Acceptance of Medical Artificial Intelligence in Skin Cancer Screening: Choice-Based Conjoint Survey

Authors: Inga Jagemann, Ole Wensing, Manuel Stegemann, Gerrit Hirschfeld

There is great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems capable of identifying melanoma could save lives, enable immediate access to screenings, and reduce unnecessary care and health care costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system.

Digital Health

Evaluation of Swin Transformer and knowledge transfer for denoising of super-resolution structured illumination microscopy data

Authors: Zafran Hussain Shah, Marcel Müller, Wolfgang Hübner, Tung-Cheng Wang, Daniel Telman, Thomas Huser, Wolfram Schenck

Convolutional neural network (CNN)–based methods have shown excellent performance in denoising and reconstruction of super-resolved structured illumination microscopy (SR-SIM) data. Therefore, CNN-based architectures have been the focus of existing studies. However, Swin Transformer, an alternative and recently proposed deep learning–based image restoration architecture, has not been fully investigated for denoising SR-SIM images. (...)

Image Restoration Neural Networks Transfer Learning

A Sensor Fault Detection and Imputation Framework for Electrical Distribution Grids

Authors: Lars Quakernack, Valerie Vaquet, Barbara Hammer, Jens Haubrock

Automated and smart methods for monitoring and controlling the low voltage grid are required in the future to ensure safe operations in the presence of increasingly fluctuating power generation caused by distributed energy resources and power peaks caused by a rising number of electrical vehicles. These algorithmic methods rely on accurate (real-time) data (...)

Localization Sensor Fault Detection

Active Learning for Handling Missing Data

Authors: Alaa Tharwat, Wolfram Schenck

Recently, the massive growth of IoT devices and Internet data, which are widely used in many applications, including industry and healthcare, has dramatically increased the amount of free unlabeled data collected. However, this unlabeled data is useless if we want to learn supervised machine learning models. The expensive and time-consuming cost of labeling makes the problem even more challenging. Here, the active learning (AL) technique provides a solution (...)

Active Learning

Distributed control of partial differential equations using convolutional reinforcement learning

Authors: Sebastian Peitz, Jan Stenner , Vikas Chidananda, Oliver Wallscheid, Steven L. Brunton, Kunihiko Taira

We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical systems governed by partial differential equations (PDEs). Exploiting translational equivariances, the high-dimensional distributed control problemcan be transformed into a multi-agent control problem with many identical, uncoupled agents. (...)

Reinforcement Learning

Using methods from dimensionality reduction for active learning with low query budget

Authors: Alaa Tharwat, Wolfram Schenck

Recently, it has been challenging to generate enough labeled data for supervised learning models from a large amount of free unlabeled data due to the high cost of the labeling process. Here, the active learning technique provides a solution by annotating a small but highly informative set of unlabeled data. This ensures high generalizability in space and improves classification performance with test data. The task is more challenging when (...)

Active Learning Training Data

EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams

Authors: Daniel Leite, Alisson SIlva, Gabriella Casalino, Arnab Sharma, Danielle Fortunato, Axel-Cyrille Ngonga Ngomo

We introduce a modified incremental learning algorithm for evolving Granular Neural Network Classifiers (eGNNC+). We use double-boundary hyper-boxes to represent granules, and customize the adaptation procedures to enhance the robustness of outer boxes for data coverage and noise suppression, while ensuring that inner boxes remain flexible to capture drifts. The classifier evolves from scratch, incorporates new classes on the fly, and performs local incremental feature weighting. As an application, we focus on the classification of emotion-related patterns within electroencephalogram (EEG) signals. Emotion recognition is crucial (...)

Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning

Authors: Jörn Tebbe, Christoph Zimmer, Ansgar Steland, Markus Lange-Hegermann, Fabian Mies

Active learning of physical systems must commonly respect practical safety constraints, which restricts the exploration of the design space. Gaussian Processes (GPs) and their calibrated uncertainty estimations are widely used for this purpose. In many technical applications the design space is explored via continuous trajectories, along which the safety needs to be assessed. This is particularly challenging for strict safety requirements in GP methods, as it employs computationally expensive Monte-Carlo sampling of high quantiles. We address these challenges by providing (...)

