LC-NPLA: Label and Community Information-Based Network Presentation Learning Algorithm

Many network presentation learning algorithms(NPLA)have originated from the process of the random walk between nodes in recent years.Despite these algorithms can obtain great embedding results,there may be also some limitations.For instance,only the structural information of nodes is considered when these kinds of algorithms are constructed.Aiming at this issue,a label and community information-based network presentation learning algorithm(LC-NPLA)is proposed in this paper.First of all,by using the community information and the label information of nodes,the first-order neighbors of nodes are reconstructed.In the next,the random walk strategy is improved by integrating the degree information and label information of nodes.Then,the node sequence obtained from random walk sampling is transformed into the node representation vector by the Skip-Gram model.At last,the experimental results on ten real-world networks demonstrate that the proposed algorithm has great advantages in the label classification,network reconstruction and link prediction tasks,compared with three benchmark algorithms.

Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

In the realm of Multi-Label Text Classification(MLTC),the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches.Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content,often overlooking intrinsic textual cues such as label statistical features.In contrast,these endogenous insights naturally align with the classification task.In our paper,to complement this focus on intrinsic knowledge,we introduce a novel Gate-Attention mechanism.This mechanism adeptly integrates statistical features from the text itself into the semantic fabric,enhancing the model’s capacity to understand and represent the data.Additionally,to address the intricate task of mining label correlations,we propose a Dual-end enhancement mechanism.This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation.We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets.These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism.Our final model unequivocally outperforms the baseline model,attesting to its robustness.These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.

本科生实验课化学试剂分类标签规范化及信息化管理的探究

针对高等学校本科生实验课所用化学药品管理的现状,深入剖析了目前在试剂使用过程中存在的关键问题,提出了以分类标签为主线的药品管理措施,同时对加强分类标签管理的具体做法和信息化建设的研究方法展开讨论。分类标签可以弥补高校本科教学实验室硬件设施不完善的管理缺憾,是使分类管理规范化的切实、有效的途径。

农业信息化标准分类方法研究

针对农业信息化建设中出现的标准缺失、标准混乱、标准配套性差的问题,提出了基于三维空间模型的农业信息化标准体系,并使用国际标准分类法对标准体系中的标准进行了详细的分类与说明,进而引入农业信息化行业标准分类标签,较好地平衡了标准分类框架的稳定性和适应性,基本满足了我国农业信息化过程中对标准的规范分类与动态组织的要求。

近距离无线通讯技术在医疗设备管理中的应用

医疗设备标签管理是医疗设备管理工作中的重要一环,本文将最新的近距离无线通讯(Near Field Communication,NFC)标签技术引入设备标签管理工作,使得管理者可以通过日常生活中常用的智能手机对设备进行管理管理,并赋予设备标签更多的实用功能,如显示设备信息、协助保修、指向相关网络空间,使得设备标签可以融入到设备管理的日常工作中,简化了设备管理人员对设备的管理工作,提高了设备标签的实用性。

基于RFID的涉密载体管理系统设计

文章在研究RFID技术的基础上,分析了传统涉密载体管理效率低下、精准度不够等方面的问题,提出了基于RFID的涉密载体管理系统总体设计方案和软硬件功能模块设计架构。该设计有效地实现了对涉密载体智能化、实时化、精准化、高效率、全寿命的监控和管理,大大地降低了人工管理难度和涉密载体安全管理风险。

无线电子标签在高警示药品管理中的应用

目的:实现高警示药品的安全有效管理,减少用药错误,提高医疗安全。方法:采用浙江汉朔电子科技有限公司的无线电子标签系统对我院住院药房高警示药品实行全方位的管理与配发。结果:无线电子标签系统符合中国高警示药品管理规范的要求,可以有效管理高警示药品,减少给药差错,提高病区医嘱的发药效率。结论:无线电子标签系统在住院药房高警示药品管理与配发的过程中大大提高了药师配发药品的准确性,减少用药错误,避免医疗纠纷,提升了用药安全。

PUMD:a PU learning-based malicious domain detection framework

Domain name system(DNS),as one of the most critical internet infrastructure,has been abused by various cyber attacks.Current malicious domain detection capabilities are limited by insufficient credible label information,severe class imbalance,and incompact distribution of domain samples in different malicious activities.This paper proposes a malicious domain detection framework named PUMD,which innovatively introduces Positive and Unlabeled(PU)learning solution to solve the problem of insuffcient label information,adopts customized sample weight to improve the impact of class imbalance,and effectively constructs evidence features based on resource overlapping to reduce the intra-class distance of malicious samples.Besides,a feature selection strategy based on permutation importance and binning is proposed to screen the most informative detection features.Finally,we conduct experiments on the open source real DNS traffic dataset provided by QI-ANXIN Technology Group to evaluate the PUMD framework's abil-ity to capture potential command and control(C&C)domains for malicious activities.The experimental results prove that PUMD can achieve the best detection performance under different label frequencies and class imbalance ratios.