BCCLR:A Skeleton-Based Action Recognition with Graph Convolutional Network Combining Behavior Dependence and Context Clues

In recent years,skeleton-based action recognition has made great achievements in Computer Vision.A graph convolutional network(GCN)is effective for action recognition,modelling the human skeleton as a spatio-temporal graph.Most GCNs define the graph topology by physical relations of the human joints.However,this predefined graph ignores the spatial relationship between non-adjacent joint pairs in special actions and the behavior dependence between joint pairs,resulting in a low recognition rate for specific actions with implicit correlation between joint pairs.In addition,existing methods ignore the trend correlation between adjacent frames within an action and context clues,leading to erroneous action recognition with similar poses.Therefore,this study proposes a learnable GCN based on behavior dependence,which considers implicit joint correlation by constructing a dynamic learnable graph with extraction of specific behavior dependence of joint pairs.By using the weight relationship between the joint pairs,an adaptive model is constructed.It also designs a self-attention module to obtain their inter-frame topological relationship for exploring the context of actions.Combining the shared topology and the multi-head self-attention map,the module obtains the context-based clue topology to update the dynamic graph convolution,achieving accurate recognition of different actions with similar poses.Detailed experiments on public datasets demonstrate that the proposed method achieves better results and realizes higher quality representation of actions under various evaluation protocols compared to state-of-the-art methods.

MSC-YOLO:Improved YOLOv7 Based onMulti-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

Modeling the Spatio-Temporal Dynamics of Local Context for a Contextualized Diffusion of Agroecological Intensification Options in Niger

Spatio-temporal variability and dynamics in Sahelian agro-pastoral zones make each local situation a special case. These specificities must be considered to guide the dissemination of agricultural options with a view to sustainable development. The territorial scale of municipalities is not sufficient for this necessary contextualization;the scale of the “village terroir” seems to be a better option. This is the hypothesis we put forward in the framework of the Global Collaboration for Resilient Food Systems program (CRFS), i.e. local context is spatially defined by village terroir. The study is based on data collected through participatory mapping and surveys in “village terroirs” in three regions of Niger (Maradi, Dosso and Tillabéri). Then the links between farm managers and their cultivated land, as well as the spatio-temporal dynamics of local context are analyzed. This study provides evidence of the existence and functional usefulness of the village terroir for farmers, their land management and their activities. It demonstrates the usefulness of contextualizing agricultural options at this scale. Their analysis elucidates the links between “terroirs village” and the specific functioning of the agrosocio-ecosystems acting on each of them, thus laying the systemic and geographical foundations for a model of the spatio- temporal dynamics of “village terroirs”. This initial work has opened up new perspectives in modeling and sustainable development.

Heritage Communities in Consumer Contexts Spatial Production of Tourism Research-Take Qixian Zhao Yu Historical City as an Example

Taking Zhaoyu Historical City in Qixian County as an example,this paper explores the production process of tourism space in Zhaoyu Historical City in the context of consumption,based on Lefebvre's triadic dialectic theory.The study reveals that,driven by the development of tourism,subjects such as the government and planners possess absolute dominance over spatial representations,while residents demonstrate receptive and adaptive action strategies and social relations are reproduced,presenting a harmonious state.Further exploring the tourism community in the environmental performance of the subject of action,social relations,consumption demand,daily life practice,cultural capital,etc.,the daily life practice of the tourism community has transcended the original logic of tourism spatial production and has a certain extension.The mechanism analysis in this paper can help guide the healthy development of tourism space in the neighboring historical cities or communities and achieve the dual purpose of promoting the economic development of the community and heritage protection.

基于Context Capture多视角影像三维重建技术在藏品数字化中的应用

藏品是博物馆全部活动的物质基础。在博物馆数字化的浪潮中,藏品数字化是核心,而三维建模是藏品数字化的关键手段。多视角三维重建技术因其设备要求低、灵活性强、操作简单等优点成为藏品建模的重要手段。在考古领域,虽然Context Capture在空三精度、纹理映射质量、模型精度等方面具有优势,但由于其不能直接用于可移动文物建模,因此该软件应用较少。通过对采集到的影像预处理或对模型成果后期处理,使之能够用于文物藏品的建模,并对两种方式的优缺点进行比较。

基于Context-dependent嵌入的省级碳排放权分层分配策略研究

考虑省际资源禀赋差异,在ZSG-DEA模型中嵌入Context-dependent,以分层的方式构建渐进式改进资源分配模型,对2030年全国30个省份碳排放权进行分配。结果表明:效率值方面,全国总体效率较好,呈现出东西中地区依次降低的状态,各省份间效率值差距较大,传统能源大省(区)碳排放效率低下;碳排放权调整方面,陕西、山西、内蒙古3个效率值较低的省份需要做出较大的缩减量,而山东、广东等处于第一层有效前沿面的省份获得了相较之前更多的碳排放量;模型对比方面,在保证2030年碳排放目标基础上,C-ZSG-DEA模型在效率值的改进上和碳排放调整上都比其他模型要小,符合渐进式改进的理念。

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.

Context Awareness by Noise-Pattern Analysis of a Smart Factory

Recently,to build a smart factory,research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology,a field of artificial intelligence.Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset.However,compared to numerical raw data,learning based on image data has the disadvantage that creating a training dataset is very time-consuming.Therefore,we devised a two-step data preprocessing method that efficiently detects machine anomalies in numerical raw data.In the first preprocessing process,sound signal information is analyzed to extract features,and in the second preprocessing process,data filtering is performed by applying the proposed algorithm.An efficient dataset was built formodel learning through a total of two steps of data preprocessing.In addition,both showed excellent performance in the training accuracy of the model that entered each dataset,but it can be seen that the time required to build the dataset was 203 s compared to 39 s,which is about 5.2 times than when building the image dataset.

Traditional medicine use during pregnancy and labor in African context:A scoping review

Traditional medicine(TM)has been more popular among pregnant women worldwide and has played a significant part in maternal health-care services in many nations.Herbs,herbal preparations,and finished herbal products all contain active substances that are derived from plant parts or other plant components that are thought to have medicinal advantages.To diagnose,prevent,and treat illnesses as well as to enhance general well-being,about 80%of people use a variety of TM,including herbal remedies.A systematic search of Google Scholar and PubMed was performed utilizing an established scoping review framework by Joanna Briggs Institute from January 2012 to December 2022.A consequent title and abstract review of articles published on TM in the African context were completed.Of over 15,000 published studies identified,15 meeting the inclusion criteria were integrated into the following seven categorical themes:prevalence of TM use,source of information on TM use,reasons for use of TM,route of administration,common herbs used in pregnancy and labor,the effect of herbs used in pregnancy and labor,and predictors of use of TM.The studies reviewed were primarily in the context of an African setting on the use of TM regarding herbal medicine.Of all the articles,the highest number of studies was conducted in Zimbabwe.This review shows increased use of TM by women during pregnancy and labor with a reported prevalence rate varying from 12%to 60%.However,a decrease in use in the third trimester of pregnancy was reported.The most frequent source of information on the use of TM was from family and friends,while age,parity,education,and income were factors affecting use.In conclusion,the participants do not often disclose the use of TM during their antenatal attendance and the reason for use was accessibility and cost.Therefore,there is a need for further study on the safety and efficacy of TM use in pregnancy and labor.

An Investigation of College English Autonomous Learning in Network Multimodal Context

In the current society, based on the growing development of network information technology, the teaching in many colleges and universities has also introduced it to adapt to the situation. This trend can provide more useful conditions for students to learn, which requires students to master enough self-learning abilities to adapt to this model. The study in the paper shows that students are usually interested in autonomous learning in a multimodal environment, but the degree of strategy choice is relatively low, and the learning process is blind and passive with the lack of self-confidence. Facing the future, schools should actively integrate into network thinking, and teachers should change their roles and train and guide students’ learning strategies and learning motivations, so as to achieve better teaching results.

