Tootfinder

This https://arxiv.org/abs/2401.08501 has been replaced.
link: https://scholar.google.com/scholar?q=a

ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation
Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap between theory and practice leaving fundamental questions unanswered: Can data-related and model-related uncertainty really be separated in practice? Which components of an uncertainty method are essent…

This https://arxiv.org/abs/2212.11055 has been replaced.
link: https://scholar.google.com/scholar?q=a

Coalgebraic Satisfiability Checking for Arithmetic $μ$-Calculi
The coalgebraic $μ$-calculus provides a generic semantic framework for fixpoint logics over systems whose branching type goes beyond the standard relational setup, e.g. probabilistic, weighted, or game-based. Previous work on the coalgebraic $μ$-calculus includes an exponential-time upper bound on satisfiability checking, which however relies on the availability of tableau rules for the next-step modalities that are sufficiently well-behaved in a formally defined sense; in particular, rule ma…

This https://arxiv.org/abs/2404.01692 has been replaced.
link: https://scholar.google.com/scholar?q=a

Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However, due to the ill-posed property of SR, it is challenging for typical SR methods to restore task-relevant high-frequency contents, which may dilute the advantage of utilizing the SR method. T…

This https://arxiv.org/abs/2404.01692 has been replaced.
link: https://scholar.google.com/scholar?q=a

Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
In real-world scenarios, image recognition tasks, such as semantic segmentation and object detection, often pose greater challenges due to the lack of information available within low-resolution (LR) content. Image super-resolution (SR) is one of the promising solutions for addressing the challenges. However, due to the ill-posed property of SR, it is challenging for typical SR methods to restore task-relevant high-frequency contents, which may dilute the advantage of utilizing the SR method. T…

This https://arxiv.org/abs/2310.01113 has been replaced.
link: https://scholar.google.com/scholar?q=a

HyperGraphDis: Leveraging Hypergraphs for Contextual and Social-Based Disinformation Detection
In light of the growing impact of disinformation on social, economic, and political landscapes, accurate and efficient identification methods are increasingly critical. This paper introduces HyperGraphDis, a novel approach for detecting disinformation on Twitter that employs a hypergraph-based representation to capture (i) the intricate social structures arising from retweet cascades, (ii) relational features among users, and (iii) semantic and topical nuances. Evaluated on four Twitter dataset…

This https://arxiv.org/abs/2212.01485 has been replaced.
link: https://scholar.google.com/scholar?q=a

A Theory of Semantic Communication
Semantic communication is an emerging research area that has gained a wide range of attention recently. Despite this growing interest, there remains a notable absence of a comprehensive and widely-accepted framework for characterizing semantic communication. This paper introduces a new conceptualization of semantic communication and formulates two fundamental problems, which we term language exploitation and language design. Our contention is that the challenge of language design can be effecti…

This https://arxiv.org/abs/2310.12931 has been replaced.
link: https://scholar.google.com/scholar?q=a

Eureka: Human-Level Reward Design via Coding Large Language Models
Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation tasks, such as dexterous pen spinning, remains an open problem. We bridge this fundamental gap and present Eureka, a human-level reward design algorithm powered by LLMs. Eureka exploits the remarkable zero-shot generation, code-writing, and in-context improvement capabilities of state-of-the-art LLMs, such as GPT-4, to …

This https://arxiv.org/abs/2205.02422 has been replaced.
link: https://scholar.google.com/scholar?q=a

Quantum Semantic Communications for Resource-Efficient Quantum Networking
Quantum communication networks (QCNs) utilize quantum mechanics for secure information transmission, but the reliance on fragile and expensive photonic quantum resources renders QCN resource optimization challenging. Unlike prior QCN works that relied on blindly compressing direct quantum embeddings of classical data, this letter proposes a novel quantum semantic communications (QSC) framework exploiting advancements in quantum machine learning and quantum semantic representations to extracts a…

This https://arxiv.org/abs/2203.05314 has been replaced.
link: https://scholar.google.com/scholar?q=a

SoK: On the Semantic AI Security in Autonomous Driving
Autonomous Driving (AD) systems rely on AI components to make safety and correct driving decisions. Unfortunately, today's AI algorithms are known to be generally vulnerable to adversarial attacks. However, for such AI component-level vulnerabilities to be semantically impactful at the system level, it needs to address non-trivial semantic gaps both (1) from the system-level attack input spaces to those at AI component level, and (2) from AI component-level attack impacts to those at the system…

