05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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    Endowing a NAO robot with practical social-touch perception
    (2022) Burns, Rachael Bevill; Lee, Hyosang; Seifi, Hasti; Faulkner, Robert; Kuchenbecker, Katherine J.
    Social touch is essential to everyday interactions, but current socially assistive robots have limited touch-perception capabilities. Rather than build entirely new robotic systems, we propose to augment existing rigid-bodied robots with an external touch-perception system. This practical approach can enable researchers and caregivers to continue to use robotic technology they have already purchased and learned about, but with a myriad of new social-touch interactions possible. This paper presents a low-cost, easy-to-build, soft tactile-perception system that we created for the NAO robot, as well as participants’ feedback on touching this system. We installed four of our fabric-and-foam-based resistive sensors on the curved surfaces of a NAO’s left arm, including its hand, lower arm, upper arm, and shoulder. Fifteen adults then performed five types of affective touch-communication gestures (hitting, poking, squeezing, stroking, and tickling) at two force intensities (gentle and energetic) on the four sensor locations; we share this dataset of four time-varying resistances, our sensor patterns, and a characterization of the sensors’ physical performance. After training, a gesture-classification algorithm based on a random forest identified the correct combined touch gesture and force intensity on windows of held-out test data with an average accuracy of 74.1%, which is more than eight times better than chance. Participants rated the sensor-equipped arm as pleasant to touch and liked the robot’s presence significantly more after touch interactions. Our promising results show that this type of tactile-perception system can detect necessary social-touch communication cues from users, can be tailored to a variety of robot body parts, and can provide HRI researchers with the tools needed to implement social touch in their own systems.
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    Client aware adaptive federated learning using UCB-based reinforcement for people re-identification
    (2025) Waref, Dinah; Alayary, Yomna; Fathallah, Nadeen; Abd El Ghany, Mohamed A.; Salem, Mohammed A.-M.
    People re-identification enables locating and identifying individuals across different camera views in surveillance environments. The surveillance data contains personally identifiable information such as facial images, behavioral patterns, and location data, which can be used for malicious purposes such as identity theft, stalking, or discrimination. This raises serious ethical and privacy concerns. The communication overhead of transporting a large number of data needed to train a global model and the diverse nature of the data from different sources are serious limitations facing the development of people re-identification technologies. We address these challenges by proposing a novel three-step federated learning framework. First, we investigate the impact of data augmentation techniques on the model generalizability and explore the effectiveness of different backbone networks. Second, we use reinforcement learning-based Upper Confidence Bounds (UCB) as a client-selection strategy in the federated round that dynamically chooses devices similar to the current model state, ensuring the model is updated with relevant data and enables faster convergence. Finally, we introduce a feature-level attention mechanism focusing on discriminative features for re-identification. Extensive experiments were conducted on nine benchmark re-ID datasets. The proposed framework outperformed the federated re-ID baseline by 10% in rank-1 accuracy and achieved results comparable to the centralized approach, with a difference of 2%. This improvement over the previous state-of-the-art establishes a new benchmark for federated re-identification.
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    How mature is requirements engineering for AI-based systems? : a systematic mapping study on practices, challenges, and future research directions
    (2024) Habiba, Umm-e-; Haug, Markus; Bogner, Justus; Wagner, Stefan
    Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward.
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    On the impact of service-oriented patterns on software evolvability: a controlled experiment and metric-based analysis
    (2019) Bogner, Justus; Wagner, Stefan; Zimmermann, Alfred
    Background: Design patterns are supposed to improve various quality attributes of software systems. However, there is controversial quantitative evidence of this impact. Especially for younger paradigms such as service- and Microservice-based systems, there is a lack of empirical studies. Objective: In this study, we focused on the effect of four service-based patterns - namely Process Abstraction, Service Façade, Decomposed Capability, and Event-Driven Messaging - on the evolvability of a system from the viewpoint of inexperienced developers. Method: We conducted a controlled experiment with Bachelor students (N = 69). Two functionally equivalent versions of a service-based web shop - one with patterns (treatment group), one without (control group) - had to be changed and extended in three tasks. We measured evolvability by the effectiveness and efficiency of the participants in these tasks. Additionally, we compared both system versions with nine structural maintainability metrics for size, granularity, complexity, cohesion, and coupling. Results: Both experiment groups were able to complete a similar number of tasks within the allowed 90 min. Median effectiveness was 1/3. Mean efficiency was 12% higher in the treatment group, but this difference was not statistically significant. Only for the third task, we found statistical support for accepting the alternative hypothesis that the pattern version led to higher efficiency. In the metric analysis, the pattern version had worse measurements for size and granularity while simultaneously having slightly better values for coupling metrics. Complexity and cohesion were not impacted. Interpretation: For the experiment, our analysis suggests that the difference in efficiency is stronger with more experienced participants and increased from task to task. With respect to the metrics, the patterns introduce additional volume in the system, but also seem to decrease coupling in some areas. Conclusions: Overall, there was no clear evidence for a decisive positive effect of using service-based patterns, neither for the student experiment nor for the metric analysis. This effect might only be visible in an experiment setting with higher initial effort to understand the system or with more experienced developers.
