05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access SalChartQA: question-driven saliency on information visualisations(2024) Wang, Yao; Wang, Weitian; Abdelhafez, Abdullah; Elfares, Mayar; Hu, Zhiming; Bâce, Mihai; Bulling, AndreasUnderstanding the link between visual attention and user’s needs when visually exploring information visualisations is under-explored due to a lack of large and diverse datasets to facilitate these analyses. To fill this gap, we introduce SalChartQA - a novel crowd-sourced dataset that uses the BubbleView interface as a proxy for human gaze and a question-answering (QA) paradigm to induce different information needs in users. SalChartQA contains 74,340 answers to 6,000 questions on 3,000 visualisations. Informed by our analyses demonstrating the tight correlation between the question and visual saliency, we propose the first computational method to predict question-driven saliency on information visualisations. Our method outperforms state-of-the-art saliency models, improving several metrics, such as the correlation coefficient and the Kullback-Leibler divergence. These results show the importance of information needs for shaping attention behaviour and paving the way for new applications, such as task-driven optimisation of visualisations or explainable AI in chart question-answering.Item Open Access Mouse2Vec: learning reusable semantic representations of mouse behaviour(2024) Zhang, Guanhua; Hu, Zhiming; Bâce, Mihai; Bulling, AndreasThe mouse is a pervasive input device used for a wide range of interactive applications. However, computational modelling of mouse behaviour typically requires time-consuming design and extraction of handcrafted features, or approaches that are application-specific. We instead propose Mouse2Vec - a novel self-supervised method designed to learn semantic representations of mouse behaviour that are reusable across users and applications. Mouse2Vec uses a Transformer-based encoder-decoder architecture, which is specifically geared for mouse data: During pretraining, the encoder learns an embedding of input mouse trajectories while the decoder reconstructs the input and simultaneously detects mouse click events. We show that the representations learned by our method can identify interpretable mouse behaviour clusters and retrieve similar mouse trajectories. We also demonstrate on three sample downstream tasks that the representations can be practically used to augment mouse data for training supervised methods and serve as an effective feature extractor.Item Open Access Saliency3D: a 3D saliency dataset collected on screen(2024) Wang, Yao; Dai, Qi; Bâce, Mihai; Klein, Karsten; Bulling, AndreasWhile visual saliency has recently been studied in 3D, the experimental setup for collecting 3D saliency data can be expensive and cumbersome. To address this challenge, we propose a novel experimental design that utilizes an eye tracker on a screen to collect 3D saliency data. Our experimental design reduces the cost and complexity of 3D saliency dataset collection. We first collect gaze data on a screen, then we map them to 3D saliency data through perspective transformation. Using this method, we collect a 3D saliency dataset (49,276 fixations) comprising 10 participants looking at sixteen objects. Moreover, we examine the viewing preferences for objects and discuss our findings in this study. Our results indicate potential preferred viewing directions and a correlation between salient features and the variation in viewing directions.Item Open Access Reading data: on digital reception studies(2018) Willand, Marcus; Beck, Jens; Reiter, NilsIn the paper we present a method for the analysis of entity associations that real readers make in their reviews on goodreads.com, a social reading platform - and first results and insights of our analysis.Item Open Access Detecting protagonists in German plays around 1800 as a classification task(2018) Reiter, Nils; Krautter, Benjamin; Pagel, Janis; Willand, MarcusIn this paper, we aim at identifying protagonists in plays automatically. To this end, we train a classifier using various features and investigate the importance of each feature. A challenging aspect here is that the number of spoken words for a character is a very strong baseline. We can show, however, that a) the stage presence of characters and b) topics used in their speech can help to detect protagonists even above the baseline.Item Open Access Will my tests tell me if I break this code?(2016) Niedermayr, Rainer; Juergens, Elmar; Wagner, StefanAutomated tests play an important role in software evolution because they can rapidly detect faults introduced during changes. In practice, code-coverage metrics are often used as criteria to evaluate the effectiveness of test suites with focus on regression faults. However, code coverage only expresses which portion of a system has been executed by tests, but not how effective the tests actually are in detecting regression faults. Our goal was to evaluate the validity of code coverage as a measure for test effectiveness. To do so, we conducted an empirical study in which we applied an extreme mutation testing approach to analyze the tests of open-source projects written in Java. We assessed the ratio of pseudo-tested methods (those tested in a way such that faults would not be detected) to all covered methods and judged their impact on the software project. The results show that the ratio of pseudo-tested methods is acceptable for unit tests but not for system tests (that execute large portions of the whole system). Therefore, we conclude that the