This https://arxiv.org/abs/2206.13690 has been replaced.
link: https://scholar.google.com/scholar?q=a

Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA
In the realm of software development, the clarity, completeness, and comprehensiveness of requirements significantly impact the success of software systems. The Software Requirement Specification (SRS) document, a cornerstone of the software development life cycle, delineates both functional and nonfunctional requirements, playing a pivotal role in ensuring the quality and timely delivery of software projects. However, the inherent natural language representation of these requirements poses cha…

This https://arxiv.org/abs/2107.11190 has been replaced.
link: https://scholar.google.com/scholar?q=a

Semantic Communications for Speech Recognition
The traditional communications transmit all the source data represented by bits, regardless of the content of source and the semantic information required by the receiver. However, in some applications, the receiver only needs part of the source data that represents critical semantic information, which prompts to transmit the application-related information, especially when bandwidth resources are limited. In this paper, we consider a semantic communication system for speech recognition by desi…

This https://arxiv.org/abs/2310.07576 has been replaced.
link: https://scholar.google.com/scholar?q=a

Analyzing Trendy Twitter Hashtags in the 2022 French Election
Regressions trained to predict the future activity of social media users need rich features for accurate predictions. Many advanced models exist to generate such features; however, the time complexities of their computations are often prohibitive when they run on enormous data-sets. Some studies have shown that simple semantic network features can be rich enough to use for regressions without requiring complex computations. We propose a method for using semantic networks as user-level features …

This https://arxiv.org/abs/2401.17859 has been replaced.
link: https://scholar.google.com/scholar?q=a

Towards Semantic Consistency: Dirichlet Energy Driven Robust Multi-Modal Entity Alignment
In Multi-Modal Knowledge Graphs (MMKGs), Multi-Modal Entity Alignment (MMEA) is crucial for identifying identical entities across diverse modal attributes. However, semantic inconsistency, mainly due to missing modal attributes, poses a significant challenge. Traditional approaches rely on attribute interpolation, but this often introduces modality noise, distorting the original semantics. Moreover, the lack of a universal theoretical framework limits advancements in achieving semantic consiste…

This https://arxiv.org/abs/2312.10441 has been replaced.
link: https://scholar.google.com/scholar?q=a

Disjunctive Policies for Database-Backed Programs
When specifying security policies for databases, it is often natural to formulate disjunctive dependencies, where a piece of information may depend on at most one of two dependencies P1 or P2, but not both. A formal semantic model of such disjunctive dependencies, the Quantale of Information, was recently introduced by Hunt and Sands as a generalization of the Lattice of Information. In this paper, we seek to contribute to the understanding of disjunctive dependencies in database-backed program…

This https://arxiv.org/abs/2206.13690 has been replaced.
link: https://scholar.google.com/scholar?q=a

Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA
In the realm of software development, the clarity, completeness, and comprehensiveness of requirements significantly impact the success of software systems. The Software Requirement Specification (SRS) document, a cornerstone of the software development life cycle, delineates both functional and nonfunctional requirements, playing a pivotal role in ensuring the quality and timely delivery of software projects. However, the inherent natural language representation of these requirements poses cha…

This https://arxiv.org/abs/2310.10347 has been replaced.
link: https://scholar.google.com/scholar?q=a

Editable-DeepSC: Cross-Modal Editable Semantic Communication Systems
Different from \emph{data-oriented} communication systems primarily focusing on how to accurately transmit every bit of data, \emph{task-oriented} semantic communication systems \iffalse, which are capturing widespread research attention recently,\fi only transmit the specific semantic information required by downstream tasks, striving to minimize the communication overhead and maintain competitive tasks execution performance in the presence of channel noise. However, it is worth noting that in…

Directed Criteria Citation Recommendation and Ranking Through Link Prediction
William Watson, Lawrence Yong
https://arxiv.org/abs/2403.18855 https://

Directed Criteria Citation Recommendation and Ranking Through Link Prediction
We explore link prediction as a proxy for automatically surfacing documents from existing literature that might be topically or contextually relevant to a new document. Our model uses transformer-based graph embeddings to encode the meaning of each document, presented as a node within a citation network. We show that the semantic representations that our model generates can outperform other content-based methods in recommendation and ranking tasks. This provides a holistic approach to exploring…

