features?TheUS American research onfree verse prosody claimsthe opposite: Modern poets like Whitman, the Imagists, the Beat poets and contemporary Slam poets have developed a postmetrical idea of prosody, using rhythmical features of everyday language, prose, and musical styles like Jazz or Hip Hop. It has spawned a large and complex variety intheir poetic prosodies
which,however,appearto bemuchharderto quantify and regularize than traditional patterns. In our project, we examinethe largest portal for spoken poetry Lyrikline and analysed and classified such rhythmical patterns by using pattern recognition and classification techniques. We integrate a human-in-the-loop approach in which we interleave manual annotation with computational modelling and data-based analysis. Our results are integrated into the website of Lyrikline. Our follow-up project makes our research results available to a wider audience, in particular to high school-level teaching.
The third-order nonlinear susceptibility of silica glass is measured via self-phase modulation in standard single mode fibers at a wavelength of 1550 nm. To minimize the influence of polarization state changes along the propagation only meter-long fibers were investigated. With pulse durations of picoseconds a quasi-instantaneous nonlinearity with ultrafast electronic and fast nuclear-vibration contributions produces under conditions of negligible dispersion a classic and clean nonlinear phase shift following exactly the shape of the pulse power. The complex pulse envelope was retrieved from frequency optical gating spectrograms. The nonlinear fiber parameter γ could be determined with an accuracy of 3.7 percent. Considering the mode field structure and the doping influence the nonlinear refractive index of silica glass as the fiber base material was found to be n2 = 2.22 · 10−16 cm2
W ± 6.0 % for picosecond-long pulses. Comparing nonlinear phase shifts from linear and circular polarized light a nuclear-vibration contribution to the cubic fiber nonlinearity of 25 percent was estimated.
Despite the relevance and maturity of the Chief Information Officer (CIO) research field, no studies exist that exhaustively summarize the current body of knowledge, focusing on the development of the field over its entire timespan. The paper at hand addresses this research gap and presents an exhaustive literature review on the CIO research field using main path analysis. We identify the central papers in CIO research and eight main research streams by quantitatively and qualitatively analyzing 466 papers. We find that established research streams, e.g., 'Evolving role of the CIO' and 'CIO hierarchical position and relationships' as well as recently emerging research streams, e.g., 'CIO as business enabler' and 'CIOs and IT security,' draw growing attention. Based on our findings, we develop promising further avenues for research in the CIO field.
We describe ongoing and proposed work concerning incremental prosody extraction and classification for a spoken dialogue system. The system described will be tightly integrated with the SDS's speech recogntion which also works incrementally. The proposed architecture should allow for more control over the user interaction experience, for example allowing more precise and timely end-of-utterance vs. hesitation distinction, and auditive or visual back-channel generation.
Ideally, a spoken dialogue system should react without much delay to a user’s utterance. Such a system would already select an object, for instance, before the user has finished her utterance about moving this particular object to a particular place. A prerequisite for such a prompt reaction is that semantic representations are built up on the fly and passed on to other modules. Few approaches to incremental semantics construction exist, and, to our knowledge, none of those has been systematically tested on a spontaneous speech corpus. In this paper, we develop measures to test empirically on transcribed spontaneous speech to what extent we can create semantic interpretation on the fly with an incremental semantic chunker that builds a frame semantics.
Affordances manifest possibilities of interaction with a spoken dialogsystem. For example, the act of asking a question affords to the recipient thepossibility of answering. In the system we present, the observable act of maneuveringaffords the possibility of controlling a motion. Our system thus uses the affordanceprinciple to shape the interaction: to trigger the usage of instructions that are easyto understand and process, the system gives immediate visual feedback to afforduser commands that can then be reacted upon. This tightening of the interactionloop requires an incremental processing paradigm to allow fast reactions and to beable to alter ongoing system actions. Our system is a hybrid of incremental andnon-incremental processing components, combining conventional, state graph-basedprocessing, which has the advantage of widely available toolkits and well-understooddialog management, with incremental dialog processing which allows for the tightfeedback loop that provides for quick reactions. We tested our approach in a smalluser study and found that users used simpler and setting-independent commandsmore often and were more efficient when faced with the affordance-based version ofour system.
The potential of using ASR n-best lists for dialogue systems has often been recognised (if less often realised): it is often the case that even when the top-ranked hypothesis is erroneous, a bet- ter one can be found at a lower rank. In this paper, we describe metrics for evaluating whether the same potential carries over to incremental dialogue systems, where ASR output is consumed and reacted upon while speech is still ongoing. We show that even small N can provide an advantage for semantic process- ing, at a cost of a computational overhead.
In incremental spoken dialogue systems, par- tial hypotheses about what was said are re- quired even while the utterance is still ongo- ing. We define measures for evaluating the quality of incremental ASR components with respect to the relative correctness of the par- tial hypotheses compared to hypotheses that can optimize over the complete input, the tim- ingof hypothesisformationrelative to the por- tion ofthe inputthey areabout, andhypothesis stability, defined as the number of times they are revised. We show that simple incremen- tal post-processing can improve stability dra- matically, at the cost of timeliness (from 90% of edits of hypotheses being spurious down to 10% at a lag of 320ms). The measures are not independent,and we show how system de- signers can find a desired operating point for their ASR. To our knowledge, we are the first to suggest and examine a variety of measures for assessing incremental ASR and improve performance on this basis.
Incremental spoken dialogue systems, which process user input as it unfolds, pose additionalengineering challenges compared to more standard non-incremental systems: Their processingcomponents must be able to accept partial, and possibly subsequently revised input, and mustproduce output that is at the same time as accurate as possible and delivered with as little delay aspossible. In this article, we define metrics that measure how well a given processor meets thesechallenges, and we identify types of gold standards for evaluation. We exemplify these metrics inthe evaluation of several incremental processors that we have developed. We also present genericmeans to optimise some of the measures, if certain trade-offs are accepted. We believe that thiswork will help enable principled comparison of components for incremental dialogue systems andportability of results.
We present INPROTK, a toolkit for building incremental spoken dia-logue systems. Incremental spoken dialogue systems (systems that may react whilethe user’s utterance is ongoing) are a fairly recent research topic and allow for ex-citing new features. Even though toolkits exist that help in building conventionaldialogue systems, INPROTK offers both a tested architecture for building incre-mental SDSs as well as many of the building blocks necessary when building suchsystems. With INPROTK a researcher can avoid many of the technical difficulties,which hopefully further fosters research in this area.
Conventional dialogue management centers around an interaction style that is best described as a ping-pong game, with full turns being the units at which speech is delivered (and expected) by the system, which greatly simplifies the interaction management, delivery and understanding components of the system. While the resulting mode of interaction works well for task-based systems, it is insufficient for more conversational interaction styles, where content is delivered and grounded in units finer than full turns (Poesio and Traum 1997) and where turns are delivered concurrently by both interlocutors and hence overlap more frequently. One domain with particularly frequent overlapping contributions is interactive storytelling: a storyteller that is responsive to listeners will integrate their feedback immediately while still speaking a current contribution, and listener’s remarks or propositions regarding the story will typically be uttered immediately when the related content is delivered. We present our work on a dialogue manager that leverages recent advances in incremental speech delivery and reception (Baumann 2013) to provide for an interactive and concurrent storytelling experience. Our system uses an interaction graph, which uses a word-by-word granularity and allows to specify for individual stretches of speech where (and with what content) users may interrupt/comment, how this is interpreted, and how it is integrated into the storytelling process, thus providing for in-utterance alternatives to be spoken even without audibly interrupting the system’s ongoing utterance. The system’s interaction graph is specified in an XML language and may be hand-crafted by a story designer but can also be automatically generated. In our current system, speech recognition results are interpreted only when the user utterance is finished; however, we plan to integrate incremental speech recognition and understanding capabilities, and to
immediately react to the start of user contributions.
