In order to study whether the random events set can be used in Rudongbank of Nantong or not, we use ADCIRC model to stimulate the storm surge affectingRudong bank based on random events set. Then we use p-III curve to...In order to study whether the random events set can be used in Rudongbank of Nantong or not, we use ADCIRC model to stimulate the storm surge affectingRudong bank based on random events set. Then we use p-III curve to fit peak-value ofsurge of all the years to get the surge of typical return periods. The result shows that theresults of fitting by ADCIRC and by historical data coincide well in lower return periods,but to higher return periods, the results of fitting by ADCIRC are significantly higher thanthat of fitting by historical data. Due to the short time, it’s not enough for the extremestorm surge events to occur, the results of higher return periods are not reliable, so wecan’t rule out the reasonability of results based on random events set. The results offitting based on random events set are accurate in lower return periods and we can alsofully estimate the surge of higher return periods based on random events set. In thesituation of lacking historical data of hundreds of years, random events set can beaccepted as a tool to compute the return period of storm surge. Consideration of globalwarming, the possibility of super typhoons’ appearance will rise, which will result inhigher surge of return periods. In order to prevent the disaster of storm surge, thegovernment needs to deepen and reinforce the coastal engineering like seawalls and embankments.展开更多
Automatically answer math word problems is a challenging task in artificial intelligence.Previous solvers constructed mathematical expressions in sequence or binary tree.However,these approaches may suffer from the fo...Automatically answer math word problems is a challenging task in artificial intelligence.Previous solvers constructed mathematical expressions in sequence or binary tree.However,these approaches may suffer from the following issues:Models relying on such structures exhibit fixed-order reasoning(e.g.,left-to-right),limiting flexibility and increasing error susceptibility;prior models rely on autoregressive reasoning in a single pass,accumulating minor errors(e.g.,incorrect math symbols)during generation,resulting in reduced accuracy.To address the above issues,we emulate the human“check and modify”process in reasoning and propose a unified M-tree self-correction solver(UTSCSolver)by iterative inference with self-correction mechanism.First,we use an iterative,non-autoregressive process for generating mathematical expressions,free from fixed generation orders to handle complex and diverse problems.Additionally,we design a self-correction mechanism based on alternating execution between a generator and a discriminator.This module iteratively detects and rectifies errors in generated expressions,leveraging previous iteration information for subsequent generation guidance.Experimental results show that our UTSC-Solver outperforms traditional models in accuracy on two popular datasets,while it improves the interpretability of mathematical reasoning.展开更多
真题回顾(2024·海南·中考真题)A hug(拥抱)is a form of human touch that happens when two or more people hold each other closely.People hug for many different reasons in their lives.For example,if a child is sad...真题回顾(2024·海南·中考真题)A hug(拥抱)is a form of human touch that happens when two or more people hold each other closely.People hug for many different reasons in their lives.For example,if a child is sad,a parent may hug him or her to give comfort.Grown-ups may hug to show each other love.Friends may hug to show friendship.Members of a team may hug after winning a game to show happiness and encourage other team members.展开更多
In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban...In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield environments.By combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on it.Due to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases significantly.Therefore,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network architecture.Experimental results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target detection.The research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.展开更多
In Christianity,the passion and resurrection of Jesus Christ are a fact of history.If his resurrection is a miracle to be accepted by faith,no rational demonstration of it is needed,although the Apostle Paul argues by...In Christianity,the passion and resurrection of Jesus Christ are a fact of history.If his resurrection is a miracle to be accepted by faith,no rational demonstration of it is needed,although the Apostle Paul argues by analogy for the resurrection in 1 Corinthians.Being a realist and using Latin,Aquinas holds that human reason can contribute to an understanding of faith;he has no strict distinction between hades and hell.He uses logos to emphasize reason and instrumental causality in explaining the relationship between humanity and divinity for Jesus.Arguing for the resurrection of Jesus,Aquinas should be consistent with his principle of the individualization of a soul through a body,and a separate soul being a substance,but he is inconsistent.Considering Jesus’soul before his resurrection,Aquinas supports the Apostles’Creed,but he develops the notion of purgatory,where departed souls sojourn temporarily.This paper argues that Aquinas,in discussing the passion and resurrection of Jesus Christ,obscures the distinction he draws between faith and reason.展开更多
