Universität Stuttgart
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Item Open Access A design space for pervasive advertising on public displays(2013) Alt, Florian; Schmidt, Albrecht (Prof. Dr.)Today, people living in cities see up to 5000 ads per day and many of them are presented on public displays. More and more of these public displays are networked and equipped with various types of sensors, making them part of a global infrastructure that is currently emerging. Such networked and interactive public displays provide the opportunity to create a benefit for society in the form of immersive experiences and relevant content. In this way, they can overcome the display blindness that evolved among passersby over the years. We see two main reasons that prevent this vision from coming true: first, public displays are stuck with traditional advertising as the driving business model, making it difficult for novel, interactive applications to enter the scene. Second, no common ground exists for researchers or advertisers that outline important challenges. The provider view and audience view need to be addressed to make open, interactive display networks, successful. The main contribution made by this thesis is presenting a design space for advertising on public displays that identifies important challenges -- mainly from a human-computer interaction perspective. Solutions to these core challenges are presented and evaluated, using empirical methods commonly applied in HCI. First, we look at challenges that arise from the shared use of display space. We conducted an observational study of traditional public notice areas that allowed us to identify different stakeholders, to understand their needs and motivations, to unveil current practices used to exercise control over the display, and to understand the interplay between space, stakeholders, and content. We present a set of design implications for open public display networks that we applied when implementing and evaluating a digital public notice area. Second, we tackle the challenge of making the user interact by taking a closer look at attracting attention, communicating interactivity, and enticing interaction. Attracting attention is crucial for any further action to happen. We present an approach that exploits gaze as a powerful input modality. By adapting content based on gaze, we are able to show a significant increase in attention and an effect on the user's attitude. In order to communicate interactivity, we show that the mirror representation of the user is a powerful interactivity cue. Finally, in order to entice interaction, we show that the user needs to be motivated to interact and to understand how interaction works. Findings from our experiments reveal direct touch and the mobile phone as suitable interaction technologies. In addition, these findings suggest that relevance of content, privacy, and security have a strong influence on user motivation. Third, this thesis makes a set of contributions towards understanding audience behavior, which is particularly important for advertisers in order to choose appropriate content and to select suitable locations for future advertising displays. Our findings provide an in-depth understanding of the honeypot effect as a powerful interactivity cue. Furthermore, we identify a number of interesting effects (e.g., the landing effect) and explain how developers could design for them. We envision the results of this thesis to provide a basis for future research and for practitioners to shape future advertisements on public displays in a positive way.Item Open Access Cognition-aware systems to support information intake and learning(2016) Dingler, Tilman; Schmidt, Albrecht (Prof. Dr.)Knowledge is created at an ever-increasing pace putting us under constant pressure to consume and acquire new information. Information gain and learning, however, require time and mental resources. While the proliferation of ubiquitous computing devices, such as smartphones, enables us to consume information anytime and anywhere, technologies are often disruptive rather than sensitive to the current user context. While people exhibit different levels of concentration and cognitive capacity throughout the day, applications rarely take these performance variations into account and often overburden their users with information or fail to stimulate. This work investigates how technology can be used to help people effectively deal with information intake and learning tasks through cognitive context-awareness. By harvesting sensor and usage data from mobile devices, we obtain people's levels of attentiveness, receptiveness, and cognitive performance. We subsequently use this cognition-awareness in applications to help users process information more effectively. Through a series of lab studies, online surveys, and field experiments we follow six research questions to investigate how to build cognition-aware systems. Awareness of user's variations in levels of attention, receptiveness, and cognitive performance allows systems to trigger appropriate content suggestions, manage user interruptions, and adapt User Interfaces in real-time to match tasks to the user's cognitive capacities. The tools, insights, and concepts described in this