Approaches to Improve Fairness when Deploying AI-based Algorithms in Hiring – Using a Systematic Literature Review to Guide Future Research - Rieskamp, Jonas; Hofeditz, Lennart; Mirbabaie, Milad; Stieglitz, Stefan (Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS))
Algorithmic fairness in Information Systems (IS) is a concept that aims to mitigate systematic discrimination and bias in automated decision-making. However, previous research argued that different fairness criteria are often incompatible. In hiring, AI is used to assess and rank applicants according to their fit for vacant positions. However, various types of bias also exist for AI-based algorithms (e.g., using biased historical data). To reduce AI’s bias and thereby unfair treatment, we conducted a systematic literature review to identify suitable strategies for the context of hiring. We identified nine fundamental articles in this context and extracted four types of approaches to address unfairness in AI, namely pre-process, in-process, post-process, and feature selection. Based on our findings, we (a) derived a research agenda for future studies and (b) proposed strategies for practitioners who design and develop AIs for hiring purposes.
Keywords: fairness in AI; SLR; hiring; AI implementation; AI-based algorithms

Model-based condition monitoring of piezoelectric bending actuators - Bender, Amelie (Sensors and Actuators A: Physical)
With enhancing digitalization, condition monitoring is used in an increasing number of application fields across various industrial sectors. By its application, increased reliability as well as reduced risks and costs can be achieved. Based on different approaches, technical systems are monitored and measured data is analyzed to enable condition-based or predictive maintenance. To this end, machine learning approaches are usually implemented to diagnose the health states or predict the health index of the monitored system. However, these trained models are often black-box models, not intuitively explainable for a human. To overcome this shortcoming, a model-based approach based on physics is developed for piezoelectric bending actuators. Such a model enables a transparent representation of the system. Moreover, the model-based approach is extended by a parameter-estimation to account for sudden changes in behavior e. g. caused by occurring cracks.
Keywords: Condition Monitoring; Model-based approach Diagnostics; Varying conditions; Explainability; Piezoelectric bending actuators

AnnoPy. Ein digitales Tool zur Förderung von Textkompetenzen - Rezat, Sara; Scholle, Oliver (Medien im Deutschunterricht)
Im Beitrag wird ein digitales Tool zur fächerübergreifenden Förderung von Textkompetenzen vorgestellt. Zunächst werden die Funktionen des Tools beschrieben und das zugrundeliegende tool-based task Design erläutert. Anhand eines Lehr-Lern-Arrangements zum argumentierenden Schreiben wird exemplarisch gezeigt, wie das Tool im Deutschunterricht eingesetzt werden kann. Anschließend werden erste empirische Befunde aus der am Design-Based-Research orientierten Studie vorgestellt. Abstract (english): AnnoPy – A digital tool for cross-curricular text literacy development In this article we present a digital tool for cross-curricular text literacy development. At first, we describe the basic functions of the tool and the underlying tool-based task design. After that, a teaching arrangement for German lessons is used to show how the tool can be used to foster argumentative writing. Subsequently, first results of the design-based study are presented.
Keywords: Textkompetenz; Digitales Lesen und Schreiben; Kollaborativer Umgang mit Texten; Prozedurenorientierte Schreibdidaktik; Design-based-Research

Umgang mit Evidenzen angehender Lehrkräfte in den Naturwissenschaften - Epistemologie in der Lehrkräfteausbildung - Pollmeier, Pascal
Eine Forderung nach evidenzbasierter Praxis (EBP) im Bildungswesen kann sowohl vor dem Hintergrund der Bedeutung wissenschaftlicher Evidenzen innerhalb gesellschaftlicher Diskurse als auch der Evidenzbasierung in anderen Bereichen formuliert werden. Der Umgang mit und die Bewertung von Evidenzen stellen somit vor dem Hintergrund epistemologischer Überzeugungen relevante Aufgaben für den Chemieunterricht dar. Dabei ist unklar, inwiefern (angehende) Lehrkräfte über Kompetenzen in EBP verfügen. Bestehende Studien weisen auf einen Bedarf der fachdidaktischen Analyse und Förderung von Kompetenzen in EBP bei angehenden Lehrkräften hin.Im Rahmen dieser Dissertation wurde die Domänenspezifität der Kompetenzen in EBP sowie deren Förderung in einem fachdidaktischen Kontext untersucht. Dabei wurde ein weiterer Schwerpunkt auf den Umgang mit anomalen Beobachtungen im Sinne widersprüchlicher Evidenzen gelegt. Innerhalb von drei Teilstudien wurden unterschiedliche methodische Zugänge zur Kompetenz in EBP auf Grundlage epistemologischer Überzeugungen gewählt.Die Ergebnisse weisen auf einen Förderbedarf der Kompetenz in EBP für angehende Lehrkräfte aller Unterrichtsfächer hin. Eine entwickelte Intervention für angehende Chemielehrkräfte weist eine Wirksamkeit zur Förderung von Kompetenzen in EBP auf. Im Rahmen einer Modellierungsstudie kann der Einfluss von Fachwissen sowie Indizien für einen Einfluss epistemologischer Überzeugungen auf den Umgang mit anomalen Beobachtungen gezeigt werden.
Keywords: Epistemologie; Evidenzen; evidence-based practice

Designing digital actor engagement platforms for local high streets: an action design research study - Bartelheimer, Christian; zur Heiden, Philipp; Berendes, Carsten Ingo; Beverungen, Daniel (European Journal of Information Systems)
High streets across Europe continue to lose consumers to online retail, leading to business closures and the decline of city centres, impairing cities’ overall liveability. To counter this vicious cycle, our study presents smartmarket2, the first instantiation of a digital actor engagement platform designed specifically for high streets. smartmarket2 enables hybrid online-offline customer journeys by connecting consumers to stores and other high street service providers. In an action design research (ADR) project, we design, implement and evaluate smartmarket2, involving 150 high street operators and 2,300 citizens in three cycles of building, intervention and evaluation. We derive four design principles that contribute prescriptive knowledge on the design of digital actor engagement platforms. Our results reveal that such a platform is able to increase engagement, but that it is subject to actors’ engagement dispositions.
Keywords: Digital platform; action design research; design principles; actor engagement; engagement platform; location-based advertising

