The paper introduces a novel zero‑shot anomaly detection framework for multivariate IoT traffic data that combines adversarial learning and contrastive loss within a sequence‑based Variational Autoencoder. It achieves domain‑invariant latent representations and semantically structured embeddings without labeled data, using encoder/decoder adaptor layers to align feature distributions and a destination‑based segmentation strategy to model real‑world communication patterns. The method is evaluated on six diverse datasets across 44 transfer scenarios, showing strong zero‑shot generalization and competitive performance against a contrastive domain‑adaptation baseline in heterogeneous, privacy‑constrained IoT environments.
By Mahshid Rezakhani, Tolunay Seyfi, Fatemeh Afghah
arXiv:2608. 14694v1 Announce Type: new Abstract: Foundation models are emerging as a transformative paradigm for AI-native sixth-generation (6G) wireless networks by enabling scalable, transferable, and data-efficient intelligence across diverse communication tasks.
By Naveed Khan, Besan Al Sbeihi, Maryam Alshehhi, Nasir Saeed
arXiv:2606. 02974v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using WiFi signals has emerged as a transformative technology for smart homes, healthcare monitoring, security systems, and ambient assisted living.
By Maheen Arshad, Qindeel E Zahra, Muhammad Khuram Shahzad
arXiv:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.
By Mohd Halim Mohd Noor, Abdulrahman M. A. Baraka
The paper introduces Inter-Instance Generative Adversarial Networks (IIns‑GAN), a deep learning approach for synthesizing realistic labeled wireless signals. Unlike traditional environmental‑model based methods, IIns‑GAN adapts to various scenarios and produces signals that closely match the physical characteristics of real measurements. Experiments on public Ultra‑Wideband datasets show that the generated signals improve model training for tasks such as distance estimation and environment identification.
By Yuxiao Li, Keke Hu, Santiago Mazuelas, Yuan Shen
CIG-MAE is a self‑supervised framework for WiFi‑based human action recognition that uses a cross‑modal masked autoencoder to reconstruct both amplitude and phase of Channel State Information. It introduces an adaptive, information‑guided masking strategy that focuses on high‑density time‑frequency regions and employs a Barlow Twins regularizer to align cross‑modal representations without negative samples. Experiments on three public datasets show that CIG‑MAE outperforms state‑of‑the‑art SSL methods and even surpasses a fully supervised baseline, highlighting its data efficiency, robustness, and generalization.
By Gang Liu, Yanling Hao, Yixuan Zou
arXiv:2607. 25687v1 Announce Type: cross Abstract: Full-field reconstruction of air pollution is essential for evaluating pollution exposure and supporting public health decision-making.
By Abhishek A. Sabnis, Mihai Mitrea, Lya Lugon, Karine Sartelet, Marc Bocquet, Xiaoyuan Cheng, Shupeng Zhu, Sibo Cheng
arXiv:2507. 21799v2 Announce Type: replace-cross Abstract: The empirical success of deep learning has spurred its application to the radio-frequency (RF) domain, leading to significant advances in Deep Wireless Sensing (DWS).
By Xie Zhang, Yina Wang, Chenshu Wu
The paper proposes an ensemble-based self‑taught learning framework for parking space classification that uses unsupervised convolutional autoencoders to learn transferable visual representations from unlabeled data. These learned encoders serve as fixed feature extractors for supervised classification with limited annotated samples, and an ensemble of heterogeneous autoencoders with independent classifier heads is employed to enhance robustness and reduce architectural bias. Experiments on PKLot and CNRPark benchmarks demonstrate that this approach significantly lowers annotation requirements while achieving high accuracies (93–96%) under cross‑dataset evaluation protocols.
By Lucas de Oliveira Cunha, Joelton Deonei Gotz, Paulo Lisboa de Almeida, Andre Gustavo Hochuli
arXiv:2510. 22863v2 Announce Type: replace-cross Abstract: Reliable long-term forecasting of PM2.
By Amirali Ataee Naeini, Arshia Ataee Naeini, Fatemeh Karami Mohammadi, Omid Ghaffarpasand
arXiv:2610.02000v1 Announce Type: new
Abstract: Adverse conditions such as rain, snow, fog, and dust remain challenging for camera-based perception in autonomous driving. We study multi-class weather...
By Hossein Maghsoumi, George Atia, Yaser P. Fallah
The paper compares classical machine learning algorithms—such as Logistic Regression, SVM, Random Forest, XGBoost, and CatBoost—with Tabular Deep Learning models (TabNet, FT-Transformer, TabTransformer, TabSeq, and 1D CNNs) for urban land cover classification using a UCI dataset derived from high‑resolution aerial imagery. It evaluates performance across nine land cover classes, addressing challenges like high dimensionality, heterogeneous features, and class imbalance by applying weighted cross‑entropy loss for deep models and measuring accuracy, macro‑precision, macro‑recall, macro‑F1, AUC‑ROC, and confusion matrices. Results indicate that while tree ensembles remain strong baselines, Tabular Deep Learning can match or surpass them when non‑linear interactions are prominent and imbalance handling is effective.
By Muntasir Tabasum, Tanpia Tasnim, Md. Ekramul Islam, Al Zadid Sultan Bin Habib