Institution profile

University of Oulu

Academic institutioneurope · fi
Official website
Research library382linked papers
Opportunities0open roles
Selected work

Representative Papers

Understanding the Issues, Their Causes and Solutions in Microservices Systems: An Empirical Study

Feb 03, 2023arXiv.org

Microservice system developers lack empirical evidence regarding the types, root causes, and remediation strategies of recurring issues. Method: We adopt a mixed-methods approach—quantitatively analyzing 2,641 open-source issues, qualitatively interviewing 15 practitioners, and conducting a global survey with 150 practitioners. Contribution/Results: We introduce the first comprehensive, domain-specific three-level taxonomy (“Issue–Cause–Solution”) for microservices. We identify five high-frequency issue domains—including technical debt, CI/CD pipeline failures, and exception handling—and three predominant root causes, notably generic programming errors. From our analysis, we distill 177 actionable, context-aware remediation strategies. This work establishes an empirical foundation for microservice fault diagnosis and mitigation, delivers practical guidance for industry practitioners, and pinpoints critical research directions for next-generation microservice engineering.

6 citations2 influentialRead paper

Shortcut Learning in Binary Classifier Black Boxes: Applications to Voice Anti-Spoofing and Biometrics

Oct 01, 2025IEEE Journal on Selected Topics in Signal Processing

This work addresses the problem of shortcut learning in binary black-box classification models caused by dataset bias. It proposes a novel post-hoc analysis framework that integrates interventional and observational perspectives, introducing linear mixed-effects models—used here for the first time—to diagnose bias in black-box classifiers. By decomposing the influence of training and test data on model scores, the method moves beyond conventional error-rate metrics to uncover the risk of models relying on spurious correlations. The approach effectively identifies and quantifies the impact of data bias on decision-making in voice anti-spoofing and speaker verification tasks, offering a new pathway toward building reliable and interpretable AI systems.

2 citationsRead paper

Diffusion-based Generative Multicasting with Intent-aware Semantic Decomposition

Nov 04, 2024arXiv.org

To address low-latency and heterogeneous semantic requirements in multi-user semantic communications for future wireless networks, this paper proposes an intent-aware generative semantic multicast framework. The transmitter decomposes the source signal according to each user’s semantic intent, transmitting only the intended semantic classes while broadcasting a lightweight shared semantic graph; users then collaboratively reconstruct non-intended classes locally using pre-trained diffusion models. This work pioneers the integration of generative diffusion models (GDMs) into semantic multicast, enabling intent-driven semantic decomposition and generative reconstruction. We further design a communication-computation co-optimized, per-class adaptive parameter allocation mechanism that jointly optimizes transmit power, coding rate, and model inference overhead. Experimental results demonstrate that, compared to conventional non-generative and intent-agnostic baselines, the proposed framework significantly reduces end-to-end latency, improves spectral efficiency, and enhances privacy protection for non-intended semantic content.

2 citationsRead paper

A Conic Transformation Approach for Solving the Perspective-Three-Point Problem

Apr 02, 2025

This paper addresses the Perspective-Three-Point (P3P) problem—estimating camera pose from three 3D world points and their 2D image projections. We propose a novel geometric solution based on conic transformations, centered on a canonical parabola mapping framework: a coordinate transformation converts the original intersecting conic pair into a standard parabola and another conic, thereby explicitly decoupling variables, eliminating complex arithmetic, and yielding a real-coefficient quartic equation. Analytic elimination and normalized polynomial root-finding ensure efficient and robust real-root computation. Experiments demonstrate that our method achieves superior computational speed over state-of-the-art algorithms while maintaining numerical stability and pose estimation accuracy, making it suitable for real-time visual pose estimation.

1 citationsRead paper

Minimally sufficient structures for information-feedback policies

Feb 19, 2025

This paper addresses the low efficiency of internal representation and perception-decision coupling in robotic task execution within physical environments. Methodologically, it proposes a co-modeling framework integrating information-state-based filtering and feedback policies. It establishes, for the first time, necessary and sufficient conditions for the existence of an information-feedback policy; proves—under mild assumptions—the existence and uniqueness of a minimal internal system capable of losslessly compressing the action-observation history; and formally characterizes information constraints in the sensing-action closed loop via an information-state transition system, sensor mapping modeling, and multi-scale abstraction. Key contributions include: (i) deriving sufficient structural conditions for distance-optimal navigation in polygonal environments; and (ii) providing a computationally tractable algorithm for constructing the minimal filter, significantly reducing the state complexity of perception-decision systems.

1 citationsRead paper
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