Ahmed Yassine Chakor, Azmani Monir, Azmani Abdellah
Abstract Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions.
{"title":"Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision","authors":"Ahmed Yassine Chakor, Azmani Monir, Azmani Abdellah","doi":"10.2478/acss-2022-0005","DOIUrl":"https://doi.org/10.2478/acss-2022-0005","url":null,"abstract":"Abstract Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"7 1","pages":"43 - 54"},"PeriodicalIF":1.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84073330","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}
Abstract During COVID-19 pandemic, interest in mHealth rose dramatically. An ample literature review was carried out to discover whether personality traits could be the basis for mHealth personalization for human-computer interaction improvement. Moreover, the study of three most popular mHealth applications was conducted to determine data collected by users. The results showed that personality traits affected communication and physical activity preferences, motivation, and application usage. mHealth personalization based on personality traits could suggest enjoyable physical activities and motivational communication. mHealth applications already process enough user information to enable seamless inference of personality traits.
{"title":"mHealth and User Interaction Improvement by Personality Traits-Based Personalization","authors":"Jelena Avanesova, Jeļizaveta Lieldidža-Kolbina","doi":"10.2478/acss-2022-0006","DOIUrl":"https://doi.org/10.2478/acss-2022-0006","url":null,"abstract":"Abstract During COVID-19 pandemic, interest in mHealth rose dramatically. An ample literature review was carried out to discover whether personality traits could be the basis for mHealth personalization for human-computer interaction improvement. Moreover, the study of three most popular mHealth applications was conducted to determine data collected by users. The results showed that personality traits affected communication and physical activity preferences, motivation, and application usage. mHealth personalization based on personality traits could suggest enjoyable physical activities and motivational communication. mHealth applications already process enough user information to enable seamless inference of personality traits.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"3 1","pages":"55 - 61"},"PeriodicalIF":1.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73027186","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}
Abstract The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.
{"title":"Urdu Sentiment Analysis","authors":"Iffraah Rehman, Tariq Rahim Soomro","doi":"10.2478/acss-2022-0004","DOIUrl":"https://doi.org/10.2478/acss-2022-0004","url":null,"abstract":"Abstract The world is heading towards more modernized and digitalized data and therefore a significant growth is observed in the active number of social media users with each passing day. Each post and comment can give an insight into valuable information about a certain topic or issue, a product or a brand, etc. Similarly, the process to uncover the underlying information from the opinion that a person keeps about any entity is called a sentiment analysis. The analysis can be carried out through two main approaches, i.e., either lexicon-based or machine learning algorithms. A significant amount of work in the different domains has been done in numerous languages for sentiment analysis, but minimal research has been conducted on the national language of Pakistan, which is Urdu. Twitter users who are familiar with Urdu update the tweets in two different textual formats either in Urdu Script (Nastaleeq) or in Roman Urdu. Thus, the paper is an attempt to perform the sentiment analysis on the Urdu language by extracting the tweets (Nastaleeq and Roman Urdu both) from Twitter using Tweepy API. A machine learning-based approach has been adopted for this study and the tool opted for the purpose is WEKA. The best algorithm was identified based on evaluation metrics, which comprise the number of correctly and incorrectly classified instances, accuracy, precision, and recall. SMO was found to be the most suitable machine learning algorithm for performing the sentiment analysis on Urdu (Nastaleeq) tweets, while the Roman Urdu Random Forest algorithm was identified as the best one.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"85 10 1","pages":"30 - 42"},"PeriodicalIF":1.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83447783","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}
N. Ben, Sergiy Ryzhkov, A. Topalov, O. Gerasin, Xi Yan, A. Karpechenko, Oleksii Povorozniuk
Abstract The study aims to develop an information-computing complex for computer design of a centrifugal compressor with parallel calculation of stages and optimization of the geometric parameters of the impellers and the diffusers. The paper presents a universal methodology and computerized information system of the main geometry parameter determination and optimization of the centrifugal compressor impellers and vanned diffusers. Optimization of cross-sectional areas of the input and output channels of the impeller and diffuser blade channels is held using a gradient descent method by gas flowrate quadratic integral deviation criteria. The information-computing complex is built on the algorithm proposed by the authors and implemented as a computer program with a human-machine interface. Calculation data are written in the form of numerical arrays with the possibility of interpolating data and obtaining graphical dependencies.
