TY - JOUR A1 - Obremski, David A1 - Lugrin, Jean-Luc A1 - Schaper, Philipp A1 - Lugrin, Birgit T1 - Non-native speaker perception of Intelligent Virtual Agents in two languages: the impact of amount and type of grammatical mistakes JF - Journal on Multimodal User Interfaces N2 - Having a mixed-cultural membership becomes increasingly common in our modern society. It is thus beneficial in several ways to create Intelligent Virtual Agents (IVAs) that reflect a mixed-cultural background as well, e.g., for educational settings. For research with such IVAs, it is essential that they are classified as non-native by members of a target culture. In this paper, we focus on variations of IVAs’ speech to create the impression of non-native speakers that are identified as such by speakers of two different mother tongues. In particular, we investigate grammatical mistakes and identify thresholds beyond which the agents is clearly categorised as a non-native speaker. Therefore, we conducted two experiments: one for native speakers of German, and one for native speakers of English. Results of the German study indicate that beyond 10% of word order mistakes and 25% of infinitive mistakes German-speaking IVAs are perceived as non-native speakers. Results of the English study indicate that beyond 50% of omission mistakes and 50% of infinitive mistakes English-speaking IVAs are perceived as non-native speakers. We believe these thresholds constitute helpful guidelines for computational approaches of non-native speaker generation, simplifying research with IVAs in mixed-cultural settings. KW - mixed-cultural settings KW - Intelligent Virtual Agents KW - verbal behaviour Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-269984 SN - 1783-8738 VL - 15 IS - 2 ER - TY - JOUR A1 - Obremski, David A1 - Friedrich, Paula A1 - Haak, Nora A1 - Schaper, Philipp A1 - Lugrin, Birgit T1 - The impact of mixed-cultural speech on the stereotypical perception of a virtual robot JF - Frontiers in Robotics and AI N2 - Despite the fact that mixed-cultural backgrounds become of increasing importance in our daily life, the representation of multiple cultural backgrounds in one entity is still rare in socially interactive agents (SIAs). This paper’s contribution is twofold. First, it provides a survey of research on mixed-cultured SIAs. Second, it presents a study investigating how mixed-cultural speech (in this case, non-native accent) influences how a virtual robot is perceived in terms of personality, warmth, competence and credibility. Participants with English or German respectively as their first language watched a video of a virtual robot speaking in either standard English or German-accented English. It was expected that the German-accented speech would be rated more positively by native German participants as well as elicit the German stereotypes credibility and conscientiousness for both German and English participants. Contrary to the expectations, German participants rated the virtual robot lower in terms of competence and credibility when it spoke with a German accent, whereas English participants perceived the virtual robot with a German accent as more credible compared to the version without an accent. Both the native English and native German listeners classified the virtual robot with a German accent as significantly more neurotic than the virtual robot speaking standard English. This work shows that by solely implementing a non-native accent in a virtual robot, stereotypes are partly transferred. It also shows that the implementation of a non-native accent leads to differences in the perception of the virtual robot. KW - non-native accent KW - social robotics KW - intelligent virtual agents KW - stereotypes KW - mixed-cultural KW - culturally aware KW - socially interactive agents Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-293531 SN - 2296-9144 VL - 9 ER - TY - JOUR A1 - Oberdörfer, Sebastian A1 - Latoschik, Marc Erich T1 - Knowledge encoding in game mechanics: transfer-oriented knowledge learning in desktop-3D and VR JF - International Journal of Computer Games Technology N2 - Affine Transformations (ATs) are a complex and abstract learning content. Encoding the AT knowledge in Game Mechanics (GMs) achieves a repetitive knowledge application and audiovisual demonstration. Playing a serious game providing these GMs leads to motivating and effective knowledge learning. Using immersive Virtual Reality (VR) has the potential to even further increase the serious game’s learning outcome and learning quality. This paper compares the effectiveness and efficiency of desktop-3D and VR in respect to the achieved learning outcome. Also, the present study analyzes the effectiveness of an enhanced audiovisual knowledge encoding and the provision of a debriefing system. The results validate the effectiveness of the knowledge encoding in GMs to achieve knowledge learning. The study also indicates that VR is beneficial for the overall learning quality and that an enhanced audiovisual encoding has only a limited effect on the learning outcome. KW - games Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-201159 