Model-driven Continuous Experimentation on Component-based Software Architectures - Gottschalk, Sebastian; Yigitbas, Enes; Engels, Gregor (Proceedings of the 18th International Conference on Software Architecture Companion)
To build successful software products, developers continuously have to discover what features the users really need. This discovery can be achieved with continuous experimentation, testing different software variants with distinct user groups, and deploying the superior variant for all users. However, existing approaches do not focus on explicit modeling of variants and experiments, which offers advantages such as traceability of decisions and combinability of experiments. Therefore, our vision is the provision of model-driven continuous experimentation, which provides the developer with a framework for structuring the experimentation process. For that, we introduce the overall concept, apply it to the experimentation on component-based software architectures and point out future research questions. In particular, we show the applicability by combining feature models for modeling the software variants, users, and experiments (i.e., model-driven) with MAPE-K for the adaptation (i.e., continuous experimentation) and implementing the concept based on the component-based Angular framework.
Keywords: continuous experimentation; model-driven; component-based software architectures; self-adaptation

Use What You Know: Network and Service Coordination Beyond Certainty - Werner, Stefan; Schneider, Stefan Balthasar; Karl, Holger (IEEE/IFIP Network Operations and Management Symposium (NOMS))
Modern services often comprise several components, such as chained virtual network functions, microservices, or machine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. To overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. Instead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.
Keywords: network management; service management; AI; Monte Carlo Tree Search; model-based; QoS

More Efficient Techniques for Adaptively-Secure Cryptography - Niehues, David
Modern-day communication has become more and more digital. While this comes with many advantages such as a more efficient economy, it has also created more and more opportunities for various adversaries to manipulate communication or eavesdrop on it. The Snowden revelations in 2013 further highlighted the seriousness of these threats. To protect the communication of people, companies, and states from such threats, we require cryptography with strong security guarantees. Different applications may require different security properties from cryptographic schemes. For most applications, however, so-called adaptive security is considered a reasonable minimal requirement of security. Cryptographic schemes with adaptive security remain secure in the presence of an adversary that can corrupt communication partners to respond to messages of the adversaries choice, while the adversary may choose the messages based on previously observed interactions. While cryptography is associated the most with encryption, this is only one of many primitives that are essential for the security of digital interactions. This thesis presents novel identity-based encryption (IBE) schemes and verifiable random functions (VRFs) that achieve adaptive security as outlined above. Moreover, the cryptographic schemes presented in this thesis are proven secure in the standard model. That is without making use of idealized models like the random oracle model.
Keywords: public-key cryptography; lattices; pairings; verifiable random functions; identity-based encryption

HOMOGENIZATION OF TWIN-ROLL CAST Al-Li-BASED ALLOY STUDIED BY IN-SITU ELECTRON MICROSCOPY - CIESLAR, Miroslav; KŘIVSKÁ, Barbora; KRÁLÍK, Rostislav; BAJTOŠOVÁ, Lucia; Grydin, Olexandr; STOLBCHENKO, Mykhailo; Schaper, Mirko (METAL 2022 Conference Proeedings)
Transformation of Fe- and Cu-rich primary phase particles was studied in an Al-Li-based alloy prepared by twin-roll casting. Thin foils for combined STEM and SEM experiments were prepared by electrolytic twin-jet polishing. They were in-situ heated in a TEM heating stage and observed at 200 kV in the JEOL JEM 2200FS electron microscope equipped with STEM HAADF and BF detectors and SEM BSE and SE detectors working both in composition and topographic modes. The resulting structures were combined with EDS mapping performed directly in the heating holder. Dissolution and transformation of Cu- and Fe-rich particles occur above 500 °C. EDS maps acquired on the foil cooled down to room temperature show that Cu and Fe are both still present in newly formed particles, most likely indicating the presence of the Al7Cu2Fe phase.
Keywords: Al-Li-based alloy; in-situ TEM; homogenization; phase transformation

Responsibility Assignment in Systems Engineering - Gräßler, Iris; Thiele, Henrik; Grewe, Benedikt; Hieb, Michael (Proceedings of the Design Society)
Abstract Increasing system complexity can be controlled by using systems engineering processes. INCOSE defines processes with inputs and outputs (artifacts) for this purpose. Specific SE roles are used to organize the tasks of the processes within the company. In this work, the responsibilities for artifacts are evaluated by means of the RACI scheme and examined by a cluster analysis and discussed for a SE transformation project with a German automotive OEM. As a result of the study, the optimal composition for systems engineering teams is identified and the systems engineering roles are prioritized.
Keywords: systems engineering (SE); project management; model-based systems engineering (MBSE)

Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering - Götte, Ricarda-Samantha; Timmermann, Julia (2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC))
In control design most control strategies are model-based and require accurate models to be applied successfully. Due to simplifications and the model-reality-gap physics-derived models frequently exhibit deviations from real-world-systems. Likewise, purely data-driven methods often do not generalise well enough and may violate physical laws. Recently Physics-Guided Neural Networks (PGNN) and physics-inspired loss functions separately have shown promising results to conquer these drawbacks. In this contribution we extend existing methods towards the identification of non-autonomous systems and propose a combined approach PGNN-L, which uses a PGNN and a physics-inspired loss term (-L) to successfully identify the system's dynamics, while maintaining the consistency with physical laws. The proposed method is demonstrated on two real-world nonlinear systems and outperforms existing techniques regarding complexity and reliability.
Keywords: data-driven; physics-based; physics-informed; neural networks; system identification; hybrid modelling

Development and validation of a video-based test instrument to measure noticing with regard to processes ofrecognition in inclusive physical education - Langer, Wiebke; Bruns, Julia; Erhorn, Jan (German Journal of Exercise and Sport Research)
The construct of noticing is considered to be of crucial importance for professional action of (prospective) physical education (PE) teachers in the context of (inclusive) PE. In order to implement the demands of inclusive PE teaching, noticingwith regard to processes of recognition proves to be particularly significant. Due to the relevance of physicality, the design of recognition relationships that encourage pupils' development results in specific requirements for the selective attention and the theory-based interpretation of processes of recognition and disregard in complex teaching situations. This facet of competence has hardly been studied so far, partly due to the lack of instruments for standardised assessment of this construct. In the context of this paper, the development and validation of a standardised, video vignette-based test instrument for the measurement of noticingwith regard to processes of recognition of prospective PE teachers is presented. The validation focuses on the content of the test and the internal structure of the instrument as well as on correlations with related constructs in terms of content. The results from three consecutive validation studies provide indications of the reliability and validity of the test score interpretation of the instrument ViProQiS_A. The findings so far indicate that the standardised measurement of noticingwith regard to processes of recognitionin inclusive PE could be realized on the basis of the test instrument developed here.
Keywords: Physical education teacher education; Competence modelling and; measurement; Inclusive physical education; Video-based items; Noticing

Ein kritisch-multiperspektivischer Blick auf Forschendes Lernen in der Lehrkräftebildung - Bloh, Thiemo; Caruso, Carina (die hochschullehre)
Forschendes Lernen in der Lehrer:innenbildung ist seit der Ausweitung schulpraktischer Anteile bzw. der Einführung des sogenannten Praxissemesters eng verwoben mit der Lehrer:innenausbildung. Gleichzeitig wird bisher weitestgehend different beantwortet, was Forschendes Lernen ist und sein kann sowie warum es wie hochschuldidaktisch gerahmt wird und werden sollte. Der Beitrag widmet sich dieser Frage. Dabei zeigt sich einerseits, dass hochschuldidaktische Zugänge zur Realisierung Forschenden Lernens in der Lehrer:innenbildung erst vor dem Hintergrund theoretischer Annahmen zur Entwicklung von Lehrpersonen und deren Professionalität sowie zur Gestalt - gemeint ist hier der Beitrag zu dieser Entwicklung sowie der Anteil an Professionalität - Forschenden Lernens entwickelt werden können, eine solche Fundierung aber oftmals ausbleibt. Andererseits wird herausgearbeitet, inwiefern eine Differenz zwischen wissenschaftlicher Forschung und Forschung im Forschenden Lernen besteht. Daran anschließend wird eine habitustheoretische Fundierung Forschenden Lernens vorgestellt und es werden exemplarisch deren Implikationen für die Gestaltung Forschenden Lernens benannt. Abschließend wird anhand empirischer Rekonstruktionen beispielhaft eine praktische Umsetzung diskutiert.
Keywords: Forschendes Lernen; Lehrer:innenausbildung; Praxissemester; Professionalisierung / Research-based learning; teacher education; internships; professionalization

A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions - Bender, Amelie (Machines)
While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case.
Keywords: prognostics; RUL predictions; particle filter; uncertainty consideration; Multi-Model-Particle Filter; model-based approach; rubber-metal-elements; predictive maintenance

Digitalisation in Agriculture: Knowledge and Learning Requirements of German Dairy Farmers - Goller, Michael; Caruso, Carina; Harteis, Christian (International Journal for Research in Vocational Education and Training)
Purpose: This study aims at investigating how digitalisation (in the sense of industry 4.0) has changed the work of farmers and how they experience the changes from more traditional work to digitalised agriculture. It also investigates what knowledge farmers require on digitalised farms and how they acquire it. Dairy farming was used as domain of investigation since it, unlike other industries, has strongly been affected by digitalisation throughout the last years. Method: Exploratory interviews with 10 livestock farmers working on digitalised dairy farms were analysed using qualitative content analysis. A deductive and inductive coding strategy was used. Findings: Farming work has changed from more manual tasks towards symbol manipulation and data processing. Farmers must be able to use computers and other digital devices to retrieve and analyse sensor data that allow them to monitor and control the processes on their farm. For this new kind of work, farmers require elaborated mental models that link traditional farming knowledge with knowledge about digital systems, including a strong understanding of production processes underlying their farm. Learning is mostly based on instructions offered by manufacturers of the new technology as well as informal and non-formal learning modes. Even younger farmers report that digital technology was not sufficiently covered in their (vocational) degrees. In general, farmers emphasises the positive effects of digitalisation both on their working as well as private life. Conclusions: Farmers should be aware of the opportunities as well as the potential drawbacks of the digitalisation of work processes in agriculture. Providers of agricultural education (like vocational schools or training institutes) need to incorporate the knowledge and skills required to work in digitalised environments (e.g., data literacy) in their syllabi. Further studies are required to assess how digitalisation changes farming practices and what knowledge as well as skills linked to these developments are required in the future.
Keywords: Work-Based Learning; Organisational Change; Digital Competences; Qualitative Research; Digitalisation; Farming; Dairy; VET; Vocational Education and Training

Systematische Bewertung von Auswirkungsanalysen des Engineering Change Managements - Gräßler, Iris; Wiechel, Dominik (DS 111: Proceedings of the 32nd Symposium Design for X)
Challenges in decisions on technical changes are the lack of knowledge about the expected impact and change propagation. Currently, no literature study contains a systematic differentiation and evaluation of existing approaches, which is a prerequisite for practitioners to select a suitable approach. This research aims at defining differentiation criteria as well as generally applicable requirements for evaluation. A four-step approach is used: systematic literature review on approaches for impact analysis of engineering changes (1), categorization and prioritization of approaches based on reoccuring elements (2), derivation of context specific requirements for evaluation (3), and evaluation of approaches (4). The result indicates existing potential of object-oriented modeling approaches.
Keywords: Engineering Change Management; Impact Analysis; Engineering Changes; Model-based Systems Engineering; Product Developmen

An Empirical Evaluation of Cost-based Federated SPARQL Query Processing Engines - Qudus, Umair; Saleem, Muhammad; Ngonga Ngomo, Axel-Cyrille; Lee, Young-Koo (Semantic Web)
Finding a good query plan is key to the optimization of query runtime. This holds in particular for cost-based federation engines, which make use of cardinality estimations to achieve this goal. A number of studies compare SPARQL federation engines across different performance metrics, including query runtime, result set completeness and correctness, number of sources selected and number of requests sent. Albeit informative, these metrics are generic and unable to quantify and evaluate the accuracy of the cardinality estimators of cost-based federation engines. To thoroughly evaluate cost-based federation engines, the effect of estimated cardinality errors on the overall query runtime performance must be measured. In this paper, we address this challenge by presenting novel evaluation metrics targeted at a fine-grained benchmarking of cost-based federated SPARQL query engines. We evaluate five cost-based federated SPARQL query engines using existing as well as novel evaluation metrics by using LargeRDFBench queries. Our results provide a detailed analysis of the experimental outcomes that reveal novel insights, useful for the development of future cost-based federated SPARQL query processing engines.
Keywords: SPARQL; benchmarking; cost-based; cost-free; federated; querying

ProConAR: A Tool Support for Model-based AR Product Configuration - Gottschalk, Sebastian; Yigitbas, Enes; Schmidt, Eugen; Engels, Gregor (Human-Centered Software Engineering. HCSE 2020)
Mobile shopping apps have been using Augmented Reality (AR) in the last years to place their products in the environment of the customer. While this is possible with atomic 3D objects, there is is still a lack in the runtime conﬁguration of 3D object compositions based on user needs and environmental constraints. For this, we previously developed an approach for model-based AR-assisted product conﬁguration based on the concept of Dynamic Software Product Lines. In this demonstration paper, we present the corresponding tool support ProConAR in the form of a Product Modeler and a Product Conﬁgurator. While the Product Modeler is an Angular web app that splits products (e.g. table) up into atomic parts (e.g. tabletop, table legs, funnier) and saves it within a conﬁguration model, the Product Conﬁgurator is an Android client that uses the conﬁguration model to place diﬀerent product conﬁgurations within the environment of the customer. We show technical details of our ready to use tool-chain ProConAR by describing its implementation and usage as well as pointing out future research directions.
Keywords: Product Configuration; Augmented Reality; Model-based; Tool Support

Model-based Hypothesis Engineering for Supporting Adaptation to Uncertain Customer Needs - Gottschalk, Sebastian; Yigitbas, Enes; Engels, Gregor (Business Modeling and Software Design)
To build successful products, the developers have to adapt their product features and business models to uncertain customer needs. This adaptation is part of the research discipline of Hypotheses Engineering (HE) where customer needs can be seen as hypotheses that need to be tested iteratively by conducting experiments together with the customer. So far, modeling support and associated traceability of this iterative process are missing. Both, in turn, are important to document the adaptation to the customer needs and identify experiments that provide most evidence to the customer needs. To target this issue, we introduce a model-based HE approach with a twofold contribution: First, we develop a modeling language that models hypotheses and experiments as interrelated hierarchies together with a mapping between them. While the hypotheses are labeled with a score level of their current evidence, the experiments are labeled with a score level of maximum evidence that can be achieved during conduction. Second, we provide an iterative process to determine experiments that offer the most evidence improvement to the modeled hypotheses. We illustrate the usefulness of the approach with an example of testing the business model of a mobile application.
Keywords: Hypothesis Engineering; Model-based; Customer Need Adaptation; Business Model; Product Features

Distinguishing Knowledge Impact from Citation Impact: A Methodology for Analysing Knowledge Impact for the Literature Review Genre - Schryen, Guido; Wagner, Gerit; Benlian, Alexander
The scientific impact of research papers is multi-dimensional and can be determined quantitatively by means of citation analysis and qualitatively by means of content analysis. Accounting for the widely acknowledged limitations of pure citation analysis, we adopt a knowledge-based perspective on scientific impact to develop a methodology for content-based citation analysis which allows determining how papers have enabled knowledge development in subsequent research (knowledge impact). As knowledge development differs between research genres, we develop a new knowledgebased citation analysis methodology for the genre of standalone literature reviews (LRs). We apply the suggested methodology to the IS business value domain by manually coding 22 LRs and 1,228 citing papers (CPs) and show that the results challenge the assumption that citations indicate knowledge impact. We derive implications for distinguishing knowledge impact from citation impact in the LR genre. Finally, we develop recommendations for authors of LRs, scientific evaluation committees and editorial boards of journals how to apply and benefit from the suggested methodology, and we discuss its efficiency and automatization.
Keywords: Scientific impact; knowledge impact; content-based citation analysis; methodology

Aspect Phrase Extraction in Sentiment Analysis with Deep Learning - Kersting, Joschka; Geierhos, Michaela (Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) -- Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020))
This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.
Keywords: Deep Learning; Natural Language Processing; Aspect-based Sentiment Analysis

A Knowledge Development Perspective on Literature Reviews: Validation of a New Typology in the IS Field - Schryen, Guido; Wagner, Gerit; Benlian, Alexander; Paré, Guy (Communications of the AIS)
Literature reviews (LRs) play an important role in the development of domain knowledge in all fields. Yet, we observe a lack of insights into the activities with which LRs actually develop knowledge. To address this important gap, we (1) derive knowledge building activities from the extant literature on LRs, (2) suggest a knowledge-based typology of LRs that complements existing typologies, and (3) apply the suggested typology in an empirical study that explores how LRs with different goals and methodologies have contributed to knowledge development. The analysis of 240 LRs published in 40 renowned IS journals between 2000 and 2014 allows us to draw a detailed picture of knowledge development achieved by one of the most important genres in the IS field. An overarching contribution of our work is to unify extant conceptualizations of LRs by clarifying and illustrating how LRs apply different methodologies in a range of knowledge building activities to achieve their goals with respect to theory.
Keywords: Literature review; knowledge development; knowledge building activities; knowledge-based typology; information systems research

Initial Design Strategies and Their Effects on Sequential Model-Based Optimization: An Exploratory Case Study Based on BBOB - Bossek, Jakob; Doerr, Carola; Kerschke, Pascal (Proceedings of the Genetic and Evolutionary Computation Conference)
Sequential model-based optimization (SMBO) approaches are algorithms for solving problems that require computationally or otherwise expensive function evaluations. The key design principle of SMBO is a substitution of the true objective function by a surrogate, which is used to propose the point(s) to be evaluated next. SMBO algorithms are intrinsically modular, leaving the user with many important design choices. Significant research efforts go into understanding which settings perform best for which type of problems. Most works, however, focus on the choice of the model, the acquisition function, and the strategy used to optimize the latter. The choice of the initial sampling strategy, however, receives much less attention. Not surprisingly, quite diverging recommendations can be found in the literature. We analyze in this work how the size and the distribution of the initial sample influences the overall quality of the efficient global optimization (EGO) algorithm, a well-known SMBO approach. While, overall, small initial budgets using Halton sampling seem preferable, we also observe that the performance landscape is rather unstructured. We furthermore identify several situations in which EGO performs unfavorably against random sampling. Both observations indicate that an adaptive SMBO design could be beneficial, making SMBO an interesting test-bed for automated algorithm design.
Keywords: continuous black-box optimization; design of experiments; initial design; sequential model-based optimization

Deep Learning as a Competitive Feature-Free Approach for Automated Algorithm Selection on the Traveling Salesperson Problem - Seiler, Moritz; Pohl, Janina; Bossek, Jakob; Kerschke, Pascal; Trautmann, Heike (Parallel Problem Solving from Nature (PPSN XVI))
In this work we focus on the well-known Euclidean Traveling Salesperson Problem (TSP) and two highly competitive inexact heuristic TSP solvers, EAX and LKH, in the context of per-instance algorithm selection (AS). We evolve instances with nodes where the solvers show strongly different performance profiles. These instances serve as a basis for an exploratory study on the identification of well-discriminating problem characteristics (features). Our results in a nutshell: we show that even though (1) promising features exist, (2) these are in line with previous results from the literature, and (3) models trained with these features are more accurate than models adopting sophisticated feature selection methods, the advantage is not close to the virtual best solver in terms of penalized average runtime and so is the performance gain over the single best solver. However, we show that a feature-free deep neural network based approach solely based on visual representation of the instances already matches classical AS model results and thus shows huge potential for future studies.
Keywords: Automated algorithm selection; Deep learning; Feature-based approaches; Traveling Salesperson Problem

