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。

DATA CONTEXT MODEL IN THE PRO-CESS INTEGRATION FRAMEWORK

Process integration is the important aspect of product development process. The recent researches focus on project management, workflow management and process modeling. Based on the analysis of the process, product development process is divided into three levels according to different grains from macroscopy to microcosm. Our research concentrate on the workflow and the free-grained design process. According to the need of representing the data and the relationships among them for process integration, context model is introduced, and its characters are analyzed. The tree-like structure of inheritance among context model＇s classes is illustrated; The relationships of reference among them are also explained. Then, extensible markup language （XML） file is used to depict these classes. A four-tier framework of process integration has been established, in which model-view-controller pattern is designed to realize the separation between context model and its various views. The integration of applications is applied by the encapsulation of enterprise＇s business logic as distributed services. The prototype system for the design of air filter is applied in an institute.

Human-Object Interaction Recognition Based on Modeling Context

This paper proposes a method to recognize human-object interactions by modeling context between human actions and interacted objects.Human-object interaction recognition is a challenging task due to severe occlusion between human and objects during the interacting process.Since that human actions and interacted objects provide strong context information,i.e.some actions are usually related to some specific objects,the accuracy of recognition is significantly improved for both of them.Through the proposed method,both global and local temporal features from skeleton sequences are extracted to model human actions.In the meantime,kernel features are utilized to describe interacted objects.Finally,all possible solutions from actions and objects are optimized by modeling the context between them.The results of experiments demonstrate the effectiveness of our method.

Context Model in Chinese and American Reports on Yangtze Sunken Ship

Two pieces of news reports on Yangtze Sunken Ship event are analyzed in this essay to prove the influence of context model in news reports discourse by comparative analysis approach. Through illustrating the different expressions in these reports discourse, the process how cognition(or context model) influences and determines the production and understanding of discourse has been demonstrated.

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.

Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.

A New Aware-Context Collaborative Filtering Approach by Applying Multivariate Logistic Regression Model into General User Pattern

Traditional collaborative filtering (CF) does not take into account contextual factors such as time, place, companion, environment, etc. which are useful information around users or relevant to recommender application. So, recent aware-context CF takes advantages of such information in order to improve the quality of recommendation. There are three main aware-context approaches: contextual pre-filtering, contextual post-filtering and contextual modeling. Each approach has individual strong points and drawbacks but there is a requirement of steady and fast inference model which supports the aware-context recommendation process. This paper proposes a new approach which discovers multivariate logistic regression model by mining both traditional rating data and contextual data. Logistic model is optimal inference model in response to the binary question “whether or not a user prefers a list of recommendations with regard to contextual condition”. Consequently, such regression model is used as a filter to remove irrelevant items from recommendations. The final list is the best recommendations to be given to users under contextual information. Moreover the searching items space of logistic model is reduced to smaller set of items so-called general user pattern (GUP). GUP supports logistic model to be faster in real-time response.

Approach to Model-Based Development of Context-Aware Application

In order to address challenges posed by advances in mobile computing, ubiquitous devices software engineering, wireless and sensor technologies in many applications running, this paper provides an approach that simplifies the design and the implementation of context-aware Interactive systems called CAISDA (Context-Aware Interactive System Development Approach). This approach is based on the analysis and synthesis of context-aware frameworks proposed in literature.

Context-Aware System Modeling Based on Boolean Control Network

Boolean control network consists of a set of Boolean variables whose state is determined by other variables in the network. Boolean network is used for modeling complex system. In this paper, we have presented a model of a context-aware system used in smart home based on Boolean control networks. This modeling describes the relationship between the context elements (person, time, location, and activity) and services (Morning Call, Sleeping, Guarding, Entertainment, and normal), which is effective to logical inference. We apply semi tensor matrix product to describe the dynamic of the system. This matrix form of expression is a convenient and reasonable way to design logic control system.

An Analysis of Discourse Production and Cognitive Context Dynamism Based on Relevance-Adaptation Model

Taken discourse production as the research objective,it holds that discourse production is dynamic in human communication.It attempts to analyze the dynamics on the basis of Relevance-adaption model from the perspective of cognitive pragmatics and explain the role of the context dynamics that plays in the discourse production.

