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Algorithm selection (AS) tasks are dedicated to find the optimal algorithm for an unseen problem instance. With the knowledge of problem instances’ meta-features and algorithms’ landmark performances, Machine Learning (ML) approaches are applied to solve AS problems. However, the standard training process of benchmark ML approaches in AS either needs to train the models specifically for every algorithm or relies on the sparse one-hot encoding as the algorithms’ representation. To escape these intermediate steps and form the mapping function directly, we borrow the learning to rank framework from Recommender System (RS) and embed the bi-linear factorization to model the algorithms’ performances in AS. This Bi-linear Learning to Rank (BLR) has proven to work with competence in some AS scenarios and thus is also proposed as a benchmark approach. Thinking from the evaluation perspective in the modern AS challenges, precisely predicting the performance is usually the measuring goal. Though approaches’ inference time also needs to be counted for the running time cost calculation, it’s always overlooked in the evaluation process. The multi-objective evaluation metric Adjusted Ratio of Root Ratios (A3R) is therefore advocated in this paper to balance the trade-off between the accuracy and inference time in AS. Concerning A3R, BLR outperforms other benchmarks when expanding the candidates range to TOP3. The better effect of this candidates expansion results from the cumulative optimum performance during the AS process. We take the further step in the experimentation to represent the advantage of such TOPK expansion, and illustrate that such expansion can be considered as the supplement for the convention of TOP1 selection during the evaluation process.
Linear models are a mainstay in statistical pattern recognition but do not play a role in time series classification, because they fail to account for temporal variations. To overcome this limitation, we combine linear models with dynamic time warping (dtw). We analyze the resulting warped-linear models theoretically and empirically. The three main theoretical results are (i) the Representation Theorem, (ii) the Matrix Complexity Lemma, and (iii) local Lipschitz continuity of the warped softmax function. The Representation Theorem roughly states that warped-linear models correspond to polytope classifiers in Euclidean spaces. This key result is useful because it simplifies analysis of warped-linear models. For example, it provides a geometric interpretation, points to the label dependency problem, and justifies application of warped-linear models not only on temporal but also on multivariate data. The Representation Theorem together with the Matrix Complexity Lemma reveals that warped-linear models implement a weight trick by weight selection and massive weight sharing. Local Lipschitz continuity of warped softmax functions admits a principled training of warped-linear models by stochastic subgradient methods. Empirical results show that replacing the inner product of linear models with a dtw-score substantially improves its predictive performance. The theoretical and empirical contributions of this article provide a simple and efficient first-trial alternative to nearest-neighbor methods and open up new perspectives for more sophisticated classifiers such as warped deep learning.
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.
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.
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.
This paper describes the usability evaluation of the parts of the CHATU chatbot. The evaluation was conducted with 21 participants. A focus of this paper is the description of the carefully designed evaluation procedure, which aims to avoid textual priming of the participants. The general evaluation procedure can be applied to other speech- or text-based conversational systems, and additional material is provided. The evaluation results show that the usability and user experience of CHATU are positively rated. However, the naturalness and novelty of the interaction are not optimal, and the potential influence of users’ experience with LLMs on the evaluation is discussed.
In this study, we address the complex dynamics of emotional speech and
comprehensively examine the integration of rhythmic and vocal features to recognize emotional patterns. Our exploration is conducted using two German emotional corpora: VMEmo and EmoDB. Employing a combination of supervised methods (here linear discriminant analysis, LDA) and unsupervised techniques (here k-means clustering), we aim to uncover nuanced patterns within the emotional speech in these corpora. The application of LDA highlights salient patterns across different feature sets and focuses on the classification of speakers and prosodic characteristics. In addition, k-means clustering uncovers latent structures that reveal subtle mapping between emotions and speech behavior. Our results suggest that it is possible to cluster data based on prosodic behaviors that are influenced by emotional changes. Although precise mapping to the actual clusters derived from emotional labels could not be fully achieved, the results nonetheless reveal a moderate level of success in this investigation.
The on-chip integration of quantum light sources has enabled the realization of complex quantum photonic circuits. However, for the practical implementation of such circuits in quantum information applications, it is crucial to develop sources delivering entangled quantum photon states with on-demand tunability. Here we propose and experimentally demonstrate the concept of a widely tunable quantum light source based on spontaneous parametric down-conversion in a simple nonlinear directional coupler. We show that spatial photon-pair correlations and entanglement can be reconfigured on-demand by tuning the phase difference between the pump beams and the phase mismatch inside the structure. We experimentally demonstrate the generation of split states, robust N00N states, various intermediate regimes and biphoton steering on a single chip. Furthermore we theoretically investigate other regimes allowing all-optically tunable generation of all Bell states and flexible control of path-energy entanglement. Such wide-range capabilities of a structure comprised of just two coupled nonlinear waveguides are attributed to the intricate interplay between linear coupling and nonlinear phase matching. This scheme provides an important advance towards the realization of reconfigurable quantum circuitry.
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.
Translation invariant diagonal frame decomposition of inverse problems and their regularization
(2022)
Solving inverse problems is central to a variety of important applications, such as biomedical image reconstruction and non-destructive testing. These problems are characterized by the sensitivity of direct solution methods with respect to data perturbations. To stabilize the reconstruction process, regularization methods have to be employed. Well-known regularization methods are based on frame expansions, such as the wavelet-vaguelette (WVD) decomposition, which are well adapted to the underlying signal class and the forward model and allow efficient implementation. However, it is well known that the lack of translational invariance of wavelets and related systems leads to specific artifacts in the reconstruction. To overcome this problem, in this paper we introduce and analyze the concept of translation invariant diagonal frame decomposition (TI-DFD) of linear operators. We prove that a TI-DFD combined with a regularizing filter leads to a convergent regularization method with optimal convergence rates. As illustrative example, we construct a wavelet-based TI-DFD for one-dimensional integration, where we also investigate our approach numerically. The results indicate that filtered TI-DFDs eliminate the typical wavelet artifacts when using standard wavelets and provide a fast, accurate, and stable solution scheme for inverse problems.
