Pub Date : 2022-12-18DOI: 10.1109/IEIR56323.2022.10050084
Fucheng Guo, Pengpeng Jian
Geometry problem solving is a long-standing problem in artificial intelligence. The task requires generating explainable solving sequences based on text and diagram descriptions. Existing approaches have made great progress in geometry formal language extraction and interpretable solving. However, they neglect the graph structure information in formal language. This leads to poor prediction effect of the theorem, and too long reasoning time for problem solving and affects the accuracy of problem solving. In this paper, we construct the formal language graph and use a graph convolutional network to encode structure information of formal language. We propose an improved diagram parser for better diagram relation set extraction. The experimental results show that our method achieves better performance in interpretable geometry problem solving.
{"title":"A Graph Convolutional Network Feature Learning Framework for Interpretable Geometry Problem Solving","authors":"Fucheng Guo, Pengpeng Jian","doi":"10.1109/IEIR56323.2022.10050084","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050084","url":null,"abstract":"Geometry problem solving is a long-standing problem in artificial intelligence. The task requires generating explainable solving sequences based on text and diagram descriptions. Existing approaches have made great progress in geometry formal language extraction and interpretable solving. However, they neglect the graph structure information in formal language. This leads to poor prediction effect of the theorem, and too long reasoning time for problem solving and affects the accuracy of problem solving. In this paper, we construct the formal language graph and use a graph convolutional network to encode structure information of formal language. We propose an improved diagram parser for better diagram relation set extraction. The experimental results show that our method achieves better performance in interpretable geometry problem solving.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134140799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-18DOI: 10.1109/IEIR56323.2022.10050072
Xinguo Yu, Jing Xia, Weina Cheng
It is a valuable research task to construct the knowledge body and develop the method of analyzing the quality of an algorithm evaluation process since the invention of algorithms is one of the most important research tasks in information technology. To this end, this paper explores approaches and quality of algorithm evaluation through reviewing the papers involved in new approach of algorithm evaluation. Concretely, it does the following three jobs. First, it identifies four approaches of algorithm evaluation and further explores their features. Second, it builds the brief taxonomy of algorithm evaluation from the literature. Third, it proposes a scheme of analyzing the quality of a performance evaluation process. This study aims to facilitate the algorithm inventors to use the proper and high quality way to evaluate algorithms.
{"title":"Approaches and Quality of Algorithm Evaluation","authors":"Xinguo Yu, Jing Xia, Weina Cheng","doi":"10.1109/IEIR56323.2022.10050072","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050072","url":null,"abstract":"It is a valuable research task to construct the knowledge body and develop the method of analyzing the quality of an algorithm evaluation process since the invention of algorithms is one of the most important research tasks in information technology. To this end, this paper explores approaches and quality of algorithm evaluation through reviewing the papers involved in new approach of algorithm evaluation. Concretely, it does the following three jobs. First, it identifies four approaches of algorithm evaluation and further explores their features. Second, it builds the brief taxonomy of algorithm evaluation from the literature. Third, it proposes a scheme of analyzing the quality of a performance evaluation process. This study aims to facilitate the algorithm inventors to use the proper and high quality way to evaluate algorithms.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128233669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online learning community provides abundant semantic text information for investigating learning behaviors. Despite the increasing of learning behavior in higher education, few previous researches have studied online learning from multidimensional behavior. This study mainly explores the interrelationship of cognition, emotion and interaction when learners engage in online discussion. The research object is online discussion textual from a certain course. First, lag sequence analysis is adopted to analyze the dynamic of cognition from the micro perspective, and the relationship between cognition and emotion combined with emotion analysis is investigated. Furthermore, this study proposes the standards of quantifying cognition and emotion from the macro perspective, and the cognition, emotion and interaction are analyzed in a unified framework by using social network analysis. Our findings suggest that: (1) Cognitive level is stable during the learning process, and can be improved by continuous thinking and analysis. (2) Positive emotion plays a significant role in developing higher-level cognition, and its value increases gradually with the improvement of cognition level. (3) Network structure has important influence on individual cognition, who with higher cognitive levels are usually in the center of the network and have larger interaction quality. (4) The learning community with more intensive interactions show higher positive emotion, indicating that emotion transmission is realized by means of network structure. This study might give theoretical and technical supports for helping learners improve the learning quality and efficiency in the online learning.
{"title":"Explore the interrelationship of cognition, emotion and interaction when learners engage in online discussion","authors":"Zhu Su, Yue Li, Sannyuya Liu, Liang Zhao, Zhi Liu, Xian Peng","doi":"10.1109/IEIR56323.2022.10050057","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050057","url":null,"abstract":"Online learning community provides abundant semantic text information for investigating learning behaviors. Despite the increasing of learning behavior in higher education, few previous researches have studied online learning from multidimensional behavior. This study mainly explores the interrelationship of cognition, emotion and interaction when learners engage in online discussion. The research object is online discussion textual from a certain course. First, lag sequence analysis is adopted to analyze the dynamic of cognition from the micro perspective, and the relationship between cognition and emotion combined with emotion analysis is investigated. Furthermore, this study proposes the standards of quantifying cognition and emotion from the macro perspective, and the cognition, emotion and interaction are analyzed in a unified framework by using social network analysis. Our findings suggest that: (1) Cognitive level is stable during the learning process, and can be improved by continuous thinking and analysis. (2) Positive emotion plays a significant role in developing higher-level cognition, and its value increases gradually with the improvement of cognition level. (3) Network structure has important influence on individual cognition, who with higher cognitive levels are usually in the center of the network and have larger interaction quality. (4) The learning community with more intensive interactions show higher positive emotion, indicating that emotion transmission is realized by means of network structure. This study might give theoretical and technical supports for helping learners improve the learning quality and efficiency in the online learning.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114684477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BERT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BERT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. Extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms.
