Selected work

Representative Papers

An Approach for Safe and Secure Software Protection Supported by Symbolic Execution

Mar 11, 2026DEXA Workshops

This work addresses the security risks associated with executing industrial control software on unauthorized hardware, a challenge inadequately mitigated by conventional protection mechanisms that often fail to balance security and functional correctness. The authors propose a novel hardware-software binding approach that integrates Physical Unclonable Functions (PUFs) with symbolic execution to enforce program behavior constraints and verify critical security properties. This method ensures that the software operates correctly only on authorized target devices while maintaining secure behavior—even in the presence of unauthorized execution environments or PUF failures. Notably, this study is the first to leverage symbolic execution for preserving software security properties under anomalous execution conditions, thereby achieving a robust combination of strong anti-reverse-engineering capabilities and high reliability.

1 citationsRead paper

COntExt: Towards Context-Aware Ontology Extension from Operational Metrics

Jul 31, 2026

This study addresses the high cost and low efficiency of current ontology extension practices, which heavily rely on manual effort due to the underutilization of domain knowledge implicitly embedded in operational metrics. To overcome this limitation, the work proposes the first context-aware ontology extension framework that systematically leverages structured operational metrics as a source of contextual information. The framework formulates ontology extension as three subtasks: parent class prediction, relationship type prediction, and data property assignment, and integrates natural language processing with knowledge graph techniques to generate automated suggestions. Experimental evaluation on four cybersecurity ontologies demonstrates that the proposed approach significantly outperforms baseline methods relying solely on ontology-internal context, particularly in relationship type prediction and data property assignment, thereby effectively reducing the cost of ontology maintenance.

0 citationsRead paper

A Behavioural Theory of Probabilistic Algorithms Using Probabilistic Abstract State Machines

Jun 22, 2026

This work addresses the lack of a formal behavioral theory for probabilistic algorithms, which has hindered rigorous characterization of their semantics and execution. Building upon four axiomatic assumptions—stochastic branching time, abstract states, background, and stochastic bounded exploration—the study introduces the first axiomatic behavioral framework for probabilistic algorithms and proposes probabilistic Abstract State Machines (pASMs) as a formal modeling tool. The paper establishes that any algorithm satisfying this framework can be step-by-step behaviorally simulated by a pASM with identical signature and background. This result provides a rigorous behavioral theory for probabilistic algorithms and achieves a semantics-preserving mapping from abstract specifications to concrete computational models.

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Kernel Affine Hull Machines for Compute-Efficient Query-Side Semantic Encoding

May 01, 2026

This work addresses the high computational cost of semantic encoding for online queries, a major bottleneck in Transformer-based retrieval systems. The authors propose a lightweight analytical estimator that replaces repeated neural inference to enable efficient query-side adaptation under a fixed teacher model. The core innovation lies in the first integration of affine hull modeling in a reproducing kernel Hilbert space (RKHS) with normalized least mean squares (NLMS) optimization, establishing an interpretable error decomposition framework for query encoding. Efficiency is further enhanced through RKHS prototype mixture weight estimation and a geometrically driven mapping from lexical features to a frozen semantic space. Evaluated on an Austrian legal benchmark, the method achieves an MSE of 0.000091, R² of 0.9071, cosine similarity of 0.9536, MRR@20 of 0.504, and an 8.5× reduction in query latency.

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InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language

Apr 22, 2026

This study addresses the long-standing reliance on manual annotation in embryo assessment within assisted reproductive technologies, which lacks automated, standardized multimodal natural language description capabilities. To bridge this gap, the authors introduce InVitroVision, an end-to-end model that adapts the foundational vision–language model PaliGemma-2 to the in vitro fertilization (IVF) domain. By fine-tuning on a limited set of time-lapse embryo images paired with corresponding textual descriptions, the model generates high-quality natural language summaries of embryo morphology and developmental stage. Experimental results demonstrate that InVitroVision significantly outperforms both the commercial model ChatGPT 5.2 and baseline approaches under extremely few-shot settings, with performance further improving as training data scale increases, highlighting its strong potential for few-shot transfer learning in clinical embryology.

