Uncertainty in Recurrent Neural Network with Dropout

Recurrent Neural Network is a powerful tool for processing temporal data. However, assessing prediction uncertainty from recurrent models has proven challenging. This thesis attempts to evaluate the validity of uncertainty from recurrent models using dropout. Traditional neural network focuses on optimising data likelihood; in order to obtain model and predictive uncertainty, we need to, instead, optimise model posterior. Model posterior is usually intractable, thus we employ various dropout based approach, in the form of variational Bayesian Monte Carlo, to estimate the learning objective. This technique is applied to existing recurrent neural network benchmarks MIMIC-III. The thesis shows that Monte Carlo dropout applied to recurrent neural network can give comparable performance to the current state of the art methods, and meaningful uncertainty of predictions

Family of Circulant Graphs and Its Expander Properties

In this thesis, we apply spectral graph theory to show the non-existence of an expander family within the class of circulant graphs. Using the adjacency matrix and its properties, we prove Cheeger\u27s inequalities and determine when the equalities hold. In order to apply Cheeger\u27s inequalities, we compute the spectrum of a general circulant graph and approximate its second largest eigenvalue. Finally, we show that circulant graphs do not contain an expander family

A Deep Learning Model for Splicing Image Detection

With the advancement of digital technology, manipulating images has become relatively easy through many photo editing techniques. One of the techniques is the splicing image method, which crops parts of images and puts them into another image creating a new composite image. The image splicing detection system is soon regarded as an exciting topic for many researchers to solve the problems of forgery images on the Internet, especially in social networks. ResNet-50 and VGG-16 are powerful architectures of convolutional neural networks, but they reveal many weaknesses when operating on low-end computers. The ultimate goal of this research is to create a model for image splicing detection working well in limited memory machines. The study proposes the model, which is the improvement of VGG-16 applying residual network (ResNet). As a result, the proposed model achieves a test accuracy of 92.5% while the ResNet-50 gives an accuracy of 85.6% after 20 epochs of training 9,319 images from the CASIA v2.0 dataset, which are used for forgery classification. The result proves the efficiency of the proposed model for image splicing detection, especially when working on low-end computers

EFL SECONDARY AND HIGH SCHOOL STUDENTS’ PERCEPTIONS OF ADVANTAGES AND DIFFICULTIES OF WRITTEN FEEDBACK BY QUESTIONING IN WRITING

This paper reports a descriptive study to enquire into English as a Foreign Language (EFL) secondary and high school (K-12) students’ perceptions about the advantages and difficulties of written feedback by questioning in writing. This paper draws on the data collected as part of a larger project including questionnaires and focus-group interviews. The findings reveal that students held positive perceptions about the impact of written feedback by questioning in writing, particularly on motivation, writing skills, and attitudes and preferences.  Article visualizations

On the Interference Alignment Designs for Secure Multiuser MIMO Systems

In this paper, we propose two secure multiuser multiple-input multiple-output transmission approaches based on interference alignment (IA) in the presence of an eavesdropper. To deal with the information leakage to the eavesdropper as well as the interference signals from undesired transmitters (Txs) at desired receivers (Rxs), our approaches aim to design the transmit precoding and receive subspace matrices to minimize both the total inter-main-link interference and the wiretapped signals (WSs). The first proposed IA scheme focuses on aligning the WSs into proper subspaces while the second one imposes a new structure on the precoding matrices to force the WSs to zero. When the channel state information is perfectly known at all Txs, in each proposed IA scheme, the precoding matrices at Txs and the receive subspaces at Rxs or the eavesdropper are alternatively selected to minimize the cost function of an convex optimization problem for every iteration. We provide the feasible conditions and the proofs of convergence for both IA approaches. The simulation results indicate that our two IA approaches outperform the conventional IA algorithm in terms of average secrecy sum rate.Comment: Updated version, updated author list, accepted to be appear in IEICE Transaction