TY - JOUR A1 - Kaitoua, Abdulrahman A1 - Rabl, Tilmann A1 - Markl, Volker T1 - A distributed data exchange engine for polystores JF - Information technology : methods and applications of informatics and information technology JF - Information technology : Methoden und innovative Anwendungen der Informatik und Informationstechnik N2 - There is an increasing interest in fusing data from heterogeneous sources. Combining data sources increases the utility of existing datasets, generating new information and creating services of higher quality. A central issue in working with heterogeneous sources is data migration: In order to share and process data in different engines, resource intensive and complex movements and transformations between computing engines, services, and stores are necessary. Muses is a distributed, high-performance data migration engine that is able to interconnect distributed data stores by forwarding, transforming, repartitioning, or broadcasting data among distributed engines' instances in a resource-, cost-, and performance-adaptive manner. As such, it performs seamless information sharing across all participating resources in a standard, modular manner. We show an overall improvement of 30 % for pipelining jobs across multiple engines, even when we count the overhead of Muses in the execution time. This performance gain implies that Muses can be used to optimise large pipelines that leverage multiple engines. KW - distributed systems KW - data migration KW - data transformation KW - big data KW - engine KW - data integration Y1 - 2020 U6 - https://doi.org/10.1515/itit-2019-0037 SN - 1611-2776 SN - 2196-7032 VL - 62 IS - 3-4 SP - 145 EP - 156 PB - De Gruyter CY - Berlin ER - TY - JOUR A1 - Ihde, Sven A1 - Pufahl, Luise A1 - Völker, Maximilian A1 - Goel, Asvin A1 - Weske, Mathias T1 - A framework for modeling and executing task BT - specific resource allocations in business processes JF - Computing : archives for informatics and numerical computation N2 - As resources are valuable assets, organizations have to decide which resources to allocate to business process tasks in a way that the process is executed not only effectively but also efficiently. Traditional role-based resource allocation leads to effective process executions, since each task is performed by a resource that has the required skills and competencies to do so. However, the resulting allocations are typically not as efficient as they could be, since optimization techniques have yet to find their way in traditional business process management scenarios. On the other hand, operations research provides a rich set of analytical methods for supporting problem-specific decisions on resource allocation. This paper provides a novel framework for creating transparency on existing tasks and resources, supporting individualized allocations for each activity in a process, and the possibility to integrate problem-specific analytical methods of the operations research domain. To validate the framework, the paper reports on the design and prototypical implementation of a software architecture, which extends a traditional process engine with a dedicated resource management component. This component allows us to define specific resource allocation problems at design time, and it also facilitates optimized resource allocation at run time. The framework is evaluated using a real-world parcel delivery process. The evaluation shows that the quality of the allocation results increase significantly with a technique from operations research in contrast to the traditional applied rule-based approach. KW - Process Execution KW - Business Process Management KW - Resource Allocation KW - Resource Management KW - Activity-oriented Optimization Y1 - 2022 U6 - https://doi.org/10.1007/s00607-022-01093-2 SN - 0010-485X SN - 1436-5057 VL - 104 SP - 2405 EP - 2429 PB - Springer CY - Wien ER - TY - JOUR A1 - Kreowsky, Philipp A1 - Stabernack, Christian Benno T1 - A full-featured FPGA-based pipelined architecture for SIFT extraction JF - IEEE access : practical research, open solutions / Institute of Electrical and Electronics Engineers N2 - Image feature detection is a key task in computer vision. Scale Invariant Feature Transform (SIFT) is a prevalent and well known algorithm for robust feature detection. However, it is computationally demanding and software implementations are not applicable for real-time performance. In this paper, a versatile and pipelined hardware implementation is proposed, that is capable of computing keypoints and rotation invariant descriptors on-chip. All computations are performed in single precision floating-point format which makes it possible to implement the original algorithm with little alteration. Various