Pub Date : 2020-11-01DOI: 10.6688/JISE.202011_36(6).0013
Hong-Ji Wang, Xiang Xu, Baomin Xu, Yu Shuang-Yuan, Wang Quan-Xin
In the context of model compression using the student-teacher paradigm, we propose the idea of student-centric learning, where the student is less constrained by the teacher and able to learn on its own. We believe the student should have more flexibility during training. Towards student-centric learning, we propose two approaches: correlation-based learning and self-guided learning. In correlation-based learning, we propose to guide the student with two types of correlations between activations: the correlation between different channels and the correlation between different spatial locations. In self-guided learning, we propose to give the student network the opportunity to learn by itself in the form of additional self-taught neurons. We empirically validate our approaches on benchmark datasets, producing state-of-the-art results. Notably, our approaches can train a smaller and shallower student network with only 5 layers that outperforms a larger and deeper teacher network with 11 layers by nearly 1% on CIFAR-100.
{"title":"Student-Centric Network Learning for Improved Knowledge Transfer","authors":"Hong-Ji Wang, Xiang Xu, Baomin Xu, Yu Shuang-Yuan, Wang Quan-Xin","doi":"10.6688/JISE.202011_36(6).0013","DOIUrl":"https://doi.org/10.6688/JISE.202011_36(6).0013","url":null,"abstract":"In the context of model compression using the student-teacher paradigm, we propose the idea of student-centric learning, where the student is less constrained by the teacher and able to learn on its own. We believe the student should have more flexibility during training. Towards student-centric learning, we propose two approaches: correlation-based learning and self-guided learning. In correlation-based learning, we propose to guide the student with two types of correlations between activations: the correlation between different channels and the correlation between different spatial locations. In self-guided learning, we propose to give the student network the opportunity to learn by itself in the form of additional self-taught neurons. We empirically validate our approaches on benchmark datasets, producing state-of-the-art results. Notably, our approaches can train a smaller and shallower student network with only 5 layers that outperforms a larger and deeper teacher network with 11 layers by nearly 1% on CIFAR-100.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"2 1","pages":"1339-1351"},"PeriodicalIF":1.1,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82010397","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}
Pub Date : 2020-09-01DOI: 10.6688/JISE.202009_36(5).0009
Mian Mian Lau, K. Lim
Deep feedforward network (DFN) is the general structure of many well-known deep neural networks (DNN) for image classification. The recent research emphasizes on going deeper and wider network architecture to achieve higher accuracy and lower misclassification rate. This paper provides a study and investigation on stacking three basic operation of neural layers, i.e. convolutional layer, pooling layer and fully connected layer. As a result, a new framework of convolutional deep feedforward network (C-DFN) is proposed in this paper. C-DFN performed significantly better than deep feedforward network (DFN), deep belief network (DBN), and convolutional deep belief network (C-DBN) in MNIST dataset, INRIA pedestrian dataset and Daimler pedestrian dataset. The convolutional layer acts as a trainable feature extractor improving the network performance significantly. Moreover, it reduced 14% of the trainable parameters in DFN. With the use of trainable activation function such as PReLU in the C-DFN, it achieves an average misclassification rate of 9.22% of the three benchmark datasets.