This https://arxiv.org/abs/2404.00446 has been replaced.
link: https://scholar.google.com/scholar?q=a

Towards a Semantic Characterisation of Global Type Well-formedness
We address the question of characterising the well-formedness properties of multiparty session types semantically, i.e., as properties of the semantic model used to interpret types. Choosing Prime Event Structures (PESs) as our semantic model, we present semantic counterparts for the two properties that underpin global type well-formedness, namely projectability and boundedness, in this model. As a first step towards a characterisation of the class of PESs corresponding to well-formed global t…

This https://arxiv.org/abs/2208.06449 has been replaced.
link: https://scholar.google.com/scholar?q=a

When CNN Meet with ViT: Towards Semi-Supervised Learning for Multi-Class Medical Image Semantic Segmentation
Due to the lack of quality annotation in medical imaging community, semi-supervised learning methods are highly valued in image semantic segmentation tasks. In this paper, an advanced consistency-aware pseudo-label-based self-ensembling approach is presented to fully utilize the power of Vision Transformer(ViT) and Convolutional Neural Network(CNN) in semi-supervised learning. Our proposed framework consists of a feature-learning module which is enhanced by ViT and CNN mutually, and a guidance …

This https://arxiv.org/abs/2402.05391 has been replaced.
link: https://scholar.google.com/scholar?q=a

Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the semantic web community's exploration into multi-modal dimensions unlocking new avenues for innovation. In this survey, we carefully review over 300 articles, focusing on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into the MMKG realm. We begin by defining KGs and MM…

This https://arxiv.org/abs/2210.13034 has been replaced.
link: https://scholar.google.com/scholar?q=a

Subspace Representations for Soft Set Operations and Sentence Similarities
In the field of natural language processing (NLP), continuous vector representations are crucial for capturing the semantic meanings of individual words. Yet, when it comes to the representations of sets of words, the conventional vector-based approaches often struggle with expressiveness and lack the essential set operations such as union, intersection, and complement. Inspired by quantum logic, we realize the representation of word sets and corresponding set operations within pre-trained word…

This https://arxiv.org/abs/2210.09372 has been replaced.
link: https://scholar.google.com/scholar?q=a

Goal-Oriented Semantic Communications for 6G Networks
Upon the arrival of emerging devices, including Extended Reality (XR) and Unmanned Aerial Vehicles (UAVs), the traditional communication framework is approaching Shannon's physical capacity limit and fails to guarantee the massive amount of transmission within latency requirements. By jointly exploiting the context of data and its importance to the task, an emerging communication paradigm shift to semantic level and effectiveness level is envisioned to be a key revolution in Sixth Generation (6…

This https://arxiv.org/abs/2310.16838 has been replaced.
link: https://scholar.google.com/scholar?q=a

SparseDFF: Sparse-View Feature Distillation for One-Shot Dexterous Manipulation
Humans demonstrate remarkable skill in transferring manipulation abilities across objects of varying shapes, poses, and appearances, a capability rooted in their understanding of semantic correspondences between different instances. To equip robots with a similar high-level comprehension, we present SparseDFF, a novel DFF for 3D scenes utilizing large 2D vision models to extract semantic features from sparse RGBD images, a domain where research is limited despite its relevance to many tasks wit…

This https://arxiv.org/abs/2401.07278 has been replaced.
link: https://scholar.google.com/scholar?q=a

Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells
Artificial Intelligence (AI) in healthcare, especially in white blood cell cancer diagnosis, is hindered by two primary challenges: the lack of large-scale labeled datasets for white blood cell (WBC) segmentation and outdated segmentation methods. These challenges inhibit the development of more accurate and modern techniques to diagnose cancer relating to white blood cells. To address the first challenge, a semi-supervised learning framework should be devised to efficiently capitalize on the s…

This https://arxiv.org/abs/1804.11222 has been replaced.
link: https://scholar.google.com/scholar?q=a

An optimized KE-tableau-based system for reasoning in the description logic $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$ (Extended Version)
We present a KE-tableau-based procedure for the main TBox and ABox reasoning tasks for the description logic $\mathcal{DL}\langle \mathsf{4LQS^{R,\!\times}}\rangle(\mathbf{D})$, in short $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$. The logic $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$, representable in the decidable multi-sorted quantified set-theoretic fragment $\mathsf{4LQS^R}$, combines the high scalability and efficiency of rule languages such as the Semantic Web Rule Language (SWRL) with the exp…

