Adaptive System Identification using Markov Chain Monte Carlo

One of the major problems in adaptive filtering is the problem of system identification. It has been studied extensively due to its immense practical importance in a variety of fields. The underlying goal is to identify the impulse response of an unknown system. This is accomplished by placing a known system in parallel and feeding both systems with the same input. Due to initial disparity in their impulse responses, an error is generated between their outputs. This error is set to tune the impulse response of known system in a way that every change in impulse response reduces the magnitude of prospective error. This process is repeated until the error becomes negligible and the responses of both systems match. To specifically minimize the error, numerous adaptive algorithms are available. They are noteworthy either for their low computational complexity or high convergence speed. Recently, a method, known as Markov Chain Monte Carlo (MCMC), has gained much attention due to its remarkably low computational complexity. But despite this colossal advantage, properties of MCMC method have not been investigated for adaptive system identification problem. This article bridges this gap by providing a complete treatment of MCMC method in the aforementioned context

Learning style preference and critical thinking perception among engineering students

Engineering education plays a vital role towards modernization of world. Therefore, engineering students need to be nurture with multiple skills like learning preferences and critical thinking skills. This study has been conducted to identify the learning style preferences and critical thinking perception of the engineering students from three programs electrical engineering, mechanical engineering and civil engineering at Universiti Tun Hussein Onn Malaysia (UTHM), Johor. Survey research design was applied in this study. The quantitative data was collected by two questionnaires Index of Learning Styles (ILS) that is based on Felder-Silverman Learning Style Model (FSLSM) and Critical Thinking Skills (CTS) questionnaire which consists of analysis, evaluation, induction and deduction in terms of problem solving and decision making. A total of 315 final year engineering students were participated in this study. Data was analyzed in descriptive and inferential statistics involving tests Analysis of Variance (ANOVA), Pearson Correlation and linear regression. The study discovered that engineering students are preferred to be visual learners (83.80%). Visual learning style denotes FSLSM input dimension and visual learners learn best by diagrams, charts, maps and graphical presentations. This study also found that engineering students possess critical thinking perception in all dimensions. However, there is no statistical significant difference of learning style found among engineering programs as “p” value found 0.357. Whereas, there is statistical significant critical thinking difference found among engineering programs as “p” value found 0.006. Lastly, findings revealed that there is no significant relationship found between learning styles and critical thinking skills. The study findings suggested that providing preferred learning style (visual learning style) in classroom will enhance students’ academic achievement and increase their cognitive level. This study might serve as a guideline for educators to facilitate learners to enhance their learning and thinking for better outcomes in academia as well as in workplace

Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks