An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Due to the time-varying topology and possible disturbances in a conflict environment,it is still challenging to maintain the mission performance of flying Ad hoc networks(FANET),which limits the application of Unmanned Aerial Vehicle(UAV)swarms in harsh environments.This paper proposes an intelligent framework to quickly recover the cooperative coveragemission by aggregating the historical spatio-temporal network with the attention mechanism.The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model.A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure.Combined with the corresponding Laplacian matrix as the hyperparameter,a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery.Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies.The results demonstrate that the average connectivity and coverage results is improved by 17.92%and 16.96%,respectively compared with the state-of-the-art model.Furthermore,by the ablation study,the contributions of each different improvement are compared.The proposed model can be used to support resilient network design for real-time mission execution.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

Multi-Head Attention Spatial-Temporal Graph Neural Networks for Traffic Forecasting

Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.

Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

Fraud cases have been a risk in society and people’s property security has been greatly threatened.In recent studies,many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis.These algorithms are also suitable for fraudulent phone text recognition.Compared to these tasks,the semantics of fraudulent words are more complex and more difficult to distinguish.Recurrent Neural Networks(RNN),the variants ofRNN,ConvolutionalNeuralNetworks(CNN),and hybrid neural networks to extract text features are used by most text classification research.However,a single network or a simple network combination cannot obtain rich characteristic knowledge of fraudulent phone texts relatively.Therefore,a new model is proposed in this paper.In the fraudulent phone text,the knowledge that can be learned by the model includes the sequence structure of sentences,the correlation between words,the correlation of contextual semantics,the feature of keywords in sentences,etc.The new model combines a bidirectional Long-Short Term Memory Neural Network(BiLSTM)or a bidirectional Gate Recurrent United(BiGRU)and a Multi-Head attention mechanism module with convolution.A normalization layer is added after the output of the final hidden layer.BiLSTM or BiGRU is used to build the encoding and decoding layer.Multi-head attention mechanism module with convolution(MHAC)enhances the ability of the model to learn global interaction information and multi-granularity local interaction information in fraudulent sentences.A fraudulent phone text dataset is produced by us in this paper.The THUCNews data sets and fraudulent phone text data sets are used in experiments.Experiment results show that compared with the baseline model,the proposed model(LMHACL)has the best experiment results in terms of Accuracy,Precision,Recall,and F1 score on the two data sets.And the performance indexes on fraudulent phone text data sets are all above 0.94.

Discharge Summaries Based Sentiment Detection Using Multi-Head Attention and CNN-BiGRU

Automatic extraction of the patient’s health information from the unstructured data concerning the discharge summary remains challenging.Discharge summary related documents contain various aspects of the patient health condition to examine the quality of treatment and thereby help improve decision-making in the medical field.Using a sentiment dictionary and feature engineering,the researchers primarily mine semantic text features.However,choosing and designing features requires a lot of manpower.The proposed approach is an unsupervised deep learning model that learns a set of clusters embedded in the latent space.A composite model including Active Learning(AL),Convolutional Neural Network(CNN),BiGRU,and Multi-Attention,called ACBMA in this research,is designed to measure the quality of treatment based on discharge summaries text sentiment detection.CNN is utilized for extracting the set of local features of text vectors.Then BiGRU network was utilized to extract the text’s global features to solve the issues that a single CNN cannot obtain global semantic information and the traditional Recurrent Neural Network(RNN)gradient disappearance.Experiments prove that the ACBMA method can demonstrate the effectiveness of the suggested method,achieve comparable results to state-of-arts methods in sentiment detection,and outperform them with accurate benchmarks.Finally,several algorithm studies ultimately determined that the ACBMA method is more precise for discharge summaries sentiment analysis.