Renew or cancel? Drivers of customer renewal decisions for IT-based service contracts - Wangenheim, Florian v; Wünderlich, Nancy; Schumann, Jan H (Journal of Business Research)
Manufacturers increasingly integrate information and communication technologies into their products so that they can provide IT-based services. Organizations that formerly concentrated on transactional sales thus confront a new challenge associated with managing service usage—retention and extracting value from investments in smart technology. This study combines a marketing and an information systems perspective in a field study conducted jointly with a large European car manufacturer. Understanding the renewal decision for IT-based service contracts requires knowledge from both disciplines. The paper shows that combining behavioral predictor variables stemming from marketing research and technology-related perceptual variables stemming from technology acceptance research increases the explanatory power and prediction accuracy of forecasting models for customer renewal decisions. Specifically, the authors show that perceptions of usefulness become more important the longer customers use IT-based services and the more services they use within the service contract.
Keywords: IT-based service; Smart services; Contract renewal; Retention; Customer churn; Free trial

Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing - Kimotho, James Kuria; Sextro, Walter; Hemsel, Tobias (IEEE Transactions on Reliability)
Piezoelectric transducers are used in a wide range of applications. Reliability of these transducers is an important aspect in their application. Prognostics, which involve continuous monitoring of the health of technical systems and using this information to estimate the current health state and consequently predict the remaining useful lifetime (RUL), can be used to increase the reliability, safety, and availability of the transducers. This is achieved by utilizing the health state and RUL predictions to adaptively control the usage of the components or to schedule appropriate maintenance without interrupting operation. In this work, a prognostic approach utilizing self-sensing, where electric signals of a piezoelectric transducer are used as the condition monitoring data, is proposed. The approach involves training machine learning algorithms to model the degradation of the transducers through a health index and the use of the learned model to estimate the health index of similar transducers. The current health index is then used to estimate RUL of test components. The feasibility of the approach is demonstrated using piezoelectric bimorphs and the results show that the method is accurate in predicting the health index and RUL.
Keywords: Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing

Evolvable caches: Optimization of reconfigurable cache mappings for a LEON3/Linux-based multi-core processor - Ho, Nam; Kaufmann, Paul; Platzner, Marco (2017 International Conference on Field Programmable Technology (ICFPT))
Keywords: Linux; cache storage; microprocessor chips; multiprocessing systems; LEON3-Linux based multicore processor; MiBench suite; block sizes; cache adaptation; evolvable caches; memory-to-cache-index mapping function; processor caches; reconfigurable cache mapping optimization; reconfigurable hardware technology; replacement strategies; standard Linux OS; time a complete hardware implementation; Hardware; Indexes; Linux; Measurement; Multicore processing; Optimization; Training

Computational self-awareness as design approach for visual sensor nodes - Guettatfi, Zakarya; Hübner, Philipp; Platzner, Marco; Rinner, Bernhard (12th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC))
Keywords: embedded systems; image sensors; power aware computing; wireless sensor networks; Zynq-based VSN node prototype; computational self-awareness; design approach; platform levels; power consumption; visual sensor networks; visual sensor nodes; Cameras; Hardware; Middleware; Multicore processing; Operating systems; Runtime; Reconfigurable platforms; distributed embedded systems; performance-resource trade-off; self-awareness; visual sensor nodes

Mathematics-related Competence of Early Childhood Teachers Visiting a Continuous Professional Development Course: An Intervention Study - Bruns, Julia; Eichen, Lars; Gasteiger, Hedwig (Mathematics Teacher Education and Development (MTED))
Recent studies highlight early childhood teachers’ mathematics-related competence. Developing this competence should be a main aspect of early childhood teachers’ education. This is, however, not the case in all countries. Consequently, high-quality professional development courses are needed. Based on research results, we developed a competence-oriented continuous professional development course ("EmMa") and examined the effects of "EmMa" by asking: How does "EmMa" affect the development of early childhood teachers’ i) mathematical content knowledge, ii) mathematical pedagogical content knowledge and iii) beliefs towards mathematics in general? To answer these questions, we conducted a pre-test/post-test study including a control group with 99 in-service early childhood teachers. Results show that the course affected teachers’ mathematical pedagogical content knowledge and static orientation towards mathematics positively. From this we conclude that scaling-up "EmMa" might be a suitable approach to bridge the gap between pre-service education with nearly no mathematics and the challenges of early mathematics education.
Keywords: Beliefs; Competency Based Teacher Education; Control Groups; Early Childhood Education; Faculty Development; Foreign Countries; Inservice Teacher Education; Intervention; Mathematical Aptitude; Mathematics Skills; Pedagogical Content Knowledge; Preschool Teachers; Pretests Posttests; Professional Continuing Education; Statistical Analysis; Teacher Competency Testing

How to Complete Customer Requirements: Using Concept Expansion for Requirement Refinement - Geierhos, Michaela; Bäumer, Frederik Simon (Proceedings of the 21st International Conference on Applications of Natural Language to Information Systems (NLDB))
One purpose of requirement refinement is that higher-level requirements have to be translated to something usable by developers. Since customer requirements are often written in natural language by end users, they lack precision, completeness and consistency. Although user stories are often used in the requirement elicitation process in order to describe the possibilities how to interact with the software, there is always something unspoken. Here, we present techniques how to automatically refine vague software descriptions. Thus, we can bridge the gap by first revising natural language utterances from higher-level to more detailed customer requirements, before functionality matters. We therefore focus on the resolution of semantically incomplete user-generated sentences (i.e. non-instantiated arguments of predicates) and provide ontology-based gap-filling suggestions how to complete unverbalized information in the user’s demand.
Keywords: Requirement refinement; Concept expansion; Ontology-based instantiation of predicate-argument structure

