39 research outputs found
Exploring memorable gastronomic experiences: Automatic topic modelling of TripAdvisor reviews
The article aims to identify memorable gastronomic experiences reported online and verify their relationships
with the type of cuisine served and restaurant location. This study used text mining, LDA, Pearson’s chi-squared
test and sentiment analysis. All 48,378 English reviews posted by TripAdvisor users concerning 155 restaurants
in Krakow were scraped. Eight features that characterise MGEs were identified (service/staff, atmosphere,
cuisine/food (taste), drinks, local specialities, location/setting, price & value and table booking). There
are statistically significant differences in the frequency of the topic experiences depending on the location
of restaurants in the city
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Semantic Sentiment Analysis of Microblogs
Microblogs and social media platforms are now considered among the most popular forms of online communication. Through a platform like Twitter, much information reflecting people's opinions and attitudes is published and shared among users on a daily basis. This has recently brought great opportunities to companies interested in tracking and monitoring the reputation of their brands and businesses, and to policy makers and politicians to support their assessment of public opinions about their policies or political issues.
A wide range of approaches to sentiment analysis on Twitter, and other similar microblogging platforms, have been recently built. Most of these approaches rely mainly on the presence of affect words or syntactic structures that explicitly and unambiguously reflect sentiment (e.g., "great'', "terrible''). However, these approaches are semantically weak, that is, they do not account for the semantics of words when detecting their sentiment in text. This is problematic since the sentiment of words, in many cases, is associated with their semantics, either along the context they occur within (e.g., "great'' is negative in the context "pain'') or the conceptual meaning associated with the words (e.g., "Ebola" is negative when its associated semantic concept is "Virus").
This thesis investigates the role of words' semantics in sentiment analysis of microblogs, aiming mainly at addressing the above problem. In particular, Twitter is used as a case study of microblogging platforms to investigate whether capturing the sentiment of words with respect to their semantics leads to more accurate sentiment analysis models on Twitter. To this end, several approaches are proposed in this thesis for extracting and incorporating two types of word semantics for sentiment analysis: contextual semantics (i.e., semantics captured from words' co-occurrences) and conceptual semantics (i.e., semantics extracted from external knowledge sources).
Experiments are conducted with both types of semantics by assessing their impact in three popular sentiment analysis tasks on Twitter; entity-level sentiment analysis, tweet-level sentiment analysis and context-sensitive sentiment lexicon adaptation. Evaluation under each sentiment analysis task includes several sentiment lexicons, and up to 9 Twitter datasets of different characteristics, as well as comparing against several state-of-the-art sentiment analysis approaches widely used in the literature.
The findings from this body of work demonstrate the value of using semantics in sentiment analysis on Twitter. The proposed approaches, which consider words' semantics for sentiment analysis at both, entity and tweet levels, surpass non-semantic approaches in most datasets
Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020
On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges
Data and methods for a visual understanding of sign languages
Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages.
Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translationLes llengües de signes són llengües completes i naturals que utilitzen milions de persones de tot el món com mode de comunicació primer o preferit. Tanmateix, malauradament, continuen essent llengües marginades. Dissenyar, construir i avaluar tecnologies que funcionin amb les llengües de signes presenta reptes de recerca que requereixen d’esforços interdisciplinaris i col·laboratius. Els avenços recents en l’aprenentatge automàtic i la intel·ligència artificial (IA) poden millorar l’accessibilitat tecnològica dels signants, i alhora reduir la barrera de comunicació existent entre la comunitat sorda i les persones no-signants. Tanmateix, les tecnologies més modernes en IA encara no consideren les llengües de signes en les seves interfícies amb l’usuari. Això es deu principalment a que les llengües de signes són llenguatges visuals, que utilitzen característiques manuals i no manuals per transmetre informació, i no tenen una forma escrita estàndard. Els objectius principals d’aquesta tesi són la creació de recursos multimodals a gran escala adequats per entrenar models d’aprenentatge automàtic per a llengües de signes, i