Tootfinder

Normalisation of SWIFT Message Counterparties with Feature Extraction and Clustering
Thanasis Schoinas, Benjamin Guinard, Diba Esbati, Richard Chalk
https://arxiv.org/abs/2508.21081

Normalisation of SWIFT Message Counterparties with Feature Extraction and Clustering
Short text clustering is a known use case in the text analytics community. When the structure and content falls in the natural language domain e.g. Twitter posts or instant messages, then natural language techniques can be used, provided texts are of sufficient length to allow for use of (pre)trained models to extract meaningful information, such as part-of-speech or topic annotations. However, natural language models are not suitable for clustering transaction counterparties, as they are found…

RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David B\'alik, Martin Jure\v{c}ek, Mark Stamp
https://arxiv.org/abs/2506.23909 https://

RawMal-TF: Raw Malware Dataset Labeled by Type and Family
This work addresses the challenge of malware classification using machine learning by developing a novel dataset labeled at both the malware type and family levels. Raw binaries were collected from sources such as VirusShare, VX Underground, and MalwareBazaar, and subsequently labeled with family information parsed from binary names and type-level labels integrated from ClarAVy. The dataset includes 14 malware types and 17 malware families, and was processed using a unified feature extraction p…

Quenched Quantum Feature Maps
Anton Simen, Carlos Flores-Garrigos, Murilo Henrique De Oliveira, Gabriel Dario Alvarado Barrios, Juan F. R. Hern\'andez, Qi Zhang, Alejandro Gomez Cadavid, Yolanda Vives-Gilabert, Jos\'e D. Mart\'in-Guerrero, Enrique Solano, Narendra N. Hegade, Archismita Dalal
https://arxiv.org/abs/2508.20975

Quenched Quantum Feature Maps
We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding a dataset information into disordered quantum many-body spin-glass problems, followed by a nonadiabatic evolution and feature extraction via measurements of expectation values, significantly enhances machine learning (ML) models. By analyzing the performance…

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Domenico De Cristofaro, Vincenzo Norman Vitale, Alessandro Vietti
https://arxiv.org/abs/2508.17914

Evaluating the Representation of Vowels in Wav2Vec Feature Extractor: A Layer-Wise Analysis Using MFCCs
Automatic Speech Recognition has advanced with self-supervised learning, enabling feature extraction directly from raw audio. In Wav2Vec, a CNN first transforms audio into feature vectors before the transformer processes them. This study examines CNN-extracted information for monophthong vowels using the TIMIT corpus. We compare MFCCs, MFCCs with formants, and CNN activations by training SVM classifiers for front-back vowel identification, assessing their classification accuracy to evaluate pho…

Invited Paper: Feature-to-Classifier Co-Design for Mixed-Signal Smart Flexible Wearables for Healthcare at the Extreme Edge
Maha Shatta, Konstantinos Balaskas, Paula Carolina Lozano Duarte, Georgios Panagopoulos, Mehdi B. Tahoori, Georgios Zervakis
https://arxiv.org/abs/2508.19637

Invited Paper: Feature-to-Classifier Co-Design for Mixed-Signal Smart Flexible Wearables for Healthcare at the Extreme Edge
Flexible Electronics (FE) offer a promising alternative to rigid silicon-based hardware for wearable healthcare devices, enabling lightweight, conformable, and low-cost systems. However, their limited integration density and large feature sizes impose strict area and power constraints, making ML-based healthcare systems-integrating analog frontend, feature extraction and classifier-particularly challenging. Existing FE solutions often neglect potential system-wide solutions and focus on the cla…

Co-Win: Joint Object Detection and Instance Segmentation in LiDAR Point Clouds via Collaborative Window Processing
Haichuan Li, Tomi Westerlund
https://arxiv.org/abs/2507.19691 …

Co-Win: Joint Object Detection and Instance Segmentation in LiDAR Point Clouds via Collaborative Window Processing
Accurate perception and scene understanding in complex urban environments is a critical challenge for ensuring safe and efficient autonomous navigation. In this paper, we present Co-Win, a novel bird's eye view (BEV) perception framework that integrates point cloud encoding with efficient parallel window-based feature extraction to address the multi-modality inherent in environmental understanding. Our method employs a hierarchical architecture comprising a specialized encoder, a window-based b…

Microscopic and collective signatures of feature learning in neural networks
Andrea Corti, Rosalba Pacelli, Pietro Rotondo, Marco Gherardi
https://arxiv.org/abs/2508.20989 https…

Microscopic and collective signatures of feature learning in neural networks
Feature extraction - the ability to identify relevant properties of data - is a key factor underlying the success of deep learning. Yet, it has proved difficult to elucidate its nature within existing predictive theories, to the extent that there is no consensus on the very definition of feature learning. A promising hint in this direction comes from previous phenomenological observations of quasi-universal aspects in the training dynamics of neural networks, displayed by simple properties of f…

Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
Bla\v{z} \v{S}krlj, Beno\^it Guilleminot, Andra\v{z} Tori
https://arxiv.org/abs/2507.18993

Agent0: Leveraging LLM Agents to Discover Multi-value Features from Text for Enhanced Recommendations
Large language models (LLMs) and their associated agent-based frameworks have significantly advanced automated information extraction, a critical component of modern recommender systems. While these multitask frameworks are widely used in code generation, their application in data-centric research is still largely untapped. This paper presents Agent0, an LLM-driven, agent-based system designed to automate information extraction and feature construction from raw, unstructured text. Categorical f…

Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking
Zhuohao Li, Wenqing Chen, Jianxing Yu, Zhichao Lu
https://arxiv.org/abs/2508.19558

Functional Consistency of LLM Code Embeddings: A Self-Evolving Data Synthesis Framework for Benchmarking
Embedding models have demonstrated strong performance in tasks like clustering, retrieval, and feature extraction while offering computational advantages over generative models and cross-encoders. Benchmarks such as MTEB have shown that text embeddings from large language models (LLMs) capture rich semantic information, but their ability to reflect code-level functional semantics remains unclear. Existing studies largely focus on code clone detection, which emphasizes syntactic similarity and o…

PhishKey: A Novel Centroid-Based Approach for Enhanced Phishing Detection Using Adaptive HTML Component Extraction
Felipe Casta\~no, Eduardo Fidalgo, Enrique Alegre, Rocio Alaiz-Rodr\'iguez, Raul Orduna, Francesco Zola
https://arxiv.org/abs/2506.21106

PhishKey: A Novel Centroid-Based Approach for Enhanced Phishing Detection Using Adaptive HTML Component Extraction
Phishing attacks pose a significant cybersecurity threat, evolving rapidly to bypass detection mechanisms and exploit human vulnerabilities. This paper introduces PhishKey to address the challenges of adaptability, robustness, and efficiency. PhishKey is a novel phishing detection method using automatic feature extraction from hybrid sources. PhishKey combines character-level processing with Convolutional Neural Networks (CNN) for URL classification, and a Centroid-Based Key Component Phishing …