Contrasting Risk Perceptions of Technology-Based Service Innovations in Inter-Organizational Settings. - Paluch, Stefanie; Wünderlich, Nancy (Journal of business Research)
Despite the rapid growth and potential of technology-based services, managers' greatest challenges are gaining customer acceptance and increasing usage of these new innovative services. In the B2C field, studies of self-service technology show that perceived risk is an important factor influencing the use of service technology. Though prior research explores different risk types that emerge in consumer settings, risk perception in the B2B setting lacks a detailed examination of different risk types influencing technology-based service adoption. Data from 49 qualitative interviews with providers and customers in two different B2B industries inform this study. The findings emphasize the importance of functional and financial risks in a B2B context and show that business customers' personal and psychological fears hinder their use of technology-based services. Results highlight differences in risk perception and evaluation between customers and providers.
Keywords: Risk perception; Technology-based service innovations; Business-to-business context; Interview study; Risk categories; Smart service

A Multi-Theoretical Literature Review on Information Security Investments using the Resource-Based View and the Organizational Learning Theory - Weishäupl, Eva; Yasasin, Emrah; Schryen, Guido (International Conference on Information Systems)
The protection of information technology (IT) has become and is predicted to remain a key economic challenge for organizations. While research on IT security investment is fast growing, it lacks a theoretical basis for structuring research, explaining economic-technological phenomena and guide future research. We address this shortcoming by suggesting a new theoretical model emerging from a multi-theoretical perspective adopt-ing the Resource-Based View and the Organizational Learning Theory. The joint appli-cation of these theories allows to conceptualize in one theoretical model the organiza-tional learning effects that occur when the protection of organizational resources through IT security countermeasures develops over time. We use this model of IT security invest-ments to synthesize findings of a large body of literature and to derive research gaps. We also discuss managerial implications of (closing) these gaps by providing practical ex-amples.
Keywords: Information Security; Investment; Literature review; Resource-based View; Organi-zational Learning Theory; Multi-theoretical Perspective

Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement - Bossek, Jakob; Bischl, Bernd; Wagner, Tobias; Rudolph, Günter (Proceedings of the Genetic and Evolutionary Computation Conference)
The majority of algorithms can be controlled or adjusted by parameters. Their values can substantially affect the algorithms’ performance. Since the manual exploration of the parameter space is tedious – even for few parameters – several automatic procedures for parameter tuning have been proposed. Recent approaches also take into account some characteristic properties of the problem instances, frequently termed instance features. Our contribution is the proposal of a novel concept for feature-based algorithm parameter tuning, which applies an approximating surrogate model for learning the continuous feature-parameter mapping. To accomplish this, we learn a joint model of the algorithm performance based on both the algorithm parameters and the instance features. The required data is gathered using a recently proposed acquisition function for model refinement in surrogate-based optimization: the profile expected improvement. This function provides an avenue for maximizing the information required for the feature-parameter mapping, i.e., the mapping from instance features to the corresponding optimal algorithm parameters. The approach is validated by applying the tuner to exemplary evolutionary algorithms and problems, for which theoretically grounded or heuristically determined feature-parameter mappings are available.
Keywords: evolutionary algorithms; model-based optimization; parameter tuning

Wie kann über DBR die wissenschaftliche und praktische Relevanz der Forschung gesichert werden? - Brahm, Taiga; Jenert, Tobias
Auseinandersetzungen über Forschungsmethodologien haben in den Bildungswissenschaften lange Tradition. Sie werden häufig unversöhnlich und mit Profilierung der Gegensätze geführt. Für uns hängt die Sinnhaftigkeit einer Forschungskonzeption von der Problemstellung und dem Erkenntnisinteresse ab. Dies gilt auch für Design-Based Research (DBR). Dieser Ansatz entstand als Reaktion auf die Kritik an mangelnder praktischer Anwendung von Befunden aus der empirisch-analytischen Lehr-Lernforschung. Als Ergebnis werden Theorien angestrebt, die für die Praxis einen Nutzen bieten und zugleich über die Anwendung in einer singulären Situation hinausgehen. Wir wollen folgenden Fragen nachgehen: Wo liegen die Gemeinsamkeiten, wo die Spezifika von DBR gegenüber bestehenden Methodologien? Wie wird der Generalisierungsanspruch von Theorien innerhalb von DBR verstanden und umgesetzt? Für welche Erkenntnisinteressen eignet sich DBR? Welche Methoden sind für DBR sinnvoll und können wie angewendet werden?
Keywords: design-based research; gestatlungsbasierte Forschung; Erziehungswissenschaft; Design; Mixed Methods

A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE/ACM Transactions on Audio, Speech, and Language Processing)
In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data.
Keywords: computational complexity; reverberation; speech recognition; automatic speech recognition; background noise; clean speech; computational complexity; energy compensation; logarithmic mel power spectral domain; mel frequency cepstral coefficients; microphone input signals; model-based feature compensation schemes; noisy reverberant speech automatic recognition; noisy reverberant speech features; reverberation; Atmospheric modeling; Computational modeling; Noise; Noise measurement; Reverberation; Speech; Vectors; Model-based feature compensation; observation model for reverberant and noisy speech; recursive observation model; robust automatic speech recognition

Wissenschafts-Praxis-Kooperation in designbasierter Forschung: Im Spannungsfeld zwischen wissenschaftlicher Gültigkeit und praktischer Relevanz - Brahm, Taiga; Jenert, Tobias (Zeitschrift für Berufs-und Wirtschaftspädagogik-Beihefte (ZBW-B))
Designbasierte Forschung zielt darauf, praktische Problemstellungen zu lösen und gleichzeitig wissenschaftliche Theorien (weiter) zu entwickeln. Dabei durchläuft designbasierte Forschung in der Regel die Phasen der Problemdefinition, der Entwicklung eines didaktischen Designs, der zyklischen Design-Implementation sowie der Evaluation und Reflexion in enger Kooperation von Wissenschaft und Praxis. Inwieweit es mittels designbasierter Forschung gelingen kann, wissenschaftliche Gültigkeit und praktische Relevanz von Forschung gleichermassen zu steigern, wird im Beitrag anhand des Konzepts multipler Signifikanzen erörtert. Dabei wird diskutiert, wie die Gestaltung der Kooperation von Forschenden und Praktikern/-innen dazu beitragen kann, die praktische, statistische, klinische und wirtschaftliche Signifikanz des designbasierten Forschungsprozesses sicherzustellen.
Keywords: Design-based research; design research; validity; significance; Bildungsforschung; designbasierte Forschung; Wissenschafts-Praxis-Kommunikation