Active Learning Gaussian Processes

On the continuity and smoothness of the value function in reinforcement learning and optimal control

Authors: Hans Harder, Sebastian Peitz

he value function plays a crucial role as a measure for the cumulative future reward an agent receives in both reinforcement learning and optimal control. It is therefore of interest to study how similar the values of neighboring states are, i.e., to investigate the continuity of the value function. We do so by providing and verifying upper bounds on the value function's modulus of continuity. Additionally, we show that the value function is always (...)

Gaussian Processes

Comparing generative and extractive approaches to information extraction from abstracts describing randomized clinical trials

Authors: Christian Witte, David M. Schmidt, Philipp Cimiano

Systematic reviews of Randomized Controlled Trials (RCTs) are an important part of the evidence-based medicine paradigm. However, the creation of such systematic reviews by clinical experts is costly as well as time-consuming, and results can get quickly outdated after publication. Most RCTs are structured based on the Patient, Intervention, Comparison, Outcomes (PICO) framework and there exist many approaches which aim to extract PICO elements automatically. (...)

Information Extraction

Predicting PDEs Fast and Efficiently with Equivariant Extreme Learning Machines

Authors: Hans Harder, Sebastian Peitz

We utilize extreme learning machines for the prediction of partial differential equations (PDEs). Our method splits the state space into multiple windows that are predicted individually using a single model. Despite requiring only few data points (in some cases, our method can learn from a single full-state snapshot), it still achieves high accuracy and can predict the flow of PDEs over long time horizons. Moreover, we show how additional symmetries can be exploited to increase sample efficiency and to enforce equivariance. (...)

Partial Differential Equations

Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs

Authors: Stefan Werner, Sebastian Peitz

The goal of this paper is to make a strong point for the usage of dynamical models when using reinforcement learning (RL) for feedback control of dynamical systems governed by partial differential equations (PDEs). To breach the gap between the immense promises we see in RL and the applicability in complex engineering systems, the main challenges are the massive requirements in terms of the training data, as well as the lack of performance guarantees. We present a solution(...)

Reinforcement Learning

Learning Permutation-Invariant Embeddings for Description Logic Concepts

Authors: Caglar Demir, Axel-Cyrille Ngonga Ngomo

Concept learning deals with learning description logic concepts from a background knowledge and input examples. The goal is to learn a concept that covers all positive examples, while not covering any negative examples. This non-trivial task is often formulated as a search problem within an infinite quasi-ordered concept space. Although state-of-the-art models have been successfully applied to tackle this problem, their large-scale applications have been severely hindered due to their excessive exploration incurring impractical runtimes. Here, we propose a remedy for this limitation. (...)

Concept Learning Description Logics

Enhancing Comprehension and Navigation in Jupyter Notebooks with Static Analysis

Authors: Ashwin Prasad Shivarpatna Venkatesh, Jiawei Wang, Li Li, Eric Bodden

Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating and sharing machine-learning based solutions, primarily written in Python. Recent studies have demonstrated, however, that a large portion of Jupyter notebooks available on public platforms are undocumented and lacks a narrative structure. This reduces the readability of these notebooks. To address this shortcoming, this paper presents HeaderGen (...)

Programming Static Analysis

A Survey on Active Learning: State-of-the-Art, Practical Challenges and Research Directions

Authors: Alaa Tharwat, Wolfram Schenck

Despite the availability and ease of collecting a large amount of free, unlabeled data, the expensive and time-consuming labeling process is still an obstacle to labeling a sufficient amount of training data, which is essential for building supervised learning models. Here, with low labeling cost, the active learning (AL) technique could be a solution (...)

Active Learning

Unsupervised Cyclic Siamese Networks Automating Cell Imagery Analysis

Authors: Dominik Stallmann, Barbara Hammer

Novel neural network models that can handle complex tasks with fewer examples than before are being developed for a wide range of applications. In some fields, even the creation of a few labels is a laborious task and impractical, especially for data that require more than a few seconds to generate each label. In the biotechnological domain, cell cultivation experiments are usually done by varying the circumstances of the experiments (...)

Neural Networks

Identifying Slurs and Lexical Hate Speech via Light-Weight Dimension Projection in Embedding Space

Authors: Sanne Hoeken, Sina Zarrieß, Özge Alacam

The prevalence of hate speech on online platforms has become a pressing concern for society, leading to increased attention towards detecting hate speech. Prior work in this area has primarily focused on identifying hate speech at the utterance level that reflects the complex nature of hate speech. In this paper, we propose a targeted and efficient approach to identifying hate speech by detecting slurs at the lexical level using contextualized word embeddings. We hypothesize that slurs (...)

