Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Re...Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning.展开更多
The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,...The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,the effect of the interactions between perception factors on residents'willingness to evacuate is an urgent problem to be solved.Therefore,this paper introduces risk,stakeholder,and protective action perceptions from the protective action decision model as the main explanatory variables.These three core perceptions are subdivided into affective risk perception,cognitive risk perception,government perception,other-stakeholder perception,resourcerelated attributes,and hazard-related attributes.A questionnaire survey was conducted from June to July 2023 among residents of mountainous communities in nine villages in three towns in Sichuan Province,China.359 cross-sectional data were analyzed using structural equation modeling to explore the effects of six perception factors on evacuation intentions.The results of the study showed that:(1)affective risk perception,government perception,other-stakeholder perception,and hazard-related attributes all directly and positively influence residents'intentions to evacuate;(2)cognitive risk perception is mediated by stakeholder and protective action perceptions,which indirectly and positively affect residents'intentions to evacuate.Based on the hypothesized paths,strategies to improve residents'willingness to evacuate are discussed from the perspective of three core perceptions:strengthening disaster risk education,improving residents'cohesion,and building government credibility.The results of this study can provide theoretical support and practical suggestions for emergency management departments to formulate emergency evacuation strategies,which can aid decision-makers in better understanding residents'intentions to evacuate,optimizing evacuation information dissemination pathways,and strengthening disaster risk management capabilities.展开更多
The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular ...The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular visual system, the real visual images of the object will be obtained. Then through the mobile self-organizing network, a three-dimensional model is rebuilt by synthesizing the returned images. On this basis, we formalize a novel algorithm for multichannel binocular visual three-dimensional images based on fast three-dimensional modeling. Compared with the method based on single binocular visual system, the new algorithm can improve the Integrity and accuracy of the dynamic three-dimensional object modeling. The simulation results show that the new method can effectively accelerate the modeling speed, improve the similarity and not increase the data size.展开更多
Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location ...Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.展开更多
By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help s...By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.展开更多
The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of ...The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of Gen AI in EFL writing.The research involved semi-structured interviews with 13 students and 10 EFL teachers.Thematic analysis,guided by the Technology Acceptance Model(TAM),was employed to analyze the qualitative data.The findings reveal the perceptions of students and teachers regarding the role of generative AI in EFL writing.Regarding usefulness,students appreciate Gen AI for reducing writing difficulty and enhancing efficiency,though some note that it may produce logical flaws and misinformation.Teachers share similar perceptions,but stress effectiveness depends on students’language level.Some teachers also advocate traditional writing initially to build foundational skills.On the ease of use,most students find it easy interacting with Gen AI but mention dialogical understanding challenges.Both students and teachers stress clear prompts are crucial,indicating“AI interaction literacy”should be part of teaching.Moreover,teachers worry that Gen AI’s ease of use may lead to over-reliance.These results reveal contradicting goals of using Gen AI:students value efficiency,while teachers focus on ability cultivation.These insights guide more effective integration of Gen AI in EFL writing education.展开更多
With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social rela...With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social relations model (SRM) [1], this paper investigates interpersonal perception in virtual groups from a multilevel perspective. In particular, it examines the following three areas: homophily, identification, and individual attraction, and explores how much of these directional and dyadic relational evaluations can be attributed to the effect of the actor, the partner, and the relationship.展开更多