desenvolupar sistemes de visió per computador adreçats a una millor comprensió automàtica de les llengües de signes. Així, a la Part I presentem la base de dades How2Sign, una gran col·lecció multimodal i multivista de vídeos de la llengua de signes nord-americana. A la Part II, contribuïm al desenvolupament de tecnologia per a llengües de signes, presentant al capítol 4 una solució per anotar signes automàticament anomenada Spot-Align, basada en mètodes de localització de signes en seqüències contínues de signes. Després, presentem els avantatges d’aquesta solució i proporcionem uns primers resultats per la tasca de reconeixement de la llengua de signes a la base de dades How2Sign. A continuació, al capítol 5 aprofitem de les anotacions i diverses modalitats de How2Sign per explorar la cerca de vídeos en llengua de signes a partir de l’entrenament d’incrustacions multimodals. Finalment, al capítol 6, explorem la generació de vídeos en llengua de signes aplicant xarxes adversàries generatives al domini de la llengua de signes. Avaluem fins a quin punt els signants poden entendre els vídeos generats automàticament, proposant un nou protocol d’avaluació basat en les categories dins de How2Sign i la traducció dels vídeos a l’anglès escritLas lenguas de signos son lenguas completas y naturales que utilizan millones de personas
de todo el mundo como modo de comunicación primero o preferido. Sin embargo, desgraciadamente,
siguen siendo lenguas marginadas. Diseñar, construir y evaluar tecnologías
que funcionen con las lenguas de signos presenta retos de investigación que requieren
esfuerzos interdisciplinares y colaborativos. Los avances recientes en el aprendizaje automático
y la inteligencia artificial (IA) pueden mejorar la accesibilidad tecnológica de
los signantes, al tiempo que reducir la barrera de comunicación existente entre la comunidad
sorda y las personas no signantes. Sin embargo, las tecnologías más modernas en
IA todavía no consideran las lenguas de signos en sus interfaces con el usuario. Esto
se debe principalmente a que las lenguas de signos son lenguajes visuales, que utilizan
características manuales y no manuales para transmitir información, y carecen de una
forma escrita estándar. Los principales objetivos de esta tesis son la creación de recursos
multimodales a gran escala adecuados para entrenar modelos de aprendizaje automático
para lenguas de signos, y desarrollar sistemas de visión por computador dirigidos a una
mejor comprensión automática de las lenguas de signos.
Así, en la Parte I presentamos la base de datos How2Sign, una gran colección multimodal
y multivista de vídeos de lenguaje la lengua de signos estadounidense. En la Part II,
contribuimos al desarrollo de tecnología para lenguas de signos, presentando en el capítulo
4 una solución para anotar signos automáticamente llamada Spot-Align, basada en
métodos de localización de signos en secuencias continuas de signos. Después, presentamos
las ventajas de esta solución y proporcionamos unos primeros resultados por la tarea
de reconocimiento de la lengua de signos en la base de datos How2Sign. A continuación,
en el capítulo 5 aprovechamos de las anotaciones y diversas modalidades de How2Sign
para explorar la búsqueda de vídeos en lengua de signos a partir del entrenamiento de
incrustaciones multimodales. Finalmente, en el capítulo 6, exploramos la generación
de vídeos en lengua de signos aplicando redes adversarias generativas al dominio de la
lengua de signos. Evaluamos hasta qué punto los signantes pueden entender los vídeos
generados automáticamente, proponiendo un nuevo protocolo de evaluación basado en
las categorías dentro de How2Sign y la traducción de los vídeos al inglés escrito.Postprint (published version
Data and methods for a visual understanding of sign languages
Signed languages are complete and natural languages used as the first or preferred mode of communication by millions of people worldwide. However, they, unfortunately, continue to be marginalized languages. Designing, building, and evaluating models that work on sign languages presents compelling research challenges and requires interdisciplinary and collaborative efforts. The recent advances in Machine Learning (ML) and Artificial Intelligence (AI) has the power to enable better accessibility to sign language users and narrow down the existing communication barrier between the Deaf community and non-sign language users. However, recent AI-powered technologies still do not account for sign language in their pipelines. This is mainly because sign languages are visual languages, that use manual and non-manual features to convey information, and do not have a standard written form. Thus, the goal of this thesis is to contribute to the development of new technologies that account for sign language by creating large-scale multimodal resources suitable for training modern data-hungry machine learning models and developing automatic systems that focus on computer vision tasks related to sign language that aims at learning better visual understanding of sign languages.
