Currently, most anomaly detection pattern learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm m...Currently, most anomaly detection pattern learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. Focusing on this problem, in this paper, firstly a new anomaly detection measurement is proposed according to the probability characteristics of intrusion instances and normal instances. Secondly, on the basis of anomaly detection measure, we present a clustering-based unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage above. Finally, some experiments are conducted to verify the proposed algorithm is valid.展开更多
Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features...Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.展开更多
Field computation, an emerging computation technique, has inspired passion of intelligence science research. A novel field computation model based on the magnetic field theory is constructed. The proposed magnetic fie...Field computation, an emerging computation technique, has inspired passion of intelligence science research. A novel field computation model based on the magnetic field theory is constructed. The proposed magnetic field computation （MFC） model consists of a field simulator, a non-derivative optimization algo- rithm and an auxiliary data processing unit. The mathematical model is deduced and proved that the MFC model is equivalent to a quadratic discriminant function. Furthermore, the finite element prototype is derived, and the simulator is developed, combining with particle swarm optimizer for the field configuration. Two benchmark classification experiments are studied in the numerical experiment, and one notable advantage is demonstrated that less training samples are required and a better generalization can be achieved.展开更多
The rapid development of artificial intelligence technology has propelled the automated,humanized,and personalized learning services to become a core topic in the transformation of education.Generative artificial inte...The rapid development of artificial intelligence technology has propelled the automated,humanized,and personalized learning services to become a core topic in the transformation of education.Generative artificial inteligence(GenAI),represented by large language models(LLMs),hasprovidedopportunitiesfor reshaping the methods for setting personalized learning objectives,learning patterns,construction of learning resources,and evaluation systems.However,it still faces significant limitations in understanding the differences in individual static characteristics,dynamic learning processes,and students'literacy goals,as well as in actively differentiating and adapting to these differences.The study has clarified the technical strategies and application services of GenAI-empowered personalized learning,and analyzed the challenges in areas such as the lag in theoretical foundations and lack of practical guidance,weak autonomy and controllability of key technologies,insufficient understanding of the learning process,lack of mechanisms for enhancing higher-order literacy,and deficiencies in safety and ethical regulations.It has proposed implementationpathsaround interdisciplinary theoretical innovation,development of LLMs,enhancement of personalized basic services,improvement of higher-order literacy,optimization of long-term evidence-based effects,and establishment of a safety and ethical value regulation system,aiming to promote the realization of safe,efficient,and sustainable personalized learning.展开更多
Exploring the association patterns of student performance in depth can help administrators and teachers optimize the curriculum structure and teaching plans more specifically to improve teaching effectiveness in a col...Exploring the association patterns of student performance in depth can help administrators and teachers optimize the curriculum structure and teaching plans more specifically to improve teaching effectiveness in a college undergraduate major.However,these high-dimensional time-varying student performance data involve multiple associated subjects,such as student,course,and teacher,which exhibit complex interrelationships in academic semesters,knowledge categories,and student groups.This makes it challenging to conduct a comprehensive analysis of association patterns.To this end,we construct a visual analysis framework,called MAPVis,to support multi-method and multi-level interactive exploration of the association patterns in student performance.MAPVis consists of two stages:in the first stage,we extract students’learning patterns and further introduce mutual information to explore the distribution of learning patterns;in the second stage,various learning patterns and subject attributes are integrated based on a hierarchical apriori algorithm to achieve a multi-subject interactive exploration of the association patterns among students,courses,and teachers.Finally,we conduct a case study using real student performance data to verify the applicability and effectiveness of MAPVis.展开更多
In the field of hexapod robot control,the application of central pattern generators(CPG)and deep reinforcement learning(DRL)is becoming increasingly common.Compared to traditional control methods that rely on dynamic ...In the field of hexapod robot control,the application of central pattern generators(CPG)and deep reinforcement learning(DRL)is becoming increasingly common.Compared to traditional control methods that rely on dynamic models,both the CPG and the end-to-end DRL approaches significantly simplify the complexity of designing control models.However,relying solely on DRL for control also has its drawbacks,such as slow convergence speed and low exploration efficiency.Moreover,although the CPG can produce rhythmic gaits,its control strategy is relatively singular,limiting the robot's ability to adapt to complex terrains.To overcome these limitations,this study proposes a three-layer DRL control architecture.The high-level reinforcement learning controller is responsible for learning the parameters of the middle-level CPG and the low-level mapping functions,while the middle and low level controllers coordinate the joint movements within and between legs.By integrating the learning capabilities of DRL with the gait generation characteristics of CPG,this method significantly enhances the stability and adaptability of hexapod robots in complex terrains.Experimental results show that,compared to pure DRL approaches,this method significantly improves learning efficiency and control performance,when dealing with complex terrains,it considerably enhances the robot's stability and adaptability compared to pure CPG control.展开更多
