A new method for cross-matching fingerprints from different sensors.
problem Inefficiency in cross-matching fingerprints from different sensors.
method Co-occurrence of ridge orientations and Gabor-HoG descriptor fusion with CCA.
result Significant enhancement over state-of-the-art methods.
Deep learning improves website fingerprinting performance.
problem Improving website fingerprinting attacks using deep learning.
method Unsupervised deep neural networks for feature extraction and classification.
result Deep learning can predict fingerprintability of websites with 99% accuracy.
Paper tackles indoor localization issues with reduced dimensionality and improved fingerprint matching.
problem Curse of dimensionality and asymmetric matching in fingerprint-based indoor localization.
method Proposes a semi-supervised RSS dimensionality reduction algorithm and integrates it with a fingerprint-based algorithm.
result Improves indoor localization accuracy by reducing dimensionality and addressing matching issues.
Persistent homology improves fingerprint classification accuracy.
problem Classifying fingerprints into arch, loop, and whorl groups.
method Applying topological data analysis (TDA) to 3D point clouds of oriented minutiae points and ink-roll images, using supervised learning.
result Achieves near state-of-the-art classification accuracy rates.
Deep learning reduces memory and computation for MRF recovery.
problem Heavy storage and computation requirements of dictionary-matching in MRF recovery.
method MRF-Net with dimensionality reduction and manifold clustering.
result MRF-Net saves over 60 times memory and computation.
Lower bounds on private estimation of Gaussian covariance matrices.
problem Private estimation of Gaussian covariance matrices under various parameter regimes.
method Stein-Haff identity and fingerprinting lemma extensions.
result Lower bounds match existing upper bounds in the widest known parameters.
Audio fingerprinting, also named as audio hashing, has been well-known as a powerful technique to perform audio identification and synchronization. It basically involves two major steps: fingerprint (voice pattern) design and matching search. While the first step concerns the derivation of a robust and compact audio si…
AGML model improves indoor localization with sparse fingerprints using meta-learning and graph neural networks.
problem Maintaining high localization accuracy with extremely sparse fingerprints.
method Attentional Graph Neural Network (AGNN) and meta-learning framework with data augmentation strategies.
result AGML model consistently outperforms baseline methods across various metrics.
Chemception learns from molecule images to match expert QSAR models.
problem Predicting chemical properties without explicit chemistry knowledge.
method Developed a deep CNN architecture, Chemception, trained on 2D molecule images.
result Chemception outperforms MLPs in activity and solvation prediction.
New lower bounds for private covariance estimation of Gaussian distributions are proven.
problem Proving tight lower bounds for private estimation tasks under differential privacy.
method Generalized fingerprinting method for exponential families and private Assouad method.
result Tight lower bounds for private covariance estimation in Frobenius and spectral norms.
Most existing fingerprints-based indoor localization approaches are based on some single fingerprints, such as received signal strength (RSS), channel impulse response (CIR), and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environ…
Complex-valued neural networks improve wireless fingerprinting robustness.
problem Distinguish between devices sending the same message, robust against spoofing.
method Used complex-valued neural networks for supervised learning of fingerprints from wireless signals.
result Noise augmentation by adding white Gaussian noise leads to significant performance gains.
BrainSurfCNN predicts task contrasts from resting-state fingerprints, improving accuracy over baseline.
problem Predicting task-evoked activity from resting-state functional connectivity.
method Surface-based convolutional neural network (BrainSurfCNN) with reconstructive-contrastive loss.
result Significantly improved accuracy in predicting task contrasts over baseline.
Indoor localization based on SIngle Of Fingerprint (SIOF) is rather susceptible to the changing environment, multipath, and non-line-of-sight (NLOS) propagation. Building SIOF is also a very time-consuming process. Recently, we first proposed a GrOup Of Fingerprints (GOOF) to improve the localization accuracy and reduc…
SMILES Transformer learns molecular fingerprints for drug discovery.
problem Poor performance of rule-based molecular fingerprints in shallow prediction models or small datasets.
method Unsupervised pre-training of a sequence-to-sequence language model on a corpus of SMILES.
result SMILES Transformer outperformed existing methods in small-data settings.
