Tootfinder

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in A Digital World
J. Recker, R. Lukyanenko, M. A. Jabbari, B. M. Samuel, A. Castellanos
https://arxiv.org/abs/2506.18743

From Representation to Mediation: A New Agenda for Conceptual Modeling Research in A Digital World
The role of information systems (IS) as representations of real-world systems is changing in an increasingly digitalized world, suggesting that conceptual modeling is losing its relevance to the IS field. We argue the opposite: Conceptual modeling research is more relevant to the IS field than ever, but it requires an update with current theory. We develop a new theoretical framework of conceptual modeling that delivers a fundamental shift in the assumptions that govern research in this area. T…

Decentralized Federated Learning of Probabilistic Generative Classifiers
Aritz P\'erez, Carlos Echegoyen, Guzm\'an Santaf\'e
https://arxiv.org/abs/2507.17285

Decentralized Federated Learning of Probabilistic Generative Classifiers
Federated learning is a paradigm of increasing relevance in real world applications, aimed at building a global model across a network of heterogeneous users without requiring the sharing of private data. We focus on model learning over decentralized architectures, where users collaborate directly to update the global model without relying on a central server. In this context, the current paper proposes a novel approach to collaboratively learn probabilistic generative classifiers with a parame…

Identifying Explanation Needs: Towards a Catalog of User-based Indicators
Hannah Deters, Laura Reinhardt, Jakob Droste, Martin Obaidi, Kurt Schneider
https://arxiv.org/abs/2506.16997

Identifying Explanation Needs: Towards a Catalog of User-based Indicators
In today's digitalized world, where software systems are becoming increasingly ubiquitous and complex, the quality aspect of explainability is gaining relevance. A major challenge in achieving adequate explanations is the elicitation of individual explanation needs, as it may be subject to severe hypothetical or confirmation biases. To address these challenges, we aim to establish user-based indicators concerning user behavior or system events that can be captured at runtime to determine when a…

On the Power of Spatial Locality on Online Routing Problems
Swapnil Guragain, Gokarna Sharma
https://arxiv.org/abs/2506.17517 https://

On the Power of Spatial Locality on Online Routing Problems
We consider the online versions of two fundamental routing problems, traveling salesman (TSP) and dial-a-ride (DARP), which have a variety of relevant applications in logistics and robotics. The online versions of these problems concern with efficiently serving a sequence of requests presented in a real-time on-line fashion located at points of a metric space by servers (salesmen/vehicles/robots). In this paper, motivated from real-world applications, such as Uber/Lyft rides, where some limited…

3LM: Bridging Arabic, STEM, and Code through Benchmarking
Basma El Amel Boussaha, Leen AlQadi, Mugariya Farooq, Shaikha Alsuwaidi, Giulia Campesan, Ahmed Alzubaidi, Mohammed Alyafeai, Hakim Hacid
https://arxiv.org/abs/2507.15850

3LM: Bridging Arabic, STEM, and Code through Benchmarking
Arabic is one of the most widely spoken languages in the world, yet efforts to develop and evaluate Large Language Models (LLMs) for Arabic remain relatively limited. Most existing Arabic benchmarks focus on linguistic, cultural, or religious content, leaving a significant gap in domains like STEM and code which are increasingly relevant for real-world LLM applications. To help bridge this gap, we present 3LM, a suite of three benchmarks designed specifically for Arabic. The first is a set of S…

CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets
Samhita Kuili, Mohammadreza Amini, Burak Kantarci
https://arxiv.org/abs/2506.15075

CWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets
In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by incorporating additive white Gaussian noise (AWGN) into the real-world In-phase and Quadrature (I/Q) OFDM datasets. A Convolutional Autoencoder (CAE) is exploited to implement a jamming detection over various characteristics such as heterogenous I/Q datasets; extracting relevant inf…

What is a good use case for quantum computers?
Michael Marthaler, Peter Pinski, Pascal Stadler, Vladimir Rybkin, Marina Walt
https://arxiv.org/abs/2506.15426

What is a good use case for quantum computers?
Identify, Transform, Benchmark, Show Quantum Advantage (ITBQ): Evaluating use cases for quantum computers. We introduce a four-step framework for assessing quantum computing applications -- from identifying relevant industry problems to demonstrating quantum advantage -- addressing steps often overlooked in the literature, such as rigorous benchmarking against classical solutions and the challenge of translating real-world tasks onto quantum hardware. Applying this framework to cases like NMR, …

LDI: Localized Data Imputation
Soroush Omidvartehrani, Davood Rafiei
https://arxiv.org/abs/2506.16616 https://arxiv.org/pdf/2506.1661…

LDI: Localized Data Imputation
Missing values are a common challenge in real-world tabular data and can significantly impair downstream analysis. While Large Language Models (LLMs) have recently shown promise in data imputation, existing methods often rely on broad, unfiltered prompts that compromise accuracy, scalability, and explainability. We introduce LDI (Localized Data Imputation), a novel framework that improves both the accuracy and transparency of LLM-based imputation by selecting a compact, contextually relevant su…

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
Lionel Wong, Katherine M. Collins, Lance Ying, Cedegao E. Zhang, Adrian Weller, Tobias Gersternberg, Timothy O'Donnell, Alexander K. Lew, Jacob D. Andreas, Joshua B. Tenenbaum, Tyler Brooke-Wilson
https://arxiv.org/abs/2507.12547

Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models
When faced with novel situations, people are able to marshal relevant considerations from a wide range of background knowledge and put these to use in inferences and predictions. What permits us to draw in globally relevant information and reason over it coherently? Here, we explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations. We propose a computational implementation of this idea -- a `…

Continuously Updating Digital Twins using Large Language Models
Harry Amad, Nicol\'as Astorga, Mihaela van der Schaar
https://arxiv.org/abs/2506.12091 …

Continuously Updating Digital Twins using Large Language Models
Digital twins are models of real-world systems that can simulate their dynamics in response to potential actions. In complex settings, the state and action variables, and available data and knowledge relevant to a system can constantly change, requiring digital twins to continuously update with these changes to remain relevant. Current approaches struggle in this regard, as they require fixed, well-defined modelling environments, and they cannot adapt to novel variables without re-designs, or i…