Objective To enhance the quality of medical service for Chinese patients through research of service quality from Chinese medical personnel. Methods Serv Qual scale was used for infection medical staffs randomly by sa...Objective To enhance the quality of medical service for Chinese patients through research of service quality from Chinese medical personnel. Methods Serv Qual scale was used for infection medical staffs randomly by sampling questionnaire in Beijing, Shanghai, Chengdu, Chongqing, Guangzhou and Nanning. The data collected were entered and analyzed using SPSS 20.0. Statistical methods included frequency, factor analysis, reliability analysis, correlation analysis, independent samples t test, one-way analyses of variance, simultaneous regression analysis and structural equation model analysis. Results The Kaiser-Meyer-Olkin value for the factor analysis of the scale was 0.970. The Cronbach's α for the reliability analysis was 0.975. The Pearson correlation coefficients were 0.624-0.874 and statistically significant. Undergraduates felt good, Ph D students felt bad; the doctors felt bad; managers felt good. Standard 5 dimensions of the regression coefficients were positive, including empathy(β = 0.288), reliability(β = 0.241) impacting on perceived service quality mostly. The control ability and stability of the standard error of perceived service quality directly effected value were 0.646 and 0.382, respectively. Conclusions Medical staffs of infectious disease department have poor perception of service quality. Hospitals should improve awareness and of clinicians and deepen the reform of the medical care system.展开更多
This paper describes the basic connotation of risk perception, the influence factors of the risk perception for agricultural drought and the mainstream assess- ment model. Additionally, it summarizes the latest develo...This paper describes the basic connotation of risk perception, the influence factors of the risk perception for agricultural drought and the mainstream assess- ment model. Additionally, it summarizes the latest developments of research meth- ods for risk perception for the agricultural drought, and the research status of the risk perception for agricultural drought, and put forward the trends of risk perception for the agricultural drought. Finally, it proposes the research areas of the risk per- ception for agricultural drought should be improved in future.展开更多
With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much att...With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.展开更多
Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forec...Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells.展开更多
With the rapid development of rural tourism in China,community residents,as important stakeholders in the development of rural tourism,their perceptions and attitudes directly affect the sustainable and healthy develo...With the rapid development of rural tourism in China,community residents,as important stakeholders in the development of rural tourism,their perceptions and attitudes directly affect the sustainable and healthy development of local rural tourism.Taking the community residents of Xiaogucheng Village in Hangzhou as the research object,using the methods of field interviews and questionnaires,a multiple regression model was established to conduct an empirical analysis on the perception and main factors affecting the development of rural tourism of community residents.The results show that the development of rural tourism in villages with better economic development is not as popular as expected;Where community residents have made ideological progress and are willing to participate in tourism development,the development effect of rural tourism is remarkable;In addition,community residents also hope that their personal abilities can be combined with rural tourism for common development;The destruction of community environment has a slight impact on the development of rural tourism,which shows that the attention is not enough.Finally,based on the analysis conclusion,it provides new ideas and inspiration for the sustainable development of rural tourism:improving the community residents’participation in rural tourism system,establishing the guidance mechanism of community residents’tourism vocational education,and consolidating the achievements of community ecological environment management.展开更多
Despite the existence of colorectal cancer (CRC) screening guidelines, population-based studies have consistently shown under-utilization of CRC screening procedures among older adults in the United States. We examine...Despite the existence of colorectal cancer (CRC) screening guidelines, population-based studies have consistently shown under-utilization of CRC screening procedures among older adults in the United States. We examined whether symptoms of anxiety and depression are associated with colorectal cancer (CRC) screening perceptions and behaviors among older adults in a primary care setting. A cross-sectional study was conducted by using a sample of 143 family medicine patients who completed an 88-item anonymous self-administered questionnaire covering symptoms of anxiety and depression as well as CRC screening perceptions (defined based on the Health Belief Model) and behaviors (defined as ever use of or adherence to CRC testing). Moderate-to-clinically significant anxiety and depressive symptoms were, respectively, prevalent in 47% and 42% of participants. Perceived benefits and barriers were the only Health Belief Model constructs associated with anxiety. Perceived barriers were positively associated with anxiety symptoms after adjustment for confounders, including age, gender, race/ ethnicity, marital status, education, smoking history, body mass index and self-rated health. By contrast, perceived benefits were negatively associated with anxiety symptoms only in the unadjusted model. Neither anxiety nor depression was associated with ever use of or adherence to CRC testing. Symptoms of anxiety, but not depression, may potentially influence CRC screening perceptions, with implications for behavioral interventions targeting CRC testing.展开更多
