Deep neural networks improve CSI-based user authentication stability.
problem Stability in authentication when users rotate.
method Extract features from CSI measurements using a deep neural network.
result Deep neural networks enhance the stability of CSI-based authentication.
Paper proposes deep neural networks for efficient CSI-based authentication.
problem Efficiently authenticate users using channel state information (CSI) without requiring extensive channel knowledge.
method Proposes CNN, RNN, and CRNN for CSI feature extraction and dependency capture. Extends to semi-supervised learning for unlabeled data.
result Demonstrates improved authentication performance using deep neural networks and semi-supervised learning.
This paper classifies CSI Kundt metrics related to locally homogeneous degenerate Kundt metrics.
problem Classifying CSI Kundt metrics related to locally homogeneous degenerate Kundt metrics.
method Invariant classification of locally homogeneous CSI Kundt spacetimes of alignment type D.
result Any CSI Kundt metric can be constructed from the classified locally homogeneous ones.
Proposes CNN-based analog CSI feedback for FDD MIMO-OFDM systems.
problem High CSI feedback overhead in FDD MIMO systems.
method AnalogDeepCMC: maps downlink CSI to uplink channel input, reconstructs channel estimate.
result Significantly improves downlink spectral efficiency and simplifies operation.
DeepCMC compresses CSI for massive MIMO systems, reducing overhead and improving performance.
problem High CSI overhead in massive MIMO systems limits spectral efficiency.
method Deep learning-based fully convolutional neural network with residual layers and entropy coding.
result DeepCMC outperforms state-of-the-art schemes in CSI reconstruction quality for the same compression rate.
TGARCH model shows CSI-300 futures reduce spot price volatility.
problem Impact of CSI-300 futures trading on spot price volatility.
method TGARCH model applied to CSI-300 index data.
result CSI-300 futures trading significantly reduces spot price volatility.
Machine learning enhances wireless network authentication for diverse devices.
problem Complex dynamic wireless environments challenge conventional authentication methods.
method Intelligent authentication using machine learning for diverse physical layer attributes.
result Machine learning-based authentication provides cost-effective, reliable, and situation-aware security.
Enhanced ECG biometric system improves authentication accuracy.
problem Traditional authentication methods have security issues.
method Developed an ECG-based authentication system using machine learning.
result Achieved up to 92% identification accuracy.
CSI-Net learns WiFi signals for body characterization and pose recognition.
problem Unified learning of body characteristics and pose recognition.
method Unified Deep Neural Network (DNN) for WiFi signal representation and multi-task learning.
result CSI-Net solves biometrics estimation and person recognition.
Paper models and compresses wideband CSI feedback in FDD MIMO systems.
problem Fundamental limits of channel state information (CSI) feedback in FDD massive MIMO systems.
method Modeling CSI as a Gaussian-mixture source with latent geometry states, proposing Gaussian-mixture transform coding (GMTC).
result Near-optimal CSI compression achieved through state-adaptive transform coding without large neural encoders.
Study shows context-specific models improve swipe gesture authentication for smartphone users.
problem Improving swipe gesture-based continuous authentication for smartphones.
method Conducted experiments on HMOG dataset with 100 subjects, analyzing authentication error in different scenarios.
result Context-specific models are needed for different smartphone usage and human activity scenarios.
Paper shows how to trick machine learning-based authentication systems.
problem Ineffectiveness of machine learning in authentication systems against sophisticated attacks.
method Constructing a blind attack using query synthesis and Explainable-AI techniques.
result Demonstrated that machine learning models can be fooled with under 100 queries.
Deep learning improves gait biometric recognition accuracy.
problem Low accuracy in existing CSI-based gait identification systems.
method Developed an end-to-end deep CSI learning system using deep neural networks.
result Achieved a top-1 accuracy of 97.12% for a dataset of 30 people.
Deep neural networks infer downlink CSI from uplink CSI without feedback.
problem Efficient allocation of wireless resources in FDD systems requires accurate DL-CSI.
method Used convolutional neural networks and GANs to infer DL-CSI from UL-CSI.
result Deep learning can accurately predict DL-CSI from UL-CSI for various environments.