Thus, in Part I, we introduce the How2Sign dataset, which is a large-scale collection of multimodal and multiview sign language videos in American Sign Language. In Part II, we contribute to the development of technologies that account for sign languages by presenting in Chapter 4 a framework called Spot-Align, based on sign spotting methods, to automatically annotate sign instances in continuous sign language. We further present the benefits of this framework and establish a baseline for the sign language recognition task on the How2Sign dataset. In addition to that, in Chapter 5 we benefit from the different annotations and modalities of the How2Sign to explore sign language video retrieval by learning cross-modal embeddings. Later in Chapter 6, we explore sign language video generation by applying Generative Adversarial Networks to the sign language domain and assess if and how well sign language users can understand automatically generated sign language videos by proposing an evaluation protocol based on How2Sign topics and English translationLes llengües de signes són llengües completes i naturals que utilitzen milions de persones de tot el món com mode de comunicació primer o preferit. Tanmateix, malauradament, continuen essent llengües marginades. Dissenyar, construir i avaluar tecnologies que funcionin amb les llengües de signes presenta reptes de recerca que requereixen d’esforços interdisciplinaris i col·laboratius. Els avenços recents en l’aprenentatge automàtic i la intel·ligència artificial (IA) poden millorar l’accessibilitat tecnològica dels signants, i alhora reduir la barrera de comunicació existent entre la comunitat sorda i les persones no-signants. Tanmateix, les tecnologies més modernes en IA encara no consideren les llengües de signes en les seves interfícies amb l’usuari. Això es deu principalment a que les llengües de signes són llenguatges visuals, que utilitzen característiques manuals i no manuals per transmetre informació, i no tenen una forma escrita estàndard. Els objectius principals d’aquesta tesi són la creació de recursos multimodals a gran escala adequats per entrenar models d’aprenentatge automàtic per a llengües de signes, i desenvolupar sistemes de visió per computador adreçats a una millor comprensió automàtica de les llengües de signes. Així, a la Part I presentem la base de dades How2Sign, una gran col·lecció multimodal i multivista de vídeos de la llengua de signes nord-americana. A la Part II, contribuïm al desenvolupament de tecnologia per a llengües de signes, presentant al capítol 4 una solució per anotar signes automàticament anomenada Spot-Align, basada en mètodes de localització de signes en seqüències contínues de signes. Després, presentem els avantatges d’aquesta solució i proporcionem uns primers resultats per la tasca de reconeixement de la llengua de signes a la base de dades How2Sign. A continuació, al capítol 5 aprofitem de les anotacions i diverses modalitats de How2Sign per explorar la cerca de vídeos en llengua de signes a partir de l’entrenament d’incrustacions multimodals. Finalment, al capítol 6, explorem la generació de vídeos en llengua de signes aplicant xarxes adversàries generatives al domini de la llengua de signes. Avaluem fins a quin punt els signants poden entendre els vídeos generats automàticament, proposant un nou protocol d’avaluació basat en les categories dins de How2Sign i la traducció dels vídeos a l’anglès escritLas lenguas de signos son lenguas completas y naturales que utilizan millones de personas
de todo el mundo como modo de comunicación primero o preferido. Sin embargo, desgraciadamente,
siguen siendo lenguas marginadas. Diseñar, construir y evaluar tecnologías
que funcionen con las lenguas de signos presenta retos de investigación que requieren
esfuerzos interdisciplinares y colaborativos. Los avances recientes en el aprendizaje automático
y la inteligencia artificial (IA) pueden mejorar la accesibilidad tecnológica de
los signantes, al tiempo que reducir la barrera de comunicación existente entre la comunidad
sorda y las personas no signantes. Sin embargo, las tecnologías más modernas en
IA todavía no consideran las lenguas de signos en sus interfaces con el usuario. Esto
se debe principalmente a que las lenguas de signos son lenguajes visuales, que utilizan
características manuales y no manuales para transmitir información, y carecen de una
forma escrita estándar. Los principales objetivos de esta tesis son la creación de recursos
multimodales a gran escala adecuados para entrenar modelos de aprendizaje automático
para lenguas de signos, y desarrollar sistemas de visión por computador dirigidos a una
mejor comprensión automática de las lenguas de signos.
