49 research outputs found
인공지능 보안
학위논문 (박사) -- 서울대학교 대학원 : 자연과학대학 협동과정 생물정보학전공, 2021. 2. 윤성로.With the development of machine learning (ML), expectations for artificial intelligence (AI) technologies have increased daily. In particular, deep neural networks have demonstrated outstanding performance in many fields. However, if a deep-learning (DL) model causes mispredictions or misclassifications, it can cause difficulty, owing to malicious external influences.
This dissertation discusses DL security and privacy issues and proposes methodologies for security and privacy attacks. First, we reviewed security attacks and defenses from two aspects. Evasion attacks use adversarial examples to disrupt the classification process, and poisoning attacks compromise training by compromising the training data. Next, we reviewed attacks on privacy that can exploit exposed training data and defenses, including differential privacy and encryption.
For adversarial DL, we study the problem of finding adversarial examples against ML-based portable document format (PDF) malware classifiers. We believe that our problem is more challenging than those against ML models for image processing, owing to the highly complex data structure of PDFs, compared with traditional image datasets, and the requirement that the infected PDF should exhibit malicious behavior without being detected. We propose an attack using generative adversarial networks that effectively generates evasive PDFs using a variational autoencoder robust against adversarial examples.
For privacy in DL, we study the problem of avoiding sensitive data being misused and propose a privacy-preserving framework for deep neural networks. Our methods are based on generative models that preserve the privacy of sensitive data while maintaining a high prediction performance. Finally, we study the security aspect in biological domains to detect maliciousness in deoxyribonucleic acid sequences and watermarks to protect intellectual properties.
In summary, the proposed DL models for security and privacy embrace a diversity of research by attempting actual attacks and defenses in various fields.인공지능 모델을 사용하기 위해서는 개인별 데이터 수집이 필수적이다. 반면 개인의 민감한 데이터가 유출되는 경우에는 프라이버시 침해의 소지가 있다. 인공지능 모델을 사용하는데 수집된 데이터가 외부에 유출되지 않도록 하거나, 익명화, 부호화 등의 보안 기법을 인공지능 모델에 적용하는 분야를 Private AI로 분류할 수 있다. 또한 인공지능 모델이 노출될 경우 지적 소유권이 무력화될 수 있는 문제점과, 악의적인 학습 데이터를 이용하여 인공지능 시스템을 오작동할 수 있고 이러한 인공지능 모델 자체에 대한 위협은 Secure AI로 분류할 수 있다.
본 논문에서는 학습 데이터에 대한 공격을 기반으로 신경망의 결손 사례를 보여준다. 기존의 AEs 연구들은 이미지를 기반으로 많은 연구가 진행되었다. 보다 복잡한 heterogenous한 PDF 데이터로 연구를 확장하여 generative 기반의 모델을 제안하여 공격 샘플을 생성하였다. 다음으로 이상 패턴을 보이는 샘플을 검출할 수 있는 DNA steganalysis 방어 모델을 제안한다. 마지막으로 개인 정보 보호를 위해 generative 모델 기반의 익명화 기법들을 제안한다.
요약하면 본 논문은 인공지능 모델을 활용한 공격 및 방어 알고리즘과 신경망을 활용하는데 발생되는 프라이버시 이슈를 해결할 수 있는 기계학습 알고리즘에 기반한 일련의 방법론을 제안한다.Abstract i
List of Figures vi
List of Tables xiii
1 Introduction 1
2 Background 6
2.1 Deep Learning: a brief overview . . . . . . . . . . . . . . . . . . . 6
2.2 Security Attacks on Deep Learning Models . . . . . . . . . . . . . 10
2.2.1 Evasion Attacks . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 Poisoning Attack . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Defense Techniques Against Deep Learning Models . . . . . . . . . 26
2.3.1 Defense Techniques against Evasion Attacks . . . . . . . . 27
2.3.2 Defense against Poisoning Attacks . . . . . . . . . . . . . . 36
2.4 Privacy issues on Deep Learning Models . . . . . . . . . . . . . . . 38
2.4.1 Attacks on Privacy . . . . . . . . . . . . . . . . . . . . . . 39
2.4.2 Defenses Against Attacks on Privacy . . . . . . . . . . . . 40
3 Attacks on Deep Learning Models 47
3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.1 Threat Model . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1.2 Portable Document Format (PDF) . . . . . . . . . . . . . . 55
3.1.3 PDF Malware Classifiers . . . . . . . . . . . . . . . . . . . 57
3.1.4 Evasion Attacks . . . . . . . . . . . . . . . . . . . . . . . 58
3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . 60
3.2.2 Feature Selection Process . . . . . . . . . . . . . . . . . . 61
3.2.3 Seed Selection for Mutation . . . . . . . . . . . . . . . . . 62
3.2.4 Evading Model . . . . . . . . . . . . . . . . . . . . . . . . 63
3.2.5 Model architecture . . . . . . . . . . . . . . . . . . . . . . 67
3.2.6 PDF Repacking and Verification . . . . . . . . . . . . . . . 67
3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.3.1 Datasets and Model Training . . . . . . . . . . . . . . . . . 68
3.3.2 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 71
3.3.3 CVEs for Various Types of PDF Malware . . . . . . . . . . 72
3.3.4 Malicious Signature . . . . . . . . . . . . . . . . . . . . . 72
3.3.5 AntiVirus Engines (VirusTotal) . . . . . . . . . . . . . . . 76
3.3.6 Feature Mutation Result for Contagio . . . . . . . . . . . . 76
3.3.7 Feature Mutation Result for CVEs . . . . . . . . . . . . . . 78
3.3.8 Malicious Signature Verification . . . . . . . . . . . . . . . 78
3.3.9 Evasion Speed . . . . . . . . . . . . . . . . . . . . . . . . 80
3.3.10 AntiVirus Engines (VirusTotal) Result . . . . . . . . . . . . 82
3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4 Defense on Deep Learning Models 88
4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.1.1 Message-Hiding Regions . . . . . . . . . . . . . . . . . . . 91
4.1.2 DNA Steganography . . . . . . . . . . . . . . . . . . . . . 92
4.1.3 Example of Message Hiding . . . . . . . . . . . . . . . . . 94
4.1.4 DNA Steganalysis . . . . . . . . . . . . . . . . . . . . . . 95
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.2.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.2.2 Proposed Model Architecture . . . . . . . . . . . . . . . . 103
