Selected work

Representative Papers

Risk-based Design for Sustainability in Cloud Systems: Insights from an Experts' Survey

Jul 21, 2026

This study addresses the challenge of identifying sustainability risks in cloud systems during early development stages, where complexity and performance considerations often obscure such risks. To tackle this issue, the work proposes an integrated approach that combines multi-industry expert surveys with thematic analysis, grounded in Risk-Based Design (RBD) methodology, to systematically identify and categorize cloud sustainability risks. The research establishes a multi-layered risk classification framework that delineates key influencing factors for each risk category and maps them to corresponding mitigation strategies. By doing so, it offers practitioners actionable guidance and fills a critical gap in systematic risk governance for cloud system sustainability.

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From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond

Jul 06, 2026

This study addresses the challenge of effective attribution in large-scale hybrid cyber-physical Internet-of-Things systems, where traditional causal explanation methods struggle due to their reliance on explicit directed graphs and difficulties handling feedback loops and partial observability. To overcome these limitations, this work proposes a statistical mechanics–inspired undirected energy-based modeling framework that captures dependency structures among variables and analyzes shifts in the energy landscape to enable structured attribution without reconstructing a causal graph. The approach introduces a novel energy-landscape–based dependency-aware mechanism capable of reasoning about perturbation effects in systems with mixed continuous-discrete variables. Experiments on an industrial IoT platform demonstrate that the method significantly outperforms state-of-the-art graph-based approaches in attribution accuracy, robustness, and scalability, making it well-suited for high-dimensional cyber-physical and socio-technical systems.

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Facial Affect Analysis for Service-Oriented Systems: Advances, Challenges, and Future Visions

Jun 13, 2026

This study reframes facial affect analysis (FAA) from an isolated recognition task into a reusable, composable, and reliable service component within service-oriented software ecosystems (SoSEs). It systematically evaluates the suitability of CNNs, Transformers, graph neural networks, and hybrid architectures for edge–cloud collaborative deployment and, for the first time, introduces SoSE-readiness criteria encompassing uncertainty-aware outputs, runtime assurances, fairness, privacy preservation, and intervention stability. The work establishes a practical FAA service quality attribute framework tailored for real-world deployment, defining measurable interface specifications and lifecycle management mechanisms. This provides an engineering roadmap for integrating FAA capabilities into authentic service ecosystems while ensuring robustness, accountability, and operational viability.

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Sustainable Face Recognition on Low-Power Devices with VQ-VAE Embeddings

Jun 13, 2026

This work proposes a lightweight edge-based face recognition framework leveraging a vector-quantized variational autoencoder (VQ-VAE) to overcome the limitations of conventional approaches that rely on computationally intensive cloud models and are thus unsuitable for low-power edge devices. By integrating VQ-VAE with pretrained face embeddings and incorporating a knowledge distillation mechanism, the method generates compact yet semantically rich identity representations in the latent space. The resulting model significantly reduces computational, storage, and communication overhead while maintaining recognition accuracy comparable to state-of-the-art methods. This approach offers a practical and energy-efficient solution for deploying high-performance face recognition directly on resource-constrained edge platforms.

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Equilibrium Semantics and Strong Equivalence for Higher-Order Logic Programs

Jun 01, 2026

This work addresses the lack of a purely logical semantic foundation for verifying strong equivalence in higher-order logic programs, which hinders correctness guarantees in program transformation and optimization. By extending the framework of equilibrium logic, the paper establishes a formal semantics for higher-order answer set programming through the introduction of higher-order equilibrium models. It generalizes the strong equivalence theorem to the higher-order setting for the first time, showing that two programs are strongly equivalent if and only if they share the same higher-order equilibrium models. Furthermore, it proves that stratified higher-order programs admit a unique equilibrium model, thereby establishing the theoretical completeness of the proposed semantics with respect to expressiveness, model-theoretic characterization, and strong equivalence analysis.