Hate Speech Linguistics

A Unifying Formal Approach to Importance Values in Boolean Functions

Authors: Hans Harder, Simon Jantsch, Christel Baier, Clemens Dubslaff

Boolean functions and their representation through logics, circuits, machine learning classifers, or binary decision diagrams (BDDs) play a central role in the design and analysis of computing systems. Quantifying the relative impact of variables on the truth value by means of importance values can provide useful insights to steer system design and debugging. In this paper, we introduce a uniform framework for reasoning about such value (...)

Neural Class Expression Synthesis

Authors: N’Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Many applications require explainable node classification in knowledge graphs. Towards this end, a popular “white-box” approach is class expression learning: Given sets of positive and negative nodes, class expressions in description logics are learned that separate positive from negative nodes. Most existing approaches are search-based approaches generating many candidate class expressions and selecting the best one. However, they often take a long time to find suitable class expressions. In this paper, we cast class expression learning (...)

Class Expression Learning Concept Learning Neural Networks

Explainable Integration of Knowledge Graphs using Large Language Models

Authors: Abdullah Fathi Ahmed, Asep Fajar Firmansyah, Mohamed Ahmed Sherif, Diego Moussallem, Axel-Cyrille Ngonga Ngomo

Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by (...)

Knowledge Graphs LLMs Semantic Web

NELLIE: Never-Ending Linking for Linked Open Data

Authors: Abdullah Fathi Ahmed, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo

Knowledge graphs (KGs) that follow the Linked Data principles are created daily. However, there are no holistic models for the Linked Open Data (LOD). Building these models( i.e., engineering a pipeline system) is still a big challenge in order to make the LOD vision comes true. In this paper, we address this challenge by presenting NELLIE (...)

Knowledge Graphs LInked Data Semantic Web

Tutorial: Interactive Adaptive Learning

Authors: Mirko Bunse, Georg Krempl, Alaa Tharwat, Amal Saadallah

We summarize the contents of the tutorial we present as a part of the 7th Interactive Adaptive Learning workshop. This workshop is co-located with the ECML-PKDD conference, where it takes place on September 22nd, 2023 in Turin, Italy.

Active Learning

Beyond the Bias: Unveiling the Quality of Implicit Causality Prompt Continuations in Language Models

Authors: Judith Sieker, Oliver Bott, Torgrim Solstad, Sina Zarrieß

Recent studies have used human continuations of Implicit Causality (IC) prompts collected in linguistic experiments to evaluate discourse understanding in large language models (LLMs), focusing on the well-known IC coreference bias in the LLMs’ predictions of the next word following the prompt. In this study, we investigate how continuations of IC prompts can be used to evaluate the text generation capabilities of LLMs in a linguistically controlled setting. We conduct an experiment using two open-source GPT-based models (...)

Linguistics LLMs

Neuro-Symbolic Class Expression Learning

Authors: Caglar Demir, Axel-Cyrille Ngonga Ngomo

Models computed using deep learning have been effectively applied to tackle various problems in many disciplines. Yet, the predictions of these models are often at most post-hoc and locally explainable. In contrast, class expressions in description logics are ante-hoc and globally explainable. Although state-of-the-art symbolic machine learning approaches are being successfully applied to learn class expressions, their application at large scale has been hindered by their impractical runtimes. Arguably, the reliance on myopic heuristic functions contributes to this limitation. We propose (...)

Class Expression Learning

Native Execution of GraphQL Queries over RDF Graphs Using Multi-way Joins

Authors: Nikolaos Karalis, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo

The query language GraphQL has gained significant traction in recent years. In particular, it has recently gained the attention of the semantic web and graph database communities and is now often used as a means to query knowledge graphs. Most of the storage solutions that support GraphQL rely on a translation layer to map the said language to another query language that they support natively, for example SPARQL. (...)