En este trabajo se presenta una implementación de unsistema de reconocimiento distribuido del habla en tiem-po real para su aplicación en un entorno de Internet. Desa-rrollado como una aplicación cliente-servidor, el clien-te hace uso del front-end estándar definido por la ETSI.Incluye un detector de voz para sólo enviar informacióncuando el locutor habla y la transmisión de la informaciónse hace de acuerdo al RFC 3557. El servidor realiza el re-conocimiento utilizando programación dinámica, incluyea su vez un detector de voz y utiliza la técnica de mitiga-ción de pérdida de paquetes propuesta en el estándar de laETSI. La modularidad del diseño permite la utilización decualquier otro reconocedor sin que por ello se vean afec-tados los clientes. Las pruebas reales de funcionamientopara la aplicación particular desarrollada han demostradouna alta fiabilidad en varias condiciones de transmisión.
We present the novel task of predicting tem-poral features of continuations of user input,while that input is still ongoing. We show that the remaining duration of an ongoing word, aswell as the duration of the next can be predicted reasonably well, and we put this information touse in a system that synchronously completesa user’s speech. While we focus on collaborative completions, the techniques presented here may also be useful for the alignment of back-channels and immediate turn-taking in anincremental SDS, or to synchronously monitorthe user’s speech fluency for other reasons.
We present a component for incremental speech synthesis (iSS) and a set of applications that demonstrate its capabilities. This component can be used to increase the responsivity and naturalness of spoken interactive systems. While iSS can show its full strength in systems that generate output incrementally, we also discuss how even otherwise unchanged systems may profit from its capabilities.
In many environments (e. g. sports commentary), situations incrementally unfold over time and often the future appearance of a relevant event can be predicted, but not in all its details or precise timing. We have built a simulation framework that uses our incremental speech synthesis component to assemble in a timely manner complex commentary utterances. In our evaluation, the resulting output is preferred over that from a baseline system that uses a simpler commenting strategy. Even in cases where the incremental system overcommits temporally and requires a filled pause to wait for the upcoming event, the system is preferred over the baseline.
Multi-fidelity optimization of metal sheets concerning manufacturability in deep-drawing processes
(2023)
Multi-fidelity optimization, which complements an expensive high-fidelity function with cheaper low-fidelity functions, has been successfully applied in many fields of structural optimization. In the present work, an exemplary cross-die deep-drawing optimization problem is investigated to compare different objective functions and to assess the performance of a multi-fidelity efficient global optimization technique. To that end, hierarchical kriging is combined with an infill criterion called variable-fidelity expected improvement. Findings depend significantly on the choice of objective function, highlighting the importance of careful consideration when defining an objective function. We show that one function based on the share of bad elements in a forming limit diagram is not well suited to optimize the example problem. In contrast, two other definitions of objective functions, the average sheet thickness reduction and an averaged limit violation in the forming limit diagram, confirm the potential of a multi-fidelity approach. They significantly reduce computational cost at comparable result quality or even improve result quality compared to a single-fidelity optimization.
The third-order nonlinear susceptibility of silica glass is measured via self-phase modulation in standard single mode fibers at a wavelength of 1550 nm. To minimize the influence of polarization state changes along the propagation only meter-long fibers were investigated. With pulse durations of picoseconds a quasi-instantaneous nonlinearity with ultrafast electronic and fast nuclear-vibration contributions produces under conditions of negligible dispersion a classic and clean nonlinear phase shift following exactly the shape of the pulse power. The complex pulse envelope was retrieved from frequency optical gating spectrograms. The nonlinear fiber parameter γ could be determined with an accuracy of 3.7 percent. Considering the mode field structure and the doping influence the nonlinear refractive index of silica glass as the fiber base material was found to be n 2 =2.22⋅10 −16 cm 2 W±6.0% for picosecond-long pulses. Comparing nonlinear phase shifts from linear and circular polarized light a nuclear-vibration contribution to the cubic fiber nonlinearity of 25 percent was estimated.
We present a toolkit for manipulating andvisualising time-aligned linguistic datasuch as dialogue transcripts or languageprocessing data. The package comple-ments existing editing tools by allowingfor conversion between their formats, in-formation extraction from the raw files,and by adding sophisticated, and easily ex-tended methods for visualising the dynam-ics of dialogue processing. To illustratethe versatility of the package, we describeits use in three different projects at our site.
Produkt- bzw. Maschinengeräusche mit deutlich hörbaren tonalen Komponenten werden häufig als lästig empfunden. Wenn zusätzlich eine Interaktion mehrerer tonaler Komponenten in Geräuschen stattfindet, was in realen Szenarien beispielsweise durch die Überlagerung tonaler Komponenten durch verschiedene Aggregate, wie Motoren und Getrieben, stattfindet, dann können Geräusche unterschiedlich tonal, aber auch unterschiedlich konsonant und dissonant wahrgenommen werden. Vorangehende Hörexperimente haben gezeigt, dass die Dissonanz bei ähnlicher Tonhaltigkeit der Geräusche einen großen Einfluss auf die Präferenzurteile hat. Des Weiteren konnte bereits ein grundlegendes Empfindungsmodell für die ”Dissonanz” sowie ein Präferenz-Prädiktor entwickelt werden, die die Subjektivdaten in hoher Güte vorhersagen konnten. In den hier vorgestellten Versuchen wurde das Zusammenspiel zwischen der empfundenen Tonhaltigkeit und der sich aus mehreren Tonkomponenten ergebenden Dissonanz auf die Präferenzurteile untersucht. In der Analyse wird aufgezeigt, auf welche Weise und in welchem Umfang die wahrgenommene Ausgeprägtheit der Tonhaltigkeit durch Interaktion mehrerer Tonkomponenten die allgemeine Geräuschqualität beeinflusst. Da verschiedene Grade der Dissonanz nur durch die gleichzeitige Anwesenheit tonaler Komponenten entstehen können, ist das Zusammenspiel beider Größen bei der Bildung eines Präferenzurteils von einer speziellen Natur. Dies wird sowohl bei der Modellierung der Interaktion beider Größen als auch im Vorhersagemodell der Dissonanz berücksichtigt.
Comparing Local and Sequential Models for Statistical Incremental Natural Language Understanding
(2010)
Incremental natural language understanding is the task of assigning semantic representations to successively larger prefixes of utterances. We compare two types of statistical models for this task: a) local models, which predict a single class for an input; and b), sequential models, which align a sequence of classes to a sequence of input tokens. We show that, with some modifications, the first type of model can be improved and made to approximate the output of the second, even though the latter is more informative. We show on two different data sets that both types of model achieve comparable performance (significantly better than a baseline), with the first type requiring simpler training data. Results for the first type of model have been reported in the literature; we show that for our kind of data our more sophisticated variant of the model performs better.
Participants in a conversation are normally receptive to their surroundings and their interlocutors, even while they are speaking and can, if necessary, adapt their ongoing utterance. Typical dialogue systems are not receptive and cannot adapt while uttering. We present combin-able components for incremental natural lan-guage generation and incremental speech syn-thesis and demonstrate the flexibility they can achieve with an example system that adapts to a listener's acoustic understanding problems by pausing, repeating and possibly rephrasing problematic parts of an utterance. In an evaluation, this system was rated as significantly more natural than two systems representing the current state of the art that either ignore the interrupting event or just pause; it also has a lower response time.
When dialogue systems, through theuse of incremental processing, arenot bounded anymore by strict, non-overlapping turn-taking, a whole range ofadditional interactional devices becomesavailable. We explore the use of one suchdevice, trial intonation. We elaborateour approach to dialogue managementin incremental systems, based on theInformation-State-Update approach, anddiscuss an implementation in a micro-domain that lends itself to the use ofimmediate feedback, trial intonations andexpansions. In an overhearer evaluation,the incremental system was judged as sig-nificantly more human-like and reactivethan a non-incremental version.
Generating Situated Assisting Utterances to Facilitate Tactile-Map Understanding: A Prototype System
(2012)
Tactile maps are important substitutes for visual maps for blind and visually impaired people and the efficiency of tactile-map reading can largely be improved by giving assisting utterances that make use of spatial language. In this paper, we elaborate earlier ideas for a system that generates such utterances and present a prototype implementation based on a semantic conceptualization of the movements that the map user performs. A worked example shows the plausibility of the solution and the output that the prototype generates given input derived from experimental data.
