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Is it Possible to Characterize Group Fairness in Rankings in Terms of Individual Fairness and Diversity?
Full text linkRankings are ever-present in everyday life. Examples are the results of personalized recommendations and web search queries. Rankings can result from an algorithm, importance scores and human-based rankings of items. Till we are not concerned with societal applications, the “fairness“ of the ranking is often irrelevant; however, problems appear when switching from depersonalized items to individuals. Then, suddenly, fairness becomes an issue. We investigate the relationships among group fairness, individual fairness, diversity, and Shapley values. Far from being a comprehensive survey of fairness-related papers or proposing a new method, we want to raise awareness of the chaos we are trying to navigate and propose some new research direction we are trying to follow
Advancements in Neural Network Generations
Full text linkInnovations in Neural Network Generation demonstrate the continual evolution, optimization, and development of artificial neural networks (ANNs) over periods. These improvements include a combination of methodologies, approaches, and technical breakthroughs aimed at increasing the efficiency and abilities of neural network models. Researchers and engineers have repeatedly attempted to push the boundaries of neural network performance, scalability, and applicability across multiple fields. These improvements usually involve changes to network designs, training algorithms, optimization methodologies, and hardware acceleration methods. Moreover, the neural network generations are closely related to key achievements in the machine learning (ML) research domain, such as the development of deep learning (DL) designs like convolutional neural network (CNN) or spiking neural network (SNN) and using both neural generations to introduce natural language processing and advances in computer vision applications. Thus, in the field of neural network study, researchers have categorized ANN models into generations based on their computational design and capabilities. Therefore, this research study explores the continual evolution and optimization of ANNs, highlighting advancements in methodologies and technical innovation. We discuss the different generations of ANN, based on computational design and capabilities, emphasizing their role in shaping achievements in ML research. The study underscores the significance of these generational milestones in enhancing the adaptability and efficacy of neural network models for computational tasks, such as image classification
Prediction of Intermuscular Co-contraction Based on the sEMG of Only One Muscle With the Same Biomechanical Direction of Action
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Research aims to enhance physical abilities using exoskeletons and limb movement prediction. SEMG signals are used for intuitive control, but their measurement is limited to shallowly under-the-skin muscles, making deep muscle signals less frequently used.Here we extended a previously proposed method to train a virtual sensor for the difficult to access muscles (deep muscles e.g. brachialis).The method is extended from signals from the same muscle to intermuscular signals and the results confirm simple biomechanical assumptions. The trained virtual sensors are ready for further investigations by being used in a biomechanical model
Improving Trust in AI Through Sustainable and Trustworthy Reporting
Full text linkThis extended abstract outlines STREP, our (S)ustainable and (T)rustworthy (REP)orting framework. It communicates performance indicators of systems that build on artificial intelligence and thus makes them more trustworthy
Trustworthy Virtual Measurements in Battery Manufacturing
Full text linkThe growing demand for electric cars necessitates an increase in battery production efficiency and cost-effectiveness. Through a reduction of the joint testing efforts an increase of productivity can be accomplished. To achieve the reduction, remain on a high level of quality standards and increase the informational content about current production the use of virtual measurements is examined. Ensuring the trustworthiness of virtual measurements is crucial for informed decision making, necessitating validation. This paper explores the requirements and challenges in battery manufacturing for implementing trustworthy virtual measurements. Two central requirements are identified to enable virtual measurements. Firstly, a traceability system based on the production meta-model is needed to track process parameters and quality characteristics. Secondly, a framework is proposed to facilitate reliable virtual measurements. The primary challenge for virtual measurement in battery manufacturing systems from the complexity of the process chain and products. It is crucial to assess how virtual measurements perform across various processes and to evaluate their transferability to different process parameters and products
Keynot Talks
No full textTuesday, 25.06.2024
Prof. Dr. Anand Subramoney
Scalable Architectures for Neuromorphic Machine Learning
I will discuss how to design architectures for neuromorphic machine learning from first principles. These architectures take inspiration from biology without being constrained by biological details. Two major themes will be sparsity and asynchrony, and their significant role in scalable neuromorphic systems. I will present recent work from my group on using various forms of sparsity and distributed learning to improve the scalability and efficiency of neuromorphic deep learning models.