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Recent publications

Latest Papers

Risk-based Design for Sustainability in Cloud Systems: Insights from an Experts' Survey

Jul 21, 2026

This study addresses the challenge of identifying sustainability risks in cloud systems during early development stages, where complexity and performance considerations often obscure such risks. To tackle this issue, the work proposes an integrated approach that combines multi-industry expert surveys with thematic analysis, grounded in Risk-Based Design (RBD) methodology, to systematically identify and categorize cloud sustainability risks. The research establishes a multi-layered risk classification framework that delineates key influencing factors for each risk category and maps them to corresponding mitigation strategies. By doing so, it offers practitioners actionable guidance and fills a critical gap in systematic risk governance for cloud system sustainability.

0 citationsRead paper

From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond

Jul 06, 2026

This study addresses the challenge of effective attribution in large-scale hybrid cyber-physical Internet-of-Things systems, where traditional causal explanation methods struggle due to their reliance on explicit directed graphs and difficulties handling feedback loops and partial observability. To overcome these limitations, this work proposes a statistical mechanics–inspired undirected energy-based modeling framework that captures dependency structures among variables and analyzes shifts in the energy landscape to enable structured attribution without reconstructing a causal graph. The approach introduces a novel energy-landscape–based dependency-aware mechanism capable of reasoning about perturbation effects in systems with mixed continuous-discrete variables. Experiments on an industrial IoT platform demonstrate that the method significantly outperforms state-of-the-art graph-based approaches in attribution accuracy, robustness, and scalability, making it well-suited for high-dimensional cyber-physical and socio-technical systems.

0 citationsRead paper

Facial Affect Analysis for Service-Oriented Systems: Advances, Challenges, and Future Visions

Jun 13, 2026

This study reframes facial affect analysis (FAA) from an isolated recognition task into a reusable, composable, and reliable service component within service-oriented software ecosystems (SoSEs). It systematically evaluates the suitability of CNNs, Transformers, graph neural networks, and hybrid architectures for edge–cloud collaborative deployment and, for the first time, introduces SoSE-readiness criteria encompassing uncertainty-aware outputs, runtime assurances, fairness, privacy preservation, and intervention stability. The work establishes a practical FAA service quality attribute framework tailored for real-world deployment, defining measurable interface specifications and lifecycle management mechanisms. This provides an engineering roadmap for integrating FAA capabilities into authentic service ecosystems while ensuring robustness, accountability, and operational viability.

0 citationsRead paper

Sustainable Face Recognition on Low-Power Devices with VQ-VAE Embeddings

Jun 13, 2026

This work proposes a lightweight edge-based face recognition framework leveraging a vector-quantized variational autoencoder (VQ-VAE) to overcome the limitations of conventional approaches that rely on computationally intensive cloud models and are thus unsuitable for low-power edge devices. By integrating VQ-VAE with pretrained face embeddings and incorporating a knowledge distillation mechanism, the method generates compact yet semantically rich identity representations in the latent space. The resulting model significantly reduces computational, storage, and communication overhead while maintaining recognition accuracy comparable to state-of-the-art methods. This approach offers a practical and energy-efficient solution for deploying high-performance face recognition directly on resource-constrained edge platforms.

0 citationsRead paper

Equilibrium Semantics and Strong Equivalence for Higher-Order Logic Programs

Jun 01, 2026

This work addresses the lack of a purely logical semantic foundation for verifying strong equivalence in higher-order logic programs, which hinders correctness guarantees in program transformation and optimization. By extending the framework of equilibrium logic, the paper establishes a formal semantics for higher-order answer set programming through the introduction of higher-order equilibrium models. It generalizes the strong equivalence theorem to the higher-order setting for the first time, showing that two programs are strongly equivalent if and only if they share the same higher-order equilibrium models. Furthermore, it proves that stratified higher-order programs admit a unique equilibrium model, thereby establishing the theoretical completeness of the proposed semantics with respect to expressiveness, model-theoretic characterization, and strong equivalence analysis.

0 citationsRead paper