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Recent publications

Latest Papers

COntExt: Towards Context-Aware Ontology Extension from Operational Metrics

Jul 31, 2026

This study addresses the high cost and low efficiency of current ontology extension practices, which heavily rely on manual effort due to the underutilization of domain knowledge implicitly embedded in operational metrics. To overcome this limitation, the work proposes the first context-aware ontology extension framework that systematically leverages structured operational metrics as a source of contextual information. The framework formulates ontology extension as three subtasks: parent class prediction, relationship type prediction, and data property assignment, and integrates natural language processing with knowledge graph techniques to generate automated suggestions. Experimental evaluation on four cybersecurity ontologies demonstrates that the proposed approach significantly outperforms baseline methods relying solely on ontology-internal context, particularly in relationship type prediction and data property assignment, thereby effectively reducing the cost of ontology maintenance.

0 citationsRead paper

A Behavioural Theory of Probabilistic Algorithms Using Probabilistic Abstract State Machines

Jun 22, 2026

This work addresses the lack of a formal behavioral theory for probabilistic algorithms, which has hindered rigorous characterization of their semantics and execution. Building upon four axiomatic assumptions—stochastic branching time, abstract states, background, and stochastic bounded exploration—the study introduces the first axiomatic behavioral framework for probabilistic algorithms and proposes probabilistic Abstract State Machines (pASMs) as a formal modeling tool. The paper establishes that any algorithm satisfying this framework can be step-by-step behaviorally simulated by a pASM with identical signature and background. This result provides a rigorous behavioral theory for probabilistic algorithms and achieves a semantics-preserving mapping from abstract specifications to concrete computational models.

0 citationsRead paper

Kernel Affine Hull Machines for Compute-Efficient Query-Side Semantic Encoding

May 01, 2026

This work addresses the high computational cost of semantic encoding for online queries, a major bottleneck in Transformer-based retrieval systems. The authors propose a lightweight analytical estimator that replaces repeated neural inference to enable efficient query-side adaptation under a fixed teacher model. The core innovation lies in the first integration of affine hull modeling in a reproducing kernel Hilbert space (RKHS) with normalized least mean squares (NLMS) optimization, establishing an interpretable error decomposition framework for query encoding. Efficiency is further enhanced through RKHS prototype mixture weight estimation and a geometrically driven mapping from lexical features to a frozen semantic space. Evaluated on an Austrian legal benchmark, the method achieves an MSE of 0.000091, R² of 0.9071, cosine similarity of 0.9536, MRR@20 of 0.504, and an 8.5× reduction in query latency.

0 citationsRead paper

InVitroVision: a Multi-Modal AI Model for Automated Description of Embryo Development using Natural Language

Apr 22, 2026

This study addresses the long-standing reliance on manual annotation in embryo assessment within assisted reproductive technologies, which lacks automated, standardized multimodal natural language description capabilities. To bridge this gap, the authors introduce InVitroVision, an end-to-end model that adapts the foundational vision–language model PaliGemma-2 to the in vitro fertilization (IVF) domain. By fine-tuning on a limited set of time-lapse embryo images paired with corresponding textual descriptions, the model generates high-quality natural language summaries of embryo morphology and developmental stage. Experimental results demonstrate that InVitroVision significantly outperforms both the commercial model ChatGPT 5.2 and baseline approaches under extremely few-shot settings, with performance further improving as training data scale increases, highlighting its strong potential for few-shot transfer learning in clinical embryology.

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Expert-Annotated Embryo Image Dataset with Natural Language Descriptions for Evidence-Based Patient Communication in IVF

Apr 16, 2026

This study addresses the limited interpretability of existing AI-assisted embryo selection methods, which hinders their clinical integration and transparent communication between clinicians and patients. To bridge this gap, the authors construct the first expert-annotated dataset pairing time-lapse embryo images with natural language descriptions encompassing cell cycle phases, developmental stages, and key morphological features. Leveraging this dataset, they fine-tune a vision–language foundation model to automatically generate scientifically grounded and interpretable embryo assessment reports. By introducing natural language descriptions into AI-based embryo evaluation for the first time, this approach substantially enhances decision transparency and the quality of patient–clinician dialogue, offering a trustworthy intelligent support system for assisted reproductive treatments.

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