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}
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}
State of the art formal verification is based on formal methods and its goal is proving given correctness properties. For example, a PSTM scheduler was modeled in CSP in order to prove deadlock-freeness and starvation-freeness. However, as this paper shows, using solely formal methods is not sufficient. Therefore, in this paper we propose a complete formal verification of trustworthy software, which jointly uses formal verification and formal model testing. As an example, we first test the previous CSP model of PSTM transaction scheduler by comparing the model checker PAT results with the manually derived expected results, for the given test workloads. Next, according to the results of this testing, we correct and extend the CSP model. Finally, using PAT results for the new CSP model, we analyze the performance of the PSTM online transaction scheduling algorithms from the perspective of the relative speedup.
{"title":"Complete formal verification of the PSTM transaction Scheduler","authors":"M. Popovic, M. Popovic, B. Kordic, Huibiao Zhu","doi":"10.2298/csis210908058p","DOIUrl":"https://doi.org/10.2298/csis210908058p","url":null,"abstract":"State of the art formal verification is based on formal methods and its goal is proving given correctness properties. For example, a PSTM scheduler was modeled in CSP in order to prove deadlock-freeness and starvation-freeness. However, as this paper shows, using solely formal methods is not sufficient. Therefore, in this paper we propose a complete formal verification of trustworthy software, which jointly uses formal verification and formal model testing. As an example, we first test the previous CSP model of PSTM transaction scheduler by comparing the model checker PAT results with the manually derived expected results, for the given test workloads. Next, according to the results of this testing, we correct and extend the CSP model. Finally, using PAT results for the new CSP model, we analyze the performance of the PSTM online transaction scheduling algorithms from the perspective of the relative speedup.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"42 1","pages":"307-327"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76368517","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}
Recording anomalous traces in business processes diminishes an event log?s quality. The abnormalities may represent bad execution, security issues, or deviant behavior. Focusing on mitigating this phenomenon, organizations spend efforts to detect anomalous traces in their business processes to save resources and improve process execution. However, in many real-world environments, reference models are unavailable, requiring expert assistance and increasing costs. The con15 siderable number of techniques and reduced availability of experts pose an additional challenge for particular scenarios. In this work, we combine the representational power of encoding with a Meta-learning strategy to enhance the detection of anomalous traces in event logs towards fitting the best discriminative capability be tween common and irregular traces. Our approach creates an event log profile and recommends the most suitable encoding technique to increase the anomaly detetion performance. We used eight encoding techniques from different families, 80 log descriptors, 168 event logs, and six anomaly types for experiments. Results indicate that event log characteristics influence the representational capability of encodings. Moreover, we investigate the process behavior?s influence for choosing the suitable encoding technique, demonstrating that traditional process mining analysis can be leveraged when matched with intelligent decision support approaches.
{"title":"Matching business process behavior with encoding techniques via meta-learning: An anomaly detection study","authors":"G. Tavares, Sylvio Barbon Junior","doi":"10.2298/csis220110005t","DOIUrl":"https://doi.org/10.2298/csis220110005t","url":null,"abstract":"Recording anomalous traces in business processes diminishes an event log?s quality. The abnormalities may represent bad execution, security issues, or deviant behavior. Focusing on mitigating this phenomenon, organizations spend efforts to detect anomalous traces in their business processes to save resources and improve process execution. However, in many real-world environments, reference models are unavailable, requiring expert assistance and increasing costs. The con15 siderable number of techniques and reduced availability of experts pose an additional challenge for particular scenarios. In this work, we combine the representational power of encoding with a Meta-learning strategy to enhance the detection of anomalous traces in event logs towards fitting the best discriminative capability be tween common and irregular traces. Our approach creates an event log profile and recommends the most suitable encoding technique to increase the anomaly detetion performance. We used eight encoding techniques from different families, 80 log descriptors, 168 event logs, and six anomaly types for experiments. Results indicate that event log characteristics influence the representational capability of encodings. Moreover, we investigate the process behavior?s influence for choosing the suitable encoding technique, demonstrating that traditional process mining analysis can be leveraged when matched with intelligent decision support approaches.","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"91 1","pages":"1207-1233"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74968710","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}
{"title":"Guest editorial - Parallel and distributed computing and applications","authors":"Hong Shen, Hui Tian, Yingpeng Sang","doi":"10.2298/csis230100ixs","DOIUrl":"https://doi.org/10.2298/csis230100ixs","url":null,"abstract":"<jats:p>nema</jats:p>","PeriodicalId":50636,"journal":{"name":"Computer Science and Information Systems","volume":"1 1","pages":"ix"},"PeriodicalIF":1.4,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82766606","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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