Contrastive Graph Convolutional Networks with adaptive augmentation for text classification

Text classification is an important research topic in natural language processing (NLP), and Graph Neural Networks (GNNs) have recently been applied in this task. However, in existing graph-based models, text graphs constructed by rules are not real graph data and introduce massive noise. More importantly, for fixed corpus-level graph structure, these models cannot sufficiently exploit the labeled and unlabeled information of nodes. Meanwhile, contrastive learning has been developed as an effective method in graph domain to fully utilize the information of nodes. Therefore, we propose a new graph-based model for text classification named CGA2TC, which introduces contrastive learning with an adaptive augmentation strategy into obtaining more robust node representation. First, we explore word co-occurrence and document word relationships to construct a text graph. Then, we design an adaptive augmentation strategy for the text graph with noise to generate two contrastive views that effectively solve the noise problem and preserve essential structure. Specifically, we design noise-based and centrality-based augmentation strategies on the topological structure of text graph to disturb the unimportant connections and thus highlight the relatively important edges. As for the labeled nodes, we take the nodes with same label as multiple positive samples and assign them to anchor node, while we employ consistency training on unlabeled nodes to constrain model predictions. Finally, to reduce the resource consumption of contrastive learning, we adopt a random sample method to select some nodes to calculate contrastive loss. The experimental results on several benchmark datasets can demonstrate the effectiveness of CGA2TC on the text classification task.     

Negative Can Be Positive: Signed Graph Neural Networks for Recommendation

Most of the existing GNN-based recommender system models focus on learning users’ personalized preferences from these (explicit/implicit) positive feedback to achieve personalized recommendations. However, in the real-world recommender system, the users’ feedback behavior also includes negative feedback behavior (e.g., click dislike button), which also reflects users’ personalized preferences. How to utilize negative feedback is a challenging research problem. In this paper, we first qualitatively and quantitatively analyze the three kinds of negative feedback that widely existed in real-world recommender systems and investigate the role of negative feedback in recommender systems. We found that it is different from what we expected — not all negative items are ranked low, and some negative items are even ranked high in the overall items. Then, we propose a novel Signed Graph Neural Network Recommendation model (SiGRec) to encode the users’ negative feedback behavior. Our SiGRec can learn positive and negative embeddings of users and items via positive and negative graph neural network encoders, respectively. Besides, we also define a new Sign Cosine (SiC) loss function to adaptively mine the information of negative feedback for different types of negative feedback. Extensive experiments on four datasets demonstrate the proposed model outperforms several existing models. Specifically, on the Zhihu dataset, SiGRec outperforms the unsigned GNN model (i.e., LightGCN), 27.58% 29.81%, and 31.21% in P@20, R@20, and nDCG@20, respectively. We hope our work can open the door to further exploring the negative feedback in recommendations.     

新交通流模型对信号交叉路口通行能力的影响分析

合理准确的交通流模型不仅有利于理解车辆的行驶行为,而且对分析交通流状况,规划交通网络和实现交通优化控制策略都有重要的作用。车一车通信环境下多车型合作驾驶跟驰模型将车辆类型的差异化信息以及前方车辆的交通诱导信息包含其中。仿真结果表明,信号交叉路口的通行能力会随着参与合作驾驶的车辆数目的增多而得到显著增强,但其随着车辆密度的走势不会发生变化。     

线性调频信号脉冲压缩技术仿真

最大作用距离和距离分辨力是雷达应用中两个关键的性能指标,但在常规脉冲体制雷达中,这两个指标是相互矛盾的。脉冲压缩技术的出现,有效的解决了该问题。文章先介绍了脉冲压缩技术,然后通过MATLAB仿真验证了脉冲压缩技术在解决这对矛盾时的有效性。     

“信号与线性系统”课程教学改革探索与实践

信号与线性系统是测控技术与仪器专业的一门十分重要的专业基础课,该课程将物理问题与数学表达和论证相结合,具有数学推导多、理论性强、难学、枯燥等公认的特点.我们在人才培养体系设置,教学内容和教学方法等多方面进行了改革与探索.实践表明教学改革取得了较好的效果.     

动态联盟:图书馆馆际合作的新途径

提出了在信息化条件下 ,采用动态联盟的方式进行图书馆协作的思路 ,并探讨了动态联盟的总体框架和要解决的关键技术。     

一种辐射能谱数据采集新方法

本文提出了对幅射信号＂动态RC成形,定时峰值采集＂的能谱数据采集新方法。该方法能有效地减少高计数率时的脉冲堆积现象,提高闪烁探测器的能量分辨率。     

一种新的基于MANET的分布式CA系统模型研究

移动Ad Hoc网络（MANET）是一种不依赖于任何固定基础设施和集中式组织管理机构的具有全新概念的无线网络,通过无线链路实现移动节点间通信。然而Ad Hoc网络的固有特性使其面临严重的安全问题,针对这种网络环境,提出了建立安全系统所要遵循的基本要素,分析了国内外现有安全解决方案存在的问题。在对信任模型研究的基础上提出了基于局部分布式信任模型和扩展推荐信任模型的多域全分布式CA系统模型,能充分满足系统对容错性、安全性、可用性和可伸缩性的要求。     

基于复杂社会网络和局部网络效应的新产品竞争扩散的计算实验研究

采用基于复杂社会网络视角的新产品竞争微观扩散模型,应用计算实验的方法分析局部网络效应下用户基础、产品内在价值以及初始种子用户的选择等因素对产品竞争扩散的影响.研究结果表明,与全局网络效应不同的是,在局部网络效应中,影响新产品竞争扩散的关键因素不是用户基础而是社会网络中消费者的交互作用,该作用加快了信息传播、产品扩散的速度;产品自身内在价值的加大可以弥补局部网络效应强度较弱的产品竞争弱势.此外,选择hub节点作为初始种子用户将有利于产品的扩散并最终占有更有优势的竞争格局.