12 papers · ranked by Valyu relevance
Jiatong Wu, Sen Wang, Kai Niu, Yifei She + 2 more
Classical Algorithmic Information Theory (AIT) provides a rigorous foundation for information-based similarity measurement, but classical formulations and their compression-based approximations largely operate at the syntactic level, making them sensitive to surface-level variation and insufficient for semantic…
Ming-Feng Yeh, Ching-Chuan Luo, Cheng-Lin Lu, Nianbo Liu
Smart manufacturing relies on programmable logic controllers (PLCs) that translate sensor inputs into actuator commands. Generating PLC programs in legacy textual languages such as Mitsubishi FX-series Instruction List (IL) remains an expert-only task, and IL’s deprecation in IEC 61131-3 Edition 3.0 leaves it…
Zhengbin Zou, Tao Jiang, Yizheng Wang, Tiancheng Xue + 2 more
The increasing complexity of software systems has rendered code vulnerability detection a critical aspect of software security. While deep learning-based approaches have advanced this field, challenges such as coarse-grained function-level detection, scalability limitations, and constrained accuracy persist. Although…
Angeliki Mouzaki, Vasiliki Desylla, Asimina M. Ralli, Maria Vlassopoulos + 1 more
Developmental language disorder (DLD) is a neurodevelopmental condition characterized by persistent difficulties in language acquisition, affecting both comprehension and production, and typically emerging in early childhood through deficits in morphosyntax, vocabulary and phonology. Although distinct from Specific…
M. Shahbaz Ismail, Sara Shahzad, Fahmi H. Quradaa, Sajid Anwar
Semantic code clone detection plays an essential role in software maintenance and quality assurance, as it helps uncover fragments of code that express the same logic even when their syntax has been altered or deliberately obfuscated. In this study, we propose a framework that combines hybrid representation learning…
Francisco Pinto-Santos, Carolina Zato, Héctor Quintián, Tian Cheng Li + 1 more
Software vulnerability analysis is critical for maintaining secure and reliable systems, yet traditional Deep Learning (DL) models often act as “black boxes,” lacking transparency and failing to leverage the explicit structural semantics of code. In this paper, we propose KG-HiAttention, a novel neuro-symbolic…
Zi Wang, Xiaoyu Zhu, Hongqiang Wang, Yichun Yu + 2 more
Formal verification ensures software correctness but faces challenges in kernel specification writing, which is labor-intensive, expertise-dependent, and limited to specific targets. For complex microkernels like seL4, these issues significantly reduce the practicality of formal methods. To address this, we propose…
Lars Vogt
This manuscript introduces the Semantic Units Framework, a technology-agnostic representational approach to semantic modularization in which statements and compound meaning structures are treated as first-class semantic units with explicit boundaries, identity, and epistemic status. Motivated by recurring limitations…
Stergios Chatzikyriakidis, Shalom Lappin
Neuro-symbolic (NeSy) approaches promise to overcome the limitations of purely neural and purely symbolic NLP. In this paper we survey recent development in NeSy NLP and propose a system classification framework that combines Kautz's integration types with Lappin's injective-federative distinction. We then apply this…
Rasmus Blanck, Bill Noble
Neural systems for Natural Language Inference (NLI) have seen impressive performance over the last ten years, but their ability to generalize beyond their training data has repeatedly been questioned. The NLI task has long been considered as a proxy for the wider problem of Natural Language Understanding (NLU)…
Andy E. Williams
Objective We introduce the Cognitive Near-Singularity (CNS) as a falsifiable phase transition in reasoning systems: the regime in which updates are globally contracting, closed under composition, and invariant to benign reparameterizations, so that heterogeneous reasoning processes converge to a common fixed point up…
Adam Pease, Richard Thompson
Human language is often vague and ambiguous. There have been many efforts to create formal languages and many attempts to translate human language into formal languages. Logic has a great deal of flexibility, not least in how the symbols used are defined. We anchor lexical elements in a formal ontology, which helps…