This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI prog...This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI program in order to study the dynamic bending features of fabrics in a specific air flow filed. The computational fluid dynamics (CFD) model for flow and the finite element model (FEM) for fabric was set up to constitute an FSI model in which the geometric nonlinear behavior and the dynamic stress-strain variation of the relatively soft fabric material were taken into account. Several FSI cases with different time-dependent wind load and the model frequency analysis for fabric were carried out. The dynamic response of fabric and the distribution of fluid variables were investigated. The results of numerical simulation and experiments fit quite well. Hence, this work contributes to the research of modeling the dynamic bending behavior of fabrics in air field.展开更多
Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical appli...Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.展开更多
This paper focuses on the connection between the Flourishing Tang Features and Li Bai’s landscape poems,exploring their interactive relationship from three main dimensions.Firstly,the Flourishing Tang Dynasty,charact...This paper focuses on the connection between the Flourishing Tang Features and Li Bai’s landscape poems,exploring their interactive relationship from three main dimensions.Firstly,the Flourishing Tang Dynasty,characterized by political stability,economic prosperity,and cultural openness,nourished Li Bai’s landscape poems.Geographically,the expanded territory and smooth transportation broadened his creative vision,enabling his works to break away from the narrow local landscape and present a cosmic perspective covering landscapes like the Yellow River,Yangtze River,and Tianshan Mountains;spiritually,the era’s emphasis on active engagement in society shaped the core of his poems,transforming landscape writing from a tool for seclusion to a medium for expressing lofty aspirations.Secondly,Li Bai’s landscape poems projected the Flourishing Tang Features:Grand landscape images echoed the dynasty’s vast territory and national confidence;free artistic conceptions reflected the dynasty’s cultural inclusiveness and ideological emancipation;the integration of landscapes with ideals manifested the scholars’commitment to serving the country.Finally,the two achieved spiritual interaction:In spatial terms,the dynasty’s vast territory aligned with Li Bai’s broad spatial vision;in values,the era’s balance between engagement and freedom matched his combination of wandering and ambition;in spirit,the dynasty’s confidence resonated with his heroic and free emotions.In summary,the Flourishing Tang Features provided a creative foundation for Li Bai,while his landscape poems materialized and spread the Tang spirit,jointly forming a unique cultural legacy of the Tang Dynasty.展开更多
With the growing application of intelligent robots in service,manufacturing,and medical fields,efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user ...With the growing application of intelligent robots in service,manufacturing,and medical fields,efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user experience.Gesture recognition,as an intuitive and contactless interaction method,can overcome the limitations of traditional interfaces and enable real-time control and feedback of robot movements and behaviors.This study first reviews mainstream gesture recognition algorithms and their application on different sensing platforms(RGB cameras,depth cameras,and inertial measurement units).It then proposes a gesture recognition method based on multimodal feature fusion and a lightweight deep neural network that balances recognition accuracy with computational efficiency.At system level,a modular human-robot interaction architecture is constructed,comprising perception,decision,and execution layers,and gesture commands are transmitted and mapped to robot actions in real time via the ROS communication protocol.Through multiple comparative experiments on public gesture datasets and a self-collected dataset,the proposed method’s superiority is validated in terms of accuracy,response latency,and system robustness,while user-experience tests assess the interface’s usability.The results provide a reliable technical foundation for robot collaboration and service in complex scenarios,offering broad prospects for practical application and deployment.展开更多
In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,t...In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.展开更多
Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sough...Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sought)for use of interactivity features(“process motivation”)and how widely they are used.It also attempts to ascertain the gratification obtained from their use among readers.The probable relationships between use of the interactivity features(“audience interactivity”)and gratification obtained from them(“process gratification”)and the impact of the perceived credibility of the online newspapers on gratification are also examined.Past studies present mixed results on use of interactivity and gratification obtained from it.This study finds that use of interactivity in Zambian online newspapers is at a low level,although among the three broad categorisations of features of online newspapers,interactivity attracts greater use than hyper-textuality and multi-mediality.Human interactivity features-“knowing what others think about an issue”,“chat on the Facebook page of the newspaper”,“ability to navigate on the Facebook page of the newspaper”,and“posting own comments on stories”-are the main motivations for use of online newspapers,the most frequently used,and the most gratifying to the readers.While readers express an interest in interacting with other readers via online newspapers,they seem less interested in posting their own stories as“citizen journalists”and linking up with the publishers and editors.This finding challenges the notion that all new media are catalysts of participatory and cyclic communication.展开更多
The dynamical features of air-sea coupling waves and their stabilities in a simple coupled air-sea model in the tropics have been studied with respect to interaction occurring among different types of the free waves i...The dynamical features of air-sea coupling waves and their stabilities in a simple coupled air-sea model in the tropics have been studied with respect to interaction occurring among different types of the free waves in the o-cean and in the atmosphere. It is pointed out that there exist a stable and an unstable air-sea interaction modes in the tropical coupled system , respectively. The propagation of the unstable mode relies greatly on the zonal space scale, i. e. only for wave length ranging from 5 000 km to 10 000 km can the disturbance unstably move slowly eastward. The waves that slowly propagate unstably eastward agree well with the observational facts. Finally,it is also proposed that the interaction between Kelvin wave in one medium and Rossby wave in another medium is a necessary condition for the occurrence of destabilization of the coupled air-sea system in the tropics.展开更多
Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges...Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges mentioned above with a single model.To tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework.ST-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps simultaneously.Specifically,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene information.To preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder.Meanwhile,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed.Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,respectively.Therefore,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.展开更多
Three materials(agar,konjac glucomannan(KGM)andκ-carrageenan)were used to prepare ternary systems,i.e.,sol-gels and their dried composites conditioned at varied relative humidity(RH)(33%,54%and 75%).Combined methods,...Three materials(agar,konjac glucomannan(KGM)andκ-carrageenan)were used to prepare ternary systems,i.e.,sol-gels and their dried composites conditioned at varied relative humidity(RH)(33%,54%and 75%).Combined methods,e.g.,scanning electron microscopy,small-angle X-ray scattering,infrared spectroscopy(IR)and X-ray diffraction(XRD),were used to disclose howκ-carrageenan addition tailors the features of agar/KGM/κ-carrageenan ternary system.As affirmed by IR and XRD,the ternary systems withκ-carrageenan below 25%(agar/KGM/carrageenan,50:25:25,m/m)displayed proper component interactions,which increased the sol-gel transition temperature and the hardness of obtained gels.For instance,the ternary composites could show hardness about 3 to 4 times higher than that for binary counterpart.These gels were dehydrated to acquire ternary composites.Compared to agar/KGM composite,the ternary composites showed fewer crystallites and nanoscale orders,and newly-formed nanoscale structures from chain assembly.Such multi-scale structures,for composites withκ-carrageenan below 25%,showed weaker changes with RH,as revealed by especially morphologic and crystalline features.Consequently,the ternary composites with lessκ-carrageenan(below 25%)exhibited stabilized elongation at break and hydrophilicity at different RHs.This hints to us that agar/KGM/κ-carrageenan composite systems can display series applications with improved features,e.g.,increased sol-gel transition point.展开更多
Recently,vision-based gesture recognition(VGR)has become a hot research spot in human-computer interaction(HCI).Unlike other gesture recognition methods with data gloves or other wearable sensors,vision-based gesture ...Recently,vision-based gesture recognition(VGR)has become a hot research spot in human-computer interaction(HCI).Unlike other gesture recognition methods with data gloves or other wearable sensors,vision-based gesture recognition could lead to more natural and intuitive HCI interactions.This paper reviews the state-of-the-art vision-based gestures recognition methods,from different stages of gesture recognition process,i.e.,(1)image acquisition and pre-processing,(2)gesture segmentation,(3)gesture tracking,(4)feature extraction,and(5)gesture classification.This paper also analyzes the advantages and disadvantages of these various methods in detail.Finally,the challenges of vision-based gesture recognition in haptic rendering and future research directions are discussed.展开更多
