The classification accuracy of EEG signals based on traditional machine learning methods is low. Therefore, this paper proposes a new model for the feature extraction and recognition of dance motor imagery EEG, which makes full use of the advantage of anti-aliasing filter based on whale parameter optimization method. The anti-aliasing filter is used for preprocessing, and the filtered signal is extracted by two-dimensional empirical wavelet transform. The extracted feature is input to the robust support matrix machine to complete pattern recognition. In pattern recognition process, an improved whale algorithm is used to dynamically adjust the optimal parameters of individual subjects. Experiments are carried out on two public data sets to verify that anti-aliasing filter-based preprocessing can improve signal feature discrimination. The improved whale algorithm can find the optimal parameters of robust support matrix machine classification for individuals. This presented method can improve the recognition rate of dance motion image. Compared with other advanced methods, the proposed method requires less samples and computing resources, and it is suitable for the practical application of brain-computer interface.
{"title":"A2FWPO: Anti-aliasing filter based on whale parameter optimization method for feature extraction and recognition of dance motor imagery EEG","authors":"Tianliang Huang, Ziyue Luo, Yin Lyu","doi":"10.2298/csis221222033h","DOIUrl":"https://doi.org/10.2298/csis221222033h","url":null,"abstract":"The classification accuracy of EEG signals based on traditional machine learning methods is low. Therefore, this paper proposes a new model for the feature extraction and recognition of dance motor imagery EEG, which makes full use of the advantage of anti-aliasing filter based on whale parameter optimization method. The anti-aliasing filter is used for preprocessing, and the filtered signal is extracted by two-dimensional empirical wavelet transform. The extracted feature is input to the robust support matrix machine to complete pattern recognition. In pattern recognition process, an improved whale algorithm is used to dynamically adjust the optimal parameters of individual subjects. Experiments are carried out on two public data sets to verify that anti-aliasing filter-based preprocessing can improve signal feature discrimination. The improved whale algorithm can find the optimal parameters of robust support matrix machine classification for individuals. This presented method can improve the recognition rate of dance motion image. Compared with other advanced methods, the proposed method requires less samples and computing resources, and it is suitable for the practical application of brain-computer interface.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1849-1868"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464333","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}
Ferrari Halfeld, P. Ceravolo, S. Ristić, Yaser Jararweh, Dimitrios Katsaros
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{"title":"Guest editorial: Advances in intelligent data, data engineering, and information systems","authors":"Ferrari Halfeld, P. Ceravolo, S. Ristić, Yaser Jararweh, Dimitrios Katsaros","doi":"10.2298/csis230300vh","DOIUrl":"https://doi.org/10.2298/csis230300vh","url":null,"abstract":"<jats:p>nema</jats:p>","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"1 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464451","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}
Xian Guo, Baobao Wang, Yongbo Jiang, Di Zhang, Laicheng Cao
Machine learning has been widely used for intelligent forwarding strategy in Vehicular Ad-Hoc Networks (VANET). However, machine learning has serious security and privacy issues. BRFD is a smart Receiver Forwarding Decision solution based on Bayesian theory for Named Data Vehicular Ad-Hoc Networks (NDN-VANET). In BRFD, every vehicle that received an interest packet is required to make a forwarding decision according to the collected network status information. And then decides whether it will forward the received interest packet or not. Therefore, the privacy information of a vehicle can be revealed to other vehicles during information exchange of the network status. In this paper, a Privacy-Aware intelligent forwarding solution PABRFD is proposed by integrating Homomorphic Encryption (HE) into the improved BRFD. In PABRFD, a secure Bayesian classifier is used to resolve the security and privacy issues of information exchanged among vehicle nodes. We informally prove that this new scheme can satisfy security requirements and we implement our solution based on HE standard libraries CKKS and BFV. The experimental results show that PABRFD can satisfy our expected performance requirements.