The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natu...The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.展开更多
Thurstone’s Comparative Judgment Model was applied to measure characteristics of tourists’after-trip perception of landscape preference in the Confucius Temple,a famous historical block in Nanjing City.The results s...Thurstone’s Comparative Judgment Model was applied to measure characteristics of tourists’after-trip perception of landscape preference in the Confucius Temple,a famous historical block in Nanjing City.The results show that(a)as time goes by,the tourists’time perception differentiation has continuously sublimated from the general experience into the peak experience,and gradually evolved into the core experience element in the overall perception.In terms of content,perceptional contents of tourists decrease in sequence of the Confucian culture,the commercial culture and the culture of refined scholars.As a whole,tourists’after-trip perception differentiation has 3 sections:halo zone,sub-halo zone,and gray zone.(b)Because of tourism development,the Confucian culture is influenced by other cultures,and the commercial culture shows the trend of"over-generalization",and the culture of refined scholars has weakening carriers and modes of inheritance.Inheritance of its unique cultural connotations deserves increasing attention.展开更多
The enhancement of adhesive perception is crucial to maintaining a stable and comfortable grip of the skin-touch products.To study the tactile perception of adhesive surfaces,subjective evaluation,skin friction and vi...The enhancement of adhesive perception is crucial to maintaining a stable and comfortable grip of the skin-touch products.To study the tactile perception of adhesive surfaces,subjective evaluation,skin friction and vibrations,and neurophysiological response of the brain activity were investigated systematically.Silicone materials,which are commonly used for bionic materials and skin-touch products,were chosen for the tactile stimulus.The results showed that with the increasing of surface adhesion,the dominant friction transferred from a combination of adhesive friction and deformation friction to adhesive friction.The friction coefficient and vibration amplitude had strong correlations with the perceived adhesion of surfaces.The parietal lobe and occipital lobe were involved in adhesive perceptions,and the area and intensity of brain activation increased with the increasing surface adhesion.Surfaces with larger adhesion tended to excite a high P300 amplitude and short latency,indicating that the judgment was faster and that more attentional resources were involved in adhesive perception.Furthermore,the electroencephalograph signals of the adhesive perception were simulated by the neural mass model.It demonstrated that the excitability and intensity of brain activity,and the connectivity strength between two neural masses increased with the increasing surface adhesion.This study is meaningful to understand the role of surface adhesion in tactile friction and the cognitive mechanism in adhesive perception to improve the tactile experience of adhesive materials.展开更多
The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spa...The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.展开更多
To build robots that engage in intuitive communication with people by natural language, we are developing a new knowledge representation called conceptual network model. The conceptual network connects natural languag...To build robots that engage in intuitive communication with people by natural language, we are developing a new knowledge representation called conceptual network model. The conceptual network connects natural language concepts with visual perception including color perception, shape perception, size perception, and spatial perception. In the implementation of spatial perception, we present a computational model based on spatial template theory to interpret qualitative spatial expressions. Based on the conceptual network model, our mobile robot can understand user's instructions and recognize the object referred to by the user and perform appropriate action. Experimental results show our approach promising.展开更多