Prof. Dr. Kerstin Bunte
Scientific Machine Learning for Partially Observed Dynamical Systems
Nowadays, most successful machine learning (ML) techniques for the analysis of complex interdisciplinary data use significant amounts of measurements as input to a statistical system. The domain expert knowledge is often only used in data preprocessing. The subsequently trained technique appears as a “black box”, which is difficult to interpret and rarely allows insight into the underlying natural process. Especially in critical domains such as medicine and engineering, the analysis of dynamic data in the form of sequences and time series is often difficult. Due to natural or cost limitations and ethical considerations data is often irregularly and sparsely sampled and the underlying dynamic system is complex. Therefore, domain experts currently enter a time-consuming and laborious cycle of mechanistic model construction and simulation, often without direct use of the experimental data or the task at hand. We now combine the predictive power of ML and the explanatory power of mechanistic models.Therefore we perform learning in the space of dynamic models that represent the complex underlying natural processes, with potentially very few and limited measurements. We use principles of dimensionality reduction, such as subspace learning, to determine relevant areas in the parameter space of the underlying model as a first step to achieve task-driven model reduction. We furthermore incorporate identifiability analysis for informed posterior construction to improve learning with ill-posed systems caused by data limitations. Findings indicate the possibility of an alternative handling of epistemic uncertainties for scientific machine learning techniques applicable for all linear and classes of non-linear mechanistic models based on Lie symmetries.
Joint work of:Bunte, Kerstin; Tino, Peter; Oostwal, Elisa; Norden, Janis; Chappell, Michael; Smith, Dave
Prof. Dr. Holger Hoos
How and Why AI Will Shape the Future of Science and Engineering
Recent progress in artificial intelligence has elevated what used to be a highly specialised research area to a topic of public discourse and debate. In this presentation I will discuss why beyond the hype, there are good reasons to be excited, but also concerned about AI. Specifically, I will explain how and why AI will have transformative impact on all sciences and engineering disciplines. Based on my own research on the robustness of neural networks, I will discuss some of the fundamental strengths, weaknesses and limitations of current AI systems. Finally, I will share some thoughts on the most serious risks of deploying these systems quickly and broadly, as well as on what needs to be done in order to manage these risks and to realise the benefits AI can bring.
Prof. Dr. Christian Igel
Deep Learning for Large-Scale Tree Carbon Stock Estimation From Satellite Imagery
Trees play an important role for carbon sequestration, biodiversity, as well as timber and food production. We need a better characterization of woody resources at global scale to understand how they are affected by climate change and human management. Recent advances in satellite remote sensing and machine learning (ML) based computer vision makes this possible. This talk discusses large-scale mapping of individual trees using deep learning applied to high-resolution satellite imagery. The biomass of each tree, and thereby its carbon content, is estimated from the crown size using allometric equations. The parameters of these equations are learned from data. The functional relation is assumed to be non-decreasing. Such monotonicity constraints are powerful regularizers in ML in general. They can support fairness in computer-aided decision making and increase plausibility in data-driven scientific models. This talk introduces a conceptually simple and efficient neural network architecture for monotonic modelling that compares favorable to state-of-the-art alternatives. After this technical excursion, we present an application of our tree monitoring in Rwanda, where it helps quantifying progress of restoration projects and developing a pathway to reach the country’s goal of net zero emissions by 2050.
Wednesday, 26.06.24
Prof. Dr. Lucie Flek
Perspective Taking in Large Language Models
Perspective-taking, the process of conceptualizing the point of view of another person, remains a challenge for LLMs. Understanding the mental state of others – emotions, beliefs, intentions – is central for the ability to empathize in social interactions. It is also the key to choose the best action to take next. Enhancing perspective-taking capabilities of LLMs can unlock their potential to react better and safer to hints of distress, to engage in a more receptive argumentation, or to target an explanation to an audience. In this talk, I will present our recent perspective-taking experiments, and discuss further opportunities for bringing the human-centered perspectivist paradigm into the LLMs.
Prof. Dr. Henning Wachsmuth
LLM-based Argument Quality Improvement
Natural language processing (NLP) has recently seen a revolutionary breakthrough, due to the impressive capabilities of large language models (LLM). This also affects NLP research on computational argumentation: the computational analysis and synthesis of natural language arguments. While one of the core tasks studied in computational argumentation is the assessment of an argument’s quality, in this talk I look one beyond, namely at how to improve argument quality. Starting from basics of argumentation, I present insights from selected research of my group involving LLMs for improving argument quality. As part of this, I also look at the recent breakthroughs of LLMs and the paradigm shift that comes with them for computational argumentation in particular and for NLP in general.
