In synthetic aperture radar (SAR) target recognition, the interpretation of SAR images and the accurate recognition of targets are significantly affected by speckle noise. Traditional recognition methods often fail to meet the high accuracy requirements. Therefore, this letter proposes a dual-ended fusion network (DEFNet) for SAR target recognition. The network consists of three main components: the local refinement feature extraction (LRFE) network, the large-scale feature extraction (LSFE) network, and the adaptive feature interaction fusion (AFIF) module. It employs a parallel structure, utilizing the LRFE and LSFE branch modules to extract local and large-scale features, respectively. These two features are then adaptively fused through the AFIF module to further enhance feature representation. Experimental results on the moving and stationary target acquisition and recognition dataset indicate that DEFNet demonstrates significant performance improvement compared to traditional methods, showcasing its effectiveness and adaptability in SAR target recognition tasks.

Traditional radar signal modulation recognition (RSMR) methods struggle to achieve the required accuracy under low signal-to-noise ratio (SNR) conditions. To address this issue, a hybrid network architecture integrating deformable convolution and mamba (DCMNet) is proposed. Specifically, DCMNet employs a multi-view feature extraction structure that combines inverted deformable convolution (IDC) with a state space model (SSM), enabling dynamic adjustment of convolution kernel positions and capturing global information and dependencies in long sequence data. The cross-gated feature fusion (CGFF) mechanism effectively modulates and dynamically aggregates features from different perspectives. The lightweight design provides significant advantages in terms of network scale and deployment. Experimental results demonstrate that the proposed method achieves excellent performance on a dataset with ten different waveforms. Notably, at an SNR of -8 dB, the recognition accuracy exceeds 90%, significantly outperforming existing methods.

Popular privacy regulations such as General Data Protection Regulation (GDPR) often allow consumers to withdraw from providing data, e.g., the famous right to opt-out. Modern computer software, e.g., mobile applications (apps), often provide withdrawal interfaces, which stop data collection— e.g., from third-party ads and analytics libraries—to respect users’ withdrawal decisions. While such interfaces are marked as "withdrawal", their correlated withdrawal decisions are often inconsistent with the apps’ actual data collection behavior, especially from third parties, which is defined as withdrawal inconsistency in the paper.Prior works have either studied website withdrawal inconsistency or privacy leaks of mobile apps. However, the mobile withdrawal inconsistency problem is different yet more complex than those in websites due to the diversity in mobile withdrawal interface and the variety of private information. At the same time, none of the existing works detecting privacy leaks of mobile apps understand users’ withdrawal decisions let alone correlate them with withdrawal behaviors.In this paper, we design and implement a novel approach, called MowChecker, to detect mobile apps’ inconsistencies in third-party data collection. The key insight is that withdrawal choices should have either a control-flow dependency on personal information flow or a data-flow dependency on withdrawal APIs provided by third-party data collection libraries. Our evaluation of MowChecker on real-world Android apps reveals 157 manually-confirmed, zero-day withdrawal inconsistencies. We have responsibly reported them to app developers and received 23 responses with two being fixed.

To tackle the issue of space-time adaptive processing (STAP) performance degradation caused by inaccurate estimation of the clutter covariance matrix (CCM) with limited sample support, a knowledge-aided (KA) CCM estimation algorithm based on the symmetric alternating direction method of multiplier (KA-SADMM) is proposed. The CCM estimation problem is constructed based on the knowledge of persymmetric structure, the low-rank structure, and the prior covariance matrix, and the solution to the resulting problem is derived. Moreover, the contraction property of the sequence generated by KA-SADMM with respect to the solution set of the estimation problem is analyzed. With limited training samples, the simulation results show that the proposed algorithm improves the STAP performance over other similar algorithms by at least about 0.2 dB when the prior knowledge is accurate and by at least 0.3 dB when the prior knowledge is inaccurate.

Excellent performance in supervised learning-based radar signal modulation recognition (RSMR) techniques relies on the quantity and quality of labeled datasets, while the high cost and difficulty involved in analyzing and labeling radar signal samples limits its development. An RSMR algorithm using self-supervised contrastive learning (SSCL) methodology is proposed to address this issue. Specifically, within the classical contrastive learning (CL) framework MoCo V2, a customized data augmentation method is devised to capture time-frequency features of the radar signal. In addition, the feature extraction network ResNet50 is improved by decoupling spatial and channel filters, resulting in greater sensitivity to the time-frequency features. To enhance the recognition accuracy, two loss functions, alignment and uniformity, are used in place of the info noise contrastive estimation (InfoNCE) loss, and both loss functions are optimized directly. The recognition accuracy of the proposed method can reach 97.66% at a signal-to-noise ratio (SNR) of 4 dB.

Gene similarity mining through meta-paths in the heterogeneous network composed of genes-disease-phenotypes is one of the important methods in the field of gene similarity search. Since the current meta-path-based algorithms do not consider the influence of autocorrelation between disease and disease, and between phenotype and phenotype on gene similarity, the potential and implicit similar genes in the heterogeneous network cannot be fully mined. To address this issue, this paper first explores the autocorrelation expressions of diseases and phenotypes by constructing a semantic contribution graph and calculating the similarity of Gaussian kernel topology, and then by integrating the autocorrelation into the heterogeneous network, a fully linked network model with correlation weights containing autocorrelation information between diseases and phenotypes is constructed. This network is then utilized to calculate gene similarity based on the gSim-Search principle. The proposed method makes full use of autocorrelation information between diseases and phenotypes to search for potentially similar genes, and indirectly solves the problem of sparseness of heterogeneous network links. Experiments show that the proposed method improves the accuracy of prediction when calculating and ranking the similarity of the two pathogenic genes of breast cancer and obesity.

