Textual informativity is one of the seven standards of textuality. This paper focuses on the shift among three orders of textual informativity. And also probe into some strategies to compensate for the different level...Textual informativity is one of the seven standards of textuality. This paper focuses on the shift among three orders of textual informativity. And also probe into some strategies to compensate for the different level of informativity.展开更多
As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms ...As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).展开更多
Knowledge graphs are essential tools for representing real-world facts and finding wide applications in various domains. However, the process of constructing knowledge graphs often introduces noises and errors, which ...Knowledge graphs are essential tools for representing real-world facts and finding wide applications in various domains. However, the process of constructing knowledge graphs often introduces noises and errors, which can negatively impact the performance of downstream applications. Current methods for knowledge graph error detection primarily focus on graph structure and overlook the importance of textual information in error detection. Therefore, this paper proposes a novel error detection framework that combines both structural and textual information. The framework utilizes a confidence module for error detection while generating knowledge embeddings. The performance of this approach outperforms baseline methods in error detection and link prediction experiments, particularly achieving state-of-the-art performance in the error detection task.展开更多
Generalizing policies learned by agents in known environments to unseen domains is an essential challenge in advancing the development of reinforcement learning.Lately,language-conditioned policies have underscored th...Generalizing policies learned by agents in known environments to unseen domains is an essential challenge in advancing the development of reinforcement learning.Lately,language-conditioned policies have underscored the pivotal role of linguistic information in the context of cross-environments.Integrating both environmental and textual information into the observation space enables agents to accomplish similar tasks across different scenarios.However,for entities with varying forms of motion but the same name present in observations(e.g.,immovable mage and fleeing mage),existing methods are unable to learn the motion information the entities possess well.They face the problem of ambiguity caused by motion.In order to tackle this challenge,we propose the entity mapper with multi-modal attention based on behavior prediction(EMMA-BBP)framework,comprising modules for predicting motion behavior and text matching.The behavioral prediction module is used to determine the motion information of the entities present in the environment to eliminate the semantic ambiguity of the motion information.The role of the text-matching module is to match the text given in the environment with the information about the entity’s behavior under observation,thus eliminating false textual information.EMMA-BBP has been tested in the demanding environment of MESSENGER,doubling the generalization ability of EMMA.展开更多
Braille serves as an efficient means for visually impaired individuals to access textual information and engage in communication.However,the process of reading Braille can often be cumbersome and time-intensive,partic...Braille serves as an efficient means for visually impaired individuals to access textual information and engage in communication.However,the process of reading Braille can often be cumbersome and time-intensive,particularly in bidirectional human-machine interaction.In this work,a compact optical device for contactless detection of Braille is fabricated and characterized.The GaN-on-sapphire chip,which employs monolithic integration,serves as the core for both light emission and photodetection,significantly reducing its overall footprint.The incorporation of the semiellipsoid epoxy lens with optimized dimensions ensures consistent and accurate detection.The sensing device demonstrates high stability and fast response through its line-scanning capabilities on Braille codes.The captured signals are analyzed using a microcontroller,and the Braille recognition results are wirelessly transmitted to a portable mobile device,enabling the conversion into audio and visual formats.This innovative design not only facilitates Braille reading but also holds the potential to advance human-machine interaction.展开更多
文摘Textual informativity is one of the seven standards of textuality. This paper focuses on the shift among three orders of textual informativity. And also probe into some strategies to compensate for the different level of informativity.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No.2021R1F1A1049387).