Many high schools in the northeastern United States have suffered from declining enrollment due to a declining population of school-age children in the region.Administrators,counselors and teachers perceive many impac...Many high schools in the northeastern United States have suffered from declining enrollment due to a declining population of school-age children in the region.Administrators,counselors and teachers perceive many impacts of the implementation of an international student program at a rural high school.This qualitative case study reveals that the rural high school’s unique nature impacts international student programs,international students influence both school culture and programming,and international students suffer from isolation.School leaders would benefit from considerations in the form of professional development both prior to implementation of an international student program and ongoing throughout its duration.It is important for schools to find ways to respectfully honor student cultures and optimize learning experiences.School leaders would be wise to determine desired size of international student programs and make efforts accordingly to achieve that size for optimal effectiveness for the benefit of both host and international students.Implications and recommendations for future research include further exploration of the perceived experiences of European and Chinese international students,understanding the unique nature of rural high schools and international student experiences,and examination of school practices.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
文摘Knowledge distillation has become a standard technique for compressing large language models into efficient student models,but existing methods often struggle to balance prediction accuracy with explanation quality.Recent approaches such as Distilling Step-by-Step(DSbS)introduce explanation supervision,yet they apply it in a uniform manner that may not fully exploit the different learning dynamics of prediction and explanation.In this work,we propose a task-structured curriculum learning(TSCL)framework that structures training into three sequential phases:(i)prediction-only,to establish stable feature representations;(ii)joint prediction-explanation,to align task outputs with rationale generation;and(iii)explanation-only,to refine the quality of rationales.This design provides a simple but effective modification to DSbS,requiring no architectural changes and adding negligible training cost.We justify the phase scheduling with ablation studies and convergence analysis,showing that an initial prediction-heavy stage followed by a balanced joint phase improves both stability and explanation alignment.Extensive experiments on five datasets(e-SNLI,ANLI,CommonsenseQA,SVAMP,and MedNLI)demonstrate that TSCL consistently outperforms strong baselines,achieving gains of+1.7-2.6 points in accuracy and 0.8-1.2 in ROUGE-L,corresponding to relative error reductions of up to 21%.Beyond lexical metrics,human evaluation and ERASERstyle faithfulness diagnostics confirm that TSCL produces more faithful and informative explanations.Comparative training curves further reveal faster convergence and lower variance across seeds.Efficiency analysis shows less than 3%overhead in wall-clock training time and no additional inference cost,making the approach practical for realworld deployment.This study demonstrates that a simple task-structured curriculum can significantly improve the effectiveness of knowledge distillation.By separating and sequencing objectives,TSCL achieves a better balance between accuracy,stability,and explanation quality.The framework generalizes across domains,including medical NLI,and offers a principled recipe for future applications in multimodal reasoning and reinforcement learning.
基金supported by the National Natural Science Foundation of China(U20A20111)the National key R&D Program(2022YFC3080100)。
文摘The evacuation of people under threat is an effective disaster prevention and mitigation measure in response to flash floods and geological hazards,and it is also an essential element of pre-disaster planning.However,the effect of the interactions between perception factors on residents'willingness to evacuate is an urgent problem to be solved.Therefore,this paper introduces risk,stakeholder,and protective action perceptions from the protective action decision model as the main explanatory variables.These three core perceptions are subdivided into affective risk perception,cognitive risk perception,government perception,other-stakeholder perception,resourcerelated attributes,and hazard-related attributes.A questionnaire survey was conducted from June to July 2023 among residents of mountainous communities in nine villages in three towns in Sichuan Province,China.359 cross-sectional data were analyzed using structural equation modeling to explore the effects of six perception factors on evacuation intentions.The results of the study showed that:(1)affective risk perception,government perception,other-stakeholder perception,and hazard-related attributes all directly and positively influence residents'intentions to evacuate;(2)cognitive risk perception is mediated by stakeholder and protective action perceptions,which indirectly and positively affect residents'intentions to evacuate.Based on the hypothesized paths,strategies to improve residents'willingness to evacuate are discussed from the perspective of three core perceptions:strengthening disaster risk education,improving residents'cohesion,and building government credibility.The results of this study can provide theoretical support and practical suggestions for emergency management departments to formulate emergency evacuation strategies,which can aid decision-makers in better understanding residents'intentions to evacuate,optimizing evacuation information dissemination pathways,and strengthening disaster risk management capabilities.