A Fast and Light-weight Non-Iterative Visual Odometry with RGB-D Cameras
Zheng Yang, Kuan Xu, Shenghai Yuan, Lihua Xie
https://arxiv.org/abs/2507.18886 https://

A Fast and Light-weight Non-Iterative Visual Odometry with RGB-D Cameras
In this paper, we introduce a novel approach for efficiently estimating the 6-Degree-of-Freedom (DoF) robot pose with a decoupled, non-iterative method that capitalizes on overlapping planar elements. Conventional RGB-D visual odometry(RGBD-VO) often relies on iterative optimization solvers to estimate pose and involves a process of feature extraction and matching. This results in significant computational burden and time delays. To address this, our innovative method for RGBD-VO separates the …

ViFusion: In-Network Tensor Fusion for Scalable Video Feature Indexing
Yisu Wang, Yixiang Zhu, Xinjiao Li, Yulong Zhang, Ruilong Wu, Dirk Kutscher
https://arxiv.org/abs/2506.16258

ViFusion: In-Network Tensor Fusion for Scalable Video Feature Indexing
Large-scale video feature indexing in datacenters is critically dependent on efficient data transfer. Although in-network computation has emerged as a compelling strategy for accelerating feature extraction and reducing overhead in distributed multimedia systems, harnessing advanced networking resources at both the switch and host levels remains a formidable challenge. These difficulties are compounded by heterogeneous hardware, diverse application requirements, and complex multipath topologies…

Comparing and Scaling fMRI Features for Brain-Behavior Prediction
Mikkel Sch\"ottner Sieler, Thomas A. W. Bolton, Jagruti Patel, Patric Hagmann
https://arxiv.org/abs/2507.20601

Comparing and Scaling fMRI Features for Brain-Behavior Prediction
Predicting behavioral variables from neuroimaging modalities such as magnetic resonance imaging (MRI) has the potential to allow the development of neuroimaging biomarkers of mental and neurological disorders. A crucial processing step to this aim is the extraction of suitable features. These can differ in how well they predict the target of interest, and how this prediction scales with sample size and scan time. Here, we compare nine feature subtypes extracted from resting-state functional MRI…

EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow
Zeyi Lu, Xiaoxiao Ma, Yujun Huang, Minxiao Chen, Bin Chen, Baoyi An, Shu-Tao Xia
https://arxiv.org/abs/2507.18969

EDPC: Accelerating Lossless Compression via Lightweight Probability Models and Decoupled Parallel Dataflow
The explosive growth of multi-source multimedia data has significantly increased the demands for transmission and storage, placing substantial pressure on bandwidth and storage infrastructures. While Autoregressive Compression Models (ACMs) have markedly improved compression efficiency through probabilistic prediction, current approaches remain constrained by two critical limitations: suboptimal compression ratios due to insufficient fine-grained feature extraction during probability modeling, …

PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching
Guillem Cort\`es-Sebasti\`a, Benjamin Martin, Emilio Molina, Xavier Serra, Romain Hennequin
https://arxiv.org/abs/2506.21086

PeakNetFP: Peak-based Neural Audio Fingerprinting Robust to Extreme Time Stretching
This work introduces PeakNetFP, the first neural audio fingerprinting (AFP) system designed specifically around spectral peaks. This novel system is designed to leverage the sparse spectral coordinates typically computed by traditional peak-based AFP methods. PeakNetFP performs hierarchical point feature extraction techniques similar to the computer vision model PointNet++, and is trained using contrastive learning like in the state-of-the-art deep learning AFP, NeuralFP. This combination allow…

Neurosymbolic Feature Extraction for Identifying Forced Labor in Supply Chains
Zili Wang, Frank Montabon, Kristin Yvonne Rozier
https://arxiv.org/abs/2507.07217

Neurosymbolic Feature Extraction for Identifying Forced Labor in Supply Chains
Supply chain networks are complex systems that are challenging to analyze; this problem is exacerbated when there are illicit activities involved in the supply chain, such as counterfeit parts, forced labor, or human trafficking. While machine learning (ML) can find patterns in complex systems like supply chains, traditional ML techniques require large training data sets. However, illicit supply chains are characterized by very sparse data, and the data that is available is often (purposely) co…

Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
Fergal Stapleton, Daniel Garc\'ia N\'u\~nez, Yanan Sun, Edgar Galv\'an
https://arxiv.org/abs/2506.20469

Surrogate-Assisted Evolution for Efficient Multi-branch Connection Design in Deep Neural Networks
State-of-the-art Deep Neural Networks (DNNs) often incorporate multi-branch connections, enabling multi-scale feature extraction and enhancing the capture of diverse features. This design improves network capacity and generalisation to unseen data. However, training such DNNs can be computationally expensive. The challenge is further exacerbated by the complexity of identifying optimal network architectures. To address this, we leverage Evolutionary Algorithms (EAs) to automatically discover hi…

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Buddhi Wijenayake, Athulya Ratnayake, Praveen Sumanasekara, Nichula Wasalathilaka, Mathivathanan Piratheepan, Roshan Godaliyadda, Mervyn Ekanayake, Vijitha Herath
https://arxiv.org/abs/2507.11523

Precision Spatio-Temporal Feature Fusion for Robust Remote Sensing Change Detection
Remote sensing change detection is vital for monitoring environmental and urban transformations but faces challenges like manual feature extraction and sensitivity to noise. Traditional methods and early deep learning models, such as convolutional neural networks (CNNs), struggle to capture long-range dependencies and global context essential for accurate change detection in complex scenes. While Transformer-based models mitigate these issues, their computational complexity limits their applica…

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Mateusz Wasala, Mateusz Smolarczyk, Michal Danilowicz, Tomasz Kryjak
https://arxiv.org/abs/2507.07903

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Accurate position estimation is essential for modern navigation systems deployed in autonomous platforms, including ground vehicles, marine vessels, and aerial drones. In this context, Visual Simultaneous Localisation and Mapping (VSLAM) - which includes Visual Odometry - relies heavily on the reliable extraction of salient feature points from the visual input data. In this work, we propose an embedded implementation of an unsupervised architecture capable of detecting and describing feature po…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/9]:
- DeeP-Mod: Deep Dynamic Programming based Environment Modelling using Feature Extraction
Chris Child, Lam Ngo

Regularizing Learnable Feature Extraction for Automatic Speech Recognition
Peter Vieting, Maximilian Kannen, Benedikt Hilmes, Ralf Schl\"uter, Hermann Ney
https://arxiv.org/abs/2506.09804

Regularizing Learnable Feature Extraction for Automatic Speech Recognition
Neural front-ends are an appealing alternative to traditional, fixed feature extraction pipelines for automatic speech recognition (ASR) systems since they can be directly trained to fit the acoustic model. However, their performance often falls short compared to classical methods, which we show is largely due to their increased susceptibility to overfitting. This work therefore investigates regularization methods for training ASR models with learnable feature extraction front-ends. First, we e…

