Soundtrack recommendation for images

The drastic increase in production of multimedia content has emphasized the research concerning its organization and retrieval. In this thesis, we address the problem of music retrieval when a set of images is given as input query, i.e., the problem of soundtrack recommendation for images. The task at hand is to recommend appropriate music to be played during the presentation of a given set of query images. To tackle this problem, we formulate a hypothesis that the knowledge appropriate for the task is contained in publicly available contemporary movies. Our approach, Picasso, employs similarity search techniques inside the image and music domains, harvesting movies to form a link between the domains. To achieve a fair and unbiased comparison between different soundtrack recommendation approaches, we proposed an evaluation benchmark. The evaluation results are reported for Picasso and the baseline approach, using the proposed benchmark. We further address two efficiency aspects that arise from the Picasso approach. First, we investigate the problem of processing top-K queries with set-defined selections and propose an index structure that aims at minimizing the query answering latency. Second, we address the problem of similarity search in high-dimensional spaces and propose two enhancements to the Locality Sensitive Hashing (LSH) scheme. We also investigate the prospects of a distributed similarity search algorithm based on LSH using the MapReduce framework. Finally, we give an overview of the PicasSound|a smartphone application based on the Picasso approach.Der drastische Anstieg von verfügbaren Multimedia-Inhalten hat die Bedeutung der Forschung über deren Organisation sowie Suche innerhalb der Daten hervorgehoben. In dieser Doktorarbeit betrachten wir das Problem der Suche nach geeigneten Musikstücken als Hintergrundmusik für Diashows. Wir formulieren die Hypothese, dass die für das Problem erforderlichen Kenntnisse in öffentlich zugänglichen, zeitgenössischen Filmen enthalten sind. Unser Ansatz, Picasso, verwendet Techniken aus dem Bereich der Ähnlichkeitssuche innerhalb von Bild- und Musik-Domains, um basierend auf Filmszenen eine Verbindung zwischen beliebigen Bildern und Musikstücken zu lernen. Um einen fairen und unvoreingenommenen Vergleich zwischen verschiedenen Ansätzen zur Musikempfehlung zu erreichen, schlagen wir einen Bewertungs-Benchmark vor. Die Ergebnisse der Auswertung werden, anhand des vorgeschlagenen Benchmarks, für Picasso und einen weiteren, auf Emotionen basierenden Ansatz, vorgestellt. Zusätzlich behandeln wir zwei Effizienzaspekte, die sich aus dem Picasso Ansatz ergeben. (i) Wir untersuchen das Problem der Ausführung von top-K Anfragen, bei denen die Ergebnismenge ad-hoc auf eine kleine Teilmenge des gesamten Indexes eingeschränkt wird. (ii) Wir behandeln das Problem der Ähnlichkeitssuche in hochdimensionalen Räumen und schlagen zwei Erweiterungen des Lokalitätssensitiven Hashing (LSH) Schemas vor. Zusätzlich untersuchen wir die Erfolgsaussichten eines verteilten Algorithmus für die Ähnlichkeitssuche, der auf LSH unter Verwendung des MapReduce Frameworks basiert. Neben den vorgenannten wissenschaftlichen Ergebnissen beschreiben wir ferner das Design und die Implementierung von PicassSound, einer auf Picasso basierenden Smartphone-Anwendung

Deep Learning Detected Nutrient Deficiency in Chili Plant

Chili is a staple commodity that also affects the Indonesian economy due to high market demand. Proven in June 2019, chili is a contributor to Indonesia's inflation of 0.20% from 0.55%. One factor is crop failure due to malnutrition. In this study, the aim is to explore Deep Learning Technology in agriculture to help farmers be able to diagnose their plants, so that their plants are not malnourished. Using the RCNN algorithm as the architecture of this system. Use 270 datasets in 4 categories. The dataset used is primary data with chili samples in Boyolali Regency, Indonesia. The chili we use are curly chili. The results of this study are computers that can recognize nutrient deficiencies in chili plants based on image input received with the greatest testing accuracy of 82.61% and has the best mAP value of 15.57%

Radial Basis Function Neural Network in Identifying The Types of Mangoes

Mango (Mangifera Indica L) is part of a fruit plant species that have different color and texture characteristics to indicate its type. The identification of the types of mangoes uses the manual method through direct visual observation of mangoes to be classified. At the same time, the more subjective way humans work causes differences in their determination. Therefore in the use of information technology, it is possible to classify mangoes based on their texture using a computerized system. In its completion, the acquisition process is using the camera as an image processing instrument of the recorded images. To determine the pattern of mango data taken from several samples of texture features using Gabor filters from various types of mangoes and the value of the feature extraction results through artificial neural networks (ANN). Using the Radial Base Function method, which produces weight values, is then used as a process for classifying types of mangoes. The accuracy of the test results obtained from the use of extraction methods and existing learning methods is 100%