If we can model the cognitive and communicative processes underlying speech, we should be able to better predict what a speaker will do. With this idea as inspiration, we examine a number of prosodic and timing features as potential sources of information on what words the speaker is likely to say next. In spontaneous dialog we find that word probabilities do vary with such features. Using perplexity as the metric, the most informative of these included recent speaking rate, volume, and pitch, and time until end of utterance. Using simple combinations of such features to augment trigram language models gave up to a 8.4% perplexity benefit on the Switchboard corpus, and up to a 1.0% relative reduction in word error rate (0.3% absolute) on the Verbmobil II corpus.
We present a model of semantic processing of spoken language that (a) is robust against ill-formed input, such as can be expected from automatic speech recognisers, (b) respects both syntactic and pragmatic constraints in the computation of most likely interpretations, (c) uses a principled, expressive semantic representation formalism (RMRS) with a well-defined model theory, and (d) works continuously (producing meaning representations on a word-by-word basis, rather than only for full utterances) and incrementally (computing only the additional contribution by the new word, rather than re-computing for the whole utterance-so-far). We show that the joint satisfaction of syntactic and pragmatic constraints improves the performance of the NLU component (around 10 % absolute, over a syntax-only baseline).
In this paper we do two things: a) we discuss in general terms the task of incre mental reference resolution (IRR), in particular resolution of exophoric reference, and specify metrics for measuring the performance of dialogue system components tackling this task, and b) we present a simple Bayesian filtering model of IRR that performs reasonably well just using words directly (no structure information and no hand-coded semantics): it picks the right referent out of 12 for around 50 % of real world dialogue utterances in our test corpus. It is also able to learn to interpret not only words but also hesitations, just as humans have shown to do in similar situations, namely as markers of references tohard-to-describe entities.
We describe work done at three sites on designing conversational agents capable of incremental processing. We focus on the middleware layer in these systems, which takes care of passing around and maintaining incremental information between the modules of such agents. All implementations are based on the abstract model of incremental dialogue processing proposed by Schlangen and Skantze (2009), and the paper shows what different instantiations of the model can look like given specific requirements and application areas.
This project aimed to establish the feasibility of creating a procedural system for generating expressive facial animation based on an affective agent. A procedural system supporting a limited number of emotional expression changes was created alongside keyframed animations of these same emotional expression changes, and audience response to these two approaches was tested empirically. Results seem to partially support the procedural animations generated being comparable with keyframed, in terms of perceptual validity.
Speaking as part of a conversation is different from reading out aloud. Speech synthesis systems, however, are typically developed using assumptions (at least implicitly) that are more true of the latter than the former situation. We address one particular aspect, which is the assumption that a fully formulated sentence is available for synthesis. We have built a system that does not make this assumption but rather can synthesize speech given incrementally extended input. In an evaluation experiment, we found that in a dynamic domain where what is talked about changes quickly, subjects rated the output of this system as more ‘naturally pronounced’ than that of a baseline system that employed standard synthesis, despite the synthesis quality objectively being degraded. Our results highlight the importance of considering a synthesizer’s ability to support interactive use-cases when determining the adequacy of synthesized speech.
Incremental speech synthesis (iSS) accepts input and produces output in consecutive chunks that only together result in a full utterance. Systems that use iSS thus have the ability to adapt their utterances while they are ongoing. Having available less than the full utterance to plan the acoustic realisation has downsides, however, as global optimisation is not possible anymore. In this paper we present a strategy for incrementalizing the symbolic pre-processing component of speech synthesis and assess the influence of a reduction in "lookahead", i. e. in knowledge about the rest of the utterance, on prosodic quality. We found that high quality incremental output can be achieved even with a lookahead of slightly less than one phrase, allowing for timely system reaction.
We investigate the feasibility of using rare-earth iron garnet films grown on (111)-oriented substrates as magneto-optic indicator films for the visualization of magnetic leakage fields in non-destructive evaluation. In most cases the leakage field of the defect has a strong component in the film plane. The influence of this in-plane dc magnetic field on the image formation is investigated. It is shown that the presence of a strong in-plane magnetic field allows one to extend the dynamic range of the out-of-plane field imposed by the uniaxial anisotropy field HA. On the other hand, an in-plane field reduces the sensitivity. The guidelines for selecting parameters of magneto-optic indicator films are given.
Rare-earth iron garnet films with in-plane magnetic anisotropy grown on [111]-oriented substrates are promising for the visualization of magnetic leakage fields in nondestructive evaluation. Such magneto-optical films have to be specifically engineered, and we give an example of this technology. To assess the validity and accuracy of finite-element calculations of a magnetization assembly combined with the physical modeling of the image formation, comparisons between calculated and experimentally obtained magneto-optical images of metal-loss defects have been made. A convincing quantitative agreement is demonstrated. It is shown that both physical and computer modeling techniques allow for a predictive engineering design of the prospective applications and provide greater insight into the method.
A novel analytical approach for calculating the magnetic leakage field from surface defects is proposed and demonstrated for the case of a linear ferromagnetic material. The novelty of the theory is that it relates the distribution of induced magnetic charges to the surface shape. An excellent agreement between the analytical and numerical results is shown. The functional relations between different magnetic field components are discussed.
A randomly inhomogeneous composite consisting of two, ferromagnetic and nonmagnetic (para-or diamagnetic), phases is considered. The dependence of the effective permeability of the composite on the concentration of the ferromagnetic phase and on the applied magnetic field is found for the case of the negligible hysteresis loop. When the hysteresis loop is appreciable, the remanent magnetization as a function of the ferromagnet concentration is calculated.
Illumination of ac-biased photorefractive Bi12TiO20 crystals with a coherent light beam results in the development of strong nonlinear scattering. Theoretically and experimentally we investigate the angular and polarization characteristics of the scattered light for the diagonal ([1̲1̲1]) optical configuration and different polarization states of the pump. A satisfactory understanding of the observed scattering properties is achieved for most of the cases investigated.
Most of the common model-based reconstruction schemes in magnetic particle imaging (MPI) use idealized assumptions, e.g., of an ideal field-free-line (FFL) topology. However, the magnetic fields that are generated in real MPI scanners have distortions and, therefore, model-based approaches often lead to inaccurate reconstructions and may contain artifacts. In order to improve the reconstruction quality in MPI, more realistic MPI models need to be derived. In the present work, we address this problem and present a hybrid model for MPI that allows us to incorporates real measurements of the applied magnetic fields. We will explain that the measurements, that are needed to setup a model for the magnetic fields, can be obtained in a novel calibration procedure that is independent of the resolution and which is much less time-consuming than the one employed in measurement-based MPI reconstructions.We will also present a discretization strategy for this model, that can be used in context of algebraic reconstructions. The presented approach was validated on simulated data in [1], however, its evaluation on real data is a topic for future research.
In various fields of applications, inverse problems are characterized by their sensitivity to data perturbations which can cause severe reconstruction errors. Hence, regularization procedures are employed in order to ensure stability and reconstruction quality. To overcome limitations of classical approaches such as the filtered singular value decomposition (SVD), frame based
diagonalization methods have been studied in the recent years, e.g., wavelet-vagulette (WVD) decomposition. While these methods can be well adapted to the problem at hand, it is well-known, that the lack of translation invariance in multiscale systems can cause specific artifacts in the recovered object. Thus, to overcome these drawbacks we use the translation invariant diagonal frame decomposition (TI-DFD) of linear operators. For illustration, we construct a TI-WVD for one-dimensional integration operator, and confirm our theoretical findings by numerical simulations.
Magnetic garnet films prepared by liquid phase epitaxy on paramagnetic substrates of gadolinium gallium garnet are currently used for the imaging of magnetic field distributions. This application is based on the Faraday rotation which can be strongly enhanced by bismuth incorporation. For gray scale imaging the plane of the sensor film should be an easy plane of magnetization so that no domains nucleate. However, incorporation of bismuth induces a strong positive uniaxial anisotropy perpendicular to the film plane, especially if films of [111] orientation are used. To counteract this unwanted behavior neodymium and/or praseodymium are substituted in addition to bismuth. These two elements cause a very strong negative anisotropy. This is tested experimentally by growing series of garnet films with gradually changing composition and growth parameters.
