As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for many.International students,particularly,turned to Twitter to express their struggles an...As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for many.International students,particularly,turned to Twitter to express their struggles and hardships during this difficult time.To better understand the sentiments and experiences of these international students,we developed the Situational Aspect-Based Annotation and Classification(SABAC)text mining framework.This framework uses a three-layer approach,combining baseline Deep Learning(DL)models with Machine Learning(ML)models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset.Using the pro-posed aspect2class annotation algorithm,we labeled bulk unlabeled tweets according to their contained aspect terms.However,we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled datasets.To address this issue,we proposed the Volatile Stopwords Filtering(VSF)technique to reduce sparsity and enhance classifier performance.The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21%when using the random forest as a meta-classifier.Through testing on three benchmark datasets,we found that the SABAC ensemble framework performed exceptionally well.Our findings showed that international students during the pandemic faced various issues,including stress,uncertainty,health concerns,financial stress,and difficulties with online classes and returning to school.By analyzing and summarizing these annotated tweets,decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.展开更多
Switching Markov jump linear system(SMJLS),a special hybrid system,has attracted a lot of studies recently.SMJLS is governed by stochastic and deterministic commutations.This paper focuses on the switching strategy wh...Switching Markov jump linear system(SMJLS),a special hybrid system,has attracted a lot of studies recently.SMJLS is governed by stochastic and deterministic commutations.This paper focuses on the switching strategy which stabilizes the SMJLS in a finite time interval in order to further expand the existing results and investigate new aspects of such systems.Several sufficient conditions for finite-time stability of discrete-time SMJLS are provided,and the numerical problems in these sufficient conditions are solved by solving linear matrix inequalities(LMIs).Finally,numerical examples are given to show the feasibility and effectiveness of the results.展开更多
基金supported by the National Natural Science Foundation of China[Grant Number:92067106]the Ministry of Education of the People’s Republic of China[Grant Number:E-GCCRC20200309].
文摘As the COVID-19 pandemic swept the globe,social media plat-forms became an essential source of information and communication for many.International students,particularly,turned to Twitter to express their struggles and hardships during this difficult time.To better understand the sentiments and experiences of these international students,we developed the Situational Aspect-Based Annotation and Classification(SABAC)text mining framework.This framework uses a three-layer approach,combining baseline Deep Learning(DL)models with Machine Learning(ML)models as meta-classifiers to accurately predict the sentiments and aspects expressed in tweets from our collected Student-COVID-19 dataset.Using the pro-posed aspect2class annotation algorithm,we labeled bulk unlabeled tweets according to their contained aspect terms.However,we also recognized the challenges of reducing data’s high dimensionality and sparsity to improve performance and annotation on unlabeled datasets.To address this issue,we proposed the Volatile Stopwords Filtering(VSF)technique to reduce sparsity and enhance classifier performance.The resulting Student-COVID Twitter dataset achieved a sophisticated accuracy of 93.21%when using the random forest as a meta-classifier.Through testing on three benchmark datasets,we found that the SABAC ensemble framework performed exceptionally well.Our findings showed that international students during the pandemic faced various issues,including stress,uncertainty,health concerns,financial stress,and difficulties with online classes and returning to school.By analyzing and summarizing these annotated tweets,decision-makers can better understand and address the real-time problems international students face during the ongoing pandemic.
基金the National Natural Science Foundation of China(No.61573237)the“111 Project”(No.D18003)the Program of China Scholarship Council(No.201906895021)。
文摘Switching Markov jump linear system(SMJLS),a special hybrid system,has attracted a lot of studies recently.SMJLS is governed by stochastic and deterministic commutations.This paper focuses on the switching strategy which stabilizes the SMJLS in a finite time interval in order to further expand the existing results and investigate new aspects of such systems.Several sufficient conditions for finite-time stability of discrete-time SMJLS are provided,and the numerical problems in these sufficient conditions are solved by solving linear matrix inequalities(LMIs).Finally,numerical examples are given to show the feasibility and effectiveness of the results.
