The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issu...The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.展开更多
Drawing on 4.8 million unlabeled behavioural events from 17 universities on five continents,this study proposes the Contrastive Self-Supervised and Causal Inference-Based Contextual Predictive Model(CSCI-CPM)to foreca...Drawing on 4.8 million unlabeled behavioural events from 17 universities on five continents,this study proposes the Contrastive Self-Supervised and Causal Inference-Based Contextual Predictive Model(CSCI-CPM)to forecast depression risk and quantify the value of counseling outreach for 12438 internationally mobile students.Twin momentum-updated Transformers learn 128-dimensional,domain-invariant embeddings via an InfoNCE objective,sharply reducing label dependence and campus drift.A doubly robust head jointly models treatment propensity and counterfactual PHQ-9 outcomes,yielding unbiased individualized treatment-effect estimates.Leave-one-continent-out tests lift ROC-AUC from 0.882 to 0.931,cut root-meansquared PHQ-9 error by 0.41,and trim PEHE to 0.027,surpassing five baselines at p<0.01.A 16-week Thompson-sampling simulation with 25 weekly counseling slots lowers unmet-need days by 41.6%,raises outreach to low-SES learners from 21%to 34%,and shrinks equal-opportunity gaps to 0.019.Real-time inference executes in 18 ms at<0.001 kg CO₂-eq per student-day,enabling sustainable on-premise deployment.Clinician review validates 87%of alerts,while integrated-gradients explanations highlight language-switch entropy,night-screen bursts,and weekend immobility as salient risk signals.CSCI-CPM thus offers a scalable,culturally responsive,and privacy-preserving framework for proactive mental-health governance in global higher education.展开更多
The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of health.Parallel to t...The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of health.Parallel to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare costs.To gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of healthcare.This research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the coldest.Moreover,thirty-three topics are showing in-significant trends.We further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific domain.The current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary perspective.It further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based models.We also utilized corpus tuning,the mean pooling technique,and the hugging face tool.Our method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).展开更多
In this paper, we introduce and discuss the robustness of contextuality（Ro C） R_C（e） and the contextuality cost C（e） of an empirical model e. The following properties of them are proved.（i） An empirical model ...In this paper, we introduce and discuss the robustness of contextuality（Ro C） R_C（e） and the contextuality cost C（e） of an empirical model e. The following properties of them are proved.（i） An empirical model e is contextual if and only if R_C（e） ＆gt; 0;（ii） the Ro C function R_C is convex, lower semi-continuous and un-increasing under an affine mapping on the set E M of all empirical models;（iii） e is non-contextual if and only if C（e） = 0;（iv） e is contextual if and only if C（e） ＆gt; 0;（v） e is strongly contextual if and only if C（e） = 1. Also, a relationship between RC（e） and C（e） is obtained. Lastly, the Ro C of three empirical models is computed and compared. Especially, the Ro C of the PR boxes is obtained and the supremum 0.5 is found for the Ro C of all no-signaling type（2, 2, 2） empirical models.展开更多
Recently, the robustness of contextuality(RoC) of an empirical model was discussed in [Sci. China-Phys. Mech. Astron. 59,640303(2016)], many important properties of the RoC have been proved except for its boundedness ...Recently, the robustness of contextuality(RoC) of an empirical model was discussed in [Sci. China-Phys. Mech. Astron. 59,640303(2016)], many important properties of the RoC have been proved except for its boundedness and continuity. The aim of this paper is to find an upper bound for the RoC over all of empirical models and prove that the RoC is a continuous function on the set of all empirical models. Lastly, a relationship between the RoC and the extent of violating the noncontextual inequalities is established for an n-cycle contextual box. This relationship implies that the RoC can be used to quantify the contextuality of n-cycle boxes.展开更多
Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consum...Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%-100%,whereas the one-class support vectormachine approach performs within the range of 99.3%-100%.Research limitations/implications-The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns.However,it can be extended to correlate driver’s physiological state such as fatigue,sleep and stress to correlate with driving behaviour and fuel economy.The anomaly detection approach here is limited to providing feedback to driver,it can be extended to give contextual feedback to the steering controller or throttle controller.In the future,a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implications-The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts.It can also be used as a training tool for improving driving efficiency for new drivers.It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/value-This paper contributes to the existing literature by providing anMLpipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values.The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy.The main contributions for this approach are as follows:(1)a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption.(2)Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.展开更多