Así, en la Parte I presentamos la base de datos How2Sign, una gran colección multimodal
y multivista de vídeos de lenguaje la lengua de signos estadounidense. En la Part II,
contribuimos al desarrollo de tecnología para lenguas de signos, presentando en el capítulo
4 una solución para anotar signos automáticamente llamada Spot-Align, basada en
métodos de localización de signos en secuencias continuas de signos. Después, presentamos
las ventajas de esta solución y proporcionamos unos primeros resultados por la tarea
de reconocimiento de la lengua de signos en la base de datos How2Sign. A continuación,
en el capítulo 5 aprovechamos de las anotaciones y diversas modalidades de How2Sign
para explorar la búsqueda de vídeos en lengua de signos a partir del entrenamiento de
incrustaciones multimodales. Finalmente, en el capítulo 6, exploramos la generación
de vídeos en lengua de signos aplicando redes adversarias generativas al dominio de la
lengua de signos. Evaluamos hasta qué punto los signantes pueden entender los vídeos
generados automáticamente, proponiendo un nuevo protocolo de evaluación basado en
las categorías dentro de How2Sign y la traducción de los vídeos al inglés escrito.Teoria del Senyal i Comunicacion
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Integrating Recognition and Decision Making to Close the Interaction Loop for Autonomous Systems
Intelligent systems are becoming increasingly ubiquitous in daily life. Mobile devices are providing machine-generated support to users, robots are coming out of their cages in manufacturing to interact with co-workers, and cars with various degrees of self-driving capabilities operate amongst pedestrians and the driver. However, these interactive intelligent systems\u27 effectiveness depends on their understanding and recognition of human activities and goals, as well as their responses to people in a timely manner. The average person does not follow instructions step-by-step or act in a formulaic manner, but instead varies the order of actions and timing when performing a given task. People explore their surroundings, make mistakes, and may interrupt an activity to handle more urgent matters. The decisions that an autonomous intelligent system makes should account for such noise and variance regardless of the form of interaction, which includes adapting action choices and possibly its own goals.While most people take these aspects of interaction for granted, they are complex and involve many specific tasks that have primarily been studied independently within artificial intelligence. This results in open-loop interactive experiences where the user must perform a fixed input command or the intelligent system performs a hard-coded output response---one of the components of the interaction cannot adapt with respect to the other for longer-term back-and-forth interactions. This dissertation explores how developments in plan recognition, activity recognition, intent recognition, and autonomous planning can work together to develop more adaptive interactive experiences between autonomous intelligent systems and the people around them. In particular, we consider a unifying perspective of recognition algorithms that provides sufficient information to dynamically produce short-term automated planning problems, and we present ways to run these algorithms faster for the real-time needs of interaction. This exploration leads to the introduction of the Planning and Recognition Together Close the Interaction Loop (PReTCIL) framework that serves as a first step towards identifying how we can address the problem of closing the interaction loop, in addition to new questions that need to be considered
Human Practice. Digital Ecologies. Our Future. : 14. Internationale Tagung Wirtschaftsinformatik (WI 2019) : Tagungsband
Erschienen bei: universi - Universitätsverlag Siegen. - ISBN: 978-3-96182-063-4Aus dem Inhalt:
Track 1: Produktion & Cyber-Physische Systeme
Requirements and a Meta Model for Exchanging Additive Manufacturing Capacities
Service Systems, Smart Service Systems and Cyber- Physical Systems—What’s the difference? Towards a Unified Terminology
Developing an Industrial IoT Platform – Trade-off between Horizontal and Vertical Approaches
Machine Learning und Complex Event Processing: Effiziente Echtzeitauswertung am Beispiel Smart Factory
Sensor retrofit for a coffee machine as condition monitoring and predictive maintenance use case
Stakeholder-Analyse zum Einsatz IIoT-basierter Frischeinformationen in der Lebensmittelindustrie
Towards a Framework for Predictive Maintenance Strategies in Mechanical Engineering - A Method-Oriented Literature Analysis
Development of a matching platform for the requirement-oriented selection of cyber physical systems for SMEs
Track 2: Logistic Analytics
An Empirical Study of Customers’ Behavioral Intention to Use Ridepooling Services – An Extension of the Technology Acceptance Model
Modeling Delay Propagation and Transmission in Railway Networks
What is the impact of company specific adjustments on the acceptance and diffusion of logistic standards?