Knowledge Graphs

Clifford Embeddings – A Generalized Approach for Embedding in Normed Algebras

Authors: Caglar Demir, Axel-Cyrille Ngonga Ngomo

A growing number of knowledge graph embedding models exploit the characteristics of division algebras (e.g., R, C, H, and O) to learn embeddings. Yet, recent empirical results suggest that the suitability of algebras is contingent upon the knowledge graph being embedded. In this work, we tackle the challenge of selecting the algebra within which a given knowledge graph should be embedded by exploiting the fact that Clifford algebras (...)

Knowledge Graphs

COBALT: A Content-Based Similarity Approach for Link Discovery over Geospatial Knowledge Graphs

Authors: Alexander Becker, Abdullah Ahmed, Mohamed Ahmed Sherif, Axel-Cyrille Ngonga Ngomo

Data integration and applications across knowledge graphs (KGs) rely heavily on the discovery of links between resources within these KGs. Geospatial link discovery algorithms have to deal with millions of point sets containing billions of points. (...)

Knowledge Graphs LInked Data

LitCQD: Multi-Hop Reasoning in Incomplete Knowledge Graphs with Numeric Literals

Authors: Caglar Demir, Michel Wiebesiek, Renzhong Lu, Axel-Cyrille Ngonga Ngomo, Stefan Heindorf

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete. Answering queries on such incomplete graphs is an important, but challenging problem. Recently, a number of approaches, including complex query decomposition (CQD), have been proposed to answer complex, multi-hop queries with conjunctions and disjunctions on such graphs. However, these approaches only consider graphs consisting of entities and relations, neglecting literal values. In this paper, we propose LitCQD (...)

Knowledge Graphs

Neural Class Expression Synthesis in ALCHIQ(D)

Authors: N’Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Class expression learning in description logics has long been regarded as an iterative search problem in an infinite conceptual space. Each iteration of the search process invokes a reasoner and a heuristic function. The reasoner finds the instances of the current expression, and the heuristic function computes the information gain and decides on the next step to be taken. As the size of the background knowledge base grows, search-based approaches for class expression learning become prohibitively slow. Current neural class expression synthesis (NCES) approaches investigate the use of neural networks for class expression learning in the attributive language (...)

Class Expression Learning Decription Logics Neural Networks

A Topic Model for the Data Web

Authors: Michael Röder, Denis Kuchelev, Axel Ngonga

The usage of knowledge graphs in industry and at Web scale has increased steadily within recent years. However, the decentralized approach to data creation which underpins the popularity of knowledge graphs also comes with significant challenges. In particular, gaining an overview of the topics covered by existing datasets manually becomes a gargantuan if not impossible feat. Several dataset catalogs (...)

Topic Modeling

Adaptive local Principal Component Analysis improves the clustering of high-dimensional data

Authors: Nico Migenda, Ralf Möller, Wolfram Schenck

In local Principal Component Analysis (PCA), a distribution is approximated by multiple units, each representing a local region by a hyper-ellipsoid obtained through PCA. We present an extension for local PCA which adaptively adjusts both the learning rate of each unit and the potential function which guides the competition between the local units. Our local PCA method is an online neural network method where (...)

Neural Networks

Predicting grounding state for adaptive explanation generation in analogical problem-solving

Authors: Lina Mavrina, Stefan Kopp

This paper’s main contribution is a Bayesian hierarchical grounding state prediction model implemented in an adaptive explainer agent assisting users with analogical problem-solving. This model lets the agent adapt dialogue moves regarding previously unmentioned domain entities that are similar to the ones already explained when they are instances of the same generalised schema in different domains. Learning such schemata facilitates knowledge transfer between domains and plays an important role in analogical reasoning (...)


Active Learning for Regression Problems with Ensemble Methods

Authors: Bjarne Jaster, Martin Kohlhase

Traditional machine learning paradigms depend on the availability of labeled data, a luxury that is not often the reality in real-world scenarios. In domains such as industry, healthcare, autonomous systems and finances a massive amount of unlabeled data is produced every day. As the demand for accurate and robust models to deal with this data grows, the inefficiency and the cost of manual labeling motivates the research field active learning (...)

Active Learning Gaussian Processes Neural Networks

Robust Training with Adversarial Examples on Industrial Data

Authors: Julian Knaup, Christoph-Alexander Holst, Volker Lohweg

In an era where deep learning models are increasingly deployed in safety-critical domains, ensuring their reliability is paramount. The emergence of adversarial examples, which can lead to severe model misbehavior, underscores this need for robustness. Adversarial training, a technique aimed at fortifying models against such threats, is of particular interest. This paper presents an approach tailored to adversarial training on tabular data within industrial environments.


