Tootfinder

Ontological differentiation as a measure of semantic accuracy
Pablo Garcia-Cuadrillero, Fabio Revuelta, Jose Angel Capitan
https://arxiv.org/abs/2507.06208

Ontological differentiation as a measure of semantic accuracy
Understanding semantic relationships within complex networks derived from lexical resources is fundamental for network science and language modeling. While network embedding methods capture contextual similarity, quantifying semantic distance based directly on explicit definitional structure remains challenging. Accurate measures of semantic similarity allow for navigation on lexical networks based on maximizing semantic similarity in each navigation jump (Semantic Navigation, SN). This work in…

Text-guided multi-stage cross-perception network for medical image segmentation
Gaoyu Chen
https://arxiv.org/abs/2506.07475 https://a…

Text-guided multi-stage cross-perception network for medical image segmentation
Medical image segmentation plays a crucial role in clinical medicine, serving as a tool for auxiliary diagnosis, treatment planning, and disease monitoring, thus facilitating physicians in the study and treatment of diseases. However, existing medical image segmentation methods are limited by the weak semantic expression of the target segmentation regions, which is caused by the low contrast between the target and non-target segmentation regions. To address this limitation, text prompt informat…

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
Sofiia Chorna, Kateryna Tarelkina, Elo\"ise Berthier, Gianni Franchi
https://arxiv.org/abs/2507.05810

Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
While concept-based interpretability methods have traditionally focused on local explanations of neural network predictions, we propose a novel framework and interactive tool that extends these methods into the domain of mechanistic interpretability. Our approach enables a global dissection of model behavior by analyzing how high-level semantic attributes (referred to as concepts) emerge, interact, and propagate through internal model components. Unlike prior work that isolates individual neuro…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/10]:
- AASeg: Attention Aware Network for Real Time Semantic Segmentation
Abhinav Sagar

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.03248 https://

Federated Learning with Feature Reconstruction for Vector Quantization based Semantic Communication
Recent advancements in semantic communication have primarily focused on image transmission, where neural network (NN)-based joint source-channel coding (JSCC) modules play a central role. However, such systems often experience semantic communication errors due to mismatched knowledge bases between users and performance degradation from outdated models, necessitating regular model updates. To address these challenges in vector quantization (VQ)-based image semantic communication systems, we prop…

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
Shixiao Wang, Yifan Zhuang, Runsheng Zhang, Zhijun Song
https://arxiv.org/abs/2507.00540

Capsule Network-Based Semantic Intent Modeling for Human-Computer Interaction
This paper proposes a user semantic intent modeling algorithm based on Capsule Networks to address the problem of insufficient accuracy in intent recognition for human-computer interaction. The method represents semantic features in input text through a vectorized capsule structure. It uses a dynamic routing mechanism to transfer information across multiple capsule layers. This helps capture hierarchical relationships and part-whole structures between semantic entities more effectively. The mod…

Semantic Network Analysis of Achievement Standards in Physics of 2022 Revised Curriculum
Jibeom Seo, Jun Cho, Sukyung Han, Meesoon Ha
https://arxiv.org/abs/2508.02864 https://…

Semantic Network Analysis of Achievement Standards in Physics of 2022 Revised Curriculum
We investigate semantic networks of achievement standards for physics subjects in the 2022 revised curriculum to derive information embedded in the curriculum. We extract each subject's keywords with node strength and random-walk betweenness, detect communities of physics terms by the optimized greedy algorithm, and find the connectivity of physics subjects using bipartite networks. The network analysis reveals three remarkable results: First, keywords are about scientific thinking and practice…

An LLM Agent for Functional Bug Detection in Network Protocols
Mingwei Zheng, Chengpeng Wang, Xuwei Liu, Jinyao Guo, Shiwei Feng, Xiangyu Zhang
https://arxiv.org/abs/2506.00714

An LLM Agent for Functional Bug Detection in Network Protocols
Functional correctness is critical for ensuring the reliability and security of network protocol implementations. Functional bugs, instances where implementations diverge from behaviors specified in RFC documents, can lead to severe consequences, including faulty routing, authentication bypasses, and service disruptions. Detecting these bugs requires deep semantic analysis across specification documents and source code, a task beyond the capabilities of traditional static analysis tools. This p…