Robust Route Planning in Intermodal Urban Traffic
Track 3: Unternehmensmodellierung & Informationssystemgestaltung (Enterprise Modelling & Information Systems Design)
Work System Modeling Method with Different Levels of Specificity and Rigor for Different Stakeholder Purposes
Resolving Inconsistencies in Declarative Process Models based on Culpability Measurement
Strategic Analysis in the Realm of Enterprise Modeling – On the Example of Blockchain-Based Initiatives for the Electricity Sector
Zwischenbetriebliche Integration in der Möbelbranche: Konfigurationen und Einflussfaktoren
Novices’ Quality Perceptions and the Acceptance of Process Modeling Grammars
Entwicklung einer Definition für Social Business Objects (SBO) zur Modellierung von Unternehmensinformationen
Designing a Reference Model for Digital Product Configurators
Terminology for Evolving Design Artifacts
Business Role-Object Specification: A Language for Behavior-aware Structural Modeling of Business Objects
Generating Smart Glasses-based Information Systems with BPMN4SGA: A BPMN Extension for Smart Glasses Applications
Using Blockchain in Peer-to-Peer Carsharing to Build Trust in the Sharing Economy
Testing in Big Data: An Architecture Pattern for a Development Environment for Innovative, Integrated and Robust Applications
Track 4: Lern- und Wissensmanagement (e-Learning and Knowledge Management)
eGovernment Competences revisited – A Literature Review on necessary Competences in a Digitalized Public Sector
Say Hello to Your New Automated Tutor – A Structured Literature Review on Pedagogical Conversational Agents
Teaching the Digital Transformation of Business Processes: Design of a Simulation Game for Information Systems Education
Conceptualizing Immersion for Individual Learning in Virtual Reality
Designing a Flipped Classroom Course – a Process Model
The Influence of Risk-Taking on Knowledge Exchange and Combination
Gamified Feedback durch Avatare im Mobile Learning
Alexa, Can You Help Me Solve That Problem? - Understanding the Value of Smart Personal Assistants as Tutors for Complex Problem Tasks
Track 5: Data Science & Business Analytics
Matching with Bundle Preferences: Tradeoff between Fairness and Truthfulness
Applied image recognition: guidelines for using deep learning models in practice
Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting
Reading Between the Lines of Qualitative Data – How to Detect Hidden Structure Based on Codes
Online Auctions with Dual-Threshold Algorithms: An Experimental Study and Practical Evaluation
Design Features of Non-Financial Reward Programs for Online Reviews: Evaluation based on Google Maps Data
Topic Embeddings – A New Approach to Classify Very Short Documents Based on Predefined Topics
Leveraging Unstructured Image Data for Product Quality Improvement
Decision Support for Real Estate Investors: Improving Real Estate Valuation with 3D City Models and Points of Interest
Knowledge Discovery from CVs: A Topic Modeling Procedure
Online Product Descriptions – Boost for your Sales?
Entscheidungsunterstützung durch historienbasierte Dienstreihenfolgeplanung mit Pattern
A Semi-Automated Approach for Generating Online Review Templates
Machine Learning goes Measure Management: Leveraging Anomaly Detection and Parts Search to Improve Product-Cost Optimization
Bedeutung von Predictive Analytics für den theoretischen Erkenntnisgewinn in der IS-Forschung
Track 6: Digitale Transformation und Dienstleistungen
Heuristic Theorizing in Software Development: Deriving Design Principles for Smart Glasses-based Systems
Mirroring E-service for Brick and Mortar Retail: An Assessment and Survey
Taxonomy of Digital Platforms: A Platform Architecture Perspective
Value of Star Players in the Digital Age
Local Shopping Platforms – Harnessing Locational Advantages for the Digital Transformation of Local Retail Outlets: A Content Analysis
A Socio-Technical Approach to Manage Analytics-as-a-Service – Results of an Action Design Research Project
Characterizing Approaches to Digital Transformation: Development of a Taxonomy of Digital Units
Expectations vs. Reality – Benefits of Smart Services in the Field of Tension between Industry and Science
Innovation Networks and Digital Innovation: How Organizations Use Innovation Networks in a Digitized Environment
Characterising Social Reading Platforms— A Taxonomy-Based Approach to Structure the Field
Less Complex than Expected – What Really Drives IT Consulting Value