{"title":"Homomorphic encryption based privacy-aware intelligent forwarding mechanism for NDN-VANET","authors":"Xian Guo, Baobao Wang, Yongbo Jiang, Di Zhang, Laicheng Cao","doi":"10.2298/csis220210051g","DOIUrl":"https://doi.org/10.2298/csis220210051g","url":null,"abstract":"Machine learning has been widely used for intelligent forwarding strategy in Vehicular Ad-Hoc Networks (VANET). However, machine learning has serious security and privacy issues. BRFD is a smart Receiver Forwarding Decision solution based on Bayesian theory for Named Data Vehicular Ad-Hoc Networks (NDN-VANET). In BRFD, every vehicle that received an interest packet is required to make a forwarding decision according to the collected network status information. And then decides whether it will forward the received interest packet or not. Therefore, the privacy information of a vehicle can be revealed to other vehicles during information exchange of the network status. In this paper, a Privacy-Aware intelligent forwarding solution PABRFD is proposed by integrating Homomorphic Encryption (HE) into the improved BRFD. In PABRFD, a secure Bayesian classifier is used to resolve the security and privacy issues of information exchanged among vehicle nodes. We informally prove that this new scheme can satisfy security requirements and we implement our solution based on HE standard libraries CKKS and BFV. The experimental results show that PABRFD can satisfy our expected performance requirements.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"2012 1","pages":"1-24"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73786564","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}
Rural mobility research has been left aside in favor of urban transporta tion. Rural areas? low demand, the distance among settlements, and an older pop ulation on average make conventional public transportation inefficient and costly. This paper assesses the contribution that on-demand mobility has the potential to make to rural areas. First, demand-responsive transportation is described, and the related literature is reviewed to gather existing system configurations. Next, we de scribe and implement a proposal and test it on a simulation basis. The results show a clear potential of the demand-responsive mobility paradigm to serve rural demand at an acceptable quality of service. Finally, the results are discussed, and the issues of adoption rate and input data scarcity are addressed.
{"title":"A flexible approach for demand-responsive public transport in rural areas","authors":"Pasqual Martí, Jaume Jordán, Vicente Julian","doi":"10.2298/csis230115074m","DOIUrl":"https://doi.org/10.2298/csis230115074m","url":null,"abstract":"Rural mobility research has been left aside in favor of urban transporta tion. Rural areas? low demand, the distance among settlements, and an older pop ulation on average make conventional public transportation inefficient and costly. This paper assesses the contribution that on-demand mobility has the potential to make to rural areas. First, demand-responsive transportation is described, and the related literature is reviewed to gather existing system configurations. Next, we de scribe and implement a proposal and test it on a simulation basis. The results show a clear potential of the demand-responsive mobility paradigm to serve rural demand at an acceptable quality of service. Finally, the results are discussed, and the issues of adoption rate and input data scarcity are addressed.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"294 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135446186","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}
Marin Lujak, Alessio Salvatore, Alberto Fernández, Stefano Giordani, Kendal Cousy
An individually rational agent will participate in a multiagent coalition if the participation, given available information and knowledge, brings a payoff that is at least as high as the one achieved by not participating. Since agents? performance and skills may vary from task to task, the decisions about individual agent-task assignment will determine the overall performance of the coalition. Maximising the efficiency of the one-on-one assignment of tasks to agents corresponds to the conventional linear sum assignment problem, which considers efficiency as the sum of the costs or benefits of individual agent-task assignments obtained by the coalition as a whole. This approach may be unfair since it does not explicitly consider fairness and, thus, is unsuitable for individually rational agents? coalitions. In this paper, we propose two new assignment models that balance efficiency and fairness in task assignment and study the utilitarian, egalitarian, and Nash social welfare for task assignment in individually rational agents? coalitions. Since fairness is a relatively abstract term that can be difficult to quantify, we propose three new fairness measures based on equity and equality and use them to compare the newly proposed models. Through functional examples, we show that a reasonable trade-off between efficiency and fairness in task assignment is possible through the use of the proposed models.