Optimization of Magnetic Garnet Films for Magneto-Optical Imaging of Magnetic Field Distributions
(2004)
Rare-earth iron garnet films are currently applied for magneto-optical imaging of magnetic field distributions. The physical properties of the films can be controlled by the chemical composition, the growth conditions and the crystallographic orientation. The sensor properties must be optimized according to the application desired. A new optimization method is introduced based on the swing of the photoresponse. An application example is presented. Furthermore, the sensitivity can be strongly enhanced using specific crystallographic orientations which induce an easy plane of magnetization being tilted with respect to the film plane. Experimental results of a [112] oriented garnet film are in good agreement with calculations. The influence of the cubic anisotropy on the sensor performance is discussed. Finally, it is shown that also domain films can be applied for magneto-optical imaging.
This paper describes a case study of assessing student's coding behavior and skills in a realistic development setting. Students had to solve typical programming problems in the context of app development for the Android platform using the Eclipse IDE. Data was analyzed using IDE as well as browser interaction logs. In addition, screen recordings of the students' interaction with the IDE provide further insight. In this paper we present the first results of our ongoing work.
It is simple to query a relational database because all columns of the tables are known and the language SQL is easily applicable. In NoSQL, there usually is no fixed schema and no query language. In this article, we present NotaQL, a data-transformation language for wide-column stores. NotaQL is easy to use and powerful. Many MapReduce algorithms like filtering, grouping, aggregation and even breadth-first-search, PageRank and other graph and text algorithms can be expressed in two or three short lines of code.
In the context of a "Smart Grid" research project, together with industrial partners, the OTH-Regensburg realized an intelligent medium-voltage grid in the local area. Goal was to improve the current voltage regulation and to counter the problems with inconsistent energy feed-in of decentralized renewable energy producers. In this paper we discuss the possibilities of using 3rd generation (3G) cellular networks (UMTS) as basic technology to communicate the voltage-levels within a medium-voltage grid. We build an experimental
hardware setup to generate data-traffic as specified for the smart
grid. By analyzing the performance of 3G cellular networks in
terms of transmission latency and rate of failure, we tried to
evaluate the usability of this technology for such critical data
exchange. Though mobile communication in its structure is not
specified for the use of transmitting such infrastructure critical
data, the results show a promising high reliability with low
transmission latency. The experiments served just to test a
fragment of the conditions of use in a real scenario. An expanded
test scope is needed to further analyze the performance of mobile
radio for automatic control in smart grids. In the end the results
discussed in this paper led to a successful prototype of an
intelligent medium-voltage grid with mobile radio as communication technology
The Hierarchical Layer Graph (HL graph) is a promising network topology for wireless networks with variable transmission ranges. It was introduced and analyzed by Meyer auf der Heide et al. 2004. In this paper we present a distributed, localized and resource-efficient algorithm for constructing this graph. The qualtiy of the HL graph depends on the domination radius and the publication radius, which affect the amount of interference in the network. These parameters also determine whether the HL graph is a c-spanner, which implies an energy-efficient topology. We investigate the performance on randomly distributed node sets and show that the restrictions on these parameters derived from a worst case analysis are not so tight using realistic settings. Here, we present the results of our extensive experimental evaluation, measuring congestion, dilation and energy. Congestion includes the load that is induced by interfering edges. We distinguish between congestion and realistic congestion where we also take the signal-to-interference ratio into account. Our experiments show that the HL graph contains energy-efficient paths as well as paths with a few number of hops while preserving a low congestion.
This paper overviews the state of the art in reverberant speech processing from the speech recognition viewpoint. First, it points out that the key to successful reverberant speech recognition is to account for long-term dependencies between reverberant observations obtained from consecutive time frames. Then, a diversity of approaches that exploit the long-term dependencies in various ways is described, ranging from signal and feature dereverberation to acoustic model compensation tailored to reverberation. A framework for classifying those approaches is presented to highlight similarities and differences between them.
A novel framework based on conditional emission densities for
hidden Markov models (HMMs) is proposed in this contribution to integrate speech enhancement systems with automatic speech recognition systems. In the training phase, the observed feature vectors, corrupted by background noise and reverberation, together with estimates for the interference as provided by the speech enhancement system are used for training joint densities of the observations and the interference estimates. In the decoding phase, the joint densities are transformed to conditional densities of the observed features given the interference estimates.
Thus, front end processing can be exploited for obtaining interference estimates, and the estimation errors can be modeled very effectively in a data-driven way. Connected digit recognition experiments in a simulated reverberant environment show the potential of the proposed approach: HMMs with the proposed conditional densities outperform various configurations of conventional HMMs in the logarithmic melspectral domain. This is a first step towards using conditional densities for creating synergies between front end and back end.
Index Terms: speech enhancement, robust speech recognition,
dereverberation, conditional HMM emission densities, frame-
by-frame model adaptation.
Analysen auf NoSQL-Datenbanken sind oft langdauernd und die Ergebnisse fur den Benutzer haufig schwer verst ̈andlich. Wir prasentieren eine Möglichkeit, Datenmengen aus Wide-Column Stores mittels der Transformationssprache NotaQL zu transformieren sowie zu aggregieren und die Ergebnisse in Form von Diagrammen dem Benutzer darzustellen. Dabei kommen Sampling-Techniken zum Einsatz, um die Berechnung auf Kosten der Genauigkeit zu beschleunigen. Das von uns verwendete iterative Samplingverfahren sorgt fur eine kontinuierliche Verbesserung der Berechnungsgenauigkeit und bietet zudem Möglichkeiten zur Genauigkeitsabschätzung, die in Form von Konfidenzintervallen in den Diagrammen dargestellt werden kann.
An adequate network management is an essential part for each network permitting to keep the network performance at a suitable level. In this paper, we present our work focusing on an Ethernet network which is used as background interconnection technology for a smart home system in large buildings like tenements or an office block. In this case the network is larger and especially the applications are quite heterogeneous as compared to a private smart home system. Therefore, it is hard to supply a fair and quality of service aware network meeting all the demands. This is especially challenging in the case of an Ethernet network because this technology was intended to use it as a transparent connection system without the feature to support hard quality of service. We present how this problem could be solved by a middleware which best meets the special requirement of the mentioned use case. Finally, we discuss possible design improvements of this solution and present the recent Ethernet standards that could help solve this problem in an alternative way.
In this paper, an empirical study of tagging behaviour in web-based bibliographic annotation systems is presented. Starting from an initial category finding phase in which tags attributed to selected articles from Connotea were classified we have set up a category model for linguistic and functional aspects of tag usage as well as for the relationship between tags and document full text. In a second phase this model is applied to approx. 500 tagged articles from the information and computer technology domain randomly selected from Connotea. Our findings show significant differences to other tagging research which was primarily conducted using popular (non-scientific) tagging platforms like Flickr or Delicious. We observe a great overlap of tag material and document text and rather few non-content related tags. The comparison of user tags with author keywords shows that users tend to use less and more general tags. Finally, system functionality seems to play a role for users’ tagging behaviour.
Social tagging systems have become increasingly popular over the past years. Users' tagging practices have been little studied and understood so far. However, understanding tagging behaviour can contribute towards a thorough understanding of the tagging phenomenon from multiple perspectives. In the present paper, results of a comparative analysis of tag characteristics on the tagging platforms connotea.org (scientific articles), del.icio.us (bookmarks), flickr.com (photos), and youtube.com (videos) are presented. Results show that differences in tagging behaviour can be observed for different digital resource types. Finally, a short discussion of the possible implications of the results for the design of future tagging systems is presented.
We investigate the problem of path selection in radio networks for a given static set of n sites in two- and three-dimensional space. For static point-to-point communication we define measures for congestion, dilation, and energy consumption that take interferences among communication links into account. We show that energy-optimal path selection for radio networks can be computed in polynomial time. Then we introduce the diversity g(V) of a set V ⫅ ℝd for any constant d. It can be used to upper bound the number of interfering edges. For real-world applications it can be regarded as Θ(log n). A main result is that a c-spanner construction as a communication network allows one to approximate the congestion-optimal path system by a factor of O(g(V)2). Furthermore, we show that there are vertex sets where only one of the performance parameters congestion, dilation, and energy can be optimized at a time. We show trade-offs lower bounding congestion × dilation and dilation × energy. The trade-off between congestion and dilation increases with switching from two-dimensional to three-dimensional space. For congestion and energy the situation is even worse. It is only possible to find a reasonable approximation for either congestion or energy minimization, while the other parameter is at least a polynomial factor worse than in the optimal network.