This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature repr...This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.展开更多
In this article,we consider the(3+1)-dimensional generalized Kadomtsev–Petviashvili(GKP)equation in fluids.We show that a variety of nonlinear localized waves can be produced by the breath wave of the GKP model,such ...In this article,we consider the(3+1)-dimensional generalized Kadomtsev–Petviashvili(GKP)equation in fluids.We show that a variety of nonlinear localized waves can be produced by the breath wave of the GKP model,such as the(oscillating-)W-and M-shaped waves,rational W-shaped waves,multi-peak solitary waves,(quasi-)Bell-shaped and W-shaped waves and(quasi-)periodic waves.Based on the characteristic line analysis and nonlinear superposition principle,we give the transition conditions analytically.We find the interesting dynamic behavior of the converted nonlinear waves,which is known as the time-varying feature.We further offer explanations for such phenomenon.We then discuss the classification of the converted solutions.We finally investigate the interactions of the converted waves including the semi-elastic collision,perfectly elastic collision,inelastic collision and one-off collision.And the mechanisms of the collisions are analyzed in detail.The results could enrich the dynamic features of the high-dimensional nonlinear waves in fluids.展开更多
Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experi...Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such novelties.Drug-drug interaction(DDI)significantly contributed to drug administration and development.It continues as the main obstacle in offering inexpensive and safe healthcare.It normally happens for patients with extensive medication,leading them to take many drugs simultaneously.DDI may cause side effects,either mild or severe health problems.This reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery time.Several efforts were made to formulate new methods for DDI prediction to overcome this issue.In this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data environment.In the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for prediction.The input data is preprocessed at the primary level to improve its quality.Next,the salp swarm optimization algorithm(SSO)is used to select features.In this study,the deep belief network(DBN)model is exploited to predict the DDI accurately.The SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the work.The experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.展开更多
Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has at...Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image frame.The third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.展开更多
The paper concerns the issue of ELF （English as a lingua franca） in the European and Asian context. The authors start from a brief conceptual perspective to shed light on salient aspects related to ELF. Then, this p...The paper concerns the issue of ELF （English as a lingua franca） in the European and Asian context. The authors start from a brief conceptual perspective to shed light on salient aspects related to ELF. Then, this paper discusses the study investigating the interactions among NNS （non-native speakers） of English in the naturalistic settings, namely in Zhangjiajie （China）, Masouri （Kalymnos/Greece）, and Unterwasser （Switzerland）. The main objective of the research based on the qualitative methodology was to analyze the ELF interactions from the linguistic point of view focusing on lexicogrammar and pragmatic features. The secondary objective was to establish whether the identified ELF features contributed to communication intelligibility. The obtained results indicated a few significant similarities with the Seidlhofer＇s list of the ELT characteristics. Furthermore, it was established in the study that the ELF features did not interfere with effective communication between interlocutors展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
基金National Natural Science Foundations of China(No.50803010,No.60904056)
文摘This paper is devoted to the two-dimensional nonlinear modeling of the fluid-solid interaction (FSI) between fabric and air flow, which is based on the Automatic Incremental Dynamic Nonlinear Analysis (AIDNA)-FSI program in order to study the dynamic bending features of fabrics in a specific air flow filed. The computational fluid dynamics (CFD) model for flow and the finite element model (FEM) for fabric was set up to constitute an FSI model in which the geometric nonlinear behavior and the dynamic stress-strain variation of the relatively soft fabric material were taken into account. Several FSI cases with different time-dependent wind load and the model frequency analysis for fabric were carried out. The dynamic response of fabric and the distribution of fluid variables were investigated. The results of numerical simulation and experiments fit quite well. Hence, this work contributes to the research of modeling the dynamic bending behavior of fabrics in air field.
基金supported by the National Key R&D Program of China(Grant No.:2023YFC2604400)the National Natural Science Foundation of China(Grant No.:62103436).