The production of ferroalloys is a resource-intensive and energy-consuming process.To mitigate its adverse environmental effects,steel companies should implement a range of measures aiming at enhancing the utilization...The production of ferroalloys is a resource-intensive and energy-consuming process.To mitigate its adverse environmental effects,steel companies should implement a range of measures aiming at enhancing the utilization rate of ferroalloys.Therefore,a comprehensive ferroalloy model was proposed,incorporating a prediction model for alloying element yield based on case-based reasoning and support vector machine(CBR-SVM),along with a ferroalloy batching model employing an integral linear programming algorithm.In simulation calculations,the prediction model exhibited exceptional predictive performance,with a hit rate of 96.05%within 5%.The linear programming ingredient model proved effective in reducing costs by 20.7%,which was achieved through accurate adjustments to the types and quantities of ferroalloys.The proposed method and system were successfully implemented in the actual production environment of a specific steel plant,operating seamlessly for six months.This implementation has notably increased the product quality of the enterprise,with the control rate of high-quality products increasing from 46%to 79%,effectively diminishing the consumption and expenses associated with ferroalloys.The reduced usage of ferroalloys simultaneously reduces energy consumption and mitigates the adverse environmental impact of the steel industry.展开更多
Smart learning environments have been considered as vital sources and essential needs in modern digital education systems.With the rapid proliferation of smart and assistive technologies,smart learning processes have ...Smart learning environments have been considered as vital sources and essential needs in modern digital education systems.With the rapid proliferation of smart and assistive technologies,smart learning processes have become quite convenient,comfortable,and financially affordable.This shift has led to the emergence of pervasive computing environments,where user’s intelligent behavior is supported by smart gadgets;however,it is becoming more challenging due to inconsistent behavior of Artificial intelligence(AI)assistive technologies in terms of networking issues,slow user responses to technologies and limited computational resources.This paper presents a context-aware predictive reasoning based formalism for smart learning environments that facilitates students in managing their academic as well as extra-curricular activities autonomously with limited human intervention.This system consists of a three-tier architecture including the acquisition of the contextualized information from the environment autonomously,modeling the system using Web Ontology Rule Language(OWL 2 RL)and Semantic Web Rule Language(SWRL),and perform reasoning to infer the desired goals whenever and wherever needed.For contextual reasoning,we develop a non-monotonic reasoning based formalism to reason with contextual information using rule-based reasoning.The focus is on distributed problem solving,where context-aware agents exchange information using rule-based reasoning and specify constraints to accomplish desired goals.To formally model-check and simulate the system behavior,we model the case study of a smart learning environment in the UPPAAL model checker and verify the desired properties in the model,such as safety,liveness and robust properties to reflect the overall correctness behavior of the system with achieving the minimum analysis time of 0.002 s and 34,712 KB memory utilization.展开更多
From AI-powered chatbots capable of deep reasoning to humanoid robots equipped with intelligent“brains”for complex services,technological advancements continue to astonish us at an unprecedented pace.The rapid devel...From AI-powered chatbots capable of deep reasoning to humanoid robots equipped with intelligent“brains”for complex services,technological advancements continue to astonish us at an unprecedented pace.The rapid development of artificial intelligence(AI)is reshaping industries,enhancing productivity,and offering new possibilities for an intelligent life.展开更多
The rapid development of AI is unlocking new opportunities across industries and driving innovation.FROM chatbots capable of deep reasoning to humanoid robots equipped with intelligent“brains”for complex services,te...The rapid development of AI is unlocking new opportunities across industries and driving innovation.FROM chatbots capable of deep reasoning to humanoid robots equipped with intelligent“brains”for complex services,technological advancements continue to astonish us at an unprecedented pace.The rapid development of artificial intelligence(AI)is reshaping industries,enhancing productivity,and offering new possibilities for an intelligent life.展开更多
The cross-modal person re-identification task aims to match visible and infrared images of the same individual.The main challenges in this field arise from significant modality differences between individuals and the ...The cross-modal person re-identification task aims to match visible and infrared images of the same individual.The main challenges in this field arise from significant modality differences between individuals and the lack of high-quality cross-modal correspondence methods.Existing approaches often attempt to establish modality correspondence by extracting shared features across different modalities.However,these methods tend to focus on local information extraction and fail to fully leverage the global identity information in the cross-modal features,resulting in limited correspondence accuracy and suboptimal matching performance.To address this issue,we propose a quadratic graph matching method designed to overcome the challenges posed by modality differences through precise cross-modal relationship alignment.This method transforms the cross-modal correspondence problem into a graph matching task and minimizes the matching cost using a center search mechanism.Building on this approach,we further design a block reasoning module to uncover latent relationships between person identities and optimize the modality correspondence results.The block strategy not only improves the efficiency of updating gallery images but also enhances matching accuracy while reducing computational load.Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods on the SYSU-MM01,RegDB,and RGBNT201 datasets,achieving excellent matching accuracy and robustness,thereby validating its effectiveness in cross-modal person re-identification.展开更多