VL - 2019 ER - TY - JOUR A1 - Oberdörfer, Sebastian A1 - Heidrich, David A1 - Birnstiel, Sandra A1 - Latoschik, Marc Erich T1 - Enchanted by Your Surrounding? Measuring the Effects of Immersion and Design of Virtual Environments on Decision-Making JF - Frontiers in Virtual Reality N2 - Impaired decision-making leads to the inability to distinguish between advantageous and disadvantageous choices. The impairment of a person’s decision-making is a common goal of gambling games. Given the recent trend of gambling using immersive Virtual Reality it is crucial to investigate the effects of both immersion and the virtual environment (VE) on decision-making. In a novel user study, we measured decision-making using three virtual versions of the Iowa Gambling Task (IGT). The versions differed with regard to the degree of immersion and design of the virtual environment. While emotions affect decision-making, we further measured the positive and negative affect of participants. A higher visual angle on a stimulus leads to an increased emotional response. Thus, we kept the visual angle on the Iowa Gambling Task the same between our conditions. Our results revealed no significant impact of immersion or the VE on the IGT. We further found no significant difference between the conditions with regard to positive and negative affect. This suggests that neither the medium used nor the design of the VE causes an impairment of decision-making. However, in combination with a recent study, we provide first evidence that a higher visual angle on the IGT leads to an effect of impairment. KW - virtual reality KW - virtual environments KW - immersion KW - decision-making KW - iowa gambling task Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-260101 VL - 2 ER - TY - JOUR A1 - Oberdörfer, Sebastian A1 - Birnstiel, Sandra A1 - Latoschik, Marc Erich A1 - Grafe, Silke T1 - Mutual Benefits: Interdisciplinary Education of Pre-Service Teachers and HCI Students in VR/AR Learning Environment Design JF - Frontiers in Education N2 - The successful development and classroom integration of Virtual (VR) and Augmented Reality (AR) learning environments requires competencies and content knowledge with respect to media didactics and the respective technologies. The paper discusses a pedagogical concept specifically aiming at the interdisciplinary education of pre-service teachers in collaboration with human-computer interaction students. The students’ overarching goal is the interdisciplinary realization and integration of VR/AR learning environments in teaching and learning concepts. To assist this approach, we developed a specific tutorial guiding the developmental process. We evaluate and validate the effectiveness of the overall pedagogical concept by analyzing the change in attitudes regarding 1) the use of VR/AR for educational purposes and in competencies and content knowledge regarding 2) media didactics and 3) technology. Our results indicate a significant improvement in the knowledge of media didactics and technology. We further report on four STEM learning environments that have been developed during the seminar. KW - interdisciplinary education KW - virtual reality KW - augmented reality KW - serious games KW - learning environments KW - teacher education Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-241612 SN - 2504-284X VL - 6 ER - TY - THES A1 - Nogatz, Falco T1 - Defining and Implementing Domain-Specific Languages with Prolog T1 - Definition und Implementierung domänenspezifischer Sprachen mit Prolog N2 - The landscape of today’s programming languages is manifold. With the diversity of applications, the difficulty of adequately addressing and specifying the used programs increases. This often leads to newly designed and implemented domain-specific languages. They enable domain experts to express knowledge in their preferred format, resulting in more readable and concise programs. Due to its flexible and declarative syntax without reserved keywords, the logic programming language Prolog is particularly suitable for defining and embedding domain-specific languages. This thesis addresses the questions and challenges that arise when integrating domain-specific languages into Prolog. We compare the two approaches to define them either externally or internally, and provide assisting tools for each. The grammar of a formal language is usually defined in the extended Backus–Naur form. In this work, we handle this formalism as a domain-specific language in Prolog, and define term expansions that allow to translate it into equivalent definite clause grammars. We present the package library(dcg4pt) for SWI-Prolog, which enriches them by an additional argument to automatically process the term’s corresponding parse tree. To simplify the work with definite clause grammars, we visualise their application by a web-based tracer. The external integration of domain-specific languages requires the programmer to keep the grammar, parser, and interpreter in sync. In many cases, domain-specific languages can instead be directly embedded into Prolog by providing appropriate operator definitions. In addition, we propose