文摘Identifying drug-drug interactions(DDIs)is essential to prevent adverse effects from polypharmacy.Although deep learning has advanced DDI identification,the gap between powerful models and their lack of clinical application and evaluation has hindered clinical benefits.Here,we developed a Multi-Dimensional Feature Fusion model named MDFF,which integrates one-dimensional simplified molec-ular input line entry system sequence features,two-dimensional molecular graph features,and three-dimensional geometric features to enhance drug representations for predicting DDIs.MDFF was trained and validated on two DDI datasets,evaluated across three distinct scenarios,and compared with advanced DDI prediction models using accuracy,precision,recall,area under the curve,and F1 score metrics.MDFF achieved state-of-the-art performance across all metrics.Ablation experiments showed that integrating multi-dimensional drug features yielded the best results.More importantly,we obtained adverse drug reaction reports uploaded by Xiangya Hospital of Central South University from 2021 to 2023 and used MDFF to identify potential adverse DDIs.Among 12 real-world adverse drug reaction reports,the predictions of 9 reports were supported by relevant evidence.Additionally,MDFF demon-strated the ability to explain adverse DDI mechanisms,providing insights into the mechanisms behind one specific report and highlighting its potential to assist practitioners in improving medical practice.
基金supported by the projects:Guangxi Philosophy and Social Sciences Research Projects:“A Study on the Imagery System of Yuexi Poetry Collection and the Mechanism of Generating Ecological Beauty Signs of Charming Scenery in Guangxi”,and Wuzhou University Education and Teaching Reform Projects:“Research on Innovative Teaching Mode and Evaluation of‘Poetry and Ritual Interaction’Under the Background of Skill and Emotional Deficiency(Wyjg2022A033)”“Research on Improving the Quality of Ancient Literature Curriculum Centered on Student Cognitive Ladder(Wyjg2023B016)”“Research on Innovative Integration of Information Technology and Ancient Literature Curriculum Under the Goal of New Literature Construction(Wyjg2024A018)”.
文摘This paper focuses on the connection between the Flourishing Tang Features and Li Bai’s landscape poems,exploring their interactive relationship from three main dimensions.Firstly,the Flourishing Tang Dynasty,characterized by political stability,economic prosperity,and cultural openness,nourished Li Bai’s landscape poems.Geographically,the expanded territory and smooth transportation broadened his creative vision,enabling his works to break away from the narrow local landscape and present a cosmic perspective covering landscapes like the Yellow River,Yangtze River,and Tianshan Mountains;spiritually,the era’s emphasis on active engagement in society shaped the core of his poems,transforming landscape writing from a tool for seclusion to a medium for expressing lofty aspirations.Secondly,Li Bai’s landscape poems projected the Flourishing Tang Features:Grand landscape images echoed the dynasty’s vast territory and national confidence;free artistic conceptions reflected the dynasty’s cultural inclusiveness and ideological emancipation;the integration of landscapes with ideals manifested the scholars’commitment to serving the country.Finally,the two achieved spiritual interaction:In spatial terms,the dynasty’s vast territory aligned with Li Bai’s broad spatial vision;in values,the era’s balance between engagement and freedom matched his combination of wandering and ambition;in spirit,the dynasty’s confidence resonated with his heroic and free emotions.In summary,the Flourishing Tang Features provided a creative foundation for Li Bai,while his landscape poems materialized and spread the Tang spirit,jointly forming a unique cultural legacy of the Tang Dynasty.
文摘With the growing application of intelligent robots in service,manufacturing,and medical fields,efficient and natural interaction between humans and robots has become key to improving collaboration efficiency and user experience.Gesture recognition,as an intuitive and contactless interaction method,can overcome the limitations of traditional interfaces and enable real-time control and feedback of robot movements and behaviors.This study first reviews mainstream gesture recognition algorithms and their application on different sensing platforms(RGB cameras,depth cameras,and inertial measurement units).It then proposes a gesture recognition method based on multimodal feature fusion and a lightweight deep neural network that balances recognition accuracy with computational efficiency.At system level,a modular human-robot interaction architecture is constructed,comprising perception,decision,and execution layers,and gesture commands are transmitted and mapped to robot actions in real time via the ROS communication protocol.Through multiple comparative experiments on public gesture datasets and a self-collected dataset,the proposed method’s superiority is validated in terms of accuracy,response latency,and system robustness,while user-experience tests assess the interface’s usability.The results provide a reliable technical foundation for robot collaboration and service in complex scenarios,offering broad prospects for practical application and deployment.