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    Software quality models : purposes, usage scenarios and requirements
    (2009) Deißenböck, Florian; Juergens, Elmar; Lochmann, Klaus; Wagner, Stefan
    Software quality models are a well-accepted means to support quality management of software systems. Over the last 30 years, a multitude of quality models have been proposed and applied with varying degrees of success. Despite successes and standardisation efforts, quality models are still being criticised, as their application in practice exhibits various problems. To some extent, this criticism is caused by an unclear definition of what quality models are and which purposes they serve. Beyond this, there is a lack of explicitly stated requirements for quality models with respect to their intended mode of application. To remedy this, this paper describes purposes and usage scenarios of quality models and, based on the literature and experiences from the authors, collects critique of existing models. From this, general requirements for quality models are derived. The requirements can be used to support the evaluation of existing quality models for a given context or to guide further quality model development.
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    Int-HRL : towards intention-based hierarchical reinforcement learning
    (2024) Penzkofer, Anna; Schaefer, Simon; Strohm, Florian; Bâce, Mihai; Leutenegger, Stefan; Bulling, Andreas
    While deep reinforcement learning (RL) agents outperform humans on an increasing number of tasks, training them requires data equivalent to decades of human gameplay. Recent hierarchical RL methods have increased sample efficiency by incorporating information inherent to the structure of the decision problem but at the cost of having to discover or use human-annotated sub-goals that guide the learning process. We show that intentions of human players, i.e. the precursor of goal-oriented decisions, can be robustly predicted from eye gaze even for the long-horizon sparse rewards task of Montezuma’s Revenge-one of the most challenging RL tasks in the Atari2600 game suite. We propose Int-HRL : Hierarchical RL with intention-based sub-goals that are inferred from human eye gaze. Our novel sub-goal extraction pipeline is fully automatic and replaces the need for manual sub-goal annotation by human experts. Our evaluations show that replacing hand-crafted sub-goals with automatically extracted intentions leads to an HRL agent that is significantly more sample efficient than previous methods.
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    The Quamoco product quality modelling and assessment approach
    (2012) Wagner, Stefan; Lochmann, Klaus; Heinemann, Lars; Kläs, Michael; Trendowicz, Adam; Plösch, Reinhold; Seidl, Andreas; Goeb, Andreas; Streit, Jonathan
    Published software quality models either provide abstract quality attributes or concrete quality assessments. There are no models that seamlessly integrate both aspects. In the project Quamoco, we built a comprehensive approach with the aim to close this gap. For this, we developed in several iterations a meta quality model specifying general concepts, a quality base model covering the most important quality factors and a quality assessment approach. The meta model introduces the new concept of a product factor, which bridges the gap between concrete measurements and abstract quality aspects. Product factors have measures and instruments to operationalise quality by measurements from manual inspection and tool analysis. The base model uses the ISO 25010 quality attributes, which we refine by 200 factors and 600 measures for Java and C# systems. We found in several empirical validations that the assessment results fit to the expectations of experts for the corresponding systems. The empirical analyses also showed that several of the correlations are statistically significant and that the maintainability part of the base model has the highest correlation, which fits to the fact that this part is the most comprehensive. Although we still see room for extending and improving the base model, it shows a high correspondence with expert opinions and hence is able to form the basis for repeatable and understandable quality assessments in practice.
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    Mixed-mode in-memory computing : towards high-performance logic processing in a memristive crossbar array
    (2025) Du, Nan; Polian, Ilia; Bengel, Christopher; Li, Kefeng; Chen, Ziang; Zhao, Xianyue; Hübner, Uwe; Chen, Li-Wei; Liu, Feng; Di Ventra, Massimiliano; Menzel, Stephan; Krüger, Heidemarie
    In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable device behavior, which affects data accuracy and efficiency. In this work, the authors present a new computing method that combines two types of operations—those based on electrical resistance and those based on voltage-within each memory cell. This design improves reliability and avoids the need for expensive current measurements. A new software tool also helps automate the design process, supporting highly parallel operations in dense two-dimensional memory arrays. The approach balances speed and space, making it practical for advanced computing tasks. Demonstrations include a digital adder and a key part of encryption module, showing both strong performance and accuracy. This work offers a new direction for reliable and efficient in-memory computing systems with real-world applications.
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    Editorial - autonomous health monitoring and assistance systems with IoT
    (2021) Azzopardi, George; Karastoyanova, Dimka; Aiello, Marco; Schizas, Christos N.
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    Nontraditional design of dynamic logics using FDSOI for ultra-efficient computing
    (2023) Kumar, Shubham; Chatterjee, Swetaki; Dabhi, Chetan Kumar; Chauhan, Yogesh Singh; Amrouch, Hussam