book allow researchers and application designers to build systems with an awareness of momentary user states and general circadian rhythms of alertness and cognitive performance.Item Open Access Casual analytics : advancing interactive visualization by domain knowledge(2014) Bosch, Harald; Ertl, Thomas (Prof. Dr.)The often cited information explosion is not limited to volatile network traffic and massive multimedia capture data. Structured and high quality data from diverse fields of study become easily and freely available, too. This is due to crowd sourced data collections, better sharing infrastructure, or more generally speaking user generated content of the Web 2.0 and the popular transparency and open data movements. At the same time as data generation is shifting to everyday casual users, data analysis is often still reserved to large companies specialized in content analysis and distribution such as today's internet giants Amazon, Google, and Facebook. Here, fully automatic algorithms analyze metadata and content to infer interests and believes of their users and present only matching navigation suggestions and advertisements. Besides the problem of creating a filter bubble, in which users never see conflicting information due to the reinforcement nature of history based navigation suggestions, the use of fully automatic approaches has inherent problems, e.g. being unable to find the unexpected and adopt to changes, which lead to the introduction of the Visual Analytics (VA) agenda. If users intend to perform their own analysis on the available data, they are often faced with either generic toolkits that cover a broad range of applicable domains and features or specialized VA systems that focus on one domain. Both are not suited to support casual users in their analysis as they don't match the users' goals and capabilities. The former tend to be complex and targeted to analysis professionals due to the large range of supported features and programmable visualization techniques. The latter trade general flexibility for improved ease of use and optimized interaction for a specific domain requirement. This work describes two approaches building on interactive visualization to reduce this gap between generic toolkits and domain-specific systems. The first one builds upon the idea that most data relevant for casual users are collections of entities with attributes. This least common denominator is commonly employed in faceted browsing scenarios and filter/flow environments. Thinking in sets of entities is natural and allows for a very direct visual interaction with the analysis subject and it stands for a common ground for adding analysis functionality to domain-specific visualization software. Encapsulating the interaction with sets of entities into a filter/flow graph component can be used to record analysis steps and intermediate results into an explicit structure to support collaboration, reporting, and reuse of filters and result sets. This generic analysis functionality is provided as a plugin-in component and was integrated into several domain-specific data visualization and analysis prototypes. This way, the plug-in benefits from the implicit domain knowledge of the host system (e.g. selection semantics and domain-specific visualization) while being used to structure and record the user's analysis process. The second approach directly exploits encoded domain knowledge in order to help casual users interacting with very specific domain data. By observing the interrelations in the ontology, the user interface can automatically be adjusted to indicate problems with invalid user input and transform the system's output to explain its relation to the user. Here, the domain related visualizations are personalized and orchestrated for each user based on user profiles and ontology information. In conclusion, this thesis introduces novel approaches at the boundary of generic analysis tools and their domain-specific context to extend the usage of visual analytics to casual users by exploiting domain knowledge for supporting analysis tasks, input validation, and personalized information visualization.Item Open Access Verfahren zur Unterstützung der Arbeitsabläufe bei der Crash-Simulation im Fahrzeugbau(2004) Frisch, Norbert; Ertl, Thomas (Prof. Dr.)Der starke internationale Wettbewerb in der Automobilindustrie zwingt die Unternehmen zu immer kürzeren Produktzyklen bei gleichzeitiger Reduzierung der Kosten bei der Fahrzeugentwicklung. Die passive Sicherheit ist dabei ein Thema von zunehmender Bedeutung in der Karosserieentwicklung. Die Optimierung der passiven Sicherheit erfolgt heute vor allem mit Hilfe von Crash-Simulationen am Rechner. Im Rahmen der vorliegenden Arbeit wurden Verfahren zur Vorbereitung (Preprocessing) und Steuerung von Crash-Simulationen entwickelt. Damit lassen sich Crash-Simulationen effizienter und bereits in der frühen Phase der Karosserieentwicklung durchführen, in der Änderungen noch mit wenig Aufwand verbunden sind. Die Forschungsarbeiten wurden im Rahmen der BMBF-Verbundprojekte AutoBench und AutoOpt und in enger Zusammenarbeit mit dem Automobilhersteller BMW realisiert. Ziel war die Entwicklung von Softwareprototypen zur Unterstützung der Berechnungsingenieure bei der Durchführung