In this contribution, a novel two-channel acoustic front-end for robust automatic speech recognition in adverse acoustic environments with nonstationary interference and reverberation is proposed. From a MISO system perspective, a statistically optimum source signal extraction scheme based on the multichannel Wiener filter (MWF) is discussed for application in noisy and underdetermined scenarios. For free-field and diffuse noise conditions, this optimum scheme reduces to a Delay & Sum beamformer followed by a single-channel Wiener postfilter. Scenarios with multiple simultaneously interfering sources and background noise are usually modeled by a diffuse noise field. However, in reality, the free-field assumption is very weak because of the reverberant nature of acoustic environments. Therefore, we propose to estimate this simplified MWF solution in each frequency bin separately to cope with reverberation. We show that this approach can very efficiently be realized by the combination of a blocking matrix based on semi-blind source separation (‘directional BSS’), which provides a continuously updated reference of all undesired noise and interference components separated from the desired source and its reflections, and a single-channel Wiener postfilter. Moreover, it is shown, how the obtained reference signal of all undesired components can efficiently be used to realize the Wiener postfilter, and at the same time, generalizes well-known postfilter realizations. The proposed front-end and its integration into an automatic speech recognition (ASR) system are analyzed and evaluated in noisy living-room-like environments according to the PASCAL CHiME challenge. A comparison to a simplified front-end based on a free-field assumption shows that the introduced system substantially improves the speech quality and the recognition performance under the considered adverse conditions.
The rising adoption of NoSQL technology in enterprises causes a heterogeneous landscape of different data stores. Different stores provide distinct advantages and disadvantages, making it necessary for enterprises to facilitate multiple systems for specific purposes. This resulting polyglot persistence is difficult to handle for developers since some data needs to be replicated and aggregated between different and within the same stores. Currently, there are no uniform tools to perform these data transformations since all stores feature different APIs and data models. In this paper, we present the transformation language NotaQL that allows cross-system data transformations. These transformations are output-oriented, meaning that the structure of a transformation script is similar to that of the output. Besides, we provide an aggregation-centric approach, which makes aggregation operations as easy as possible.
Wir pr asentieren SQL Island, ein neuartiges browserbasiertes Lernspiel,welches auf dem Konzept der Text-Adventures basiert. Nach einem Flugzeugabsturzlandet die Spielfigur auf einer Insel. Man redet mit Bewohnern, sammelt Gegenst ̈andeund k ̈apft gegen B ̈osewichte. Die Besonderheit bei diesem Spiel ist jedoch, dass derSpieler seine Figur lediglich mittels SQL-Befehlen steuert. Alle n ̈otigen Befehle wer-den zun ̈achst pr ̈asentiert, sodass keine Vorerfahrung notwendig ist. Nach etwa einerStunde Spielzeit beherrscht der Spieler SELECT-, UPDATE- und DELETE-Anfragensowie Gruppierungen, Aggregationsfunktionen und Joins. Das Spiel kann online aufhttp://www.sql-island.de kostenlos und ohne Registrierung gespielt werden.
After leaving the lecture hall, students hardly reflect about the lesson's contents. To foster learning, the lesson content should be reorganised, elaborated and critically reflected. Tagging and blogging offer the opportunity to actively engage students in follow-up course work. This paper presents the results of a case study exploring the use of a social web application to encourage student contributions within the context of an undergraduate university seminar. The tagblog combines blogging, tagging and rating as three forms of online user contribution on Web 2.0 to develop a shared, emergent group knowledge repository. Blog posts, tags and comments are analysed to examine how user contributions reflect the active processing of learning content.
While high-level software components simplify the programming of grid applications and Web services increase their interoperability, developing such components and configuring the interconnecting services is a demanding task. In this paper, we consider the combination of Higher-Order Components (HOCs) with the Fractal component model and the ProActive library.
HOCs are parallel programming components, made accessible on the grid via Web services that use a special class loader enabling code mobility: executable code can be uploaded to a HOC, allowing one to customize the HOC. Fractal simplifies the composition of components and the ProActive library offers a generator for automatically creating Web services from components composed with Fractal, as long as all the parameters of these services have primitive types.
Taking all the advantages of HOCs, ProActive and Fractal together, the obvious conclusion is that composing HOCs using Fractal and automatically exposing them as Web services on the grid via ProActive minimizes the required efforts for building complex grid systems. In this context, we solved the problem of exchanging code-carrying parameters in automatically generated Web services by integrating the HOC class loading mechanism into the ProActive library.
In this paper we investigate the relations between spanners, weak spanners, and power spanners in for any dimension and apply our results to topology control in wireless networks. For , a c-spanner is a subgraph of the complete Euclidean graph satisfying the condition that between any two vertices there exists a path of length at most c-times their Euclidean distance. Based on this ability to approximate the complete Euclidean graph, sparse spanners have found many applications, e.g., in FPTAS, geometric searching, and radio networks. In a weak c-spanner, this path may be arbitrarily long, but must remain within a disk or sphere of radius c-times the Euclidean distance between the vertices. Finally in a c-power spanner, the total energy consumed on such a path, where the energy is given by the sum of the squares of the edge lengths on this path, must be at most c-times the square of the Euclidean distance of the direct edge or communication link.
While it is known that any c-spanner is also both a weak -spanner and a -power spanner for appropriate , depending only on c but not on the graph under consideration, we show that the converse is not true: there exists a family of -power spanners that are not weak C-spanners and also a family of weak -spanners that are not C-spanners for any fixed C. However a main result of this paper reveals that any weak c-spanner is also a C-power spanner for an appropriate constant C.
We further generalize the latter notion by considering -power spanners where the sum of the δth powers of the lengths has to be bounded; so ()-power spanners coincide with the usual power spanners and ()-power spanners are classical spanners. Interestingly, these ()-power spanners form a strict hierarchy where the above results still hold for any some even hold for while counter-examples exist for . We show that every self-similar curve of fractal dimension is not a -power spanner for any fixed C, in general.
Finally, we consider the sparsified Yao-graph (SparsY-graph or YY) that is a well-known sparse topology for wireless networks. We prove that all SparsY-graphs are weak c-spanners for a constant c and hence they allow us to approximate energy-optimal wireless networks by a constant factor.
Holding non-co-located conversationswhile driving is dangerous (Horrey and- Wickens, 2006; Strayer et al., 2006), much more so than conversations with physically present, “situated” interlocutors
(Drews et al., 2004). In-car dialogue systems typically resemble non-co-located conversations more, and share their negative impact (Strayer et al., 2013). We implemented and tested a simple strategy
for making in-car dialogue systems aware of the driving situation, by giving them the capability to interrupt themselves when a dangerous situation is detected,and resume when over. We show that this improves both driving performance and recall of system-presented information,
compared to a non-adaptive strategy.
When humans speak, they do not plan their full utterance inall detail before beginning to speak, nor do they speak piece-by-piece and ignoring their full message – instead humans usepartial representations in which they fill in the missing partsas the utterance unfolds. Incremental speech synthesizers, incontrast, have not yet made use of partial representations and theinformation contained there-in.We analyze the quality of prosodic parameter assignments(pitch and duration) generated from partial utterance specifi-cations (substituting defaults for missing features) in order todetermine the requirements that symbolic incremental prosodymodelling should meet. We find that broader, higher-level infor-mation helps to improve prosody even if lower-level informationabout the near future is yet unavailable. Furthermore, we findthat symbolic phrase-level or utterance-level information is mosthelpful towards the end of the phrase or utterance, respectively,that is, when this information is becoming available even in theincremental case. Thus, the negative impact of incremental pro-cessing can be minimized by using partial representations thatare filled in incrementally.
Automatic speech recognition (ASR) technology has been developed to such a level that off-the-shelf distributed speech recognition services are available (free of cost), which allow researchers to integrate speech into their applications with little development effort or expert knowledge leading to better results compared with previously used open-source tools.
