5 Search Results for "Dang, Hoang-Hai"

Document
DOI: 10.4230/LIPIcs.SAT.2025.32
Efficient Certified Reasoning for Binarized Neural Networks

Authors: Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel

Published in: LIPIcs, Volume 341, 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)

Abstract
Neural networks have emerged as essential components in safety-critical applications - these use cases demand complex, yet trustworthy computations. Binarized Neural Networks (BNNs) are a type of neural network where each neuron is constrained to a Boolean value; they are particularly well-suited for safety-critical tasks because they retain much of the computational capacities of full-scale (floating-point or quantized) deep neural networks, but remain compatible with satisfiability solvers for qualitative verification and with model counters for quantitative reasoning. However, existing methods for BNN analysis suffer from either limited scalability or susceptibility to soundness errors, which hinders their applicability in real-world scenarios. In this work, we present a scalable and trustworthy approach for both qualitative and quantitative verification of BNNs. Our approach introduces a native representation of BNN constraints in a custom-designed solver for qualitative reasoning, and in an approximate model counter for quantitative reasoning. We further develop specialized proof generation and checking pipelines with native support for BNN constraint reasoning, ensuring trustworthiness for all of our verification results. Empirical evaluations on a BNN robustness verification benchmark suite demonstrate that our certified solving approach achieves a 9× speedup over prior certified CNF and PB-based approaches, and our certified counting approach achieves a 218× speedup over the existing CNF-based baseline. In terms of coverage, our pipeline produces fully certified results for 99% and 86% of the qualitative and quantitative reasoning queries on BNNs, respectively. This is in sharp contrast to the best existing baselines which can fully certify only 62% and 4% of the queries, respectively.
Cite as

Jiong Yang, Yong Kiam Tan, Mate Soos, Magnus O. Myreen, and Kuldeep S. Meel. Efficient Certified Reasoning for Binarized Neural Networks. In 28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 341, pp. 32:1-32:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{yang_et_al:LIPIcs.SAT.2025.32,
  author =	{Yang, Jiong and Tan, Yong Kiam and Soos, Mate and Myreen, Magnus O. and Meel, Kuldeep S.},
  title =	{{Efficient Certified Reasoning for Binarized Neural Networks}},
  booktitle =	{28th International Conference on Theory and Applications of Satisfiability Testing (SAT 2025)},
  pages =	{32:1--32:22},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-381-2},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{341},
  editor =	{Berg, Jeremias and Nordstr\"{o}m, Jakob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2025.32},
  URN =		{urn:nbn:de:0030-drops-237665},
  doi =		{10.4230/LIPIcs.SAT.2025.32},
  annote =	{Keywords: Neural network verification, proof certification, SAT solving, approximate model counting}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2025.14
IsaBIL: A Framework for Verifying (In)correctness of Binaries in Isabelle/HOL

Authors: Matt Griffin, Brijesh Dongol, and Azalea Raad

Published in: LIPIcs, Volume 333, 39th European Conference on Object-Oriented Programming (ECOOP 2025)

Abstract
This paper presents IsaBIL, a binary analysis framework in Isabelle/HOL that is based on the widely used Binary Analysis Platform (BAP). Specifically, in IsaBIL, we formalise BAP’s intermediate language, called BIL and integrate it with Hoare logic (to enable proofs of correctness) as well as incorrectness logic (to enable proofs of incorrectness). IsaBIL inherits the full flexibility of BAP, allowing us to verify binaries for a wide range of languages (C, C++, Rust), toolchains (LLVM, Ghidra) and target architectures (x86, RISC-V), and can also be used when the source code for a binary is unavailable. To make verification tractable, we develop a number of big-step rules that combine BIL’s existing small-step rules at different levels of abstraction to support reuse. We develop high-level reasoning rules for RISC-V instructions (our main target architecture) to further optimise verification. Additionally, we develop Isabelle proof tactics that exploit common patterns in C binaries for RISC-V to discharge large numbers of proof goals (often in the 100s) automatically. IsaBIL includes an Isabelle/ML based parser for BIL programs, allowing one to automatically generate the associated Isabelle/HOL program locale from a BAP output. Taken together, IsaBIL provides a highly flexible proof environment for program binaries. As examples, we prove correctness of key examples from the Joint Strike Fighter coding standards and the MITRE database.
Cite as