rotation resolutions and filter kernel sizes are supported for images of any resolution up to ultra-high definition. For full high definition images, 84 fps can be processed. Ultra high definition images can be processed at 21 fps. KW - Field programmable gate arrays KW - Convolution KW - Signal processing KW - algorithms KW - Kernel KW - Image resolution KW - Histograms KW - Feature extraction KW - Scale-invariant feature transform (SIFT) KW - field-programmable gate array KW - (FPGA) KW - image processing KW - computer vision KW - parallel processing KW - architecture KW - real-time KW - hardware architecture Y1 - 2021 U6 - https://doi.org/10.1109/ACCESS.2021.3104387 SN - 2169-3536 VL - 9 SP - 128564 EP - 128573 PB - Inst. of Electr. and Electronics Engineers CY - New York, NY ER - TY - JOUR A1 - Schneider, Sven A1 - Lambers, Leen A1 - Orejas, Fernando T1 - A logic-based incremental approach to graph repair featuring delta preservation JF - International journal on software tools for technology transfer : STTT N2 - We introduce a logic-based incremental approach to graph repair, generating a sound and complete (upon termination) overview of least-changing graph repairs from which a user may select a graph repair based on non-formalized further requirements. This incremental approach features delta preservation as it allows to restrict the generation of graph repairs to delta-preserving graph repairs, which do not revert the additions and deletions of the most recent consistency-violating graph update. We specify consistency of graphs using the logic of nested graph conditions, which is equivalent to first-order logic on graphs. Technically, the incremental approach encodes if and how the graph under repair satisfies a graph condition using the novel data structure of satisfaction trees, which are adapted incrementally according to the graph updates applied. In addition to the incremental approach, we also present two state-based graph repair algorithms, which restore consistency of a graph independent of the most recent graph update and which generate additional graph repairs using a global perspective on the graph under repair. We evaluate the developed algorithms using our prototypical implementation in the tool AutoGraph and illustrate our incremental approach using a case study from the graph database domain. KW - Nested graph conditions KW - Graph repair KW - Model repair KW - Consistency KW - restoration KW - Delta preservation KW - Graph databases KW - Model-driven KW - engineering Y1 - 2021 U6 - https://doi.org/10.1007/s10009-020-00584-x SN - 1433-2779 SN - 1433-2787 VL - 23 IS - 3 SP - 369 EP - 410 PB - Springer CY - Berlin ; Heidelberg ER - TY - JOUR A1 - Hartung, Niklas A1 - Borghardt, Jens Markus T1 - A mechanistic framework for a priori pharmacokinetic predictions of orally inhaled drugs JF - PLoS Computational Biology : a new community journal N2 - Author summary
The use of orally inhaled drugs for treating lung diseases is appealing since they have the potential for lung selectivity, i.e. high exposure at the site of action -the lung- without excessive side effects. However, the degree of lung selectivity depends on a large number of factors, including physiochemical properties of drug molecules, patient disease state, and inhalation devices. To predict the impact of these factors on drug exposure and thereby to understand the characteristics of an optimal drug for inhalation, we develop a predictive mathematical framework (a "pharmacokinetic model"). In contrast to previous approaches, our model allows combining knowledge from different sources appropriately and its predictions were able to adequately predict different sets of clinical data. Finally, we compare the impact of different factors and find that the most important factors are the size of the inhaled particles, the affinity of the drug to the lung tissue, as well as the rate of drug dissolution in the lung. In contrast to the common belief, the solubility of a drug in the lining fluids is not found to be relevant. These findings are important to understand how inhaled drugs should be designed to achieve best treatment results in patients.
