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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Towards an Ontology-Driven Approach for Process-Aware Ris...
Gal Engelberg, Mattia Fumagalli, Adrian Kuboszek, Dan Klein, Pni · 2022-12-22 · via cs.IT updates on arXiv.org

The rapid development of cyber-physical systems creates an increasing demand for a general approach to risk, especially considering how physical and digital components affect the processes of the system itself. In risk analytics and management, risk propagation is a central technique, which allows the calculation of the cascading effect of risk within a system and supports risk mitigation activities. However, one open challenge is to devise a process-aware risk propagation solution that can be used to assess the impact of risk at different levels of abstraction, accounting for actors, processes, physical-digital objects, and their interrelations. To address this challenge, we propose a process-aware risk propagation approach that builds on two main components: i. an ontology, which supports functionalities typical of Semantic Web technologies (SWT), and semantics-based intelligent systems, representing a system with processes and objects having different levels of abstraction, and ii. a method to calculate the propagation of risk within the given system. We implemented our approach in a proof-of-concept tool, which was validated and demonstrated in the cybersecurity domain.