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.

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.

Extended context-based semantic communication system for text transmission

Context information is significant for semantic extraction and recovery of messages in semantic communication.However,context information is not fully utilized in the existing semantic communication systems since re-lationships between sentences are often ignored.In this paper,we propose an Extended Context-based Semantic Communication(ECSC)system for text transmission,in which context information within and between sentences is explored for semantic representation and recovery.At the encoder,self-attention and segment-level relative attention are used to extract context information within and between sentences,respectively.In addition,a gate mechanism is adopted at the encoder to incorporate the context information from different ranges.At the decoder,Transformer-XL is introduced to obtain more semantic information from the historical communication processes for semantic recovery.Simulation results show the effectiveness of our proposed model in improving the semantic accuracy between transmitted and recovered messages under various channel conditions.

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.

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。

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.

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.

Strategies for Fostering Interaction in Online Classrooms:A Conversation Analysis of Teacher-Student Verbal Interaction in Random Questioning in Pandemic-Initiated Online Teaching

This paper aims to explore how a veteran teacher organizes online teaching initiated by the pandemic and how she deals with the problems in online teacher-student verbal interaction.By analyzing a corpus of 20 audio-recorded online lessons between a math teacher and her students during the COVID-19 pandemic from April 11 to May 10,2022,four interactional segments are selected as the focus of the study.The results of the conversation analysis of the segments showed that students’modesty,lack of confidence,lack of ability,and network delay are the main factors affecting online teacher-student interaction.By encouraging students to answer questions,enlightening students to give answers,enriching students’answers,and entertaining the teaching atmosphere(“4Es”strategies),the teacher solved the problems successfully.The findings from this study can provide pedagogical experience and implications for practical teaching.

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。

A Study on Ways of Improving Nonverbal Communication in Business Context

As the counterpart of verbal communication,nonverbal communication plays an essential role in communicating with others.This thesis aims at introducing ways of improving nonverbal communication in business context,basing on the types of communication and its functions.Nonverbal communication has great impact in business communication.To be a successful communicator in business world,we need to improve our nonverbal communication skills.

Cultural Context and Appreciation to Obama's Verbal Humor

This paper aims to explain the restraint exerted by the deep structure of culture,such as history,religion,social convention and family,in understanding and appreciating foreign verbal humor,especially foreign statesman's verbal humor;whereby the significance of the deep structure of culture is revealed.In this paper,Herbert Paul Grice's Conversational Implicature Theory,i.e.the violation of cooperative principle,is taken as the theoretic base;the deep structure of culture is the analytical background and some of Obama's remarks and speeches given informal occasions are taken as analyzed examples,from which the essential role of the deep structure of culture is emphasized and focused in cross-cultural communication.

Verbal Assistance within the Context of an IADL Evaluation

Objective: The aim of this study was to identify the types of verbal assistance that facilitate task progression in individuals with cognitive deficits secondary to traumatic brain injury (TBI). Methods: Two individuals with moderate-to-severe TBI needing verbal assistance to complete the “Obtaining Information task” of the Instrumental Activities of Daily Living Profile were selected. A qualitative conversational analysis was conducted on the complete verbatim of the interactions that occurred between the evaluator and each participant while planning how they would find the information. The evaluator provided the least possible assistance to observe the maximal levels of independence of each individual. Results and Outcomes: Six types of verbal assistance, offered in response to each participant’s specific problems, facilitated goal formulation for finding information: restarting, scaffolding, cueing, action priming, offer of a strategy, and explicit advice. Explicit advice that involved the therapist thinking for the person was only provided after numerous other types of more implicit assistance had failed to facilitate task progression. Conclusions: Therapists can facilitate task-related goal formulation and attainment in individuals with cognitive limitations using several types of well-adjusted verbal assistance.

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。

Verbal Dyspraxia: A Case Study

Introduction: While approaching the aspect of learning disorders, particular attention is paid to verbal dyspraxia, a phenomenon that runs its course regularly over the last years. Verbal dyspraxia is inherent in the person without mental disorders and accompanies them throughout the whole spectrum of life. Comorbidity is an added issue. Although dyspraxia is met in homogeneous groups, some common elements such as intelligence, difficulty regarding linguistic skills, low learning performance and low self-esteem are present. Purpose: The object is to research how dyspraxia is manifested and how it affects a 6-year-old boy as well as the possibility of promptly interfering and simplifying his everyday life. Method: In the current case study, Achenbach’s questionnaire was used, combined with the use of expressive vocabulary. Results: The results of the research were unveiled through experts’ references in coordinance with the conferences conducted. Conclusion: Winding up, dyspraxia is a learning disorder that exists within the person through their lifespan. Immediate diagnosis, combined with experts’ personalized intervention programs (and perhaps, a differentiated curriculum, where applicable) can guide the person to live up to the educational needs. Family’s role is to be supportive, intending to eliminate possible emotional strains.

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.

Effect of Specific Verbal Instructions on the Identification of Pain Location during a Passive Straight Leg Raise Test

Musculoskeletal pain is common. Because pain is subjective, objectively describing it is crucial. However, pain assessment may cause distress in patients;therefore, physical therapists (PTs) should conduct these tests quickly and accurately. Simple and clear instructions are recommended for pain assessment. However, few studies have provided evidence to support this hypothesis. Correspondingly, this study aimed to confirm the effectiveness of specific verbal instructions for pain location during five consecutive Passive Straight Leg Raise (PSLR) tests. The 28 asymptomatic participants (age 27.4 ± 9.6 years) who provided informed consent received five consecutive PSLR tests: three without and two with specific verbal instructions to ascertain pain intensity, quality, and location. The participants drew pain locations on a body chart and described the pain intensity and quality after each test. All participants were interviewed regarding the differences they noted in the presence and absence of specific verbal instructions. Each pain location was classified into one of ten areas for statistical analysis. The proportion of participants who changed the pain location was compared between the tests using McNemar’s test, and the kappa coefficient was confirmed for consistency of pain location. There was a significant difference in the proportion of participants who changed their pain location between the second and third tests and from the third to the fourth test (McNemar’s test: p = 0.003). Kappa coefficients had low consistency (κ = 0.28) just after receiving the specific verbal instructions in the fourth test compared to the third test. Consistency improved in the fifth test (κ = 0.57);93% of the participants answered that the pain location had become clearer. This study revealed the effects of specific verbal instructions in identifying pain locations. This detailed information may help PTs provide appropriate treatment and contribute to reducing pain in clinical settings.