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.3.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . 105
4.3.2 Environment . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.3.3 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.4 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 107
4.3.5 Message Hiding Procedure . . . . . . . . . . . . . . . . . . 108
4.3.6 Evaluation Procedure . . . . . . . . . . . . . . . . . . . . . 109
4.3.7 Performance Comparison . . . . . . . . . . . . . . . . . . . 109
4.3.8 Analyzing Malicious Code in DNA Sequences . . . . . . . 112
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5 Privacy: Generative Models for Anonymizing Private Data 115
5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.1.1 Notations . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.1.2 Anonymization using GANs . . . . . . . . . . . . . . . . . 119
5.1.3 Security Principle of Anonymized GANs . . . . . . . . . . 123
5.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.2.2 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 126
5.2.3 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 126
5.2.4 Evaluation Process . . . . . . . . . . . . . . . . . . . . . . 126
5.2.5 Comparison to Differential Privacy . . . . . . . . . . . . . 128
5.2.6 Performance Comparison . . . . . . . . . . . . . . . . . . . 128
5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
6 Privacy: Privacy-preserving Inference for Deep Learning Models 132
6.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 135
6.1.2 Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
6.1.3 Deep Private Generation Framework . . . . . . . . . . . . . 137
6.1.4 Security Principle . . . . . . . . . . . . . . . . . . . . . . . 141
6.1.5 Threat to the Classifier . . . . . . . . . . . . . . . . . . . . 143
6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.2.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.2.2 Experimental Process . . . . . . . . . . . . . . . . . . . . . 146
6.2.3 Target Classifiers . . . . . . . . . . . . . . . . . . . . . . . 147
6.2.4 Model Training . . . . . . . . . . . . . . . . . . . . . . . . 147
6.2.5 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . 149
6.2.6 Performance Comparison . . . . . . . . . . . . . . . . . . . 150
6.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
7 Conclusion 153
7.0.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 154
7.0.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 155
Bibliography 157
Abstract in Korean 195Docto
Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations
The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov
Hardware and software platforms to deploy and evaluate non-intrusive load monitoring systems
The work in this PhD thesis addresses the practical implications of deploying and testing Non-Intrusive Load Monitoring (NILM) and eco-feedback solutions in real-world scenarios. The contributions to this topic are centered around the design and development of NILM frameworks that have been deployed in the wild, supporting long-term research in ecofeedback and also serving the purpose of producing real-world datasets and furthering the state of the art regarding the performance metrics used to evaluate NILM algorithms. This thesis consists of three main parts: i) the development of tools and datasets for NILM and eco-feedback research, ii) the design, implementation and deployment of NILM and eco-feedback technologies in real world scenarios, and iii) an experimental comparison of performance metrics for event detection and event classification algorithms. In the first part we describe the Energy Monitoring and Disaggregation Data Format (EMD-DF) and the SustData and SustDataED public datasets. In second part we discuss the development and deployment of two hardware and software platforms in real households, to support eco-feedback research. We then report on more than five years of experience in deploying and maintaining such platforms. Our findings suggest that the main practical issues can be divided in two categories, technological (e.g., system installation) and social (e.g., maintaining a steady sample throughout the whole study). In the final part of this thesis we analyze experimentally the behavior of a number of performance metrics for event detection and event classification, identifying clusters and relationships between the different measures. Our results evidence some considerable differences in the behavior of the performance metrics when applied to the different problems.O trabalho desenvolvido nesta tese de doutoramento aborda as implicações praticas da instalação e avaliação de soluções de monitorização não intrusiva de cargas elétricas (NILM) e eco-feedback em cenários reais. As contribuições para este tópico estão centradas em torno da concepção e desenvolvimento de plataformas NILM que foram instaladas em ambientes não controlados, suportando a pesquisa de longo termo em eco-feedback e servindo também o propósito de produzir conjuntos de dados científicos, bem como promover o avanço do estado da arte acerca das métricas de desempenho utilizadas para avaliar algoritmos NILM. Esta tese é constituída por três partes principais: i) o desenvolvimento de ferramentas e conjuntos de dados científicos para investigação em NILM e eco-feedback, ii) a concepção, desenho e instalação de tecnologias NILM e eco-feedback em cenários reais, e iii) uma comparação experimental de métricas de desempenho para algoritmos de detecção e de classificação de eventos. Na primeira parte descrevemos o Energy Monitoring and Disaggregation Data Format (EMD-DF) e os conjuntos de dados científicos SustData e SustDataED. Na segunda parte discutimos o desenvolvimento e instalação de duas plataformas de hardware e software em residências atuais com a finalidade de suportar