{"title":"A Methodology and Information System for Computing and Optimization of Impellers and Vanned Diffusers Geometry Parameters","authors":"N. Ben, Sergiy Ryzhkov, A. Topalov, O. Gerasin, Xi Yan, A. Karpechenko, Oleksii Povorozniuk","doi":"10.2478/acss-2022-0007","DOIUrl":"https://doi.org/10.2478/acss-2022-0007","url":null,"abstract":"Abstract The study aims to develop an information-computing complex for computer design of a centrifugal compressor with parallel calculation of stages and optimization of the geometric parameters of the impellers and the diffusers. The paper presents a universal methodology and computerized information system of the main geometry parameter determination and optimization of the centrifugal compressor impellers and vanned diffusers. Optimization of cross-sectional areas of the input and output channels of the impeller and diffuser blade channels is held using a gradient descent method by gas flowrate quadratic integral deviation criteria. The information-computing complex is built on the algorithm proposed by the authors and implemented as a computer program with a human-machine interface. Calculation data are written in the form of numerical arrays with the possibility of interpolating data and obtaining graphical dependencies.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"92 1","pages":"62 - 74"},"PeriodicalIF":1.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80454328","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}
Abstract In the modern online world, users are often asked for a permission to track their actions as a permission to “allow cookies”. The gathered information could be very valuable for a potential advertiser. However, online tracking is not only a benefit for a user but also a threat to the user’s privacy. This information combined with a targeted advertisement on a mass scale has potential to alter behaviour of large groups. This study summarises previous academic work on online user tracking and anti-tracking measures. As a result, it describes the current mechanisms used to track a user, as well as some methods that can be applied to reduce tracking. The study concludes that government legislation and open dialog between Internet users and advertisers might be the only way to ensure online privacy.
{"title":"Internet User Trackers and Where to Find Them","authors":"Vitalijs Teze, Erika Nazaruka","doi":"10.2478/acss-2022-0008","DOIUrl":"https://doi.org/10.2478/acss-2022-0008","url":null,"abstract":"Abstract In the modern online world, users are often asked for a permission to track their actions as a permission to “allow cookies”. The gathered information could be very valuable for a potential advertiser. However, online tracking is not only a benefit for a user but also a threat to the user’s privacy. This information combined with a targeted advertisement on a mass scale has potential to alter behaviour of large groups. This study summarises previous academic work on online user tracking and anti-tracking measures. As a result, it describes the current mechanisms used to track a user, as well as some methods that can be applied to reduce tracking. The study concludes that government legislation and open dialog between Internet users and advertisers might be the only way to ensure online privacy.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"126 1","pages":"75 - 82"},"PeriodicalIF":1.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74897955","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}
Abstract One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class.
