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 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.Item Open Access Correntropy-based constructive one hidden layer neural network(2024) Nayyeri, Mojtaba; Rouhani, Modjtaba; Yazdi, Hadi Sadoghi; Mäkelä, Marko M.; Maskooki, Alaleh; Nikulin, YuryOne of the main disadvantages of the traditional mean square error (MSE)-based constructive networks is their poor performance in the presence of non-Gaussian noises. In this paper, we propose a new incremental constructive network based on the correntropy objective function (correntropy-based constructive neural network (C2N2)), which is robust to non-Gaussian noises. In the proposed learning method, input and output side optimizations are separated. It is proved theoretically that the new hidden node, which is obtained from the input side optimization problem, is not orthogonal to the residual error function. Regarding this fact, it is proved that the correntropy of the residual error converges to its optimum value. During the training process, the weighted linear least square problem is iteratively applied to update the parameters of the newly added node. Experiments on both synthetic and benchmark datasets demonstrate the robustness of the proposed method in comparison with the MSE-based constructive network, the radial basis function (RBF) network. Moreover, the proposed method outperforms other robust learning methods including the cascade correntropy network (CCOEN), Multi-Layer Perceptron based on the Minimum Error Entropy objective function (MLPMEE), Multi-Layer Perceptron based on the correntropy objective function (MLPMCC) and the Robust Least Square Support Vector Machine (RLS-SVM).Item Open Access The child factor in child-robot interaction : discovering the impact of developmental stage and individual characteristics(2024) Rudenko, Irina; Rudenko, Andrey; Lilienthal, Achim J.; Arras, Kai O.; Bruno, BarbaraSocial robots, owing to their embodied physical presence in human spaces and the ability to directly interact with the users and their environment, have a great potential to support children in various activities in education, healthcare and daily life. Child-Robot Interaction (CRI), as any domain involving children, inevitably faces the major challenge of designing generalized strategies to work with unique, turbulent and very diverse individuals. Addressing this challenging endeavor requires to combine the standpoint of the robot-centered perspective, i.e. what robots technically can and are best positioned to do, with that of the child-centered perspective, i.e. what children may gain from the robot and how the robot should act to best support them in reaching the goals of the interaction. This article aims to help researchers bridge the two perspectives and proposes to address the development of CRI scenarios with insights from child psychology and child development theories. To that end, we review the outcomes of the CRI studies, outline common trends and challenges, and identify two key factors from child psychology that impact child-robot interactions, especially in a long-term perspective: developmental stage and individual characteristics. For both of them we discuss prospective experiment designs which support building naturally engaging and sustainable interactions.Item Open Access eyeNotate : interactive annotation of mobile eye tracking data based on few-shot image classification(2025) Barz, Michael; Bhatti, Omair Shahzad; Alam, Hasan Md Tusfiqur; Nguyen, Duy Minh Ho; Altmeyer, Kristin; Malone, Sarah; Sonntag, DanielMobile eye tracking is an important tool in psychology and human-centered interaction design for understanding how people process visual scenes and user interfaces. However, analyzing recordings from head-mounted eye trackers, which typically include an egocentric video of the scene and a gaze signal, is a time-consuming and largely manual process. To address this challenge, we develop eyeNotate, a web-based annotation tool that enables semi-automatic data annotation and learns to improve from corrective user feedback. Users can manually map fixation events to areas of interest (AOIs) in a video-editing-style interface (baseline version). Further, our tool can generate fixation-to-AOI mapping suggestions based on a few-shot image classification model (IML-support version). We conduct an expert study with trained annotators (n = 3) to compare the baseline and IML-support versions. We measure the perceived usability, annotations’ validity and reliability, and efficiency during a data annotation task. We asked our participants to re-annotate data from a single individual using an existing dataset (n = 48). Further, we conducted a semi-structured interview to understand how participants used the provided IML features and assessed our design decisions. In a post hoc experiment, we investigate the performance of three image classification models in annotating data of the remaining 47 individuals.Item Open Access L2XGNN : learning to explain graph neural networks(2024) Serra, Giuseppe; Niepert, MathiasGraph Neural Networks (GNNs) are a popular class of machine learning models. Inspired by the learning to explain (L2X) paradigm, we propose L2xGnn , a framework for explainable GNNs which provides faithful explanations by design. L2xGnn learns a mechanism for selecting explanatory subgraphs (motifs) which are exclusively used in the GNNs message-passing operations. L2xGnn is able to select, for each input graph, a subgraph with specific properties such as being sparse and connected. Imposing such constraints on the motifs often leads to more interpretable and effective explanations. Experiments on several