In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the bio...In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.展开更多
An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical re...An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.展开更多
The problem of spam short message （SMS） recognition involves many aspects of natural language pro- cessing. A good solution to solving the problem can not only improve the quality of people experiencing the mobile l...The problem of spam short message （SMS） recognition involves many aspects of natural language pro- cessing. A good solution to solving the problem can not only improve the quality of people experiencing the mobile life, but also has a positive role on promoting the analysis of short text occurring in current mobile applications, such as We- bchat and microblog. As spam SMSes have characteristics of sparsity, transformation and real-timedness, we propose three methods at different levels, i.e., recognition based on sym- bolic features, recognition based on text similarity, and recog- nition based on pattern matching. By combining these meth- ods, we obtain a multi-level approach to spam SMS recog- nition. In order to enrich the pattern base to reduce manual labor and time, we propose a quasi-pattern learning method, which utilizes quasi-pattern matching results in the pattern matching process. The method can learn many interesting and new patterns from the SMS corpus. Finally, a comprehensive analysis indicates that our spare SMS recognition approach achieves a precision rate as high as 95.18%, and a recall rate of 95.51%.展开更多
文摘Currently, most anomaly detection pattern learning algorithms require a set of purely normal data from which they train their model. If the data contain some intrusions buried within the training data, the algorithm may not detect these attacks because it will assume that they are normal. In reality, it is very hard to guarantee that there are no attack items in the collected training data. Focusing on this problem, in this paper, firstly a new anomaly detection measurement is proposed according to the probability characteristics of intrusion instances and normal instances. Secondly, on the basis of anomaly detection measure, we present a clustering-based unsupervised anomaly detection patterns learning algorithm, which can overcome the shortage above. Finally, some experiments are conducted to verify the proposed algorithm is valid.
基金supported by European Community s Seventh Framework Programme FP7/2007-2013,Challenge 2,Cognitive Systems,Interaction,Robotics(No.248311AMARSi)
文摘Articulated movements are fundamental in many human and robotic tasks.While humans can learn and generalise arbitrarily long sequences of movements,and particularly can optimise them to ft the constraints and features of their body,robots are often programmed to execute point-to-point precise but fxed patterns.This study proposes a new approach to interpreting and reproducing articulated and complex trajectories as a set of known robot-based primitives.Instead of achieving accurate reproductions,the proposed approach aims at interpreting data in an agent-centred fashion,according to an agent s primitive movements.The method improves the accuracy of a reproduction with an incremental process that seeks frst a rough approximation by capturing the most essential features of a demonstrated trajectory.Observing the discrepancy between the demonstrated and reproduced trajectories,the process then proceeds with incremental decompositions and new searches in sub-optimal parts of the trajectory.The aim is to achieve an agent-centred interpretation and progressive learning that fts in the frst place the robots capability,as opposed to a data-centred decomposition analysis.Tests on both geometric and human generated trajectories reveal that the use of own primitives results in remarkable robustness and generalisation properties of the method.In particular,because trajectories are understood and abstracted by means of agent-optimised primitives,the method has two main features： 1） Reproduced trajectories are general and represent an abstraction of the data.2） The algorithm is capable of reconstructing highly noisy or corrupted data without pre-processing thanks to an implicit and emergent noise suppression and feature detection.This study suggests a novel bio-inspired approach to interpreting,learning and reproducing articulated movements and trajectories.Possible applications include drawing,writing,movement generation,object manipulation,and other tasks where the performance requires human-like interpretation and generalisation capabilities.
基金supported by the National Natural Science Foundation of China(60903005)the National Basic Research Program of China(973 Program)(2012CB821206)
文摘Field computation, an emerging computation technique, has inspired passion of intelligence science research. A novel field computation model based on the magnetic field theory is constructed. The proposed magnetic field computation （MFC） model consists of a field simulator, a non-derivative optimization algo- rithm and an auxiliary data processing unit. The mathematical model is deduced and proved that the MFC model is equivalent to a quadratic discriminant function. Furthermore, the finite element prototype is derived, and the simulator is developed, combining with particle swarm optimizer for the field configuration. Two benchmark classification experiments are studied in the numerical experiment, and one notable advantage is demonstrated that less training samples are required and a better generalization can be achieved.
基金supported by the National Natural Science Foundation of China(Grant Nos.62037001 and 62337001).