This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to im...This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.展开更多
The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitatio...The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.展开更多
This study examines how generative artificial intelligence(AI)reshapes creative identity in design education.Drawing on post-humanist and network-based theories,it frames AI as a cognitive collaborator in ideation and...This study examines how generative artificial intelligence(AI)reshapes creative identity in design education.Drawing on post-humanist and network-based theories,it frames AI as a cognitive collaborator in ideation and authorship.Mixed-methods data reveal student anxiety and stylistic confusion,contrasted with designers’adaptive strategies.The AI–Cognition–Identity framework supports curricula that promote reflective,ethical,and epistemically informed AI-integrated pedagogy.展开更多
Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,su...Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,susceptibility to catastrophic forgetting,and a deficiency in logical reasoning capabilities within black-box models.To address these challenges,we draw insights from human memory mechanisms to introduce“machine memory,”which we define as a storage structure formed by encoding external information into a machine-representable and computable format.Centered on machine memory,we propose the brand-new machine memory intelligence(M^(2)I)framework,which encompasses representation,learning,and reasoning modules and loops.We explore the key issues and recent advances in the four core aspects of M^(2)I,including neural mechanisms,associative representation,continual learning,and collaborative reasoning within machine memory.M^(2)I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models,driving a qualitative leap from weak to strong AI.展开更多
BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To inv...BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients.展开更多
In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential r...In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)methodology.The proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the attributes.DE optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each alternative.Then the score values of alternatives are computed based on the aggregated q-RLDFVs.An alternative with the maximum score value is selected as a better one.The applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning management.Moreover,we have validated the proposed approach with a numerical example.Finally,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.展开更多
This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal...This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.展开更多
文摘In order to study whether the random events set can be used in Rudongbank of Nantong or not, we use ADCIRC model to stimulate the storm surge affectingRudong bank based on random events set. Then we use p-III curve to fit peak-value ofsurge of all the years to get the surge of typical return periods. The result shows that theresults of fitting by ADCIRC and by historical data coincide well in lower return periods,but to higher return periods, the results of fitting by ADCIRC are significantly higher thanthat of fitting by historical data. Due to the short time, it’s not enough for the extremestorm surge events to occur, the results of higher return periods are not reliable, so wecan’t rule out the reasonability of results based on random events set. The results offitting based on random events set are accurate in lower return periods and we can alsofully estimate the surge of higher return periods based on random events set. In thesituation of lacking historical data of hundreds of years, random events set can beaccepted as a tool to compute the return period of storm surge. Consideration of globalwarming, the possibility of super typhoons’ appearance will rise, which will result inhigher surge of return periods. In order to prevent the disaster of storm surge, thegovernment needs to deepen and reinforce the coastal engineering like seawalls and embankments.
基金supported by the National Natural Science Foundation of China(62106244)the Fundamental Research Funds for the Central Universities(WK2150110021)the University Synergy Innovation Program of Anhui Province(GXXT-2022-042).
文摘Automatically answer math word problems is a challenging task in artificial intelligence.Previous solvers constructed mathematical expressions in sequence or binary tree.However,these approaches may suffer from the following issues:Models relying on such structures exhibit fixed-order reasoning(e.g.,left-to-right),limiting flexibility and increasing error susceptibility;prior models rely on autoregressive reasoning in a single pass,accumulating minor errors(e.g.,incorrect math symbols)during generation,resulting in reduced accuracy.To address the above issues,we emulate the human“check and modify”process in reasoning and propose a unified M-tree self-correction solver(UTSCSolver)by iterative inference with self-correction mechanism.First,we use an iterative,non-autoregressive process for generating mathematical expressions,free from fixed generation orders to handle complex and diverse problems.Additionally,we design a self-correction mechanism based on alternating execution between a generator and a discriminator.This module iteratively detects and rectifies errors in generated expressions,leveraging previous iteration information for subsequent generation guidance.Experimental results show that our UTSC-Solver outperforms traditional models in accuracy on two popular datasets,while it improves the interpretability of mathematical reasoning.