Diffusion-based Task-oriented Semantic Communications with Model Inversion Attack
Xuesong Wang, Mo Li, Xingyan Shi, Zhaoqian Liu, Shenghao Yang
https://arxiv.org/abs/2506.19886

Diffusion-based Task-oriented Semantic Communications with Model Inversion Attack
Semantic communication has emerged as a promising neural network-based system design for 6G networks. Task-oriented semantic communication is a novel paradigm whose core goal is to efficiently complete specific tasks by transmitting semantic information, optimizing communication efficiency and task performance. The key challenge lies in preserving privacy while maintaining task accuracy, as this scenario is susceptible to model inversion attacks. In such attacks, adversaries can restore or even…

Learning Constraints Directly from Network Data
Hongyu H\`e, Minhao Jin, Maria Apostolaki
https://arxiv.org/abs/2506.23964 https://ar…

Learning Constraints Directly from Network Data
Network data conforms to a wide range of rules that arise from protocols, design principles, and deployment decisions (e.g., a packet's queuing delay must be less than its end-to-end delay). Formalizing such rules as logic constraints can (i) improve the quality of synthetic data, (ii) reduce the brittleness of machine learning (ML) models, and (iii) improve semantic understanding of network measurements. However, these benefits remain out of reach if rule extraction is manual or solely reliant…

Unified Network-Based Representation of BIM Models for Embedding Semantic, Spatial, and Topological Data
Jin Han, Xin-Zheng Lu, Jia-Rui Lin
https://arxiv.org/abs/2505.22670

Unified Network-Based Representation of BIM Models for Embedding Semantic, Spatial, and Topological Data
Building Information Modeling (BIM) has revolutionized the construction industry by providing a comprehensive digital representation of building structures throughout their lifecycle. However, existing research lacks effective methods for capturing the complex spatial and topological relationships between components in BIM models, which are essential for understanding design patterns and enhancing decision-making. This study proposes a unified network-based representation method that integrates…

This https://arxiv.org/abs/2505.08052 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

NAZM: Network Analysis of Zonal Metrics in Persian Poetic Tradition
This study formalizes a computational model to simulate classical Persian poets' dynamics of influence through constructing a multi-dimensional similarity network. Using a rigorously curated dataset based on Ganjoor's corpus, we draw upon semantic, lexical, stylistic, thematic, and metrical features to demarcate each poet's corpus. Each is contained within weighted similarity matrices, which are then appended to generate an aggregate graph showing poet-to-poet influence. Further network investi…

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Rajpreet Singh, Novak Bo\v{s}kov, Aditya Gudal, Manzoor A. Khan
https://arxiv.org/abs/2506.01571

A Ranking Framework for Network Resource Allocation and Scheduling via Hypergraphs
Resource allocation and scheduling are a common problem in various distributed systems. Although widely studied, the state-of-the-art solutions either do not scale or lack the expressive power to capture the most complex instances of the problem. To that end, we present a mathematical framework for hypergraph ranking and analysis, unifying graph theory, lattice theory, and semantic analysis. In our fundamental theorem, we prove the existence of partial order on entities of hypergraphs, extendin…

Feature Reconstruction Aided Federated Learning for Image Semantic Communication
Yoon Huh, Bumjun Kim, Wan Choi
https://arxiv.org/abs/2508.02048 https://ar…

Feature Reconstruction Aided Federated Learning for Image Semantic Communication
Research in semantic communication has garnered considerable attention, particularly in the area of image transmission, where joint source-channel coding (JSCC)-based neural network (NN) modules are frequently employed. However, these systems often experience performance degradation over time due to an outdated knowledge base, highlighting the need for periodic updates. To address this challenge in the context of training JSCC modules for image transmission, we propose a federated learning (FL)…

SDC-Net: A Domain Adaptation Framework with Semantic-Dynamic Consistency for Cross-Subject EEG Emotion Recognition
Jiahao Tang, Youjun Li, Xiangting Fan, Yangxuan Zheng, Siyuan Lu, Xueping Li, Peng Fang, Chenxi Li, Zi-Gang Huang
https://arxiv.org/abs/2507.17524

