Wireless power transfer (WPT) technology holds the key to unlock the massive deployment of wirelessly powered battery-free IoT devices and has become an emerging topic in IoT applications. As IoT devices are distributed randomly in the environment, antennas featuring broad circularly polarized (CP) radiation coverage are highly promising as a power source to wirelessly charge them. Herein, in this article, an electrically small pattern reconfigurable Huygens CP antenna with quasi-isotropic beam coverage is presented for WPT enabled IoT applications. It consists of a pair of orthogonally placed crossed Egyptian axe dipoles (EADs), customized reconfigurable capacitively loaded loops (CLLs), and an unbalanced reconfigurable crossed driven-dipole structure. Pattern reconfigurability is realized due to the phase reversal of the EADs by switching the diodes at the center of the additional crossed shorter strips beneath the upper section of the CLLs. Furthermore, to ensure overlapping bandwidths for impedance matching and AR, the effective lengths of the driven-dipole arms are reconfigurable by controlling the diodes. An optimized prototype was fabricated and validated, showing a peak realized gain of 2.45 dBic and a wide 3-dB AR beamwidth of larger than 100° for both modes in an electrically small size (ka=0.79).

Steel surface defect detection is a crucial aspect of the steel manufacturing process, serving as an essential guarantee for enhancing the quality of steel production. Detecting surface defects during the industrial production process has become a critical area of research aimed at enhancing the quality of strip steel. Existing methods based on YOLO demonstrate strong real-time capabilities but often lack precision, while DETR methods offer higher accuracy yet face challenges in training, low real-time performance, and substantial computational demands. In this paper, we introduce the LJ-DETR model, designed to overcome these challenges by integrating various lightweight modules and capitalizing on the strengths of the YOLO architecture through a Joint Hybrid Encoder. Our extensive experiments conducted on the NEU-DET dataset demonstrate that the improved model significantly surpasses four RT-DETR baselines, achieving a mAP@50 of 75.1%, which is a 7.6% improvement over RT-DETR-X. Our model reduces the parameter count to 20.5M and enhances the frames per second (FPS) to 166.7. Additionally, our model improves performance on the GC10-DET dataset by 4% compared to the best-performing RT-DETR-R50. These advancements enable our approach to effectively meet the stringent demands for real-time detection and high accuracy in industrial defect inspection.

Detecting Twitter bots is essential for combating misinformation and maintaining the integrity of online social networks. Existing methods often overlook the high-order interactions and heterogeneous relationships among users and tweets, limiting their effectiveness in addressing sophisticated bot behaviors. This article introduces HHG-Bot, a novel hyper-heterogeneous graph-based framework for Twitter bot detection. The proposed approach integrates heterogeneous graph convolutional networks with a trainable hypergraph aggregation model to capture complex, high-order interactions. To overcome the challenge of labeled data scarcity, HHG-Bot employs a meta-learning paradigm that enhances the model’s generalization capability across different bot types. Experiments conducted on the Twibot-20 benchmark dataset demonstrate that HHG-Bot achieves state-of-the-art performance, surpassing existing methods in terms of accuracy (86.17%), F1-score (87.51%), and Matthews correlation coefficient (MCC) (71.75%). The results validate the effectiveness of leveraging hypergraphs and meta-learning for detecting Twitter bots, particularly in scenarios with limited labeled data.

Android malware poses a significant challenge for mobile platforms. To evade detection, contemporary malware variants use API substitution or obfuscation techniques to hide malicious activities and mask their shallow semantic characteristics. However, existing research lacks analysis of the hierarchical semantic associated with Android apps. To address this problem, we propose ArchSentry, an enhanced Android malware detection via hierarchical semantic extraction. First, we select entities and their relationships relevant to Android software behavior through the software architecture and represent them using a heterogeneous graph. Then, we structure meta-paths to represent rich semantic information to achieve semantic enhancement and improve efficiency. Next, we design a meta-path semantic selection method based on KL Divergence to identify and eliminate redundant features. To achieve a comprehensive representation of the overall software semantics and improve performance, we construct a feature fusion approach based on Restricted Boltzmann Machines (RBM) and AutoEncoder (AE) during the pre-training phase, while preserving the probability distribution characteristics of various meta-paths. Finally, Deep Neural Networks (DNN) process fusion features for comprehensive feature sets. Experimental results on real-world application samples indicate that ArchSentry achieves a remarkable 99.2% detection rate for Android malware, with a low false positive rate below 1%. These results surpass the performance of current state-of-the-art approaches.