{"title":"How to fairly and efficiently assign tasks in individually rational agents’ coalitions? Models and fairness measures","authors":"Marin Lujak, Alessio Salvatore, Alberto Fernández, Stefano Giordani, Kendal Cousy","doi":"10.2298/csis230119075l","DOIUrl":"https://doi.org/10.2298/csis230119075l","url":null,"abstract":"An individually rational agent will participate in a multiagent coalition if the participation, given available information and knowledge, brings a payoff that is at least as high as the one achieved by not participating. Since agents? performance and skills may vary from task to task, the decisions about individual agent-task assignment will determine the overall performance of the coalition. Maximising the efficiency of the one-on-one assignment of tasks to agents corresponds to the conventional linear sum assignment problem, which considers efficiency as the sum of the costs or benefits of individual agent-task assignments obtained by the coalition as a whole. This approach may be unfair since it does not explicitly consider fairness and, thus, is unsuitable for individually rational agents? coalitions. In this paper, we propose two new assignment models that balance efficiency and fairness in task assignment and study the utilitarian, egalitarian, and Nash social welfare for task assignment in individually rational agents? coalitions. Since fairness is a relatively abstract term that can be difficult to quantify, we propose three new fairness measures based on equity and equality and use them to compare the newly proposed models. Through functional examples, we show that a reasonable trade-off between efficiency and fairness in task assignment is possible through the use of the proposed models.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"157 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135446191","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}
Halil Arslan, Yunus Işik, Yasin Görmez, Mustafa Temiz
The growing demand for information systems has significantly increased the workload of consulting and software development firms, requiring them to man age multiple projects simultaneously. Usually, these firms rely on a shared pool of staff to carry out multiple projects that require different skills and expertise. How ever, since the number of employees is limited, the assignment of staff to projects should be carefully decided to increase the efficiency in job-sharing. Therefore, assigning tasks to the most appropriate personnel is one of the challenges of multi project management. Assign a staff to the project by team leaders or researchers is a very demanding process. For this reason, researchers are working on automatic assignment, but most of these studies are done using historical data. It is of great importance for companies that personnel assignment systems work with real-time data. However, a model designed with historical data has the risk of getting un successful results in real-time data. In this study, unlike the literature, a machine learning-based decision support system that works with real-time data is proposed. The proposed system analyses the description of newly requested tasks using text mining and machine-learning approaches and then, predicts the optimal available staff that meets the needs of the project task. Moreover, personnel qualifications are iteratively updated after each completed task, ensuring up-to-date information on staff capabilities. In addition, because our system was developed as a microservice architecture, it can be easily integrated into companies? existing enterprise resource planning (ERP) or portal systems. In a real-world implementation at Detaysoft, the system demonstrated high assignment accuracy, achieving up to 80% accuracy in matching tasks with appropriate personnel.
{"title":"Machine learning and text mining based real-time semi-autonomous staff assignment system","authors":"Halil Arslan, Yunus Işik, Yasin Görmez, Mustafa Temiz","doi":"10.2298/csis220922065a","DOIUrl":"https://doi.org/10.2298/csis220922065a","url":null,"abstract":"The growing demand for information systems has significantly increased the\u0000 workload of consulting and software development firms, requiring them to man\u0000 age multiple projects simultaneously. Usually, these firms rely on a shared\u0000 pool of staff to carry out multiple projects that require different skills\u0000 and expertise. How ever, since the number of employees is limited, the\u0000 assignment of staff to projects should be carefully decided to increase the\u0000 efficiency in job-sharing. Therefore, assigning tasks to the most\u0000 appropriate personnel is one of the challenges of multi project management.\u0000 Assign a staff to the project by team leaders or researchers is a very\u0000 demanding process. For this reason, researchers are working on automatic \u0000 assignment, but most of these studies are done using historical data. It is\u0000 of great importance for companies that personnel assignment systems work\u0000 with real-time data. However, a model designed with historical data has the\u0000 risk of getting un successful results in real-time data. In this study,\u0000 unlike the literature, a machine learning-based decision support system that\u0000 works with real-time data is proposed. The proposed system analyses the\u0000 description of newly requested tasks using text mining and machine-learning\u0000 approaches and then, predicts the optimal available staff that meets the\u0000 needs of the project task. Moreover, personnel qualifications are\u0000 iteratively updated after each completed task, ensuring up-to-date\u0000 information on staff capabilities. In addition, because our system was\u0000 developed as a microservice architecture, it can be easily integrated into\u0000 companies? existing enterprise resource planning (ERP) or portal systems. In\u0000 a real-world implementation at Detaysoft, the system demonstrated high\u0000 assignment accuracy, achieving up to 80% accuracy in matching tasks with\u0000 appropriate personnel.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135446195","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 tourism industry, millions of card transactions generate a massive volume of big data. The card transactions eventually reflect customers? consumption behaviors and patterns. Additionally, recommender systems that incorporate users? personal preferences and consumption is an important subject of smart tourism. However, challenges exist such as handling the absence of rating data and considering spatial factor that significantly affects recommendation performance. This paper applies well-known Doc2Vec techniques to the tourism recommendation. We use them on non-textual features, card transaction dataset, to recommend tourism business services to target user groups who visit a specific location while addressing the challenges above. For the experiments, a card transaction dataset among eight years from Shinhan, which is one of the major card companies in the Republic of Korea, is used. The results demonstrate that the use of vector space representations trained by the Doc2Vec techniques considering spatial information is promising for tourism recommendations.