基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别方法

识别非驾驶行为是提高驾驶安全性的重要手段之一。目前基于骨架序列和图像的融合识别方法具有计算量大和特征融合困难的问题。针对上述问题,本文提出一种基于多尺度骨架图和局部视觉上下文融合的驾驶员行为识别模型(skeleton-image based behavior recognition network,SIBBR-Net)。SIBBR-Net通过基于多尺度图的图卷积网络和基于局部视觉及注意力机制的卷积神经网络,充分提取运动和外观特征,较好地平衡了模型表征能力和计算量间的关系。基于手部运动的特征双向引导学习策略、自适应特征融合模块和静态特征空间上的辅助损失,使运动和外观特征间互相引导更新并实现自适应融合。最终在Drive&Act数据集进行算法测试,SIBBR-Net在动态标签和静态标签条件下的平均正确率分别为61.78%和80.42%,每秒浮点运算次数为25.92G,较最优方法降低了76.96%。

场景驱动下新零售商业模式创新及其价值创造的机理与策略

数字技术颠覆了零售行业的商业模式,通过重塑人、货、场关系驱动的新零售场景,为零售商与消费者带来全新的价值体验,也为我国商品经济发展带来新活力。在文献梳理的基础上,界定新零售场景和场景价值的内涵,识别出社群、内容、时空等场景要素,分析功能、效率、体验、情绪等场景价值的构成维度并探究其互动关系,构建“关系—场景—价值”概念模型。此外,引入商业模式创新的概念,并将其作为价值创造的抓手,以“场景—模式—价值”为主线,建构场景驱动下新零售商业模式创新的价值创造机理理论框架,提出“应用场景新颖化创造功能价值”“交易场景时空化创造效率价值”“互动场景内容化创造体验价值”“关注场景社群化创造情绪价值”等4条创造场景价值的策略。最后,以步步高为样本开展案例分析,为价值创造机理理论提供经验证据。研究丰富了商业模式场景化理论,也为新零售企业开展商业模式创新提供了理论参考。

基于形状描述符的滞回曲线相似性指标的形成

为了形成全面的相似度指标以评价滞回曲线的拟合程度,引入了已广泛用于图像特征提取的形状描述符,并提出了基于这些描述符形成计算数值模拟曲线相对于实验曲线的相似度指标的具体流程.根据该流程,形成了一个基于形状上下文的相似度指标.先计算两曲线滞回圈之间的相似度,再对这些相似度进行加权组合,形成整个滞回环的相似度.为了验证该指标,采用Bouc-Wen模型进行数值模拟研究.改变Bouc-Wen模型的5个参数,并形成51条数值模拟曲线,计算这些曲线相对于中值曲线的相似度与力峰值点、耗能、刚度误差.结果表明,相似度与误差之间的斯皮尔曼相关系数绝对值均大于0.78,有强烈的相关性,从而验证了该指标的可行性和指标形成流程的有效性.

基于多尺度上下文的英文作文自动评分研究

目前作文自动评分模型缺乏对不同尺度上下文语义特征的提取,未能从句子级别计算与作文主题关联程度的特征。提出基于多尺度上下文的英文作文自动评分研究方法MSC。采用XLNet英文预训练模型提取原始作文文本单词嵌入和句嵌入,避免在处理长序列文本时无法准确捕捉到符合上下文语境的向量嵌入,提升动态向量语义表征质量,解决一词多义问题,并通过一维卷积模块提取不同尺度的短语级别嵌入。多尺度上下文网络通过结合内置自注意力简单循环单元和全局注意力机制,分别捕捉单词、短语和句子级别的作文高维潜在上下文语义关联关系,利用句向量与作文主题计算语义相似度提取篇章主题层次特征,将所有特征输入融合层通过线性层得到自动评分结果。在公开的标准英文作文评分数据集ASAP上的实验结果表明,MSC模型平均二次加权的Kappa值达到了80.5%,且在多个子集上取得了最佳效果,优于实验对比的深度学习自动评分模型,证明了MSC在英文作文自动评分任务上的有效性。