von Crash-Simulationen. Zunächst werden Techniken zur Erkennung, Visualisierung und Beseitigung der bei der Diskretisierung des CAD-Modells entstandenen Netzinkonsistenzen präsentiert. Ergänzt werden diese Verfahren durch einen Algorithmus zur Gitterrelaxation, der die Gleichmäßigkeit der Finiten Elemente nach der Beseitigung von Netzinkonsistenzen wiederherstellt. Anschließend wird ein Verfahren zur Flanscherkennung beschrieben, welches als Grundlage für weitere Algorithmen dient. Darauf aufbauend wird eine Vorgehensweise zur automatischen Definition von Schweißpunktlinien auf Flanschen vorgestellt. Ein breites Spektrum von Änderungen der Geometrie von Bauteilen durch Verformung bietet die sogenannte Free-Form Deformation. Im Rahmen dieser Arbeit wurde dieses Verfahren weiterentwickelt und hinsichtlich Benutzerfreundlichkeit und Effizienz angepasst. In Verbindung mit der Flanscherkennung wurde darauf aufbauend ein iterativer Algorithmus zur Justierung des Abstandes von Flanschen entwickelt. Damit können außerdem Durchdringungen von Finiten Elementen auf Flanschen behoben werden. Beim sogenannten Massentrimm geht es schliesslich um die vereinfachte Darstellung von nichttragenden Teilen. Dies vereinfacht den Berechnungsaufwand, da weniger Finite Elemente bei der Simulation berücksichtigt werden müssen. Durch die in dieser Arbeit entwickelten Preprocessing-Verfahren lässt sich das Finite-Elemente-Netz für die Simulation aufbereiten, und es können Änderungen und Ergänzungen am Netz vorgenommen werden. So kann z.B. die Auswirkung kleiner Änderungen auf das Simulationsverhalten rasch untersucht werden, und durch Ergänzung eines noch unvollständigen Finite-Elemente Modells lassen sich bereits in der frühen Entwicklungsphase Erkenntnisse über das Crashverhalten gewinnen. Die hier vorgestellten Verfahren wurden innerhalb einer Anwendung zur Visualisierung und Modellierung von Finite-Elemente-Modellen realisiert. Zusätzlich wurde diese Anwendung an die Integrationsumgebung CAE-Bench angebunden. CAE-Bench bietet eine Web-basierte Benutzerführung und eine einheitliche Bedienoberfläche für die verschiedenen Anwendungen bei der Crash-Simulation. Es wurde ein spezielles Java-Applet entwickelt, welches in die CAE-Bench Web-Seite eingebettet wird. Dieses Applet kommuniziert mit der Anwendung über CORBA und mit der CAE-Bench Web-Seite mit Hilfe von Java und Javascript Methodenaufrufen. Eine weitere CORBA-Schnittstelle der Anwendung ermöglicht den Abruf und die Visualisierung von Zwischenergebnissen der laufenden Simulation. So lässt sich frühzeitig Einfluss auf die Simulation nehmen, ein Vorgehen, das als Simulation Steering bezeichnet wird. Die vorliegende Arbeit kombiniert Ansätze aus den verschiedenen Bereichen der Informatik, z.B. aus dem Bereich der geometrischen Algorithmen, der Computergraphik, der Visualisierung und der geometrischen Modellierung, sowie aus dem Bereich der Benutzerschnittstellen und der Web-basierten und Middleware-Technologien. Durch die Beiträge dieser Arbeit wird eine schnelle und frühzeitige Durchführung von Crash-Simulationen unterstützt. Dies führt durch Simultaneous Engineering zu einer signifikanten Verkürzung der Entwicklungszeiten bei der Fahrzeugkonstruktion.Item Open Access Empirical assessment and improvement of ubiquitous notifications(2023) Weber, Dominik; Henze, Niels (Prof. Dr.)Smart devices have become ubiquitous. Devices like smartphones, smartwatches, tablets, laptops, and smart TVs accompany us throughout the day. Advancements in computational efficiency and wireless technologies allow these devices to be always on and always connected. While some devices are used situationally, other devices like smartphones are always with the user. This inherently changed how we interact with these devices. Instead of manually looking for news and new messages, these devices can proactively inform us about new events through notifications around the clock. From new messages, reminders, to system updates, notifications are fundamentally personal and cover a wide range of topics. While notifications are valued by users and make them feel connected, they can also cause interruptions and distractions. With more and more services making use of notifications on more and more devices, potential adverse effects are amplified. For instance, a single email might alert a user on multiple devices using multiple modalities. To reduce adverse effects, an understanding of different categories of notifications, different devices, and user needs is required. Notification management is a balancing act between satisfying users' information needs and respecting their attention. This thesis investigates the empirical assessment and improvements of ubiquitous notifications. We present multiple user studies, from online surveys, lab studies, in-situ studies to large-scale in-the-wild studies. We first focus on the assessment and management of mobile notifications on smartphones, tackling the challenges of conducting in-situ controlled and in-the-wild user studies while preserving the users' privacy. We present a notification data set, propose user types, and introduce new approaches for users to reflect on and manage their notifications. We then expand the scope to include other device types such as smartwatches, tablets, and