文摘真题回顾(2024·海南·中考真题)A hug(拥抱)is a form of human touch that happens when two or more people hold each other closely.People hug for many different reasons in their lives.For example,if a child is sad,a parent may hug him or her to give comfort.Grown-ups may hug to show each other love.Friends may hug to show friendship.Members of a team may hug after winning a game to show happiness and encourage other team members.
基金supported by the National Natural Science Foundation of China(61806024,62206257)the Jilin Province Science and Technology Development Plan Key Research and Development Project(20210204050YY)+1 种基金the Wuxi University Research Start-up Fund for Introduced Talents(2023r004,2023r006)Jiangsu Engineering Research Center of Hyperconvergence Application and Security of IoT Devices,Jiangsu Foreign Expert Workshop,Wuxi City Internet of Vehicles Key Laboratory.
文摘In this paper,a reasoning enhancement method based on RGCN(Relational Graph Convolutional Network)is proposed to improve the detection capability of UAV(Unmanned Aerial Vehicle)on fast-moving military targets in urban battlefield environments.By combining military images with the publicly available VisDrone2019 dataset,a new dataset called VisMilitary was built and multiple YOLO(You Only Look Once)models were tested on it.Due to the low confidence problem caused by fuzzy targets,the performance of traditional YOLO models on real battlefield images decreases significantly.Therefore,we propose an improved RGCN inference model,which improves the performance of the model in complex environments by optimizing the data processing and graph network architecture.Experimental results show that the proposed method achieves an improvement of 0.4%to 1.7%on mAP@0.50,which proves the effectiveness of the model in military target detection.The research of this paper provides a new technical path for UAV target detection in urban battlefield,and provides important enlightenment for the application of deep learning in military field.
基金The Study of the Separation of Judaism and Early Christianity on the Texts,Ideas,and Community(犹太教和早期基督教“文本、思想和社群”的分离研究),awarded by the Ministry of Education of the People’s Republic of China,Number:22JJD73001.
文摘In Christianity,the passion and resurrection of Jesus Christ are a fact of history.If his resurrection is a miracle to be accepted by faith,no rational demonstration of it is needed,although the Apostle Paul argues by analogy for the resurrection in 1 Corinthians.Being a realist and using Latin,Aquinas holds that human reason can contribute to an understanding of faith;he has no strict distinction between hades and hell.He uses logos to emphasize reason and instrumental causality in explaining the relationship between humanity and divinity for Jesus.Arguing for the resurrection of Jesus,Aquinas should be consistent with his principle of the individualization of a soul through a body,and a separate soul being a substance,but he is inconsistent.Considering Jesus’soul before his resurrection,Aquinas supports the Apostles’Creed,but he develops the notion of purgatory,where departed souls sojourn temporarily.This paper argues that Aquinas,in discussing the passion and resurrection of Jesus Christ,obscures the distinction he draws between faith and reason.
基金supported by the National Natural Science Foundation of China(No.52174297).
文摘The production of ferroalloys is a resource-intensive and energy-consuming process.To mitigate its adverse environmental effects,steel companies should implement a range of measures aiming at enhancing the utilization rate of ferroalloys.Therefore,a comprehensive ferroalloy model was proposed,incorporating a prediction model for alloying element yield based on case-based reasoning and support vector machine(CBR-SVM),along with a ferroalloy batching model employing an integral linear programming algorithm.In simulation calculations,the prediction model exhibited exceptional predictive performance,with a hit rate of 96.05%within 5%.The linear programming ingredient model proved effective in reducing costs by 20.7%,which was achieved through accurate adjustments to the types and quantities of ferroalloys.The proposed method and system were successfully implemented in the actual production environment of a specific steel plant,operating seamlessly for six months.This implementation has notably increased the product quality of the enterprise,with the control rate of high-quality products increasing from 46%to 79%,effectively diminishing the consumption and expenses associated with ferroalloys.The reduced usage of ferroalloys simultaneously reduces energy consumption and mitigates the adverse environmental impact of the steel industry.
基金supported by the National Research Foundation(NRF),Republic of Korea,under project BK21 FOUR(4299990213939).