A novel method to prevent bias in authentication models.
problem Bias in data-driven authentication models trained in one domain but required to apply in others.
method Two-stage method involving one-versus-rest disentangle learning and additive adversarial learning.
result Demonstrated effectiveness and superiority of the proposed method through comprehensive evaluation.
New attacks bypass authentication models using mouse dynamics data.
problem Adversarial attacks on behavioural mouse authentication.
method Generative approaches to create adversarial mouse trajectories.
result Imitation-based attacks often outperform surrogate-based attacks.
Unified Siamese network for wireless positioning and channel charting.
problem Wireless positioning and channel charting using CSI.
method Unified Siamese neural network architecture for both supervised and unsupervised learning.
result Siamese networks achieve similar or better performance than existing methods.
Wi-Fringe recognizes named gestures using WiFi CSI without training data.
problem Lack of training data for WiFi-based gesture recognition.
method WiFi CSI values paired with activity names for recognition.
result Wi-Fringe detects activities with or without training data.
DNNs improve localization from channel estimates, overcoming practical impairments.
problem Improving localization accuracy from channel estimates in Massive MIMO systems.
method Principled feature design for DNNs invariant to practical impairments.
result DNN achieves high localization accuracy and generalization capability.
New algorithm APHEN improves tensor decomposition for mobile banking user-device authentication.
problem Enhancing user-device authentication in mobile banking for financial services.
method Tensor decomposition using Paratuck2 and APHEN algorithm for faster and more accurate computation.
result Improved user-device authentication for financial services through faster and more accurate tensor decomposition.
CSI method learns conditional distributions by estimating flow equations.
problem Learning conditional distributions in generative models.
method Estimates probability flow equations to transport reference to target distribution.
result Derives explicit expressions for conditional drift and score functions.
Aims to eliminate domain bias in authentication without domain labels.
problem Authentication models are biased due to domain differences.
method Discover latent domains and eliminate domain difference alternately, using a meta-learning framework.
result Eliminates domain difference in authentication without domain labels.
New system uses wearable bio-signals for easy authentication.
problem Security of private information on wearables is a concern.
method Context-dependent soft-biometric authentication using heart rate, gait, and breathing audio.
result Binary SVM with RBF kernel achieves high accuracy and low EER.
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.
Deep reinforcement learning optimizes power allocation in wireless networks.
problem Challenges in optimizing power allocation in large wireless networks.
method Distributively executed dynamic power allocation scheme using deep Q-learning.
result Achieves near-optimal power allocation in real-time with delayed CSI.
DeepSIC learns to detect multiple symbols in MIMO systems without assuming a specific channel model.
problem Challenges in multiuser MIMO detection due to non-linear channels and lack of accurate channel state information.
method Integrates machine learning into iterative soft interference cancellation (SIC) algorithm to learn from limited training samples.
result Significantly outperforms conventional methods in linear and non-linear channels, even with CSI uncertainty.
ViterbiNet uses deep learning to detect symbols without channel info.
problem Symbol detection in digital receivers without channel state info.
method Integrates DNNs into Viterbi algorithm, leaving structure intact.
result Performance of ViterbiNet approaches CSI-based Viterbi, tracks dynamic channels.
The growing trend of using wearable devices for context-aware computing and pervasive sensing systems has raised its potentials for quick and reliable authentication techniques. Since personal writing habitats differ from each other, it is possible to realize user authentication through writing. This is of great signif…
This paper models CSI 300 index volatility using machine learning and addresses jump prediction.
problem Volatility modeling and jump prediction for high-frequency CSI 300 index data.
method Generalized Barndorff-Nielsen and Shephard model with machine learning algorithms for parameter estimation and forecast evaluation.
result Deterministic component of stochastic volatility processes can be captured over short and longer-term windows.
Edge devices learn a global model collaboratively over wireless channels.
problem Learning a global model from edge devices with imperfect channel state information.
method Proposed analog aggregation scheme, receive beamforming at PS, and convergence analysis.
result Performance improvement with more PS antennas, even with imperfect CSI.
Paper compares machine learning models for food authenticity.
problem Determining correct food labelling from NIR spectroscopic data.
method Applied and compared various classification, dimension reduction, and variable selection approaches to NIR datasets of meat, olive oil, and honey samples.
result Partial least squares outperformed other approaches in classifying food authenticity.