laptops to create a holistic understanding of how these devices differ regarding user expectations for receiving notifications by combining activity logging on multiple devices with experience sampling. Afterward, we expand the scope again to include large and pervasive displays. Finally, we present an open-source logging framework for mobile devices to enable other developers and researchers to build on top of this work. The contribution of this thesis is threefold. First, this thesis introduces multiple approaches to conducting research on ubiquitous notifications, from controlled lab studies to large-scale in-the-wild studies. Second, the thesis provides insights into users' notification preferences and interactions on different types of devices. Third, a technical contribution, including an open-source notification logging framework and notification data set. These contributions are a foundation for future research on ubiquitous notifications.Item Open Access Visual analytics of human mobility behavior(2017) Krüger, Robert; Ertl, Thomas (Prof. Dr.)Human mobility plays an important role in many domains of today’s society, such as security, logistics, transportation, urban planning, and geo-marketing. Both, government and industry thus have great interest in understanding mobility patterns and their driving social, economical, and environmental causes and effects. While stakeholders had to rely on manual traffic surveys for a long time, improvements in tracking technology made analyses based on large digital datasets possible. Recently, the omnipresence of mobile devices significantly increased the amounts of collected movement and context data. People are willing to reveal their position, but also further personal details such as visited places, observations, events, news, and sentiments in exchange for personalized services and social networking. This opens up new possibilities for many domains where a semantic mobility understanding is required but also raises major challenges. To reveal a holistic picture, heterogeneous datasets of different services with different resolution and format have to be fused and analyzed. However, social sensing data is vast, has varying scale, is unevenly distributed, and constantly updated. Especially content from social media services is often inconsistent, unreliable, and incomplete, which requires special treatment. Fully automatic mapping approaches are not trustworthy as they do not take into account these uncertainties. At the same time, manual approaches become insufficient with large amounts of data. Even when data is perfectly aligned, analysts cannot purely rely on existing techniques. Answering questions about reasons for movement requires a broader perspective that takes into account environmental and social context, the driving forces for human mobility behavior. Visual analytics is an emerging research field to tackle such challenges. It creates added value by combining the processing power and accuracy of machines with human capabilities to perceive information visually. Automatic means are used to fuse and aggregate data and to detect hidden patterns therein. Interactive visualizations allow to explore and query the data and to steer the automatic processes with domain knowledge. This increases trust in data, models, and results, which is especially important when critical decisions need to be made. The strengths of visual analytics have been shown to be particularly advantageous when problems and goals are underspecified and exploratory means are needed to discover yet unknown patterns. This thesis presents novel visual analytics approaches to derive meaning and reasons behind movement, by taking into account the aforementioned characteristics. The approaches are aligned in a holistic process model covering all steps from data retrieval, enrichment, exploration, and verification to externalization of gained knowledge for various fields of application such as electric mobility, event management, and law enforcement. It is shown how data from social media can not only be used to retrieve up-to-date movement information, but also to enrich movement trajectories from other sources with structured and unstructured information about places, events, transactions, and other observations. Through highly interactive visual interfaces analysts can bring in domain knowledge to deal with uncertainties during data fusion and to steer the subsequent semantic analysis. Exploratory and confirmatory analysis techniques are presented to create hypotheses, refine them, and find support in the data. Analysts can discover routines and abnormal behavior with assistance of automatic pattern detection methods to cope with the vast amounts of data. Spatial drill-down is supported by a set-based focus+context technique, while a more abstract visual query language allows to explicitly formulate, extract, and query for movement patterns. The approaches are applied in different scenarios and are integrated in a visual analytics system. Evaluation with experts and novice users, case studies, and comparisons to ground truth data reveal the need and effectiveness of the contributions. Overall, the thesis contributes a visual analytics process for human mobility behavior with