Often, however, such services do not accept language models or grammars but process free speech from any domain. While results are very good given the enormous size of the search space, results frequently contain out-of-domain words or constructs that cannot be understood by subsequent domain-dependent natural language understanding (NLU) components. We present a versatile post-processing technique based on phonetic distance that integrates domain knowledge with open-domain ASR results, leading to improved ASR performance. Notably, our technique is able to make use of domain restrictions using various degrees of domain knowledge, ranging from pure vocabulary restrictions via grammars or N-Grams to restrictions of the acceptable utterances. We present results for a variety of corpora (mainly from human-robot interaction) where our combined approach significantly outperforms Google ASR as well as a plain open-source ASR solution.
Human speakers plan and deliver their utterances incrementally, piece-by-piece, and it is obvious that their choice regarding phonetic details (and the details' peculiarities) is rarely determined by globally optimal solutions. In contrast, parametric speech synthesizers use a full-utterance context when optimizing vocoding parameters and when determing HMM states. Apart from being cognitively implausible, this impedes incremental use-cases, where the future context is often at least partially unavailable. This paper investigates the `locality' of features in parametric speech synthesis voices and takes some missing steps towards better HMM state selection and prosody modelling for incremental speech synthesis.
It is established that driver distraction is the result of sharing cognitive resources between the primary task (driving) and any other secondary task. In the case of holding conversations, a human passenger who is aware of the driving conditions can choose to interrupt his speech in situations potentially requiring more attention from the driver, but in-car information systems typically do not exhibit such sensitivity. We have designed and tested such a system in a driving simulation environment. Unlike other systems, our system delivers information via speech (calendar entries with scheduled meetings) but is able to react to signals from the environment to interrupt when the driver needs to be fully attentive to the driving task and subsequently resume its delivery. Distraction is measured by a secondary short-term memory task. In both tasks, drivers perform significantly worse when the system does not adapt its speech, while they perform equally well to control conditions (no concurrent task) when the system intelligently interrupts and resumes.
When a passenger speaks to a driver, he or she is co-located with the driver, is generally aware of the situation, and can stop speaking to allow the driver to focus on the driving task. In-car dialogue systems ignore these important aspects, making them more distracting than even cell-phone conversations. We developed and tested a "situationally-aware" dialogue system that can interrupt its speech when a situation which requires more attention from the driver is detected, and can resume when driving conditions return to normal. Furthermore, our system allows driver-controlled resumption of interrupted speech via verbal or visual cues (head nods). Over two experiments, we found that the situationally-aware spoken dialogue system improves driving performance and attention to the speech content, while driver-controlled speech resumption does not hinder performance in either of these two tasks
Robots should appropriately give reasons for their actions
when these actions affect a human’s action or goal space. Communicating reasons may help the human understand the robot’s intents and may initiate joint action, i. e., accepting the robot’s goals and cooperating on the robot’s actions. However, to be efficient, the communication of reasons should be limited to the necessary rather than to completeness, conforming to the Gricean Maxim of Quantity. Furthermore, what is necessary only becomes apparent as the situation evolves and hence, for seamless interaction, ongoing utterances must be adapted as they happen. We present a system that flexibly gives reasons in a reduced setting in which the robot needs to intrude a human’s personal space in order to reach its goal.
We propose to use a model of personal space to initiate communication while passing a human thereby acknowledging that humans are not just a special kind of obstacle to be avoided but potential interaction partners. As a simple form of interaction, our system communicates an apology while closely passing a human. To this end, we present a software architecture that integrates a social-spaces knowledge base and a component for incremental speech production. Incrementality ensures that the robot’s utterance can be adapted to fit the developing situation in a natural way. Observer ratings show that personal-space intrusion is perceived as both natural and polite if the robot has the capability to utter and adapt an apology in an incremental way whereas it is perceived as unfriendly if the robot intrudes personal space without saying anything. Moreover, the robot is perceived as less natural if it does not adapt.
Incremental speech synthesis aims at delivering the synthetic voice while the sentence is still being typed. One of the main challenges is the online estimation of the target prosody from a partial knowledge of the sentence's syntactic structure. In the context of HMM-based speech synthesis, this typically results in missing segmental and suprasegmental features, which describe the linguistic context of each phoneme. This study describes a voice training procedure which integrates explicitly a potential uncertainty on some contextual features. The proposed technique is compared to a baseline approach (previously published), which consists in substituting a missing contextual feature by a default value calculated on the training set. Both techniques were implemented in a HMM-based Text-To-Speech system for French, and compared using objective and perceptual measurements. Experimental results show that the proposed strategy outperforms the baseline technique for this language.
The Spoken Wikipedia project unites volunteer readers of encyclopedic entries. Their recordings make encyclopedic knowledge accessible to persons who are unable to read (out of alexia, visual impairment, or because their sight is currently occupied, e. g. while driving). However, on Wikipedia, recordings are available as raw audio files that can only be consumed linearly, without the possibility for targeted navigation or search. We present a reading application which uses an alignment between the recording, text and article structure and which allows to navigate spoken articles, through a graphical or voice-based user interface (or a combination thereof). We present the results of a usability study in which we compare the two interaction modalities. We find that both types of interaction enable users to navigate articles and to find specific information much more quickly compared to a sequential presentation of the full article. In particular when the VUI is not restricted by speech recognition and understanding issues, this interface is on par with the graphical interface and thus a real option for browsing the Wikipedia without the need for vision or reading.
We present a corpus of time-aligned spoken data of Wikipedia articles as well as the pipeline that allows to generate such corpora for many
languages. There are initiatives to create and sustain spoken Wikipedia versions in many languages and hence the data is freely available,
grows over time, and can be used for automatic corpus creation. Our pipeline automatically downloads and aligns this data. The resulting
German corpus currently totals 293h of audio, of which we align 71h in full sentences and another 86h of sentences with some missing
words. The English corpus consists of 287h, for which we align 27h in full sentence and 157h with some missing words. Results are publically available.
Das ständige Umblättern von Noten ist für Musiker ein wiederkehrendes Problem. Dieses wird häufig durch einen Assistenten des Musikers, dem sogenannten Notenwender, gelöst. Diese Unterstützung haben allerdings viele Musiker nur selten während des Übens. In diesem Artikel stellen wir eine Anwendung für mobile Geräte vor, die auf verschiedene Arten das Umblättern von Klavierpartituren unterstützt. In einer Studie mit professionellen Musikern und Klavierschülern wurden diese Arten gegeneinander abgewogen. Die Ergebnisse zeigen auf, dass computer-unterstütztes Blättern Vorteile gegenüber herkömmlichem Blättern hat.
A recently proposed concept for training reverberation-robust acoustic models for automatic speech recognition using pairs of clean and reverberant data is extended from word models to tied-state triphone models in this paper. The key idea of the concept, termed ICEWIND, is to use the clean data for the temporal alignment and the reverberant data for the estimation of the emission densities. Experiments with the 5000-word Wall Street Journal corpus confirm the benefits of ICEWIND with tied-state triphones: While the training time is reduced by more than 90%, the word accuracy is improved at the same time, both for room-specific and multi-style hidden Markov models. Since the acoustic models trained with ICEWIND need less Gaussian components for the emission densities to achieve comparable recognition rates as Baum-Welch acoustic models, ICEWIND also allows for a reduced decoding complexity.
Many MapReduce jobs for analyzing Big Data require many hours and have to be repeated again and again because the base data changes continuously. In this paper we propose Marimba, a framework for making MapReduce jobs incremental. Thus, a recomputation of a job only needs to process the changes since the last computation. This accelerates the execution and enables more frequent recomputations, which leads to results which are more up-to-date. Our approach is based on concepts that are popular in the area of materialized views in relational database systems where a view can be updated only by aggregating changes in base data upon the previous result.
With the increase of centralization of resources in IT-infrastructure and the growing amount of cloud services, database management systems (DBMS) will be more and more outsourced to Infrastructure-as-a-Service (IaaS) providers. The outsourcing of entire databases, or the computation power for processing Big Data to an external provider also means that the provider has full access to the information contained in the database. In this article we propose a feasible solution with Order-Preserving Encryption (OPE) and further, state of the art, encryption methods to sort and process Big Data on external resources without exposing the unencrypted data to the IaaS provider. We also introduce a proof-of-concept client for Google BigQuery as example IaaS Provider.
