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Learning the dynamics of complex systems features a large number of applications in data science. Graph-based modeling and inference underpins the most prominent family of approaches to learn complex dynamics due to their ability to capture the intrinsic sparsity of direct interactions in such systems. They also provide the user with interpretable graphs that unveil behavioral patterns and changes.


Developing recommendation system for fashion images is challenging due to the inherent ambiguity associated with what criterion a user is looking at. Suggesting multiple images where each output image is similar to the query image on the basis of a different feature or part is one way to mitigate the problem. Existing works for fashion recommendation have used Siamese or Triplet network to learn features between a similar pair and a similar dissimilar triplet respectively.


Fly Local Sensitive Hashing (FLSH) is a biomimetic data-independent hashing method inspired by the mechanism of odor processing system in drosophila. In this paper,we propose a novel Randomized Sampling-based Fly Local Sensitive Hashing (rs-FLSH) to model the randomness occurred during the establishment of synapses between neurons.Significant performance improvement can be achieved by applying a novel randomized sampling scheme in rs-FLSH,in which the sample rate is modeled by a Gaussian random variable rather than a fixed value in FLSH.


We consider autoencoders (AEs) for matrix completion (MC) with application to collaborative filtering (CF) for recommedation systems. It is observed that for a given sparse user-item rating matrix, denoted asM, an AE performs matrix factorization so that the recovered matrix is represented as a product of user and item feature matrices.


Multiple classification techniques have been employed for different business applications. In the particular case of credit scoring, a classifier which maximizes the total profit is preferable. The recently proposed expected maximum profit (EMP) measure for credit scoring allows to select the most profitable classifier. Taking the idea of the EMP one step further, it is desirable to integrate the measure into model construction, and thus obtain a profit maximizing model.


In recent years, neural networks (NN) have achieved remarkable
performance improvement in text classification due to
their powerful ability to encode discriminative features by
incorporating label information into model training. Inspired
by the success of NN in text classification, we propose a
pseudo-supervised neural network approach for text clustering.
The neural network is trained in a supervised fashion
with pseudo-labels, which are provided by the cluster labels
of pre-clustering on unsupervised document representations.


Winter 2017 Seasonal School Workshop, Malaysia. The Arduino Simple Programming at school was held at five different schools near Parit Raja, Batu Pahat, Johor from October-November 2017. The schools are SK Jelutong, SMK Sri gading, SK Pintas Puding, SK Bukit Kuari and SK Seri Sabak Uni. The objective of Professional Knowledge Transfer Workshop is to give an exposure to the students of primary and secondary schools on engineering and to inspire them to be engineers. This program was organized by the Institute of Electrical and Electronics Engineers (IEEE) UTHM Student Branch with cooperation of IEEE Signal Processing Society (SPS) Malaysia Chapter. The program is fully funded by IEEE Signal Processing Society (SPS) under the IEEE SPS Member-Driven Initiative Program with the amount of USD 1,400. The fund had been used to buy 30 Arduino Uno kits, purchasing stationery, and refreshments for 30 students from each school, 10 postgraduate students from UTHM IEEE student branch and 13 Academic staff from FKEE, UTHM.


Automatic question-answering is a classical problem in natural language processing, which aims at designing systems that can automatically answer a question, in the same way as human does. In this work, we propose a deep learning based model for automatic question-answering. First the questions and answers are embedded using neural probabilistic modeling. Then a deep similarity neural network is trained to find the similarity score of a pair of answer and question. Then for each question, the best answer is found as the one with the highest similarity score.


We solve the compressive sensing problem via convolutional factor analysis, where the convolutional dictionaries are learned in situ from the compressed measurements. An alternating direction method of multipliers (ADMM) paradigm for compressive sensing inversion based on convolutional factor analysis is developed. The proposed algorithm provides reconstructed images as well as features, which can be directly used for recognition (e:g:, classification) tasks.


One of the longstanding problems in spectral graph clustering (SGC) is the so-called model order selection problem: automated selection of the correct number of clusters. This is equivalent to the problem of finding the number of connected components or communities in an undirected graph. In this paper, we propose AMOS, an automated model order selection algorithm for SGC.