基金supported by HiTech Researchand Development Program of China under Grant No.2007AA10Z235
文摘The dynamic multichannel binocular visual image modeling is studied based on Internet of Things (IoT) Perception Layer, using mobile robot self-organizing network. By employing multigroup mobile robots with binocular visual system, the real visual images of the object will be obtained. Then through the mobile self-organizing network, a three-dimensional model is rebuilt by synthesizing the returned images. On this basis, we formalize a novel algorithm for multichannel binocular visual three-dimensional images based on fast three-dimensional modeling. Compared with the method based on single binocular visual system, the new algorithm can improve the Integrity and accuracy of the dynamic three-dimensional object modeling. The simulation results show that the new method can effectively accelerate the modeling speed, improve the similarity and not increase the data size.
文摘Vehicle recognition system (VRS) plays a very important role in the field of intelligent transportation systems.A novel and intuitive method is proposed for vehicle location.The method we provide for vehicle location is based on human visual perception model technique. The perception color space HSI in this algorithm is adopted.Three color components of a color image and more potential edge patterns are integrated for solving the feature extraction problem.A fast and automatic threshold technique based on human visual perception model is also developed.The vertical edge projection and horizontal edge projection are adopted for locating left-right boundary of vehicle and top-bottom boundary of vehicle, respectively. Very promising experimental results are obtained using real-time vehicle image sequences, which have confirmed that this proposed location vehicle method is efficient and reliable, and its calculation speed meets the needs of the VRS.
文摘By comparing price plans offered by several retail energy firms,end users with smart meters and controllers may optimize their energy use cost portfolios,due to the growth of deregulated retail power markets.To help smart grid end-users decrease power payment and usage unhappiness,this article suggests a decision system based on reinforcement learning to aid with electricity price plan selection.An enhanced state-based Markov decision process(MDP)without transition probabilities simulates the decision issue.A Kernel approximate-integrated batch Q-learning approach is used to tackle the given issue.Several adjustments to the sampling and data representation are made to increase the computational and prediction performance.Using a continuous high-dimensional state space,the suggested approach can uncover the underlying characteristics of time-varying pricing schemes.Without knowing anything regarding the market environment in advance,the best decision-making policy may be learned via case studies that use data from actual historical price plans.Experiments show that the suggested decision approach may reduce cost and energy usage dissatisfaction by using user data to build an accurate prediction strategy.In this research,we look at how smart city energy planners rely on precise load forecasts.It presents a hybrid method that extracts associated characteristics to improve accuracy in residential power consumption forecasts using machine learning(ML).It is possible to measure the precision of forecasts with the use of loss functions with the RMSE.This research presents a methodology for estimating smart home energy usage in response to the growing interest in explainable artificial intelligence(XAI).Using Shapley Additive explanations(SHAP)approaches,this strategy makes it easy for consumers to comprehend their energy use trends.To predict future energy use,the study employs gradient boosting in conjunction with long short-term memory neural networks.
文摘The advancement of generative AI has reshaped EFL education,particularly in EFL writing.This qualitative case study investigates the perceptions of Chinese college students and EFL teachers towards the integration of Gen AI in EFL writing.The research involved semi-structured interviews with 13 students and 10 EFL teachers.Thematic analysis,guided by the Technology Acceptance Model(TAM),was employed to analyze the qualitative data.The findings reveal the perceptions of students and teachers regarding the role of generative AI in EFL writing.Regarding usefulness,students appreciate Gen AI for reducing writing difficulty and enhancing efficiency,though some note that it may produce logical flaws and misinformation.Teachers share similar perceptions,but stress effectiveness depends on students’language level.Some teachers also advocate traditional writing initially to build foundational skills.On the ease of use,most students find it easy interacting with Gen AI but mention dialogical understanding challenges.Both students and teachers stress clear prompts are crucial,indicating“AI interaction literacy”should be part of teaching.Moreover,teachers worry that Gen AI’s ease of use may lead to over-reliance.These results reveal contradicting goals of using Gen AI:students value efficiency,while teachers focus on ability cultivation.These insights guide more effective integration of Gen AI in EFL writing education.
