The rapid expansion of materials databases offers unprecedented opportunities for accelerating materials discovery via machine learning. However, the widespread assumption that larger datasets inherently produce better models does not hold in practice. We propose FUSION (Fusing Uncertainty with Structural Information for Optimal Neural training), an offline dataset pruning strategy that synergistically combines uncertainty quantification with crystallographic structure analysis via geometric fingerprinting, framing dataset pruning as a discrete optimization problem. Through evaluation across 3 benchmark datasets, FUSION consistently outperforms baselines, including random pruning, uncertainty sampling, weighting factor pruning, diversity sampling, and active learning. It demonstrates robust transferability across 11 diverse architectures, outperforming random pruning by 1.91–13.65% across different datasets, with an average improvement of 6.36%. Moreover, our analysis suggests that different models exhibit varying robustness characteristics when faced with pruned training data, highlighting the importance of model selection tailored to dataset composition. We identify optimal pruning points where removing just 0–8% of training data improves model performance, yielding gains up to 12.67% in specific model–dataset combinations. These results establish a new paradigm for materials informatics that prioritizes data quality over quantity, offering a pathway toward more efficient and sustainable machine learning workflows in computational materials science.

As smart homes become increasingly prevalent, intelligent models are widely used for tasks such as anomaly detection and behavior prediction. These models are typically trained on static datasets, making them brittle to behavioral drift caused by seasonal changes, lifestyle shifts, or evolving routines. However, collecting new behavior data for retraining is often impractical due to its slow pace, high cost, and privacy concerns. In this paper, we propose SmartGen, an LLM-based framework that synthesizes context-aware user behavior data to support continual adaptation of downstream smart home models. SmartGen consists of four key components. First, we design a Time and Semantic-aware Split module to divide long behavior sequences into manageable, semantically coherent subsequences under dual time-span constraints. Second, we propose Semantic-aware Sequence Compression to reduce input length while preserving representative semantics by clustering behavior mapping in latent space. Third, we introduce Graph-guided Sequence Synthesis, which constructs a behavior relationship graph and encodes frequent transitions into prompts, guiding the LLM to generate data aligned with contextual changes while retaining core behavior patterns. Finally, we design a Two-stage Outlier Filter to identify and remove implausible or semantically inconsistent outputs, aiming to improve the factual coherence and behavioral validity of the generated sequences. Experiments on three real-world datasets demonstrate that SmartGen significantly enhances model performance on anomaly detection and behavior prediction tasks under behavioral drift, with anomaly detection improving by 85.43% and behavior prediction by 70.51% on average. The code is available at https://github.com/xzyvoid/SmartGen.

Machine Learning-based Network Intrusion Detection Systems (ML-NIDSes) are vital for cyber-security, yet their inherent vulnerability to adversarial attacks poses a persistent challenge. Among these, zero-query transfer-based attacks present a particularly realistic and formidable threat, as adversaries operate with no knowledge of or interaction with the target NIDS. However, the efficacy of such attacks is critically hampered by poor adversarial transferability across diverse NIDS model architectures and by strict protocol-defined constraints on network traffic modifications. To probe the robustness of NIDSes under zero-query attacks, we introduce ARTEMIS, a novel hybrid attack framework engineered to dramatically enhance adversarial transferability for zero-query attacks. ARTEMIS leverages the generalization capabilities of meta-learning to adapt to unknown target models by simulating diverse black-box transfer tasks. Simultaneously, it combines a reinforcement learning-inspired adaptive reweighing mechanism to maximize transfer potential by promoting the effective use of heterogeneous substitute ensembles. Extensive evaluations on the BCCC-CIC-IDS2017/2018 datasets across closed-set, open-set, and cross-set zero-query scenarios confirm that ARTEMIS significantly outperforms state-of-the-art baselines. Our work presents a powerful methodology for NIDS vulnerability assessment and provides crucial insights for developing defenses against transfer-based evasions.

In this paper, we introduce CFDONEval, a comprehensive evaluation of 12 operator-learning-based neural network (ON) models to simulate 7 benchmark fluid dynamics problems. These problems cover a range of 2D scenarios, including Darcy flow, two-phase flow, Taylor-Green vortex, lid-driven cavity flow, tube flow, circular cylinder flow, and 3D periodic hill flow. For a rigorous evaluation, we establish 22 fluid dynamics datasets for these benchmark problems, 18 of which are newly generated using traditional numerical methods, such as the finite element method. Our evaluation tackles 5 key challenges: multiscale phenomena, convection dominance, long-term predictions, multiphase flows, and unstructured meshes over complex geometries. We assess computational accuracy, efficiency, and flow field visualization, offering valuable insights into the application of ON models in fluid dynamics research. Our findings show that attention-based models perform well in handling almost all challenges; models with a U-shaped structure excel in handling multiscale problems; and the NU-FNO model demonstrates the smallest relative error in L2 norm when processing nonuniform grid data. The related code, dataset, and appendix are publicly available at: https://github.com/Sysuzqs/CFDNNEval.

Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have been proposed to identify abnormal behaviors and mitigate potential risks. However, their performance often falls short because they do not effectively learn less frequent behaviors, consider temporal context, or account for the impact of noise in human behaviors. In this paper, we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly detection framework. First, we design a Loss-guided Dynamic Mask Strategy (LDMS) to encourage the model to learn less frequent behaviors, which are often overlooked during learning. Second, we propose a Three-level Time-aware Position Embedding (TTPE) to incorporate temporal information into positional embedding to detect temporal context anomaly. Third, we propose a Noise-aware Weighted Reconstruction Loss (NWRL) that assigns different weights for routine behaviors and noise behaviors to mitigate the interference of noise behaviors during inference. Comprehensive experiments on three datasets with ten types of anomaly behaviors demonstrates that SmartGuard consistently outperforms state-of-the-art baselines and also offers highly interpretable results.