{"title":"Detection of Driver Dynamics with VGG16 Model","authors":"Alper Aytekin, Vasfiye Mençik","doi":"10.2478/acss-2022-0009","DOIUrl":"https://doi.org/10.2478/acss-2022-0009","url":null,"abstract":"Abstract One of the most important factors triggering the occurrence of traffic accidents is that drivers continue to drive in a tired and drowsy state. It is a great opportunity to regularly control the dynamics of the driver with transfer learning methods while driving, and to warn the driver in case of possible drowsiness and to focus their attention in order to prevent traffic accidents due to drowsiness. A classification study was carried out with the aim of detecting the drowsiness of the driver by the position of the eyelids and the presence of yawning movement using the Convolutional Neural Network (CNN) architecture. The dataset used in the study includes the face shapes of drivers of different genders and different ages while driving. Accuracy and F1-score parameters were used for experimental studies. The results achieved are 91 % accuracy for the VGG16 model and an F1-score of over 90 % for each class.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"92 1","pages":"83 - 88"},"PeriodicalIF":1.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76391002","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}
Abstract The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October selection was performed using 14 different feature selection methods based on the biochemical parameters available to us. As a result of the study, the performance of the algorithms and feature selection methods was evaluated using performance evaluation criteria. The dataset used in the study consists of 100 covid-negative and 121 covid-positive data from a total of 221 patients. The dataset includes 16 biochemical parameters used for the diagnosis of Covid-19. Feature selection methods were used to reduce the number of parameters and perform the classification process. The result of the study shows that the new feature set obtained using feature selection algorithms yields very similar results to the set containing all features. Overall, 5 features obtained from 16 features by feature selection methods yielded the best performance for the K-Nearest Neighbour algorithm with the FSVFS feature selection method of 86.4 %.
{"title":"Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis","authors":"Çağla Danacı, S. Tuncer","doi":"10.2478/acss-2022-0002","DOIUrl":"https://doi.org/10.2478/acss-2022-0002","url":null,"abstract":"Abstract The aim of the study is to diagnose Covid-19 by machine learning algorithms using biochemical parameters. In addition to the aim of the study, October selection was performed using 14 different feature selection methods based on the biochemical parameters available to us. As a result of the study, the performance of the algorithms and feature selection methods was evaluated using performance evaluation criteria. The dataset used in the study consists of 100 covid-negative and 121 covid-positive data from a total of 221 patients. The dataset includes 16 biochemical parameters used for the diagnosis of Covid-19. Feature selection methods were used to reduce the number of parameters and perform the classification process. The result of the study shows that the new feature set obtained using feature selection algorithms yields very similar results to the set containing all features. Overall, 5 features obtained from 16 features by feature selection methods yielded the best performance for the K-Nearest Neighbour algorithm with the FSVFS feature selection method of 86.4 %.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"16 1","pages":"13 - 18"},"PeriodicalIF":1.0,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80096460","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}
Abstract There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric methods. Recently, methods using behavioural biometric features have gained popularity. Identification made using gait pattern is also one of these methods. The present study proposes a machine learning based system performing identification in real time via gait features using a Kinect device. The data set is composed of 23 individuals’ skeleton model data obtained by the authors. From these data, 147 handcrafted features have been extracted. Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting (GB), XG-Boost (XGB) and K-Nearest Neighbour (KNN) classifiers have been trained with these features. Furthermore, the output of these five machine learning models has been combined with a voting approach. The highest classification has been obtained with 97.5 % accuracy via a voting approach. The classification accuracies of the RF, DNN, XGB, GB and KNN classifiers are 95 %, 87.5 %, 85 %, 80 % and 65 %, respectively. The classification accuracy obtained via a voting approach is higher than in the previous studies. The developed system successfully performs real-time identification.