文摘Smart learning environments have been considered as vital sources and essential needs in modern digital education systems.With the rapid proliferation of smart and assistive technologies,smart learning processes have become quite convenient,comfortable,and financially affordable.This shift has led to the emergence of pervasive computing environments,where user’s intelligent behavior is supported by smart gadgets;however,it is becoming more challenging due to inconsistent behavior of Artificial intelligence(AI)assistive technologies in terms of networking issues,slow user responses to technologies and limited computational resources.This paper presents a context-aware predictive reasoning based formalism for smart learning environments that facilitates students in managing their academic as well as extra-curricular activities autonomously with limited human intervention.This system consists of a three-tier architecture including the acquisition of the contextualized information from the environment autonomously,modeling the system using Web Ontology Rule Language(OWL 2 RL)and Semantic Web Rule Language(SWRL),and perform reasoning to infer the desired goals whenever and wherever needed.For contextual reasoning,we develop a non-monotonic reasoning based formalism to reason with contextual information using rule-based reasoning.The focus is on distributed problem solving,where context-aware agents exchange information using rule-based reasoning and specify constraints to accomplish desired goals.To formally model-check and simulate the system behavior,we model the case study of a smart learning environment in the UPPAAL model checker and verify the desired properties in the model,such as safety,liveness and robust properties to reflect the overall correctness behavior of the system with achieving the minimum analysis time of 0.002 s and 34,712 KB memory utilization.
文摘From AI-powered chatbots capable of deep reasoning to humanoid robots equipped with intelligent“brains”for complex services,technological advancements continue to astonish us at an unprecedented pace.The rapid development of artificial intelligence(AI)is reshaping industries,enhancing productivity,and offering new possibilities for an intelligent life.
文摘The rapid development of AI is unlocking new opportunities across industries and driving innovation.FROM chatbots capable of deep reasoning to humanoid robots equipped with intelligent“brains”for complex services,technological advancements continue to astonish us at an unprecedented pace.The rapid development of artificial intelligence(AI)is reshaping industries,enhancing productivity,and offering new possibilities for an intelligent life.
文摘The cross-modal person re-identification task aims to match visible and infrared images of the same individual.The main challenges in this field arise from significant modality differences between individuals and the lack of high-quality cross-modal correspondence methods.Existing approaches often attempt to establish modality correspondence by extracting shared features across different modalities.However,these methods tend to focus on local information extraction and fail to fully leverage the global identity information in the cross-modal features,resulting in limited correspondence accuracy and suboptimal matching performance.To address this issue,we propose a quadratic graph matching method designed to overcome the challenges posed by modality differences through precise cross-modal relationship alignment.This method transforms the cross-modal correspondence problem into a graph matching task and minimizes the matching cost using a center search mechanism.Building on this approach,we further design a block reasoning module to uncover latent relationships between person identities and optimize the modality correspondence results.The block strategy not only improves the efficiency of updating gallery images but also enhances matching accuracy while reducing computational load.Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods on the SYSU-MM01,RegDB,and RGBNT201 datasets,achieving excellent matching accuracy and robustness,thereby validating its effectiveness in cross-modal person re-identification.
基金supported by the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091)Seok-Won Lee’s work was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2024-RS-2023-00255968)grant funded by the Korea government(MSIT).
文摘This research addresses the performance challenges of ontology-based context-aware and activity recognition techniques in complex environments and abnormal activities,and proposes an optimized ontology framework to improve recognition accuracy and computational efficiency.The method in this paper adopts the event sequence segmentation technique,combines location awareness with time interval reasoning,and improves human activity recognition through ontology reasoning.Compared with the existing methods,the framework performs better when dealing with uncertain data and complex scenes,and the experimental results show that its recognition accuracy is improved by 15.6%and processing time is reduced by 22.4%.In addition,it is found that with the increase of context complexity,the traditional ontology inferencemodel has limitations in abnormal behavior recognition,especially in the case of high data redundancy,which tends to lead to a decrease in recognition accuracy.This study effectively mitigates this problem by optimizing the ontology matching algorithm and combining parallel computing and deep learning techniques to enhance the activity recognition capability in complex environments.
基金supported by the Natural Science Foundation of China(Nos.U22A2099,62273113,62203461,62203365)the Innovation Project of Guangxi Graduate Education under Grant YCBZ2023130by the Guangxi Higher Education Undergraduate Teaching Reform Project Key Project,grant number 2022JGZ130.