{"title":"Convolutional and Fully Connected Layer in DFN","authors":"Mian Mian Lau, K. Lim","doi":"10.6688/JISE.202009_36(5).0009","DOIUrl":"https://doi.org/10.6688/JISE.202009_36(5).0009","url":null,"abstract":"Deep feedforward network (DFN) is the general structure of many well-known deep neural networks (DNN) for image classification. The recent research emphasizes on going deeper and wider network architecture to achieve higher accuracy and lower misclassification rate. This paper provides a study and investigation on stacking three basic operation of neural layers, i.e. convolutional layer, pooling layer and fully connected layer. As a result, a new framework of convolutional deep feedforward network (C-DFN) is proposed in this paper. C-DFN performed significantly better than deep feedforward network (DFN), deep belief network (DBN), and convolutional deep belief network (C-DBN) in MNIST dataset, INRIA pedestrian dataset and Daimler pedestrian dataset. The convolutional layer acts as a trainable feature extractor improving the network performance significantly. Moreover, it reduced 14% of the trainable parameters in DFN. With the use of trainable activation function such as PReLU in the C-DFN, it achieves an average misclassification rate of 9.22% of the three benchmark datasets.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"27 1","pages":"1069-1078"},"PeriodicalIF":1.1,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87095144","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}
Pub Date : 2020-09-01DOI: 10.6688/JISE.202009_36(5).0012
Huanhuan Zhao, Zuchang Ma, Yining Sun
Mobile crowd sensing (MCS) makes full use of the sensing and wireless communication capabilities of smart mobile devices to collect real-time information effectively. It makes it possible to monitor people's health condition in real time. Our health information collected through MCS can be used to improve healthcare service. Hypertension is a widespread chronic disease, and preventing hypertension can effectively reduce the incidence of cardiovascular disease. In this paper, we propose a hypertension risk assessment approach based on mobile crowd sensing, which allows for real time health monitoring and warning. In order to stimulate the enthusiasm of MCS volunteers, optimized communication model is used to reduce the communication cost of non-data-users. Additionally, the current hypertension risk status of patients will be feed back to them in real time. In our approach, binary logistic regression is used to select risk factors of hypertension, and then the risk factors are used as the inputs of BP neural network to construct the risk prediction model. Furthermore, the hypertension risk is further divided into low risk, medium risk and high risk through cumulative distribution function. 4498 samples from a community health service center in Hefei area were used to evaluate the performance of the proposed approach. The experimental results show that the proposed approach can provide real-time, effective monitoring and dynamic feedback of the hypertension risk, offering a novel clinical tool for the early warning of hypertension. The proposed approach also provides a general framework for risk assessment of other chronic diseases.
{"title":"A Risk Assessment Approach of Hypertension Based on Mobile Crowd Sensing","authors":"Huanhuan Zhao, Zuchang Ma, Yining Sun","doi":"10.6688/JISE.202009_36(5).0012","DOIUrl":"https://doi.org/10.6688/JISE.202009_36(5).0012","url":null,"abstract":"Mobile crowd sensing (MCS) makes full use of the sensing and wireless communication capabilities of smart mobile devices to collect real-time information effectively. It makes it possible to monitor people's health condition in real time. Our health information collected through MCS can be used to improve healthcare service. Hypertension is a widespread chronic disease, and preventing hypertension can effectively reduce the incidence of cardiovascular disease. In this paper, we propose a hypertension risk assessment approach based on mobile crowd sensing, which allows for real time health monitoring and warning. In order to stimulate the enthusiasm of MCS volunteers, optimized communication model is used to reduce the communication cost of non-data-users. Additionally, the current hypertension risk status of patients will be feed back to them in real time. In our approach, binary logistic regression is used to select risk factors of hypertension, and then the risk factors are used as the inputs of BP neural network to construct the risk prediction model. Furthermore, the hypertension risk is further divided into low risk, medium risk and high risk through cumulative distribution function. 4498 samples from a community health service center in Hefei area were used to evaluate the performance of the proposed approach. The experimental results show that the proposed approach can provide real-time, effective monitoring and dynamic feedback of the hypertension risk, offering a novel clinical tool for the early warning of hypertension. The proposed approach also provides a general framework for risk assessment of other chronic diseases.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"36 1","pages":"1107-1124"},"PeriodicalIF":1.1,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45750214","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}
Pub Date : 2020-09-01DOI: 10.6688/JISE.202009_36(5).0006
Sharaf Hussain, Samita Bai, S. Khoja
This paper discusses the formation of math grammar rules for LATEX math equations. These rules are used to generate Abstract Syntax Tree (AST) which extracts structural information from mathematical expressions given in LATEX format. Later AST is used to generate XML structure of mathematical expressions that make mathematical expressions machine-readable in heterogeneous environments. A rule-based algorithm is also proposed that converts LATEX math expressions into Content MathML (CMML), which produces semantic enrichment in web documents. The rules for writing LATEX math equations are formulated and implemented as LATEX Math Grammar (LMG), which are used for generating AST. Further, AST is converted into XML structure which is used to generate CMML encoding. Initially, the conversion algorithm is tested on 20 equations used in an NTCIR-12 math competition, then the algorithm is tested on NTCIR-12 Wikipedia-MathIR and ArXiv data sets. The results show that our algorithm is capable of converting LATEX complex equations into CMML extensively as compared to the existing ones as well as its time efficiency is better than contemporary systems.