上下文建模与推理的视频异常事件检测

视频异常事件检测旨在从视频中自动地检测出不符合正常事件规律的视频事件.视频中许多正常和异常的事件是由目标与场景或其它目标交互而产生的,即它们是以目标为中心且高度上下文相关的.如何从底层的视频特征中提取事件高层语义上下文信息,并根据上下文信息进行视频异常事件检测仍是一个开放的难题.为此,本文提出了一种新的上下文建模与推理的视频异常事件检测方法.本文方法通过建立视频的上下文图,自动地推理事件相关的语义上下文信息,以缩小底层视觉特征与异常事件高层语义之间的差距,实现异常事件检测.具体来说,首先使用了预训练的目标检测网络,提取目标初始的表观特征、目标之间的时空关系特征和场景特征;其次设计了一个上下文图推理模块,通过建模时空上下文图,将提取到的特征显式地建模为三类语义上下文,包括事件目标的个体行为、不同目标之间的时空关系以及目标与场景之间的交互,其中图的节点表示目标/场景,图的边表示时空关系;最后构建了一个异常预测模块,根据推理到的语义上下文信息进行异常事件检测.本文的上下文图推理模块基于平均场理论,通过使用多个带有消息传递模块的循环神经网络,迭代更新图的节点和边的状态,目的是从底层的视觉特征中推理得到高层的语义上下文.本文的异常预测模块包括注意力池化网络层和全连接网络层,通过输入语义上下文信息,计算视频帧的异常分数,从而正确地进行异常事件检测.实验中,设计了一个自训练策略,分别使用了无监督、半监督、弱监督和监督四种训练策略,以端到端的方式训练时空上下文图推理模块和异常预测模块.本文方法在四个公开的数据集上进行了实验,包括三个半监督的数据集Subway (Entrance/Exit)、Avenue和ShanghaiTech,以及一个监督的数据集UCF-Crime.与不使用上下文的方法相比,本文方法在Subway(Entrance/Exit)、Avenue和ShanghaiTech数据集上的无监督AUC指标分别提高了2.7%/3.1%、2.0%和2.9%,半监督AUC指标分别提高了3.5%/3.3%、4.0%和4.3%.在监督数据集UCF-Crime上,与没有使用上下文的方法相比,本文方法在半监督AUC、弱监督AUC和监督AUC的指标上分别提高了2.1%、0.4%和9.2%,取得了有竞争力的表现.

基于context模型的contourlet域图像去噪

在分析contourlet域系数分布特征的基础上提出了一种基于context模型的contourlet域图像去噪算法。算法的关键点在于:基于contourlet变换系数的分布特性,确定合适的去噪门限;利用context模型建立图像contourlet变换后的系数分类模型并根据分类使用不同的门限去噪。实验表明,本方法能较好地去除图像噪声,在提高去噪图像PSNR值和改善主观视觉效果方面都表现出了良好的性能。

基于视觉提示学习的天气退化图像恢复

尽管现有的天气退化图像恢复方法在单一天气去除任务上已经取得良好表现,但其无法适应真实场景下多变的天气类型.为此,本文提出一种基于视觉提示学习的天气退化图像恢复算法,其是预训练语言图像模型与天气退化图像恢复任务结合的新范式.该算法首先设计一个查询提示约束网络(Query Prompt Contrained Network,QPC-Net),其利用对比语言图像预训练模型中的文本编码器和图像编码器来根据给定的退化图像直接编码其对应真实背景的潜在描述特征.同时,该算法还包括一个示例提示引导网络(Example Prompt Guided Network,EPG-Net),其利用给定的示例图像来引导预训练扩散模型去除查询图像上对应的天气退化.相比类似设定的现有算法,本文算法在8个天气退化数据集上平均改善峰值信噪比2.11 dB,平均改善结构相似性4.74%.

大数据背景下社交平台伪健康信息传播影响因素研究

探究大数据背景下社交平台伪健康信息传播影响因素,为有关部门更好地制定伪健康信息管理策略提供参考与借鉴。整合S-O-R模型和MOA理论,引入个性化推荐这一新变量,构建社交平台伪健康信息传播影响因素模型,利用问卷调查和结构方程模型验证模型和假设的合理性。研究表明信息交换、健康关注度、信息质量、信息新颖和个性化推荐正向影响用户感知,进而正向影响用户伪健康信息传播行为,而健康信息素养负向影响用户伪健康信息传播行为,并据此提出管理社交平台伪健康信息传播的可行性建议。