coverage metric is only a valid effectiveness indicator for unit tests.Item Open Access Layered symbolic security analysis in DY*(2023) Bhargavan, Karthikeyan; Bichhawat, Abhishek; Hosseyni, Pedram; Küsters, Ralf; Pruiksma, Klaas; Schmitz, Guido; Waldmann, Clara; Würtele, TimWhile cryptographic protocols are often analyzed in isolation, they are typically deployed within a stack of protocols, where each layer relies on the security guarantees provided by the protocol layer below it, and in turn provides its own security functionality to the layer above. Formally analyzing the whole stack in one go is infeasible even for semi-automated verification tools, and impossible for pen-and-paper proofs. The DY* protocol verification framework offers a modular and scalable technique that can reason about large protocols, specified as a set of F* modules. However, it does not support the compositional verification of layered protocols since it treats the global security invariants monolithically. In this paper, we extend DY* with a new methodology that allows analysts to modularly analyze each layer in a way that compose to provide security for a protocol stack. Importantly, our technique allows a layer to be replaced by another implementation, without affecting the proofs of other layers. We demonstrate this methodology on two case studies. We also present a verified library of generic authenticated and confidential communication patterns that can be used in future protocol analyses and is of independent interest.Item Open Access Designing for noticeability: understanding the impact of visual importance on desktop notifications(2022) Müller, Philipp; Staal, Sander; Bâce, Mihai; Bulling, AndreasDesktop notifications should be noticeable but are also subject to a number of design choices, e.g. concerning their size, placement, or opacity. It is currently unknown, however, how these choices interact with the desktop background and their influence on noticeability. To address this limitation, we introduce a software tool to automatically synthesize realistically looking desktop images for major operating systems and applications. Using these images, we present a user study (N=34) to investigate the noticeability of notifications during a primary task. We are first to show that visual importance of the background at the notification location significantly impacts whether users detect notifications. We analyse the utility of visual importance to compensate for suboptimal design choices with respect to noticeability, e.g. small notification size. Finally, we introduce noticeability maps - 2D maps encoding the predicted noticeability across the desktop and inform designers how to trade-off notification design and noticeability.Item Open Access PrivacyScout: assessing vulnerability to shoulder surfing on mobile devices(2022) Bâce, Mihai; Saad, Alia; Khamis, Mohamed; Schneegass, Stefan; Bulling, AndreasOne approach to mitigate shoulder surfing attacks on mobile devices is to detect the presence of a bystander using the phone’s front-facing camera. However, a person’s face in the camera’s field of view does not always indicate an attack. To overcome this limitation, in a novel data collection study (N=16), we analysed the influence of three viewing angles and four distances on the success of shoulder surfing attacks. In contrast to prior works that mainly focused on user authentication, we investigated three common types of content susceptible to shoulder surfing: text, photos, and PIN authentications. We show that the vulnerability of text and photos depends on the observer’s location relative to the device, while PIN authentications are vulnerable independent of the observation location. We then present PrivacyScout – a novel method that predicts the shoulder-surfing risk based on visual features extracted from the observer’s face as captured by the front-facing camera. Finally, evaluations from our data collection study demonstrate our method’s feasibility to assess the risk of a shoulder surfing attack more accurately.Item Open Access Is the stack distance between test case and method correlated with test effectiveness?(2019) Niedermayr, Rainer; Wagner, StefanMutation testing is a means to assess the effectiveness of a test suite and its outcome is considered more meaningful than code coverage metrics. However, despite several optimizations, mutation testing requires a significant computational effort and has not been widely adopted in industry. Therefore, we study in this paper whether test effectiveness can be approximated using a more light-weight approach. We hypothesize that a test case is more likely to detect faults in methods that are close to the test case on the call stack than in methods that the test case accesses indirectly through many other methods. Based on this hypothesis, we propose the minimal stack distance between test case and method as a new test measure, which expresses how close any test case comes to a given method, and study its correlation with test effectiveness. We conducted an empirical study with 21 open-source projects, which comprise in total 1.8 million LOC, and show that a correlation exists between stack distance and test effectiveness. The correlation reaches a strength up to 0.58. We further show that a classifier using the minimal stack distance along with additional easily computable measures can predict the mutation testing result of a method with 92.9% precision and 93.4% recall. Hence, such a classifier can be taken into consideration as a light-weight alternative to mutation testing or as a preceding, less costly step to that.