Replaced article(s) found for cs.MS. https://arxiv.org/list/cs.MS/new
[1/1]:
- A Sparse Tensor Generator with Efficient Feature Extraction
Tugba Torun, Ameer Taweel, Didem Unat

CWEFS: Brain volume conduction effects inspired channel-wise EEG feature selection for multi-dimensional emotion recognition
Xueyuan Xu, Wenjia Dong, Fulin Wei, Li Zhuo
https://arxiv.org/abs/2508.05228

CWEFS: Brain volume conduction effects inspired channel-wise EEG feature selection for multi-dimensional emotion recognition
Due to the intracranial volume conduction effects, high-dimensional multi-channel electroencephalography (EEG) features often contain substantial redundant and irrelevant information. This issue not only hinders the extraction of discriminative emotional representations but also compromises the real-time performance. Feature selection has been established as an effective approach to address the challenges while enhancing the transparency and interpretability of emotion recognition models. Howev…

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Jintao Liu, Mohammad Goudarzi, Adel Nadjaran Toosi
https://arxiv.org/abs/2507.15233

An ML-Driven Participant Selection Technique for Federated Recommendation System in Edge-Cloud Computing
Recommendation systems (RS) personalize content by analyzing user preferences, but typically require centralized collection of user data, raising privacy and scalability concerns. Federated Recommendation Systems (FRS) address these issues by enabling distributed, privacy-preserving model training across edge devices, keeping raw data on-device. Although existing FRS frameworks benefit from on-device feature extraction and privacy preservation, they suffer from heterogeneous device capabilities…

FENIX: Enabling In-Network DNN Inference with FPGA-Enhanced Programmable Switches
Xiangyu Gao (Tsinghua University), Tong Li (Renmin University of China), Yinchao Zhang (Tsinghua University), Ziqiang Wang (Southeast University), Xiangsheng Zeng (Huazhong University of Science and Technology), Su Yao (Tsinghua University), Ke Xu (Tsinghua University)
…

FENIX: Enabling In-Network DNN Inference with FPGA-Enhanced Programmable Switches
Machine learning (ML) is increasingly used in network data planes for advanced traffic analysis. However, existing solutions (such as FlowLens, N3IC, and BoS) still struggle to simultaneously achieve low latency, high throughput, and high accuracy. To address these challenges, we present FENIX, a hybrid in-network ML system that performs feature extraction on programmable switch ASICs and deep neural network inference on FPGAs. FENIX introduces a Data Engine that leverages a probabilistic token…

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Zahid Ullah, Jihie Kim
https://arxiv.org/abs/2506.12363 …

Hierarchical Deep Feature Fusion and Ensemble Learning for Enhanced Brain Tumor MRI Classification
Accurate brain tumor classification is crucial in medical imaging to ensure reliable diagnosis and effective treatment planning. This study introduces a novel double ensembling framework that synergistically combines pre-trained deep learning (DL) models for feature extraction with optimized machine learning (ML) classifiers for robust classification. The framework incorporates comprehensive preprocessing and data augmentation of brain magnetic resonance images (MRI), followed by deep feature e…

Deep Generative Models in Condition and Structural Health Monitoring: Opportunities, Limitations and Future Outlook
Xin Yang, Chen Fang, Yunlai Liao, Jian Yang, Konstantinos Gryllias, Dimitrios Chronopoulos
https://arxiv.org/abs/2507.15026

Deep Generative Models in Condition and Structural Health Monitoring: Opportunities, Limitations and Future Outlook
Condition and structural health monitoring (CM/SHM) is a pivotal component of predictive maintenance (PdM) strategies across diverse industrial sectors, including mechanical rotating machinery, airplane composite wings, offshore wind turbines, and civil engineering structures. Conventional deep learning models, while effective in fault diagnosis and anomaly detection through supervised feature extraction and rule-based data augmentation, often struggle with operational variability, imbalanced o…

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
HyeYoung Lee, Muhammad Nadeem, Pavel Tsoi
https://arxiv.org/abs/2507.10622 h…

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
The rapid expansion of Internet of Things (IoT) networks has led to a surge in security vulnerabilities, emphasizing the critical need for robust anomaly detection and classification techniques. In this work, we propose a novel approach for identifying anomalies in IoT network traffic by leveraging the Mel-frequency cepstral coefficients (MFCC) and ResNet-18, a deep learning model known for its effectiveness in feature extraction and image-based tasks. Learnable MFCCs enable adaptive spectral f…

Computing-In-Memory Dataflow for Minimal Buffer Traffic
Choongseok Song, Doo Seok Jeong
https://arxiv.org/abs/2508.14375 https://arxiv.org/pdf/2508.14375…

Computing-In-Memory Dataflow for Minimal Buffer Traffic
Computing-In-Memory (CIM) offers a potential solution to the memory wall issue and can achieve high energy efficiency by minimizing data movement, making it a promising architecture for edge AI devices. Lightweight models like MobileNet and EfficientNet, which utilize depthwise convolution for feature extraction, have been developed for these devices. However, CIM macros often face challenges in accelerating depthwise convolution, including underutilization of CIM memory and heavy buffer traffi…

Revisiting R: Statistical Envelope Analysis for Lightweight RF Modulation Classification
Srinivas Rahul Sapireddy, Mostafizur Rahman
https://arxiv.org/abs/2506.19956

Revisiting R: Statistical Envelope Analysis for Lightweight RF Modulation Classification
Modulation classification plays a crucial role in wireless communication systems, enabling applications such as cognitive radio, spectrum monitoring, and electronic warfare. Conventional techniques often involve deep learning or complex feature extraction, which, while effective, require substantial computational resources and memory. An early approach by Chan and Gadbois in 1985 introduced a theoretical method for modulation classification using a mathematically derived parameter called R. The…

Machine learning-based classification of variable stars using phase-folded light curves
Almat Akhmetali, Alisher Zhunuskanov, Timur Namazbayev, Marat Zaidyn, Aknur Sakan, Dana Turlykozhayeva, Nurzhan Ussipov
https://arxiv.org/abs/2508.11964

Machine learning-based classification of variable stars using phase-folded light curves
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques continues to grow. Traditional methods often rely on manual feature extraction and selection, which can be labor-intensive and less effective for managing extensive datasets. In this study, we present a convolutional neural network (CNN)-based method for cla…

MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization
Tingting Liu, Yuan Liu, Jinhui Tang, Liyin Yuan, Chengyu Liu, Chunlai Li, Xiubao Sui, Qian Chen
https://arxiv.org/abs/2506.17540

MTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization
Thermal infrared (TIR) images, acquired through thermal radiation imaging, are unaffected by variations in lighting conditions and atmospheric haze. However, TIR images inherently lack color and texture information, limiting downstream tasks and potentially causing visual fatigue. Existing colorization methods primarily rely on single-band images with limited spectral information and insufficient feature extraction capabilities, which often result in image distortion and semantic ambiguity. In …

