Study analyzes WiFi check-ins to predict student activities.
problem Limited understanding of daily routines in POI prediction.
method Heterogeneous graph-based method to encode correlations.
result Improved POI prediction on education check-in data.
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.
Study compares distances for indoor WiFi mapping, finding Earth Mover's Distance effective.
problem Indoor localization and mapping using WiFi signals.
method Comparison of distance measures and kernel density estimation.
result Earth Mover's Distance is most beneficial for indoor localization.
Paper presents a WiFi-based indoor sensor localization technique.
problem Indoor localization in wireless sensor networks.
method Zoning-based localization using statistical learning.
result Efficient sensor zone determination in indoor environments.
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.
Deep learning model classifies concurrent human interactions from WiFi data with high accuracy.
problem Classifying concurrent human interactions from WiFi data with high accuracy.
method Attention-BiGRU deep learning model using Multiple Input Multiple Output radio link.
result Maximum benchmark accuracy of 94% for a single subject-pair, 88% for ten subject pairs.
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.
Neural networks predict airport passenger behavior using WiFi traces.
problem Predicting airport passenger activity choices inside the terminal.
method Three neural network architectures: FNN, LSTM, and their combination. Inputs include static and dynamic passenger data. Real-world case study at Bologna Airport.
result LSTM approach, especially with short prediction horizons, outperforms FNN.
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.
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.
Paper uses RNNs for more accurate indoor WiFi localization.
problem Accurate indoor WiFi localization using RSSI measurements.
method Proposes recurrent neural networks (RNNs) for trajectory positioning of RSSI data.
result Achieves an average localization error of 0.75 m with 80% under 1 m, outperforming conventional algorithms.
Model trains from wifi traces to infer public transport levels.
problem Estimate the level of service of a city-wide public transport network.
method Robust unsupervised clustering for train movement inference; classification model for real-time commuter patterns.
result Accurately estimated demand-supply gap from connected devices.
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users. In this paper, an approach adopting receiv…
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.
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold geodesic distance between any pair of points using both the labeled and unlabeled instances; and (ii) apply a k nearest neighbor regressor b…
We utilize Wi-Fi communications from smartphones to predict their mobility mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four strategic locations in a closed loop on streets in downtown Toronto. Deep neural network (Multilayer Perceptron) along with three decision tree based classifiers (Decisi…
We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage. Given a training radio map (TRM), i.e., a set of coordinat…
Population migration is valuable information which leads to proper decision in urban-planning strategy, massive investment, and many other fields. For instance, inter-city migration is a posterior evidence to see if the government's constrain of population works, and inter-community immigration might be a prior evidenc…
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.
Landmark2Vec maps unknown landmarks without GPS.
problem Estimate positions of unknown landmarks without GPS.
method Unsupervised neural network trained on landmark signals.
result Maps landmarks up to scale, rotation, and shift.
Active authentication is the problem of continuously verifying the identity of a person based on behavioral aspects of their interaction with a computing device. In this study, we collect and analyze behavioral biometrics data from 200subjects, each using their personal Android mobile device for a period of at least 30…
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.
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.
Paper proposes methods to localize sources in WSNs without knowing sensor parameters.
problem Source localization in WSNs without sensor parameter knowledge.
method Hitting set approach and feature selection method.
result Effective source localization methods validated through simulations.
WiPIN uses Wi-Fi signals to identify people without requiring them to walk.
problem Identification requires walking and is unreliable with many users.
method Extracts body information from Wi-Fi signals without user movement.
result Achieves 92% accuracy with 30 users, robust to various settings.
Method discovers user habits from mobile data.
problem Understanding human mobility patterns and habits.
method Density-based clustering for spatio-temporal data and Gaussian Mixture Model (GMM).
result Many unique habits were identified from the datasets.
Deep learning identifies wireless interference sources efficiently.
problem Interference source identification in 2.4 GHz ISM Band.
method Deep learning algorithms trained on 10 MHz band samples.
result CNN architecture reduces training time by 60% with minimal accuracy loss.
Study neural communication systems with bandwidth-limited channels.
problem Reliable message transmission despite noisy channels.
method Jointly model compression and error correction with neural networks; introduce prior for missing information; use auxiliary latent variables.
result Joint neural communication systems outperform separate models under expected information loss.
Paper proves integrability and entropy compactness for Kähler potentials with uniform log-log threshold.
problem Integrability and entropy compactness for Kähler potentials with specific density.
method Skoda-Zeriahi type integrability theorem and log-log threshold detection.
result Positivity of integrability threshold and entropy compactness for uniform log-log threshold.
In this paper, we prove that the zero-locus of any global holomorphic log-one-form on a projective log-smooth pair (X,D) of log-general type must be non-empty. Applying this result, we give an answer to the algebraic hyperbolicity part of Shafarevich's conjecture, with the generic fiber being Kawamata-log-…
Deep learning models detect and classify log anomalies.
problem Anomaly detection in unstructured log data.
method Auto-LSTM, Auto-BLSTM, and Auto-GRU models for feature extraction.
result Models outperform other algorithms on various log data sets.
