Within the domain of data mining, one critical objective is the discovery of sequential rules with high utility. The goal is to discover sequential rules that exhibit both high utility and strong confidence, which are valuable in real-world applications. However, existing high-utility sequential rule mining algorithms suffer from redundant utility computations, as different rules may consist of the same sequence of items. When these items can form multiple distinct rules, additional utility calculations are required. To address this issue, this study proposes a sequential rule mining algorithm that utilizes segmentation guided by confidence (RSC), which employs confidence-guided segmentation to reduce redundant utility computation. It adopts a method that precomputes the confidence of segmented rules by leveraging the support of candidate subsequences in advance. Once the segmentation point is determined, all rules with different antecedents and consequents are generated simultaneously. RSC uses a utility-linked table to accelerate candidate sequence generation and introduces a stricter utility upper bound, called the reduced remaining utility of a sequence, to address sequences with duplicate items. Finally, the proposed RSC method was evaluated on multiple datasets, and the results demonstrate improvements over state-of-the-art approaches.

Existing reversible audio watermarking (RAW) techniques are often vulnerable to intentional or even unintentional attacks on the cover object. This paper proposes a robust RAW scheme based on lattices, which is referred to as Meet-in-the-Middle Embedding (MME). In MME, the lattice quantization errors are properly scaled and added back to the quantized host signals such that the receiver can estimate the cover. Scaling factor serves as a key factor to the reversibility of MME, whose feasible range is rigorously justified. Both theoretically and experimentally, we demonstrate the superiority of MME to improved quantization index modulation (IQIM) in terms of signal-to-watermark ratio (SWR) and generalized signal-to-noise ratio (GSNR). Moreover, simulations show that MME also outperforms other state-of-the-arts in SWR, objective difference grade (ODG), and bit error rate (BER).

Synthetic aperture radar (SAR) is an active surveillance system which is able to work under all-day and all-weather conditions. Due to the rapid development of SAR, automatic target recognition (ATR) as one of SAR image applications has become an important research direction in recent years, and its key process is target classification. In this paper, a novel target classification model based on amplitude and phase information of sparse SAR image is proposed. Firstly, we use a novel iterative soft thresholding (BiIST) algorithm to acquire sparse SAR image with better quality than traditional matched filtering (MF)-based SAR image. Moreover, the amplitude and phase information are utilized to classify the targets based on the proposed amplitude-phase convolutional neural network (AP-CNN). Experimental results based on real MSTAR dataset show that AP-CNN performs better than amplitude-based CNN under standard operating conditions (SOC). In the case of limited samples, the performance gap between the proposed network and amplitude-based CNN is more obvious.

Different from optical image limited in time and space, sparse synthetic aperture radar (SAR) can obtain high-resolution image in all day and all weather conditions. Thus, SAR image has been widely used in military fields. With rapid development of technology and the growth of data volume, it is obviously impractical to interpret SAR image manually. Therefore, in this paper, we propose a novel sparse SAR automatic target recognition (ATR) framework, which is composed by the regularization sparse recovery algorithm and YOLO networks, so as to identify the SAR target quickly and accurately. In the proposed framework, we first use the complex approximate message passing (CAMP) based sparse image recovery algorithm to construct the sparse SAR image dataset, then identify the SAR targets by YOLOv3 and YOLOv4 networks. Experimental results based on MSTAR dataset validate the proposed framework effectively.