With the growth of mobile computing techniques, mobile gambling scams have seen a rampant increase in the recent past. In mobile gambling scams, miscreants deliver scamming messages via mobile instant messaging, host scam gambling platforms on mobile apps, and adopt mobile payment channels. To date, there is little quantitative knowledge about how this trending cybercrime operates, despite causing daily fraud losses estimated at more than ${\$}$522,262 USD. This paper presents the first empirical study based on ground-truth data of mobile gambling scams, associated with 1,461 scam incident reports and 1,487 gambling scam apps, spanning from January 1, 2020 to December 31, 2020. The qualitative and quantitative analysis of this ground-truth data allows us to characterize the operational pipeline and full fraud kill chain of mobile gambling scams. In particular, we study the social engineering tricks used by scammers and reveal their effectiveness. Our work provides a systematic analysis of 1,068 confirmed Android and 419 iOS scam apps, including their development frameworks, declared permissions, compatibility, and backend network infrastructure. Perhaps surprisingly, our study unveils that public online app generators have been abused to develop gambling scam apps. Our analysis reveals several payment channels (ab)used by gambling scam app and uncovers a new type of money mule-based payment channel with the average daily gambling deposit of ${\$}$400,000 USD. Our findings enable a better understanding of the mobile gambling scam ecosystem, and suggest potential avenues to disrupt these scam activities.

Electronic medical record (EMR) text word segmentation is the basis of natural language processing in medicine. Due to the characteristics of EMR, such as strong specialization, high cost of annotation, special writing style and sustained growth of terminology, the current Chinese word segmentation (CWS) methods cannot fully meet the requirements of the application of EMR. In order to solve this problem, an EMR word segmentation model based on Graph Neural Network (GNN), bidirectional Long Short-Term Memory network (Bi-LSTM) and conditional random field (CRF) is designed in this paper to improve the segmentation effect and reduce the dependence on data set. In the model, GNN based on the domain lexicon is used to learn the local composition features, Bi-LSTM is used to capture the long-term dependence and context sequence information, and CRF is used to obtain the optimal annotation sequence based on the sentence level label information. Through multi-feature interaction, the ambiguity resolution and new word recognition in the EMR word segmentation are effectively carried out. Compared with CWS tools such as Jieba and Pkuseg, as well as baseline models and state-of-the-art methods, the precision and recall rate of the model in this paper have been significantly improved.

Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance structure. The resulting constrained optimization problem is solved globally resorting to the Dykstra' projection framework. Each step of the procedure involves the solution of two convex sub-problems, whose minimizers are available in closed form. Simulation results related to typical radar environments highlight the effectiveness of the devised method.

This letter deals with the robust joint design of radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case Signal-to-Interference-plus-Noise-Ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

Heterogeneous data sources and multi-label are two important characteristics of protein function prediction. They describe protein data from two different aspects. However, it is of considerable challenge to integrate multiple data sources and multi-label simultaneously for predicting protein functions, especially when there are only a limited number of labeled proteins. In this paper, we propose a generative model with hypergraph regularizers algorithm, called GMHR, for predicting proteins with multiple functions. The GMHR algorithm integrates all data sources that are available, including protein attribute features, interaction networks, label correlations, and unlabeled data. Experimental results on the real-world datasets predicting the functions of proteins demonstrate the superiority of our proposed method compared with the state-of-the-art baselines.

Deriving a representative sample from a big social network is essential for many Internet services that rely on accurate analysis of big social data. A good sampling method for social network should be able to generate small sample networks with similar structures as original big network. In this paper, we propose SGP, a new big social network sampling algorithm based on graph partition. In SGP, original network is firstly partitioned into several sub-networks that will be sampled evenly. This procedure enables SGP to effectively maintain the topological similarity and community structure similarity between the sampled network and its original network. We have evaluated SGP on several well-known data sets. The experimental results show that SGP outperforms six state-of-the-art methods.

The authors consider data reorganization in a dynamically reconfigurable distributed database system. The network environment they study is a dynamic system prototype called Driftwood. They present reallocation strategies corresponding to different network reconfiguration and then address the nonblocking reorganization approach. They cover the implementation strategies, impact on user transaction processing, and efficiency. The authors present an algorithm to construct a parallel reorganization scheduler that is expected to minimize the system recovery time.>

The authors address the data allocation problem in a dynamically reconfigurable distributed database system. Following the description of the system model and the consequences of node movement, several dynamic allocation strategies are presented. A distributed candidate-selection algorithm using the data from the accounting system in the database servers is proposed to reduce the number of files and nodes considered in reallocation. The allocation algorithms choose the optimal assignment using heuristic benefit functions and greedy search strategy. The experimental results demonstrate the accuracy and efficiency of the allocation algorithms.>