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis
Anna Seo Gyeong Choi, Alexander Richardson, Ryan Partlan, Sunny Tang, Sunghye Cho
https://arxiv.org/abs/2506.01129

Comparative Evaluation of Acoustic Feature Extraction Tools for Clinical Speech Analysis
This study compares three acoustic feature extraction toolkits (OpenSMILE, Praat, and Librosa) applied to clinical speech data from individuals with schizophrenia spectrum disorders (SSD) and healthy controls (HC). By standardizing extraction parameters across the toolkits, we analyzed speech samples from 77 SSD and 87 HC participants and found significant toolkit-dependent variations. While F0 percentiles showed high cross-toolkit correlation (r=0.962 to 0.999), measures like F0 standard devia…

I have updated Inspireface4J.
- Multiple detection sessions
- Configuration of logging on the FFM lib part
- Face angular / attribute handling
- Improved landmark extraction
- Configureable feature selection for the detection pipeline (e.g. turn off attribute handling)
https://github.com/metaloom/inspir…

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
Stephan Sturm
https://arxiv.org/abs/2506.01815

Path Signatures for Feature Extraction. An Introduction to the Mathematics Underpinning an Efficient Machine Learning Technique
We provide an introduction to the topic of path signatures as means of feature extraction for machine learning from data streams. The article stresses the mathematical theory underlying the signature methodology, highlighting the conceptual character without plunging into the technical details of rigorous proofs. These notes are based on an introductory presentation given to students of the Research Experience for Undergraduates in Industrial Mathematics and Statistics at Worcester Polytechnic …

Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems
Florian Bayer, Maximilian Russo, Christian Rathgeb
https://arxiv.org/abs/2508.11419

Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems
Biometric recognition is widely used, making the privacy and security of extracted templates a critical concern. Biometric Template Protection schemes, especially those utilizing Homomorphic Encryption, introduce significant computational challenges due to increased workload. Recent advances in deep neural networks have enabled state-of-the-art feature extraction for face, fingerprint, and iris modalities. The ubiquity and affordability of biometric sensors further facilitate multi-modal fusion…

A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction
Yijun Geng, Jianzhou Wang, Jinze Li, Zhiwu Li
https://arxiv.org/abs/2507.05761

A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction
Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This sys…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
Ahmed Farooq
https://arxiv.org/abs/2507.08766 https://…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that b…

Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing
Buddhi Wijenayake, Athulya Ratnayake, Praveen Sumanasekara, Roshan Godaliyadda, Parakrama Ekanayake, Vijitha Herath, Nichula Wasalathilaka
https://arxiv.org/abs/2508.08232

Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing
Semantic Change Detection (SCD) from remote sensing imagery requires models balancing extensive spatial context, computational efficiency, and sensitivity to class-imbalanced land-cover transitions. While Convolutional Neural Networks excel at local feature extraction but lack global context, Transformers provide global modeling at high computational costs. Recent Mamba architectures based on state-space models offer compelling solutions through linear complexity and efficient long-range modeli…

Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning
Bojun Zhang
https://arxiv.org/abs/2508.13474 https://arxiv.org/pdf/2508.…

Electromagnetic Signal Modulation Recognition based on Subgraph Embedding Learning
Automatic Modulation Recognition (AMR) detects modulation schemes of received signals for further processing of signals without any priori information, which is critically important for civil spectrum regulation, information countermea sures, and communication security. Due to the powerful feature extraction and classification capabilities of Deep Learning (DL), DL-based AMR algorithms have achieved excellent performance gains compared with traditional modulation detection algorithm…

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping
Qingyu Fan, Yinghao Cai, Chao Li, Chunting Jiao, Xudong Zheng, Tao Lu, Bin Liang, Shuo Wang
https://arxiv.org/abs/2507.02672

MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping
Robotic grasping faces challenges in adapting to objects with varying shapes and sizes. In this paper, we introduce MISCGrasp, a volumetric grasping method that integrates multi-scale feature extraction with contrastive feature enhancement for self-adaptive grasping. We propose a query-based interaction between high-level and low-level features through the Insight Transformer, while the Empower Transformer selectively attends to the highest-level features, which synergistically strikes a balanc…

Wavelet-Based Time-Frequency Fingerprinting for Feature Extraction of Traditional Irish Music
Noah Shore
https://arxiv.org/abs/2508.00479 https://arxiv.org…

Wavelet-Based Time-Frequency Fingerprinting for Feature Extraction of Traditional Irish Music
This work presents a wavelet-based approach to time-frequency fingerprinting for time series feature extraction, with a focus on audio identification from live recordings of traditional Irish tunes. The challenges of identifying features in time-series data are addressed by employing a continuous wavelet transform to extract spectral features and wavelet coherence analysis is used to compare recorded audio spectrograms to synthetically generated tunes. The synthetic tunes are derived from ABC n…

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Abdul Basit, Maha Nawaz, Saim Rehman, Muhammad Shafique
https://arxiv.org/abs/2508.07731

CognitiveArm: Enabling Real-Time EEG-Controlled Prosthetic Arm Using Embodied Machine Learning
Efficient control of prosthetic limbs via non-invasive brain-computer interfaces (BCIs) requires advanced EEG processing, including pre-filtering, feature extraction, and action prediction, performed in real time on edge AI hardware. Achieving this on resource-constrained devices presents challenges in balancing model complexity, computational efficiency, and latency. We present CognitiveArm, an EEG-driven, brain-controlled prosthetic system implemented on embedded AI hardware, achieving real-t…

This https://arxiv.org/abs/2506.04354 has been replaced.
initial toot: https://mastoxiv.page/@ar…

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
BridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations (FPEs). Traditional PINNs, which typically rely on fully connected architectures, often struggle to capture complex spatial hierarchies and enforce intricate boundary conditions. In contrast, BridgeNet leverages adaptive CNN layers for effective local feature extraction and incorporates a dynamically w…

Quantum Algorithm for Estimating Intrinsic Geometry
Nhat A. Nghiem, Tuan K. Do, Tzu-Chieh Wei, Trung V. Phan
https://arxiv.org/abs/2508.06355 https://arxiv…

Quantum Algorithm for Estimating Intrinsic Geometry
High-dimensional datasets typically cluster around lower-dimensional manifolds but are also often marred by severe noise, obscuring the intrinsic geometry essential for downstream learning tasks. We present a quantum algorithm for estimating the intrinsic geometry of a point cloud -- specifically its local intrinsic dimension and local scalar curvature. These quantities are crucial for dimensionality reduction, feature extraction, and anomaly detection -- tasks that are central to a wide range …

Unraveling the Biomarker Prospects of High-Altitude Diseases: Insights from Biomolecular Event Network Constructed using Text Mining
Balu Bhasuran, Sabenabanu Abdulkadhar, Jeyakumar Natarajan
https://arxiv.org/abs/2507.10953