Develops CSI for predicting disease progression from sparse data.
problem Lack of frequent clinical metrics for disease progression.
method Machine learning framework (CSI) for analyzing disease progression from sparse observations.
result CSI effectively predicts disease progression from sparse data.
CSI detects novelty by contrasting shifted instances, outperforming existing methods.
problem Detecting samples from outside the training distribution.
method Contrastive learning with distributionally shifted augmentations.
result CSI outperforms existing methods in various novelty detection scenarios.
Deep learning reduces training overhead in massive MIMO systems.
problem Reducing training overhead in massive MIMO systems.
method Use of deep learning (NNs) to improve CSI acquisition and feedback processes.
result Significant improvements in performance and reduced complexity.
SONA improves conditional generation by balancing authenticity and alignment.
problem Challenges in balancing authenticity and conditional alignment in conditional generative models.
method SONA integrates unconditional discrimination, matching-aware supervision, and adaptive weighting to balance authenticity and alignment.
result SONA achieves superior sample quality and conditional alignment compared to state-of-the-art methods.
Paper proposes CNN-LSTM for WiFi indoor localization.
problem Classical WiFi localization methods using only spatial data.
method Combines CNN and LSTM for CSI feature extraction.
result Achieves 2.5~m average localization error with 80% under 4~m.
A new framework for mobile authentication using deep metric learning.
problem Challenges in mobile authentication using behavioral biometrics.
method Deep metric learning, private data protection, flexible training scheduling.
result 95% authentication accuracy on public datasets, robust against attacks.
Study provides long-term EMG data for multi-day biometric authentication.
problem Limited long-term EMG data for multi-day biometric authentication.
method Collected EMG data from 43 participants over three days.
result Multi-day biometric authentication results in low error rates.
FaceSigns embeds a secret watermark in images to authenticate and detect deepfakes.
problem Realistic image and video manipulation threats, especially deepfakes.
method Semi-fragile watermarking using neural networks, robust to face-swapping but fragile to deepfake manipulations.
result FaceSigns can reliably detect deepfake content with high accuracy.
Deep learning model predicts stock market direction.
problem Forecasting stock market direction in finance.
method Stacked Denoising Autoencoder (SDAE) applied to financial prediction.
result Deep learning model outperforms traditional methods in predicting CSI 300 index.
FedUA trains UA models privately without raw data.
problem Training UA models requires raw user data, compromising privacy.
method Federated learning framework for privacy-preserving UA model training.
result FedUA reliably rejects unseen user data at high true positive rates.
We study the existence of a non-spacelike isometry, ζ, in higher dimensional Kundt spacetimes with constant scalar curvature invariants (CSI). We present the particular forms for the null or timelike Killing vectors and a set of constraints for the metric functions in each case. Within the class of N dimensional CSI Ku…
Enhanced smartphone authentication using app usage patterns.
problem Low-latency continuous authentication for smartphones.
method Markovian process modeling, Hidden Markov Models (HMMs), modified edit-distance algorithm.
result Effective incorporation of unforeseen events improves user verification performance.
Paper proposes a new portfolio model for better investment decisions.
problem Traditional portfolio models fail to adapt to nonstationary markets.
method Developed a mean-detrended cross-correlation portfolio model (M-DCCP model).
result The M-DCCP model outperforms traditional models in constructing optimal portfolios.
Paper improves channel charting using autoencoders with spatial constraints.
problem Improving logical positioning of UEs using channel-state information.
method Representation-constrained autoencoders to enhance channel charts.
result Improved quality of learned channel charts for UE positioning.
Deep learning predicts downlink channel from uplink data, reducing signaling overhead.
problem Large signaling overhead for full DL CSI in FDD MIMO.
method Deep learning-based channel extrapolation (prediction).
result Deep learning can infer DL CSI from UL CSI without additional overhead.
Deep model improves option pricing for CSI 300 index with sentiment and volatility features.
problem Challenges in real market option pricing, especially with constant volatility assumption.
method Deep Forward-Backward Stochastic Differential Equation (FBSDE) framework with dual-network architecture.
result Significant reduction in MAE and MAPE compared to BSM model.
Deep learning uses WiFi CSI for reliable human presence detection.
problem Reliable human presence detection using ambient RF signals.
method Pre-processing of estimated CSI followed by deep learning.
result Near perfect presence detection during multiple extended periods.