In the post-Moore era, the development of active phased array antennas will inevitably trend towards active array microsystems. In this paper, the characteristics and composition of the active array antenna are briefly described. Owing to the high efficiency, low profile, and light weight of the active array microsystems, the application prospects and advantages in the engineering of multi-functional airborne radar, spaceborne radar, and communication systems are analyzed. Moreover, according to the characteristics of the post-Moore era of integrated circuits, scientific and technological problems in the active array microsystems are presented, including multi-scale, multi-signal, and multi-physics field coupling. The challenges are also discussed, such as new architectures and algorithms, miniaturization of passive components, novel materials and processes, ultra-wideband technology, and new interdisciplinary technological applications. This paper is expected to inspire in-depth research on active array microsystems.
Graph neural networks (GNNs) have achieved remarkable performance in a variety of graph-related tasks. Recent evidence in the GNN community shows that such good performance can be attributed to the homophily prior; i.e., connected nodes tend to have similar features and labels. However, in heterophilic settings where the features of connected nodes may vary significantly, GNN models exhibit notable performance deterioration. In this work, we formulate this problem as prior-data conflict and propose a model called the mixture-prior graph neural network (MPGNN). First, to address the mismatch of homophily prior on heterophilic graphs, we introduce the non-informative prior, which makes no assumptions about the relationship between connected nodes and learns such relationship from the data. Second, to avoid performance degradation on homophilic graphs, we implement a soft switch to balance the effects of homophily prior and non-informative prior by learnable weights. We evaluate the performance of MPGNN on both synthetic and real-world graphs. Results show that MPGNN can effectively capture the relationship between connected nodes, while the soft switch helps select a suitable prior according to the graph characteristics. With these two designs, MPGNN outperforms state-of-the-art methods on heterophilic graphs without sacrificing performance on homophilic graphs.
Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning networks. However, after embedding watermark signals by convolution, the feature fusion efficiency of convolution is relatively low; this can easily lead to distortion in the embedded image. When distortion occurs in medical images, especially in diffusion tensor images (DTIs), the clinical value of the DTI is lost. To address this issue, a robust watermarking algorithm for DTIs implemented by fusing convolution with a Transformer is proposed to ensure the robustness of the watermark and the consistency of sampling distance, which enhances the quality of the reconstructed image of the watermarked DTIs after embedding the watermark signals. In the watermark-embedding network, T1-weighted (T1w) images are used as prior knowledge. The correlation between T1w images and the original DTI is proposed to calculate the most significant features from the T1w images by using the Transformer mechanism. The maximum of the correlation is used as the most significant feature weight to improve the quality of the reconstructed DTI. In the watermark extraction network, the most significant watermark features from the watermarked DTI are adequately learned by the Transformer to robustly extract the watermark signals from the watermark features. Experimental results show that the average peak signal-to-noise ratio of the watermarked DTI reaches 50.47 dB, the diffusion characteristics such as mean diffusivity and fractional anisotropy remain unchanged, and the main axis deflection angle αAC is close to 1. Our proposed algorithm can effectively protect the copyright of the DTI and barely affects the clinical diagnosis.
Data imputation is an essential pre-processing task for data governance, aimed at filling in incomplete data. However, conventional data imputation methods can only partly alleviate data incompleteness using isolated tabular data, and they fail to achieve the best balance between accuracy and efficiency. In this paper, we present a novel visual analysis approach for data imputation. We develop a multi-party tabular data association strategy that uses intelligent algorithms to identify similar columns and establish column correlations across multiple tables. Then, we perform the initial imputation of incomplete data using correlated data entries from other tables. Additionally, we develop a visual analysis system to refine data imputation candidates. Our interactive system combines the multi-party data imputation approach with expert knowledge, allowing for a better understanding of the relational structure of the data. This significantly enhances the accuracy and efficiency of data imputation, thereby enhancing the quality of data governance and the intrinsic value of data assets. Experimental validation and user surveys demonstrate that this method supports users in verifying and judging the associated columns and similar rows using their domain knowledge.