Dynamic reliability management: Reconfiguring reliability-levels of hardware designs at runtime - Anwer, Jahanzeb; Meisner, Sebastian; Platzner, Marco (Reconfigurable Computing and FPGAs (ReConFig), 2013 International Conference on)
Keywords: fault tolerant computing; field programmable gate arrays; logic design; reliability; BYU-LANL tool; DRM tool flow; FPGA based hardware designs; avionic application; device technologies; dynamic reliability management; fault-tolerant operation; hardware designs; reconfiguring reliability levels; space applications; Field programmable gate arrays; Hardware; Redundancy; Reliability engineering; Runtime; Tunneling magnetoresistance

MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations - Chinaev, Aleksej; Haeb-Umbach, Reinhold (38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013))
In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner.
Keywords: Gaussian noise; maximum likelihood estimation; parameter estimation; GMM parameter; Gaussian mixture model; MAP estimation; Map-based estimation; maximum a-posteriori estimation; maximum likelihood technique; noisy observation; sequential estimation framework; white Gaussian noise; Additive noise; Gaussian mixture model; Maximum likelihood estimation; Noise measurement; Gaussian mixture model; Maximum a posteriori estimation; Maximum likelihood estimation

Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data.
Keywords: Bayes methods; compensation; error statistics; reverberation; speech recognition; Bayesian feature enhancement; background noise; clean speech feature vectors; compensation; connected digits recognition task; error statistics; memory requirements; noisy reverberant data; posteriori probability density function; recursive formulation; reverberant logarithmic mel power spectral coefficients; robust automatic speech recognition; signal-to-noise ratios; time-variant observation; word error rate reduction; Robust automatic speech recognition; model-based Bayesian feature enhancement; observation model for reverberant and noisy speech; recursive observation model

Server based indoor navigation using RSSI and inertial sensor information - Hoang, Manh Kha; Schmitz, Sarah; Drueke, Christian; Vu, Dang Hai Tran; Schmalenstroeer, Joerg; Haeb-Umbach, Reinhold (Positioning Navigation and Communication (WPNC), 2013 10th Workshop on)
In this paper we present a system for indoor navigation based on received signal strength index information of Wireless-LAN access points and relative position estimates. The relative position information is gathered from inertial smartphone sensors using a step detection and an orientation estimate. Our map data is hosted on a server employing a map renderer and a SQL database. The database includes a complete multilevel office building, within which the user can navigate. During navigation, the client retrieves the position estimate from the server, together with the corresponding map tiles to visualize the user's position on the smartphone display.
Keywords: SQL; navigation; smart phones; wireless LAN; RSSI; SQL database; complete multilevel office building; inertial sensor information; inertial smartphone sensors; map renderer; received signal strength index information; relative position estimates; server based indoor navigation; step detection; wireless-LAN access points; Smartphone; fingerprint; indoor navigation; map tile

Non-perturbing cavitation detection / monitoring in sonochemical reactors - Bornmann, Peter; Hemsel, Tobias; Sextro, Walter; Maeda, Takafumi; Morita, Takeshi (Ultrasonics Symposium (IUS), 2012 IEEE International)
To optimize the ultrasound irradiation for cavitation based ultrasound applications like sonochemistry or ultrasound cleaning, the correlation between cavitation intensity and the resulting effect on the process is of interest. Furthermore, changing conditions like temperature and pressure result in varying acoustic properties of the liquid. That might necessitate an adaption of the ultrasound irradiation. To detect such changes during operation, process monitoring is desired. Labor intensive processes, that might be carried out for several hours, also require process monitoring to increase their reliability by detection of changes or malfunctions during operation. In some applications cavitation detection and monitoring can be achieved by the application of sensors in the sound field. Though the application of sensors is possible, this necessitates modifications on the system and the sensor might disturb the sound field. In other applications harsh, process conditions prohibit the application of sensors in the sound field. Therefore alternative techniques for cavitation detection and monitoring are desired. The applicability of an external microphone and a self-sensing ultrasound transducer for cavitation detection were experimentally investigated. Both methods were found to be suitable and easily applicable.
Keywords: cavitation; chemical reactors; microphones; process monitoring; reliability; ultrasonic applications; ultrasonic waves; acoustic properties; cavitation based ultrasound applications; cavitation intensity; change detection reliability; external microphone; malfunction detection reliability; nonperturbing cavitation detection; nonperturbing cavitation monitoring; process monitoring; self-sensing ultrasound transducer; sonochemical reactors; sonochemistry; ultrasound cleaning; ultrasound irradiation; Acoustics; Liquids; Monitoring; Sensors; Sonar equipment; Transducers; Ultrasonic imaging

MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations - Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011))
The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is superposed by additive Gaussian observation errors of known variance. Simulations on artificially generated data demonstrate the superiority of the proposed method. While reducing to the ordinary MAP approach in the absence of observation noise, the improvement becomes the more pronounced the larger the variance of the observation noise. The method is further extended to track the parameters in case of non-stationary Gaussian processes.
Keywords: Gaussian processes; MAP-based estimation; maximum a posteriori method; maximum likelihood estimation; nonstationary Gaussian processes

Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation - Krueger, Alexander; Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we present a novel blocking matrix and fixed beamformer design for a generalized sidelobe canceler for speech enhancement in a reverberant enclosure. They are based on a new method for estimating the acoustical transfer function ratios in the presence of stationary noise. The estimation method relies on solving a generalized eigenvalue problem in each frequency bin. An adaptive eigenvector tracking utilizing the power iteration method is employed and shown to achieve a high convergence speed. Simulation results demonstrate that the proposed beamformer leads to better noise and interference reduction and reduced speech distortions compared to other blocking matrix designs from the literature.
Keywords: acoustical transfer function ratio; adaptive eigenvector tracking; array signal processing; beamformer design; blocking matrix; eigenvalues and eigenfunctions; eigenvector-based transfer function ratios estimation; generalized sidelobe canceler; interference reduction; iterative methods; power iteration method; reduced speech distortions; reverberant enclosure; reverberation; speech enhancement; stationary noise

Model-Based Feature Enhancement for Reverberant Speech Recognition - Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications.
Keywords: ASR; AURORA5 database; automatic speech recognition; Bayesian inference; belief networks; CMLLR; computational complexity; constrained maximum likelihood linear regression; least mean squares methods; LMPSC computation; logarithmic Mel power spectrum; maximum likelihood estimation; Mel frequency cepstral coefficients; MFCC feature vectors; microphone signal; minimum mean square error estimation; model-based feature enhancement; regression analysis; reverberant speech recognition; reverberation; RIR energy; room impulse response; speech recognition; stochastic observation model; stochastic processes