Multi-Headed Deep Learning Models to Detect Abnormality of Alzheimer’s Patients

Worldwide,many elders are suffering from Alzheimer’s disease(AD).The elders with AD exhibit various abnormalities in their activities,such as sleep disturbances,wandering aimlessly,forgetting activities,etc.,which are the strong signs and symptoms of AD progression.Recognizing these symptoms in advance could assist to a quicker diagnosis and treatment and to prevent the progression of Disease to the next stage.The proposed method aims to detect the behavioral abnormalities found in Daily activities of AD patients(ADP)using wearables.In the proposed work,a publicly available dataset collected using wearables is applied.Currently,no real-world data is available to illustrate the daily activities of ADP.Hence,the proposed method has synthesized the wearables data according to the abnormal activities of ADP.In the proposed work,multi-headed(MH)architectures such as MH Convolutional Neural Network-Long Short-Term Mem-ory Network(CNN-LSTM),MH one-dimensional Convolutional Neural Network(1D-CNN)and MH two dimensional Convolutional Neural Network(2D-CNN)as well as conventional methods,namely CNN-LSTM,1D-CNN,2D-CNN have been implemented to model activity pattern.A multi-label prediction technique is applied to detect abnormal activities.The results obtained show that the proposed MH architectures achieve improved performance than the conventional methods.Moreover,the MH models for activity recognition perform better than the abnormality detection.

基于RoBERTa-Span-Attack的标签指针网络军事命名实体识别

军事领域文本中存在大量军事实体信息,准确识别这些信息是军事文本信息提取和构建军事知识图谱的基础性任务。首先,提出了一种基于RoBERTa预训练模型、跨度和对抗训练的标签指针网络的融合深度模型(RoBERTa-Span-Attack),用于中文军事命名实体识别;然后,采用了一种基于Span的标签指针网络,同时完成实体的起止位置和类别的识别任务;最后,在模型训练过程中加入对抗训练策略,通过添加一些扰动来生成对抗样本进行训练。在军事领域数据集上的实验结果表明:所提出的军事领域命名实体识别模型相较于BERT-CRF、BERT-Softmax和BERT-Span,在识别准确度上具有更优的效果。

基于连续提示注入与指针网络的农业病害命名实体识别

针对农业病害领域命名实体识别过程中存在的预训练语言模型利用不充分、外部知识注入利用率低、嵌套命名实体识别率低的问题,本文提出基于连续提示注入和指针网络的命名实体识别模型CP-MRC(Continuous prompts for machine reading comprehension)。该模型引入BERT(Bidirectional encoder representation from transformers)预训练模型,通过冻结BERT模型原有参数,保留其在预训练阶段获取到的文本表征能力;为了增强模型对领域数据的适用性,在每层Transformer中插入连续可训练提示向量;为提高嵌套命名实体识别的准确性,采用指针网络抽取实体序列。在自建农业病害数据集上开展了对比实验,该数据集包含2933条文本语料,8个实体类型,共10414个实体。实验结果显示,CP-MRC模型的精确率、召回率、F1值达到83.55%、81.4%、82.4%,优于其他模型;在病原、作物两类嵌套实体的识别率较其他模型F1值提升3个百分点和13个百分点,嵌套实体识别率明显提升。本文提出的模型仅采用少量可训练参数仍然具备良好识别性能,为较大规模预训练模型在信息抽取任务上的应用提供了思路。

近视针刺法联合指针疗法防控儿童低中度近视的疗效观察

目的 探讨近视针刺法联合指针疗法防控儿童低中度近视临床疗效。方法 选取2020年1月—2022年10月期间首都医科大学附属北京同仁医院就诊的低中度近视儿童86例,按随机数字表方法分为对照组和观察组,每组各43例。对照组给予配镜治疗,观察组在对照组基础上予近视针刺法联合指针疗法。治疗1个月后,观察两组患儿治疗前后临床疗效,治疗前后中医证候评分,裸眼视力,屈光度,眼轴长度,调节幅度。结果 治疗后两组患儿观察组临床总有效率93.02%(40/43)明显高于对照组74.42%(32/43),差异有统计学意义(P<0.05)。治疗后两组患儿中医证候评分均较治疗前降低,差异有统计学意义(P<0.05);且观察组中医证候评分明显低于对照组,差异有统计学意义(P<0.05)。治疗后两组患儿裸眼视力及屈光度均较治疗前升高,差异有统计学意义(P<0.05);且观察组裸眼视力及屈光度均明显高于对照组,差异有统计学意义(P<0.05)。治疗后两组患儿眼轴长度及调节幅度均较治疗前升高,差异有统计学意义(P<0.05);且观察组眼轴增长幅度低于对照组,调节幅度高于对照组,差异有统计学意义(P<0.05)。结论 近视针刺法联合指针疗法防控儿童低中度近视临床疗效显著。