a investigação em eco-feedback. Aqui, reportamos sobre mais de cinco anos de experiência na instalação e manutenção destes sistemas. Os nossos resultados sugerem que as principais implicações práticas podem ser divididas em duas categorias, físicas (e.g., instalação do sistema) e sociais (e.g., manter uma amostra constante ao longo de todo o estudo). Na terceira parte analisamos experimentalmente o comportamento de uma série de métricas de desempenho quando estas são utilizadas para avaliar algoritmos de detecção e de classificação de eventos. Calculamos as correlações lineares e não lineares entre os vários pares de métricas, e com base nesses valores procuramos agrupar as métricas que evidenciam um comportamento semelhante. Os nossos resultados sugerem a existência de diferenças evidentes no comportamento das métricas quando aplicadas a ambos dos problemas.Fundação para a Ciência e a Tecnologi
Recommended from our members
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
Toward Understanding Visual Perception in Machines with Human Psychophysics
Over the last several years, Deep Learning algorithms have become more and more powerful.
As such, they are being deployed in increasingly many areas including ones that can directly affect human lives.
At the same time, regulations like the GDPR or the AI Act are putting the request and need to better understand these artificial algorithms on legal grounds.
How do these algorithms come to their decisions?
What limits do they have?
And what assumptions do they make?
This thesis presents three publications that deepen our understanding of deep convolutional neural networks (DNNs) for visual perception of static images.
While all of them leverage human psychophysics, they do so in two different ways: either via direct comparison between human and DNN behavioral data or via an evaluation of the helpfulness of an explainability method.
Besides insights on DNNs, these works emphasize good practices:
For comparison studies, we propose a checklist on how to design, conduct and interpret experiments between different systems.
And for explainability methods, our evaluations exemplify that quantitatively testing widely spread intuitions can help put their benefits in a realistic perspective.
In the first publication, we test how similar DNNs are to the human visual system, and more specifically its capabilities and information processing.
Our experiments reveal that DNNs (1)~can detect closed contours, (2)~perform well on an abstract visual reasoning task and (3)~correctly classify small image crops.
On a methodological level, these experiments illustrate that (1)~human bias can influence our interpretation of findings, (2)~distinguishing necessary and sufficient mechanisms can be challenging, and (3)~the degree of aligning experimental conditions between systems can alter the outcome.
In the second and third publications, we evaluate how helpful humans find the explainability method feature visualization.
The purpose of this tool is to grant insights into the features of a DNN.
To measure the general informativeness and causal understanding supported via feature visualizations, we test participants on two different psychophysical tasks.
Our data unveil that humans can indeed understand the inner DNN semantics based on this explainability tool.
However, other visualizations such as natural data set samples also provide useful, and sometimes even \emph{more} useful, information.
On a methodological level, our work illustrates that human evaluations can adjust our expectations toward explainability methods and that different claims have to match the experiment
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with
arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
The last decade has seen a revolution in the theory and application of
machine learning and pattern recognition. Through these advancements, variable
ranking has emerged as an active and growing research area and it is now
beginning to be applied to many new problems. The rationale behind this fact is
that many pattern recognition problems are by nature ranking problems. The main
objective of a ranking algorithm is to sort objects according to some criteria,
so that, the most relevant items will appear early in the produced result list.
Ranking methods can be analyzed from two different methodological perspectives:
ranking to learn and learning to rank. The former aims at studying methods and
techniques to sort objects for improving the accuracy of a machine learning
model. Enhancing a model performance can be challenging at times. For example,
in pattern classification tasks, different data representations can complicate
and hide the different explanatory factors of variation behind the data. In
particular, hand-crafted features contain many cues that are either redundant
or irrelevant, which turn out to reduce the overall accuracy of the classifier.
In such a case feature selection is used, that, by producing ranked lists of
features, helps to filter out the unwanted information. Moreover, in real-time
systems (e.g., visual trackers) ranking approaches are used as optimization
procedures which improve the robustness of the system that deals with the high
variability of the image streams that change over time. The other way around,
learning to rank is necessary in the construction of ranking models for
information retrieval, biometric authentication, re-identification, and
recommender systems. In this context, the ranking model's purpose is to sort
objects according to their degrees of relevance, importance, or preference as
defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with
arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author
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