This https://arxiv.org/abs/2404.01705 has been replaced.
link: https://scholar.google.com/scholar?q=a

Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model
High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT). CNN-based methods struggle with handling such high-resolution images due to their limited receptive field, while ViT faces challenges in handling long sequences. Inspired by Mamba, which adopts a State Space Model (SSM) to efficiently capture global semantic information, we propose a semantic segmentation framework for h…

This https://arxiv.org/abs/1804.11222 has been replaced.
link: https://scholar.google.com/scholar?q=a

An optimized KE-tableau-based system for reasoning in the description logic $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$ (Extended Version)
We present a KE-tableau-based procedure for the main TBox and ABox reasoning tasks for the description logic $\mathcal{DL}\langle \mathsf{4LQS^{R,\!\times}}\rangle(\mathbf{D})$, in short $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$. The logic $\mathcal{DL}_{\mathbf{D}}^{4,\!\times}$, representable in the decidable multi-sorted quantified set-theoretic fragment $\mathsf{4LQS^R}$, combines the high scalability and efficiency of rule languages such as the Semantic Web Rule Language (SWRL) with the exp…

This https://arxiv.org/abs/2310.05116 has been replaced.
link: https://scholar.google.com/scholar?q=a

Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Document-level event argument extraction (EAE) is a vital but challenging subtask in information extraction. Most existing approaches focus on the interaction between arguments and event triggers, ignoring two critical points: the information of contextual clues and the semantic correlations among argument roles. In this paper, we propose the CARLG model, which consists of two modules: Contextual Clues Aggregation (CCA) and Role-based Latent Information Guidance (RLIG), effectively leveraging c…

DWE : Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Shezheng Song, Shasha Li, Shan Zhao, Xiaopeng Li, Chengyu Wang, Jie Yu, Jun Ma, Tianwei Yan, Bin Ji, Xiaoguang Mao
https://arxiv.org/abs/2404.04818

DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base. Current methods facing main issues: (1)treating the entire image as input may contain redundant information. (2)the insufficient utilization of entity-related information, such as attributes in images. (3)semantic inconsistency between the entity in knowledge base and its representation. To this end, we propose DWE+…

This https://arxiv.org/abs/2402.03703 has been replaced.
link: https://scholar.google.com/scholar?q=a

Hierarchical Large Language Models in Cloud Edge End Architecture for Heterogeneous Robot Cluster Control
Despite their powerful semantic understanding and code generation capabilities, Large Language Models (LLMs) still face challenges when dealing with complex tasks. Multi agent strategy generation and motion control are highly complex domains that inherently require experts from multiple fields to collaborate. To enhance multi agent strategy generation and motion control, we propose an innovative architecture that employs the concept of a cloud edge end hierarchical structure. By leveraging mult…

This https://arxiv.org/abs/2401.16184 has been replaced.
link: https://scholar.google.com/scholar?q=a

On the Semantics of LM Latent Space: A Vocabulary-defined Approach
Understanding the latent space of language models (LM) is crucial to refining their performance and interpretability. Existing analyses often fall short in providing disentangled (model-centric) insights into LM semantics, and neglect essential aspects of LM adaption. In response, we introduce a pioneering method called vocabulary-defined semantics, which establishes a reference frame within the LM latent space, ensuring disentangled semantic analysis grounded in LM vocabulary. Our approach tra…

This https://arxiv.org/abs/2402.05872 has been replaced.
link: https://scholar.google.com/scholar?q=a

You've Got to Feel It To Believe It: Multi-Modal Bayesian Inference for Semantic and Property Prediction
Robots must be able to understand their surroundings to perform complex tasks in challenging environments and many of these complex tasks require estimates of physical properties such as friction or weight. Estimating such properties using learning is challenging due to the large amounts of labelled data required for training and the difficulty of updating these learned models online at run time. To overcome these challenges, this paper introduces a novel, multi-modal approach for representing …

This https://arxiv.org/abs/2304.00848 has been replaced.
link: https://scholar.google.com/scholar?q=a

