This paper proposes a hierarchical framework for industrial Internet device authentication and trusted access as well as a mechanism for industrial security state perception, and designs a cross-domain authentication scheme for devices on this basis. The scheme obtains hardware device platform configuration register (PCR) values and platform integrity measure through periodic perception, completes device identity identification and integrity measure verification when device accessing and data transmission requesting, ensures secure and trustworthy access and interoperation of devices, and designs a cross-domain authentication model for trustworthy access of devices and related security protocols. Through the security analysis, this scheme has good anti-attack abilities, and it can effectively protect against common replay attacks, impersonation attacks, and man-in-the-middle attacks.
{"title":"A Lightweight Cross-Domain Authentication Protocol for Trusted Access to Industrial Internet","authors":"Tiantian Zhang, Zhiyong Zhang, Kejing Zhao, Brij B. Gupta, Varsha Arya","doi":"10.4018/ijswis.333481","DOIUrl":"https://doi.org/10.4018/ijswis.333481","url":null,"abstract":"This paper proposes a hierarchical framework for industrial Internet device authentication and trusted access as well as a mechanism for industrial security state perception, and designs a cross-domain authentication scheme for devices on this basis. The scheme obtains hardware device platform configuration register (PCR) values and platform integrity measure through periodic perception, completes device identity identification and integrity measure verification when device accessing and data transmission requesting, ensures secure and trustworthy access and interoperation of devices, and designs a cross-domain authentication model for trustworthy access of devices and related security protocols. Through the security analysis, this scheme has good anti-attack abilities, and it can effectively protect against common replay attacks, impersonation attacks, and man-in-the-middle attacks.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":" 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135341002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Applying sharding protocol to address scalability challenges in alliance chain is popular. However, inevitable cross-shard transactions significantly hamper performance even at low ratios, negating scalability benefits when they dominate as shard scale grows. This article proposes a new sharding protocol suitable for alliance chain that reduces cross-shard transaction impact, improving system performance. It adopts a directed acyclic graph ledger, enabling parallel transaction processing, and employs dynamic transaction confirmation consensus for simplicity. The protocol's sharding process and node score mechanism can deter malicious behavior. Experiments show that compared with mainstream sharding protocols, the protocol performs better when affected by cross-shard transactions. Moreover, its throughput has shown improvement compared to high-performance protocols without cross-shard transactions. This solution suits systems requiring high throughput and reliability, maintaining a stable performance advantage even as cross-shard transactions increase to the usual maximum ratio.
{"title":"A Scalable Sharding Protocol Based on Cross-Shard Dynamic Transaction Confirmation for Alliance Chain in Intelligent Systems","authors":"Nigang Sun, Junlong Li, Yining Liu, Varsha Arya","doi":"10.4018/ijswis.333063","DOIUrl":"https://doi.org/10.4018/ijswis.333063","url":null,"abstract":"Applying sharding protocol to address scalability challenges in alliance chain is popular. However, inevitable cross-shard transactions significantly hamper performance even at low ratios, negating scalability benefits when they dominate as shard scale grows. This article proposes a new sharding protocol suitable for alliance chain that reduces cross-shard transaction impact, improving system performance. It adopts a directed acyclic graph ledger, enabling parallel transaction processing, and employs dynamic transaction confirmation consensus for simplicity. The protocol's sharding process and node score mechanism can deter malicious behavior. Experiments show that compared with mainstream sharding protocols, the protocol performs better when affected by cross-shard transactions. Moreover, its throughput has shown improvement compared to high-performance protocols without cross-shard transactions. This solution suits systems requiring high throughput and reliability, maintaining a stable performance advantage even as cross-shard transactions increase to the usual maximum ratio.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"107 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135539376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Khalid M. O. Nahar, Mustafa Banikhalaf, Firas Ibrahim, Mohammed Abual-Rub, Ammar Almomani, Brij B. Gupta
A healthy diet and daily physical activity are a cornerstone in preventing serious diseases and conditions such as heart disease, diabetes, high blood pressure, and hypertension. They also play an important role in the healthy growth and cognitive development for young and old people. Thus, this paper presents a new restaurant advisory system (RAS) using artificial intelligence (AI) techniques such as machine learning, decision tree, and rule-based methods. The proposed system makes a smart decision based on the user's input information to generate a list of appropriate meals that fit his/her health condition. For accuracy and efficiency measurement procedure in the decision-making process, a dataset from 1100 participants suffering from several diseases such as allergy, age, and body has been created and validated. The performance of the RAS was tested using Visual Basic.net Framework and prolog language. The RAS achieves an accuracy of 100% by testing 30 different live cases.