文摘The field of artificial intelligence has advanced significantly in recent years,but achieving a human-like or Artificial General Intelligence(AGI)remains a theoretical challenge.One hypothesis suggests that a key issue is the formalisation of extracting meaning from information.Meaning emerges through a three-stage interpretative process,where the spectrum of possible interpretations is collapsed into a singular outcome by a particular context.However,this approach currently lacks practical grounding.In this research,we developed a model based on contexts,which applies interpretation principles to the visual information to address this gap.The field of computer vision and object recognition has progressed essentially with artificial neural networks,but these models struggle with geometrically transformed images,such as those that are rotated or shifted,limiting their robustness in real-world applications.Various approaches have been proposed to address this problem.Some of them(Hu moments,spatial transformers,capsule networks,attention and memory mechanisms)share a conceptual connection with the contextual model(CM)discussed in this study.This paper investigates whether CM principles are applicable for interpreting rotated images from the MNIST and Fashion MNIST datasets.The model was implemented in the Rust programming language.It consists of a contextual module and a convolutional neural network(CNN).The CMwas trained on the rotated Mono Icons dataset,which is significantly different from the testing datasets.The CNN module was trained on the original MNIST and Fashion MNIST datasets for interpretation recognition.As a result,the CM was able to recognise the original datasets but encountered rotated images only during testing.The findings show that the model effectively interpreted transformed images by considering them in all available contexts and restoring their original form.This provides a practical foundation for further development of the contextual hypothesis and its relation to theAGI domain.
文摘Drawing on 4.8 million unlabeled behavioural events from 17 universities on five continents,this study proposes the Contrastive Self-Supervised and Causal Inference-Based Contextual Predictive Model(CSCI-CPM)to forecast depression risk and quantify the value of counseling outreach for 12438 internationally mobile students.Twin momentum-updated Transformers learn 128-dimensional,domain-invariant embeddings via an InfoNCE objective,sharply reducing label dependence and campus drift.A doubly robust head jointly models treatment propensity and counterfactual PHQ-9 outcomes,yielding unbiased individualized treatment-effect estimates.Leave-one-continent-out tests lift ROC-AUC from 0.882 to 0.931,cut root-meansquared PHQ-9 error by 0.41,and trim PEHE to 0.027,surpassing five baselines at p<0.01.A 16-week Thompson-sampling simulation with 25 weekly counseling slots lowers unmet-need days by 41.6%,raises outreach to low-SES learners from 21%to 34%,and shrinks equal-opportunity gaps to 0.019.Real-time inference executes in 18 ms at<0.001 kg CO₂-eq per student-day,enabling sustainable on-premise deployment.Clinician review validates 87%of alerts,while integrated-gradients explanations highlight language-switch entropy,night-screen bursts,and weekend immobility as salient risk signals.CSCI-CPM thus offers a scalable,culturally responsive,and privacy-preserving framework for proactive mental-health governance in global higher education.
文摘The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of health.Parallel to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare costs.To gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of healthcare.This research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the coldest.Moreover,thirty-three topics are showing in-significant trends.We further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific domain.The current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary perspective.It further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based models.We also utilized corpus tuning,the mean pooling technique,and the hugging face tool.Our method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).
基金supported by the National Natural Science Foundation of China(Grant Nos.1137101211401359+1 种基金11471200 and 11571213)the Fundamental Research Funds for the Central Universities(Grant No.GK201301007)
文摘In this paper, we introduce and discuss the robustness of contextuality（Ro C） R_C（e） and the contextuality cost C（e） of an empirical model e. The following properties of them are proved.（i） An empirical model e is contextual if and only if R_C（e） ＆gt; 0;（ii） the Ro C function R_C is convex, lower semi-continuous and un-increasing under an affine mapping on the set E M of all empirical models;（iii） e is non-contextual if and only if C（e） = 0;（iv） e is contextual if and only if C（e） ＆gt; 0;（v） e is strongly contextual if and only if C（e） = 1. Also, a relationship between RC（e） and C（e） is obtained. Lastly, the Ro C of three empirical models is computed and compared. Especially, the Ro C of the PR boxes is obtained and the supremum 0.5 is found for the Ro C of all no-signaling type（2, 2, 2） empirical models.