Hochschulen für angewandte Wissenschaften (HAW) kommt als regionalen Innovationstreibern eine gesellschaftliche Verantwortung durch ihre Zusammenarbeit mit Unternehmen und Gesellschaft zu. Diese Zusammenarbeit geschieht im Rahmen von Aktivitäten des Wissens- und Technologietransfers. Die Beurteilung des Erfolgs dieser Transferaktivitäten verursacht jedoch regelmäßig Schwierigkeiten, da geeignete Indikatoren zur Erfolgsmessung fehlen. Im Rahmen des vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Verbundforschungsprojektes Transfer_i wurde ein Modell zur Objektivierung und Messung von Forschungsleistung, forschungsbasiertem Transfer sowie dessen Umsetzung am Markt in Form von Innovationen erarbeitet. Mit dem Forschungsprojekt wurden kausale Zusammenhänge für das Gelingen von Transfer identifiziert und daraufhin entsprechende Indikatoren definiert, um auf dieser Basis die Steuerung von Transferprozessen zu ermöglichen.
In diesem Beitrag werden zwei im Projekt Transfer_i entwickelte Modelle und Indikatoren vorgestellt, die die Voraussetzungen für einen erfolgreichen Transferprozess abbilden können. Auf dieser Grundlage zeigen wir anhand eines Projekts (MAGGIE) der Ostbayerischen Technischen Hochschule (OTH) Regensburg mit mehreren regionalen Partnern, wie die vorher definierten Modelle und Indikatoren in einem konkreten Anwendungsfall verwendet werden können. Abschließend beschreiben wir die erforderlichen Rahmenbedingungen für die erfolgreiche Umsetzung von Transfer an Hochschulen und wie darauf bezogene Indikatoren effektiv eingeführt werden können.
The translation of poetry is a complex, multifaceted challenge: the translated text should communicate the same meaning, similar metaphoric expressions, and also match the style and prosody of the original poem. Research on machine poetry translation is existing since 2010, but for four reasons it is still rather insufficient:
1. The few approaches existing completely lack any knowledge about current developments in both lyric theory and translation theory.
2. They are based on very small datasets.
3. They mostly ignored the neural learning approach that superseded the long-standing dominance of phrase-based approaches within machine translation.
4. They have no concept concerning the pragmatic function of their research and the resulting tools.
Our paper describes how to improve the existing research and technology for poetry translations in exactly these four points. With regards to 1) we will describe the “Poetics of Translation”. With regards to 2) we will introduce the Worlds largest corpus for poetry translations from lyrikline. With regards to 3) we will describe first steps towards a neural machine translation of poetry. With regards to 4) we will describe first steps towards the development of a poetry translation mapping system.
The growing prevalence of voice assistants has sparked privacy concerns with respect to content privacy and potential human-based attacks such as eavesdropping which make users feel uncomfortable utilizing them in public. To address these challenges, understanding human privacy perceptions in acoustic environments becomes paramount. This understanding can empower voice assistants to accurately quantify privacy perceptions, adapt conversational patterns, and ultimately enhance human-machine interaction. This study draws inspiration from human-tohuman interactions and previous research on acoustic privacy, to quantify privacy perceptions in environments characterized by babble noise. The primary objective is a comprehensive evaluation of both objective and subjective measures to quantitatively capture privacy perceptions in acoustic environments.
In simultaneous interpreting, human experts incrementally construct and extend partial hypotheses about the source speaker’s message, and start to verbalize a corresponding message in the target language, based on a partial translation – which may have to be corrected occasionally. They commence the target utterance in the hope that they will be able to finish understanding the source speaker’s message and determine its translation in time for the unfolding delivery. Of course, both incremental understanding and translation by humans can be garden-pathed, although experts are able to optimize their delivery so as to balance the goals of minimal latency, translation quality and high speech fluency with few corrections. We investigate the temporal properties of both translation input and output to evaluate the tradeoff between low latency and translation quality. In addition, we estimate the improvements that can be gained with a tempo-elastic
speech synthesizer.
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.
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.
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.
Due to technological advances, IT is increasingly introduced and managed outside of (or even hidden from) an organisation's IT department. As a result, researchers and practitioners have created multiple concepts describing different facets and trends of this development. While the individual concepts typically focus on different aspects, there are overlaps between these concepts. This effect hampers research progress as it creates redundancies and separate streams of knowledge development. We propose a taxonomy of the concepts Lightweight IT, IT Consumerisation, Bring Your Own Device, Shadow IT, and Business-Managed IT to solve this research problem based on a systematic literature review. For this purpose, we derive characterising dimensions of each concept from existing studies and analyse literature according to the usage of terms. As a result, we can describe and delineate the concepts. Thus, we contribute a taxonomy of IT outside the IT department to improve the transfer of research results between research streams.
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.
This contribution focuses on structural similarities between tonality and cadences in music on the one hand, and rhythmical patterns in poetic languages respectively poetry on the other hand.
We investigate two exemplary rhythmical patterns in modern and postmodern poetry to detect these tonality-like features in poetic language: The Parlando and the Variable Foot. German poems
readout from the original poets are collected from the webpage of our partner lyrikline. We compared these rhythmical features with tonality rules, explained in two important theoretical volumes: The Generative Theory of Tonal Music and the Rhythmic Phrasing in English Verse. Using both volumes, we focused on a certain combination of four different features: The grouping
structure, the metrical structure, the time-span-variation and the prolongation, in order to detect the two important rhythmical patterns which use tonality-like features in poetic language (Parlando and Variable Foot). Different features including pause and parser information are used in this classification process. The best classification result, calculated by the f-measure, for
Parlando and Variable Foot is 0.69.