文摘In recent years,deep learning has been widely applied in the fields of recommendation systems and click-through rate(CTR)prediction,and thus recommendation models incorporating deep learning have emerged.In addition,the design and implementation of recommendation models using information related to user behavior sequences is an important direction of current research in recommendation systems,and models calculate the likelihood of users clicking on target items based on their behavior sequence information.In order to explore the relationship between features,this paper improves and optimizes on the basis of deep interest network(DIN)proposed by Ali’s team.Based on the user behavioral sequences information,the attentional factorization machine(AFM)is integrated to obtain richer and more accurate behavioral sequence information.In addition,this paper designs a new way of calculating attention weights,which uses the relationship between the cosine similarity of any two vectors and the absolute value of their modal length difference to measure their relevance degree.Thus,a novel deep learning CTR prediction mode is proposed,that is,the CTR prediction network based on user behavior sequence and feature interactions deep interest and machines network(DIMN).We conduct extensive comparison experiments on three public datasets and one private music dataset,which are more recognized in the industry,and the results show that the DIMN obtains a better performance compared with the classical CTR prediction model.
文摘Interactivity in online newspapers is the focus of this chapter in eliciting readers’evaluation of Zambian online newspapers.This aspect of the study investigates and characterises the motivations(gratification sought)for use of interactivity features(“process motivation”)and how widely they are used.It also attempts to ascertain the gratification obtained from their use among readers.The probable relationships between use of the interactivity features(“audience interactivity”)and gratification obtained from them(“process gratification”)and the impact of the perceived credibility of the online newspapers on gratification are also examined.Past studies present mixed results on use of interactivity and gratification obtained from it.This study finds that use of interactivity in Zambian online newspapers is at a low level,although among the three broad categorisations of features of online newspapers,interactivity attracts greater use than hyper-textuality and multi-mediality.Human interactivity features-“knowing what others think about an issue”,“chat on the Facebook page of the newspaper”,“ability to navigate on the Facebook page of the newspaper”,and“posting own comments on stories”-are the main motivations for use of online newspapers,the most frequently used,and the most gratifying to the readers.While readers express an interest in interacting with other readers via online newspapers,they seem less interested in posting their own stories as“citizen journalists”and linking up with the publishers and editors.This finding challenges the notion that all new media are catalysts of participatory and cyclic communication.
文摘The dynamical features of air-sea coupling waves and their stabilities in a simple coupled air-sea model in the tropics have been studied with respect to interaction occurring among different types of the free waves in the o-cean and in the atmosphere. It is pointed out that there exist a stable and an unstable air-sea interaction modes in the tropical coupled system , respectively. The propagation of the unstable mode relies greatly on the zonal space scale, i. e. only for wave length ranging from 5 000 km to 10 000 km can the disturbance unstably move slowly eastward. The waves that slowly propagate unstably eastward agree well with the observational facts. Finally,it is also proposed that the interaction between Kelvin wave in one medium and Rossby wave in another medium is a necessary condition for the occurrence of destabilization of the coupled air-sea system in the tropics.
基金Basic and Advanced Research Projects of CSTC,Grant/Award Number:cstc2019jcyj-zdxmX0008Science and Technology Research Program of Chongqing Municipal Education Commission,Grant/Award Numbers:KJQN202100634,KJZDK201900605National Natural Science Foundation of China,Grant/Award Number:62006065。
文摘Scene perception and trajectory forecasting are two fundamental challenges that are crucial to a safe and reliable autonomous driving(AD)system.However,most proposed methods aim at addressing one of the two challenges mentioned above with a single model.To tackle this dilemma,this paper proposes spatio-temporal semantics and interaction graph aggregation for multi-agent perception and trajectory forecasting(STSIGMA),an efficient end-to-end method to jointly and accurately perceive the AD environment and forecast the trajectories of the surrounding traffic agents within a unified framework.ST-SIGMA adopts a trident encoder-decoder architecture to learn scene semantics and agent interaction information on bird’s-eye view(BEV)maps simultaneously.Specifically,an iterative aggregation network is first employed as the scene semantic encoder(SSE)to learn diverse scene information.To preserve dynamic interactions of traffic agents,ST-SIGMA further exploits a spatio-temporal graph network as the graph interaction encoder.Meanwhile,a simple yet efficient feature fusion method to fuse semantic and interaction features into a unified feature space as the input to a novel hierarchical aggregation decoder for downstream prediction tasks is designed.Extensive experiments on the nuScenes data set have demonstrated that the proposed ST-SIGMA achieves significant improvements compared to the state-of-theart(SOTA)methods in terms of scene perception and trajectory forecasting,respectively.Therefore,the proposed approach outperforms SOTA in terms of model generalisation and robustness and is therefore more feasible for deployment in realworld AD scenarios.
