vb000/SemanticHearing
Real-time binaural target sound extraction model. (⭐ 97)
Semantic Importance-Aware Communications Using Pre-trained Language Models
This letter proposes a semantic importance-aware communication (SIAC) scheme using pre-trained language models (e.g., ChatGPT, BERT, etc.). Specifically, we propose a cross-layer design with a pre-trained language model embedded in/connected by the c...
A lemma on closures and its application to modularity in logic programming semantics
This note points out a lemma on closures of monotonic increasing functions and shows how it is applicable to decomposition and modularity for semantics defined as the least fixedpoint of some monotonic function. In particular it applies to numerous s...
Semantic integration - Wikipedia
structure but also context or value. In enterprise application integration (EAI), semantic integration can facilitate or even automate the communication between
Semantic Caching for Improving Web Affordability
The rapid growth of web content has led to increasingly large webpages, posing significant challenges for Internet affordability, especially in developing countries where data costs remain prohibitively high. We propose semantic caching using Large L...
Screen2Vec: Semantic Embedding of GUI Screens and GUI Components
Representing the semantics of GUI screens and components is crucial to data-driven computational methods for modeling user-GUI interactions and mining GUI designs. Existing GUI semantic representations are limited to encoding either the textual conte...
MESSI: A Multi-Elevation Semantic Segmentation Image Dataset of an Urban Environment
This paper presents a Multi-Elevation Semantic Segmentation Image (MESSI) dataset comprising 2525 images taken by a drone flying over dense urban environments. MESSI is unique in two main features. First, it contains images from various altitudes, al...
Three Steps to Heaven: Semantic Publishing in a Real World Workflow
Semantic publishing offers the promise of computable papers, enriched visualisation and a realisation of the linked data ideal. In reality, however, the publication process contrives to prevent richer semantics while culminating in a `lumpen' PDF. In...
Patton: Language Model Pretraining on Text-Rich Networks
A real-world text corpus sometimes comprises not only text documents but also semantic links between them (e.g., academic papers in a bibliographic network are linked by citations and co-authorships). Text documents and semantic connections form a te...
Single-Stage Semantic Segmentation from Image Labels
Recent years have seen a rapid growth in new approaches improving the accuracy of semantic segmentation in a weakly supervised setting, i.e. with only image-level labels available for training. However, this has come at the cost of increased model co...
Weakly Supervised Co-training with Swapping Assignments for Semantic Segmentation
Class activation maps (CAMs) are commonly employed in weakly supervised semantic segmentation (WSSS) to produce pseudo-labels. Due to incomplete or excessive class activation, existing studies often resort to offline CAM refinement, introducing addit...
Widening - Wikipedia
Look up widening in Wiktionary, the free dictionary. Widening may refer to: Semantic change § Types of semantic change Widening (computer science), several
Semantic Modeling with Foundries
We analyze challenges for the development of the Human Activities and Infrastructures Foundry. We explore a rich semantic modeling approach to describe two Korean ceramic water droppers used to mix ink for calligraphy, how they were produced and the...
Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
Semantic scene completion (SSC) aims to infer both the 3D geometry and semantics of a scene from single images. In contrast to prior work on SSC that heavily relies on expensive ground-truth annotations, we approach SSC in an unsupervised setting. Ou...
SENT Map -- Semantically Enhanced Topological Maps with Foundation Models
We introduce SENT-Map, a semantically enhanced topological map for representing indoor environments, designed to support autonomous navigation and manipulation by leveraging advancements in foundational models (FMs). Through representing the environm...
Dung's semantics satisfy attack removal monotonicity
We show that preferred, stable, complete, and grounded semantics satisfy attack removal monotonicity. This means that if an attack from b to a is removed, the status of a cannot worsen, e.g. if a was skeptically accepted, it cannot become rejected....
Nail Polish Try-On: Realtime Semantic Segmentation of Small Objects for Native and Browser…
We provide a system for semantic segmentation of small objects that enables nail polish try-on AR applications to run client-side in realtime in native and web mobile applications. By adjusting input resolution and neural network depth, our model des...
Can You Learn Semantics Through Next-Word Prediction? The Case of Entailment
Do LMs infer the semantics of text from co-occurrence patterns in their training data? Merrill et al. (2022) argue that, in theory, sentence co-occurrence probabilities predicted by an optimal LM should reflect the entailment relationship of the cons...
Feature2Vec: Distributional semantic modelling of human property knowledge
Feature norm datasets of human conceptual knowledge, collected in surveys of human volunteers, yield highly interpretable models of word meaning and play an important role in neurolinguistic research on semantic cognition. However, these datasets are...
Cambridge Semantics - Wikipedia
finalist. Cambridge Semantics named KMWorld’s 2016 ‘100 Companies That Matter in Knowledge Management’ and KMWorld Trend-Setting Products of 2015. Cambridge