{"title":"Tourism recommendation based on word embedding from card transaction data","authors":"Minsung Hong, Namho Chung, C. Koo","doi":"10.2298/csis220620002h","DOIUrl":"https://doi.org/10.2298/csis220620002h","url":null,"abstract":"In the tourism industry, millions of card transactions generate a massive volume of big data. The card transactions eventually reflect customers? consumption behaviors and patterns. Additionally, recommender systems that incorporate users? personal preferences and consumption is an important subject of smart tourism. However, challenges exist such as handling the absence of rating data and considering spatial factor that significantly affects recommendation performance. This paper applies well-known Doc2Vec techniques to the tourism recommendation. We use them on non-textual features, card transaction dataset, to recommend tourism business services to target user groups who visit a specific location while addressing the challenges above. For the experiments, a card transaction dataset among eight years from Shinhan, which is one of the major card companies in the Republic of Korea, is used. The results demonstrate that the use of vector space representations trained by the Doc2Vec techniques considering spatial information is promising for tourism recommendations.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"911-931"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90938311","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}
Personalized training systems and augmented reality are two of the most promising educational technologies since they could enhance engineering students? spatial ability. Prior research has examined the benefits of the integration of augmented reality in increasing students? motivation and enhancing their spatial skills. However, based on the review of the literature, current training systems do not provide adaptivity to students? individual needs. In view of the above, this paper presents a novel adaptive augmented reality training system, which teaches the knowledge domain of technical drawing. The novelty of the proposed system is that it proposes using fuzzy sets to represent the students? knowledge levels more accurately in the adaptive augmented reality training system. The system determines the amount and the level of difficulty of the learning activities delivered to the students, based on their progress. The main contribution of the system is that it is student-centered, providing the students with an adaptive training experience. The evaluation of the system took place during the 2021-22 and 2022-23 winter semesters, and the results are very promising.
{"title":"PARSAT: Fuzzy logic for adaptive spatial ability training in an augmented reality system","authors":"Christos Papakostas, Christos Troussas, Akrivi Krouska, Cleo Sgouropoulou","doi":"10.2298/csis230130043p","DOIUrl":"https://doi.org/10.2298/csis230130043p","url":null,"abstract":"Personalized training systems and augmented reality are two of the most promising educational technologies since they could enhance engineering students? spatial ability. Prior research has examined the benefits of the integration of augmented reality in increasing students? motivation and enhancing their spatial skills. However, based on the review of the literature, current training systems do not provide adaptivity to students? individual needs. In view of the above, this paper presents a novel adaptive augmented reality training system, which teaches the knowledge domain of technical drawing. The novelty of the proposed system is that it proposes using fuzzy sets to represent the students? knowledge levels more accurately in the adaptive augmented reality training system. The system determines the amount and the level of difficulty of the learning activities delivered to the students, based on their progress. The main contribution of the system is that it is student-centered, providing the students with an adaptive training experience. The evaluation of the system took place during the 2021-22 and 2022-23 winter semesters, and the results are very promising.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783604","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}
Xia Lei, Jia-Jiang Lin, Xiong-Lin Luo, Yongkai Fan
Understanding deep residual networks (ResNets) decisions are receiving much attention as a way to ensure their security and reliability. Recent research, however, lacks theoretical analysis to guarantee the faithfulness of explanations and could produce an unreliable explanation. In order to explain ResNets predictions, we suggest a provably faithful explanation for ResNet using a surrogate explainable model, a neural ordinary differential equation network (Neural ODE). First, ResNets are proved to converge to a Neural ODE and the Neural ODE is regarded as a surrogate model to explain the decision-making attribution of the ResNets. And then the decision feature and the explanation map of inputs belonging to the target class for Neural ODE are generated via the symplectic adjoint method. Finally, we prove that the explanations of Neural ODE can be sufficiently approximate to ResNet. Experiments show that the proposed explanation method has higher faithfulness with lower computational cost than other explanation approaches and it is effective for troubleshooting and optimizing a model by the explanation.