文摘As location information of numerous Internet of Thing(IoT)devices can be recognized through IoT sensor technology,the need for technology to efficiently analyze spatial data is increasing.One of the famous algorithms for classifying dense data into one cluster is Density-Based Spatial Clustering of Applications with Noise(DBSCAN).Existing DBSCAN research focuses on efficiently finding clusters in numeric data or categorical data.In this paper,we propose the novel problem of discovering a set of adjacent clusters among the cluster results derived for each keyword in the keyword-based DBSCAN algorithm.The existing DBSCAN algorithm has a problem in that it is necessary to calculate the number of all cases in order to find adjacent clusters among clusters derived as a result of the algorithm.To solve this problem,we developed the Genetic algorithm-based Keyword Matching DBSCAN(GKM-DBSCAN)algorithm to which the genetic algorithm was applied to discover the set of adjacent clusters among the cluster results derived for each keyword.In order to improve the performance of GKM-DBSCAN,we improved the general genetic algorithm by performing a genetic operation in groups.We conducted extensive experiments on both real and synthetic datasets to show the effectiveness of GKM-DBSCAN than the brute-force method.The experimental results show that GKM-DBSCAN outperforms the brute-force method by up to 21 times.GKM-DBSCAN with the index number binarization(INB)is 1.8 times faster than GKM-DBSCAN with the cluster number binarization(CNB).
基金supported by the National Key R&D Plan(No.2022YFC3303303)the National Natural Science Foundation of China(Nos.61976204,31900979,and U1811461)+1 种基金the Zhengzhou Collaborative Innovation Major Project(No.20XTZX11020)Project of Youth Innovation Promotion Association CAS and Beijing Nova Program(No.Z201100006820062).
文摘Knowledge graphs are essential tools for representing real-world facts and finding wide applications in various domains. However, the process of constructing knowledge graphs often introduces noises and errors, which can negatively impact the performance of downstream applications. Current methods for knowledge graph error detection primarily focus on graph structure and overlook the importance of textual information in error detection. Therefore, this paper proposes a novel error detection framework that combines both structural and textual information. The framework utilizes a confidence module for error detection while generating knowledge embeddings. The performance of this approach outperforms baseline methods in error detection and link prediction experiments, particularly achieving state-of-the-art performance in the error detection task.
基金supported by the Program of Natural Science Foundation of Shanghai(no.23ZR1422800).
文摘Generalizing policies learned by agents in known environments to unseen domains is an essential challenge in advancing the development of reinforcement learning.Lately,language-conditioned policies have underscored the pivotal role of linguistic information in the context of cross-environments.Integrating both environmental and textual information into the observation space enables agents to accomplish similar tasks across different scenarios.However,for entities with varying forms of motion but the same name present in observations(e.g.,immovable mage and fleeing mage),existing methods are unable to learn the motion information the entities possess well.They face the problem of ambiguity caused by motion.In order to tackle this challenge,we propose the entity mapper with multi-modal attention based on behavior prediction(EMMA-BBP)framework,comprising modules for predicting motion behavior and text matching.The behavioral prediction module is used to determine the motion information of the entities present in the environment to eliminate the semantic ambiguity of the motion information.The role of the text-matching module is to match the text given in the environment with the information about the entity’s behavior under observation,thus eliminating false textual information.EMMA-BBP has been tested in the demanding environment of MESSENGER,doubling the generalization ability of EMMA.
基金financial support from the National Natural Science Foundation of China under Grant 12074170in part by the Shenzhen Fundamental Research Program under Grant JCYJ20220530113201003.
文摘Braille serves as an efficient means for visually impaired individuals to access textual information and engage in communication.However,the process of reading Braille can often be cumbersome and time-intensive,particularly in bidirectional human-machine interaction.In this work,a compact optical device for contactless detection of Braille is fabricated and characterized.The GaN-on-sapphire chip,which employs monolithic integration,serves as the core for both light emission and photodetection,significantly reducing its overall footprint.The incorporation of the semiellipsoid epoxy lens with optimized dimensions ensures consistent and accurate detection.The sensing device demonstrates high stability and fast response through its line-scanning capabilities on Braille codes.The captured signals are analyzed using a microcontroller,and the Braille recognition results are wirelessly transmitted to a portable mobile device,enabling the conversion into audio and visual formats.This innovative design not only facilitates Braille reading but also holds the potential to advance human-machine interaction.