Matt Griffin, Brijesh Dongol, and Azalea Raad. IsaBIL: A Framework for Verifying (In)correctness of Binaries in Isabelle/HOL. In 39th European Conference on Object-Oriented Programming (ECOOP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 333, pp. 14:1-14:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{griffin_et_al:LIPIcs.ECOOP.2025.14,
  author =	{Griffin, Matt and Dongol, Brijesh and Raad, Azalea},
  title =	{{IsaBIL: A Framework for Verifying (In)correctness of Binaries in Isabelle/HOL}},
  booktitle =	{39th European Conference on Object-Oriented Programming (ECOOP 2025)},
  pages =	{14:1--14:30},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-373-7},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{333},
  editor =	{Aldrich, Jonathan and Silva, Alexandra},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2025.14},
  URN =		{urn:nbn:de:0030-drops-233070},
  doi =		{10.4230/LIPIcs.ECOOP.2025.14},
  annote =	{Keywords: Binary Analysis Platform, Isabelle/HOL, Hoare Logic, Incorrectness Logic}
}
Document
Survey
DOI: 10.4230/TGDK.1.1.7
Rule Learning over Knowledge Graphs: A Review

Authors: Hong Wu, Zhe Wang, Kewen Wang, Pouya Ghiasnezhad Omran, and Jiangmeng Li

Published in: TGDK, Volume 1, Issue 1 (2023): Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge, Volume 1, Issue 1

Abstract
Compared to black-box neural networks, logic rules express explicit knowledge, can provide human-understandable explanations for reasoning processes, and have found their wide application in knowledge graphs and other downstream tasks. As extracting rules manually from large knowledge graphs is labour-intensive and often infeasible, automated rule learning has recently attracted significant interest, and a number of approaches to rule learning for knowledge graphs have been proposed. This survey aims to provide a review of approaches and a classification of state-of-the-art systems for learning first-order logic rules over knowledge graphs. A comparative analysis of various approaches to rule learning is conducted based on rule language biases, underlying methods, and evaluation metrics. The approaches we consider include inductive logic programming (ILP)-based, statistical path generalisation, and neuro-symbolic methods. Moreover, we highlight important and promising application scenarios of rule learning, such as rule-based knowledge graph completion, fact checking, and applications in other research areas.
Cite as

Hong Wu, Zhe Wang, Kewen Wang, Pouya Ghiasnezhad Omran, and Jiangmeng Li. Rule Learning over Knowledge Graphs: A Review. In Special Issue on Trends in Graph Data and Knowledge. Transactions on Graph Data and Knowledge (TGDK), Volume 1, Issue 1, pp. 7:1-7:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@Article{wu_et_al:TGDK.1.1.7,
  author =	{Wu, Hong and Wang, Zhe and Wang, Kewen and Omran, Pouya Ghiasnezhad and Li, Jiangmeng},
  title =	{{Rule Learning over Knowledge Graphs: A Review}},
  journal =	{Transactions on Graph Data and Knowledge},
  pages =	{7:1--7:23},
  ISSN =	{2942-7517},
  year =	{2023},
  volume =	{1},
  number =	{1},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.7},
  URN =		{urn:nbn:de:0030-drops-194813},
  doi =		{10.4230/TGDK.1.1.7},
  annote =	{Keywords: Rule learning, Knowledge graphs, Link prediction}
}
Document
DOI: 10.4230/DARTS.3.2.15
Strong Logic for Weak Memory: Reasoning About Release-Acquire Consistency in Iris (Artifact)

Authors: Jan-Oliver Kaiser, Hoang-Hai Dang, Derek Dreyer, Ori Lahav, and Viktor Vafeiadis

Published in: DARTS, Volume 3, Issue 2, Special Issue of the 31st European Conference on Object-Oriented Programming (ECOOP 2017)