本文讨论了LATEX数学方程数学语法规则的形成。这些规则用于生成抽象语法树(AST)， AST从以LATEX格式给出的数学表达式中提取结构信息。后来使用AST生成数学表达式的XML结构，使数学表达式在异构环境中具有机器可读性。提出了一种基于规则的算法，将LATEX数学表达式转换为内容MathML (Content MathML)，从而在web文档中产生语义丰富。LATEX数学公式的编写规则被表述为LATEX数学语法(LATEX math Grammar, LMG)，用于生成AST，并将AST转换为XML结构，用于生成cml编码。首先，在ntcirr -12数学竞赛中使用的20个方程上测试了转换算法，然后在ntcirr -12 Wikipedia-MathIR和ArXiv数据集上测试了该算法。结果表明，与现有算法相比，该算法能够将LATEX复杂方程广泛地转换为cml，并且时间效率优于现有系统。
{"title":"Rule Based Conversion of L A T E X Math Equations into Content MathML (CMML)","authors":"Sharaf Hussain, Samita Bai, S. Khoja","doi":"10.6688/JISE.202009_36(5).0006","DOIUrl":"https://doi.org/10.6688/JISE.202009_36(5).0006","url":null,"abstract":"This paper discusses the formation of math grammar rules for LATEX math equations. These rules are used to generate Abstract Syntax Tree (AST) which extracts structural information from mathematical expressions given in LATEX format. Later AST is used to generate XML structure of mathematical expressions that make mathematical expressions machine-readable in heterogeneous environments. A rule-based algorithm is also proposed that converts LATEX math expressions into Content MathML (CMML), which produces semantic enrichment in web documents. The rules for writing LATEX math equations are formulated and implemented as LATEX Math Grammar (LMG), which are used for generating AST. Further, AST is converted into XML structure which is used to generate CMML encoding. Initially, the conversion algorithm is tested on 20 equations used in an NTCIR-12 math competition, then the algorithm is tested on NTCIR-12 Wikipedia-MathIR and ArXiv data sets. The results show that our algorithm is capable of converting LATEX complex equations into CMML extensively as compared to the existing ones as well as its time efficiency is better than contemporary systems.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"23 1","pages":"1021-1034"},"PeriodicalIF":1.1,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87300611","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}
Pub Date : 2020-09-01DOI: 10.6688/JISE.202009_36(5).0005
H. Tan, K. Lim
Deep learning neural network is often associated with high complexity classification problems by stacking multiple hidden layers between input and output. The measured error is backpropagated layer-by-layer in a network with gradual vanishing gradient value due to the differentiation of activation function. In this paper, Stochastic Diagonal Approximate Greatest Descent (SDAGD) is proposed to tackle the issue of vanishing gradient in the deep learning neural network using the adaptive step length derived based on the second-order derivatives information. The proposed SDAGD optimizer trajectory is demonstrated using three-dimensional error surfaces, i:e: (a) a hilly error surface with two local minima and one global minimum; (b) a deep Gaussian trench to simulate drastic gradient changes experienced with ravine topography and (c) small initial gradient to simulate a plateau terrain. As a result, SDAGD is able to converge at the fastest rate to the global minimum without the interference of vanishing gradient issue as compared to other benchmark optimizers such as Gradient Descent (GD), AdaGrad and AdaDelta. Experiments are tested on saturated and unsaturated activation functions using sequential added hidden layers to evaluate the vanishing gradient mitigation with the proposed optimizer. The experimental results show that SDAGD is able to obtain good performance in the tested deep feedforward networks while stochastic GD obtain worse misclassification error when the network has more than three hidden layers due to the vanishing gradient issue. SDAGD can mitigate the vanishing gradient by adaptively control the step length element in layers using the second-order information. At the constant training iteration setup, SDAGD with ReLU can achieve the lowest misclassification rate of 1.77% as compared to other optimization methods.