{"title":"A Rule-Based Expert Advisory System for Restaurants Using Machine Learning and Knowledge-Based Systems Techniques","authors":"Khalid M. O. Nahar, Mustafa Banikhalaf, Firas Ibrahim, Mohammed Abual-Rub, Ammar Almomani, Brij B. Gupta","doi":"10.4018/ijswis.333064","DOIUrl":"https://doi.org/10.4018/ijswis.333064","url":null,"abstract":"A healthy diet and daily physical activity are a cornerstone in preventing serious diseases and conditions such as heart disease, diabetes, high blood pressure, and hypertension. They also play an important role in the healthy growth and cognitive development for young and old people. Thus, this paper presents a new restaurant advisory system (RAS) using artificial intelligence (AI) techniques such as machine learning, decision tree, and rule-based methods. The proposed system makes a smart decision based on the user's input information to generate a list of appropriate meals that fit his/her health condition. For accuracy and efficiency measurement procedure in the decision-making process, a dataset from 1100 participants suffering from several diseases such as allergy, age, and body has been created and validated. The performance of the RAS was tested using Visual Basic.net Framework and prolog language. The RAS achieves an accuracy of 100% by testing 30 different live cases.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"106 S3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.
{"title":"Intelligent Systems in Motion","authors":"Yiyi Cai, Tuanfa Qin, Yang Ou, Rui Wei","doi":"10.4018/ijswis.333056","DOIUrl":"https://doi.org/10.4018/ijswis.333056","url":null,"abstract":"Simultaneous localization and mapping (SLAM) serves as a cornerstone in autonomous systems and has seen exponential growth in its roles, particularly in facilitating advanced path planning solutions. One emerging avenue of research that is rapidly evolving is the incorporation of multi-sensor fusion techniques to enhance SLAM-based path planning. The paper initiates with a thorough review of various sensor types and their attributes before covering a broad spectrum of both traditional and contemporary algorithms for multi-sensor fusion within SLAM. Performance evaluation metrics pertinent to SLAM and sensor fusion are explored. A special focus is laid on the interconnected roles and applications of multi-sensor fusion in SLAM-based path planning, discussing its significance in navigation scenarios as well as addressing challenges such as computational burden and real-time implementation. This paper sets the stage for future developments in creating more robust, resilient, and efficient SLAM-based path planning systems enabled by multi-sensor fusion.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"19 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135326240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An improved SegNet semantic segmentation model is proposed to address the issue of traditional classification algorithms and shallow learning algorithms not being suitable for extracting information from high-resolution remote sensing images. During the research process, space remote sensing images obtained from the GF-1 satellite were used as the data source. In order to improve the operational efficiency of the encoding network, the pooling layer in the encoding network is removed and the ordinary convolutional layer is replaced with a depth-wise separable convolution. By decoding the last layer of the network to obtain the reshaped output results, and then calculating the probability of each classification using a Softmax classifier, the classification of pixels can be achieved. The output result of the classifier is the final result of the remote sensing image semantic segmentation model. The results showed that the proposed algorithm had the highest Kappa coefficient of 0.9531, indicating good classification performance.
{"title":"A Novel Semantic Segmentation Approach Using Improved SegNet and DSC in Remote Sensing Images","authors":"Wanjun Chang, Dongfang Zhang","doi":"10.4018/ijswis.332769","DOIUrl":"https://doi.org/10.4018/ijswis.332769","url":null,"abstract":"An improved SegNet semantic segmentation model is proposed to address the issue of traditional classification algorithms and shallow learning algorithms not being suitable for extracting information from high-resolution remote sensing images. During the research process, space remote sensing images obtained from the GF-1 satellite were used as the data source. In order to improve the operational efficiency of the encoding network, the pooling layer in the encoding network is removed and the ordinary convolutional layer is replaced with a depth-wise separable convolution. By decoding the last layer of the network to obtain the reshaped output results, and then calculating the probability of each classification using a Softmax classifier, the classification of pixels can be achieved. The output result of the classifier is the final result of the remote sensing image semantic segmentation model. The results showed that the proposed algorithm had the highest Kappa coefficient of 0.9531, indicating good classification performance.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yifang Xu, Yunzhuo Sun, Zien Xie, Benxiang Zhai, Youyao Jia, Sidan Du
With the surge in online video content, finding highlights and key video segments have garnered widespread attention. Given a textual query, video highlight detection (HD) and temporal grounding (TG) aim to predict frame-wise saliency scores from a video while concurrently locating all relevant spans. Despite recent progress in DETR-based works, these methods crudely fuse different inputs in the encoder, which limits effective cross-modal interaction. To solve this challenge, the authors design QD-Net (query-guided refinement and dynamic spans network) tailored for HD&TG. Specifically, they propose a query-guided refinement module to decouple the feature encoding from the interaction process. Furthermore, they present a dynamic span decoder that leverages learnable 2D spans as decoder queries, which accelerates training convergence for TG. On QVHighlights dataset, the proposed QD-Net achieves 61.87 HD-HIT@1 and 61.88 TG-mAP@0.5, yielding a significant improvement of +1.88 and +8.05, respectively, compared to the state-of-the-art method.