Being ignored is not the only possible form of social exclusion in human-agent interaction

Authors: Clarissa Sabrina Arlinghaus, Günter W. Maier

In a world where humans and technical agents (e.g., robots, AI) work collaboratively, processes of social inclusion and exclusion in human-agent interaction (HAI) gain importance. However, the current focus of social exclusion in HAI is too narrowminded and neglects many forms of social exclusion (e.g., averted eye gazes, microaggressions, hurtful laughter). To change this, the effects of different types of social exclusion will be explored in a series of experiments against the background of William's need-threat mode (...)

Social Exclusion

Social exclusion in personnel selection – The risk of discriminating AI biases

Authors: Clarissa Sabrina Arlinghaus, Günter W. Maier

Work plays a central role in the life of adults as it opens up access to a wide range of valuable resources (e.g., financial security, time structure, social contacts). Thereby work contributes to the social inclusion of people in most societies. Therefore, personnel selection processes carry a high level of social responsibility. Nowadays, artificial intelligence (AI) is widely used in human resources (HR), but the unreflected use of AI in recruitment can lead to the exclusion of vulnerable groups. (...)

Discrimination Social Exclusion

Adaptive Koopman-Based Models for Holistic Controller and Observer Design

Authors: Annika Junker, Keno Pape, Julia Timmermann, Ansgar Trächtler

We present a method to obtain a data-driven Koopman operator-based model that adapts itself during operation and can be straightforwardly used for the controller and observer design. The adaptive model is able to accurately describe different state-space regions and additionally consider unpredictable system changes that occur during operation. Furthermore, we show that this adaptive model is applicable to state-space control, which requires complete knowledge of the state vector. (...)

Adaptive Control Data-Based Control

When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle

Authors: Judith Sieker, Sina Zarrieß

The increasing interest in probing the linguistic capabilities of large language models (LLMs) has long reached the area of semantics and pragmatics, including the phenomenon of presuppositions. In this study, we investigate a phenomenon that, however, has not yet been investigated, i.e., the phenomenon of anti-presupposition and the principle that accounts for it, the Maximize Presupposition! principle (MP!). (...)

Linguistics LLMs

TEMPORALFC: A Temporal Fact Checking approach over Knowledge Graphs

Authors: Umair Qudus, Michael Röder, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo

Verifying assertions is an essential part of creating and maintaining knowledge graphs. Most often, this task cannot be carried out manually due to the sheer size of modern knowledge graphs. Hence, automatic fact-checking approaches have been proposed over the last decade. These approaches aim to compute automatically whether a given assertion is correct or incorrect. (...)

Fact Checking Knowledge Graphs

Layered Neural Networks with GELU Activation, a Statistical Mechanics Analysis

Authors: Frederieke Richert, Michiel Straat, Elisa Oostwal, Michael Biehl

Understanding the influence of activation functions on the learning behaviour of neural networks is of great practical interest. The GELU, being similar to swish and ReLU, is analysed for soft committee machines in the statistical physics framework of off-line learning. We find phase transitions with respect to the relative training set size, which are always continuous. This result rules out the hypothesis that convexity is necessary for continuous phase transitions. Moreover, we show that even a small contribution of a sigmoidal function like erf in combination with GELU leads to a discontinuous transition.

Neural Networks

Towards Detecting Lexical Change of Hate Speech in Historical Data

Authors: Sanne Hoeken, Sophie Spliethoff, Silke Schwandt, Sina Zarrieß, Özge Alacam

The investigation of lexical change has predominantly focused on generic language evolution, not suited for detecting shifts in a particular domain, such as hate speech. Our study introduces the task of identifying changes in lexical semantics related to hate speech within historical texts. We present an interdisciplinary approach that brings together NLP and History, yielding a pilot dataset comprising 16th-century Early Modern English religious writings during the Protestant Reformation. We provide annotations for both semantic shifts and hatefulness on this data and, thereby, combine the tasks of Lexical Semantic Change Detection and Hate Speech Detection. Our framework and resulting dataset facilitate the evaluation of our applied methods, advancing the analysis of hate speech evolution.