Fingerprint deep neural networks by conferrable adversarial examples.
problem Detecting model stealing attacks in machine learning as a service.
method Extract conferrable adversarial examples to fingerprint deep neural networks.
result First method to reach 1.0 ROC AUC in verifying surrogates.
A new ML framework for RF fingerprinting across various applications.
problem Extracting unique RF fingerprints for specific emitter identification.
method Generic machine learning framework for automatic RF fingerprinting.
result Framework achieves superior performance compared to traditional methods.
CNNs improve positioning accuracy in massive MIMO systems.
problem Improving positioning accuracy in massive MIMO systems.
method Applying CNNs to learn sparse massive MIMO channel fingerprints.
result Moderately deep CNNs achieve fractional-wavelength positioning accuracy.
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-d…
Indoor localization is a supporting technology for a broadening range of pervasive wireless applications. One promis- ing approach is to locate users with radio frequency fingerprints. However, its wide adoption in real-world systems is challenged by the time- and manpower-consuming site survey process, which builds a …
This study improves UAV identification using RF signals with one-shot generative methods.
problem Limited RF environments and signal variability make traditional RF identification ineffective.
method Introduces one-shot generative methods to augment RF signals for UAV identification.
result One-shot generative methods outperform traditional methods in low-data regimes.
Paper proposes a SIMO DNN for indoor localization using Wi-Fi fingerprints.
problem Indoor localization with high accuracy and efficiency.
method Single-input and multi-output deep neural network architecture.
result SIMO-DNN scheme outperforms existing methods in floor detection and location accuracy.
Cover trees speed up MRI fingerprint recovery by reducing computation.
problem Efficiently reconstructing MRI fingerprint signals from compressed sensing data.
method Use cover trees for fast approximate nearest neighbor searches in IPG algorithm.
result Achieves 2-3 orders of magnitude reduction in computations.
PHINN: A generative model for rare-event time series using persistent homology
problem Generating rare events in time series
method Flow-matching framework with dynamic Betti curves and persistence landscape loss
result Outperforms statistical and diffusion baselines in topological fidelity and tail coverage
Deep learning models can learn confounding factors instead of device fingerprints in wireless signals.
problem Learning device fingerprints from complex-valued deep neural networks in the presence of confounding factors.
method Investigating complex-valued deep neural networks (DNNs) to distinguish between wireless transmitters, focusing on clock drift and channel variations.
result DNNs learn confounding features rather than device-specific characteristics, requiring strategies to promote generalization.
CMDRNN predicts user location using WiFi fingerprints with deep learning.
problem Predicting user activity with WiFi fingerprints is challenging due to high dimensionality.
method Combines CNN, RNN, and MDN to model high-dimensional time-series data.
result CMDRNN effectively predicts user location using WiFi fingerprints.
The paper tackles AI for fingerprinting localization, reducing training data and adapting models.
problem Training cumbersome databases and adapting algorithms for similar environments.
method Data augmentation and transfer learning for deep neural networks.
result Models can be fine-tuned for similar environments with significantly less training data.
This paper introduces a new method to evaluate multiple policies simultaneously.
problem Estimating the value of many policies for a single set of states.
method Developed a scalable, differentiable fingerprinting mechanism to represent complex policies.
result The method can produce policies that outperform those that generated the training data, in zero-shot manner.
Paper presents a new method to detect process differences at the trace level using mutual fingerprints.
problem Low-level granularity in process variant analysis leads to many false differences.
method Develops a mutual fingerprint technique to encode entire process traces for comparison.
result Mutual fingerprint method reveals significant differences not detected by existing techniques.
CDM improves fingerprinting-based positioning accuracy.
problem Quantifying similarity of collections with missing attributes.
method Combining vector-based distance metrics and set operations.
result Improves positioning accuracy by 5% on average.
Study optimizes Sigfox-based RSSI fingerprinting for outdoor localization.
problem Lack of publicly available datasets for Sigfox-based outdoor localization.
method Tuning hyperparameters and data transformations for kNN method.
result Mean localization error of 298 meters, median error of 109 meters.