文摘With the penetration of the Internet, virtual groups have become more and more popular. The reliability and accuracy of interpersonal perception in the virtual environment is an intriguing issue. Using the Social relations model (SRM) [1], this paper investigates interpersonal perception in virtual groups from a multilevel perspective. In particular, it examines the following three areas: homophily, identification, and individual attraction, and explores how much of these directional and dyadic relational evaluations can be attributed to the effect of the actor, the partner, and the relationship.
基金supported by the year 2014 Key Research Project of the Party of the Education and Health of Shanghai(NO:201420)Scientific Research in Hospital Construction Project of Chinese Medical Doctor Assoclation
文摘Objective To enhance the quality of medical service for Chinese patients through research of service quality from Chinese medical personnel. Methods Serv Qual scale was used for infection medical staffs randomly by sampling questionnaire in Beijing, Shanghai, Chengdu, Chongqing, Guangzhou and Nanning. The data collected were entered and analyzed using SPSS 20.0. Statistical methods included frequency, factor analysis, reliability analysis, correlation analysis, independent samples t test, one-way analyses of variance, simultaneous regression analysis and structural equation model analysis. Results The Kaiser-Meyer-Olkin value for the factor analysis of the scale was 0.970. The Cronbach's α for the reliability analysis was 0.975. The Pearson correlation coefficients were 0.624-0.874 and statistically significant. Undergraduates felt good, Ph D students felt bad; the doctors felt bad; managers felt good. Standard 5 dimensions of the regression coefficients were positive, including empathy(β = 0.288), reliability(β = 0.241) impacting on perceived service quality mostly. The control ability and stability of the standard error of perceived service quality directly effected value were 0.646 and 0.382, respectively. Conclusions Medical staffs of infectious disease department have poor perception of service quality. Hospitals should improve awareness and of clinicians and deepen the reform of the medical care system.
基金Supported by the National Natural Foundation of China(4161100)the Fund Program of Yunnan University(2013CG011)~~
文摘This paper describes the basic connotation of risk perception, the influence factors of the risk perception for agricultural drought and the mainstream assess- ment model. Additionally, it summarizes the latest developments of research meth- ods for risk perception for the agricultural drought, and the research status of the risk perception for agricultural drought, and put forward the trends of risk perception for the agricultural drought. Finally, it proposes the research areas of the risk per- ception for agricultural drought should be improved in future.
文摘With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.
基金supported by the Science and Technology Grant No.520120210003,Jibei Electric Power Company of the State Grid Corporation of China。
文摘Convective storms and lightning are among the most important weather phenomena that are challenging to forecast.In this study,a novel multi-task learning(MTL)encoder-decoder U-net neural network was developed to forecast convective storms and lightning with lead times for up to 90 min,using GOES-16 geostationary satellite infrared brightness temperatures(IRBTs),lightning flashes from Geostationary Lightning Mapper(GLM),and vertically integrated liquid(VIL)from Next Generation Weather Radar(NEXRAD).To cope with the heavily skewed distribution of lightning data,a spatiotemporal exponent-weighted loss function and log-transformed lightning normalization approach were developed.The effects of MTL,single-task learning(STL),and IRBTs as auxiliary input features on convection and lightning nowcasting were investigated.The results showed that normalizing the heavily skew-distributed lightning data along with a log-transformation dramatically outperforms the min-max normalization method for nowcasting an intense lightning event.The MTL model significantly outperformed the STL model for both lightning nowcasting and VIL nowcasting,particularly for intense lightning events.The MTL also helped delay the lightning forecast performance decay with the lead times.Furthermore,incorporating satellite IRBTs as auxiliary input features substantially improved lightning nowcasting,but produced little difference in VIL forecasting.Finally,the MTL model performed better for forecasting both lightning and the VIL of organized convective storms than for isolated cells.