A method, system, and computer-usable non-transitory storage device for dynamic voice codec adaptation are disclosed. The voice codec adapts in real time to devote more bits to audio quality when it is most needed, and fewer bits to less important parts of utterances are disclosed. Dialog knowledge is utilized for compression opportunities to adjust the bitrate moment-by-moment, based on the inferred value of each frame. Frame importance and appropriate transmission fidelity is predicted based on prosodic features and models of dialog dynamics. This technique provides the same communications quality with less spectrum needs, fewer antennas, and less battery drain.
Automatic speech recognition (ASR) is not only becoming increasingly
accurate, but also increasingly adapted for producing timely, incremental output. However, overall accuracy and timeliness alone are insufficient when it comes to interactive dialogue systems which require stability in the output and responsivity to the utterance as it is unfolding. Furthermore, for a dialogue system to deal with
phenomena such as disfluencies, to achieve deep understanding of user utterances these should be preserved or marked up for use by downstream components, such as language understanding, rather than be filtered out. Similarly, word timing can be informative for analyzing deictic expressions in a situated environment and should
be available for analysis. Here we investigate the overall accuracy and incremental performance of three widely used systems and discuss their suitability for the aforementioned perspectives. From the differing performance along these measures we provide a picture of the requirements for incremental ASR in dialogue systems and describe freely available tools for using and evaluating incremental ASR.
Most modern and post-modern poems have developed a post-metrical idea of lyrical prosody that employs rhythmical features of everyday language and prose instead of a strict adherence to rhyme and metrical schemes. This development is subsumed under the term free verse prosody. We present our methodology for the large-scale analysis of modern and post-modern poetry in both their written form and as spoken aloud by the author. We employ language processing tools to align text and speech, to generate a null-model of how the poem would be spoken by a naïve reader, and to extract contrastive prosodic features used by the poet. On these, we intend to build our model of free verse prosody, which will help to understand, differentiate and relate the different styles of free verse poetry. We plan to use our processing scheme on large amounts of data to iteratively build models of styles, to validate and guide manual style annotation, to identify further rhythmical categories, and ultimately to broaden our understanding of free verse poetry. In this paper, we report on a proof-of-concept of our methodology using smaller amounts of poems and a limited set of features. We find that our methodology helps to extract differentiating features in the authors’ speech that can be explained by philological insight. Thus, our automatic method helps to guide the literary analysis and this in turn helps to improve our computational models.
Predictive incremental parsing produces syntactic representations of sentences as they are produced, e.g. by typing or speaking. In order to generate connected parses for such unfinished sentences, upcoming word types can be hypothesized and structurally integrated with already realized words. For example, the presence of a determiner as the last word of a sentence prefix may indicate that a noun will appear somewhere in the completion of that sentence, and the determiner can be attached to the predicted noun. We combine the forward-looking parser predictions with backward-looking N-gram histories and analyze in a set of experiments the impact on language models, i.e. stronger discriminative power but also higher data sparsity. Conditioning N-gram models, MaxEnt models or RNN-LMs on parser predictions yields perplexity reductions of about 6%. Our method (a) retains online decoding capabilities and (b) incurs relatively little computational overhead which sets it apart from previous approaches that use syntax for language modeling. Our method is particularly attractive for modular systems that make use of a syntax parser anyway, e.g. as part of an understanding pipeline where predictive parsing improves language modeling at no additional cost.
Engineering is based on the understanding of causes and effects. Thus, causality should also guide the safety assessment of complex systems such as autonomous driving cars. To ensure the safety of the intended functionality of these systems, normative regulations like ISO 21448 recommend scenario-based testing. An important task here is to identify critical scenarios, so-called edge and corner cases. Data-driven approaches to this task (e.g. based on machine learning) cannot adequately address a constantly changing operational design domain. Model-based approaches offer a remedy – they allow including different sources of knowledge (e.g. data, human experts) into safety considerations. With this paper, we outline a novel approach for ensuring automotive system safety. We propose to use structural causal models as a probabilistic modelling language to combine knowledge about an open-context environment from different sources. Based on these models, we investigate parameter configurations that are candidates for critical scenarios. In this paper, we first discuss some aspects of scenario-based testing. We then provide an informal introduction to causal models and relate their development lifecycle to the established V-model. Finally, we outline a generic workflow for using causal models to identify critical scenarios and highlight some challenges that arise in the process.
With the increase in demand of services in the automotive industry, automotive enterprises prefer to collaborate with other qualified cross-domain partners to provide complex automotive functions (or services), such as autonomous driving, OTA (Over The Air) vehicle update, V2X (Vehicle-to-Vehicle communication), etc. One key element in cross-domain enterprise collaboration is the mutual agreement between interfaces of software components. In this context, model-to-model mappings of software component models of heterogeneous frameworks for automotive services and to explore the synergies in their interface semantics, have become an essential factor in improving the interoperability among the automotive and other cross-domain enterprises. However, one of the challenges in achieving cross-domain component interface model-to-model mappings at an application level lies in detecting the interface semantics and the semantic relations that are conveyed in different component models in different frameworks. This paper addresses this challenge using a Model Driven Architecture (MDA) based analytical approach to explore interface semantic synergies in the cross-domain component meta-models that are used for automotive services. The approach applies manual semantic checking measurements at an application interface level to understand the meanings and relations between the different meta-model entities of cross-domain framework software components. In this research, we attempt to ensure that interface description models of software components from heterogeneous frameworks can be compared, correlated and re-used for automotive services based on semantic synergies. We have demonstrated our approach using component meta-models from cross-domain enterprises, that are used for the automotive application domain.
In recent years, mapping of application software components’ ontologies semantically emerged as a big research challenge in automotive application domain that manipulates several cross-enterprise synergy knowledge application frameworks. The same knowledge formalized by different experts in different vehicle application frameworks leads to heterogeneous representations of components’ interface data. Consequently, this causes the most daunting impediment in semantic interoperability between the service components in cooperative automotive systems. From a modeling perspective, in the absence of standardized domain-based unified modeling techniques, the orchestration and resolution of semantic data interoperability between various vehicle application frameworks’ components’ interface models remain a challenge. However, this challenge could be addressed using ontological metamodeling by specifying semantic associations between components’ interface model concepts based on the domain knowledge. Apart from the semantic mapping of interface ontological metamodels, this work also defines quality metrics to determine the degree of semantic alignment achieved between the various interface ontologies. Additionally, to reduce development time and cost towards semantic interoperability, this work proposes a semi-automated plugin tool for the applicability of the evaluated quality metrics to semantic mapping of real-world components’ interface models.
Development and verification of modern, dependable automotive systems require appropriate modelling approaches. Classic automotive safety is described by the normative regulations ISO 26262, its relative ISO/PAS 21448, and their respective methodologies. In recent publications, an emerging demand to combine environmental influences, machine learning, or reasoning under uncertainty with standard-compliant analysis techniques can be noticed. Therefore, adapting established methods like FTA and proper tool support is necessary. We argue that Bayesian Networks (BNs) can be used as a central component to address and merge these demands. In this paper, we present our Open-Source Python package BayesianSafety. First, we review how BNs relate to data-driven methods, model-to-model transformations, and causal reasoning. Together with FTA and ETA, these models form the core functionality of our software. After describing currently implemented features and possibilities of combining individual modelling approaches, we provide an informal view of the tool’s architecture and of the resulting software ecosystem. By comparing selected publicly available safety and reliability analysis libraries, we outline that many relevant methodologies yield specialized implementations. Finally, we show that there is a demand for a flexible, unifying analysis tool that allows researching system safety by using multi-model and multi-domain approaches.