There is high confidence for the hypothesis that in speech perception the cycles of a θ-oscillation segment the auditory signal into syllables [8]. Yet the functionality of the oscillator generating the θ-oscillation is unknown. We follow the finding that, within an auditory scene, speech is perceived as a stream given by temporal coherence [12]. We work with the hypotheses that the θ-oscillator is driven by temporal features providing this coherence. We propose a new temporal feature called O-distance, which detects the onset of a syllable - the starting point to of a θ-cycle–triggered by the temporal distance from to to the instance of the maximal rise of the loudness curve of the vowel. To extract to from the auditory signal, we use the statistical properties of this distance based on the C-center hypothesis [25], which predicts a close temporal relation of the onset consonants to the onset of a vowel. The statistics are derived from reference O-distance extracted from an articulatory database, where the minima and maxima of the loudness are related to maxima and minima of the lower incisor and tongue tip. To judge the quality of the O-distance extracted from the auditory signal, we regard the temporal deviation of the O-distance to the reference O-distance. Currently we achieve a mean deviation of 34ms.
Dubbing has two shades; synchronisation constraints are applied only when the actor’s mouth is visible on screen, while the translation is unconstrained for off-screen dubbing. Consequently, different synchronisation requirements, and therefore translation strategies, are applied depending on the type of dubbing. In this work, we manually annotate an existing dubbing corpus (Heroes) for this dichotomy. We show that, even though we did not observe distinctive features between on- and off-screen dubbing at the textual level, on-screen dubbing is more difficult for MT (-4 BLEU points). Moreover, synchronisation constraints dramatically decrease translation quality for off-screen dubbing. We conclude that, distinguishing between on-screen and off-screen dubbing is necessary for determining successful strategies for dubbing-customised Machine Translation.
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.
The Spoken Wikipedia Corpus collection: Harvesting, alignment and an application to hyperlistening
(2019)
Spoken corpora are important for speech research, but are expensive to create and do not necessarily reflect (read or spontaneous) speech ‘in the wild’. We report on our conversion of the preexisting and freely available Spoken Wikipedia into a speech resource. The Spoken Wikipedia project unites volunteer readers of Wikipedia articles. There are initiatives to create and sustain Spoken Wikipedia versions in many languages and hence the available data grows over time. Thousands of spoken articles are available to users who prefer a spoken over the written version. We turn these semi-structured collections into structured and time-aligned corpora, keeping the exact correspondence with the original hypertext as well as all available metadata. Thus, we make the Spoken Wikipedia accessible for sustainable research. We present our open-source software pipeline that downloads, extracts, normalizes and text–speech aligns the Spoken Wikipedia. Additional language versions can be exploited by adapting configuration files or extending the software if necessary for language peculiarities. We also present and analyze the resulting corpora for German, English, and Dutch, which presently total 1005 h and grow at an estimated 87 h per year. The corpora, together with our software, are available via http://islrn.org/resources/684-927-624-257-3/. As a prototype usage of the time-aligned corpus, we describe an experiment about the preferred modalities for interacting with information-rich read-out hypertext. We find alignments to help improve user experience and factual information access by enabling targeted interaction.
The SMOOTH-robot is a mobile robot that-due to its modularity-combines a relatively low price with the possibility to be used for a large variety of tasks in a wide range of domains. In this article, we demonstrate the potential of the SMOOTH-robot through three use cases, two of which were performed in elderly care homes. The robot is designed so that it can either make itself ready or be quickly changed by staff to perform different tasks. We carefully considered important design parameters such as the appearance, intended and unintended interactions with users, and the technical complexity, in order to achieve high acceptability and a sufficient degree of utilization of the robot. Three demonstrated use cases indicate that such a robot could contribute to an improved work environment, having the potential to free resources of care staff which could be allocated to actual care-giving tasks. Moreover, the SMOOTH-robot can be used in many other domains, as we will also exemplify in this article.
The REVERB challenge is a benchmark task designed to evaluate reverberation-robust automatic speech recognition techniques under various conditions. A particular novelty of the REVERB challenge database is that it comprises both real reverberant speech recordings and simulated reverberant speech, both of which include tasks to evaluate techniques for 1-, 2-, and 8-microphone situations. In this chapter, we describe the problem of reverberation and characteristics of the REVERB challenge data, and finally briefly introduce some results and findings useful for reverberant speech processing in the current deep-neural-network era.
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.
Cybersecurity is a complex global phenomenon where the risk for individuals, organisation and the society at large are at risk. These risks need to be in focus and solutions for the prevention and developing countermeasures. In this paper, we describe the Joint Effort Workshop as an approach to raise awareness to these threads and the possibility to generate and exchange knowledge between students and experts. We conclude that mechanisms for systematic response to attacks need the developed technical requirements, but foremost human behaviour, knowledge and resilience to response to risks, which can be experienced through the collaborative environment of the Joint Effort Workshop
The INPROTK 2012 release
(2012)
We describe the 2012 release of INPROTK1, our “Incremental Processing Toolkit“ which combines a powerful and extensible architecture for incremental processing with components for incremental speech recognition and, new to this release, incremental speech synthesis. These components work domainindependently; we also provide example implementations of higher-level components such as natural language understanding and dialogue management that are somewhat more tied to a particular domain. The toolkit is accompanied by evaluation tools for analysing timing behaviour, and we highlight some timing results on conversational speech input in this paper. We offer our toolkit to foster research in this new and exciting area, which promises to help increase the naturalness of behaviours that can be modelled in such systems.
This study investigates the effects of speech segmentation methods on speaker recognition models, particularly with regard to the use of rhythmic feature sets. Using three automatic methods and one manual method on the German database of Kiel corpus, segmentation was performed based on the identification of vowel onsets. Subsequently rhythmic variability indices derived from these intervals were calculated and used for principal component analysis and support vector machine model in order to investigate the variation among speakers. The results underline the influence of signal segmentation methods on speaker recognition models.
Eye tracking is a powerful technique that helps reveal how people process visual information. This paper discusses a novel metric for indicating expertise in visual information processing. Named the Gaze
Relational Index (GRI), this metric is defined as the ratio of mean fixation duration to fixation count.