In this paper, we propose DGCL, a dual-graph neural networks (GNNs)-based contrastive learning (CL) integrated with mixed molecular fingerprints (MFPs) for molecular property prediction. The DGCL-MFP method contains two stages. In the first pretraining stage, we utilize two different GNNs as encoders to construct CL, rather than using the method of generating enhanced graphs as before. Precisely, DGCL aggregates and enhances features of the same molecule by the Graph Isomorphism Network and the Graph Attention Network, with representations extracted from the same molecule serving as positive samples, and others marked as negative ones. In the downstream tasks training stage, features extracted from the two above pretrained graph networks and the meticulously selected MFPs are concated together to predict molecular properties. Our experiments show that DGCL enhances the performance of existing GNNs by achieving or surpassing the state-of-the-art self-supervised learning models on multiple benchmark datasets. Specifically, DGCL increases the average performance of classification tasks by 3.73$\%$ and improves the performance of regression task Lipo by 0.126. Through ablation studies, we validate the impact of network fusion strategies and MFPs on model performance. In addition, DGCL's predictive performance is further enhanced by weighting different molecular features based on the Extended Connectivity Fingerprint. The code and datasets of DGCL will be made publicly available.

Existing IoT Network Anomaly Detection Systems (NADSes) typically treat IoT devices as independent entities and model them by Euclidean space features. These approaches suffer from low accuracies on new attacks (e.g., platform-based attacks and evasion attacks), since they do not fully consider the semantic information including traffic periodicity and device/environment interactions. In this paper, we propose SeIoT, a knowledge graph-based bimodal anomaly detection framework for smart homes. We propose a knowledge graph structure to represent the semantic information of a smart home. First, we propose the Action Fingerprint module, an efficient and effective traffic classification approach to extract the device actions and features required by the knowledge graph. Then, we propose a bimodal anomaly detection framework including interaction-related and time-related detectors to detect the knowledge graph. We propose a feature separation-based heterogeneous graph attention network that can accurately model the interactions among devices and environments, and a method to represent traffic periodicity for the time-related detector. For evaluation, we set up a real-world testbed and evaluate the detection performance of both device-targeted attacks and platform-based attacks. Experiment results show that SeIoT can achieve better detection capability than prior work on both of the attacks.

Synthetic traffic generation can produce sufficient data for model training of various traffic analysis tasks for IoT networks with few costs and ethical concerns. However, with the increasing functionalities of the latest smart devices, existing approaches can neither customize the traffic generation of various device functions nor generate traffic that preserves the sequentiality among packets as the real traffic. To address these limitations, this paper proposes IoTGemini, a novel framework for high-quality IoT traffic generation, which consists of a Device Modeling Module and a Traffic Generation Module. In the Device Modeling Module, we propose a method to obtain the profiles of the device functions and network behaviors, enabling IoTGemini to customize the traffic generation like using a real IoT device. In the Traffic Generation Module, we design a Packet Sequence Generative Adversarial Network (PS-GAN), which can generate synthetic traffic with high fidelity of both per-packet fields and sequential relationships. We set up a real-world IoT testbed to evaluate IoTGemini. The experiment result shows that IoTGemini can achieve great effectiveness in device modeling, high fidelity of synthetic traffic generation, and remarkable usability to downstream tasks on different traffic datasets and downstream traffic analysis tasks.

Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some important data, e.g., 5-tuples and flow statistics, are often obscured, rendering many existing approaches invalid. It is further challenged by the high-speed traffic of hundreds of terabytes per day in ISP networks. This paper proposes DeviceRadar, an online IoT device fingerprinting framework that achieves accurate, real-time processing in ISPs using programmable switches. We innovatively exploit “key packets” as a basis of fingerprints only using packet sizes and directions, which appear periodically while exhibiting differences across different IoT devices. To utilize them, we propose a packet size embedding model to discover the spatial relationships between packets. Meanwhile, we design an algorithm to extract the “key packets” of each device, and propose an approach that jointly considers the spatial relationships and the key packets to produce a neighboring key packet distribution, which can serve as a feature vector for machine learning models for inference. Last, we design a model transformation method and a feature extraction process to deploy the model on a programmable data plane within its constrained arithmetic operations and memory to achieve line-speed processing. Our experiments show that DeviceRadar can achieve state-of-the-art accuracy across 77 IoT devices with 40 Gbps throughput, and requires only 1.3% of the processing time compared to GPU-accelerated approaches.

As a fundamental machine learning problem, graph clustering has facilitated various real-world applications, and tremendous efforts had been devoted to it in the past few decades. However, most of the existing methods like spectral clustering suffer from the sparsity, scalability, robustness and handling high dimensional raw information in clustering. To address this issue, we propose a deep probabilistic model, called Variational Graph Embedding and Clustering with Laplacian Eigenmaps (VGECLE), which learns node embeddings and assigns node clusters simultaneously. It represents each node as a Gaussian distribution to disentangle the true embedding position and the uncertainty from the graph. With a Mixture of Gaussian (MoG) prior, VGECLE is capable of learning an interpretable clustering by the variational inference and generative process. In order to learn the pairwise relationships better, we propose a Teacher-Student mechanism encouraging node to learn a better Gaussian from its instant neighbors in the stochastic gradient descent (SGD) training fashion. By optimizing the graph embedding and the graph clustering problem as a whole, our model can fully take the advantages in their correlation. To our best knowledge, we are the first to tackle graph clustering in a deep probabilistic viewpoint. We perform extensive experiments on both synthetic and real-world networks to corroborate the effectiveness and efficiency of the proposed framework.