Fusing Spatio-Temporal Contexts into DeepFM for Taxi Pick-Up Area Recommendation

Short-term GPS data based taxi pick-up area recommendation can improve the efficiency and reduce the overheads.But how to alleviate sparsity and further enhance accuracy is still challenging.Addressing at these issues,we propose to fuse spatio-temporal contexts into deep factorization machine(STC_DeepFM)offline for pick-up area recommendation,and within the area to recommend pick-up points online using factorization machine(FM).Firstly,we divide the urban area into several grids with equal size.Spatio-temporal contexts are destilled from pick-up points or points-of-interest(POIs)belonged to the preceding grids.Secondly,the contexts are integrated into deep factorization machine(DeepFM)to mine high-order interaction relationships from grids.And a novel algorithm named STC_DeepFM is presented for offline pick-up area recommendation.Thirdly,we devise the architecture of offline-to-online(O2O)recommendation respectively based on DeepFM and FM model in order to tradeoff the accuracy and efficiency.Some experiments are designed on the DiDi dataset to evaluate step by step the performance of spatio-temporal contexts,different recommendation models,and the O2O architecture.The results show that the proposed STC_DeepFM algorithm exceeds several state-of-the-art methods,and the O2O architecture achieves excellent real-time performance.

新颖词语义韵的发生机制:“双枣树”效应的证据

以中文母语者为被试开展词汇学习实验,既操控新颖词出现的语境情感(积极、消极、中性),又操控语境的变异性(重复、变化),检验语境情感是否可通过阅读接触,从语境迁移到新颖词以及这种迁移是否影响新颖词习得的效果,从而探索新颖词语义韵的发生机制。196名被试参加了实验,他们在不同的语境情感和语境的变异性条件下一共阅读了45个篇章,然后对9个新颖词进行情感效价评分并参加了三种不同的词汇知识测试。结果显示,只有在重复阅读相同材料的条件下,语境的情感才顺利地迁移到新颖词,表现出明显的“双枣树”效应,而与此相反的是,只有在变化语境下,语境情感才对新颖词词形及词义的学习具有显著的预测作用,在越积极的情感语境里,词形和词义的习得效果也越好。“双枣树”效应有效地解释了新颖词语义韵的发生机制,也为新词学习提供了重要启示。

大国竞争背景下新质生产力形成的理论逻辑与实现路径

新质生产力是习近平总书记基于大国竞争背景下我国发展阶段、发展环境、发展条件变化,作出的具有根本性、全局性、长远性的重大战略判断。在新一代信息技术加速突破应用、先进制造技术加速产业转型的新发展阶段,形成新质生产力既是构建新发展格局、推动高质量发展的必然要求,也是我国建设现代化经济体系的关键动能,关系我国在未来发展和国际竞争中赢得战略主动。新质生产力的内涵是以引领国际经济体系变革、重塑现代化产业体系、提升企业核心竞争力为目的,从传统生产力向新质生产力的演化升级应该包含国际竞争、国家优势和企业发展三个层面整个生产范式的转变。文章以马克思主义政治经济学、习近平经济思想、习近平生态文明思想为理论支撑,提出“新质生产力”的理论构建包括三个维度:一是微观载体——打造世界一流企业;二是中观治理——提升国际竞争优势;三是宏观发展——构建人类命运共同体。文章将新质生产力形成的理论逻辑和实践路径融入中国当前的发展战略中,从转变企业发展范式、重塑现代化产业体系和建设新的全球治理体系三个方面提出形成新质生产力的改革路径,三条路径相辅相成、协同发力,共同为新质生产力的形成注入活力,使得生产力发展更好地服务于中国高质量发展的战略定位。