FPO optimizes policies for robust reinforcement learning by adjusting environment variables.
problem Slow learning or suboptimal policies due to unobservable environment variables.
method FPO uses Bayesian optimization to select optimal environment variable distributions.
result FPO efficiently learns robust policies for rare events.
PS-VAE extracts multi-parameter MRI biomarkers with uncertainty quantification.
problem Uncertainty in inverse problems limits clinical acceptance of quantitative MRI methods.
method Physics-Structured Variational Autoencoder (PS-VAE) integrating physics simulator and self-supervised learning.
result PS-VAE provides full covariance of inter-parameter correlations and accelerates multi-parametric MRI quantification.
We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We sho…
A multiple classifiers fusion localization technique using received signal strengths (RSSs) of visible light is proposed, in which the proposed system transmits different intensity modulated sinusoidal signals by LEDs and the signals received by a Photo Diode (PD) placed at various grid points. First, we obtain some {\…
Two deep learning models improve indoor location prediction from WiFi fingerprints.
problem Indoor location prediction from WiFi fingerprints.
method Convolutional mixture density recurrent neural network and VAE-based semi-supervised learning model.
result Proposed models outperform existing methods in real-world datasets.
Python package for SPD matrix distances, reproducible and extensible.
problem Computing distances between SPD matrices for various applications.
method Unified, extensible framework supporting multiple SPD metrics.
result Reproducible and accessible SPD matrix comparison tool.
New DNN architecture for indoor localization in multi-story buildings.
problem Scalable indoor localization in complex multi-story buildings.
method Stacked autoencoder and feed-forward classifier for multi-label classification.
result Near state-of-the-art performance with lower complexity and energy consumption.
Transformer models show distinct spectral fingerprints under voice changes.
problem Detecting architectural biases in transformer models.
method Spectral analysis of attention-induced token graphs.
result Clear architectural signatures in model fingerprints correlate with language-specific behavior.
Symmetry-electronic fingerprints reveal competing magnetic phases in two-dimensional materials.
problem Predicting magnetic ground states, moments, and anisotropy in two-dimensional magnets.
method Introduce the symmetry-electronic fingerprint (SEF), a physically interpretable representation that encodes crystallographic symmetry operations, Wyckoff-site geometry, and site-resolved electronic structure.
result SEF-trained models accurately classify magnetic ordering and regress moments alongside anisotropy energies.
We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These fingerprints are further transformed non-linearly, their inner-product …
GEE detects and explains network anomalies without labeled data.
problem Detecting and explaining network anomalies without labeled data.
method GEE combines VAE for anomaly detection and gradient-based fingerprinting for explanation.
result GEE effectively detects and explains various network anomalies.
DeepPos uses deep learning to improve indoor location accuracy.
problem Indoor GPS accuracy issues with mobile devices.
method Supervised autoencoder network for modeling CSI-based environments.
result Robust and efficient indoor localization system developed.
New method uses multi-hop assortativity to classify network functionalities.
problem Classifying network functionalities based on structure and metadata.
method Introduces multi-hop assortativity to capture node similarity in paths.
result Multi-hop assortativity features outperform state-of-the-art methods in network classification.
Method finds reference products for a given item.
problem Finding relevant products for a given item.
method Product representation learning and fingerprint-type vector searching.
result The method outperforms peer services in search return rate and precision.
New algorithm estimates intrinsic dimension of discrete datasets.
problem Inaccuracies in using continuous methods for discrete datasets.
method Introduced an algorithm to infer intrinsic dimension of discrete spaces.
result Demonstrated accuracy on benchmark datasets and found a small intrinsic dimension in a metagenomic dataset.
New method uses SVBI to quickly generate radio maps for indoor positioning.
problem Time-consuming site surveys for creating radio maps in large indoor spaces.
method Combines fingerprinting positioning and SVBI for efficient radio map generation.
result Significantly reduces the cost and time of creating radio maps for indoor positioning.
Selective prediction framework reduces errors in molecular structure identification from MS/MS.
problem High-stakes applications require reliable molecular structure identification from MS/MS data.
method Selective prediction framework using risk-coverage tradeoff and uncertainty quantification.
result First-order confidence measures and retrieval-level aleatoric uncertainty achieve strong risk-coverage tradeoffs.