基金supported by the National Natural Science Foundation of China(Grant Nos.11371012,11401359,11471200,11571211 and11571213)the Fundamental Research Funds for the Central Universities(Grant No.GK201604001)the Innovation Fund Project for Graduate Program of Shaanxi Normal University(Grant No.2016CBY005)
文摘Recently, the robustness of contextuality(RoC) of an empirical model was discussed in [Sci. China-Phys. Mech. Astron. 59,640303(2016)], many important properties of the RoC have been proved except for its boundedness and continuity. The aim of this paper is to find an upper bound for the RoC over all of empirical models and prove that the RoC is a continuous function on the set of all empirical models. Lastly, a relationship between the RoC and the extent of violating the noncontextual inequalities is established for an n-cycle contextual box. This relationship implies that the RoC can be used to quantify the contextuality of n-cycle boxes.
文摘Purpose-A cost-effective way to achieve fuel economy is to reinforce positive driving behaviour.Driving behaviour can be controlled if drivers can be alerted for behaviour that results in poor fuel economy.Fuel consumption must be tracked and monitored instantaneously rather than tracking average fuel economy for the entire trip duration.A single-step application of machine learning(ML)is not sufficient to model prediction of instantaneous fuel consumption and detection of anomalous fuel economy.The study designs an ML pipeline to track and monitor instantaneous fuel economy and detect anomalies.Design/methodology/approach-This research iteratively applies different variations of a two-step ML pipeline to the driving dataset for hatchback cars.The first step addresses the problem of accurate measurement and prediction of fuel economy using time series driving data,and the second step detects abnormal fuel economy in relation to contextual information.Long short-term memory autoencoder method learns and uses the most salient features of time series data to build a regression model.The contextual anomaly is detected by following two approaches,kernel quantile estimator and one-class support vector machine.The kernel quantile estimator sets dynamic threshold for detecting anomalous behaviour.Any error beyond a threshold is classified as an anomaly.The one-class support vector machine learns training error pattern and applies the model to test data for anomaly detection.The two-step ML pipeline is further modified by replacing long short term memory autoencoder with gated recurrent network autoencoder,and the performance of both models is compared.The speed recommendations and feedback are issued to the driver based on detected anomalies for controlling aggressive behaviour.Findings-A composite long short-term memory autoencoder was compared with gated recurrent unit autoencoder.Both models achieve prediction accuracy within a range of 98%-100%for prediction as a first step.Recall and accuracy metrics for anomaly detection using kernel quantile estimator remains within 98%-100%,whereas the one-class support vectormachine approach performs within the range of 99.3%-100%.Research limitations/implications-The proposed approach does not consider socio-demographics or physiological information of drivers due to privacy concerns.However,it can be extended to correlate driver’s physiological state such as fatigue,sleep and stress to correlate with driving behaviour and fuel economy.The anomaly detection approach here is limited to providing feedback to driver,it can be extended to give contextual feedback to the steering controller or throttle controller.In the future,a controller-based system can be associated with an anomaly detection approach to control the acceleration and braking action of the driver.Practical implications-The suggested approach is helpful in monitoring and reinforcing fuel-economical driving behaviour among fleet drivers as per different environmental contexts.It can also be used as a training tool for improving driving efficiency for new drivers.It keeps drivers engaged positively by issuing a relevant warning for significant contextual anomalies and avoids issuing a warning for minor operational errors.Originality/value-This paper contributes to the existing literature by providing anMLpipeline approach to track and monitor instantaneous fuel economy rather than relying on average fuel economy values.The approach is further extended to detect contextual driving behaviour anomalies and optimises fuel economy.The main contributions for this approach are as follows:(1)a prediction model is applied to fine-grained time series driving data to predict instantaneous fuel consumption.(2)Anomalous fuel economy is detected by comparing prediction error against a threshold and analysing error patterns based on contextual information.