People modify their tutoring behavior in robot-directed interaction for action learning - Vollmer, Anna-Lisa; Lohan, Katrin Solveig; Fischer, Kerstin; Nagai, Yukie; Pitsch, Karola; Fritsch, Jannik; Rohlfing, Katharina; Wrede, Britta (Development and Learning, 2009. ICDL 2009. IEEE 8th International Conference on Development and Learning)
In developmental research, tutoring behavior has been identified as scaffolding infants' learning processes. It has been defined in terms of child-directed speech (Motherese), child-directed motion (Motionese), and contingency. In the field of developmental robotics, research often assumes that in human-robot interaction (HRI), robots are treated similar to infants, because their immature cognitive capabilities benefit from this behavior. However, according to our knowledge, it has barely been studied whether this is true and how exactly humans alter their behavior towards a robotic interaction partner. In this paper, we present results concerning the acceptance of a robotic agent in a social learning scenario obtained via comparison to adults and 8-11 months old infants in equal conditions. These results constitute an important empirical basis for making use of tutoring behavior in social robotics. In our study, we performed a detailed multimodal analysis of HRI in a tutoring situation using the example of a robot simulation equipped with a bottom-up saliency-based attention model. Our results reveal significant differences in hand movement velocity, motion pauses, range of motion, and eye gaze suggesting that for example adults decrease their hand movement velocity in an Adult-Child Interaction (ACI), opposed to an Adult-Adult Interaction (AAI) and this decrease is even higher in the Adult-Robot Interaction (ARI). We also found important differences between ACI and ARI in how the behavior is modified over time as the interaction unfolds. These findings indicate the necessity of integrating top-down feedback structures into a bottom-up system for robots to be fully accepted as interaction partners.
Keywords: robot simulation; hand movement velocity; robotic interaction partner; robotic agent; robot-directed interaction; multimodal analysis; Motionese; Motherese; intelligent tutoring systems; immature cognitive capability; human computer interaction; eye gaze; child-directed speech; child-directed motion; bottom-up system; bottom-up saliency-based attention model; adult-robot interaction; adult-child interaction; adult-adult interaction; human-robot interaction; action learning; social learning scenario; social robotics; software agents; top-down feedback structures; tutoring behavior

A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition - Ion, Valentin; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we derive an uncertainty decoding rule for automatic speech recognition (ASR), which accounts for both corrupted observations and inter-frame correlation. The conditional independence assumption, prevalent in hidden Markov model-based ASR, is relaxed to obtain a clean speech posterior that is conditioned on the complete observed feature vector sequence. This is a more informative posterior than one conditioned only on the current observation. The novel decoding is used to obtain a transmission-error robust remote ASR system, where the speech capturing unit is connected to the decoder via an error-prone communication network. We show how the clean speech posterior can be computed for communication links being characterized by either bit errors or packet loss. Recognition results are presented for both distributed and network speech recognition, where in the latter case common voice-over-IP codecs are employed.
Keywords: automatic speech recognition; bit errors; codecs; communication links; corrupted observations; decoding; distributed speech recognition; error-prone communication network; feature vector sequence; hidden Markov model-based ASR; hidden Markov models; inter-frame correlation; Internet telephony; network speech recognition; packet loss; speech posterior; speech recognition; transmission error robust speech recognition; uncertainty decoding; voice-over-IP codecs

UML for Electronic Systems Design – A Comprehensive Overview - Vanderperren, Yves; Müller, Wolfgang; Dahaene, Wim (Design Automation for Embedded Systems)
UML has been widely accepted by the software community for several years. As electronic systems design can no longer be seen as an isolated hardware design activity, UML becomes of significant interest as a unification language for systems description combining both HW and SW components. This article provides a comprehensive view of the UML applied to System-on-Chip (SoC) and hardware-related embedded systems design. The modeling concepts in the UML language are first introduced, including major diagrams for the representation of the behavior and the structure of systems. The principles behind application specific UML customizations (UML profiles) are summarized, and several examples relevant for SoC design are given, such as the SysML (System Modeling Language) and the SoC Profile. Thereafter, various approaches associating UML with existing HW/SW design languages are presented. Beyond language aspects, the article addresses the question of UML-based design flows, and shows how UML can be applied concretely to the development of electronic-based systems. The current situation about tool support constitutes the last focus of the article. In particular, we show how UML tools can be combined with well-known simulation environments, such as MATLAB.
Keywords: UML SysML Model-based design System specification Modelling languages

Server-based execution of periodic tasks on dynamically reconfigurable hardware - Danne, Klaus; Mühlenbernd, Roland; Platzner, Marco (IET Computers Digital Techniques)
Keywords: reconfigurable architectures; resource allocation; device reconfiguration time; dynamic hardware reconfiguration; dynamically reconfigurable hardware; light-weight runtime system; merge server distribute load; periodic real-time tasks; runtime system overheads; schedulability analysis; scheduling technique; server-based execution; synthesis tool flow

A Low-Cost Positioning System for Location-Aware Applications in Smart Homes - Schäfer, Robbie; Müller, Wolfgang; Deimann, Roman; Kleinjohann, Bernd (Proceedings of the Workshop on Mobile Spatial Interaction at CHI 2007)
Location-aware services for private use such as GPS- based navigation systems and GSM-based offerings have become quite a success for outdoor applications, while indoor positioning systems are still mainly employed for professional use only. The main reasons are cost issues and the complexity of setup and maintenance of those systems. In this paper we present CaMPTrack (Camera-based Multiple Person Tracker), a prototype of a webcam-based positioning system and discuss its application and development challenges.
Keywords: Positioning Systems; Camera Based; Cost Efficiency; Smart Home Applications

Automatic generation of phonetic regression class trees for MLLR adaptation - Haeb-Umbach, Reinhold (IEEE Transactions on Speech and Audio Processing)
In this paper, it is shown that a correlation criterion is the appropriate criterion for bottom-up clustering to obtain broad phonetic class regression trees for maximum likelihood linear regression (MLLR)-based speaker adaptation. The correlation structure among speech units is estimated on the speaker-independent training data. In adaptation experiments the tree outperformed a regression tree obtained from clustering according to closeness in acoustic space and achieved results comparable with those of a manually designed broad phonetic class tree
Keywords: acoustic space; adaptation experiments; automatic generation; bottom-up clustering; broad phonetic class regression trees; correlation criterion; correlation methods; maximum likelihood estimation; maximum likelihood linear regression based speaker adaptation; MLLR adaptation; pattern clustering; phonetic regression class trees; speaker-independent training data; speech recognition; speech units; statistical analysis; trees (mathematics)