novel semantic analysis approaches, ranging from global movements of many to local activities of a few people, for a wide range of application domains.Item Open Access Interactive volume rendering in virtual environments(2003) Schulze-Döbold, Jürgen Peter; Ertl, Thomas (Prof. Dr.)This dissertation is about the interactive visualization of volume data in virtual environments. Only data on regular grids will be discussed. Research was conducted on three major topics: visualization algorithms, user interfaces, and parallelization of the visualization algorithms. Because the shear-warp algorithm is a very fast CPU-based volume rendering algorithm, it was investigated how it could be adapted to the characteristics of virtual environments. This required the support of perspective projection, as well as specific developments for interactive work, for instance a variable frame rate or the application of clipping planes. Another issue was the improvement of image quality by the utilization of pre-integration for the compositing. Concerning the user interface, a transfer function editor was created, which was tailored to the conditions of virtual environments. It should be usable as intuitively as possible, even with imprecise input devices or low display resolutions. Further research was done in the field of direct interaction, for instance a detail probe was developed which is useful to look inside of a dataset. In order to run the user interface on a variety of output devices, a device independent menu and widget system was developed. The shear-warp algorithm was accelerated by a parallelization which is based on MPI. For the actual volume rendering, a remote parallel computer can be employed, which needs to be linked to the display computer via a network connection. Because the image transfer turned out to be the bottleneck of this solution, it is compressed before being transferred. Furthermore, it will be described how all the above developments were combined to a volume rendering system, and how they were integrated into an existing visualization toolkit.Item Open Access Displaying text using head-mounted displays(2021) Rzayev, Rufat; Henze, Niels (Prof. Dr.)Reading text is a fundamental activity to gain information both in the real and the digital world. Although digital text is ubiquitous and accessible through various devices, several challenges need to be addressed for efficient display of text on head-mounted displays (HMDs). Specifically, the display of text in the user's field of view or in the vicinity can lead to occlusion of objects in the real or the virtual environment, distraction from the current activity and decrease of visibility of the environment or the text itself. To overcome these challenges and to understand when they occur, it is important to investigate the presentation of text on HMDs. In this thesis, through a series of lab studies and an online survey, we investigate text presentation using HMDs. Particularly, we study the effects of spatial characteristics and presentation types of text on HMDs and the social implication of displaying text using HMDs. First, we show how short texts can be efficiently displayed using HMDs considering the context. Second, we present design recommendations for displaying long texts on HMDs regarding their spatial characteristics and presentation types. Finally, we show how displaying text on HMDs in a social setting affects the social interaction and how the priority of the text can affect the users' preference during social interaction. This thesis contributes with three main insights to provide designers with recommendations for designing reading interfaces for HMD-based applications.Item Open Access Deep learning based prediction and visual analytics for temporal environmental data(2022) Harbola, Shubhi; Coors, Volker (Prof. Dr.)The objective of this thesis is to focus on developing Machine Learning methods and their visualisation for environmental data. The presented approaches primarily focus on devising an accurate Machine Learning framework that supports the user in understanding and comparing the model accuracy in relation to essential aspects of the respective parameter selection, trends, time frame, and correlating together with considered meteorological and pollution parameters. Later, this thesis develops approaches for the interactive visualisation of environmental data that are wrapped over the time series prediction as an application. Moreover, these approaches provide an interactive application that supports: 1. a Visual Analytics platform to interact with the sensors data and enhance the representation of the environmental data visually by identifying patterns that mostly go unnoticed in large temporal datasets, 2. a seasonality deduction platform presenting analyses of the results that clearly demonstrate the relationship between these parameters in a combined temporal activities frame, and 3. air quality analyses that successfully discovers spatio-temporal relationships among complex air quality data interactively in different time frames by harnessing the user’s knowledge of factors influencing the past, present, and future behaviour with Machine Learning models' aid. Some of the above pieces of work contribute to the field of Explainable