基金the National Natural Science Foundation of China(32172240)BL19U2 beamline of National Facility for Protein Science in Shanghai(NFPS)at Shanghai Synchrotron Radiation Facility,for their assistance during data collection。
文摘Three materials(agar,konjac glucomannan(KGM)andκ-carrageenan)were used to prepare ternary systems,i.e.,sol-gels and their dried composites conditioned at varied relative humidity(RH)(33%,54%and 75%).Combined methods,e.g.,scanning electron microscopy,small-angle X-ray scattering,infrared spectroscopy(IR)and X-ray diffraction(XRD),were used to disclose howκ-carrageenan addition tailors the features of agar/KGM/κ-carrageenan ternary system.As affirmed by IR and XRD,the ternary systems withκ-carrageenan below 25%(agar/KGM/carrageenan,50:25:25,m/m)displayed proper component interactions,which increased the sol-gel transition temperature and the hardness of obtained gels.For instance,the ternary composites could show hardness about 3 to 4 times higher than that for binary counterpart.These gels were dehydrated to acquire ternary composites.Compared to agar/KGM composite,the ternary composites showed fewer crystallites and nanoscale orders,and newly-formed nanoscale structures from chain assembly.Such multi-scale structures,for composites withκ-carrageenan below 25%,showed weaker changes with RH,as revealed by especially morphologic and crystalline features.Consequently,the ternary composites with lessκ-carrageenan(below 25%)exhibited stabilized elongation at break and hydrophilicity at different RHs.This hints to us that agar/KGM/κ-carrageenan composite systems can display series applications with improved features,e.g.,increased sol-gel transition point.
基金Supported by the National Natural Science Foundation of China(61773205,61773219)the Fundamental Research Funds for the Central Universities(NS2016032,NS2019018,Nanjing University of Aeronautics and Astronautics)+1 种基金the Scholarship from China Scholarship Council(201906835020)the Fundamental Research Funds for the Central Universities(the Graduate Student Innovation Base Open Fund Project of NUAA,kfjj20190307)。
文摘Recently,vision-based gesture recognition(VGR)has become a hot research spot in human-computer interaction(HCI).Unlike other gesture recognition methods with data gloves or other wearable sensors,vision-based gesture recognition could lead to more natural and intuitive HCI interactions.This paper reviews the state-of-the-art vision-based gestures recognition methods,from different stages of gesture recognition process,i.e.,(1)image acquisition and pre-processing,(2)gesture segmentation,(3)gesture tracking,(4)feature extraction,and(5)gesture classification.This paper also analyzes the advantages and disadvantages of these various methods in detail.Finally,the challenges of vision-based gesture recognition in haptic rendering and future research directions are discussed.
基金Project(51678075)supported by the National Natural Science Foundation of ChinaProject(2017GK2271)supported by Hunan Provincial Science and Technology Department,China
文摘This paper proposed a novel multi-view interactive behavior recognition method based on local self-similarity descriptors and graph shared multi-task learning. First, we proposed the composite interactive feature representation which encodes both the spatial distribution of local motion of interest points and their contexts. Furthermore, local self-similarity descriptor represented by temporal-pyramid bag of words(BOW) was applied to decreasing the influence of observation angle change on recognition and retaining the temporal information. For the purpose of exploring latent correlation between different interactive behaviors from different views and retaining specific information of each behaviors, graph shared multi-task learning was used to learn the corresponding interactive behavior recognition model. Experiment results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA, i3Dpose dataset and self-built database for interactive behavior recognition.