{"title":"Vanishing Gradient Analysis in Stochastic Diagonal Approximate Greatest Descent Optimization","authors":"H. Tan, K. Lim","doi":"10.6688/JISE.202009_36(5).0005","DOIUrl":"https://doi.org/10.6688/JISE.202009_36(5).0005","url":null,"abstract":"Deep learning neural network is often associated with high complexity classification problems by stacking multiple hidden layers between input and output. The measured error is backpropagated layer-by-layer in a network with gradual vanishing gradient value due to the differentiation of activation function. In this paper, Stochastic Diagonal Approximate Greatest Descent (SDAGD) is proposed to tackle the issue of vanishing gradient in the deep learning neural network using the adaptive step length derived based on the second-order derivatives information. The proposed SDAGD optimizer trajectory is demonstrated using three-dimensional error surfaces, i:e: (a) a hilly error surface with two local minima and one global minimum; (b) a deep Gaussian trench to simulate drastic gradient changes experienced with ravine topography and (c) small initial gradient to simulate a plateau terrain. As a result, SDAGD is able to converge at the fastest rate to the global minimum without the interference of vanishing gradient issue as compared to other benchmark optimizers such as Gradient Descent (GD), AdaGrad and AdaDelta. Experiments are tested on saturated and unsaturated activation functions using sequential added hidden layers to evaluate the vanishing gradient mitigation with the proposed optimizer. The experimental results show that SDAGD is able to obtain good performance in the tested deep feedforward networks while stochastic GD obtain worse misclassification error when the network has more than three hidden layers due to the vanishing gradient issue. SDAGD can mitigate the vanishing gradient by adaptively control the step length element in layers using the second-order information. At the constant training iteration setup, SDAGD with ReLU can achieve the lowest misclassification rate of 1.77% as compared to other optimization methods.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"40 1","pages":"1007-1019"},"PeriodicalIF":1.1,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88373898","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}
Pub Date : 2020-09-01DOI: 10.6688/JISE.202009_36(5).0002
K. H. Law
Quasi Z-source inverter (qZSI) has been proven to be reliable for inverting DC electricity to AC. When compared with conventional two stage DC-AC boost inverter topology, qZSI is a single stage topology which can be simply constructed via connecting a quasi Z-source (qZS) impedance network in series with an H-bridge inverter. This resulted an attractive topology without needing any switching element and resistor in the DC-link. In this paper, the investigation of qZSI with battery topology is extended to regulate its output voltage from a fluctuating DC input source. Through extensive mathematic derivation, a control scheme which comprised of battery current controller, battery management algorithm, and electronic circuit breaker (ECB) algorithm, is proposed to achieve the aforementioned outcome plus ensuring the effectiveness of battery charging and discharging capability as well as prevention of over-charging and over-discharging of the battery according to the DC input voltage level. All theoretical findings are validated with simulation results using Matlab / Simulink software package.
{"title":"Modelling of Charging Control Scheme for QZSI with Battery Topology","authors":"K. H. Law","doi":"10.6688/JISE.202009_36(5).0002","DOIUrl":"https://doi.org/10.6688/JISE.202009_36(5).0002","url":null,"abstract":"Quasi Z-source inverter (qZSI) has been proven to be reliable for inverting DC electricity to AC. When compared with conventional two stage DC-AC boost inverter topology, qZSI is a single stage topology which can be simply constructed via connecting a quasi Z-source (qZS) impedance network in series with an H-bridge inverter. This resulted an attractive topology without needing any switching element and resistor in the DC-link. In this paper, the investigation of qZSI with battery topology is extended to regulate its output voltage from a fluctuating DC input source. Through extensive mathematic derivation, a control scheme which comprised of battery current controller, battery management algorithm, and electronic circuit breaker (ECB) algorithm, is proposed to achieve the aforementioned outcome plus ensuring the effectiveness of battery charging and discharging capability as well as prevention of over-charging and over-discharging of the battery according to the DC input voltage level. All theoretical findings are validated with simulation results using Matlab / Simulink software package.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"391 1","pages":"967-979"},"PeriodicalIF":1.1,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76596161","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}
Guided by an interdisciplinary approach, this study seeks to illustrate the digital practices of international speakers on social media. The practices of international users are especially valuable due to the presence of various audiences in their networks, some rarely researched. For this purpose, the study examines the social media practices of 16 international graduate students (IGSs), who experience a transnational mobility in the United States. The data is collected through semi-structured interviews with participants and their social media data. The analysis includes quantitative assessment of participants’ social media activities and qualitative analyses of interviews and digital practices. The findings of the study illustrate how individuals with transborder experiences engage in identity work by sharing transcultural content with a multitude of audiences in their networks. The study concludes that digital practices involving the transcultural flow of content present opportunities for IGSs to work and realign various facets of their identities.