文摘The evidential reasoning(ER)rule framework has been widely applied in multi-attribute decision analysis and system assessment to manage uncertainty.However,traditional ER implementations rely on two critical limitations:1)unrealistic assumptions of complete evidence independence,and 2)a lack of mechanisms to differentiate causal relationships from spurious correlations.Existing similarity-based approaches often misinterpret interdependent evidence,leading to unreliable decision outcomes.To address these gaps,this study proposes a causality-enhanced ER rule(CER-e)framework with three key methodological innovations:1)a multidimensional causal representation of evidence to capture dependency structures;2)probabilistic quantification of causal strength using transfer entropy,a model-free information-theoretic measure;3)systematic integration of causal parameters into the ER inference process while maintaining evidential objectivity.The PC algorithm is employed during causal discovery to eliminate spurious correlations,ensuring robust causal inference.Case studies in two types of domains—telecommunications network security assessment and structural risk evaluation—validate CER-e’s effectiveness in real-world scenarios.Under simulated incomplete information conditions,the framework demonstrates superior algorithmic robustness compared to traditional ER.Comparative analyses show that CER-e significantly improves both the interpretability of causal relationships and the reliability of assessment results,establishing a novel paradigm for integrating causal inference with evidential reasoning in complex system evaluation.
文摘This study examines how generative artificial intelligence(AI)reshapes creative identity in design education.Drawing on post-humanist and network-based theories,it frames AI as a cognitive collaborator in ideation and authorship.Mixed-methods data reveal student anxiety and stylistic confusion,contrasted with designers’adaptive strategies.The AI–Cognition–Identity framework supports curricula that promote reflective,ethical,and epistemically informed AI-integrated pedagogy.
基金supported by the National Natural Science Foun-dation of China(62137002,62250009,62202367,82025020,and 82230072).
文摘Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,susceptibility to catastrophic forgetting,and a deficiency in logical reasoning capabilities within black-box models.To address these challenges,we draw insights from human memory mechanisms to introduce“machine memory,”which we define as a storage structure formed by encoding external information into a machine-representable and computable format.Centered on machine memory,we propose the brand-new machine memory intelligence(M^(2)I)framework,which encompasses representation,learning,and reasoning modules and loops.We explore the key issues and recent advances in the four core aspects of M^(2)I,including neural mechanisms,associative representation,continual learning,and collaborative reasoning within machine memory.M^(2)I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models,driving a qualitative leap from weak to strong AI.
基金Supported by Wuxi Taihu Talent Project,No.WXTTP 2021the General Scientific Research Program of Wuxi Municipal Health Commission,No.M202447.
文摘BACKGROUND Patients with major depression(MD)exhibit conditional reasoning dysfunction;however,no studies on the event-related potential(ERP)characteristics of conditional reasoning in MD have been reported.AIM To investigate the ERP characteristics of conditional reasoning in MD patients and explore the neural mechanism of cognitive processing.METHODS Thirty-four patients with MD and 34 healthy controls(HCs)completed ERP measurements while performing the Wason selection task(WST).The clusterbased permutation test in FieldTrip was used to compare the differences in the mean amplitudes between the patients with MD and HCs on the ERP components under different experimental conditions.Behavioral data[accuracy(ACC)and reaction times(RTs)],the ERP P100 and late positive potentials(LPPs)were analyzed.RESULTS Although the mean ACC was greater and the mean of RTs was shorter in HCs than in MD patients,the differences were not statistically significant.However,across both groups,the ACC in the precautionary WST was greater than that in the other tasks,and the RTs in the abstract task were greater than those in the other tasks.Importantly,compared with that of HCs,the P100 of the left centroparietal sites was significantly increased,and the early LPP was attenuated at parietal sites and increased at left frontocentral sites;the medium LPP and late LPP were increased at the left frontocentral sites.CONCLUSION Patients with MD have conditional reasoning dysfunction and exhibit abnormal ERP characteristics evoked by the WST,which suggests neural correlates of abnormalities in conditional reasoning function in MD patients.
文摘In this paper,a robust and consistent COVID-19 emergency decision-making approach is proposed based on q-rung linear diophantine fuzzy set(q-RLDFS),differential evolutionary(DE)optimization principles,and evidential reasoning(ER)methodology.The proposed approach uses q-RLDFS in order to represent the evaluating values of the alternatives corresponding to the attributes.DE optimization is used to obtain the optimal weights of the attributes,and ER methodology is used to compute the aggregated q-rung linear diophantine fuzzy values(q-RLDFVs)of each alternative.Then the score values of alternatives are computed based on the aggregated q-RLDFVs.An alternative with the maximum score value is selected as a better one.The applicability of the proposed approach has been illustrated in COVID-19 emergency decision-making system and sustainable energy planning management.Moreover,we have validated the proposed approach with a numerical example.Finally,a comparative study is provided with the existing models,where the proposed approach is found to be robust to perform better and consistent in uncertain environments.
基金supported by the National Key Research&Development Program of China(2021YFB3301100)the National Natural Science Foundation of China(52004014)the Fundamental Research Funds for the Central Universities(ZY2406).
文摘This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.
