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
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
In Part 2 of David Hume’s Dialogues Concerning Natural Religion,Cleanthes puts forth the analogical Argument from Design,the argument intended to establish that the designer of the world possesses an intelligence sim...In Part 2 of David Hume’s Dialogues Concerning Natural Religion,Cleanthes puts forth the analogical Argument from Design,the argument intended to establish that the designer of the world possesses an intelligence similar to human intelligence,in light of Cleanthes’claim that the design of the world resembles machines of human contrivance.Philo argues that this argument fails,because the world does not bear a specific resemblance to any type of machine,and,therefore,there is no basis for reasoning analogically to an intelligent cause of design.In Part 3,Cleanthes attempts to strengthen his case through two illustrative analogies:I will examine the first of these-the Articulate Voice speaking from the clouds.Scholarship generally regards the Articulate Voice illustration to fail,precisely because nothing in this illustrative analogy assists Philo in understanding that the world is a machine.My paper/talk reveals that Philo provides additional criticisms of the Articulate Voice illustration in Parts 6 and 7 of the Dialogues,which make Philo’s critique even stronger and more enlightening regarding his critical approach to the Design Argument than can be learned from Part 2 alone.展开更多
Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,ca...Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.展开更多
Objective This study aimed to investigate the level of knowledge among urologists of usage of fluoroscopy during percutaneous nephrolithotomy.MethodsWe conducted an anonymous internet-based survey addressed to the EAU...Objective This study aimed to investigate the level of knowledge among urologists of usage of fluoroscopy during percutaneous nephrolithotomy.MethodsWe conducted an anonymous internet-based survey addressed to the EAU Section of Uro-Technology and the International Alliance of Urolithiasis members with particular interest in the stone treatment at all levels of expertise.The final version of the questionnaire included 31 questions,evaluated the level of knowledge on X-ray utilization and exposure,and identified correlations between geographic areas,levels of seniority,surgical volumes,and awareness on radiation protection.ResultsIn total,586 respondents were included.Knowledge of fluoroscopy settings appeared low,particularly among trainees(up to 87.5%were uninformed,p=0.008).Precautions to reduce exposure appeared poorly followed as up to 25.4%of respondents used regularly continuous fluoroscopy,and up to 20.5%used regularly high-frequency setting and this trend was more obvious among senior specialists(6.2%of trainees used high-frequency settings vs.21.3%of consultants,p<0.05).Additionally,only 24.9%of respondents would provide X-ray protection to patients too.ConclusionAlthough high and routinary utilization of X-rays,the level of awareness and adhesion to“as low as reasonably achievable”principles among endourologists seems suboptimal in 65.0%of all respondents.Highest volume surgeons,inevitably at higher risk,do not seem to adopt more precautions.More efforts should be addressed to improve these results,reducing the risk related to excessive radiation exposure for both surgical staff and patients in order to minimize health related issues.展开更多
Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artific...Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.展开更多
This study investigated the relationship between parental cognitive ability and child logical reasoning ability,and the role of academic expectation and family environment in that relationship.Based on the 2020 China ...This study investigated the relationship between parental cognitive ability and child logical reasoning ability,and the role of academic expectation and family environment in that relationship.Based on the 2020 China Family Panel Studies(CFPS)data,1491 children(girls ratio=53.78%;average grade=6.023 years,school grade standard deviation=1.825 years).Results following multiple regression model(OLS)show that the higher the parental cognitive ability,the higher the children’s logical reasoning ability.Secondly,parental academic expectation serves as a mediator between their cognitive ability and children’s logical reasoning ability for higher logical reasoning by children.Third,a possible family environment acts as a mediator in the relationship between parents’cognitive ability and children’s logical reasoning ability to be higher.We conclude from thesefindings that parents with high cognitive abilities can enhance their children’s logical reasoning skills not only by setting higher academic expectations,but also by cultivating a supportive family environment.Thesefindings imply a need for intervention to improve family quality of life to enhance children’s thinking abilities to optimize their academic learning.展开更多
In recent years,the application scenarios of electrical and electronic products have become increasingly diverse worldwide.The impact of climatic environmental tests on the performance of related products has attracte...In recent years,the application scenarios of electrical and electronic products have become increasingly diverse worldwide.The impact of climatic environmental tests on the performance of related products has attracted much attention,and formulating scientific and reasonable environmental test plans has become an important step to ensure product quality and reliability.展开更多
0 INTRODUCTION Due to the rapid population growth and the accelerated urbanization process,the contradiction between the demand for expanding ground space and the limited available land scale is becoming increasingly ...0 INTRODUCTION Due to the rapid population growth and the accelerated urbanization process,the contradiction between the demand for expanding ground space and the limited available land scale is becoming increasingly prominent.China has implemented and completed several largescale land infilling and excavation projects(Figure 1),which have become the main way to increase land resources and expand construction land.展开更多
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.
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.
文摘In Part 2 of David Hume’s Dialogues Concerning Natural Religion,Cleanthes puts forth the analogical Argument from Design,the argument intended to establish that the designer of the world possesses an intelligence similar to human intelligence,in light of Cleanthes’claim that the design of the world resembles machines of human contrivance.Philo argues that this argument fails,because the world does not bear a specific resemblance to any type of machine,and,therefore,there is no basis for reasoning analogically to an intelligent cause of design.In Part 3,Cleanthes attempts to strengthen his case through two illustrative analogies:I will examine the first of these-the Articulate Voice speaking from the clouds.Scholarship generally regards the Articulate Voice illustration to fail,precisely because nothing in this illustrative analogy assists Philo in understanding that the world is a machine.My paper/talk reveals that Philo provides additional criticisms of the Articulate Voice illustration in Parts 6 and 7 of the Dialogues,which make Philo’s critique even stronger and more enlightening regarding his critical approach to the Design Argument than can be learned from Part 2 alone.
