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Exploring the Influence of context on Emotional Mimicry and Intention: An Affirmation of the Correction Hypothesis

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context-aware local abnormality detection in crowded scene

Behavioral Sciences 2023年第8期13卷

作者： Xu X. Hu P. Department of psychology Renmin University of China Beijing 100872 China

Background: Emotional mimicry, a phenomenon frequently observed in our everyday interactions, is the act of replicating another individual’s facial expression. The Emotion Mimicry in context View and the Correction Hypothesis underscore the critical role of context and intention within emotional mimicry. Methods: In two distinct studies, participants were presented with facial expressions of models (happiness and anger) within various contexts (affiliative, distancing, and neutral). Concurrently, we recorded electromyography (EMG) to index emotional mimicry, while participants explicitly rated the models’ intentions. Results: We observed context swiftly influences emotional mimicry within 500 ms, notably when the intentions of contexts are opposing to the intentions of facial expressions, leading to weakened muscle responses and diminished perceived intention. Furthermore, a notable correlation was discovered in the mimicry of angry faces; the more distancing the context, the stronger the corrugator supercilii (CS) muscle activity after context processing. Conclusions: First, emotional mimicry should not be simply viewed as an output corresponding to the expresser’s facial expressions but the dynamic process involving the active participation of the observer. Second, intention serves as a pivotal anchor, effectively integrating facial and contextual information. As such, we provided empirical support for the Correction Hypothesis. © 2023 by the authors.

关键词： affiliative intention context distancing intention electromyography (EMG) emotional mimicry

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Science China(Information Sciences) 2015年第5期58卷 134-144页

作者： ZHU XiaoBin JIN Xin ZHANG XiaoYu LI ChangSheng HE FuGang WANG Lei Beijing Technology and Business University Beijing Electronic Science and Technology Institute Information Technology Research Base of Civil Aviation Administration of China Civil Aviation University of China

In this paper, we propose a novel algorithm by jointly modeling motion and context information targeting at detecting abnormal events in crowded scenes. In our algorithm, context pattern information,extracted through volume local binary patterns computation on three orthogonal planes(LBP-TOP) between local target areas with surrounding areas, is explicitly taken into consideration for localizing abnormality. To capture motion information, a novel feature descriptor named Multi-scale Histogram of Frequency Coefficient is explored by taking Fourier Transform on the extracted dense trajectories. For detection of abnormality, sparse reconstruction cost from a learned event dictionary is adopted to classify local normal and abnormal events.Experiments conducted on three benchmark datasets show superior performance to many related state-of-the-art methods.

关键词： local abnormality detection crowded scene context local binary pattern sparse coding

context-aware end-to-end QoS diagnosis and quantitative guarantee based on Bayesian network

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The Journal of China Universities of Posts and Telecommunications 2010年第6期17卷 106-112页

作者： LIN Xiang-tao,CHNEG Bo,CHEN Jun-liang,QIAO Xiu-quan State Key Laboratory of Networking and Switching Technology,Beijing University of Posts and Telecommunications,Beijing 100876,China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing 100876 China

To support quality of service （QoS） management on current Internet working with best effort,we bring forth a systematic approach for end-to-end QoS diagnosis and quantitative guarantee. For QoS diagnosis,we take contexts of a service into consideration in a comprehensive way that is realized by exploiting causal relationships between a QoS metric and its contexts with the help of Bayesian network （BN） structure learning. context discretization algorithm and node ordering algorithm are proposed to facilitate BN structure learning. The QoS metric is diagnosed to be causally related to its causal contexts,and the QoS metric can be quantitatively guaranteed by its causal contexts. For quantitative QoS guarantee,those causal relationships are first modeled quantitatively by BN parameter learning. Then,the QoS metric is guaranteed to certain value with a probability given its causal contexts tuned to suitable values,that is,quantitative QoS guarantee is reached. Simulations with three sequential stages：context discretization,QoS diagnosis and quantitative QoS guarantee,on a peer-to-peer （P2P） network,are discussed and our approach is validated to be effective.

关键词： context context discretization QoS qualitative diagnosis QoS quantitative guarantee Bayesian network

A Heuristic Approach for context-Aware Recommendation Using Rough Set Theory

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Chinese Journal of Electronics 2018年第3期27卷 500-506页

作者： HE Ming DONG Tao LIU Yi College of Computer Science Beijing University of Technology

context-aware recommender systems, aiming to further improve performance accuracy and user satisfaction by fully utilizing contextual information, have recently become one of the hottest topics in the domain of recommender systems. However, not all contextual information might be relevant or useful for recommendation purposes, and little work has been done on measuring how important the contextual information for recommendation.We propose a heuristic optimization algorithm based on rough set theory and collaborative filtering to using contextual information more efficiently for boosting recommendation. Our approach involves three processes. First, significant attributes to represent contextual information are extracted and measured to identify recommended items using rough set theory. Second, the user similarity is evaluated in a target context consideration. Third, collaborative filtering is applied to recommend appropriate items.We perform an empirical comparison of three approaches on two real-world data sets. The experimental results show that the proposed approach generates more accurate predictions.