{"title":"Explaining deep residual networks predictions with symplectic adjoint method","authors":"Xia Lei, Jia-Jiang Lin, Xiong-Lin Luo, Yongkai Fan","doi":"10.2298/csis230310047l","DOIUrl":"https://doi.org/10.2298/csis230310047l","url":null,"abstract":"Understanding deep residual networks (ResNets) decisions are receiving much attention as a way to ensure their security and reliability. Recent research, however, lacks theoretical analysis to guarantee the faithfulness of explanations and could produce an unreliable explanation. In order to explain ResNets predictions, we suggest a provably faithful explanation for ResNet using a surrogate explainable model, a neural ordinary differential equation network (Neural ODE). First, ResNets are proved to converge to a Neural ODE and the Neural ODE is regarded as a surrogate model to explain the decision-making attribution of the ResNets. And then the decision feature and the explanation map of inputs belonging to the target class for Neural ODE are generated via the symplectic adjoint method. Finally, we prove that the explanations of Neural ODE can be sufficiently approximate to ResNet. Experiments show that the proposed explanation method has higher faithfulness with lower computational cost than other explanation approaches and it is effective for troubleshooting and optimizing a model by the explanation.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136209985","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}
A. Khan, F. Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira
Online discussion forums are repositories of valuable information where users interact and articulate their ideas, opinions, and share experiences about nu merous topics. They are internet-based online communities where users can ask for help and find the solution to a problem. On online discussion forums, a new user becomes exhausted from reading the significant number of replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome this limitation, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the per formance of DTS. The empirical result in the context of average precision, recall, and F-measure of the proposed approach with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets proves the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks by boosting the performance of the automated DTS model.
{"title":"Sentence embedding approach using LSTM auto-encoder for discussion threads summarization","authors":"A. Khan, F. Al-Obeidat, Afsheen Khalid, Adnan Amin, Fernando Moreira","doi":"10.2298/csis221210055k","DOIUrl":"https://doi.org/10.2298/csis221210055k","url":null,"abstract":"Online discussion forums are repositories of valuable information where users interact and articulate their ideas, opinions, and share experiences about nu merous topics. They are internet-based online communities where users can ask for help and find the solution to a problem. On online discussion forums, a new user becomes exhausted from reading the significant number of replies in a discussion. An automated discussion thread summarizing system (DTS) is necessary to create a candid view of the entire discussion of a query. Most of the previous approaches for automated DTS use the continuous bag of words (CBOW) model as a sentence embedding tool, which is poor at capturing the overall meaning of the sentence and is unable to grasp word dependency. To overcome this limitation, we introduce the LSTM Auto-encoder as a sentence embedding technique to improve the per formance of DTS. The empirical result in the context of average precision, recall, and F-measure of the proposed approach with respect to ROGUE-1 and ROUGE-2 of two standard experimental datasets proves the effectiveness and efficiency of the proposed approach and outperforms the state-of-the-art CBOW model in sentence embedding tasks by boosting the performance of the automated DTS model.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"20 1","pages":"1367-1387"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68464284","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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