Hate Speech

Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models

Authors: Sanne Hoeken, Özge Alacam, Antske Fokkens, Pia Sommerauer

In this paper, we investigate automatic detection of subtle semantic shifts between social communities of different political convictions in Dutch and English. We perform a methodological study comparing methods using static and contextualized language models. We investigate the impact of specializing contextualized models through fine-tuning on target corpora, word sense disambiguation and sentiment. We furthermore propose a new approach using masked token prediction, that relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. Our results show (...)


Supporting wound infection diagnosis: advancements and challenges with electronic noses

Authors: Julius Wörner, Maurice Moelleken, Joachim Dissemon, Miriam Pein-Hackelbusch

Wound infections are a major problem worldwide, both for the healthcare system and for patients affected. Currently available diagnostic methods to determine the responsible germs are time-consuming and costly. Wound infections are mostly caused by various bacteria, which in turn produce volatile organic compounds. From clinical experience, we know that depending on the bacteria involved, a specific odor impression can be expected. For this reason, we hypothesized that electronic noses, (...)

Key Indicators for the Discrimination of Wines by Electronic Noses

Authors: Julius Wörner, Helene Dörksen, Miriam Pein-Hackelbusch

In the food industry, and especially in wines as products thereof, ethanol and sulfur dioxide play an equally important role. Both substances are important wine quality characteristics as they influence the taste and odor. As both substances comprise volatile matter, electronic noses should be applicable to discriminate the different qualities of wines. Our study investigates the influence of alcohol and sulfur dioxide on the discrimination ability of wines (especially those of the same grape variety) using two different electronic nose systems. (...)

Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients

Authors: Marc Härkönen, Markus Lange-Hegermann, Bogdan Raiță

Partial differential equations (PDEs) are important tools to model physical systems and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP, such that all realizations are exact solutions of this system. We apply the Ehrenpreis-Palamodov fundamental principle (...)

Gaussian Processes Partial Differential Equations

Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems

Authors: Sebastian Peitz, Hans Harder, Feliks Nüske, Friedrich Philipp, Manuel Schaller, Karl Worthmann

The Koopman operator has become an essential tool for data-driven analysis, prediction and control of complex systems, the main reason being the enormous potential of identifying linear function space representations of nonlinear dynamics from measurements. Until now, the situation where for large-scale systems, we (i) only have access to partial observations (i.e., measurements, as is very common for experimental data) or (ii) deliberately perform coarse graining (for efficiency reasons) has not been treated to its full extent. In this paper, we address the pitfall associated (...)

Towards designing assistants for well-being: clarifying the relationship between users’ intrinsic motivation and expectations from assistants

Authors: Hitesh Dhiman, Yutaro Nemoto, Holger Mühlan, Michael Fellmann, Carsten Röcker

Although considerable research effort has been devoted to understanding the adoption and use of commercially available intelligent assistants, the relationship between user expectations from assistants and users’ endogenous intrinsic motivation to perform an activity has not been explored. Doing so is important to meet user expectations, prevent adoption failures, and design for well-being. In this paper, we investigate whether a person's intrinsic motivation (...)

Assistant Design Intelligent Assistants

Exploring Semantic Spaces for Detecting Clustering and Switching in Verbal Fluency

Authors: Özge Alacam, Simeon Schüz, Martin Wegrzyn, Johanna Kißler, Sina Zarrieß

In this work, we explore the fitness of various word/concept representations in analyzing an experimental verbal fluency dataset providing human responses to 10 different category enumeration tasks. Based on human annotations of so-called clusters and switches between sub-categories in the verbal fluency sequences, we analyze whether lexical semantic knowledge represented in word embedding spaces (GloVe, fastText, ConceptNet, BERT) is suitable for detecting (...)


Modeling Referential Gaze in Task-oriented Settings of Varying Referential Complexity

Authors: Özge Alacam, Eugen Ruppert, Sina Zarrieß, Ganeshan Malhotra, Chris Biemann

Referential gaze is a fundamental phenomenon for psycholinguistics and human-human communication. However, modeling referential gaze for real-world scenarios, e.g. for task-oriented communication, is lacking the well-deserved attention from the NLP community. In this paper, we address this challenging issue by proposing a novel multimodal NLP task; namely predicting when the gaze is referential. We further investigate (...)

Linguistics NLP