{"title":"Transcultural Practices of International Students as Identity Performances in Digital Settings","authors":"Osman Solmaz","doi":"10.32674/jise.v9i2.2175","DOIUrl":"https://doi.org/10.32674/jise.v9i2.2175","url":null,"abstract":"Guided by an interdisciplinary approach, this study seeks to illustrate the digital practices of international speakers on social media. The practices of international users are especially valuable due to the presence of various audiences in their networks, some rarely researched. For this purpose, the study examines the social media practices of 16 international graduate students (IGSs), who experience a transnational mobility in the United States. The data is collected through semi-structured interviews with participants and their social media data. The analysis includes quantitative assessment of participants’ social media activities and qualitative analyses of interviews and digital practices. The findings of the study illustrate how individuals with transborder experiences engage in identity work by sharing transcultural content with a multitude of audiences in their networks. The study concludes that digital practices involving the transcultural flow of content present opportunities for IGSs to work and realign various facets of their identities.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"9 1","pages":"285-309"},"PeriodicalIF":1.1,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49235730","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}
This is a book about the history of Muslim girls/women in South Asia and their difficulties in acquiring an education.
这是一本关于南亚穆斯林女孩/妇女的历史和她们在获得教育方面的困难的书。
{"title":"Forging the ideal educated girl: the production of desirable subjects in Muslim South Asia.","authors":"Taiwo Adenuga","doi":"10.32674/jise.v9i2.1666","DOIUrl":"https://doi.org/10.32674/jise.v9i2.1666","url":null,"abstract":"This is a book about the history of Muslim girls/women in South Asia and their difficulties in acquiring an education.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2020-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46435825","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}
Pub Date : 2020-08-14DOI: 10.32674/jise.v9isi.2810
Prince Paa-Kwesi Heto, H. Indangasi
{"title":"Mindset, Heartset, and Skillset","authors":"Prince Paa-Kwesi Heto, H. Indangasi","doi":"10.32674/jise.v9isi.2810","DOIUrl":"https://doi.org/10.32674/jise.v9isi.2810","url":null,"abstract":"","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"9 1","pages":"1-13"},"PeriodicalIF":1.1,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48884099","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}
Pub Date : 2020-08-11DOI: 10.32674/jise.v9is(1).1857
M. Odari
Soka (value creating) education is a Japanese concept propounded by Tsunesaburo Makiguchi and further developed by Josei Toda and Daisaku Ikeda. This educational philosophy aims to foster individuals who can find meaning in their lives and contribute to the well-being of others to better society. Ubuntu, an African philosophy, espouses togetherness and collectivism. Like value creating education, Ubuntu promotes working for the good of all not solely the individual. Examining these two philosophies, this paper explored their role in promoting humanism. Focusing on the education system in Kenya, this paper investigated how the institutionalization of both philosophies can foster global citizens and realize a more humane Kenya. Furthermore, this paper illustrated the importance of educators as agents of change, aiding students to become global citizens who work towards building a more humanistic society. This paper concluded that integrating both value creating education and Ubuntu in the education system can serve as a tool to nurture individuals who will not only improve their quality of life but also contribute positively to promote a more just and prosperous world.
{"title":"Role of Value Creating Education and Ubuntu Philosophy in Fostering Humanism in Africa","authors":"M. Odari","doi":"10.32674/jise.v9is(1).1857","DOIUrl":"https://doi.org/10.32674/jise.v9is(1).1857","url":null,"abstract":"Soka (value creating) education is a Japanese concept propounded by Tsunesaburo Makiguchi and further developed by Josei Toda and Daisaku Ikeda. This educational philosophy aims to foster individuals who can find meaning in their lives and contribute to the well-being of others to better society. Ubuntu, an African philosophy, espouses togetherness and collectivism. Like value creating education, Ubuntu promotes working for the good of all not solely the individual. Examining these two philosophies, this paper explored their role in promoting humanism. Focusing on the education system in Kenya, this paper investigated how the institutionalization of both philosophies can foster global citizens and realize a more humane Kenya. Furthermore, this paper illustrated the importance of educators as agents of change, aiding students to become global citizens who work towards building a more humanistic society. This paper concluded that integrating both value creating education and Ubuntu in the education system can serve as a tool to nurture individuals who will not only improve their quality of life but also contribute positively to promote a more just and prosperous world.","PeriodicalId":50177,"journal":{"name":"Journal of Information Science and Engineering","volume":"9 1","pages":"56-68"},"PeriodicalIF":1.1,"publicationDate":"2020-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47625561","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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