{"title":"Real-Time Identification from Gait Features Using Cascade Voting Method","authors":"Berk Ercin, Abdulkadir Karacı","doi":"10.2478/acss-2021-0020","DOIUrl":"https://doi.org/10.2478/acss-2021-0020","url":null,"abstract":"Abstract There are several biometric methods for identification. These are generally classified under two main groups as physiological and behavioural biometric methods. Recently, methods using behavioural biometric features have gained popularity. Identification made using gait pattern is also one of these methods. The present study proposes a machine learning based system performing identification in real time via gait features using a Kinect device. The data set is composed of 23 individuals’ skeleton model data obtained by the authors. From these data, 147 handcrafted features have been extracted. Deep Neural Network (DNN), Random Forest (RF), Gradient Boosting (GB), XG-Boost (XGB) and K-Nearest Neighbour (KNN) classifiers have been trained with these features. Furthermore, the output of these five machine learning models has been combined with a voting approach. The highest classification has been obtained with 97.5 % accuracy via a voting approach. The classification accuracies of the RF, DNN, XGB, GB and KNN classifiers are 95 %, 87.5 %, 85 %, 80 % and 65 %, respectively. The classification accuracy obtained via a voting approach is higher than in the previous studies. The developed system successfully performs real-time identification.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"38 1","pages":"164 - 172"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90375917","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}
Abstract Nowadays, natural language processing (NLP) is increasingly relaying on pre-trained word embeddings for use in various tasks. However, there is little research devoted to Latvian – a language that is much more morphologically complex than English. In this study, several experiments were carried out in three NLP tasks on four different methods of creating word embeddings: word2vec, fastText, Structured Skip-Gram and ngram2vec. The obtained results can serve as a baseline for future research on the Latvian language in NLP. The main conclusions are the following: First, in the part-of-speech task, using a training corpus 46 times smaller than in a previous study, the accuracy was 91.4 % (versus 98.3 % in the previous study). Second, fastText demonstrated the overall best effectiveness. Third, the best results for all methods were observed for embeddings with a dimension size of 200. Finally, word lemmatization generally did not improve results.
{"title":"Evaluation of Word Embedding Models in Latvian NLP Tasks Based on Publicly Available Corpora","authors":"Rolands Laucis, Gints Jēkabsons","doi":"10.2478/acss-2021-0016","DOIUrl":"https://doi.org/10.2478/acss-2021-0016","url":null,"abstract":"Abstract Nowadays, natural language processing (NLP) is increasingly relaying on pre-trained word embeddings for use in various tasks. However, there is little research devoted to Latvian – a language that is much more morphologically complex than English. In this study, several experiments were carried out in three NLP tasks on four different methods of creating word embeddings: word2vec, fastText, Structured Skip-Gram and ngram2vec. The obtained results can serve as a baseline for future research on the Latvian language in NLP. The main conclusions are the following: First, in the part-of-speech task, using a training corpus 46 times smaller than in a previous study, the accuracy was 91.4 % (versus 98.3 % in the previous study). Second, fastText demonstrated the overall best effectiveness. Third, the best results for all methods were observed for embeddings with a dimension size of 200. Finally, word lemmatization generally did not improve results.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"28 3","pages":"132 - 138"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72628444","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}
Abstract The classic Internet of Things-Cloud Computing model faces challenges like high response latency, high bandwidth consumption, and high storage requirement with increasing velocity and volume of generated data. Fog computing offers better services to end users by bringing processing, storage, and networking closer to them. Recently, there has been significant research addressing architectural and algorithmic aspects of fog computing. In the existing literature, a systematic study of architectural designs is widely conducted for various applications. Algorithms are seldom examined. Algorithms play a crucial role in fog computing. This survey aims to performing a comparative study of existing algorithms. The study also presents a systematic classification of the current fog computing algorithms and highlights the key challenges and research issues associated with them.
{"title":"Fog Computing Algorithms: A Survey and Research Opportunities","authors":"Shaifali P. Malukani, C. Bhensdadia","doi":"10.2478/acss-2021-0017","DOIUrl":"https://doi.org/10.2478/acss-2021-0017","url":null,"abstract":"Abstract The classic Internet of Things-Cloud Computing model faces challenges like high response latency, high bandwidth consumption, and high storage requirement with increasing velocity and volume of generated data. Fog computing offers better services to end users by bringing processing, storage, and networking closer to them. Recently, there has been significant research addressing architectural and algorithmic aspects of fog computing. In the existing literature, a systematic study of architectural designs is widely conducted for various applications. Algorithms are seldom examined. Algorithms play a crucial role in fog computing. This survey aims to performing a comparative study of existing algorithms. The study also presents a systematic classification of the current fog computing algorithms and highlights the key challenges and research issues associated with them.","PeriodicalId":41960,"journal":{"name":"Applied Computer Systems","volume":"199 1","pages":"139 - 149"},"PeriodicalIF":1.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75955174","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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