基金supported in part by the National Natural Science Foundation of China(No.62302507)and the funding of Harbin Institute of Technology(Shenzhen)(No.20210035).
文摘Extrapolation on Temporal Knowledge Graphs(TKGs)aims to predict future knowledge from a set of historical Knowledge Graphs in chronological order.The temporally adjacent facts in TKGs naturally form event sequences,called event evolution patterns,implying informative temporal dependencies between events.Recently,many extrapolation works on TKGs have been devoted to modelling these evolutional patterns,but the task is still far from resolved because most existing works simply rely on encoding these patterns into entity representations while overlooking the significant information implied by relations of evolutional patterns.However,the authors realise that the temporal dependencies inherent in the relations of these event evolution patterns may guide the follow-up event prediction to some extent.To this end,a Temporal Relational Context-based Temporal Dependencies Learning Network(TRenD)is proposed to explore the temporal context of relations for more comprehensive learning of event evolution patterns,especially those temporal dependencies caused by interactive patterns of relations.Trend incorporates a semantic context unit to capture semantic correlations between relations,and a structural context unit to learn the interaction pattern of relations.By learning the temporal contexts of relations semantically and structurally,the authors gain insights into the underlying event evolution patterns,enabling to extract comprehensive historical information for future prediction better.Experimental results on benchmark datasets demonstrate the superiority of the model.
文摘Objective This study aimed to investigate the level of knowledge among urologists of usage of fluoroscopy during percutaneous nephrolithotomy.MethodsWe conducted an anonymous internet-based survey addressed to the EAU Section of Uro-Technology and the International Alliance of Urolithiasis members with particular interest in the stone treatment at all levels of expertise.The final version of the questionnaire included 31 questions,evaluated the level of knowledge on X-ray utilization and exposure,and identified correlations between geographic areas,levels of seniority,surgical volumes,and awareness on radiation protection.ResultsIn total,586 respondents were included.Knowledge of fluoroscopy settings appeared low,particularly among trainees(up to 87.5%were uninformed,p=0.008).Precautions to reduce exposure appeared poorly followed as up to 25.4%of respondents used regularly continuous fluoroscopy,and up to 20.5%used regularly high-frequency setting and this trend was more obvious among senior specialists(6.2%of trainees used high-frequency settings vs.21.3%of consultants,p<0.05).Additionally,only 24.9%of respondents would provide X-ray protection to patients too.ConclusionAlthough high and routinary utilization of X-rays,the level of awareness and adhesion to“as low as reasonably achievable”principles among endourologists seems suboptimal in 65.0%of all respondents.Highest volume surgeons,inevitably at higher risk,do not seem to adopt more precautions.More efforts should be addressed to improve these results,reducing the risk related to excessive radiation exposure for both surgical staff and patients in order to minimize health related issues.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R435),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Psychological distress detection plays a critical role in modern healthcare,especially in ambient environments where continuous monitoring is essential for timely intervention.Advances in sensor technology and artificial intelligence(AI)have enabled the development of systems capable of mental health monitoring using multimodal data.However,existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings.This paper addresses these challenges by proposing TRANS-HEALTH,a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention(BDI)reasoning for real-time psychological distress detection.The framework utilizes a multimodal dataset containing EEG,GSR,heart rate,and activity data to predict distress while adapting to individual contexts.The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making.The novelty of the approach lies in its seamless integration of transformermodelswith BDI reasoning,providing both high accuracy and contextual relevance in real time.Performance metrics such as accuracy,precision,recall,and F1-score are employed to evaluate the system’s performance.The results show that TRANS-HEALTH outperforms existing models,achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts,with an enhanced ability to engage users,making it suitable for deployment in wearable and remote healthcare environments.
基金supported by scientific research fund of Jiangxi Provincial Social Sciences“14th Five-Year Plan”(No.23SH05).
文摘This study investigated the relationship between parental cognitive ability and child logical reasoning ability,and the role of academic expectation and family environment in that relationship.Based on the 2020 China Family Panel Studies(CFPS)data,1491 children(girls ratio=53.78%;average grade=6.023 years,school grade standard deviation=1.825 years).Results following multiple regression model(OLS)show that the higher the parental cognitive ability,the higher the children’s logical reasoning ability.Secondly,parental academic expectation serves as a mediator between their cognitive ability and children’s logical reasoning ability for higher logical reasoning by children.Third,a possible family environment acts as a mediator in the relationship between parents’cognitive ability and children’s logical reasoning ability to be higher.We conclude from thesefindings that parents with high cognitive abilities can enhance their children’s logical reasoning skills not only by setting higher academic expectations,but also by cultivating a supportive family environment.Thesefindings imply a need for intervention to improve family quality of life to enhance children’s thinking abilities to optimize their academic learning.
文摘In recent years,the application scenarios of electrical and electronic products have become increasingly diverse worldwide.The impact of climatic environmental tests on the performance of related products has attracted much attention,and formulating scientific and reasonable environmental test plans has become an important step to ensure product quality and reliability.
基金funded by the Key Research and Development Program of Shaanxi Province(No.2024SFYBXM-669)the National Natural Science Foundation of China(No.42271078)。
文摘0 INTRODUCTION Due to the rapid population growth and the accelerated urbanization process,the contradiction between the demand for expanding ground space and the limited available land scale is becoming increasingly prominent.China has implemented and completed several largescale land infilling and excavation projects(Figure 1),which have become the main way to increase land resources and expand construction land.