SDC-Net: A Domain Adaptation Framework with Semantic-Dynamic Consistency for Cross-Subject EEG Emotion Recognition
Electroencephalography(EEG) based emotion recognition holds great promise for affective brain-computer interfaces (aBCIs), yet practical deployment remains challenging due to substantial inter-subject variability and the lack of labeled data in target domains. To overcome these limitations, we present a novel unsupervised Semantic-Dynamic Consistency domain adaptation network for fully label-free cross-subject EEG emotion recognition. First, we introduce a Same-Subject Same-Trial Mixup strategy…

SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Peican Zhu, Yubo Jing, Le Cheng, Bin Chen, Xiaodong Cui, Lianwei Wu, Keke Tang
https://arxiv.org/abs/2507.13415

SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection
Previous studies on multimodal fake news detection mainly focus on the alignment and integration of cross-modal features, as well as the application of text-image consistency. However, they overlook the semantic enhancement effects of large multimodal models and pay little attention to the emotional features of news. In addition, people find that fake news is more inclined to contain negative emotions than real ones. Therefore, we propose a novel Semantic Enhancement and Emotional Reasoning (SE…

Learning-Based Interface for Semantic Communication with Bit Importance Awareness
Wenzheng Kong, Wenyi Zhang
https://arxiv.org/abs/2507.12850 https://

Learning-Based Interface for Semantic Communication with Bit Importance Awareness
Joint source-channel coding (JSCC) is an effective approach for semantic communication. However, current JSCC methods are difficult to integrate with existing communication network architectures, where application and network providers are typically different entities. Recently, a novel paradigm termed Split DeepJSCC has been under consideration to address this challenge. Split DeepJSCC employs a bit-level interface that enables separate design of source and channel codes, ensuring compatibilit…

Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network
Yanjun Dai, Haoyang Feng, Yuan Gao
https://arxiv.org/abs/2506.13787

Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network
While online advertising is highly dependent on implicit interaction networks of anonymous users for engagement inference, and for the selection and optimization of delivery strategies, existing graph models seldom can capture the multi-scale temporal, semantic and higher-order dependency features of these interaction networks, thus it's hard to describe the complicated patterns of the anonymous behavior. In this paper, we propose Decoupled Temporal-Hierarchical Graph Neural Network (DTH-GNN), …

MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection
Zhe Yee Tan
https://arxiv.org/abs/2507.02668 https://

MEGANet-W: A Wavelet-Driven Edge-Guided Attention Framework for Weak Boundary Polyp Detection
Colorectal polyp segmentation is critical for early detection of colorectal cancer, yet weak and low contrast boundaries significantly limit automated accuracy. Existing deep models either blur fine edge details or rely on handcrafted filters that perform poorly under variable imaging conditions. We propose MEGANet-W, a Wavelet Driven Edge Guided Attention Network that injects directional, parameter free Haar wavelet edge maps into each decoder stage to recalibrate semantic features. Our two ma…

SoundSculpt: Direction and Semantics Driven Ambisonic Target Sound Extraction
Tuochao Chen, D Shin, Hakan Erdogan, Sinan Hersek
https://arxiv.org/abs/2506.00273

SoundSculpt: Direction and Semantics Driven Ambisonic Target Sound Extraction
This paper introduces SoundSculpt, a neural network designed to extract target sound fields from ambisonic recordings. SoundSculpt employs an ambisonic-in-ambisonic-out architecture and is conditioned on both spatial information (e.g., target direction obtained by pointing at an immersive video) and semantic embeddings (e.g., derived from image segmentation and captioning). Trained and evaluated on synthetic and real ambisonic mixtures, SoundSculpt demonstrates superior performance compared to …

This https://arxiv.org/abs/2505.06612 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSI_…

Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation
In the era of rapid development of social media, social recommendation systems as hybrid recommendation systems have been widely applied. Existing methods capture interest similarity between users to filter out interest-irrelevant relations in social networks that inevitably decrease recommendation accuracy, however, limited research has a focus on the mutual influence of semantic information between the social network and the user-item interaction network for further improving social recommend…

Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
Simone Fiorellino, Claudio Battiloro, Emilio Calvanese Strinati, Paolo Di Lorenzo
https://arxiv.org/abs/2507.17835

Frame-Based Zero-Shot Semantic Channel Equalization for AI-Native Communications
In future AI-native wireless networks, the presence of mismatches between the latent spaces of independently designed and trained deep neural network (DNN) encoders may impede mutual understanding due to the emergence of semantic channel noise. This undermines the receiver's ability to interpret transmitted representations, thereby reducing overall system performance. To address this issue, we propose the Parseval Frame Equalizer (PFE), a zero-shot, frame-based semantic channel equalizer that a…

Hybrid Semantic-Complementary Transmission for High-Fidelity Image Reconstruction
Hyelin Nam, Jihong Park, Jinho Choi, Seong-Lyun Kim
https://arxiv.org/abs/2507.17196 https://…

Hybrid Semantic-Complementary Transmission for High-Fidelity Image Reconstruction
Recent advances in semantic communication (SC) have introduced neural network (NN)-based transceivers that convey semantic representation (SR) of signals such as images. However, these NNs are trained over diverse image distributions and thus often fail to reconstruct fine-grained image-specific details. To overcome this limited reconstruction fidelity, we propose an extended SC framework, hybrid semantic communication (HSC), which supplements SR with complementary representation (CR) capturing…

SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization
Hao Zhang, Shuo Shao, Song Li, Zhenyu Zhong, Yan Liu, Zhan Qin, Kui Ren
https://arxiv.org/abs/2506.16981

SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization
End-point monitoring solutions are widely deployed in today's enterprise environments to support advanced attack detection and investigation. These monitors continuously record system-level activities as audit logs and provide deep visibility into security events. Unfortunately, existing methods of semantic analysis based on audit logs have low granularity, only reaching the system call level, making it difficult to effectively classify highly covert behaviors. Additionally, existing works main…

AI Enabled 6G for Semantic Metaverse: Prospects, Challenges and Solutions for Future Wireless VR
Muhammad Ahmed Mohsin, Sagnik Bhattacharya, Abhiram Gorle, Muhammad Ali Jamshed, John M. Cioffi
https://arxiv.org/abs/2507.19124

AI Enabled 6G for Semantic Metaverse: Prospects, Challenges and Solutions for Future Wireless VR
Wireless support of virtual reality (VR) has challenges when a network has multiple users, particularly for 3D VR gaming, digital AI avatars, and remote team collaboration. This work addresses these challenges through investigation of the low-rank channels that inevitably occur when there are more active users than there are degrees of spatial freedom, effectively often the number of antennas. The presented approach uses optimal nonlinear transceivers, equivalently generalized decision-feedback…

LLMs Between the Nodes: Community Discovery Beyond Vectors
Ekta Gujral, Apurva Sinha
https://arxiv.org/abs/2507.22955 https://arxiv.org/pdf/2507.22955

LLMs Between the Nodes: Community Discovery Beyond Vectors
Community detection in social network graphs plays a vital role in uncovering group dynamics, influence pathways, and the spread of information. Traditional methods focus primarily on graph structural properties, but recent advancements in Large Language Models (LLMs) open up new avenues for integrating semantic and contextual information into this task. In this paper, we present a detailed investigation into how various LLM-based approaches perform in identifying communities within social grap…

A GenAI System for Improved FAIR Independent Biological Database Integration
Syed N. Sakib, Kallol Naha, Sajratul Y. Rubaiat, Hasan M. Jamil
https://arxiv.org/abs/2506.17934

A GenAI System for Improved FAIR Independent Biological Database Integration
Life sciences research increasingly requires identifying, accessing, and effectively processing data from an ever-evolving array of information sources on the Linked Open Data (LOD) network. This dynamic landscape places a significant burden on researchers, as the quality of query responses depends heavily on the selection and semantic integration of data sources --processes that are often labor-intensive, error-prone, and costly. While the adoption of FAIR (Findable, Accessible, Interoperable,…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/8]:
Latent Structured Hopfield Network for Semantic Association and Retrieval