基金supported by the Soft Science Project of Zhejiang Province(Grant No.2020C 35084)Scientific Research Project of Qianjiang College of Hangzhou Normal University
文摘With the rapid development of rural tourism in China,community residents,as important stakeholders in the development of rural tourism,their perceptions and attitudes directly affect the sustainable and healthy development of local rural tourism.Taking the community residents of Xiaogucheng Village in Hangzhou as the research object,using the methods of field interviews and questionnaires,a multiple regression model was established to conduct an empirical analysis on the perception and main factors affecting the development of rural tourism of community residents.The results show that the development of rural tourism in villages with better economic development is not as popular as expected;Where community residents have made ideological progress and are willing to participate in tourism development,the development effect of rural tourism is remarkable;In addition,community residents also hope that their personal abilities can be combined with rural tourism for common development;The destruction of community environment has a slight impact on the development of rural tourism,which shows that the attention is not enough.Finally,based on the analysis conclusion,it provides new ideas and inspiration for the sustainable development of rural tourism:improving the community residents’participation in rural tourism system,establishing the guidance mechanism of community residents’tourism vocational education,and consolidating the achievements of community ecological environment management.
文摘Despite the existence of colorectal cancer (CRC) screening guidelines, population-based studies have consistently shown under-utilization of CRC screening procedures among older adults in the United States. We examined whether symptoms of anxiety and depression are associated with colorectal cancer (CRC) screening perceptions and behaviors among older adults in a primary care setting. A cross-sectional study was conducted by using a sample of 143 family medicine patients who completed an 88-item anonymous self-administered questionnaire covering symptoms of anxiety and depression as well as CRC screening perceptions (defined based on the Health Belief Model) and behaviors (defined as ever use of or adherence to CRC testing). Moderate-to-clinically significant anxiety and depressive symptoms were, respectively, prevalent in 47% and 42% of participants. Perceived benefits and barriers were the only Health Belief Model constructs associated with anxiety. Perceived barriers were positively associated with anxiety symptoms after adjustment for confounders, including age, gender, race/ ethnicity, marital status, education, smoking history, body mass index and self-rated health. By contrast, perceived benefits were negatively associated with anxiety symptoms only in the unadjusted model. Neither anxiety nor depression was associated with ever use of or adherence to CRC testing. Symptoms of anxiety, but not depression, may potentially influence CRC screening perceptions, with implications for behavioral interventions targeting CRC testing.
文摘The process of human natural scene categorization consists of two correlated stages: visual perception and visual cognition of natural scenes.Inspired by this fact,we propose a biologically plausible approach for natural scene image classification.This approach consists of one visual perception model and two visual cognition models.The visual perception model,composed of two steps,is used to extract discriminative features from natural scene images.In the first step,we mimic the oriented and bandpass properties of human primary visual cortex by a special complex wavelets transform,which can decompose a natural scene image into a series of 2D spatial structure signals.In the second step,a hybrid statistical feature extraction method is used to generate gist features from those 2D spatial structure signals.Then we design a cognitive feedback model to realize adaptive optimization for the visual perception model.At last,we build a multiple semantics based cognition model to imitate human cognitive mode in rapid natural scene categorization.Experiments on natural scene datasets show that the proposed method achieves high efficiency and accuracy for natural scene classification.
基金Sponsored by National Natural Science Foundation(41271149)Colleges and Universities Philosophy,Social Sciences Foundation of Jiangxi Provincial Department of Education(2012SJB790028)2013 Key Program of Nanjing Institute of Industry Technology(YK13-05-03)
文摘Thurstone’s Comparative Judgment Model was applied to measure characteristics of tourists’after-trip perception of landscape preference in the Confucius Temple,a famous historical block in Nanjing City.The results show that(a)as time goes by,the tourists’time perception differentiation has continuously sublimated from the general experience into the peak experience,and gradually evolved into the core experience element in the overall perception.In terms of content,perceptional contents of tourists decrease in sequence of the Confucian culture,the commercial culture and the culture of refined scholars.As a whole,tourists’after-trip perception differentiation has 3 sections:halo zone,sub-halo zone,and gray zone.(b)Because of tourism development,the Confucian culture is influenced by other cultures,and the commercial culture shows the trend of"over-generalization",and the culture of refined scholars has weakening carriers and modes of inheritance.Inheritance of its unique cultural connotations deserves increasing attention.