syntactic extensions for Prolog to expand its expressiveness, for instance to state logic formulas with their connectives verbatim. This allows to use all tools that were originally written for Prolog, for instance code linters and editors with syntax highlighting. We present the package library(plammar), a standard-compliant parser for Prolog source code, written in Prolog. It is able to automatically infer from example sentences the required operator definitions with their classes and precedences as well as the required Prolog language extensions. As a result, we can automatically answer the question: Is it possible to model these example sentences as valid Prolog clauses, and how? We discuss and apply the two approaches to internal and external integrations for several domain-specific languages, namely the extended Backus–Naur form, GraphQL, XPath, and a controlled natural language to represent expert rules in if-then form. The created toolchain with library(dcg4pt) and library(plammar) yields new application opportunities for static Prolog source code analysis, which we also present. N2 - Die Landschaft der heutigen Programmiersprachen ist vielfältig. Mit ihren unterschiedlichen Anwendungsbereichen steigt zugleich die Schwierigkeit, die eingesetzten Programme adäquat anzusprechen und zu spezifizieren. Immer häufiger werden hierfür domänenspezifische Sprachen entworfen und implementiert. Sie ermöglichen Domänenexperten, Wissen in ihrem bevorzugten Format auszudrücken, was zu lesbareren Programmen führt. Durch ihre flexible und deklarative Syntax ohne vorbelegte Schlüsselwörter ist die logische Programmsprache Prolog besonders geeignet, um domänenspezifische Sprachen zu definieren und einzubetten. Diese Arbeit befasst sich mit den Fragen und Herausforderungen, die sich bei der Integration von domänenspezifischen Sprachen in Prolog ergeben. Wir vergleichen die zwei Ansätze, sie entweder extern oder intern zu definieren, und stellen jeweils Hilfsmittel zur Verfügung. Die Grammatik einer formalen Sprache wird häufig in der erweiterten Backus–Naur–Form definiert. Diesen Formalismus behandeln wir in dieser Arbeit als eine domänenspezifische Sprache in Prolog und definieren Termexpansionen, die es erlauben, ihn in äquivalente Definite Clause Grammars für Prolog zu übersetzen. Durch das Modul library(dcg4pt) werden sie um ein zusätzliches Argument erweitert, das den Syntaxbaum eines Terms automatisch erzeugt. Um die Arbeit mit Definite Clause Grammars zu erleichtern, visualisieren wir ihre Anwendung in einem webbasierten Tracer. Meist können domänenspezifische Sprachen jedoch auch mittels passender Operatordefinitionen direkt in Prolog eingebettet werden. Dies ermöglicht die Verwendung aller Werkzeuge, die ursprünglich für Prolog geschrieben wurden, z.B. zum Code-Linting und Syntax-Highlighting. In dieser Arbeit stellen wir den standardkonformen Prolog-Parser library(plammar) vor. Er ist in Prolog geschrieben und in der Lage, aus Beispielsätzen automatisch die erforderlichen Operatoren mit ihren Klassen und Präzedenzen abzuleiten. Um die Ausdruckskraft von Prolog noch zu erweitern, schlagen wir Ergänzungen zum ISO Standard vor. Sie erlauben es, weitere Sprachen direkt einzubinden, und werden ebenfalls von library(plammar) identifiziert. So ist es bspw. möglich, logische Formeln direkt mit den bekannten Symbolen für Konjunktion, Disjunktion, usw. als Prolog-Programme anzugeben. Beide Ansätze der internen und externen Integration werden für mehrere domänen-spezifische Sprachen diskutiert und beispielhaft für GraphQL, XPath, die erweiterte Backus–Naur–Form sowie Expertenregeln in Wenn–Dann–Form umgesetzt. Die vorgestellten Werkzeuge um library(dcg4pt) und library(plammar) ergeben zudem neue Anwendungsmöglichkeiten auch für die statische Quellcodeanalyse von Prolog-Programmen. KW - PROLOG KW - Domänenspezifische Sprache KW - logic programming KW - knowledge representation KW - definite clause grammars Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-301872 ER - TY - THES A1 - Niebler, Thomas T1 - Extracting and Learning Semantics from Social Web Data T1 - Extraktion und Lernen von Semantik aus Social Web-Daten N2 - Making machines understand natural language is a dream of mankind that existed since a very long time. Early attempts at programming machines to converse with humans in a supposedly intelligent way with humans relied on phrase lists and simple keyword matching. However, such approaches cannot provide semantically adequate answers, as they do not consider the specific meaning of the conversation. Thus, if we want to enable machines to actually understand language, we need to be able to access semantically relevant background knowledge. For this, it is possible to query so-called ontologies, which are large networks containing knowledge about real-world entities and their semantic relations. However, creating such ontologies is a tedious task, as often extensive expert knowledge is required. Thus, we need to find ways to automatically construct and update ontologies that fit human intuition of semantics and semantic relations. More specifically, we need to determine semantic entities