Unraveling the Biomarker Prospects of High-Altitude Diseases: Insights from Biomolecular Event Network Constructed using Text Mining
High-altitude diseases (HAD), encompassing acute mountain sickness (AMS), high-altitude cerebral edema (HACE), and high-altitude pulmonary edema (HAPE), are triggered by hypobaric hypoxia at elevations above 2,500 meters. These conditions pose significant health risks, yet the molecular mechanisms remain insufficiently understood. In this study, we developed a biomolecular event extraction pipeline integrating supervised machine learning with feature-based and multiscale Laplacian graph kernels…

Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis
Stan Mu\~noz Guti\'errez, Franz Wotawa
https://arxiv.org/abs/2506.12375 http…

Optimized Spectral Fault Receptive Fields for Diagnosis-Informed Prognosis
This paper introduces Spectral Fault Receptive Fields (SFRFs), a biologically inspired technique for degradation state assessment in bearing fault diagnosis and remaining useful life (RUL) estimation. Drawing on the center-surround organization of retinal ganglion cell receptive fields, we propose a frequency-domain feature extraction algorithm that enhances the detection of fault signatures in vibration signals. SFRFs are designed as antagonistic spectral filters centered on characteristic fau…

Multi-Sampling-Frequency Naturalness MOS Prediction Using Self-Supervised Learning Model with Sampling-Frequency-Independent Layer
Go Nishikawa, Wataru Nakata, Yuki Saito, Kanami Imamura, Hiroshi Saruwatari, Tomohiko Nakamura
https://arxiv.org/abs/2507.14647

Multi-Sampling-Frequency Naturalness MOS Prediction Using Self-Supervised Learning Model with Sampling-Frequency-Independent Layer
We introduce our submission to the AudioMOS Challenge (AMC) 2025 Track 3: mean opinion score (MOS) prediction for speech with multiple sampling frequencies (SFs). Our submitted model integrates an SF-independent (SFI) convolutional layer into a self-supervised learning (SSL) model to achieve SFI speech feature extraction for MOS prediction. We present some strategies to improve the MOS prediction performance of our model: distilling knowledge from a pretrained non-SFI-SSL model and pretraining …

An Overview of Algorithms for Contactless Cardiac Feature Extraction from Radar Signals: Advances and Challenges
Yuanyuan Zhang, Rui Yang, Yutao Yue, Eng Gee Lim, Zidong Wang
https://arxiv.org/abs/2508.02122

An Overview of Algorithms for Contactless Cardiac Feature Extraction from Radar Signals: Advances and Challenges
Contactless cardiac monitoring has vast potential to replace contact-based monitoring in various future scenarios such as smart home and in-cabin monitoring. Various contactless sensors can be potentially implemented for cardiac monitoring, such as cameras, acoustic sensors, Wi-Fi routers and radars. Among all these sensors, radar could achieve unobtrusive monitoring with high accuracy and robustness at the same time. The research about radar-based cardiac monitoring can be generally divided in…

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Qibiao Wu, Yagang Wang, Qian Zhang
https://arxiv.org/abs/2507.06581 https://

Airway Segmentation Network for Enhanced Tubular Feature Extraction
Manual annotation of airway regions in computed tomography images is a time-consuming and expertise-dependent task. Automatic airway segmentation is therefore a prerequisite for enabling rapid bronchoscopic navigation and the clinical deployment of bronchoscopic robotic systems. Although convolutional neural network methods have gained considerable attention in airway segmentation, the unique tree-like structure of airways poses challenges for conventional and deformable convolutions, which oft…

ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework
Abrar Faiyaz, Nhat Hoang, Giovanni Schifitto, Md Nasir Uddin
https://arxiv.org/abs/2507.07920

ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework
Cerebrovascular pathology significantly contributes to cognitive decline and neurological disorders, underscoring the need for advanced tools to assess vascular integrity. Three-dimensional Time-of-Flight Magnetic Resonance Angiography (3D TOF MRA) is widely used to visualize cerebral vasculature, however, clinical evaluations generally focus on major arterial abnormalities, overlooking quantitative metrics critical for understanding subtle vascular changes. Existing methods for extracting stru…

Online Training and Pruning of Deep Reinforcement Learning Networks
Valentin Frank Ingmar Guenter, Athanasios Sideris
https://arxiv.org/abs/2507.11975 http…

Online Training and Pruning of Deep Reinforcement Learning Networks
Scaling deep neural networks (NN) of reinforcement learning (RL) algorithms has been shown to enhance performance when feature extraction networks are used but the gained performance comes at the significant expense of increased computational and memory complexity. Neural network pruning methods have successfully addressed this challenge in supervised learning. However, their application to RL is underexplored. We propose an approach to integrate simultaneous training and pruning within advance…

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Chungang Lin, Yilong Jiang, Weiyao Zhang, Xuying Meng, Tianyu Zuo, Yujun Zhang
https://arxiv.org/abs/2506.14151

TraGe: A Generic Packet Representation for Traffic Classification Based on Header-Payload Differences
Traffic classification has a significant impact on maintaining the Quality of Service (QoS) of the network. Since traditional methods heavily rely on feature extraction and large scale labeled data, some recent pre-trained models manage to reduce the dependency by utilizing different pre-training tasks to train generic representations for network packets. However, existing pre-trained models typically adopt pre-training tasks developed for image or text data, which are not tailored to traffic d…

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
Xiang Li, Yifan Lin, Yuanzhe Zhang
https://arxiv.org/abs/2507.12098

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
To mitigate privacy leakage and performance issues in personalized advertising, this paper proposes a framework that integrates federated learning and differential privacy. The system combines distributed feature extraction, dynamic privacy budget allocation, and robust model aggregation to balance model accuracy, communication overhead, and privacy protection. Multi-party secure computing and anomaly detection mechanisms further enhance system resilience against malicious attacks. Experimental…

This https://arxiv.org/abs/2409.06990 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SIS: Seam-Informed Strategy for T-shirt Unfolding
Seams are information-rich components of garments. The presence of different types of seams and their combinations helps to select grasping points for garment handling. In this paper, we propose a new Seam-Informed Strategy (SIS) for finding actions for handling a garment, such as grasping and unfolding a T-shirt. Candidates for a pair of grasping points for a dual-arm manipulator system are extracted using the proposed Seam Feature Extraction Method (SFEM). A pair of grasping points for the ro…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
Bujar Raufi
https://arxiv.org/abs/2508.07283 https://arxiv.o…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction pa…

Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM
Zicheng Guo, Shuqi Wu, Meixing Zhu, He Guandi
https://arxiv.org/abs/2508.11215 https://arxiv.org…

Air Quality PM2.5 Index Prediction Model Based on CNN-LSTM
With the intensification of global climate change, accurate prediction of air quality indicators, especially PM2.5 concentration, has become increasingly important in fields such as environmental protection, public health, and urban management. To address this, we propose an air quality PM2.5 index prediction model based on a hybrid CNN-LSTM architecture. The model effectively combines Convolutional Neural Networks (CNN) for local spatial feature extraction and Long Short-Term Memory (LSTM) net…