The fate of orally inhaled drugs is determined by pulmonary pharmacokinetic processes such as particle deposition, pulmonary drug dissolution, and mucociliary clearance. Even though each single process has been systematically investigated, a quantitative understanding on the interaction of processes remains limited and therefore identifying optimal drug and formulation characteristics for orally inhaled drugs is still challenging. To investigate this complex interplay, the pulmonary processes can be integrated into mathematical models. However, existing modeling attempts considerably simplify these processes or are not systematically evaluated against (clinical) data. In this work, we developed a mathematical framework based on physiologically-structured population equations to integrate all relevant pulmonary processes mechanistically. A tailored numerical resolution strategy was chosen and the mechanistic model was evaluated systematically against data from different clinical studies. Without adapting the mechanistic model or estimating kinetic parameters based on individual study data, the developed model was able to predict simultaneously (i) lung retention profiles of inhaled insoluble particles, (ii) particle size-dependent pharmacokinetics of inhaled monodisperse particles, (iii) pharmacokinetic differences between inhaled fluticasone propionate and budesonide, as well as (iv) pharmacokinetic differences between healthy volunteers and asthmatic patients. Finally, to identify the most impactful optimization criteria for orally inhaled drugs, the developed mechanistic model was applied to investigate the impact of input parameters on both the pulmonary and systemic exposure. Interestingly, the solubility of the inhaled drug did not have any relevant impact on the local and systemic pharmacokinetics. Instead, the pulmonary dissolution rate, the particle size, the tissue affinity, and the systemic clearance were the most impactful potential optimization parameters. In the future, the developed prediction framework should be considered a powerful tool for identifying optimal drug and formulation characteristics. Y1 - 2020 U6 - https://doi.org/10.1371/journal.pcbi.1008466 SN - 1553-734X SN - 1553-7358 VL - 16 IS - 12 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Navarro, Marisa A1 - Orejas, Fernando A1 - Pino, Elvira A1 - Lambers, Leen T1 - A navigational logic for reasoning about graph properties JF - Journal of logical and algebraic methods in programming N2 - Graphs play an important role in many areas of Computer Science. In particular, our work is motivated by model-driven software development and by graph databases. For this reason, it is very important to have the means to express and to reason about the properties that a given graph may satisfy. With this aim, in this paper we present a visual logic that allows us to describe graph properties, including navigational properties, i.e., properties about the paths in a graph. The logic is equipped with a deductive tableau method that we have proved to be sound and complete. KW - Graph logic KW - Algebraic methods KW - Formal modelling KW - Specification Y1 - 2021 U6 - https://doi.org/10.1016/j.jlamp.2020.100616 SN - 2352-2208 SN - 2352-2216 VL - 118 PB - Elsevier Science CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Neher, Dieter A1 - Kniepert, Juliane A1 - Elimelech, Arik A1 - Koster, L. Jan Anton T1 - A New Figure of Merit for Organic Solar Cells with Transport-limited Photocurrents JF - Scientific reports N2 - Compared to their inorganic counterparts, organic semiconductors suffer from relatively low charge carrier mobilities. Therefore, expressions derived for inorganic solar cells to correlate characteristic performance parameters to material properties are prone to fail when applied to organic devices. This is especially true for the classical Shockley-equation commonly used to describe current-voltage (JV)-curves, as it assumes a high electrical conductivity of the charge transporting material. Here, an analytical expression for the JV-curves of organic solar cells is derived based on a previously published analytical model. This expression, bearing a similar functional dependence as the Shockley-equation, delivers a new figure of merit α to express the balance between free charge recombination and extraction in low mobility photoactive materials. This figure of merit is shown to determine critical device parameters such as the apparent series resistance and the fill factor. KW - Electronic and spintronic devices KW - Semiconductors Y1 - 2016 U6 - https://doi.org/10.1038/srep24861 SN - 2045-2322 VL - 6 PB - Nature Publishing Group CY - London ER - TY - CHAP A1 - Grüner, Andreas A1 - Mühle, Alexander A1 - Gayvoronskaya, Tatiana A1 - Meinel, Christoph T1 - A quantifiable trustmModel for Blockchain-based identity management T2 - IEEE 2018 International Congress on Cybermatics / 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, cyber, physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology KW - Blockchain KW - distributed ledger technology KW - digital identity KW - self-sovereign identity KW - trust KW - identity management Y1 - 2019 SN - 978-1-5386-7975-3 U6 - https://doi.org/10.1109/Cybermatics_2018.2018.00250 SP - 1475 EP - 1482 PB - IEEE CY - New York ER - TY - JOUR A1 - Doerr, Benjamin A1 - Krejca, Martin Stefan T1 - A simplified run time analysis of the univariate marginal distribution algorithm on LeadingOnes JF - Theoretical computer science N2 - With elementary means, we prove a stronger run time guarantee for the univariate marginal distribution algorithm (UMDA) optimizing the LEADINGONES benchmark function in the desirable regime with low genetic drift. If the population size is at least quasilinear, then, with high probability, the UMDA samples the optimum in a number of iterations that is linear in the problem size divided by the logarithm of the UMDA's selection rate. This improves over the previous guarantee, obtained by Dang and Lehre (2015) via the deep level-based population method, both in terms of the run time and by demonstrating further run time gains from small selection rates. Under similar assumptions, we prove a lower bound that matches our upper bound up to constant factors. KW - Theory KW - Estimation-of-distribution algorithm KW - Run time analysis Y1 - 2021 U6 - https://doi.org/10.1016/j.tcs.2020.11.028 SN - 0304-3975 SN - 1879-2294 VL - 851 SP - 121 EP - 128 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Respondek, Tobias T1 - A workflow for computing potential areas for wind turbines JF - Process design for natural scientists: an agile model-driven approach N2 - This paper describes the implementation of a workflow model for service-oriented computing of potential areas for wind turbines in jABC. By implementing a re-executable model the manual effort of a multi-criteria site analysis can be reduced. The aim is to determine the shift of typical geoprocessing tools of geographic information systems (GIS) from the desktop to the web. The analysis is based on a vector data set and mainly uses web services of the “Center for Spatial Information Science and Systems” (CSISS). This paper discusses effort, benefits and problems associated with the use of the web services. Y1 - 2014 SN - 978-3-662-45005-5 IS - 500 SP - 200 EP - 215 PB - Springer CY - Berlin ER - TY - JOUR A1 - Krause, Hannes-Vincent A1 - Große Deters, Fenne A1 - Baumann, Annika A1 - Krasnova, Hanna T1 - Active social media use and its impact on well-being BT - an experimental study on the effects of posting pictures on Instagram JF - Journal of computer-mediated communication : a journal of the International Communication Association N2 - Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established hypothesis is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with considerable heterogeneity among existing studies on the hypothesis and causal evidence still limited, a final verdict on its robustness is still pending. To contribute to this ongoing debate, we conducted a week-long randomized control trial with N = 381 adult Instagram users recruited via Prolific. Specifically, we tested how active SNS use, operationalized as picture postings on Instagram, affects different dimensions of well-being. The results depicted a positive effect on users' positive affect but null findings for other well-being outcomes. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs.
Lay Summary Active use of social networking sites (SNSs) has long been assumed to benefit users' well-being. However, this established assumption is increasingly being challenged, with scholars criticizing its lack of empirical support and the imprecise conceptualization of active use. Nevertheless, with great diversity among conducted studies on the hypothesis and a lack of causal evidence, a final verdict on its viability is still pending. To contribute to this ongoing debate, we conducted a week-long experimental investigation with 381 adult Instagram users. Specifically, we tested how posting pictures on Instagram affects different aspects of well-being. The results of this study depicted a positive effect of posting Instagram pictures on users' experienced positive emotions but no effects on other aspects of well-being. The findings broadly align with the recent criticism against the active use hypothesis and support the call for a more nuanced view on the impact of SNSs on users. KW - social networking sites KW - social media KW - Instagram KW - well-being KW - experiment KW - randomized control trial Y1 - 2022 U6 - https://doi.org/10.1093/jcmc/zmac037 SN - 1083-6101 VL - 28 IS - 1 PB - Oxford Univ. Press CY - Oxford ER - TY - GEN A1 - Hesse, Guenter A1 - Matthies, Christoph A1 - Sinzig, Werner A1 - Uflacker, Matthias T1 - Adding Value by Combining Business and Sensor Data BT - an Industry 4.0 Use Case T2 - Database Systems for Advanced Applications N2 - Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data’s full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs. KW - Industry 4.0 KW - Internet