Data from two eye-tracking studies of professional vision and visual expertise in using 3D dynamic medical visualizations are presented as cases to illustrate the suitability and additional benefits of the
GRI. Calculated values of the GRI were higher for novices than for experts, and higher in non-representative, semi-familiar / unfamiliar task conditions than in domain-representative familiar tasks.
These differences in GRI suggest that, compared to novices, experts engaged in more knowledge-driven, top-down processing that was characterized by quick, exploratory visual search. We discuss future
research aiming to replicate the GRI in professional domains with complex visual stimuli and to identify the moderating role of cognitive ability on GRI estimates.
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.
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.
Synchrony of Glottal Area Waveform Parameters During the Production of Obstruents in Vowel Context
(2024)
Obstruents are phonemes which require partial or total obstruction of airflow through the vocal tract. Their articulation also requires adjustments of the laryngeal settings, e. g., an abduction gesture to stop vocal fold vibration for voiceless obstruents. This study investigated the laryngeal settings during the production of voiced and voiceless obstruents in vowel context to analyze the degree of synchrony of the involved glottal gestures. High-speed laryngoscopy images were used to determine the glottal area waveform, from which the time functions of the parameters open quotient (OQ), fundamental frequency (f0), and AC and DC amplitude (ACA and DCA) were calculated and analyzed. Significant correlations were found between all pairs of parameters, with strong correlations between some of them, e.g. Open Quotient and AC Amplitude. Correlations were also either consistently positive or negative for specific pairs of parameters across all investigated phonemes. These results could point to consistent patterns in laryngeal gestures that could enhance articulatory speech synthesis.
In fault tolerant systems, applications are replicated and executed to enable error detection and recovery. If one replica application fails, another is able to take its place and provide the correct results. This concept can benefit from parallel execution on separate execution units. The rise of multicore platforms supports the development of parallel software, by providing the adequate hardware. However, this raises challenges regarding the synchronization of the redundant strings of execution. Replica determinism means that given the same input, identical programs provide the same output. To ensure replica determinism, requirements regarding the synchronization can be split in two domains: data and time. This paper examines the state of the art of synchronization techniques for parallel replicated execution in the context of fault tolerant systems. We analyze the requirements regarding synchronization within the time and data domain and compare different concepts of hardware (multicore, multiprocessor and multi-PCB) and software (processes, threads).
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.
Despite the potential of AI, only a small percentage of small and medium-sized enterprises (SMEs) are adopting it due to data issues, expertise gaps, and implementation barriers. Zero-shot learning offers a promising approach for SMEs by minimizing these obstacles. This paper explores the use of zero-shot learning in a real-world NLP classification task on online comments (comparable with intent classification tasks) from the e-learning platform Sofatutor. While finetuning has achieved high accuracy (82.3–86.5%), zero-shot models have shown lower performance (39.3–61.4%) due to different label selection, grouping of different scenarios in one class and the type of classification task. Even if the current accuracy is not sufficient for practical application, pre-filtering the data using zeroshot learning might be a promising option for SMEs.
Sufficient conditions for the existence of a sample mean of time series under dynamic time warping
(2020)
Time series averaging is an important subroutine for several time series data mining tasks. The most successful approaches formulate the problem of time series averaging as an optimization problem based on the dynamic time warping (DTW) distance. The existence of an optimal solution, called sample mean, is an open problem for more than four decades. Its existence is a necessary prerequisite to formulate exact algorithms, to derive complexity results, and to study statistical consistency. In this article, we propose sufficient conditions for the existence of a sample mean. A key result for deriving the proposed sufficient conditions is the Reduction Theorem that provides an upper bound for the minimum length of a sample mean.
Although the average tenure of CIOs has increased over the last years, the majority of CIOs have been in their positions for only three years or less. Nevertheless, some CIOs have been successful in their position for a long time. In this study, we use tenure as a proxy for success as a CIO. The goal of this paper is to examine factors that are critical to the success of long-term CIOs. For this purpose, we created and analyzed resumes of 384 CIOs. Out of these 384, we conducted 19 interviews with CIOs from top-tier companies and collected and analyzed both qualitative and quantitative data. In the process, we were able to identify nine factors that are critical for the success (CSF) of CIOs. These factors fall into three categories. Category “Personality” includes “Accepting and embracing change” (CSF #1), “Being perseverant to pursue long-term goals” (CSF #2), “Anticipating the future through visionary thinking” (CSF #3), and “Being empathetic to deal with uncertainty felt by co-workers” (CSF #4). The “Role Fulfilment” category includes “Cross-functional involvement and integration of the IT organization” (CSF #5), “Positioning and restructuring of the IT organization” (CSF #6), and “Well-connected and communicative leadership” (CSF #7). The “Organizational Environment” category consists of “Availability of skilled workforce” (CSF #8) and “Reporting line to the CEO” (CSF #9). CSFs 1, 2, and 3 were perceived as most important by the participating CIOs. The results may be of particular interest both to aspiring CIOs and equally their employing organizations, as they reflect what long-term CIOs value during their time in office.
Modern and post-modern free verse poems feature a large and complex variety in their poetic prosodies that falls along a continuum from a more fluent to a more disfluent and choppy style. As the poets of modernism overcame rhyme and meter, they oriented themselves in these two opposing directions, creating a free verse spectrum that calls for new analyses of prosodic forms. We present a method, grounded in philological analysis and current research on cognitive (dis)fluency, for automatically analyzing this spectrum. We define and relate six classes of poetic styles (ranging from parlando to lettristic decomposition) by their gradual differentiation. Based on this discussion, we present a model for automatic prosodic classification of spoken free verse poetry that uses deep hierarchical attention networks to integrate the source text and audio and predict the assigned class. We evaluate our model on a large corpus of German author-read post-modern poetry and find that classes can reliably be differentiated, reaching a weighted f-measure of 0.73, when combining textual and phonetic evidence. In our further analyses, we validate the model’s decision-making process, the philologically hypothesized continuum of fluency and investigate the relative importance of various features.
