With the rise of neural networks and pre‐trained models such as BERT, abstractive text summarization techniques have received widespread attention. Nevertheless, traditional extractive text summarization methods still hold substantial research value due to their low computational cost, interpretability, and robustness. In algorithms like TextRank and its variants, graph nodes are typically constructed based on surface‐level lexical features. These graphs often fail to incorporate many contextual relationships, such as coreference relationships among nodes, resulting in fragmented representations of key concepts. For edge construction, a sliding window of size T is commonly used to connect word nodes within the window. However, these methods often fall short in modeling the rich contextual dependencies embedded in the document. Several recent studies have demonstrated that semantic graphs can effectively improve the accuracy of text summarization. In this paper, we construct a more interpretable semantic graph from syntax trees and propose a novel unsupervised algorithm based on the personalized PageRank algorithm for summary extraction. We utilize tree transformation methods to enrich word‐level information for graph construction, define node‐merging rules to reduce graph complexity, use coreference chains to merge coreferring entities across sentences for enriching contextual links, and introduce the concept of Meta Node sets to capture thematic relationships that are not fully represented by syntactic dependencies or coreference chains alone. By clustering semantically related words, Meta Nodes enhance the graph’s ability to reflect deeper contextual coherence across the document. Compared with previous TextRank‐based methods, our improvement yields significant ROUGE score boosts on the CNN‐DM dataset. While the method was developed and evaluated using English‐language datasets, its underlying design is language agnostic and can be adapted to other languages with suitable linguistic tools.

The Visible-Infrared Person Re-Identification (VI-ReID) task aims to retrieval pedestrian images with the same labels across different modalities. VI-ReID is a very challenging task due to the huge intra-modality variation and cross-modality gap. Existing methods are mainly based on the feature alignment to mitigate the modality’s gap, however, using only feature-level constraints does not mitigate cross-modality gap well. We propose a fusional modality and distribution alignment learning network (FMADALNet) to mitigate modality’s gap and align modality’s distribution to learn modality-shared feature representations. FMADALNet contains a lightweight fusional modality generation module (FMGM). FMGM constructs a fusional modality that incorporates heterogeneous image features and contains only modality-shared information to mitigate modality gap at the pixel-level. In addition, to mitigate the differences in the distribution of the different modalities, we design a Hetero-center Maximum Mean Discrepancy loss (HcMMD), which reduces the differences in the distribution of the different modalities in a displaying manner. Extensive experimental results on two public datasets show that our proposed method achieves impressive performance compared to state-of-the-art methods.

Abstract The security of most lattice-based cryptography schemes are based on two computational hard problems which are the short integer solution (SIS) and learning with errors (LWE) problems. The computational complexity of SIS and LWE problems are related to approximating shortest vector problem and bounded distance decoding (BDD) problem. Approximating BDD is a special case of approximating closest vector problem (CVP). In this paper, we revisit the study for approximating CVP. We give a proof that approximating the CVP over $\ell _\infty $-norm (CVP$_\infty $) within any constant factor is NP-hard. The result is obtained by the gap-preserving reduction from Min Total Label Cover problem in $\ell _1$-norm to to CVP$_\infty $. This proof is simpler than known proofs [ 10].

Visible-Infrared cross-modality person reidentification (VI-ReID), whose aim is to match person images between visible and infrared modality, is a challenging cross-modality image retrieval task. Batch Hard Triplet loss is widely used in person re-identification tasks, but it does not perform well in the Visible-Infrared person re-identification task. Because it only optimizes the hardest triplet for each anchor image within the mini-batch, samples in the hardest triplet may all belong to the same modality, which will lead to the imbalance problem of modality optimization. To address this problem, we adopt the batch all triplet selection strategy, which selects all the possible triplets among samples to optimize instead of the hardest triplet. Furthermore, we introduce Unified Batch All Triplet loss and Cosine Softmax loss to collaboratively optimize the cosine distance between image vectors. Similarly, we modify the Hetero Center Triplet loss, which is proposed for VI-ReID task, into a batch all form to improve model performance. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.

The limited spectrum resources need to provide safe and efficient spectrum service for the intensive users. Malicious spectrum work nodes will affect the normal operation of the entire system. Using the blockchain model, consensus algorithm Praft based on optimized Raft is to solve the consensus problem in Byzantine environment. Message digital signatures give the spectrum node some fault tolerance and tamper resistance. Spectrum sharing among spectrum nodes is carried out in combination with game theory. The existing game theoretical algorithm does not consider the influence of spectrum occupancy of primary users and cognitive users on primary users' utility and enthusiasm at the same time. We elicits a reinforcement factor and analyzes the effect of the reinforcement factor on strategy performance. This scheme optimizes the previous strategy so that the profits of spectrum nodes are improved and a good Nash equilibrium is shown, while Praft solves the Byzantine problem left by Raft.

As a method for increasing throughput and improving reliability of routing, network coding has been widely used in decentralized IoT systems. When files are shared in the system, network coding signature techniques can help authenticate whether a modified packet in files is injected or not. However, in an IoT system, there are often multiple source devices each of which has its own authentication key, where existing single-source network coding signature schemes cannot work. In this paper, we study the problem of designing secure network coding signatures in the network with multiple sources and propose the multisource homomorphic network coding signature. We also give construction and prove its security.