Modularity Canvas – A Framework for Visualizing Potentials of Service Modularity
Towards a Conceptualization of Capabilities for Innovating Business Models in the Industrial Internet of Things
A Taxonomy of Barriers to Digital Transformation
Ambidexterity in Service Innovation Research: A Systematic Literature Review
Design and success factors of an online solution for cross-pillar pension information
Track 7: IT-Management und -Strategie
A Frugal Support Structure for New Software Implementations in SMEs
How to Structure a Company-wide Adoption of Big Data Analytics
The Changing Roles of Innovation Actors and Organizational Antecedents in the Digital Age
Bewertung des Kundennutzens von Chatbots für den Einsatz im Servicedesk
Understanding the Benefits of Agile Software Development in Regulated Environments
Are Employees Following the Rules? On the Effectiveness of IT Consumerization Policies
Agile and Attached: The Impact of Agile Practices on Agile Team Members’ Affective Organisational Commitment
The Complexity Trap – Limits of IT Flexibility for Supporting Organizational Agility in Decentralized Organizations
Platform Openness: A Systematic Literature Review and Avenues for Future Research
Competence, Fashion and the Case of Blockchain
The Digital Platform Otto.de: A Case Study of Growth, Complexity, and Generativity
Track 8: eHealth & alternde Gesellschaft
Security and Privacy of Personal Health Records in Cloud Computing Environments – An Experimental Exploration of the Impact of Storage Solutions and Data Breaches
Patientenintegration durch Pfadsysteme
Digitalisierung in der Stressprävention – eine qualitative Interviewstudie zu Nutzenpotenzialen
User Dynamics in Mental Health Forums – A Sentiment Analysis Perspective
Intent and the Use of Wearables in the Workplace – A Model Development
Understanding Patient Pathways in the Context of Integrated Health Care Services - Implications from a Scoping Review
Understanding the Habitual Use of Wearable Activity Trackers
On the Fit in Fitness Apps: Studying the Interaction of Motivational Affordances and Users’ Goal Orientations in Affecting the Benefits Gained
Gamification in Health Behavior Change Support Systems - A Synthesis of Unintended Side Effects
Investigating the Influence of Information Incongruity on Trust-Relations within Trilateral Healthcare Settings
Track 9: Krisen- und Kontinuitätsmanagement
Potentiale von IKT beim Ausfall kritischer Infrastrukturen: Erwartungen, Informationsgewinnung und Mediennutzung der Zivilbevölkerung in Deutschland
Fake News Perception in Germany: A Representative Study of People’s Attitudes and Approaches to Counteract Disinformation
Analyzing the Potential of Graphical Building Information for Fire Emergency Responses: Findings from a Controlled Experiment
Track 10: Human-Computer Interaction
Towards a Taxonomy of Platforms for Conversational Agent Design
Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis
Self-Tracking and Gamification: Analyzing the Interplay of Motivations, Usage and Motivation Fulfillment
Erfolgsfaktoren von Augmented-Reality-Applikationen: Analyse von Nutzerrezensionen mit dem Review-Mining-Verfahren
Designing Dynamic Decision Support for Electronic Requirements Negotiations
Who is Stressed by Using ICTs? A Qualitative Comparison Analysis with the Big Five Personality Traits to Understand Technostress
Walking the Middle Path: How Medium Trade-Off Exposure Leads to Higher Consumer Satisfaction in Recommender Agents
Theory-Based Affordances of Utilitarian, Hedonic and Dual-Purposed Technologies: A Literature Review
Eliciting Customer Preferences for Shopping Companion Apps: A Service Quality Approach
The Role of Early User Participation in Discovering Software – A Case Study from the Context of Smart Glasses
The Fluidity of the Self-Concept as a Framework to Explain the Motivation to Play Video Games
Heart over Heels? An Empirical Analysis of the Relationship between Emotions and Review Helpfulness for Experience and Credence Goods
Track 11: Information Security and Information Privacy
Unfolding Concerns about Augmented Reality Technologies: A Qualitative Analysis of User Perceptions
To (Psychologically) Own Data is to Protect Data: How Psychological Ownership Determines Protective Behavior in a Work and Private Context
Understanding Data Protection Regulations from a Data Management Perspective: A Capability-Based Approach to EU-GDPR
On the Difficulties of Incentivizing Online Privacy through Transparency: A Qualitative Survey of the German Health Insurance Market