文摘The rapid development of artificial intelligence technology has propelled the automated,humanized,and personalized learning services to become a core topic in the transformation of education.Generative artificial inteligence(GenAI),represented by large language models(LLMs),hasprovidedopportunitiesfor reshaping the methods for setting personalized learning objectives,learning patterns,construction of learning resources,and evaluation systems.However,it still faces significant limitations in understanding the differences in individual static characteristics,dynamic learning processes,and students'literacy goals,as well as in actively differentiating and adapting to these differences.The study has clarified the technical strategies and application services of GenAI-empowered personalized learning,and analyzed the challenges in areas such as the lag in theoretical foundations and lack of practical guidance,weak autonomy and controllability of key technologies,insufficient understanding of the learning process,lack of mechanisms for enhancing higher-order literacy,and deficiencies in safety and ethical regulations.It has proposed implementationpathsaround interdisciplinary theoretical innovation,development of LLMs,enhancement of personalized basic services,improvement of higher-order literacy,optimization of long-term evidence-based effects,and establishment of a safety and ethical value regulation system,aiming to promote the realization of safe,efficient,and sustainable personalized learning.
基金supported by the National Natural Science Foundation of China(No.60873093).
文摘Exploring the association patterns of student performance in depth can help administrators and teachers optimize the curriculum structure and teaching plans more specifically to improve teaching effectiveness in a college undergraduate major.However,these high-dimensional time-varying student performance data involve multiple associated subjects,such as student,course,and teacher,which exhibit complex interrelationships in academic semesters,knowledge categories,and student groups.This makes it challenging to conduct a comprehensive analysis of association patterns.To this end,we construct a visual analysis framework,called MAPVis,to support multi-method and multi-level interactive exploration of the association patterns in student performance.MAPVis consists of two stages:in the first stage,we extract students’learning patterns and further introduce mutual information to explore the distribution of learning patterns;in the second stage,various learning patterns and subject attributes are integrated based on a hierarchical apriori algorithm to achieve a multi-subject interactive exploration of the association patterns among students,courses,and teachers.Finally,we conduct a case study using real student performance data to verify the applicability and effectiveness of MAPVis.
基金supported by the Beijing Natural Science Foundation-Xiaomi Innovation Joint Fund(L243013)the National Natural Science Foundation of China(62172392).
文摘In the field of hexapod robot control,the application of central pattern generators(CPG)and deep reinforcement learning(DRL)is becoming increasingly common.Compared to traditional control methods that rely on dynamic models,both the CPG and the end-to-end DRL approaches significantly simplify the complexity of designing control models.However,relying solely on DRL for control also has its drawbacks,such as slow convergence speed and low exploration efficiency.Moreover,although the CPG can produce rhythmic gaits,its control strategy is relatively singular,limiting the robot's ability to adapt to complex terrains.To overcome these limitations,this study proposes a three-layer DRL control architecture.The high-level reinforcement learning controller is responsible for learning the parameters of the middle-level CPG and the low-level mapping functions,while the middle and low level controllers coordinate the joint movements within and between legs.By integrating the learning capabilities of DRL with the gait generation characteristics of CPG,this method significantly enhances the stability and adaptability of hexapod robots in complex terrains.Experimental results show that,compared to pure DRL approaches,this method significantly improves learning efficiency and control performance,when dealing with complex terrains,it considerably enhances the robot's stability and adaptability compared to pure CPG control.
文摘In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.
文摘An incremental time-delay neural network based on synapse growth, which is suitable for dynamic control and learning of autonomous robots, is proposed to improve the learning and retrieving performance of dynamical recurrent associative memory architecture. The model allows steady and continuous establishment of associative memory for spatio-temporal regularities and time series in discrete sequence of inputs. The inserted hidden units can be taken as the long-term memories that expand the capacity of network and sometimes may fade away under certain condition. Preliminary experiment has shown that this incremental network may be a promising approach to endow autonomous robots with the ability of adapting to new data without destroying the learned patterns. The system also benefits from its potential chaos character for emergence.
文摘The problem of spam short message （SMS） recognition involves many aspects of natural language pro- cessing. A good solution to solving the problem can not only improve the quality of people experiencing the mobile life, but also has a positive role on promoting the analysis of short text occurring in current mobile applications, such as We- bchat and microblog. As spam SMSes have characteristics of sparsity, transformation and real-timedness, we propose three methods at different levels, i.e., recognition based on sym- bolic features, recognition based on text similarity, and recog- nition based on pattern matching. By combining these meth- ods, we obtain a multi-level approach to spam SMS recog- nition. In order to enrich the pattern base to reduce manual labor and time, we propose a quasi-pattern learning method, which utilizes quasi-pattern matching results in the pattern matching process. The method can learn many interesting and new patterns from the SMS corpus. Finally, a comprehensive analysis indicates that our spare SMS recognition approach achieves a precision rate as high as 95.18%, and a recall rate of 95.51%.