In the traditional centralized Android malware classification framework, privacy concerns exist due to collected users’ apps containing sensitive information. A new classification framework based on Federated Learning (FL) has emerged to protect privacy. However, significant spatiotemporal heterogeneity exists in the distribution of Android malware samples in different clients. It presents a huge challenge to existing FL schemes, as trained local models differ significantly, resulting in slower model convergence and lower classification accuracy. To bridge this gap, we propose FedDRC, a robust FL-based Android malware classifier. First, we design a functional semantic embedding mechanism of API features, FSEM, using word embedding to improve the robustness of the model to the time heterogeneity of the client’s samples. Secondly, we use the idea of Information Bottleneck (IB) and transfer learning to design a robust local model, PAMIB, to deal with the model degradation caused by the space heterogeneity of the distribution of client samples. Extensive experiments on the Androzoo dataset show that FedDRC has the best robustness for Android malware classification tasks in various heterogeneity distribution settings: fastest convergence and best classification accuracy.

Devising diffusion graph to learn user representations is a crucial step in studying information propagation prediction. However, previous works mainly focused on structural and temporal features. To better incorporate content features, we introduce the Backward Decomposition and Forward Preservation mechanisms. The former involves decomposing content features for initializing node signals in the diffusion graph, thus fusing user features with content features. The latter aims to maintain node features generated by graph encoder consistent with the original content features. A series of experiments demonstrate that our model outperforms state-of-the-art models, and both mechanisms significantly enhance the prediction performance. Furthermore, our methods enables the features generated by the diffusion graph to more effectively incorporate features from various semantic spaces, whether encoded by language models or generated by graph embedding algorithms.

The federated Android malware classifier has attracted much attention owing to its advantages of privacy protection and multi-party joint modeling. However, the research indicates that the gradient transmitted within the federated classifier still encodes the user's sensitive information, exposing it to indirect privacy inference threats from curious servers. Differential privacy is a recognized and effective way to address this privacy breach threat by adding noise to the user's model parameters to limit the attacker's inference of sensitive information. However, the protection effect of existing differential privacy methods is at the cost of significantly reducing the model's classification accuracy, and it cannot be reasonably balanced. To address this challenge, we propose a privacy protection method, FedDADP. FedDADP performs adaptive, lightweight privacy configuration in its training time dimension and model space dimension according to the privacy risk distribution law in the federated Android malware classifier to protect users' privacy while maintaining the model's utility. Numerous experiments on the Androzoo dataset and multiple baseline classifiers show that FedDADP protects users' sensitive information better (7% more effectiveness against adversaries' inference) than baseline differential privacy methods and achieves better model utility (classification accuracy improves by about 8%) with the same privacy budget.

Information diffusion prediction captures diffusion dynamics of online messages in social networks. Thus, it is the basis of many essential tasks such as popularity prediction and viral marketing. However, there are two thorny problems caused by the loss of spatial-temporal properties of cascade data: "position-hopping" and "branch-independency." The former means no exact propagation relationship between any two consecutive infected users. The latter indicates that not all previously infected users contribute to the prediction of the next infected user. This article proposes the GRU-like Attention Unit and Structural Spreading (GRASS) model for microscopic cascade prediction to overcome the above two problems. First, we introduce the attention mechanism into the gated recurrent unit (GRU) component to expand the restricted receptive field of the recurrent neural network (RNN)-type module, thus addressing the "position-hopping" problem. Second, the structural spreading (SS) mechanism leverages structural features to filter out related users and controls the generation of cascade hidden states, thereby solving the "branch-independency" problem. Experiments on multiple real-world datasets show that our model significantly outperforms state-of-the-art baseline models on both hits@κ and map@κ metrics. Furthermore, the visualization of latent representations by t-distributed stochastic neighbor embedding (t-SNE) indicates that our model makes different cascades more discriminative during the encoding process.

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

The hierarchical network is the more effective platform, which provides multiple channels for various worm propagation. Thus, emerging worms can infect vulnerable hosts by scanning strategy and social media. However, the spread of scan-based worm is restrained due to uneven distribution of vulnerable hosts and NAT (Network Address Translation) technique. Meanwhile, topological dependency dictates to topology-based worm only infecting those hosts in social networks. To avoid their respective disadvantages, modern hybrid worm, which combines the above two propagation mechanisms, can implement efficient IP-address scanning by enhanced combination-scanning strategy, and spread more aggressively in social networks using enhanced reinfection mechanism. This paper presents a Hierarchical-Stochastic Propagation model to understand hybrid worm propagation. Inspired by hybrid worm, we design a new vaccine based on the Hierarchical-Measure Immunization strategy. For physical networking layer, we can estimate vulnerable-host distribution to find vulnerable hosts effectively through Maximum Likelihood estimation. For social networking layer, we use a novel propagation centrality measure to discover vital social nodes accurately. The experimental results show that our model can characterize the propagation mechanism of hybrid worms more comprehensively, and greatly outperforms state of the art models in terms of estimation accuracy. Meanwhile, our strategy is more effective to restrain the hybrid worm from spreading in networks.