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
Bruce Fang, Danyi Gao
https://arxiv.org/abs/2507.02233 …

Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems
This paper addresses the challenge of fault root cause identification in cloud computing environments. The difficulty arises from complex system structures, dense service coupling, and limited fault information. To solve this problem, an intelligent identification algorithm based on transfer learning is proposed. The method introduces a shared feature extraction module and a domain adversarial mechanism to enable effective knowledge transfer from the source domain to the target domain. This imp…

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
Mana Mohammadi, Amirhesam Jafari Rad, Ashkan Behrouzi
https://arxiv.org/abs/2506.15364

Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images
This paper presents a lightweight framework for classifying brain stroke types from Diffusion-Weighted Imaging (DWI) MRI scans, employing a Multi-Layer Perceptron (MLP) neural network with Wavelet Transform for feature extraction. Accurate and timely stroke detection is critical for effective treatment and improved patient outcomes in neuroimaging. While Convolutional Neural Networks (CNNs) are widely used for medical image analysis, their computational complexity often hinders deployment in re…

CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Prediction
Zhiwei Ning, Zhaojiang Liu, Xuanang Gao, Yifan Zuo, Jie Yang, Yuming Fang, Wei Liu
https://arxiv.org/abs/2508.12917

CMF-IoU: Multi-Stage Cross-Modal Fusion 3D Object Detection with IoU Joint Prediction
Multi-modal methods based on camera and LiDAR sensors have garnered significant attention in the field of 3D detection. However, many prevalent works focus on single or partial stage fusion, leading to insufficient feature extraction and suboptimal performance. In this paper, we introduce a multi-stage cross-modal fusion 3D detection framework, termed CMF-IOU, to effectively address the challenge of aligning 3D spatial and 2D semantic information. Specifically, we first project the pixel inform…

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
Xiaoxiao Li, An Zhu, Youhai Jiang, Fengjie Zhu
https://arxiv.org/abs/2508.14916 https://

Transsion Multilingual Speech Recognition System for MLC-SLM 2025 Challenge
This paper presents the architecture and performance of a novel Multilingual Automatic Speech Recognition (ASR) system developed by the Transsion Speech Team for Track 1 of the MLC-SLM 2025 Challenge. The proposed system comprises three key components: 1) a frozen Whisper-large-v3 based speech encoder, leveraging large-scale pretraining to ensure robust acoustic feature extraction; 2) a trainable adaptor module using Linear-ReLU-Linear transformation mechanisms to effectively align speech and t…

Foundation Models for Brain Signals: A Critical Review of Current Progress and Future Directions
Gayal Kuruppu, Neeraj Wagh, Yogatheesan Varatharajah
https://arxiv.org/abs/2507.11783

Foundation Models for Brain Signals: A Critical Review of Current Progress and Future Directions
Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigations. The inability of supervised EEG encoders to learn robust EEG patterns and their over-reliance on expensive signal annotations have sparked a transition towards general-purpose self-supervised EEG encoders, i.e., EEG foundation models (EEG-FMs), for robust and scalable EEG feature extraction. However, the real-world readiness of early EEG-FMs and the rub…

Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU
Petar Jaku\v{s}, Hrvoje D\v{z}apo
https://arxiv.org/abs/2506.08911 https:/…

Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU
This paper presents a keyword spotting (KWS) system implemented on the NXP MCXN947 microcontroller with an integrated Neural Processing Unit (NPU), enabling real-time voice interaction on resource-constrained devices. The system combines MFCC feature extraction with a CNN classifier, optimized using Quantization Aware Training to reduce model size with minimal accuracy drop. Experimental results demonstrate a 59x speedup in inference time when leveraging the NPU compared to CPU-only execution, …

Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation
Md Tauhidul Islam, Wu Da-Wen, Tang Qing-Qing, Zhao Kai-Yang, Yin Teng, Li Yan-Fei, Shang Wen-Yi, Liu Jing-Yu, Zhang Hai-Xian
https://arxiv.org/abs/2506.02312

Dual encoding feature filtering generalized attention UNET for retinal vessel segmentation
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases. Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field, yet issues like limited training data, imbalance data distribution, and inadequate feature extraction persist, hindering both the segmentation performance and optimal model generalization. Addressing these critical issues, the DEFFA-Unet is proposed featuring an additional encoder to process domain-invar…

MiSTR: Multi-Modal iEEG-to-Speech Synthesis with Transformer-Based Prosody Prediction and Neural Phase Reconstruction
Mohammed Salah Al-Radhi, G\'eza N\'emeth, Branislav Gerazov
https://arxiv.org/abs/2508.03166

MiSTR: Multi-Modal iEEG-to-Speech Synthesis with Transformer-Based Prosody Prediction and Neural Phase Reconstruction
Speech synthesis from intracranial EEG (iEEG) signals offers a promising avenue for restoring communication in individuals with severe speech impairments. However, achieving intelligible and natural speech remains challenging due to limitations in feature representation, prosody modeling, and phase reconstruction. We introduce MiSTR, a deep-learning framework that integrates: 1) Wavelet-based feature extraction to capture fine-grained temporal, spectral, and neurophysiological representations o…

MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Caixu Xu, Junming Wei, Huizhen Chen, Pengchen Liang, Bocheng Liang, Ying Tan, Xintong Wei
https://arxiv.org/abs/2506.12441

MS-UMamba: An Improved Vision Mamba Unet for Fetal Abdominal Medical Image Segmentation
Recently, Mamba-based methods have become popular in medical image segmentation due to their lightweight design and long-range dependency modeling capabilities. However, current segmentation methods frequently encounter challenges in fetal ultrasound images, such as enclosed anatomical structures, blurred boundaries, and small anatomical structures. To address the need for balancing local feature extraction and global context modeling, we propose MS-UMamba, a novel hybrid convolutional-mamba mo…

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
Elmira Mirzabeigi, Rezvan Salehi, Kourosh Parand
https://arxiv.org/abs/2506.04354

BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
BridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations (FPEs). Traditional PINNs, which typically rely on fully connected architectures, often struggle to capture complex spatial hierarchies and enforce intricate boundary conditions. In contrast, BridgeNet leverages adaptive CNN layers for effective local feature extraction and incorporates a dynamically w…

This https://arxiv.org/abs/2505.16133 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framework that integrates deep hashing techniques with systematic optimizations to address these limitations. Our queries directly learn binary hash codes from knowledgebase code, eliminating intermediate feature extraction steps, and significantly reducing storage and computational overhead. Building upon this hash-based eff…

Inland-LOAM: Voxel-Based Structural Semantic Mapping for Inland Waterways
Zhongbi Luo, Yunjia Wang, Jan Swevers, Peter Slaets, Herman Bruyninckx
https://arxiv.org/abs/2508.03672