What is Your Selfie Worth? A Field Study on Individuals’ Valuation of Personal Data
Justification of Mass Surveillance: A Quantitative Study
An Exploratory Study of Risk Perception for Data Disclosure to a Network of Firms
Track 12: Umweltinformatik und nachhaltiges Wirtschaften
Kommunikationsfäden im Nadelöhr – Fachliche Prozessmodellierung der Nachhaltigkeitskommunikation am Kapitalmarkt
Potentiale und Herausforderungen der Materialflusskostenrechnung
Computing Incentives for User-Based Relocation in Carsharing
Sustainability’s Coming Home: Preliminary Design Principles for the Sustainable Smart District
Substitution of hazardous chemical substances using Deep Learning and t-SNE
A Hierarchy of DSMLs in Support of Product Life-Cycle Assessment
A Survey of Smart Energy Services for Private Households
Door-to-Door Mobility Integrators as Keystone Organizations of Smart Ecosystems: Resources and Value Co-Creation – A Literature Review
Ein Entscheidungsunterstützungssystem zur ökonomischen Bewertung von Mieterstrom auf Basis der Clusteranalyse
Discovering Blockchain for Sustainable Product-Service Systems to enhance the Circular Economy
Digitale Rückverfolgbarkeit von Lebensmitteln: Eine verbraucherinformatische Studie
Umweltbewusstsein durch audiovisuelles Content Marketing? Eine experimentelle Untersuchung zur Konsumentenbewertung nachhaltiger Smartphones
Towards Predictive Energy Management in Information Systems: A Research Proposal
A Web Browser-Based Application for Processing and Analyzing Material Flow Models using the MFCA Methodology
Track 13: Digital Work - Social, mobile, smart
On Conversational Agents in Information Systems Research: Analyzing the Past to Guide Future Work
The Potential of Augmented Reality for Improving Occupational First Aid
Prevent a Vicious Circle! The Role of Organizational IT-Capability in Attracting IT-affine Applicants
Good, Bad, or Both? Conceptualization and Measurement of Ambivalent User Attitudes Towards AI
A Case Study on Cross-Hierarchical Communication in Digital Work Environments
‘Show Me Your People Skills’ - Employing CEO Branding for Corporate Reputation Management in Social Media
A Multiorganisational Study of the Drivers and Barriers of Enterprise Collaboration Systems-Enabled Change
The More the Merrier? The Effect of Size of Core Team Subgroups on Success of Open Source Projects
The Impact of Anthropomorphic and Functional Chatbot Design Features in Enterprise Collaboration Systems on User Acceptance
Digital Feedback for Digital Work? Affordances and Constraints of a Feedback App at InsurCorp
The Effect of Marker-less Augmented Reality on Task and Learning Performance
Antecedents for Cyberloafing – A Literature Review
Internal Crowd Work as a Source of Empowerment - An Empirical Analysis of the Perception of Employees in a Crowdtesting Project
Track 14: Geschäftsmodelle und digitales Unternehmertum
Dividing the ICO Jungle: Extracting and Evaluating Design Archetypes
Capturing Value from Data: Exploring Factors Influencing Revenue Model Design for Data-Driven Services
Understanding the Role of Data for Innovating Business Models: A System Dynamics Perspective
Business Model Innovation and Stakeholder: Exploring Mechanisms and Outcomes of Value Creation and Destruction
Business Models for Internet of Things Platforms: Empirical Development of a Taxonomy and Archetypes
Revitalizing established Industrial Companies: State of the Art and Success Principles of Digital Corporate Incubators
When 1+1 is Greater than 2: Concurrence of Additional Digital and Established Business Models within Companies
Special Track 1: Student Track
Investigating Personalized Price Discrimination of Textile-, Electronics- and General Stores in German Online Retail
From Facets to a Universal Definition – An Analysis of IoT Usage in Retail
Is the Technostress Creators Inventory Still an Up-To-Date Measurement Instrument? Results of a Large-Scale Interview Study
Application of Media Synchronicity Theory to Creative Tasks in Virtual Teams Using the Example of Design Thinking
TrustyTweet: An Indicator-based Browser-Plugin to Assist Users in Dealing with Fake News on Twitter
Application of Process Mining Techniques to Support Maintenance-Related Objectives
How Voice Can Change Customer Satisfaction: A Comparative Analysis between E-Commerce and Voice Commerce
Business Process Compliance and Blockchain: How Does the Ethereum Blockchain Address Challenges of Business Process Compliance?