Recent Developments in Smart Healthcare

Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine

Federated Machine Learning in Edge Computing

Machine Learning (ML) is transforming the way that computers are used to solve problems in computer vision, natural language processing, scientific modelling, and much more. The rising number of devices connected to the Internet generate huge quantities of data that can be used for ML purposes. Traditionally, organisations require user data to be uploaded to a single location (i.e., cloud datacentre) for centralised ML. However, public concerns regarding data-privacy are growing, and in some domains such as healthcare, there exist strict laws governing the access of data. The computational power and connectivity of devices at the network edge is also increasing: edge computing is a paradigm designed to move computation from the cloud to the edge to reduce latency and traffic. Federated Learning (FL) is a new and swiftly-developing field that has huge potential for privacy-preserving ML. In FL, edge devices collaboratively train a model without users sharing their personal data with any other party. However, there exist multiple challenges for designing useful FL algorithms, including: the heterogeneity of data across participating clients; the low computing power, intermittent connectivity and unreliability of clients at the network edge compared to the datacentre; and the difficulty of limiting information leakage whilst still training high-performance models. This thesis proposes new methods for improving the process of FL in edge computing and hence making it more practical for real-world deployments. First, a novel approach is designed that accelerates the convergence of the FL model through adaptive optimisation, reducing the time taken to train a model, whilst lowering the total quantity of information uploaded from edge clients to the coordinating server through two new compression strategies. Next, a Multi-Task FL framework is proposed that allows participating clients to train unique models that are tailored to their own heterogeneous datasets whilst still benefiting from FL, improving model convergence speed and generalisation performance across clients. Then, the principle of decreasing the total work that clients perform during the FL process is explored. A theoretical analysis (and subsequent experimental evaluation) suggests that this approach can reduce the time taken to reach a desired training error whilst lowering the total computational cost of FL and improving communication-efficiency. Lastly, an algorithm is designed that applies adaptive optimisation to FL in a novel way, through the use of a statistically-biased optimiser whose values are kept fixed on clients. This algorithm can leverage the convergence guarantees of centralised algorithms, with the addition of FL-related error-terms. Furthermore, it shows excellent performance on benchmark FL datasets whilst possessing lower computation and upload costs compared to competing adaptive-FL algorithms

Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

User-centred video abstraction

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonThe rapid growth of digital video content in recent years has imposed the need for the development of technologies with the capability to produce condensed but semantically rich versions of the input video stream in an effective manner. Consequently, the topic of Video Summarisation is becoming increasingly popular in multimedia community and numerous video abstraction approaches have been proposed accordingly. These recommended techniques can be divided into two major categories of automatic and semi-automatic in accordance with the required level of human intervention in summarisation process. The fully-automated methods mainly adopt the low-level visual, aural and textual features alongside the mathematical and statistical algorithms in furtherance to extract the most significant segments of original video. However, the effectiveness of this type of techniques is restricted by a number of factors such as domain-dependency, computational expenses and the inability to understand the semantics of videos from low-level features. The second category of techniques however, attempts to alleviate the quality of summaries by involving humans in the abstraction process to bridge the semantic gap. Nonetheless, a single user’s subjectivity and other external contributing factors such as distraction will potentially deteriorate the performance of this group of approaches. Accordingly, in this thesis we have focused on the development of three user-centred effective video summarisation techniques that could be applied to different video categories and generate satisfactory results. According to our first proposed approach, a novel mechanism for a user-centred video summarisation has been presented for the scenarios in which multiple actors are employed in the video summarisation process in order to minimise the negative effects of sole user adoption. Based on our recommended algorithm, the video frames were initially scored by a group of video annotators ‘on the fly’. This was followed by averaging these assigned scores in order to generate a singular saliency score for each video frame and, finally, the highest scored video frames alongside the corresponding audio and textual contents were extracted to be included into the final summary. The effectiveness of our approach has been assessed by comparing the video summaries generated based on our approach against the results obtained from three existing automatic summarisation tools that adopt different modalities for abstraction purposes. The experimental results indicated that our proposed method is capable of delivering remarkable outcomes in terms of Overall Satisfaction and Precision with an acceptable Recall rate, indicating the usefulness of involving user input in the video summarisation process. In an attempt to provide a better user experience, we have proposed our personalised video summarisation method with an ability to customise the generated summaries in accordance with the viewers’ preferences. Accordingly, the end-user’s priority levels towards different video scenes were captured and utilised for updating the average scores previously assigned by the video annotators. Finally, our earlier proposed summarisation method was adopted to extract the most significant audio-visual content of the video. Experimental results indicated the capability of this approach to deliver superior outcomes compared with our previously proposed method and the three other automatic summarisation tools. Finally, we have attempted to reduce the required level of audience involvement for personalisation purposes by proposing a new method for producing personalised video summaries. Accordingly, SIFT visual features were adopted to identify the video scenes’ semantic categories. Fusing this retrieved data with pre-built users’ profiles, personalised video abstracts can be created. Experimental results showed the effectiveness of this method in delivering superior outcomes comparing to our previously recommended algorithm and the three other automatic summarisation techniques

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