Inland-LOAM: Voxel-Based Structural Semantic Mapping for Inland Waterways
Accurate geospatial information is crucial for safe, autonomous Inland Waterway Transport (IWT), as existing charts (IENC) lack real-time detail and conventional LiDAR SLAM fails in waterway environments. These challenges lead to vertical drift and non-semantic maps, hindering autonomous navigation. This paper introduces Inland-LOAM, a LiDAR SLAM framework for waterways. It uses an improved feature extraction and a water surface planar constraint to mitigate vertical drift. A novel pipeline t…

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Tasfiq E. Alam, Md Manjurul Ahsan, Shivakumar Raman
https://arxiv.org/abs/2508.07536

Physics-Informed Multimodal Bearing Fault Classification under Variable Operating Conditions using Transfer Learning
Accurate and interpretable bearing fault classification is critical for ensuring the reliability of rotating machinery, particularly under variable operating conditions where domain shifts can significantly degrade model performance. This study proposes a physics-informed multimodal convolutional neural network (CNN) with a late fusion architecture, integrating vibration and motor current signals alongside a dedicated physics-based feature extraction branch. The model incorporates a novel physi…

When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
Ruining Sun, Hongsheng Hu, Wei Luo, Zhaoxi Zhang, Yanjun Zhang, Haizhuan Yuan, Leo Yu Zhang
https://arxiv.org/abs/2506.05743

When Better Features Mean Greater Risks: The Performance-Privacy Trade-Off in Contrastive Learning
With the rapid advancement of deep learning technology, pre-trained encoder models have demonstrated exceptional feature extraction capabilities, playing a pivotal role in the research and application of deep learning. However, their widespread use has raised significant concerns about the risk of training data privacy leakage. This paper systematically investigates the privacy threats posed by membership inference attacks (MIAs) targeting encoder models, focusing on contrastive learning framew…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Jingsong Xia, Yue Yin, Xiuhan Li
https://arxiv.org/abs/2508.11532 htt…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture. Through structural optimization and loss function design, the proposed method enhances feature extraction capability and classification performance while redu…

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Xuemei Cao, Hanlin Gu, Xin Yang, Bingjun Wei, Haoyang Liang, Xiangkun Wang, Tianrui Li
https://arxiv.org/abs/2506.09347

ErrorEraser: Unlearning Data Bias for Improved Continual Learning
Continual Learning (CL) primarily aims to retain knowledge to prevent catastrophic forgetting and transfer knowledge to facilitate learning new tasks. Unlike traditional methods, we propose a novel perspective: CL not only needs to prevent forgetting, but also requires intentional forgetting.This arises from existing CL methods ignoring biases in real-world data, leading the model to learn spurious correlations that transfer and amplify across tasks. From feature extraction and prediction resul…

Audio-Visual Speech Enhancement: Architectural Design and Deployment Strategies
Anis Hamadouche, Haifeng Luo, Mathini Sellathurai, Tharm Ratnarajah
https://arxiv.org/abs/2508.08468

Audio-Visual Speech Enhancement: Architectural Design and Deployment Strategies
This paper introduces a new AI-based Audio-Visual Speech Enhancement (AVSE) system and presents a comparative performance analysis of different deployment architectures. The proposed AVSE system employs convolutional neural networks (CNNs) for spectral feature extraction and long short-term memory (LSTM) networks for temporal modeling, enabling robust speech enhancement through multimodal fusion of audio and visual cues. Multiple deployment scenarios are investigated, including cloud-based, edg…

Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
Zhanwen Liu, Yujing Sun, Yang Wang, Nan Yang, Shengbo Eben Li, Xiangmo Zhao
https://arxiv.org/abs/2508.10704

Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
The dynamic range limitation of conventional RGB cameras reduces global contrast and causes loss of high-frequency details such as textures and edges in complex traffic environments (e.g., nighttime driving, tunnels), hindering discriminative feature extraction and degrading frame-based object detection. To address this, we integrate a bio-inspired event camera with an RGB camera to provide high dynamic range information and propose a motion cue fusion network (MCFNet), which achieves optimal s…

Embedded Acoustic Intelligence for Automotive Systems
Renjith Rajagopal, Peter Winzell, Sladjana Strbac, Konstantin Lindstr\"om, Petter H\"orling, Faisal Kohestani, Niloofar Mehrzad
https://arxiv.org/abs/2506.11071

Embedded Acoustic Intelligence for Automotive Systems
Transforming sound insights into actionable streams of data, this abstract leverages findings from degree thesis research to enhance automotive system intelligence, enabling us to address road type [1].By extracting and interpreting acoustic signatures from microphones installed within the wheelbase of a car, we focus on classifying road type.Utilizing deep neural networks and feature extraction powered by pre-trained models from the Open AI ecosystem (via Hugging Face [2]), our approach enable…

This https://arxiv.org/abs/2407.07589 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

MSC-LIO: An MSCKF-Based LiDAR-Inertial Odometry with Same-Plane Cluster Tracking
The multi-state constraint Kalman filter (MSCKF) has been proven to be more efficient than graph optimization for visual-based odometry while with similar accuracy. However, it has not been adequately considered and studied for LiDAR-based odometry. In this paper, we propose a novel tightly-coupled LiDAR-inertial odometry based on the MSCKF framework, named MSC-LIO. An efficient LiDAR same-plane cluster (LSPC) tracking method, without explicit feature extraction, is present for frame-to-frame d…

Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders
Dimitrios Bralios, Jonah Casebeer, Paris Smaragdis
https://arxiv.org/abs/2507.07867 http…

Re-Bottleneck: Latent Re-Structuring for Neural Audio Autoencoders
Neural audio codecs and autoencoders have emerged as versatile models for audio compression, transmission, feature-extraction, and latent-space generation. However, a key limitation is that most are trained to maximize reconstruction fidelity, often neglecting the specific latent structure necessary for optimal performance in diverse downstream applications. We propose a simple, post-hoc framework to address this by modifying the bottleneck of a pre-trained autoencoder. Our method introduces a …

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Ning Zhu, Xiaochuan Ma, Shaoting Zhang, Guotai Wang
https://arxiv.org/abs/2508.03441

MedCAL-Bench: A Comprehensive Benchmark on Cold-Start Active Learning with Foundation Models for Medical Image Analysis
Cold-Start Active Learning (CSAL) aims to select informative samples for annotation without prior knowledge, which is important for improving annotation efficiency and model performance under a limited annotation budget in medical image analysis. Most existing CSAL methods rely on Self-Supervised Learning (SSL) on the target dataset for feature extraction, which is inefficient and limited by insufficient feature representation. Recently, pre-trained Foundation Models (FMs) have shown powerful f…

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
Hang Fan, Mingxuan Li, Jingshi Cui, Zuhan Zhang, Wencai Run, Dunnan Liu
https://arxiv.org/abs/2506.03728