With autonomous driving, the system complexity of vehicles will increase drastically. This requires new approaches to ensure system safety. Looking at standards like ISO 26262 or ISO/PAS 21448 and their suggested methodologies, an increasing trend in the recent literature can be noticed to incorporate uncertainty. Often this is done by using Bayesian Networks as a framework to enable probabilistic reasoning. These models can also be used to represent causal relationships. Many publications claim to model cause-effect relations, yet rarely give a formal introduction of the implications and resulting possibilities such an approach may have. This paper aims to link the domains of causal reasoning and automotive system safety by investigating relations between causal models and approaches like FMEA, FTA, or GSN. First, the famous “Ladder of Causation” and its implications on causality are reviewed. Next, we give an informal overview of common hazard and reliability analysis techniques and associate them with probabilistic models. Finally, we analyse a mixed-model methodology called Hybrid Causal Logic, extend its idea, and build the concept of a causal shell model of automotive system safety.
Supervisory Control and Data Acquisition (SCADA) systems are used to control and monitor components within the energy grid, playing a significant role in the stability of the system. As a part of critical infrastructures, components in these systems have to fulfill a variety of different requirements regarding their dependability and must also undergo strict audit procedures in order to comply with all relevant standards. This results in a slow adoption of new functionalities. Due to the emerged threat of cyberattacks against critical infrastructures, extensive security measures are needed within these systems to protect them from adversaries and ensure a stable operation. In this work, a solution is proposed to integrate extensive security measures into current systems. By deploying additional security-gateways into the communication path between two nodes, security features can be integrated transparently for the existing components. The developed security-gateway is compliant to all regulatory requirements and features an internal architecture based on the separation-of-concerns principle to increase its security and longevity. The viability of the proposed solution has been verified in different scenarios, consisting of realistic field tests, security penetration tests and various performance evaluations.
Today's cyberphysical systems are increasingly prone to misuse. To secure existing and future software systems, introducing concepts of IT-Security and Secure Software Engineering (SecSE) in Software Engineering (SE) courses is essential for academic education of future software engineers. This is not only important for computer science students, but also for engineering students studying topics of computing and SE. However, only little research exists on integrating these topics into traditional SE courses, especially for engineering students in non-computer science majors. To narrow this gap, this paper contributes with the design and evaluation of an exercise on modeling misuse cases alongside use cases, based on the inductive teaching method problem-based learning (PBL). The exercise is part of an educational design research investigating which learning content and teaching methods are suitable for integrating IT-Security and SecSE topics into traditional SE education of engineering students to convey factual knowledge as well as raise awareness and interest for both topics during software development. We present the integration of the exercise design into a traditional SE course for engineering students and its evaluation to examine its suitability. We evaluated the exercise design regarding the suitability of the design components, the learning content of misuse cases and the intended learning goals as well as its impact on students' motivation, and their interest in IT-security. The paper then presents indications on the feasibility and success of the exercise design for teaching misuse cases to engineering students and sparking their interest in IT-Security.
In the real-time systems sector, various task models and corresponding tests exist to model and verify the schedulability of task sets on the system at hand. While those models and schedulability tests have intensively been studied from a theoretical point of view, it is hard to ma e use of them to compare the actual execution behavior of scheduling algorithms on a real system. In contrast to schedulability tests, simulators can help to investigate the performance of specific scheduling algorithms. One of the most generalized task models to describe parallel tasks is the Directed Acyclic Graph model that allows to represent tasks as a series of subtasks that depict the potentially parallel computations and precedence constraints that denote the order in which the subtasks are allowed to execute.
In this paper, we investigate various scheduling algorithms for the Directed Acyclic Graph model. For that, we first recapitulate the examined scheduling algorithms in detail and point out relevant differences. Subsequently, we present the evaluation of different global and federated scheduling algorithms using fine-grained parallel tasks. To this end, we generate random Directed Acyclic Graph tasks and simulate their execution on multiprocessor systems using scheduling algorithms such as global rate-monotonic and semi-federated scheduling as well as global scheduling policies using the thread pool model.
Networked control systems as e.g., battery management systems, smart grids or vehicular systems, consist of sensors, actuators and controllers with a communication network in the control loop. The data rate and the reliability of the underlying communication network are key factors since delays or message losses directly affect the system control. In addition, the processor load caused by the communication is significant as it influences the calculation of system states and the setting of control parameters. The power consumption of the communication network has a further impact on the energy efficiency of the respective application. In this paper, the communication technologies Controller Area Network (CAN), Controller Area Network Flexible Data-rate (CAN FD) and Ethernet are compared in the context of networked control systems with focus on a decentralized battery management system. First, the message processing time and the processor load are measured. With regard to energy efficiency, the maximum power consumption is determined. The Bit Error Rates (BER) and the Residual Error Rates (RER) are calculated to evaluate the reliability. Finally, the receive FIFO load under high traffic conditions is examined. Index Terms—Networked control systems, decentralized battery management system, microcontrollers, communication systems, Ethernet, Controller Area Network (CAN), Controller Area Network Flexible Data-rate (CAN FD) energy efficiency, energy consumption, bit error rate, residual error rate, processor load.
Modern cyber-physical systems, such as autonomous vehicles, advanced driver assistance systems, automation systems and battery management systems, result in extended communication requirements regarding the reliability and the availability. The Controller Area Network (CAN) is a broadcast-based protocol which is still used as a standard for serial communication between individual microcontrollers due to its reliability and low power consumption. In addition, it provides mechanisms for detecting transmission errors and retransmitting messages in the event of an error. The enhancement CAN Flexible Data-Rate (CAN FD) offers increased data rates and transmission rates in order to meet the data throughput requirements. In this paper, the mechanisms for reliable data transmission in a CAN FD network are analyzed. To improve reliability, a second identical CAN-FD network is added to the system, using the additional CAN interface already available on common microcontrollers. The redundant communication network is examined in terms of failure rates and the mean time to failure. The reliability over the operation time is calculated for the single and the redundant version of the CAN FD network using the failure rate limits of the ASIL levels.
We present a comprehensive analysis of the neural audio-visual synchrony evaluation tool SyncNet. We assess the agreement of SyncNet scores vis-a-vis human perception and whether we can use these as a reliable metric for evaluating audio-visual lip-synchrony in generation tasks with no ground truth reference audio-video pair. We further look into the underlying elements in audio and video which vitally affect synchrony using interpretable explanations from SyncNet predictions and analyse its susceptibility by introducing adversarial noise. SyncNet has been used in numerous papers on visually-grounded text-to-speech for scenarios such as dubbing. We focus on this scenario which features many local asynchronies (something that SyncNet isn’t made for).
Over the last three decades, the Controller Area Network (CAN) has become the dominant communication in embedded systems. Especially for automotive systems it offers advantages including high robustness, low error rate and high reliability combined with low power consumption. Therefore, learning the basics of this bus system is substantial in this field. Nowadays, various media about the functionality and use of CAN exist which make it easy to read into the topic. But often, theory alone is not sufficient. To deepen the understanding, practical implementation contributes significantly. However, affordable and easy-to-use CAN devices for training purposes are scarce. Existing equipment can be divided into expensive professional devices, which have many functions and inexpensive ones for hobbyists, which require difficult configurations. Therefore, a practical solution is a low-budget device equipped with an overlay which deals with the time consuming configurations. This paper covers the development of a python interface for a purchasable cost effective CAN device for Windows OS. The intention is to create an easy-to-use program that enables beginners to get in touch with CAN and collect practical experience. At the start, a brief explanation of the CAN functionality is given. After that, we introduce the hardware used in this project. Next, the software part covers the development of the interface and the integration of this interface into python-can. Furthermore, a virtual playground is introduced for testing purposes. Also, to demonstrate the functionality of the interface, a test program is executed in conjunction with a logic analyzer.
The transition towards services has been imperative for manufacturing firms for years. The change from a productoriented to a more service-dominant business model affects the organizational structure of firms. However, literature provides limited insights into how manufacturing firms organize themselves in this transition. Even though digital technologies are critical for the transition, it is unclear how to orchestrate digital and traditional Information Technology (IT) resources in manufacturing firms accordingly. We analyze the case of a typical manufacturing firm that has adjusted its structure to reorganize for solution offerings based on product, service, and digital components. Our results describe a hybrid organizational structure that splits front- and back-end units. The back-end units are split along solution components. Digital IT resources are internalized and governed decentrally, with traditional IT resources being outsourced and steered centrally. Our findings contribute to digital servitization research by clarifying the overarching as well as the digital and traditional IT-related organization for manufacturing firms.