数字情境下产品创新对新企业成长的影响

以数字情境下新企业为研究对象,构建产品创新、投机导向、不良竞争和新企业成长间的关系模型。通过对北京、深圳、上海等城市的350份新企业样本的实证研究发现:数字情境下产品创新积极影响新企业成长,即数字技术赋能新企业高效率整合内外部资源,以更好地发挥产品创新的推动作用;投机导向抑制了产品创新对新企业成长的积极影响,即投机导向越高,产品创新的积极影响受到制约越强;不良竞争行为抑制了产品创新对新企业成长的积极影响,即不良竞争行为越强,产品创新的积极影响受到制约越强。

自然疗愈体系的当代发展及公共健康服务潜力

【目的】探讨自然疗愈(nature-based intervention,NBI)体系作为现代医疗技术的辅助手段在公共健康服务方面的应用潜力并提出未来展望。【方法】通过对自然疗愈蕴含的人类思想脉络和历史发展的梳理,界定自发的疗愈行为与专业化的自然疗法(nature-based therapy,NBT)的界限,揭示自然疗愈体系由民间自发的、依靠自然医学的疗愈方法,演化成为现代专业化、系统化医疗技术辅助手段的社会动因和运行机制,进一步归纳不同国家医疗照护机构中自然疗愈体系的分布与特点。【结果】结合欧美国家自然疗愈重要理论模型,进一步解析自然疗愈作用机制和广泛的健康效能,以及实施场所和服务人群。从历史演变及当代发展趋势来看,自然疗愈体系源自朴素的自然医学和民间传统,有着广泛的社会实施基础,在现代医疗技术的进步和社会需求的变化中,显示出更强大的生命力,具有天然的“平医”结合的良好背景。【结论】自然疗愈体系充分地利用了人与自然环境的同一性和基因同源性,在不同的历史进程中均获得了人类的青睐,成为一种低成本、高产出且科学便捷的疾病恢复和健康促进手段。基于循证研究,建立科学的分类、分级体系,形成完善的园艺实操模块和干预流程,自然疗愈将具有巨大的优势,有望成为人类未来潜在的非药物治疗或替代医学方法。

轻量级重参数化的遥感图像超分辨率重建网络设计

针对当前基于深度学习的遥感图像超分辨率重建模型部署时对硬件要求较高,本文设计了一种轻量级基于重参数化的残差特征遥感图像超分辨率重建网络。首先,采用重参数化方法设计了一种残差局部特征模块,以有效地提取图像局部特征;同时考虑到图像内部出现的相似特征,设计了一个轻量级的全局上下文模块对图像的相似特征进行关联以提升网络的特征表达能力,并通过调整该模块的通道压缩倍数来减少模型的参数量和改善模型的性能;最后,在上采样模块前使用多层特征融合模块聚合所有的深度特征,以产生更全面的特征表示。在UC Merced遥感数据集上进行测试,该算法在遥感图像3倍超分辨率下的参数量为539 K,峰值信噪比为30.01 dB,结构相似性为0.8449,模型的推理时间为0.010 s;而HSENet算法的参数量为5470 K,峰值信噪比为30.00 dB,结构相似性为0.8420,模型的推理时间为0.059 s。实验结果表明,该算法相比HSENet算法,参数量更少,运行速度较快,且峰值信噪比与结构相似性也有一定的提高。在DIV2K自然图像数据集上进行测试,该算法的峰值信噪比和结构相似性相比其他算法也有一定的优势,表明该算法的泛化能力较强。