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Der zweite ITG-Workshop „Sprachassistenten – Anwendungen, Implikationen, Entwicklungen“ fand am 5. März 2024 in Regensburg statt. Er bot eine organisatorische und inhaltliche Fortführung des ersten Workshops vor vier Jahren in Magdeburg 2020. Auch in diesem Jahr war er wieder der Konferenz Elektronische Sprachsignalverarbeitung angegliedert. Auf dem Workshop wurden vielfältige und interdisziplinäre Beiträge in eingeladenen Vorträgen und als eingereichte Poster präsentiert. Durch die gute Mischung von Beitragenden sowohl aus der Hochschullandschaft als auch aus der Industrie wurden die verschiedensten Aspekte anwendungsnah diskutiert.
Spherical aberrations of lenses lead to increased spot radii on the focal plane. Several methods, such as optimizing thickness and radii or building lens groups, are known to minimize these spherical aberrations. Here, an innovative method for spherical aberration minimization is introduced; it can be used for short-range free-space optical communication systems, such as unit power afocal relay trains, lens waveguides, and periodic lens systems. This spherical aberration compensation principle is based on the combination of two identical spherical convex lenses at an optimal distance. Due to the higher refractive power for rays with an larger axis distance, rays from the outer area of the first lens intersect the inner area of the second lens, and vice versa. With this setup, the radial refractive power deviation of the two lenses compensate each other. Analytic calculations and numerical simulations are done to confirm this behavior, and measurements using two symmetric spherical lenses with polymer optical fibers as light feed confirm the calculations. Simulations and measurements show a very good matching behavior except for an unknown systematic error. At a lens distance of 300 mm, the optical attenuation decreased by ∼2 dB compared with a very small lens distance. This leads to the conclusion that this proposed spherical aberration minimization method works as theoretically predicted.
One of the tasks PAULE[1, 2] solves is finding suitable control parameter (cp-)trajectories for a given target acoustic. These cp-trajectories can be used to synthesize speech with the articulatory speech synthesizer of the VocalTractLab (VTL) [3]. If the target acoustic contains substantial microphone noise or other background noises, occasionally PAULE optimizes not for the speech in the target, but for this background noises. By adding a speech/non-speech classifier to the feedback and planning-loop in PAULE this resynthesis of background noises should be mitigated. Unfortunately, the improvements were minor, which might be due to uninformative gradients of the classifier. The importance of informative gradients and the use classifiers to adapt PAULE to different tasks are explained and discussed.
Speech Recognition Errors in ASR Engines and Their Impact on Linguistic Analysis in Psychotherapies
(2024)
Modern intervention planning in psychotherapies may benefit from predicting process relevant psychotherapy constructs by automated speech analysis. One essential step is the extraction of relevant linguistic speech markers by ASR engines, which because of highly sensible data, work offline. We analyze transcription errors from NeMo, Whisper, and Wav2Vec2.0, focusing on their impact on linguistic markers usually requiring high quality transcripts. By utilizing part-of-speech tagging, we examine error occurrences among different word types. The Linguistic Inquiry and Word Count (LIWC) software aids in extracting markers. We highlight challenges in transcribing spontaneous speech, prevalent in therapy, and compare results with the Mozilla CommonVoice dataset, which features read speech.
Summary form only given. The analytical breather-solutions of the Nonlinear Schrödinger Equation (NLS) [1] have been intensively studied and verified experimentally in the time-space system of optical pulse propagation in fibers [2]. In space-space systems, i.e. in optical beam propagation breathers in ultra-fast nonlinear media have not been observed due to the breather’s infinite background and a resulting extremely large power. In a lithium niobate slab waveguide with two second-harmonic (SH) resonances the resulting quasi-cubic cascaded quadratic nonlinearity provided together with the intrinsic cubic susceptibility enough nonlinearity for breather excitation at experimentally reachable powers. We could characterize the fi rst ultra -fast spatial -spatial optical breathers in a 5 -cm -long titanium indiffused lithium niobate slab waveguide at power levels down to tens of kW. The guided fundamental wave (FW) TM° fi lm mode at A = 1.32μm is phase -matched for type -I SH generation to TE 0 and TEi SH modes at temperatures near 295 and 344C. With temperature tuning the phase -mismatch and the two effective cascaded nonlinearities were adjusted. For breather observation, we aimed for a large phase -mismatch with low SH levels such that the cascaded nonlinearity is quasi -cubic and the propagation is well approximated by the NLS and its breather solutions. A frequency -doubled Nd:YAG-pumped OPA with CW-seeding delivered 5-ps long pulses with up to 200kW peak power in the waveguide. With a cylindrical telescope the beam was transformed into a very wide elliptical beam to approximate the breather background. The beam was end -fire coupled into the FW TM () mode. A variable few % of the beam were separated and coupled with a tilt and good overlap to the main beam into the waveguide to produce a spatial modulation of the input with adjustable period and modulation depth. A beam width of 1.5mm was large enough in the compromise between available beam power and infinite beam width. A beam with a transverse modulation with periods between 130 to 300μm approximates a constant background with modulation well enough to trigger modulation instability that develops eventually into the breather.
We investigate the spatial nonlinear localization of light on a quasi-plane-wave background with a harmonic perturbation induced by modulation instability in a quadratic nonlinear optical medium. In particular, we demonstrate experimentally the excitation of deterministic Akhmediev breathers and thus the growth-decay dynamics of modulation instability in a LiNbO3 slab waveguide. The results should stimulate new interest in modulation instability, extreme events, turbulence, recurrence, and supercontinuum generation in quadratic nonlinear optics.
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.
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.
SIM SIMulator
(2019)
Der Vortrag präsentierte ein Tool, das die Simulation einer SIM-Karte durch einen Standard-Mikrocontroller ermöglicht. Mit diesem Täuschungsmanöver kann die Authentifizierung des 3G-Mobilfunkstandards seitens der SIM-Karte umgangen werden. Tritt zusätzliche Hardware hinzu, die eine 3G-Basisstation vortäuscht, lässt sich eine Man-in-the-Middle-Attacke im 3G-Netz durchführen.