{"title":"A BERT-Based Pre-Training Model for Solving Math Application Problems","authors":"Yuhao Jia, Pingheng Wang, Zhen Zhang, Chi Cheng, Zhifei Li, Xinguo Yu","doi":"10.1109/IEIR56323.2022.10050073","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050073","url":null,"abstract":"Solving the math application problem is hot research in intelligence education. An increasing number of research scholars are using pre-trained models to tackle machine solution problems. Noteworthily, the semantic relationships required in the machine solution task are for describing math problems, while those of the BERT model with pre-training weights are of general significance, which will cause a mismatched word vector representation. To solve this problem, we proposed a self-supervised pre-training method based on loss priority. We use the input data from the downstream task datasets to fine-tune the existing BERT model so that the dynamic word vector it obtained can better match the downstream tasks. And the size of the loss value of each data batch in each round of training will be recorded to decide which data should be trained in the next round, so that the model has a faster convergence speed. Furthermore, considering that in large-scale mathematics application problems, some problems have almost the same forms of solution. We proposed a machine solution model training algorithm based on the analogy of the same problem type. Extensive experiments on two well-known datasets show the superiority of our proposed algorithms compared to other state-of-the-art algorithms.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129727652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-18DOI: 10.1109/IEIR56323.2022.10050075
Yazeed AlZahrani, Jun Shen, Jun Yan
This paper deals with a review-based study on the efficient development methodologies for the deployment of IoT systems. Efficient hardware and software development reduces the risk of system bugs and faults. However, the optimal placement of the IoT devices is one of the major challenges for the monitoring applications. In this paper, a combined Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodology is proposed to build IoT software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional data-oriented methodologies. This methodology directs software systems to the specific goal in obtaining outputs inherent to the process. The hybrid methodology includes the support of tools integrated, and also fits the general purpose. This methodology repeats the structure of spatio-temporal reasoning. Segmentation and object detection are used for the division of the region into sub-regions. Furthermore, the coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms.
{"title":"The Spatio-temporal Hybrid Development Methodology for Smart IoT: A Review based Study","authors":"Yazeed AlZahrani, Jun Shen, Jun Yan","doi":"10.1109/IEIR56323.2022.10050075","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050075","url":null,"abstract":"This paper deals with a review-based study on the efficient development methodologies for the deployment of IoT systems. Efficient hardware and software development reduces the risk of system bugs and faults. However, the optimal placement of the IoT devices is one of the major challenges for the monitoring applications. In this paper, a combined Qualitative Spatial Reasoning (QSR) and Qualitative Temporal Reasoning (QTR) methodology is proposed to build IoT software systems. The proposed hybrid methodology includes the features of QSR, QTR, and traditional data-oriented methodologies. This methodology directs software systems to the specific goal in obtaining outputs inherent to the process. The hybrid methodology includes the support of tools integrated, and also fits the general purpose. This methodology repeats the structure of spatio-temporal reasoning. Segmentation and object detection are used for the division of the region into sub-regions. Furthermore, the coverage and connectivity are maintained by the optimal placement of the IoT devices using RCC8 and TPCC algorithms.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"45 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127836786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-18DOI: 10.1109/IEIR56323.2022.10050063
Hao Meng, Liang Xue, Bin He, Xinguo Yu
Most existing neural models solve arithmetic word problems from explicit problem text. However, they can hardly give the solution procedure for problems that contain implicit quantity relations. This paper proposes a missing entity recovery(MER) model to solve arithmetic word problems(AWPs) with implicit knowledge. Given an AWP, the model effectively identifies and represents its explicit expressions into the Nodes Dependency Graph(NDG). Then the nodes on the graph get implicit knowledge from the knowledge base in a recursive way. The group of selected nodes is finally transformed into a group of equations using the solving engine to obtain the answers. The proposed algorithm is evaluated practically based on a collection of established datasets Math23K, showcasing its high accuracy in problem-solving and application in various application situations.
{"title":"Prompt-Based Missing Entity Recovery for Solving Arithmetic Word Problems","authors":"Hao Meng, Liang Xue, Bin He, Xinguo Yu","doi":"10.1109/IEIR56323.2022.10050063","DOIUrl":"https://doi.org/10.1109/IEIR56323.2022.10050063","url":null,"abstract":"Most existing neural models solve arithmetic word problems from explicit problem text. However, they can hardly give the solution procedure for problems that contain implicit quantity relations. This paper proposes a missing entity recovery(MER) model to solve arithmetic word problems(AWPs) with implicit knowledge. Given an AWP, the model effectively identifies and represents its explicit expressions into the Nodes Dependency Graph(NDG). Then the nodes on the graph get implicit knowledge from the knowledge base in a recursive way. The group of selected nodes is finally transformed into a group of equations using the solving engine to obtain the answers. The proposed algorithm is evaluated practically based on a collection of established datasets Math23K, showcasing its high accuracy in problem-solving and application in various application situations.","PeriodicalId":183709,"journal":{"name":"2022 International Conference on Intelligent Education and Intelligent Research (IEIR)","volume":"353 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115230716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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