双注意力随机选择全局上下文细粒度识别网络

针对细粒度图像识别任务中易忽视微小潜在性特征且外观差异细微等问题,提出一种基于双注意力随机选择全局上下文细粒度识别网络。首先,使用ConvNeXt作为主干网络,提出双注意力随机选择模块,对不同阶段提取到的特征进行通道随机选择和空间随机选择,使网络能够关注到其他潜在微小判别性特征;其次,利用全局上下文注意力模块将深层特征的语义信息融合到中间层,增强中间层定位微小特征的能力;最后,提出一种多分支损失,对中间层、深层和拼接层特征引入分类损失,结合不同分支提取到的特征,诱导网络获得多样性的判别特征。所提网络在Stanford-cars、CUB-200-2011、FGVC-Aircraft 3个公开细粒度数据集和真实场景下车型数据集VMRURS上分别达到了95.2%、92.1%、94.0%和97.0%的识别准确率,其性能相比其他对比方法有较大幅度提升。

基于自适应上下文匹配网络的小样本知识图谱补全

知识图谱在构建过程中需要面对繁杂的现实世界信息,无法建模所有知识,因此需要补全。真实的知识图谱中很多类型的关系通常只有少量的训练实体样本对。因此,如何进行小样本知识图谱补全是一个十分有价值的问题。目前基于嵌入的方法一般通过注意力机制等方法聚合实体上下文信息,通过学习关系嵌入的方式来补全知识图谱,仅考虑关系层面的匹配程度,虽然能够预测未知关系,但往往准确度不高。针对小样本知识图谱补全问题,提出了一个自适应上下文匹配网络(Adaptive Context Matching Network,ACMN)。首先提出一个共性邻居感知编码器,聚合参考集实体上下文,即一跳邻居实体,获得共性邻居感知编码;接着提出一个任务相关实体编码器,挖掘任务实体上下文与共性上下文的相似度信息,区分一跳邻居对当前任务的贡献,增强实体表征;然后提出一个上下文关系编码器获得动态关系表征;最后通过加权求和综合考虑实体上下文和关系的匹配程度,完成补全。ACMN从实体上下文相似度和关系匹配程度两个方面综合评价查询三元组是否成立,能够在小样本的背景下有效提高预测准确性。在两个公共数据集上和其他8个广泛使用的算法进行比较,ACMN在不同规模的小样本情况下,取得了目前最好的补全结果。

中国金融集聚研究的前沿演进、热点分析与趋势展望——基于CiteSpace的文献计量分析

随着国内经济结构转型升级和区域金融中心战略目标的提出,学者们逐渐转向研究金融集聚问题,探索金融集聚的理论内涵及其社会经济效应。以CNKI数据库中2003-2022年427篇核心期刊为样本,借助CiteSpace文献可视化软件对我国金融集聚的发展脉络与研究热点进行分析。结果表明:不同时期金融集聚研究前沿依次是“金融集聚从何处开始形成”“金融集聚的测度及其功能”“金融集聚的作用机理”;国内学者对金融集聚的研究主要集中在金融集聚的形成机理、金融集聚的测度结果及特征分析、金融集聚与产业结构、金融集聚与科技创新、金融集聚与城镇化、金融集聚与绿色经济、金融集聚与经济增长等7个重要主题。新时代关于金融集聚的研究应注重多学科交叉,重点从金融集聚时空演进特征分析、县域金融集聚促进共同富裕、金融集聚与中国式现代化的关系以及金融集聚服务乡村全面振兴和农业强国建设等方面进行延伸。

情境、文本、话语:研究生思想政治理论课叙事建构的三重场域

教育叙事是提升研究生思想政治理论课实效性的重要方法。将教育叙事融入研究生思想政治理论课是契合研究生需求特点的探索,也是符合研究生接受特点的尝试。研究生思想政治理论课教师需从场所、氛围和技术三个维度进行叙事情境的建构,创设师生互动的共情场域;从宏大叙事、微观生活和热度话题三个部分实现叙事文本的架构,营造师生互通的共享机制;从风格、表达和体系三个层面推动叙事话语的转换,达到师生互融的共鸣效果,充分发挥思想政治理论课温润人心、教化育人的作用。