Critical functionality and huge influence of the hot trend/topic page (HTP) in microblogging sites have driven the creation of a new kind of underground service called the bogus traffic service (BTS). BTS provides a kind of illegal service which hijacks the HTP by pushing the controlled topics into it for malicious customers with the goal of guiding public opinions. To hijack HTP, the agents of BTS maintain an army of black-market accounts called bogus traffic accounts (BTAs) and control BTAs to generate a burst of fake traffic by massively retweeting the tweets containing the customer desired topic (hashtag). Although this service has been extensively exploited by malicious customers, little has been done to understand it. In this paper, we conduct a systematic measurement study of the BTS. We first investigate and collect 125 BTS agents from a variety of sources and set up a honey pot account to capture BTAs from these agents. We then build a BTA detector that detects 162 218 BTAs from Weibo, the largest Chinese microblogging site, with a precision of 94.5%. We further use them as a bridge to uncover 296 916 topics that might be involved in bogus traffic. Finally, we uncover the operating mechanism from the perspectives of the attack cycle and the attack entity. The highlights of our findings include the temporal attack patterns and intelligent evasion tactics of the BTAs. These findings bring BTS into the spotlight. Our work will help in understanding and ultimately eliminating this threat.
Low-rank matrix decomposition with first-order total variation (TV) regularization exhibits excellent performance in exploration of image structure. Taking advantage of its excellent performance in image denoising, we apply it to improve the robustness of deep neural networks. However, although TV regularization can improve the robustness of the model, it reduces the accuracy of normal samples due to its over-smoothing. In our work, we develop a new low-rank matrix recovery model, called LRTGV, which incorporates total generalized variation (TGV) regularization into the reweighted low-rank matrix recovery model. In the proposed model, TGV is used to better reconstruct texture information without over-smoothing. The reweighted nuclear norm and L1-norm can enhance the global structure information. Thus, the proposed LRTGV can destroy the structure of adversarial noise while re-enhancing the global structure and local texture of the image. To solve the challenging optimal model issue, we propose an algorithm based on the alternating direction method of multipliers. Experimental results show that the proposed algorithm has a certain defense capability against black-box attacks, and outperforms state-of-the-art low-rank matrix recovery methods in image restoration.
Active anomaly detection queries labels of sampled instances and uses them to incrementally update the detection model, and has been widely adopted in detecting network attacks. However, existing methods cannot achieve desirable performance on dynamic network traffic streams because (1) their query strategies cannot sample informative instances to make the detection model adapt to the evolving stream and (2) their model updating relies on limited query instances only and fails to leverage the enormous unlabeled instances on streams. To address these issues, we propose an active tree based model, adaptive and augmented active prior-knowledge forest (A3PF), for anomaly detection on network traffic streams. A prior-knowledge forest is constructed using prior knowledge of network attacks to find feature subspaces that better distinguish network anomalies from normal traffic. On one hand, to make the model adapt to the evolving stream, a novel adaptive query strategy is designed to sample informative instances from two aspects: the changes in dynamic data distribution and the uncertainty of anomalies. On the other hand, based on the similarity of instances in the neighborhood, we devise an augmented update method to generate pseudo labels for the unlabeled neighbors of query instances, which enables usage of the enormous unlabeled instances during model updating. Extensive experiments on two benchmarks, CIC-IDS2017 and UNSW-NB15, demonstrate that A3PF achieves significant improvements over previous active methods in terms of the area under the receiver operating characteristic curve (AUC-ROC) (20.9% and 21.5%) and the area under the precision-recall curve (AUC-PR) (44.6% and 64.1%).
Electronic healthcare systems can offer convenience but face the risk of data forgery and information leakage. To solve these issues, we propose an identity-based searchable attribute signcryption in lattice for a blockchain-based medical system (BCMS-LIDSASC). BCMS-LIDSASC achieves decentralization and anti-quantum security in the blockchain environment, and provides fine-grained access control and searchability. Furthermore, smart contracts are used to replace traditional trusted third parties, and the interplanetary file system (IPFS) is used for ciphertext storage to alleviate storage pressure on the blockchain. Compared to other schemes, BCMS-LIDSASC requires smaller key size and less storage, and has lower computation cost. It contributes to secure and efficient management of medical data and can protect patient privacy and ensure the integrity of electronic healthcare systems.
To realize low harmonic distortion of the vibration waveform output from electromagnetic vibrators, we propose a vibration harmonic suppression technology based on an improved sensorless feedback control method. Without changing the original driving circuit, the alternating current (AC) equivalent resistance of the driving coil is used to obtain high-precision vibration velocity information, and then a simple and reliable velocity feedback control system is established. Through the study of the effect of different values of key parameters on the system, we have achieved an effective expansion of the velocity characteristic frequency band of low-frequency vibration, resulting in an enhanced harmonic suppression capability of velocity feedback control. We present extensive experiments to prove the effectiveness of the proposed method and make comparisons with conventional control methods. In the frequency range of 0.01–1.00 Hz, without using any sensors, the method proposed in this study can reduce the harmonic distortion of the vibration waveform by about 40% compared to open-loop control and by about 20% compared to a conventional sensorless feedback control method.