基于深度字词融合的小麦种质信息实体关系联合抽取

为获得结构化的小麦品种表型和遗传描述,针对非结构化小麦种质数据中存在的实体边界模糊以及关系重叠问题,提出一种基于深度字词融合的小麦种质信息实体关系联合抽取模型WGIE-DCWF(wheat germplasm information extraction model based on deep character and word fusion)。模型编码层通过深度字词融合和上下文语义特征融合,提高密集实体特征识别能力;模型三元组抽取层建立层叠指针网络,提高重叠关系的提取能力。在小麦种质数据集和公开数据集上的一系列对比实验结果表明,WGIE-DCWF模型能够有效提高小麦种质数据实体关系联合抽取效果,同时拥有较好的泛化性,可以为小麦种质信息知识库构建提供技术支撑。

C语言指针的铺垫式教学方法探索与实践

“C语言程序设计基础”是大多数高校的非计算机专业必修的计算机基础公共课程,其中的指针知识体系是重点和难点。为了帮助学生较容易地过渡到复杂的指针学习阶段,采取铺垫式教学方法,从四个知识体系来进行教学设计,从而使得指针的基石——地址的概念,不断地被重复,而逐渐被学生理解和接受。从学生的学习过程反馈和备考全国计算机等级考试的自学过程的反馈来看,此方法得到了学生的认可,取得了较好的教学效果。

指针式仪表读数识别的研究现状与发展

在高温高压强磁场等恶劣环境下,指针式仪表有着优于数显式仪表的良好性能表现,对指针式仪表读数识别进行研究具有重要的现实意义。近年来,深度学习与计算机视觉紧密结合,基于深度学习的指针式仪表读数识别技术是前沿研究的重点方向。文中首先概述指针式仪表读数识别的过程,然后从3个方面论述指针式仪表读数识别技术的研究现状与进展:仪表图像的预处理、仪表表盘区域的检测和仪表的读数识别,论述过程中分别阐述基于传统机器学习的方法和基于深度学习的方法。最后介绍公开的仪表数据集和应用场景,并从深度学习算法、巡检机器人工作特点和公共数据集的建立3个方面对未来的研究进行展望与建议。

基于轻量化的指针仪表检测算法研究

通过轻量化的神经网络算法部署在边缘计算设备是解决老式仪表读数问题的主流方向。该文提出了YOLOv5s-Pointer轻量化的指针仪表检测网络模型,以YOLOv5s网络模型作为基础,通过引入Mixup数据增强技术,使用MobileNetV3网络替换CSPDarkNet53特征提取网络,采用SLoU Loss定位损失函数,并引入动态样本权重思想,对网络进行改进。实验结果表明,相较于YOLOv5s网络在验证集上的参数量和计算量分别减少了78%和57%,精确率提升了1.3%。

基于多头指针的司法事件检测方法

针对如何解决中文司法事件检测中触发词与上下文关系不足以判定事件实例、案件触发词表述相似以及同一个案件中多个触发词识别和分类模糊的问题,本研究提出一种基于多头指针的司法事件检测方法。首先,该方法将上下文信息和罪名特征融合作为输入,使用双向长短期记忆(Bi-directional Long Short-Term Memory,BiLSTM)网络捕获数据依赖关系,深入提取特征;然后,使用多头指针网络对字符间的依赖关系进行建模,有效捕捉句子中的触发词;最后,利用指针标注技术抽取触发词,实现司法事件的有效检测。在公开司法数据集LEVEN上实验验证该方法的有效性,其中微平均和宏平均的F1指标达到了87.53%和78.05%,优于现有模型。该方法不仅显著提高了事件触发词的识别精度,而且也增强了对复杂司法文本中事件上下文关系的把握能力。