{"title":"Query-Guided Refinement and Dynamic Spans Network for Video Highlight Detection and Temporal Grounding in Online Information Systems","authors":"Yifang Xu, Yunzhuo Sun, Zien Xie, Benxiang Zhai, Youyao Jia, Sidan Du","doi":"10.4018/ijswis.332768","DOIUrl":"https://doi.org/10.4018/ijswis.332768","url":null,"abstract":"With the surge in online video content, finding highlights and key video segments have garnered widespread attention. Given a textual query, video highlight detection (HD) and temporal grounding (TG) aim to predict frame-wise saliency scores from a video while concurrently locating all relevant spans. Despite recent progress in DETR-based works, these methods crudely fuse different inputs in the encoder, which limits effective cross-modal interaction. To solve this challenge, the authors design QD-Net (query-guided refinement and dynamic spans network) tailored for HD&TG. Specifically, they propose a query-guided refinement module to decouple the feature encoding from the interaction process. Furthermore, they present a dynamic span decoder that leverages learnable 2D spans as decoder queries, which accelerates training convergence for TG. On QVHighlights dataset, the proposed QD-Net achieves 61.87 HD-HIT@1 and 61.88 TG-mAP@0.5, yielding a significant improvement of +1.88 and +8.05, respectively, compared to the state-of-the-art method.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135167104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the coastal areas of China, scientists have collected nearly 500 species of coastal plants and seaweeds. The collected information includes species description, morphological characteristics, habitat distribution and resource value of plants in China. By effectively extracting Chinese text information, this article establishes a Chinese text information extraction model based on DL. This article is based on short-term and short-term memory artificial neural networks for short text classification. In addition, this article also integrates the L-MFCNN models of MFCNN for short text classification. Comparing the two methods with traditional text recognition algorithms, information extraction based on syntax analysis and deep learning, the results show that, compared with the comparison method, the recognition accuracy of Chinese text information of this neural network model can reach 96.69%. Through model training and parameter adjustment, Chinese text information of coastal biodiversity can be quickly extracted, and species categories or names can be identified.
{"title":"Deep Learning in Chinese Text Information Extraction Model for Coastal Biodiversity","authors":"Xiujuan Wang, Xuerong Li","doi":"10.4018/ijswis.331756","DOIUrl":"https://doi.org/10.4018/ijswis.331756","url":null,"abstract":"In the coastal areas of China, scientists have collected nearly 500 species of coastal plants and seaweeds. The collected information includes species description, morphological characteristics, habitat distribution and resource value of plants in China. By effectively extracting Chinese text information, this article establishes a Chinese text information extraction model based on DL. This article is based on short-term and short-term memory artificial neural networks for short text classification. In addition, this article also integrates the L-MFCNN models of MFCNN for short text classification. Comparing the two methods with traditional text recognition algorithms, information extraction based on syntax analysis and deep learning, the results show that, compared with the comparison method, the recognition accuracy of Chinese text information of this neural network model can reach 96.69%. Through model training and parameter adjustment, Chinese text information of coastal biodiversity can be quickly extracted, and species categories or names can be identified.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136293686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abdullah Almuqrin, Ibrahim Mutambik, Abdulaziz Alomran, Justin Zuopeng Zhang
Every year brings numerous security breaches that lead to highly destructive ransomware attacks, data leaks, and reputational damage to governments, companies, and other organizations around the world. As a result, there is a growing need to ensure that workers comply with critical policies put in place to avoid such incidents. This study investigated how factors from social bond theory and involvement theory affected compliance with information security policies and procedures. All of the factors examined were found to have a significant influence on attitudes about compliance, and attitude had a significant impact on intention to comply. The findings of this study revealed that it is vital to raise employees' awareness about compliance with security policies by improving their information security behavior. Moreover, all the factors were found to have a significant influence on the attitude of employees towards compliance with their organizational information security policies and procedures.