基金supported by the National Natural Science Foundation of China(Grant Nos.11875126,61705006,and 11947230)the China Postdoctoral Science Foundation(No.2019M660430).
文摘In this article,we consider the(3+1)-dimensional generalized Kadomtsev–Petviashvili(GKP)equation in fluids.We show that a variety of nonlinear localized waves can be produced by the breath wave of the GKP model,such as the(oscillating-)W-and M-shaped waves,rational W-shaped waves,multi-peak solitary waves,(quasi-)Bell-shaped and W-shaped waves and(quasi-)periodic waves.Based on the characteristic line analysis and nonlinear superposition principle,we give the transition conditions analytically.We find the interesting dynamic behavior of the converted nonlinear waves,which is known as the time-varying feature.We further offer explanations for such phenomenon.We then discuss the classification of the converted solutions.We finally investigate the interactions of the converted waves including the semi-elastic collision,perfectly elastic collision,inelastic collision and one-off collision.And the mechanisms of the collisions are analyzed in detail.The results could enrich the dynamic features of the high-dimensional nonlinear waves in fluids.
文摘Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such novelties.Drug-drug interaction(DDI)significantly contributed to drug administration and development.It continues as the main obstacle in offering inexpensive and safe healthcare.It normally happens for patients with extensive medication,leading them to take many drugs simultaneously.DDI may cause side effects,either mild or severe health problems.This reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery time.Several efforts were made to formulate new methods for DDI prediction to overcome this issue.In this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data environment.In the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for prediction.The input data is preprocessed at the primary level to improve its quality.Next,the salp swarm optimization algorithm(SSO)is used to select features.In this study,the deep belief network(DBN)model is exploited to predict the DDI accurately.The SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the work.The experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2023-2018-0-01426)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)This work has also been supported by PrincessNourah bint Abdulrahman UniversityResearchers Supporting Project Number(PNURSP2022R239),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.Alsothis work was partially supported by the Taif University Researchers Supporting Project Number(TURSP-2020/115),Taif University,Taif,Saudi Arabia.
文摘Identifying human actions and interactions finds its use in manyareas, such as security, surveillance, assisted living, patient monitoring, rehabilitation,sports, and e-learning. This wide range of applications has attractedmany researchers to this field. Inspired by the existing recognition systems,this paper proposes a new and efficient human-object interaction recognition(HOIR) model which is based on modeling human pose and scene featureinformation. There are different aspects involved in an interaction, includingthe humans, the objects, the various body parts of the human, and the backgroundscene. Themain objectives of this research include critically examiningthe importance of all these elements in determining the interaction, estimatinghuman pose through image foresting transform (IFT), and detecting the performedinteractions based on an optimizedmulti-feature vector. The proposedmethodology has six main phases. The first phase involves preprocessing theimages. During preprocessing stages, the videos are converted into imageframes. Then their contrast is adjusted, and noise is removed. In the secondphase, the human-object pair is detected and extracted from each image frame.The third phase involves the identification of key body parts of the detectedhumans using IFT. The fourth phase relates to three different kinds of featureextraction techniques. Then these features are combined and optimized duringthe fifth phase. The optimized vector is used to classify the interactions in thelast phase. TheMSRDaily Activity 3D dataset has been used to test this modeland to prove its efficiency. The proposed system obtains an average accuracyof 91.7% on this dataset.
文摘The paper concerns the issue of ELF （English as a lingua franca） in the European and Asian context. The authors start from a brief conceptual perspective to shed light on salient aspects related to ELF. Then, this paper discusses the study investigating the interactions among NNS （non-native speakers） of English in the naturalistic settings, namely in Zhangjiajie （China）, Masouri （Kalymnos/Greece）, and Unterwasser （Switzerland）. The main objective of the research based on the qualitative methodology was to analyze the ELF interactions from the linguistic point of view focusing on lexicogrammar and pragmatic features. The secondary objective was to establish whether the identified ELF features contributed to communication intelligibility. The obtained results indicated a few significant similarities with the Seidlhofer＇s list of the ELT characteristics. Furthermore, it was established in the study that the ELF features did not interfere with effective communication between interlocutors
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