In diesem Szenario ist es möglich, die sensiblen Kommunikationsdaten, also den gesamten Datenverkehr beispielsweise zwischen einem Pkw und den Backend-Servern des Herstellers, auszulesen und zu untersuchen. Zudem eignet sich das Tool zum Pentesten von Modems oder SIM- beziehungsweise Smartcard-Applikationen. Der komplette Aufbau des sogenannten SIMulators ist als Open-Source-Software frei verfügbar und auf GitHub abrufbar.
This The vehicle is evolving to a complex network of heterogeneous subsystems of ECUs, sensors and actuators, each with different computational requirements. These sub-systems are connected via bus systems following different communication paradigms like e.g. signal based or service-oriented communications. This has led to the heterogeneous syntax of describing interfaces even though the semantics of the interfaces are similar. The wide variety of Interface Description Languages (IDLs) in automotive industry hinders partly with the efficient collaboration between different suppliers and the OEMs in the automotive industry. Given this wide variety of automotive IDLs, what could be more beneficial, from a software engineering point of view, is a generic automotive domain specific IDL that can satisfy all the fundamental requirements of the heterogeneous subsystems. This paper describes an approach to compare and correlate IDLs based on semantic similarities of the languages, considering the two aspects: application description and underlying message frameworks used in the different domains of given automotive subsystems. With the exploration of semantic synergies between the IDLs, various domain specific and domain-agnostic frameworks can be compared and correlated. The results can be generalized and abstracted to define a generic Meta IDL which could support use cases like e.g. domain-agnostic functional models and migration of software components between different kinds of automotive subsystems.
Dubbing is the art of finding a translation from a source into a target language that can be lip-synchronously revoiced, i. e., that makes the target language speech appear as if it was spoken by the very actors all along. Lip synchrony is essential for the full-fledged reception of foreign audiovisual media, such as movies and series, as violated constraints of synchrony between video (lips) and audio (speech) lead to cognitive dissonance and reduce the perceptual quality. Of course, synchrony constraints only apply to the translation when the speaker's lips are visible on screen. Therefore, deciding whether to apply synchrony constraints requires an automatic method for detecting whether an actor's lips are visible on screen for a given stretch of speech or not. In this paper, we attempt, for the first time, to classify on- from off-screen speech based on a corpus of real-world television material that has been annotated word-by-word for the visibility of talking lips on screen. We present classification experiments in which we classify
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.
The number of IoT devices in SCADA and ICS systems is rising quickly, especially in the domain of critical infrastructures. But these kinds of systems are performing mission critical tasks like controlling devices in industrial facilities or substations in the smart grid. Therefore, they are subject to a lot of regulatory standards. Yet, to provide remote access over the internet, special architectures are developed to integrate a network interface into these devices without inferring with the actual functionality. However, these architectures either lack security measures against cyber-attacks or do not offer the necessary performance for time-critical communication interfaces. To solve that, an architecture consisting of three units is introduced in this paper to provide a network interface with extensive security measures and a high performance. The main feature is the isolation of the cryptographic functionality onto an additional MCU. After proposing the basic concept, the paper presents many implementation details. Based on the current state of implementation, a concept validation of the realized architecture is described.
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.
Search Engine Bias
(2011)
Der Name ‚Google’ wird heute synonym für ‚Suchmaschine’ genutzt;
das Verb ‚googeln’ hat es zu einem Eintrag im Duden gebracht: Google ist allgegenwärtig.
Dies bestätigen Statistiken, die aufweisen, dass Google in vielen
Ländern andere Suchmaschinen völlig marginalisiert hat. Außerdem zeigen Untersuchungen, dass sich viele Nutzerinnen und Nutzer auf die Ergebnisse einer einzigen Suchmaschine – oft eben Google – verlassen. Daher stellt sich die Frage, ob die tatsächliche Qualität der Suchergebnisse das Vertrauen in die angenommene Qualität einer Suchmaschine rechtfertigen kann. Nach einleitenden Bemerkungen zum Suchmaschinenmarkt und einer kurzen Beschreibung der generellen Funktionsweise heute verbreiteter Suchmaschinen wird evaluiert, welche Faktoren die Ergebnisse einer Suchmaschine determinieren. Dabei wird sich zeigen, dass Suchergebnisse durch vielfältige Faktoren beeinflusst werden, so bspw. durch das Nutzerverhalten auf verschiedenen Ebenen, durch die verwendeten Indexierungs- und Suchalgorithmen oder durch die Inhalte des Netzes selbst. Diese und weitere Faktoren führen zu Verzerrungen der Suchergebnisse gegenüber einem unterstellten Idealergebnis. Es zeigt sich, dass es in vielen Situationen sinnvoll ist, sich nicht nur auf eine Suchmaschine zu verlassen bzw. den Ergebnissen einer Suchmaschine nicht blind zu vertrauen. So ist im wissenschaftlichen oder journalistischen Kontext dringend die Nutzung von bereichsspezifischen Suchmaschinen und anderer Rechercheformen angeraten.
Die vorliegende Studie untersucht Schlüsselfaktoren erfolgrei-
cher CIOs in deutschen Großunternehmen. Mit einer mittleren Verweildauer (Median) von 4,0 Jahren weisen deutsche CIOs, die mit 43 % noch überwiegend an den CFO berichten, im Vergleich zu anderen C-Level-Positionen eine deutlich kürzere Verweildauer im Amt auf. Die Ergebnisse aus 60 Interviews mit erfolgreichen deutschsprachigen CIOs, die primär über eine überdurchschnittlich lange Verweildauer verfügen, lassen verschiedene Schlüsselfaktoren für den Erfolg erkennen: Grundvoraussetzung ist stets die Gewährleistung eines sicheren und effizienten IT-Betriebs. Über effektive und innovative Change-Projekte machen die interviewten CIOs den IT-Mehrwert transparent und agieren als Brückenbauer zwischen IT und Fachbereichen. Dadurch wirken sie positiv auf die Firmenkultur ein und etablieren die IT nachhaltig in den Fachbereichen als Erfolgsfaktor. Erfolgreiche CIOs selbst sind keine „Techies“, sondern zeichnen sich durch hohe Führungskompetenz und ein hohes Geschäftsverständnis, gepaart mit visionärem Denken aus. Dadurch gelingt es ihnen, die IT zukunftsorientiert auszurichten und Anforderungen und Potenziale für und aus den Fachbereichen frühzeitig zu antizipieren. Die zukünftige Entwicklung der CIO-Organisation und der Paradigmen in der IT wird durch die Studienteilnehmer hingegen teilweise kontrovers diskutiert – so gibt es beispielsweise bei der Beurteilung der Sinnhaftigkeit und Relevanz der CDO-Position noch kein einheitliches Meinungsbild.