{"title":"Enforcing Information System Security","authors":"Abdullah Almuqrin, Ibrahim Mutambik, Abdulaziz Alomran, Justin Zuopeng Zhang","doi":"10.4018/ijswis.331396","DOIUrl":"https://doi.org/10.4018/ijswis.331396","url":null,"abstract":"Every year brings numerous security breaches that lead to highly destructive ransomware attacks, data leaks, and reputational damage to governments, companies, and other organizations around the world. As a result, there is a growing need to ensure that workers comply with critical policies put in place to avoid such incidents. This study investigated how factors from social bond theory and involvement theory affected compliance with information security policies and procedures. All of the factors examined were found to have a significant influence on attitudes about compliance, and attitude had a significant impact on intention to comply. The findings of this study revealed that it is vital to raise employees' awareness about compliance with security policies by improving their information security behavior. Moreover, all the factors were found to have a significant influence on the attitude of employees towards compliance with their organizational information security policies and procedures.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135046595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rupeng Ren, Jun Fang, Jun Hu, Xiaotong Ma, Xiaoyao Li
A risk assessment method for urban railway investment and financing based on an improved SVM model under big data is proposed. First, the inner product in the traditional SVM is replaced by a kernel function to obtain a more accurate non-linear SVM, and a classifier with high classification accuracy is achieved by finding the optimal separating hyperplane. Then, a risk index system is constructed based on the grounded theory combining with intuitionistic fuzzy sets, interval intuitionistic fuzzy sets, weighted averaging operators and the distance measure, and the selection method of assessment indexes is analyzed based on the statistical methods. Finally, the SVM model with fuzzy membership is obtained by fuzzifying the input samples of the SVM based on the given rules of fuzzy membership design. The results show that the maximum relative error between the final test results and the actual value is 0.316%, and the minimum relative error is 0.133% with three different test sets being tested in the proposed method, which can accurately assess the investment.
{"title":"Risk Assessment Modeling of Urban Railway Investment and Financing Based on Improved SVM Model for Advanced Intelligent Systems","authors":"Rupeng Ren, Jun Fang, Jun Hu, Xiaotong Ma, Xiaoyao Li","doi":"10.4018/ijswis.331596","DOIUrl":"https://doi.org/10.4018/ijswis.331596","url":null,"abstract":"A risk assessment method for urban railway investment and financing based on an improved SVM model under big data is proposed. First, the inner product in the traditional SVM is replaced by a kernel function to obtain a more accurate non-linear SVM, and a classifier with high classification accuracy is achieved by finding the optimal separating hyperplane. Then, a risk index system is constructed based on the grounded theory combining with intuitionistic fuzzy sets, interval intuitionistic fuzzy sets, weighted averaging operators and the distance measure, and the selection method of assessment indexes is analyzed based on the statistical methods. Finally, the SVM model with fuzzy membership is obtained by fuzzifying the input samples of the SVM based on the given rules of fuzzy membership design. The results show that the maximum relative error between the final test results and the actual value is 0.316%, and the minimum relative error is 0.133% with three different test sets being tested in the proposed method, which can accurately assess the investment.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135047417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent studies, graph convolutional neural networks (GCNs) have been used to solve different natural language processing (NLP) tasks. However, few researches apply graph convolutional networks to short text classification. Emoji prediction, as a complex sentiment analysis task, has received even less attention. In this work, the authors propose TGCN-Bert which combines pre-trained BERT temporal convolutional networks (TCNs) and graph convolutional networks for short text classification and emoji prediction. They initialize the nodes with the help of BERT and define the edges in text graph based on the term frequency-inverse document frequency (TF-IDF) and positive point-wise mutual information (PPMI). They employ the model for emoji prediction task, and a metric based on emoji clustering is developed to better measure the validity of emoji prediction results. To validate the performance of TGCN-Bert, they compare it with other GCN variants on short text classification datasets and emoji prediction datasets; experiments show that TGCN-Bert achieves better performance.
{"title":"TGCN-Bert Emoji Prediction in Information Systems Using TCN and GCN Fusing Features Based on BERT","authors":"Zhangping Yang, Xia Ye, Hantao Xu","doi":"10.4018/ijswis.331082","DOIUrl":"https://doi.org/10.4018/ijswis.331082","url":null,"abstract":"In recent studies, graph convolutional neural networks (GCNs) have been used to solve different natural language processing (NLP) tasks. However, few researches apply graph convolutional networks to short text classification. Emoji prediction, as a complex sentiment analysis task, has received even less attention. In this work, the authors propose TGCN-Bert which combines pre-trained BERT temporal convolutional networks (TCNs) and graph convolutional networks for short text classification and emoji prediction. They initialize the nodes with the help of BERT and define the edges in text graph based on the term frequency-inverse document frequency (TF-IDF) and positive point-wise mutual information (PPMI). They employ the model for emoji prediction task, and a metric based on emoji clustering is developed to better measure the validity of emoji prediction results. To validate the performance of TGCN-Bert, they compare it with other GCN variants on short text classification datasets and emoji prediction datasets; experiments show that TGCN-Bert achieves better performance.","PeriodicalId":54934,"journal":{"name":"International Journal on Semantic Web and Information Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135193404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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