A novel GPUM constructs Gaussian Processes for unknown manifolds with probabilistic metrics.
problem High-dimensional data on unknown manifolds with non-Euclidean geometry.
method Bayesian Gaussian Processes latent variable models (BGPLVM), Riemannian geometry, probabilistic metric tensor, Brownian Motion.
result GPUM provides more accurate predictions on unknown manifolds compared to traditional methods.
We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire population, not just conditioned on the logging policy. Prior work addresses this problem either when only logged data is available, or purely…
Anomaly detection system for network logs using ELK, Spark, and Hadoop.
problem Detecting anomalies in network connection logs.
method Streaming architecture, ELK, Spark, Hadoop for data collection and analysis; unsupervised learning with clustering and classification algorithms.
result Identifies subtle variances in log data, highlighting outliers.
Establishes log-concavity estimates for convex domains' first Dirichlet eigenfunctions.
problem Quantifying the Hessian of log-concave eigenfunctions on convex domains.
method Analyzes log-concavity properties of the first Dirichlet eigenfunction on convex domains.
result Obtains quantitative estimates for the Hessian of logu. Alternative proof of semipositivity and nefness for K-semistable log-Fano pairs.
problem Semipositivity and nefness of Chow-Mumford line bundle for K-semistable log-Fano pairs.
method Alternative proof using families of K-semistable log-Fano pairs.
result Proof of semipositivity and nefness for K-semistable log-Fano pairs.
Study improves sampling from non-log-concave distributions using Fisher information.
problem Sampling from non-log-concave distributions with high Fisher information guarantees.
method Proximal sampler with RGO implementation, leveraging log-concave sampling results.
result Improved complexity guarantee in relative Fisher information for non-log-concave sampling.
Study confirms boundedness of certain singularities in log Fano geometry.
problem Boundedness of log Fano cone singularities and minimal log discrepancies.
method Analyzing local volumes and minimal log discrepancies of Kollár components.
result Boundedness of K-semistable log Fano cone singularities confirmed in dimension three.
Paper proves log-Brunn-Minkowski inequality in 3D space.
problem Proving stronger geometric inequalities in higher dimensions.
method Establishes inequalities for convex bodies in R^3.
result Log-Brunn-Minkowski inequality holds in 3D.
New algorithms improve convergence rates for non-log-concave sampling and log-partition estimation.
problem Efficiently sampling from non-log-concave distributions and estimating their log-partition function.
method Analysis of information-based complexity, study of polynomial-time sampling algorithms.
result Optimal rates for sampling and log-partition estimation sometimes exceed those for optimization.
AppsPred predicts smartphone app usage based on context.
problem Predicting personalized usage behavior of smartphone apps based on contexts.
method Random Forest machine learning technique considering multi-dimensional contexts.
result AppsPred significantly outperforms other machine learning approaches in predicting smartphone apps.
LogAnMeta detects anomalies from log events using meta learning.
problem Poor performance of current log anomaly detection on new or unseen anomalies.
method Meta-learning-based hybrid few-shot classifier trained in an episodic manner.
result Demonstrates efficacy of LogAnMeta on detecting anomalies with few samples.
Defines log Floer cohomology for symplectic surfaces with a degenerate part.
problem Extending Floer cohomology to degenerate symplectic structures.
method Definition of log Floer cohomology for oriented log symplectic surfaces.
result Log Floer cohomology is invariant under isotopies and isomorphic to log de Rham cohomology for a single Lagrangian.
Using stable log maps, we introduce log twisted differentials extending the notion of abelian differentials to the Deligne-Mumford boundary of stable curves. The moduli stack of log twisted differentials provides a compactification of the strata of abelian differentials. The open strata can have up to three connected c…
Test log-likelihood comparisons can be misleading.
problem Misinterpretation of test log-likelihood in model comparison.
method Simple examples of model comparison and forecast accuracy.
result Test log-likelihood does not always correlate with model accuracy.
Constructs Kahler-Einstein metrics near isolated log canonical singularities.
problem Constructing metrics near singularities in complex geometry.
method Constructs Kahler-Einstein metrics with negative scalar curvature near isolated log canonical singularities.
result Metrics are complete near the singularity if the underlying space has complex dimension 2 or if the singularity is smoothable.
We study topological properties of log-symplectic structures and produce examples of compact manifolds with such structures. Notably we show that several symplectic manifolds do not admit log-symplectic structures and several log-symplectic manifolds do not admit symplectic structures, for example #m CP^2 # n bar(CP^2)…
We consider the problem of learning a general graph G=(V,E) using edge-detecting queries, where the number of vertices ∣V∣=n is given to the learner. The information theoretic lower bound gives mlogn for the number of queries, where m=∣E∣ is the number of edges. In case the number of edges m is also given t…