Towards Goal-Oriented Semantic Communications: New Metrics, Open Challenges, and Future Research Directions
Since Shannon's pioneering masterpiece which established the prototype of modern information theory, the goal of communications has long been promising accurate message reconstruction at a refined bit-by-bit level, which deliberately neglects the semantics/effectiveness aspect of information. Nevertheless, the recent development of wireless technologies and the spurt of deep learning (DL) techniques allow us to reclaim the meaning/usefulness aspect in the design of future 6G communication parad…

This https://arxiv.org/abs/2311.07277 has been replaced.
link: https://scholar.google.com/scholar?q=a

AdaCCD: Adaptive Semantic Contrasts Discovery Based Cross Lingual Adaptation for Code Clone Detection
Code Clone Detection, which aims to retrieve functionally similar programs from large code bases, has been attracting increasing attention. Modern software often involves a diverse range of programming languages. However, current code clone detection methods are generally limited to only a few popular programming languages due to insufficient annotated data as well as their own model design constraints. To address these issues, we present AdaCCD, a novel cross-lingual adaptation method that can…

This https://arxiv.org/abs/2404.03528 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering
Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting information flow in a very effective manner. Despite being widely used globally, Bangla is relatively underrepresented in KGs due to a lack of comprehensive datasets, encoders, NER (named entity recognition) models, POS (part-of-speech) taggers, and le…

This https://arxiv.org/abs/2403.01818 has been replaced.
link: https://scholar.google.com/scholar?q=a

AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling, which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However, the two training flows are separate, which allows labeled data to dominate the training process, resulting in low-quality pseudo labels and, consequently, sub-o…

This https://arxiv.org/abs/2402.05872 has been replaced.
link: https://scholar.google.com/scholar?q=a

You've Got to Feel It To Believe It: Multi-Modal Bayesian Inference for Semantic and Property Prediction
Robots must be able to understand their surroundings to perform complex tasks in challenging environments and many of these complex tasks require estimates of physical properties such as friction or weight. Estimating such properties using learning is challenging due to the large amounts of labelled data required for training and the difficulty of updating these learned models online at run time. To overcome these challenges, this paper introduces a novel, multi-modal approach for representing …

This https://arxiv.org/abs/2402.01740 has been replaced.
link: https://scholar.google.com/scholar?q=a

Compensatory Biases Under Cognitive Load: Reducing Selection Bias in Large Language Models
Large Language Models (LLMs) like gpt-3.5-turbo and claude-instant-1.2 have become instrumental in interpreting and executing semantic-based tasks. Unfortunately, these models' inherent biases, akin to human cognitive biases, adversely affect their performance. Particularly affected is object selection from lists; a fundamental operation in digital navigation and decision-making. This research critically examines these biases and quantifies the effects on a representative list selection task. T…

This https://arxiv.org/abs/2404.00685 has been replaced.
link: https://scholar.google.com/scholar?q=a

Scaling Properties of Speech Language Models
Speech Language Models (SLMs) aim to learn language from raw audio, without textual resources. Despite significant advances, our current models exhibit weak syntax and semantic abilities. However, if the scaling properties of neural language models hold for the speech modality, these abilities will improve as the amount of compute used for training increases. In this paper, we use models of this scaling behavior to estimate the scale at which our current methods will yield a SLM with the Englis…

This https://arxiv.org/abs/2404.01887 has been replaced.
link: https://scholar.google.com/scholar?q=a

3D scene generation from scene graphs and self-attention
Synthesizing realistic and diverse indoor 3D scene layouts in a controllable fashion opens up applications in simulated navigation and virtual reality. As concise and robust representations of a scene, scene graphs have proven to be well-suited as the semantic control on the generated layout. We present a variant of the conditional variational autoencoder (cVAE) model to synthesize 3D scenes from scene graphs and floor plans. We exploit the properties of self-attention layers to capture high-le…

This https://arxiv.org/abs/2404.01655 has been replaced.
link: https://scholar.google.com/scholar?q=a

FashionEngine: Interactive Generation and Editing of 3D Clothed Humans
We present FashionEngine, an interactive 3D human generation and editing system that allows us to design 3D digital humans in a way that aligns with how humans interact with the world, such as natural languages, visual perceptions, and hand-drawing. FashionEngine automates the 3D human production with three key components: 1) A pre-trained 3D human diffusion model that learns to model 3D humans in a semantic UV latent space from 2D image training data, which provides strong priors for diverse g…