Improving Business Model Configuration through a Question-based Approach
The Influence of Situational Factors and Gamification on Intrinsic Motivation and Learning
Evaluation von ITSM-Tools für Integration und Management von Cloud-Diensten am Beispiel von ServiceNow
How Software Promotes the Integration of Sustainability in Business Process Management
Criteria Catalog for Industrial IoT Platforms from the Perspective of the Machine Tool Industry
Special Track 3: Demos & Prototyping
Privacy-friendly User Location Tracking with Smart Devices: The BeaT Prototype
Application-oriented robotics in nursing homes
Augmented Reality for Set-up Processe
Mixed Reality for supporting Remote-Meetings
Gamification zur Motivationssteigerung von Werkern bei der Betriebsdatenerfassung
Automatically Extracting and Analyzing Customer Needs from Twitter: A “Needmining” Prototype
GaNEsHA: Opportunities for Sustainable Transportation in Smart Cities
TUCANA: A platform for using local processing power of edge devices for building data-driven services
Demonstrator zur Beschreibung und Visualisierung einer kritischen Infrastruktur
Entwicklung einer alltagsnahen persuasiven App zur Bewegungsmotivation für ältere Nutzerinnen und Nutzer
A browser-based modeling tool for studying the learning of conceptual modeling based on a multi-modal data collection approach
Exergames & Dementia: An interactive System for People with Dementia and their Care-Network
Workshops
Workshop Ethics and Morality in Business Informatics (Workshop Ethik und Moral in der Wirtschaftsinformatik – EMoWI’19)
Model-Based Compliance in Information Systems - Foundations, Case Description and Data Set of the MobIS-Challenge for Students and Doctoral Candidates
Report of the Workshop on Concepts and Methods of Identifying Digital Potentials in Information Management
Control of Systemic Risks in Global Networks - A Grand Challenge to Information Systems Research
Die Mitarbeiter von morgen - Kompetenzen künftiger Mitarbeiter im Bereich Business Analytics
Digitaler Konsum: Herausforderungen und Chancen der Verbraucherinformati
Beyond Question Answering: Understanding the Information Need of the User
Intelligent interaction between humans and computers has been a dream of artificial intelligence since the beginning of digital era and one of the original motivations behind the creation of artificial intelligence. A key step towards the achievement of such an ambitious goal is to enable the Question Answering systems understand the information need of the user.
In this thesis, we attempt to enable the QA system's ability to understand the user's information need by three approaches. First, an clarification question generation method is proposed to help the user clarify the information need and bridge information need gap between QA system and the user. Next, a translation based model is obtained from the large archives of Community Question Answering data, to model the information need behind a question and boost the performance of question recommendation. Finally, a fine-grained classification framework is proposed to enable the systems to recommend answered questions based on information need satisfaction
Word Embeddings for Natural Language Processing
Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for capturing both syntactic and semantic information about words. Word embeddings, when used as the underlying input representation, have been shown to be a great asset for a large variety of natural language processing (NLP) tasks. Recent techniques to obtain such word embeddings are mostly based on neural network language models (NNLM). In such systems, the word vectors are randomly initialized and then trained to predict optimally the contexts in which the corresponding words tend to appear. Because words occurring in similar contexts have, in general, similar meanings, their resulting word embeddings are semantically close after training. However, such architectures might be challenging and time-consuming to train. In this thesis, we are focusing on building simple models which are fast and efficient on large-scale datasets. As a result, we propose a model based on counts for computing word embeddings. A word co-occurrence probability matrix can easily be obtained by directly counting the context words surrounding the vocabulary words in a large corpus of texts. The computation can then be drastically simplified by performing a Hellinger PCA of this matrix. Besides being simple, fast and intuitive, this method has two other advantages over NNLM. It first provides a framework to infer unseen words or phrases. Secondly, all embedding dimensions can be obtained after a single Hellinger PCA, while a new training is required for each new size with NNLM. We evaluate our word embeddings on classical word tagging tasks and show that we reach similar performance than with neural network based word embeddings. While many techniques exist for computing word embeddings, vector space models for phrases remain a challenge. Still based on the idea of proposing simple and practical tools for NLP, we introduce a novel model that jointly learns word embeddings and their summation. Sequences of words (i.e. phrases) with different sizes are thus embedded in the same semantic space by just averaging word embeddings. In contrast to previous methods which reported a posteriori some compositionality aspects by simple summation, we simultaneously train words to sum, while keeping the maximum information from the original vectors. These word and phrase embeddings are then used in two different NLP tasks: document classification and sentence generation. Using such word embeddings as inputs, we show that good performance is achieved in sentiment classification of short and long text documents with a convolutional neural network. Finding good compact representations of text documents is crucial in classification systems. Based on the summation of word embeddings, we introduce a method to represent documents in a low-dimensional semantic space. This simple operation, along with a clustering method, provides an efficient framework for adding semantic information to documents, which yields better results than classical approaches for classification. Simple models for sentence generation can also be designed by leveraging such phrase embeddings. We propose a phrase-based model for image captioning which achieves similar results than those obtained with more complex models. Not only word and phrase embeddings but also embeddings for non-textual elements can be helpful for sentence generation. We, therefore, explore to embed table elements for generating better sentences from structured data. We experiment this approach with a large-scale dataset of biographies, where biographical infoboxes were available. By parameterizing both words and fields as vectors (embeddings), we significantly outperform a classical model