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 on Multi-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.

Palaeogene Sandstones of the Manika Plateau in Kolwezi (DR Congo): Sedimentological and Geochemical Characterization, Provenance, Palaeoalteration and Tectonic Context

This work presents a study of the Paleogene sandstones of the Manika plateau in Kolwezi, DR Congo. These sandstones belong to the “Grès polymorphes” group, which together with the overlying “Sables ocre” makes up the Kalahari Supergroup. Sedimentological and geochemical analyses have enabled us to characterize these sandstones and determine their origin, the conditions of their formation and the tectonic context in which they were developed. The results show that the sandstones are quartz arenites with a high level of mineralogical, textural and chemical maturity. They are recycled sandstones, formed in an intracratonic sedimentary basin, in the context of a passive continental margin, after a long fluvial transport of sediments. These sandstones initially come from intense alteration of magmatic rocks with felsic composition, mainly tonalite-trondhjemite-granodiorite (TTG) complexes, in hot, humid palaeoclimatic conditions and oxidizing environments.

A Comparative Study of the L2 Pragmatic Competence of University Students in Hong Kong and the Chinese Mainland:The Contributions of Sociocultural Context and Linguistic Proficiency

This study investigates the differences in pragmatic competence between Hong Kong and Chinese mainland university students.Participants included 19 native speakers of English,115 Chinese mainland students,divided into those who had spent time abroad in an English-speaking country(CM A)and those who had not(CM NA),and 97 Hong Kong students,divided into those from an English-medium secondary school(Hong Kong EMI)and those from a Chinese-medium school(Hong Kong CMI).Linguistic proficiency was measured by a C-test,and pragmatic competence by a Metapragmatic Knowledge Test,an Irony Test and a Monologic Role Play.Group scores were compared using ANCOVAs to control for differences in proficiency.The results point to a continuum of pragmatic competence—EMI>CMI>CM A>CM NA—reflecting the groups’access to English in real-life contexts.The differences between the Hong Kong groups and the Chinese mainland groups were clearest in those tests measuring processing capacity(i.e.,Irony Response Time and the Monologic Role Play).CM A,but not CM NA,performed as well as the Hong Kong groups on measures of metapragmatic awareness.The results are discussed in terms of Bialystok’s(1993)distinction between analyzed representation and control of processing.

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.

Context Capture在车辆三维建模中的应用

无人机倾斜摄影技术和Context Capture因具有自动化程度高、建模精度和效率高等特点而被广泛应用于三维建模中。本文使用无人机倾斜摄影技术和Context Capture分别对一辆SUV和一辆三厢轿车进行了三维建模,并对其进行了精度分析。结果表明,采用多条航线的无人机拍摄策略可以充分捕捉车辆各个侧面的细节信息。使用Context Capture构建的车辆三维模型细节清晰可辨,真实地反映了车辆外形。车辆特征点实测数据与航拍数据之间的最大相对误差为0.9%,确保了车辆三维建模的精度要求。

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。

通用人工智能中的Long-Context技术分析

阐述通用人工智能中的Long-Context技术原理,提出该技术的解决方案,包括位置编码外推、注意力机制计算优化、模型结构优化和检索增强生成。分析硬件系统的瓶颈,探讨Long-Context在通用人工智能时代的发展趋势。

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.

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。

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.

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.

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.

一种感知网Context存储策略

将contextaware理论融入感知网,有助于解决感知网的自适应问题。为此必须研究面向感知网的适当的context描述和存储机制。该文介绍了一种面向感知网的context存储策略――RBLS。RBLS在节点本地保存context,并根据节点持有的角色动态分配其额外需要的context存储空间。“快照”被用来记录邻居节点的私有context。仿真结果显示RBLS是简单且能源有效的。

Context概念与内涵及其在信息融合中的应用

阐述了Context的基本概念及其在人类认识世界中的作用,建立了Context敏感周期环,介绍了Context在知识获取中的作用。同时描述了人类认识阶段的划分及Context在各阶段的作用。最后阐述了信息融合与人类认识相对照的诸阶段中,Context的具体内涵和外延作用。

基于改进K均值的Context量化模型

为了提高图形编码系统压缩性能,可以通过使用Context模型来得到当前所要编码符号的概率。但是事实证明由于高阶Context模型很难在统计中有效收敛于信号的真实分布,结果使得编码效果降低,这就是所谓的"模型代价"问题。为了解决这一问题,一种有效的方法就是对高阶Context模型进行量化。由于Context量化问题与一般矢量量化问题相似,可以在设定合适的失真度量准则的条件下,使用聚类算法来实现Context量化。目前,K均值是使用比较广泛的一种聚类算法,但K均值算法必须具有确定的类数和选定的初始聚类中心。但在实际中,K均值往往难以准确界定,从而导致了聚类效果不佳。基于此,为了找到最佳的聚类数,本文提出采用聚类有效性函数来改进K均值聚类算法。

High-context and Low-context communication: A Case Study of A Chinese Refusal

Edward Hall offers us an effective means of examining cultural similarities and differences,that is the classification of high-context and low-context culture.The paper attempts to analyze a case of a Chinese refusal from the perspective of highcontext and low-context communication and explores the differences between the two types of communication.

Mobile and Context-Aware GeoBI Applications: A Multilevel Model for Structuring and Sharing of Contextual Information

With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.