Institution profile

University of Southern Denmark

Academic institutioneurope · dk
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Research library353linked papers
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Selected work

Representative Papers

A systematic data characteristic understanding framework towards physical-sensor big data challenges

Jun 12, 2024Journal of Big Data

Physical sensor big data inherently exhibits heterogeneity, sparsity, and dynamics, leading to analytical bottlenecks in timeliness and comprehensiveness. To address this, we propose the Multi-Granularity Data Feature Spectrum (DF-Spectrum) framework—the first to jointly incorporate physical constraints and statistical semantics for interpretable modeling of intrinsic patterns, quality dimensions, and evolutionary dynamics in sensor data. Methodologically, DF-Spectrum integrates physics-informed embedding, adaptive feature disentanglement, time-varying entropy-based measurement, and lightweight meta-feature distillation, enabling cross-device and cross-scenario quantification of data comparability and diagnostic root-cause attribution. Evaluated on three real-world datasets—industrial vibration monitoring, smart metering, and environmental sensing—DF-Spectrum achieves a 23.7% improvement in feature identification accuracy and accelerates anomaly root-cause localization by 5.8× compared to state-of-the-art baselines.

3 citationsRead paper

On Propositional Dynamic Logic and Concurrency

Mar 27, 2024arXiv.org

Traditional Propositional Dynamic Logic (PDL) struggles to formalize interleaved execution traces in concurrent program verification, as it inherently relies on sequential operational semantics. Method: This paper introduces Operational Propositional Dynamic Logic (OPDL), the first extension of PDL to arbitrary operational semantic frameworks—decoupling it from sequential execution assumptions. OPDL enables direct modeling of program collections at the operational semantics level, precisely capturing concurrent interleavings. We develop a sequent calculus for OPDL and establish its cut-elimination theorem and semantic soundness and completeness. Contribution/Results: OPDL is successfully instantiated for both CCS and choreographic programming models. Its design ensures semantic agnosticism and extensibility, providing a unified, rigorous logical foundation for formal verification of concurrent programs. The framework supports modular reasoning about concurrency without presupposing specific computational models, thereby advancing the theoretical underpinnings of program logics for concurrency.

3 citationsRead paper

An Overview of Cyber Security Funding for Open Source Software

Dec 08, 2024arXiv.org

Open-source software (OSS) serves as critical infrastructure yet faces persistent security maintenance and sustainability crises due to chronic human resource shortages. Method: This study investigates cybersecurity-oriented OSS funding mechanisms, conducting a qualitative thematic analysis of policy documents, project reports, and funding data from two specialized funding organizations. Drawing on critical infrastructure theory, OSS sustainability research, and cybersecurity regulatory frameworks (e.g., GDPR, NIS2), it integrates these perspectives for the first time. Contribution/Results: The analysis identifies core funded domains—including network supply chains, cryptographic libraries, programming languages, and OS-level components—and reveals that funding decisions are jointly driven by cybersecurity imperatives and sustainability goals—neither alone suffices. A multidimensional funding logic framework is proposed, explicitly linking technical, regulatory, and socio-organizational dimensions. Findings provide both theoretical grounding and actionable guidance for refining OSS security funding strategies.

2 citationsRead paper

Approximating Matroid Basis Testing for Partition Matroids using Budget-In-Expectation

Jan 10, 2026arXiv.org

This study addresses the adaptive evaluation of random Boolean functions over partition matroids: given a ground set whose elements have unknown active states, the goal is to determine whether there exists a basis consisting entirely of active elements, using the minimum expected number of queries. To this end, the work proposes a novel approach that integrates adaptive randomized strategies, optimization under expected budget constraints, and interleaved solving across multiple instances, yielding the first polynomial-time constant-factor approximation algorithm for this problem. This result overcomes the limitation of prior methods, which lacked provable approximation guarantees, and establishes an effective algorithmic framework for stochastic query problems with expected-cost constraints.

1 citationsRead paper

Fully Dynamic Graph Algorithms with Edge Differential Privacy

Sep 26, 2024arXiv.org

This paper studies real-time statistical analysis of fully dynamic graphs—supporting arbitrary edge insertions and deletions—under edge differential privacy. For fundamental graph statistics—including triangle counting, number of connected components, size of maximum matching, and degree histogram—we propose the first fully dynamic algorithms satisfying both event-level and the stronger item-level edge privacy. Methodologically, we integrate dynamic sensitivity analysis, temporal privacy budget allocation, customized perturbation mechanisms, and composition-based privacy amplification. Theoretically, we establish tight error upper and lower bounds for all problems; under item-level privacy, several algorithms achieve the optimal lower bound, yielding the first tightness breakthroughs. This work provides the first unified theoretical framework that jointly optimizes accuracy, dynamic update efficiency, and strong privacy guarantees across multiple core graph statistics.

1 citationsRead paper
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