A Simple but Accurate Approximation for Multivariate Gaussian Rate-Distortion Function and Its Application in Maximal Coding Rate Reduction
Zhenglin Huang, Qifa Yan, Bin Dai, Xiaohu Tang
https://arxiv.org/abs/2506.18613

A Simple but Accurate Approximation for Multivariate Gaussian Rate-Distortion Function and Its Application in Maximal Coding Rate Reduction
The multivariate Gaussian rate-distortion (RD) function is crucial in various applications, such as digital communications, data storage, or neural networks. However, the complex form of the multivariate Gaussian RD function prevents its application in many neural network-based scenarios that rely on its analytical properties, for example, white-box neural networks, multi-device task-oriented communication, and semantic communication. This paper proposes a simple but accurate approximation for …

Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission
Chen Zhu, Siyun Liang, Zhouxiang Zhao, Jianrong Bao, Zhaohui Yang, Zhaoyang Zhang, Dusit Niyato
https://arxiv.org/abs/2507.11913

Scene Graph-Aided Probabilistic Semantic Communication for Image Transmission
Semantic communication emphasizes the transmission of meaning rather than raw symbols. It offers a promising solution to alleviate network congestion and improve transmission efficiency. In this paper, we propose a wireless image communication framework that employs probability graphs as shared semantic knowledge base among distributed users. High-level image semantics are represented via scene graphs, and a two-stage compression algorithm is devised to remove predictable components based on le…

Conan: A Chunkwise Online Network for Zero-Shot Adaptive Voice Conversion
Yu Zhang, Baotong Tian, Zhiyao Duan
https://arxiv.org/abs/2507.14534 https://

Conan: A Chunkwise Online Network for Zero-Shot Adaptive Voice Conversion
Zero-shot online voice conversion (VC) holds significant promise for real-time communications and entertainment. However, current VC models struggle to preserve semantic fidelity under real-time constraints, deliver natural-sounding conversions, and adapt effectively to unseen speaker characteristics. To address these challenges, we introduce Conan, a chunkwise online zero-shot voice conversion model that preserves the content of the source while matching the voice timbre and styles of referenc…

The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment
Lorenz Wendlinger, Simon Alexander Nonn, Abdullah Al Zubaer, Michael Granitzer
https://arxiv.org/abs/2506.22165

The Missing Link: Joint Legal Citation Prediction using Heterogeneous Graph Enrichment
Legal systems heavily rely on cross-citations of legal norms as well as previous court decisions. Practitioners, novices and legal AI systems need access to these relevant data to inform appraisals and judgments. We propose a Graph-Neural-Network (GNN) link prediction model that can identify Case-Law and Case-Case citations with high proficiency through fusion of semantic and topological information. We introduce adapted relational graph convolutions operating on an extended and enriched versio…

Rule-Assisted Attribute Embedding
Sibo Zhao, Michael Bewong, Selasi Kwashie, Junwei Hu, Zaiwen Feng
https://arxiv.org/abs/2506.08314 https://

Rule-Assisted Attribute Embedding
Recommendation systems often overlook the rich attribute information embedded in property graphs, limiting their effectiveness. Existing graph convolutional network (GCN) models either ignore attributes or rely on simplistic triples, failing to capture deeper semantic structures. We propose RAE (Rule- Assisted Approach for Attribute Embedding), a novel method that improves recommendations by mining semantic rules from property graphs to guide attribute embedding. RAE per…

Multi-domain anomaly detection in a 5G network
Thomas Hoger (LAAS-SARA), Philippe Owezarski (LAAS-SARA)
https://arxiv.org/abs/2506.12070 https://

Multi-domain anomaly detection in a 5G network
With the advent of 5G, mobile networks are becoming more dynamic and will therefore present a wider attack surface. To secure these new systems, we propose a multi-domain anomaly detection method that is distinguished by the study of traffic correlation on three dimensions: temporal by analyzing message sequences, semantic by abstracting the parameters these messages contain, and topological by linking them in the form of a graph. Unlike traditional approaches, which are limited to considering …