The most important development in modern and postmodern poetry is the replacement of traditional meter by new rhythmical patterns. Ever since Walt Whitman's Leaves of Grass (1855), modern (nineteenth-to twenty-first-century) poets have been searching for novel forms of prosody, accent, rhythm, and intonation. Along with the rejection of older metrical units such as the iamb or trochee, a structure of lyrical language was developed that renounced traditional forms like rhyme and meter. This development is subsumed under the term free verse prosody. Our project will test this theory by applying machine learning or deep learning techniques to a corpus of modern and postmodern poems as read aloud by the original authors. To this end, we examine “lyrikline”, the most famous online portal for spoken poetry. First, about 17 different patterns being characteristic for the lyrikline-poems have been identified by the philological scholar of this project. This identification was based on a certain philological method including three different steps: a) grammetrical ranking; b) rhythmic phrasing; and c) mapping rubato and prosodic phrasing. In this paper we will show how to combine this philological and a digital analysis by using the prosody detection available in speech processing technology. In order to analyse the data, we want to use different tools for the following tasks: PoS-tagging, alignment, intonation, phrases and pauses, and tempo. We also analyzed the lyrikline-data by identifying the occurrence of the mentioned patterns. This analysis is a first step towards an automatic classification based on machine learning or deep learning techniques.
The continuous advancement of digitization extends beyond educational institutions, giving rise to numerous innovations, particularly in the realm of study information [1]. One avenue for incorporating digital methodologies involves leveraging conversational agents (CAs) [2], serving as interactive interfaces bridging the gap between humans and computers. In the broader context, conversational agents are gaining prominence, offering several benefits to their users. The overarching goal is to comprehensively assist users through these intelligent systems. Consequently, exploring existing university chatbots becomes imperative to discern the areas where they excel. This research aims to scrutinize diverse chatbot systems, delving into their use cases and the challenges they encounter, employing a systematic review. Here it turns out that chatbots support universities the most in the fields of administration, e-learning and mental health. Furthermore, the study will investigate practical experiences on the potential applications and implementation of these systems in university settings, incorporating insights from an online survey and interviews, both made with experts. Here it comes to conclusion that preparation in relation to a chatbot implementation is the key factor to success. Otherwise, a failed system is nearly impossible to be saved, once users lost trust in the system. Therefore, carefully made preparations in the technical and organisational field are necessary to provide a helpful assistant.
The vernier or nonius (in other languages) goes back to a measuring tool used in navigation and astronomy named after its inventor Pedro Nunes (1502–1578; Latin: Petrus Nonius), a Portuguese mathematician and navigator. The nonius was created in 1542 to take finer measurements on circular instruments such as the astrolabe. In 1631 the French mathematician Pierre Vernier (1580–1637) adapted and simplified the system which was later denoted “vernier”.
We present a method to construct a vernier-like scale for logarithmic scales (as used for typical slide rules), which results in variable tick spacings. The idea is to put the non-linear scale on a spiral. The method can be applied to any non-linear scale, not only logarithmic scales.
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.
Our paper focuses on the computational analysis of “readout poetry” (german: Hördichtung) – recordings of poets reading their own work – with regards to the most important type of this genre, the modern “sound poetry” (german: Lautdichtung). Whereas “readout poetry” often uses normal words and sentences, the “sound poetry”, developed by dadaistic poets like Hugo Ball and Kurt Schwitters or concrete poets like Ernst Jandl, Oskar Pastior, or Bob Cobbing, combines the “microparticles of the human voice” like the segments in Ernst Jandls sound poem “schtzngrmm” (“schtzngrmm / schtzngrmm / tttt / tttt / grrrmmmmm / tttt / sch / tzngrmm”). Within the genre of sound poetry, there are two main forms: The lettristic and the syllabic decomposition. A short anecdote will explain this difference: The dadaist Raoul Hausmann developed the lettristic sound poetry in his early dadaistic poem “fmsbw” from 1918. This is said to have inspired his successor Schwitters, whose famous “Ursonate” [The Sonata in Primal Speech] begins with the words “Fümms bö wö tää zää Uu”. With the “Ursonate”, Schwitters developed a syllabic variation of the lettristic poems of Hausmann. The paper shows how to train a bidirectional LSTM network in order to differ between these “dadaistic” sound poems and the “normal” read out poems. In a further step, we will also show how to distinguish between the lettristic and the syllabic decomposition. Based on a bidirectional LSTM network that reads encodings of the character sequence in the poem and uses the output of each directional layer, we identify poems of the sound poetry genre and differentiate between its two types of compositions. The classification results of sound poetry vs. other poetry as well as lettristic vs. syllabic decomposition are with a high performance, yielding a f-scores of 0.86 and 0.84, respectively.
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.
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.