融合RoBERTa-WWM和全局指针网络的农业病害实体关系联合抽取研究

针对实体和关系抽取过程中存在的一词多义、实体嵌套、三元组重叠的问题,本文提出了1种融合RoBERTa-WWM和全局指针网络的联合抽取模型RBGPL。该模型引入RoBERTa-WWM预训练模型,利用上下文的语境信息融合克服了不同语境下一词多义问题;采用全局指针网络Global pointer标注方式解决了实体嵌套问题;通过全局指针联合解码模型将三重抽取转变为五重提取,解决了三元组重叠问题。在自建农业病害数据集上,模型RBGPL的精确率、召回率、F1值达到76.23%,91.18%,83.04%,与其他联合抽取模型相对比F1值均取最优,有效地克服了一词多义问题和三元组重叠问题。此外,在病原(Pathogeny)和作物名称(Crop)2种易嵌套实体的F1值上提升了3%和18%,实体嵌套得到了显著缓解。本文方法提高了中文农业病害领域实体关系抽取性能,可为农业病害领域知识图谱的构建提供技术支持。

基于RGB模型的汽车指针仪表示数的识别

本文对汽车指针仪表示数的识别,提出了一种基于RGB彩色空间图像处理的识别方案。首先,根据仪表盘图像的R、G、B分量的直方图,利用该分量的欧几里德距离法分割仪表盘的圆心和指针;然后,提取圆心的骨架和圆心的坐标,建立新的坐标系;最后,将指针的质心和仪表盘的圆心连成一条直线,通过直线位置识别指针读数。实验证明:该方法可用于指针式汽车仪表的自动化测试。

基于改进YOLOv5s的指针式水表读数检测

针对光照不均匀和水表表盘雾化的指针式水表在读数检测时出现漏检、误检等问题,提出一种基于改进YOLOv5s的指针式水表读数检测方法。首先,采用Mosaic、Mixup等数据增强方法,提高模型的泛化能力;其次,引入加权双向特征金字塔网络(bilateral feature pyramid network, BiFPN)实现更高层次的特征融合使得水表图像的深层特征图和浅层特征图充分融合,提高网络的表达能力;然后,嵌入卷积注意力机制(convolutional block attention module, CBAM),在通道和空间双重维度上强化指针式水表子表盘示数特征;最后将完全交并比损失函数(complete intersection over union loss, CIoU-Loss)替换为SIoU_Loss(scylla intersection over union loss),提升边界框的回归精度。改进算法的mAP@0.5达到97.8%,比YOLOv5s原始网络提升了3.2%。实验结果表明:该算法能有效提高指针式水表的读数检测精度。

基于RoFormer预训练模型的指针网络农业病害命名实体识别

[目的/意义]针对实体嵌套、实体类型混淆等问题导致的农业病害命名实体识别(Named Entities Recognition,NER)准确率不高的情况,以PointerNet为基准模型,提出一种基于RoFormer预训练模型的指针网络农业病害NER方法RoFormer-PointerNet。[方法]采用RoFormer预训练模型对输入的文本进行向量化,利用其独特的旋转位置嵌入方法来捕捉位置信息,丰富字词特征信息,从而解决一词多义导致的类型易混淆的问题。使用指针网络进行解码,利用指针网络的首尾指针标注方式抽取句子中的所有实体,首尾指针标注方式可以解决实体抽取中存在的嵌套问题。[结果和讨论]自建农业病害数据集,数据集中包含2867条标注语料,共10282个实体。为验证RoFormer预训练模型在实体抽取上的优越性,采用Word2Vec、BERT、RoBERTa等多种向量化模型进行对比试验,RoFormer-PointerNet与其他模型相比,模型精确率、召回率、F1值均为最优,分别为87.49%,85.76%和86.62%。为验证RoFormer-PointerNet在缓解实体嵌套的优势,与使用最为广泛的双向长短期记忆神经网络(Bidirectional Long Short-Term Memory,BiLSTM)和条件随机场(Conditional Random Field,CRF)模型进行对比试验,RoFormer-PointerNet比RoFormer-BiLSTM模型、RoFormer-CRF模型和RoFormer-BiLSTM-CRF模型分别高出4.8%、5.67%和3.87%,证明用指针网络模型可以很好解决实体嵌套问题。最后验证RoFormer-PointerNet方法在农业病害数据集中的识别性能,针对病害症状、病害名称、防治方法等8类实体进行了识别实验,本方法识别的精确率、召回率和F1值分别为87.49%、85.76%和86.62%,为同类最优。[结论]本研究提出的方法能有效识别中文农业病害文本中的实体,识别效果优于其他模型。在解决实体抽取过程中的实体嵌套和类型混淆等问题方面具有一定优势。