基金support from the National Natural Science Foundation of China(Nos.52375224 and 51875566)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘The enhancement of adhesive perception is crucial to maintaining a stable and comfortable grip of the skin-touch products.To study the tactile perception of adhesive surfaces,subjective evaluation,skin friction and vibrations,and neurophysiological response of the brain activity were investigated systematically.Silicone materials,which are commonly used for bionic materials and skin-touch products,were chosen for the tactile stimulus.The results showed that with the increasing of surface adhesion,the dominant friction transferred from a combination of adhesive friction and deformation friction to adhesive friction.The friction coefficient and vibration amplitude had strong correlations with the perceived adhesion of surfaces.The parietal lobe and occipital lobe were involved in adhesive perceptions,and the area and intensity of brain activation increased with the increasing surface adhesion.Surfaces with larger adhesion tended to excite a high P300 amplitude and short latency,indicating that the judgment was faster and that more attentional resources were involved in adhesive perception.Furthermore,the electroencephalograph signals of the adhesive perception were simulated by the neural mass model.It demonstrated that the excitability and intensity of brain activity,and the connectivity strength between two neural masses increased with the increasing surface adhesion.This study is meaningful to understand the role of surface adhesion in tactile friction and the cognitive mechanism in adhesive perception to improve the tactile experience of adhesive materials.
文摘The human motion generation model can extract structural features from existing human motion capture data,and the generated data makes animated characters move.The 3D human motion capture sequences contain complex spatial-temporal structures,and the deep learning model can fully describe the potential semantic structure of human motion.To improve the authenticity of the generated human motion sequences,we propose a multi-task motion generation model that consists of a discriminator and a generator.The discriminator classifies motion sequences into different styles according to their similarity to the mean spatial-temporal templates from motion sequences of 17 crucial human joints in three-freedom degrees.And target motion sequences are created with these styles by the generator.Unlike traditional related works,our model can handle multiple tasks,such as identifying styles and generating data.In addition,by extracting 17 crucial joints from 29 human joints,our model avoids data redundancy and improves the accuracy of model recognition.The experimental results show that the discriminator of the model can effectively recognize diversified movements,and the generated data can correctly fit the actual data.The combination of discriminator and generator solves the problem of low reuse rate of motion data,and the generated motion sequences are more suitable for actual movement.
文摘To build robots that engage in intuitive communication with people by natural language, we are developing a new knowledge representation called conceptual network model. The conceptual network connects natural language concepts with visual perception including color perception, shape perception, size perception, and spatial perception. In the implementation of spatial perception, we present a computational model based on spatial template theory to interpret qualitative spatial expressions. Based on the conceptual network model, our mobile robot can understand user's instructions and recognize the object referred to by the user and perform appropriate action. Experimental results show our approach promising.
文摘Many high schools in the northeastern United States have suffered from declining enrollment due to a declining population of school-age children in the region.Administrators,counselors and teachers perceive many impacts of the implementation of an international student program at a rural high school.This qualitative case study reveals that the rural high school’s unique nature impacts international student programs,international students influence both school culture and programming,and international students suffer from isolation.School leaders would benefit from considerations in the form of professional development both prior to implementation of an international student program and ongoing throughout its duration.It is important for schools to find ways to respectfully honor student cultures and optimize learning experiences.School leaders would be wise to determine desired size of international student programs and make efforts accordingly to achieve that size for optimal effectiveness for the benefit of both host and international students.Implications and recommendations for future research include further exploration of the perceived experiences of European and Chinese international students,understanding the unique nature of rural high schools and international student experiences,and examination of school practices.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