datasets suggest that L2xGnn achieves the same classification accuracy as baseline methods using the entire input graph while ensuring that only the provided explanations are used to make predictions. Moreover, we show that L2xGnn is able to identify motifs responsible for the graph’s properties it is intended to predict.Item Open Access MBFair : a model-based verification methodology for detecting violations of individual fairness(2024) Ramadan, Qusai; Konersmann, Marco; Ahmadian, Amir Shayan; Jürjens, Jan; Staab, SteffenDecision-making systems are prone to discrimination against individuals with regard to protected characteristics such as gender and ethnicity. Detecting and explaining the discriminatory behavior of implemented software is difficult. To avoid the possibility of discrimination from the onset of software development, we propose a model-based methodology called MBFair that allows for verifying UML-based software designs with regard to individual fairness. The verification in MBFair is performed by generating temporal logic clauses, whose verification results enable reporting on the individual fairness of the targeted software. We study the applicability of MBFair using three case studies in real-world settings including a bank services system, a delivery system, and a loan system. We empirically evaluate the necessity of MBFair in a user study and compare it against a baseline scenario in which no modeling and tool support is offered. Our empirical evaluation indicates that analyzing the UML models manually produces unreliable results with a high chance of 46% that analysts overlook true-positive discrimination. We conclude that analysts require support for fairness-related analysis, such as our MBFair methodology.Item Open Access Multiset semantics in SPARQL, relational algebra, and datalog(2026) Angles, Renzo; Gutierrez, Claudio; Hernández, DanielThe paper analyzes and characterizes the algebraic and logical structure of the multiset semantics for SPARQL patterns involving AND, UNION, FILTER, EXCEPT, and SELECT. To do this, we align SPARQL with two well-established query languages: Datalog and Relational Algebra. Specifically, we study (i) a version of nonrecursive Datalog with safe negation extended to support multisets, and (ii) a multiset relational algebra comprising projection, selection, natural join, arithmetic union, and except. We prove that these three formalisms are expressively equivalent under multiset semantics.Item Open Access The aluminum standard : using generative Artificial Intelligence tools to synthesize and annotate non-structured patient data(2024) Diaz Ochoa, Juan G.; Mustafa, Faizan E.; Weil, Felix; Wang, Yi; Kama, Kudret; Knott, MarkusBackground. Medical narratives are fundamental to the correct identification of a patient’s health condition. This is not only because it describes the patient’s situation. It also contains relevant information about the patient’s context and health state evolution. Narratives are usually vague and cannot be categorized easily. On the other hand, once the patient’s situation is correctly identified based on a narrative, it is then possible to map the patient’s situation into precise classification schemas and ontologies that are machine-readable. To this end, language models can be trained to read and extract elements from these narratives. However, the main problem is the lack of data for model identification and model training in languages other than English. First, gold standard annotations are usually not available due to the high level of data protection for patient data. Second, gold standard annotations (if available) are difficult to access. Alternative available data, like MIMIC (Sci Data 3:1, 2016) is written in English and for specific patient conditions like intensive care. Thus, when model training is required for other types of patients, like oncology (and not intensive care), this could lead to bias. To facilitate clinical narrative model training, a method for creating high-quality synthetic narratives is needed. Method. We devised workflows based on generative AI methods to synthesize narratives in the German language to avoid the disclosure of patient’s health data. Since we required highly realistic narratives, we generated prompts, written with high-quality medical terminology, asking for clinical narratives containing both a main and co-disease. The frequency of distribution of both the main and co-disease was extracted from the hospital’s structured data, such that the synthetic narratives reflect the disease distribution among the patient’s cohort. In order to validate the quality of the synthetic narratives, we annotated them to train a Named Entity Recognition (NER) algorithm. According to our assumptions, the validation of this system implies that the synthesized data used for its training are of acceptable quality. Result. We report precision, recall and F1 score for the NER model while also considering metrics that take into account both exact and partial entity matches. Trained models are cautious, with a precision up to 0.8 for Entity Type match metric and a F1 score of 0.3. Conclusion. Despite its inherent limitations, this technology has the potential to allow data interoperability by using encoded diseases across languages and regions without compromising data safety. Additionally, it facilitates the synthesis of unstructured patient data. In this way, the identification and training of models can be accelerated. We believe that this method may be able to generate discharge letters for any combination of main and co-diseases, which will significantly reduce the amount of time spent writing these letters by healthcare professionals.