of Things KW - Data integration Y1 - 2019 SN - 978-3-030-18590-9 SN - 978-3-030-18589-3 U6 - https://doi.org/10.1007/978-3-030-18590-9_80 SN - 0302-9743 SN - 1611-3349 VL - 11448 SP - 528 EP - 532 PB - Springer CY - Cham ER - TY - THES A1 - Grütze, Toni T1 - Adding value to text with user-generated content N2 - In recent years, the ever-growing amount of documents on the Web as well as in closed systems for private or business contexts led to a considerable increase of valuable textual information about topics, events, and entities. It is a truism that the majority of information (i.e., business-relevant data) is only available in unstructured textual form. The text mining research field comprises various practice areas that have the common goal of harvesting high-quality information from textual data. These information help addressing users' information needs. In this thesis, we utilize the knowledge represented in user-generated content (UGC) originating from various social media services to improve text mining results. These social media platforms provide a plethora of information with varying focuses. In many cases, an essential feature of such platforms is to share relevant content with a peer group. Thus, the data exchanged in these communities tend to be focused on the interests of the user base. The popularity of social media services is growing continuously and the inherent knowledge is available to be utilized. We show that this knowledge can be used for three different tasks. Initially, we demonstrate that when searching persons with ambiguous names, the information from Wikipedia can be bootstrapped to group web search results according to the individuals occurring in the documents. We introduce two models and different means to handle persons missing in the UGC source. We show that the proposed approaches outperform traditional algorithms for search result clustering. Secondly, we discuss how the categorization of texts according to continuously changing community-generated folksonomies helps users to identify new information related to their interests. We specifically target temporal changes in the UGC and show how they influence the quality of different tag recommendation approaches. Finally, we introduce an algorithm to attempt the entity linking problem, a necessity for harvesting entity knowledge from large text collections. The goal is the linkage of mentions within the documents with their real-world entities. A major focus lies on the efficient derivation of coherent links. For each of the contributions, we provide a wide range of experiments on various text corpora as well as different sources of UGC. The evaluation shows the added value that the usage of these sources provides and confirms the appropriateness of leveraging user-generated content to serve different information needs. N2 - Die steigende Zahl an Dokumenten, welche in den letzten Jahren im Web sowie in geschlossenen Systemen aus dem privaten oder geschäftlichen Umfeld erstellt wurden, führte zu einem erheblichen Zuwachs an wertvollen Informationen über verschiedenste Themen, Ereignisse, Organisationen und Personen. Die meisten Informationen liegen lediglich in unstrukturierter, textueller Form vor. Das Forschungsgebiet des "Text Mining" befasst sich mit dem schwierigen Problem, hochwertige Informationen in strukturierter Form aus Texten zu gewinnen. Diese Informationen können dazu eingesetzt werden, Nutzern dabei zu helfen, ihren Informationsbedarf zu stillen. In dieser Arbeit nutzen wir Wissen, welches in nutzergenerierten Inhalten verborgen ist und aus unterschiedlichsten sozialen Medien stammt, um Text Mining Ergebnisse zu verbessern. Soziale Medien bieten eine Fülle an Informationen mit verschiedenen Schwerpunkten. Eine wesentliche Funktion solcher Medien ist es, den Nutzern zu ermöglichen, Inhalte mit ihrer Interessensgruppe zu teilen. Somit sind die ausgetauschten Daten in diesen Diensten häufig auf die Interessen der Nutzerbasis ausgerichtet. Die Popularität sozialer Medien wächst stetig und führt dazu, dass immer mehr inhärentes Wissen verfügbar wird. Dieses Wissen kann unter anderem für drei verschiedene Aufgabenstellungen genutzt werden. Zunächst zeigen wir, dass Informationen aus Wikipedia hilfreich sind, um Ergebnisse von Personensuchen im Web nach den in ihnen diskutierten Personen aufzuteilen. Dazu führen wir zwei Modelle zur Gruppierung der Ergebnisse und verschiedene Methoden zum Umgang mit fehlenden Wikipedia Einträgen ein, und zeigen, dass die entwickelten Ansätze traditionelle Methoden zur Gruppierung von Suchergebnissen übertreffen. Des Weiteren diskutieren wir, wie die Klassifizierung von Texten auf Basis von "Folksonomien" Nutzern dabei helfen kann, neue Informationen zu identifizieren, die