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models
The rapid growth of EVs and the subsequent increase in charging demand pose significant challenges for load grid scheduling and the operation of EV charging stations. Effectively harnessing the spatiotemporal correlations among EV charging stations to improve forecasting accuracy is complex. To tackle these challenges, we propose EV-LLM for EV charging loads based on LLMs in this paper. EV-LLM integrates the strengths of Graph Convolutional Networks (GCNs) in spatiotemporal feature extraction w…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Ailin Huang, Bingxin Li, Bruce Wang, Boyong Wu, Chao Yan, Chengli Feng, Heng Wang, Hongyu Zhou, Hongyuan Wang, Jingbei Li, Jianjian Sun, Joanna Wang, Mingrui Chen, Peng Liu, Ruihang Miao, Shilei Jiang, Tian Fei, Wang You, Xi Chen, Xuerui Yang, Yechang Huang, Yuxiang Zhang, Zheng Ge, Zheng Gong, Zhewei Huang, Zixin Zhang, Bin Wang, Bo Li, Buyun Ma, Changxin Miao, Changyi Wan, Chen Xu, Dapeng Shi, Dingyuan Hu, Enle…

Step-Audio-AQAA: a Fully End-to-End Expressive Large Audio Language Model
Large Audio-Language Models (LALMs) have significantly advanced intelligent human-computer interaction, yet their reliance on text-based outputs limits their ability to generate natural speech responses directly, hindering seamless audio interactions. To address this, we introduce Step-Audio-AQAA, a fully end-to-end LALM designed for Audio Query-Audio Answer (AQAA) tasks. The model integrates a dual-codebook audio tokenizer for linguistic and semantic feature extraction, a 130-billion-parameter…

Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
Javier Salazar Cavazos, Scott Peltier
https://arxiv.org/abs/2506.02060

Alzheimers Disease Classification in Functional MRI With 4D Joint Temporal-Spatial Kernels in Novel 4D CNN Model
Previous works in the literature apply 3D spatial-only models on 4D functional MRI data leading to possible sub-par feature extraction to be used for downstream tasks like classification. In this work, we aim to develop a novel 4D convolution network to extract 4D joint temporal-spatial kernels that not only learn spatial information but in addition also capture temporal dynamics. Experimental results show promising performance in capturing spatial-temporal data in functional MRI compared to 3D…

MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation
Qilong Xing, Zikai Song, Youjia Zhang, Na Feng, Junqing Yu, Wei Yang
https://arxiv.org/abs/2507.06992

MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation
Despite significant advancements in adapting Large Language Models (LLMs) for radiology report generation (RRG), clinical adoption remains challenging due to difficulties in accurately mapping pathological and anatomical features to their corresponding text descriptions. Additionally, semantic agnostic feature extraction further hampers the generation of accurate diagnostic reports. To address these challenges, we introduce Medical Concept Aligned Radiology Report Generation (MCA-RG), a knowled…

SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
Zhenyu Yu, Mohd. Yamani Idna Idris, Pei Wang, Yuelong Xia, Fei Ma, Rizwan Qureshi
https://arxiv.org/abs/2506.06176

SatelliteFormula: Multi-Modal Symbolic Regression from Remote Sensing Imagery for Physics Discovery
We propose SatelliteFormula, a novel symbolic regression framework that derives physically interpretable expressions directly from multi-spectral remote sensing imagery. Unlike traditional empirical indices or black-box learning models, SatelliteFormula combines a Vision Transformer-based encoder for spatial-spectral feature extraction with physics-guided constraints to ensure consistency and interpretability. Existing symbolic regression methods struggle with the high-dimensional complexity of…

ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion
Meng Zhou, Farzad Khalvati
https://arxiv.org/abs/2508.03008 https://

ClinicalFMamba: Advancing Clinical Assessment using Mamba-based Multimodal Neuroimaging Fusion
Multimodal medical image fusion integrates complementary information from different imaging modalities to enhance diagnostic accuracy and treatment planning. While deep learning methods have advanced performance, existing approaches face critical limitations: Convolutional Neural Networks (CNNs) excel at local feature extraction but struggle to model global context effectively, while Transformers achieve superior long-range modeling at the cost of quadratic computational complexity, limiting cl…

Neural Spectral Band Generation for Audio Coding
Woongjib Choi, Byeong Hyeon Kim, Hyungseob Lim, Inseon Jang, Hong-Goo Kang
https://arxiv.org/abs/2506.06732

Neural Spectral Band Generation for Audio Coding
Audio bandwidth extension is the task of reconstructing missing high frequency components of bandwidth-limited audio signals, where bandwidth limitation is a common issue for audio signals due to several reasons, including channel capacity and data constraints. While conventional spectral band replication is a well-established parametric approach to audio bandwidth extension, the SBR usually entails coarse feature extraction and reconstruction techniques, which leads to limitations when process…

AttZoom: Attention Zoom for Better Visual Features
Daniel DeAlcala, Aythami Morales, Julian Fierrez, Ruben Tolosana
https://arxiv.org/abs/2508.03625 https://

AttZoom: Attention Zoom for Better Visual Features
We present Attention Zoom, a modular and model-agnostic spatial attention mechanism designed to improve feature extraction in convolutional neural networks (CNNs). Unlike traditional attention approaches that require architecture-specific integration, our method introduces a standalone layer that spatially emphasizes high-importance regions in the input. We evaluated Attention Zoom on multiple CNN backbones using CIFAR-100 and TinyImageNet, showing consistent improvements in Top-1 and Top-5 cla…

Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
Chia-Ming Lee, Bo-Cheng Qiu, Ting-Yao Chen, Ming-Han Sun, Fang-Ying Lin, Jung-Tse Tsai, I-An Tsai, Yu-Fan Lin, Chih-Chung Hsu
https://arxiv.org/abs/2507.01564

Multi Source COVID-19 Detection via Kernel-Density-based Slice Sampling
We present our solution for the Multi-Source COVID-19 Detection Challenge, which classifies chest CT scans from four distinct medical centers. To address multi-source variability, we employ the Spatial-Slice Feature Learning (SSFL) framework with Kernel-Density-based Slice Sampling (KDS). Our preprocessing pipeline combines lung region extraction, quality control, and adaptive slice sampling to select eight representative slices per scan. We compare EfficientNet and Swin Transformer architectur…

Pitfalls and Limits in Automatic Dementia Assessment
Franziska Braun, Christopher Witzl, Andreas Erzigkeit, Hartmut Lehfeld, Thomas Hillemacher, Tobias Bocklet, Korbinian Riedhammer
https://arxiv.org/abs/2508.04512

Pitfalls and Limits in Automatic Dementia Assessment
Current work on speech-based dementia assessment focuses on either feature extraction to predict assessment scales, or on the automation of existing test procedures. Most research uses public data unquestioningly and rarely performs a detailed error analysis, focusing primarily on numerical performance. We perform an in-depth analysis of an automated standardized dementia assessment, the Syndrom-Kurz-Test. We find that while there is a high overall correlation with human annotators, due to cert…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/6]:
- Knowledge Distillation for Underwater Feature Extraction and Matching via GAN-synthesized Images
Jinghe Yang, Mingming Gong, Ye Pu