关键词： Recommendation context context-aware recommendation Collaborative filtering Rough set

Model Failure and context Switching Using Logic-Based Stochastic Models

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Journal of Computer Science & Technology 2010年第4期25卷 665-680页

作者： Nikita A.Sakhanenko George F.Luger Institute for Systems Biology Computer Science University of New Mexico

This paper addresses parameter drift in stochastic models. We define a notion of context that represents invariant, stable-over-time behavior and we then propose an algorithm for detecting context changes in processing a stream of data. A context change is seen as model failure, when a probabilistic model representing current behavior is no longer able to ＂fit＂ newly encountered data. We specify our stochastic models using a first-order logic-based probabilistic modeling language called Generalized Loopy Logic （GLL）. An important component of GLL is its learning mechanism that can identify context drift. We demonstrate how our algorithm can be incorporated into a failure-driven context-switching probabilistic modeling framework and offer several examples of its application.

关键词： context failure-driven online learning probabilistic reasoning

What and where: A context-based recommendation system for object insertion

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Computational Visual Media 2020年第1期6卷 79-93页

作者： Song-Hai Zhang Zheng-Ping Zhou Bin Liu Xi Dong Peter Hall Tsinghua University Beijing 100084China Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijing 100084China Department of Computer Science Media Technology Research Center University of BathBath BA27AYUK

We propose a novel problem revolving around two tasks:(i)given a scene,recommend objects to insert,and(ii)given an object category,retrieve suitable background scenes.A bounding box for the inserted object is predicted in both tasks,which helps downstream applications such as semiautomated advertising and video composition.The major challenge lies in the fact that the target object is neither present nor localized in the input,and furthermore,available datasets only provide scenes with existing objects.To tackle this problem,we build an unsupervised algorithm based on object-level contexts,which explicitly models the joint probability distribution of object categories and bounding boxes using a Gaussian mixture model.Experiments on our own annotated test set demonstrate that our system outperforms existing baselines on all sub-tasks,and does so using a unified framework.Future extensions and applications are suggested.

关键词： object recommendation bounding box prediction image composition object-level context

A self-optimizing QoS-aware service composition approach in a context sensitive environment

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Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第3期12卷 221-238页

作者： Yuan-hong SHEN Xiao-hu YANG School of Computer Science and Technology Zhejiang University Hangzhou 310027 China

QoS-aware service composition is aimed to maximize the global QoS of a composite service when selecting candidate services.In a context sensitive service execution environment in pervasive computing,the context information for service composition is not static:device,policy,and user constraints,and QoS requirements may change,new services may be deployed,old ones withdrawn,or existing ones change their QoS parameters.This results in the current service composition plan failing or its QoS degrading from the optimum.In this paper,a runtime self-optimizing service composition framework is proposed.An implementation of a prototype for this framework is presented,addressing the issues of reducing extra delay while increasing global QoS in service composition in a dynamic context environment.Three service re-plan algorithms are compared that can be used in dynamic context environment,i.e.,minimal-conflict hill-climbing repair genetic algorithm(MCHC-repair GA),an improved penalty-based GA,and our multi-population conflicts sorted repair genetic algorithm(MP-CS-repair GA),as well as three kinds of service composition mechanisms-with backup,without backup,and our context-aware service re-selection mechanisms.The results show that our MP-CS-repair GA and context-aware service re-selection method can reduce more extra delay while acquiring a higher global QoS for the composite service in a context sensitive environment.This context-aware service re-selection mechanism also shows some adaptability to different context change frequencies and user requirements for reducing computation cost in the self-optimizing process.

关键词： Service composition Quality of service(QoS) context Repair genetic algorithm Self-optimizing

Minimum barrier distance based tracking via spatio-temporal context learning

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Optoelectronics Letters 2019年第1期15卷 75-80页

作者：杨志远吴斌 The State Key Laboratory of Precision Measuring Technology and Instruments Tianjin University

We propose an efficient and robust tracking method based on minimum barrier distance (MBD) and spatio-temporal context (STC) learning. It is robust to noise and blur, and can be evaluated approximately through a Dijkstra-like algorithm, leading to fast computation. We adopt the MBD transform to measure the weights of context pixels, and formulate the spatio-temporal relationship between the object and its surrounding region based on a Bayesian framework to estimate the most likely location of the target. A bounded scale update model is proposed to estimate the size of the object. The whole proposed method runs at nearly 160 frames per second (FPS) on an i5 machine. Extensive experimental results show it has comparable or better comprehensive performance than the original STC and some other leading methods.

关键词： Minimum barrier tracking context learning

CARM:context Based Association Rule Mining for Conventional Data

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Computers, Materials & Continua 2021年第9期68卷 3305-3322页

作者： Muhammad Shaheen Umair Abdullah Faculty of Engineering&Information Technology Foundation UniversityIslamabadPakistan Department of Computer Science Barani Institute of Information TechnologyRawalpindiPakistan

This paper is aimed to develop an algorithm for extracting association rules,called context-Based Association Rule Mining algorithm(CARM),which can be regarded as an extension of the context-Based Positive and Negative Association Rule Mining algorithm(CBPNARM).CBPNARM was developed to extract positive and negative association rules from Spatiotemporal(space-time)data only,while the proposed algorithm can be applied to both spatial and non-spatial data.The proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative associations.Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique.The context,in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data.Conditional Probability Increment Ratio(CPIR)is also added in the proposed algorithm that was not used in CBPNARM.The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use.Also,the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM.The rules created by the proposed algorithm are more meaningful,significant,relevant and insightful.The accuracy of the proposed algorithm is compared with the Apriori,PNARM and CBPNARM algorithms.The results demonstrated enhanced accuracy,relevance and timeliness.

关键词： Association rules context CBPNARM non-spatial data CPIR support confidence interestingness