ihren Interessen entsprechen. Wir konzentrieren uns insbesondere auf temporäre Änderungen in den nutzergenerierten Inhalten, um zu zeigen, wie stark ihr Einfluss auf die Qualität verschiedener "Tag"-Empfehlungsmethoden ist. Zu guter Letzt führen wir einen Algorithmus ein, der es ermöglicht, Nennungen von Echtweltinstanzen in Texten zu disambiguieren und mit ihren Repräsentationen in einer Wissensdatenbank zu verknüpfen. Das Hauptaugenmerk liegt dabei auf der effizienten Erkennung von kohärenten Verknüpfungen. Wir stellen für jeden Teil der Arbeit eine große Vielfalt an Experimenten auf diversen Textkorpora und unterschiedlichen Quellen von nutzergenerierten Inhalten an. Damit heben wir das Potential hervor, das die Nutzung jener Quellen bietet, um die unterschiedlichen Informationsbedürfnisse abzudecken. T2 - Mehrwert für Texte mittels nutzergenerierter Inhalte KW - nutzergenerierte Inhalte KW - text mining KW - Klassifikation KW - Clusteranalyse KW - Entitätsverknüpfung KW - user-generated content KW - text mining KW - classification KW - clustering KW - entity linking Y1 - 2018 ER - TY - CHAP A1 - Rojahn, Marcel A1 - Ambros, Maximilian A1 - Biru, Tibebu A1 - Krallmann, Hermann A1 - Gronau, Norbert A1 - Grum, Marcus ED - Rutkowski, Leszek ED - Scherer, Rafał ED - Korytkowski, Marcin ED - Pedrycz, Witold ED - Tadeusiewicz, Ryszard ED - Zurada, Jacek M. T1 - Adequate basis for the data-driven and machine-learning-based identification T2 - Artificial intelligence and soft computing N2 - Process mining (PM) has established itself in recent years as a main method for visualizing and analyzing processes. However, the identification of knowledge has not been addressed adequately because PM aims solely at data-driven discovering, monitoring, and improving real-world processes from event logs available in various information systems. The following paper, therefore, outlines a novel systematic analysis view on tools for data-driven and machine learning (ML)-based identification of knowledge-intensive target processes. To support the effectiveness of the identification process, the main contributions of this study are (1) to design a procedure for a systematic review and analysis for the selection of relevant dimensions, (2) to identify different categories of dimensions as evaluation metrics to select source systems, algorithms, and tools for PM and ML as well as include them in a multi-dimensional grid box model, (3) to select and assess the most relevant dimensions of the model, (4) to identify and assess source systems, algorithms, and tools in order to find evidence for the selected dimensions, and (5) to assess the relevance and applicability of the conceptualization and design procedure for tool selection in data-driven and ML-based process mining research. KW - data mining KW - knowledge engineering KW - various applications Y1 - 2023 SN - 978-3-031-42504-2 SN - 978-3-031-42505-9 U6 - https://doi.org/10.1007/978-3-031-42505-9_48 SP - 570 EP - 588 PB - Springer CY - Cham ER - TY - JOUR A1 - Brewka, Gerhard A1 - Ellmauthaler, Stefan A1 - Kern-Isberner, Gabriele A1 - Obermeier, Philipp A1 - Ostrowski, Max A1 - Romero, Javier A1 - Schaub, Torsten H. A1 - Schieweck, Steffen T1 - Advanced solving technology for dynamic and reactive applications JF - Künstliche Intelligenz Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0538-8 SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 199 EP - 200 PB - Springer CY - Heidelberg ER - TY - CHAP A1 - Abramova, Olga A1 - Gladkaya, Margarita A1 - Krasnova, Hanna T1 - An unusual encounter with oneself BT - exploring the impact of self-view on online meeting outcomes T2 - ICIS 2021: IS and the future of work N2 - Helping overcome distance, the use of videoconferencing tools has surged during the pandemic. To shed light on the consequences of videoconferencing at work, this study takes a granular look at the implications of the self-view feature for meeting outcomes. Building on self-awareness research and self-regulation theory, we argue that by heightening the state of self-awareness, self-view engagement depletes participants’ mental resources and thereby can undermine online meeting outcomes. Evaluation of our theoretical model on a sample of 179 employees reveals a nuanced picture. Self-view engagement while speaking and while listening is positively associated with self-awareness, which, in turn, is negatively associated with satisfaction with meeting process, perceived productivity, and meeting enjoyment. The criticality of the communication role is put forward: looking at self while listening to other attendees has a negative direct and indirect effect on meeting outcomes; however, looking at self while speaking produces equivocal effects. Y1 - 2021 UR - https://aisel.aisnet.org/icis2021/is_future_work/is_future_work/16 PB - AIS Electronic Library (AISeL) CY - [Erscheinungsort nicht ermittelbar] ER - TY - JOUR A1 - Schindler, Daniel A1 - Moldenhawer, Ted A1 - Stange, Maike A1 - Lepro, Valentino A1 - Beta, Carsten A1 - Holschneider, Matthias A1 - Huisinga, Wilhelm T1 - Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows JF - PLoS Computational Biology : a new community journal N2 - Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach.