Artificial Intelligence which is an area concerned with the development of methods that help understand, explain and interpret Machine Learning algorithms. In summary, this thesis describes Machine Learning prediction algorithms together with several visualisation approaches for visually analysing the temporal relationships among complex environmental data in different time frames interactively in a robust web platform. The developed interactive visualisation system for environmental data assimilates visual prediction, sensors’ spatial locations, measurements of the parameters, detailed patterns analyses, and change in conditions over time. This provides a new combined approach to the existing visual analytics research. The algorithms developed in this thesis can be used to infer spatio-temporal environmental data, enabling the interactive exploration processes, thus helping manage the cities smartly.Item Open Access Bridging cognitive and deep learning models of attention(2025) Sood, Ekta; Bulling, Andreas (Prof. Dr.)Neural attention mechanisms, drawing inspiration from the cognitive modeling of human attention, have led to significant advancements in deep learning models across the fields of computer vision (CV) and natural language processing (NLP) (Gupta et al., 2021). Despite these technological strides, AI models still fall short of human performance in tasks demanding nuanced comprehension (e.g., reading comprehension), as well as in out-of-the-box data domains and novel modalities (Sarker, 2021). The goal of this dissertation is to bridge human and data-driven models of attention to enhance the performance of neural systems for CV and NLP tasks. We hypothesize that the human–machine performance gap is due to a lack of adequate human-like attention functionalities in AI systems, given the relationship between attention functionality and task performance in humans (Pashler et al., 2001). To address this gap, we focus on three aspects that currently hamper the performance of attention-based deep neural networks (DNNs) (Kotseruba et al., 2016). First, the lack of interpretability, obscuring our knowledge of how these models process and prioritize information. Second, the challenge of generalizability across datasets and domains. Third, the substantial data dependency, hindering the development and scalability of the models for certain tasks. We explore if we can mitigate these issues by integrating DNNs with cognitive models of attention, especially for the tasks of reading and scene perception, where human attention has been widely studied and where DNNs fall short of human capabilities (Das et al., 2017; Mathias et al., 2021). Accordingly, the manuscript develops along three research questions. The first is: What is the relationship between neural and human attention? Focusing on reading comprehension tasks, we uncover correlations between models and human-like attention on reading comprehension tasks. Our findings demonstrate that: a closer alignment with human attention patterns can in fact significantly improve DNNs task performance in both mono- and multimodal settings; that there is a trade-off between model complexity and attention-based interpretability; and that specifically text attention is significantly correlated to model accuracy. Second, we ask: How does incorporating cognitive theories of attention into DNNs enhance model generalizability? We illustrate that using cognitive simulations as an inductive bias, along with specialized training, effectively compensates for the absence of human ground truth attention data in novel domains. We pioneer a method (known as deep saliency prediction (Wang et al., 2021)) to initiate training a DNN for visual saliency prediction by using cognitive model simulations as an inductive bias. Our text and image saliency models, informed by generalized eye movement behaviors simulated from cognitive models, are further refined with limited eye-tracking data, achieving significant performance improvements comparable to the state of the art across various domains and datasets. Lastly, our third research question is: Can methods informed by cognitive models of attention effectively mitigate data dependency requirements? We apply our saliency prediction model in mono- and multimodal NLP tasks using a novel joint semi-supervised training method: we generate task-specific human-like attention by training our downstream task models and allowing for gradient flow in the saliency prediction model. Hence, we supervise neural attention layers of different downstream DNNs with different saliency predictions from the same model. This way, by supervising neural attention mechanisms with human-like attention, and jointly training both models for a given task end-to-end, we circumvent the need for task-specific human data. Put together, our studies set forth a structured approach towards addressing key limitations of current data-driven deep learning models of attention. This thesis demonstrates that integrating them with cognitive science frameworks of human attention opens up new research possibilities, allowing to obtain models that are more efficient, more aligned with human cognitive processes, and that better perceive and understand the world in a human-like manner.