and find relations between them. While this is usually done on large corpora of unstructured text, previous work has shown that we can at least facilitate the first issue of extracting entities by considering special data such as tagging data or human navigational paths. Here, we do not need to detect the actual semantic entities, as they are already provided because of the way those data are collected. Thus we can mainly focus on the problem of assessing the degree of semantic relatedness between tags or web pages. However, there exist several issues which need to be overcome, if we want to approximate human intuition of semantic relatedness. For this, it is necessary to represent words and concepts in a way that allows easy and highly precise semantic characterization. This also largely depends on the quality of data from which these representations are constructed. In this thesis, we extract semantic information from both tagging data created by users of social tagging systems and human navigation data in different semantic-driven social web systems. Our main goal is to construct high quality and robust vector representations of words which can the be used to measure the relatedness of semantic concepts. First, we show that navigation in the social media systems Wikipedia and BibSonomy is driven by a semantic component. After this, we discuss and extend methods to model the semantic information in tagging data as low-dimensional vectors. Furthermore, we show that tagging pragmatics influences different facets of tagging semantics. We then investigate the usefulness of human navigational paths in several different settings on Wikipedia and BibSonomy for measuring semantic relatedness. Finally, we propose a metric-learning based algorithm in adapt pre-trained word embeddings to datasets containing human judgment of semantic relatedness. This work contributes to the field of studying semantic relatedness between words by proposing methods to extract semantic relatedness from web navigation, learn highquality and low-dimensional word representations from tagging data, and to learn semantic relatedness from any kind of vector representation by exploiting human feedback. Applications first and foremest lie in ontology learning for the Semantic Web, but also semantic search or query expansion. N2 - Einer der großen Träume der Menschheit ist es, Maschinen dazu zu bringen, natürliche Sprache zu verstehen. Frühe Versuche, Computer dahingehend zu programmieren, dass sie mit Menschen vermeintlich intelligente Konversationen führen können, basierten hauptsächlich auf Phrasensammlungen und einfachen Stichwortabgleichen. Solche Ansätze sind allerdings nicht in der Lage, inhaltlich adäquate Antworten zu liefern, da der tatsächliche Inhalt der Konversation nicht erfasst werden kann. Folgerichtig ist es notwendig, dass Maschinen auf semantisch relevantes Hintergrundwissen zugreifen können, um diesen Inhalt zu verstehen. Solches Wissen ist beispielsweise in Ontologien vorhanden. Ontologien sind große Datenbanken von vernetztem Wissen über Objekte und Gegenstände der echten Welt sowie über deren semantische Beziehungen. Das Erstellen solcher Ontologien ist eine sehr kostspielige und aufwändige Aufgabe, da oft tiefgreifendes Expertenwissen benötigt wird. Wir müssen also Wege finden, um Ontologien automatisch zu erstellen und aktuell zu halten, und zwar in einer Art und Weise, dass dies auch menschlichem Empfinden von Semantik und semantischer Ähnlichkeit entspricht. Genauer gesagt ist es notwendig, semantische Entitäten und deren Beziehungen zu bestimmen. Während solches Wissen üblicherweise aus Textkorpora extrahiert wird, ist es möglich, zumindest das erste Problem - semantische Entitäten zu bestimmen - durch Benutzung spezieller Datensätze zu umgehen, wie zum Beispiel Tagging- oder Navigationsdaten. In diesen Arten von Datensätzen ist es nicht notwendig, Entitäten zu extrahieren, da sie bereits aufgrund inhärenter Eigenschaften bei der Datenakquise vorhanden sind. Wir können uns also hauptsächlich auf die Bestimmung von semantischen Relationen und deren Intensität fokussieren. Trotzdem müssen hier noch einige Hindernisse überwunden werden. Beispielsweise ist es notwendig, Repräsentationen für semantische Entitäten zu finden, so dass es möglich ist, sie einfach und semantisch hochpräzise zu charakterisieren. Dies hängt allerdings auch erheblich von der Qualität der Daten ab, aus denen diese Repräsentationen konstruiert werden. In der vorliegenden Arbeit extrahieren wir semantische Informationen sowohl aus Taggingdaten, von Benutzern sozialer Taggingsysteme erzeugt, als auch aus Navigationsdaten von Benutzern semantikgetriebener Social Media-Systeme. Das Hauptziel dieser Arbeit ist es, hochqualitative und robuste Vektordarstellungen von Worten zu konstruieren, die dann dazu benutzt werden können, die semantische Ähnlichkeit von Konzepten zu bestimmen. Als erstes zeigen wir, dass Navigation in Social Media Systemen unter anderem durch eine semantische Komponente getrieben wird. Danach diskutieren und erweitern wir Methoden, um die semantische Information in Taggingdaten als niedrigdimensionale sogenannte “Embeddings” darzustellen. Darüberhinaus demonstrieren wir, dass die Taggingpragmatik verschiedene Facetten der Taggingsemantik beeinflusst. Anschließend untersuchen wir, inwieweit wir menschliche Navigationspfade zur Bestimmung semantischer Ähnlichkeit benutzen können. Hierzu betrachten wir mehrere Datensätze, die Navigationsdaten in verschiedenen Rahmenbedingungen beinhalten. Als letztes stellen wir einen neuartigen Algorithmus vor, um bereits trainierte Word Embeddings im Nachhinein an menschliche Intuition von Semantik anzupassen. Diese Arbeit steuert wertvolle Beiträge zum Gebiet der Bestimmung von semantischer Ähnlichkeit bei: Es werden Methoden vorgestellt werden, um hochqualitative semantische Information aus Web-Navigation und Taggingdaten zu extrahieren, diese mittels niedrigdimensionaler Vektordarstellungen zu modellieren und selbige schließlich besser an menschliches Empfinden von semantischer Ähnlichkeit anzupassen, indem aus genau diesem Empfinden gelernt wird. Anwendungen liegen in erster Linie darin, Ontologien für das Semantic Web zu lernen, allerdings auch in allen Bereichen, die Vektordarstellungen von semantischen Entitäten benutzen. KW - Semantik KW - Maschinelles Lernen KW - Soziale Software KW - Semantics KW - User Behavior KW - Social Web KW - Machine Learning Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-178666 ER - TY - RPRT A1 - Nguyen, Kien A1 - Loh, Frank A1 - Hoßfeld, Tobias T1 - Challenges of Serverless Deployment in Edge-MEC-Cloud T2 - KuVS Fachgespräch - Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS’23) N2 - The emerging serverless computing may meet Edge Cloud in a beneficial manner as the two offer flexibility and dynamicity in optimizing finite hardware resources. However, the lack of proper study of a joint platform leaves a gap in literature about consumption and performance of such integration. To this end, this paper identifies the key questions and proposes a methodology to answer them. KW - Edge-MEC-Cloud Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322025 ER - TY - RPRT A1 - Navade, Piyush A1 - Maile, Lisa A1 - German, Reinhard T1 - Multiple DCLC Routing Algorithms for Ultra-Reliable and Time-Sensitive Applications T2 - KuVS Fachgespräch - Würzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS’23) N2 - This paper discusses the problem of finding multiple shortest disjoint paths in modern communication networks, which is essential for ultra-reliable and time-sensitive applications. Dijkstra’s algorithm has been a popular solution for the shortest path problem, but repetitive use of it to find multiple paths is not scalable. The Multiple Disjoint Path Algorithm (MDPAlg), published in 2021, proposes the use of a single full graph to construct multiple disjoint paths. This paper proposes modifications to the algorithm to include a delay constraint, which is important in time-sensitive applications. Different delay constraint least-cost routing algorithms are compared in a comprehensive manner to evaluate the benefits of the adapted MDPAlg algorithm. Fault tolerance, and thereby reliability, is ensured by generating multiple link-disjoint paths from source to destination. KW - Dijkstra’s algorithm KW - shortest path routing KW - disjoint multi-paths KW - delay constrained KW - least cost Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322177 ER - TY - JOUR A1 - Müller, Konstantin A1 - Leppich, Robert A1 - Geiß, Christian A1 - Borst, Vanessa A1 - Pelizari, Patrick Aravena A1 - Kounev, Samuel A1 - Taubenböck, Hannes T1 - Deep neural network regression for normalized digital surface model generation with Sentinel-2 imagery JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing N2 - In recent history, normalized digital surface models (nDSMs) have been constantly gaining importance as a means to solve large-scale geographic problems. High-resolution surface models are precious, as they can provide detailed information for a specific area. However, measurements with a high resolution are time consuming and costly. Only a few approaches exist to create high-resolution nDSMs for extensive areas. This article explores approaches to extract high-resolution nDSMs from low-resolution Sentinel-2 data, allowing us to derive large-scale models. We thereby utilize the advantages of Sentinel 2 being open access, having global coverage, and providing steady updates through a high repetition rate. Several deep learning models are trained to overcome the gap in producing high-resolution surface maps from low-resolution input data. With U-Net as a base architecture, we extend the capabilities of our model by integrating tailored multiscale encoders with differently sized kernels in the convolution as well as conformed self-attention inside the skip connection gates. Using pixelwise regression, our U-Net base models can achieve a mean height error of approximately 2 m. Moreover, through our enhancements to the model architecture, we reduce the model error by more than 7%. KW - Deep learning KW - multiscale encoder KW - sentinel KW - surface model Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-349424 SN - 1939-1404 VL - 16 ER -