Author summary Amoeboid motion is a crawling-like cell migration that plays an important key role in multiple biological processes such as wound healing and cancer metastasis. This type of cell motility results from expanding and simultaneously contracting parts of the cell membrane. From fluorescence images, we obtain a sequence of points, representing the cell membrane, for each time step. By using regression analysis on these sequences, we derive smooth representations, so-called contours, of the membrane. Since the number of measurements is discrete and often limited, the question is raised of how to link consecutive contours with each other. In this work, we present a novel mathematical framework in which these links are described by regularized flows allowing a certain degree of concentration or stretching of neighboring reference points on the same contour. This stretching rate, the so-called local dispersion, is used to identify expansions and contractions of the cell membrane providing a fully automated way of extracting properties of these cell shape changes. We applied our methods to time-lapse microscopy data of the social amoeba Dictyostelium discoideum. Y1 - 2021 U6 - https://doi.org/10.1371/journal.pcbi.1009268 SN - 1553-734X SN - 1553-7358 VL - 17 IS - 8 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Schaub, Torsten H. A1 - Woltran, Stefan T1 - Answer set programming unleashed! JF - Künstliche Intelligenz N2 - Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)] Y1 - 2018 U6 - https://doi.org/10.1007/s13218-018-0550-z SN - 0933-1875 SN - 1610-1987 VL - 32 IS - 2-3 SP - 105 EP - 108 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Steinert, Fritjof A1 - Stabernack, Benno T1 - Architecture of a low latency H.264/AVC video codec for robust ML based image classification how region of interests can minimize the impact of coding artifacts JF - Journal of Signal Processing Systems for Signal, Image, and Video Technology N2 - The use of neural networks is considered as the state of the art in the field of image classification. A large number of different networks are available for this purpose, which, appropriately trained, permit a high level of classification accuracy. Typically, these networks are applied to uncompressed image data, since a corresponding training was also carried out using image data of similar high quality. However, if image data contains image errors, the classification accuracy deteriorates drastically. This applies in particular to coding artifacts which occur due to image and video compression. Typical application scenarios for video compression are narrowband transmission channels for which video coding is required but a subsequent classification is to be carried out on the receiver side. In this paper we present a special H.264/Advanced Video Codec (AVC) based video codec that allows certain regions of a picture to be coded with near constant picture quality in order to allow a reliable classification using neural networks, whereas the remaining image will be coded using constant bit rate. We have combined this feature with the ability to run with lowest latency properties, which is usually also required in remote control applications scenarios. The codec has been implemented as a fully hardwired High Definition video capable hardware architecture which is suitable for Field Programmable Gate Arrays. KW - H.264 KW - Advanced Video Codec (AVC) KW - Low Latency KW - Region of Interest KW - Machine Learning KW - Inference KW - FPGA KW - Hardware accelerator Y1 - 2022 U6 - https://doi.org/10.1007/s11265-021-01727-2 SN - 1939-8018 SN - 1939-8115 VL - 94 IS - 7 SP - 693 EP - 708 PB - Springer CY - New York ER - TY - JOUR A1 - Schäfer, Robin A1 - Stede, Manfred T1 - Argument mining on twitter BT - a survey JF - Information technology : it ; Methoden und innovative Anwendungen der Informatik und Informationstechnik ; Organ der Fachbereiche 3 und 4 der GI e.V. und des Fachbereichs 6 der ITG N2 - In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook. KW - Argument Mining KW - Twitter KW - Stance Detection Y1 - 2021 U6 - https://doi.org/10.1515/itit-2020-0053 SN - 1611-2776 SN - 2196-7032 VL - 63 IS - 1 SP - 45 EP - 58 PB - De Gruyter CY - Berlin ER -