Abstract
This artifact provides the soundness proofs for the encodings in Iris the RSL and GPS logics, as well as the verification for all standard examples known to be verifiable in those logics. All of these proofs are formalized in Coq, which is the main content of this artifact. The formalization is provided in a virtual machine for the convenience of testing, but can also be built from source.
Cite as

Jan-Oliver Kaiser, Hoang-Hai Dang, Derek Dreyer, Ori Lahav, and Viktor Vafeiadis. Strong Logic for Weak Memory: Reasoning About Release-Acquire Consistency in Iris (Artifact). In Special Issue of the 31st European Conference on Object-Oriented Programming (ECOOP 2017). Dagstuhl Artifacts Series (DARTS), Volume 3, Issue 2, pp. 15:1-15:2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@Article{kaiser_et_al:DARTS.3.2.15,
  author =	{Kaiser, Jan-Oliver and Dang, Hoang-Hai and Dreyer, Derek and Lahav, Ori and Vafeiadis, Viktor},
  title =	{{Strong Logic for Weak Memory: Reasoning About Release-Acquire Consistency in Iris (Artifact)}},
  pages =	{15:1--15:2},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2017},
  volume =	{3},
  number =	{2},
  editor =	{Kaiser, Jan-Oliver and Dang, Hoang-Hai and Dreyer, Derek and Lahav, Ori and Vafeiadis, Viktor},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.3.2.15},
  URN =		{urn:nbn:de:0030-drops-72966},
  doi =		{10.4230/DARTS.3.2.15},
  annote =	{Keywords: weak memory models, release-acquire, concurrency, separation logic}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2017.17
Strong Logic for Weak Memory: Reasoning About Release-Acquire Consistency in Iris

Authors: Jan-Oliver Kaiser, Hoang-Hai Dang, Derek Dreyer, Ori Lahav, and Viktor Vafeiadis

Published in: LIPIcs, Volume 74, 31st European Conference on Object-Oriented Programming (ECOOP 2017)

Abstract
The field of concurrent separation logics (CSLs) has recently undergone two exciting developments: (1) the Iris framework for encoding and unifying advanced higher-order CSLs and formalizing them in Coq, and (2) the adaptation of CSLs to account for weak memory models, notably C11's release-acquire (RA) consistency. Unfortunately, these developments are seemingly incompatible, since Iris only applies to languages with an operational interleaving semantics, while C11 is defined by a declarative (axiomatic) semantics. In this paper, we show that, on the contrary, it is not only feasible but useful to marry these developments together. Our first step is to provide a novel operational characterization of RA+NA, the fragment of C11 containing RA accesses and "non-atomic" (normal data) accesses. Instantiating Iris with this semantics, we then derive higher-order variants of two prominent RA+NA logics, GPS and RSL. Finally, we deploy these derived logics in order to perform the first mechanical verifications (in Coq) of several interesting case studies of RA+NA programming. In a nutshell, we provide the first foundationally verified framework for proving programs correct under C11's weak-memory semantics.
Cite as

Jan-Oliver Kaiser, Hoang-Hai Dang, Derek Dreyer, Ori Lahav, and Viktor Vafeiadis. Strong Logic for Weak Memory: Reasoning About Release-Acquire Consistency in Iris. In 31st European Conference on Object-Oriented Programming (ECOOP 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 74, pp. 17:1-17:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{kaiser_et_al:LIPIcs.ECOOP.2017.17,
  author =	{Kaiser, Jan-Oliver and Dang, Hoang-Hai and Dreyer, Derek and Lahav, Ori and Vafeiadis, Viktor},
  title =	{{Strong Logic for Weak Memory: Reasoning About Release-Acquire Consistency in Iris}},
  booktitle =	{31st European Conference on Object-Oriented Programming (ECOOP 2017)},
  pages =	{17:1--17:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-035-4},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{74},
  editor =	{M\"{u}ller, Peter},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2017.17},
  URN =		{urn:nbn:de:0030-drops-72753},
  doi =		{10.4230/LIPIcs.ECOOP.2017.17},
  annote =	{Keywords: Weak memory models, release-acquire, concurrency, separation logic}
}
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