Speech quality and likability is a multi-faceted phenomenon consisting of a combination of perceptory features that cannot easily be computed nor weighed automatically. Yet, it is often easy to decide which of two voices one likes better, even though it would be hard to describe why, or to name the underlying basic perceptory features. Although likability is inherently subjective and individual preferences differ, generalizations are useful and there is often a broad intersubjective consensus about whether one speaker is more likeable than another. We present a methodology to efficiently create a likability ranking for many speakers from crowdsourced pairwise likability ratings which focuses manual rating effort on pairs of similar quality using an active sampling technique. Using this methodology, we collected pairwise likability ratings for many speakers (>220) from many raters (>160). We analyze listener preferences by correlating the resulting ranking with various acoustic and prosodic features. We also present a neural network that is able to model the complexity of listener preferences and the underlying temporal evolution of features. The recurrent neural network achieves remarkably high performance in estimating the pairwise decisions and an ablation study points toward the criticality of modeling temporal aspects in speech quality assessment.
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 analyze the addressee detection task for complexity-identical dialog for both human conversation and device-directed speech. Our recurrent neural model performs at least as good as humans, who have problems with this task, even native speakers, who profit from the relevant linguistic skills. We perform ablation experiments on the features used by our model and show that fundamental frequency variation is the single most relevant feature class. Therefore, we conclude that future systems can detect whether they are addressed based only on speech prosody which does not (or only to a very limited extent) reveal the content of conversations not intended for the system.
This paper is directed towards IT executives aiming to promote the adoption of cloud computing (CC) with in their company. We conducted a longitudinal case study on the evolving CC strategy and its implementation at the multinational company Continental, based on a previous case study by Loebbecke et al. (2012). We narrate Continental's pathway towards CC adoption, which comprised the experimentation, professionalization, and utilization of CC, and discuss current and previous barriers encountered during the implementation. We derive five lessons learned that can serve as practical guidance for executives aiming to accelerate CC adoption within their own organization: (1) differentiate the CC strategy by delivery model; (2)drive proof of concepts to generate reusable blueprints; (3) preinvest in the integration of IaaS and PaaS providers; (4) implement CC gradually, transforming applications during the transition to the cloud; and (5) disseminate knowledge within the organization to enable change.
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.
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.
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.
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.
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.
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
Concatenative text-to-speech (TTS) systems remain a widely used cheaper alternative to neural TTS systems. Yet concatenation of prerecorded units entails some drawbacks, such as spectral distortion, the perceptual consequences of which remain unclear. In an attempt to bridge this gap, our study focused on the effect of spectral distortion in vowel formants on perceived speech quality in
naturally-read manipulated German words as well as non-words. More specifically, we explored the distortion effect on a varying number of affected formants, at different magnitude and directionality in two corner vowels /a:/ and /i:/. The results indicate that single formant manipulations have a less pronounced effect on the listeners’ perception compared to multiple formant perturbations. The threshold at which the distortion became generally audible was estimated to lie between 0.4 and 1.0 bandwidth. The directionality of the distortion was not found to be significant.
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.
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.
A basic task in the design of an industrial robot application
is the relative placement of robot and workpiece. Process points are defined in Cartesian coordinates relative to the workpiece coordinate system, and the workpiece has to be located such that the robot can reach all points. Finding such a location is still an iterative procedure based on the developers' intuition. One difficulty is the choice of one of the several solutions of the backward transform of a typical 6R robot.
We present a novel algorithm that simultaneously optimizes the workpiece location and the robot configuration at all process points using higher order optimization algorithms. A key ingredient is the extension of the robot with a virtual prismatic axis. The practical feasibility of the approach is shown with an example using a commercial industrial robot.
We propose and experimentally demonstrate an all-optically tunable biphoton quantum light source using a nonlinear directional coupler. The source can generate high-fidelity N00N states, completely split states, and states with variable degrees of entanglement.
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.
We present a fully automatic solution for German video subtitling, with a focus on lecture videos. We rely entirely on open source models and scripts for German ASR, automatic punctuation reconstruction and subtitle segmentation. All training scripts, 1000h of German speech training data, pre-trained models and the final subtitling program are publicly available. It can readily be integrated into lecture video platforms such as Lecture2Go. The automatically generated subtitles can also serve as a basis to make the video material more accessible (e.g. via search, keyword clouds, and the like) or for further manual revision, potentially helping in significantly speeding up manual work. A particular challenge that we observe in lectures are technical terms that are frequent in a particular lecture, but infrequent in a typical language model and that might be out of vocabulary for a general purpose ASR. We approach this challenge by extracting texts from accompanying lecture slides to adapt the language model of our TDNN-HMM based ASR system. We demonstrate the usability of the full system and its generated subtitles and evaluate on a dataset of manually transcribed lectures with an average of 26.3% WER.
Ontologies used in robotics
(2017)
Full autonomy of road vehicles is a major goal of the automotive industry. To reach such high autonomy it is necessary to provide an accurate and comprehensible situation description for the environment and the vehicle itself. A consistent depiction is essential to facilitate data exchange and communication between internal modules, e.g. collision check and environment model, as well as communication with further information sources like traffic participants vehicle to vehicle (V2V) or the infrastructure, e.g. smart traffic lights, road signs or radio traffic service (V2I). One necessary tool to create such a model could be an ontology which represents the given information and its dependencies. Initially this work provides a summary of given approaches in the literature for use of ontologies in robotics in general. Therefore approaches are stated sorted by application and task. Further approaches with focus on autonomous robots and in particular on autonomous vehicles are listed and described. Finally we give an outlook for further research topics in the domain of ontologies.
As a result of the enormous growth in data traffic for autonomous driving, the conventional in-vehicle network is no longer sufficient and requires new types of network concepts in a vehicle. This part of the automobile is known as the next generation communication network. Since the new car-systems can be extended by various services at any time, the network must adapt dynamically to new requirements wherever possible. For example, data flow must be configured dynamically between new services. Also data rates will be much higher in the future than today. This is one of the main reasons why we need to search for new technologies for data transfer in vehicles. This is based on an in-vehicle ethernet network. The process of configuring networks automatically has been discussed several times in recent years. One of the next steps is verifying and validating the automatic configuration process during the development of the new communication network. This research paper identifies several ways to ensure the automatically generated network configuration leads to a secure system. To achieve that, other parts of the company’s enterprise IT architecture and network technologies, the conventional vehicle network and other options for verification and validation are analysed