Thursday, 27.06.2024
Prof. Dr. Sebastian Trimpe
Trustworthy AI for Physical Machines: Integrating Machine Learning and Control
AI promises significant advancements in engineering, enhancing both design and operation processes. Given that engineering focuses on physical machines like vehicles or robots, ensuring trustworthy solutions is crucial. This talk will explore how combining classical control methods with modern machine learning can create reliable algorithms for real-world applications. Specifically, we will discuss some of our recent research on (i) Bayesian optimization for controller learning, (ii) deep reinforcement learning, and (iii) approximate model-predictive control via imitation learning. The effectiveness of the developed algorithms will be demonstrated through experimental results on robotic hardware.
Prof. Dr. Malte Schilling
Biological Biases for Learning Robust Robot Behavior: Does Deep Reinforcement Learning Run into the Alignment Problem
Einblicke in die ‚Alma-SAP-Werkstatt‘
No full textDer Wunsch eines automatisierten Datenaustauschs zwischen SAP und dem Bibliothekssystem bestand schon sehr lange. Mit einem pragmatischen Ansatz konnte dieses Projekt nun endlich bewerkstelligt werden. In diesem „Werkstattbericht“ soll es mehr um die technischen Aspekte dieses Vorhabens gehen
OpenAlex im Kompetenznetzwerk Bibliometrie
Full text linkNachdem es über Jahrzehnte hinweg die proprietären Datenbanken Web of Science und Scopus waren, die für bibliometrische Analysen herangezogen wurde, ist mit OpenAlex eine interessante, für jedermann frei zugängliche und nutzbare Alternative entstanden. Ebenso wie eine Vielzahl anderer Akteure weltweit hat auch das BMBF-geförderte Kompetenzzentrum Bibliometrie das Potential der Datenbanken erkannt und fördert nun ein Projekt zur Kuratierung von Daten, die unter Beteiligung deutscher Institutionen entstanden sind. Der Vortrag führt in OpenAlex ein und gibt eine Übersicht über den Stand des Vorhabens
Bioinspired Decentralized Hexapod Control with a Graph Neural Network
Full text linkLegged locomotion enables animals to navigate challenging terrains. However, it demands intricate coordination between the legs, with varying levels of information exchange depending on the task. For instance, in more demanding scenarios such as an insect climbing on a twig, greater coordination between the legs is necessary to achieve adaptive behavior. To address this challenge for legged robots, we present a concept and preliminary results of a decentralized biologically inspired controller for a hexapod robot: Based on insights of coordination influences between legs in stick insects, our approach models inter-leg information flow as message passing through a Graph Neural Network
Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models
Full text linkIn recent years, with the increasing importance of dataset privacy in machine learning (ML) applications, there has been an increased demand for secure and privacy-preserving solutions. Consequently, encryption techniques have become known as a critical tool for protecting data privacy in an era of massive data use, exchange, and analysis. Encryption protects data against illegal access and disclosure by changing it into unreadable ciphertext that can only be decrypted by authorized parties. In the field of ML, where sensitive data is often utilized, in such a process the use of encryption techniques has significant potential for providing privacy-preserving model training and inference. Therefore, this article analyzes, investigates, and compares three widely used encryption techniques. Each encryption method offers unique advantages and trade-offs. Thus, we evaluate the performance of Convolutional Neural Network (CNN) models trained on encrypted datasets using these encryption techniques to provide detailed information on the effectiveness, practical concerns, and applicability of various methods for real-world applications by completely analyzing them within the context of computer vision. We test the performance of CNN models trained on encrypted data with several encryption approaches using neural models based-architecture. Parameters such as training time, memory usage, and classification accuracy are analyzed and compared between encryption methods. We also look into the effect of encryption on model interpretability and robustness against adversarial attacks. Furthermore, to support our study we demonstrate our approach by using practical implementation—to showcase the performance and efficiency of each encryption strategy in protecting data privacy while keeping model accuracy and testing in a real-time recognition application using an edge device such as NVIDIA Jetson. Through this comparative analysis, researchers and developers can achieve a more in-depth understanding of the importance and issues involved with the integration of encryption techniques into ML especially in computer vision application workflows