Emotion detection in Arabic text is an emerging research area, but the efforts in this new field have been hindered by the very limited availability of Arabic datasets annotated with emotions. In this paper, we review work that has been carried out in the area of emotion analysis in Arabic text. We then present an Arabic tweet dataset that we have built to serve this task. The efforts and methodologies followed to collect, clean, and annotate our dataset are described and preliminary experiments carried out on this dataset for emotion detection are presented. The results of these experiments

Black-blood cardiac Magnetic Resonance Imaging (MRI) plays an important role in diagnosing a number of heart diseases. The technique suffers inherently from low contrast-tonoise ratio between the myocardium and the blood. In this work, we examined the performance of different classification techniques that can be used. The three techniques successfully removed the noise with different performance. Numerical simulation has been done to quantitatively evaluate the performance of each technique. © 2008 IEEE.

Computation of maximal exact matches (MEMs) is an important problem in comparing genomic sequences. Optimal sequential algorithms for computing MEMs have been already introduced and integrated in a number of software tools. To cope with large data and exploit new computing paradigms like cloud computing, it is important to develop efficient and ready-to-use solutions running on distributed parallel architecture. In a previous work, we have introduced a distributed algorithm running on a computer cluster for computing the MEMs. In this paper, we extend this work in two directions: First, we

Given two long strings S and T, representing two genomic sequences, and given a user defined threshold ℓ, the problem of computing maximal exact matches (MEMs) is to find each triple (p 1,p 2,l) specifying two matching substrings S[p 1..p 1 + l - 1] = T[p 2..p 2 + l - 1], such that l ≥ ℓ and S[p 1 - 1] ≠ T[p 2 - 1] and S[p 1 + l] ≠ T[p 2 + l]. Computing MEMs is a major problem in bioinformitcs, because it is a primary step in identifying regions of common similarity among genomic sequences. Faster solutions to this problem are still demanded to overcome the ever increasing amount of genomic

Harmonic phase (HARP) tracking is one of the most commonly used techniques for estimating the myocardium regional function from tagged cardiac Magnetic Resonance Imaging sequences. Nevertheless, tag fading and phase distortion can severely limit the tracking accuracy of the technique. In this work, we propose to modify the HARP tracking algorithm to impose a constraint of locally uniform displacement field while tracking the different myocardium points. A numerical contracting phantom and a dataset of 11 patients are used to study the performance of the proposed technique at the different

Composite Strain Encoding (CSENC) is a new Magnetic Resonance Imaging (MRI) technique for simultaneously acquiring cardiac functional and viability images. It combines the use of Delayed Enhancement (DE) and the Strain Encoding (SENC) imaging techniques to identify the infracted (dead) tissue and to image the myocardial deformation inside the heart muscle. In this work, a new unsupervised segmentation method is proposed to identify infarcted left ventricular tissue in the images provided by CSENC MRI. The proposed method is based on the sequential application of Bayesian classifier, Otsu's

Most existing research in the area of emotions recognition has focused on short segments or utterances of speech. In this paper we propose a machine learning system for classifying the overall sentiment of long conversations as being Positive or Negative. Our system has three main phases, first it divides a call into short segments, second it applies machine learning to recognize the emotion for each segment, and finally it learns a binary classifier that takes the recognized emotions of individual segments as features. We investigate different approaches for this final phase by varying how

Nowadays, most service providers adopt Cloud Computing technology. Moving to Cloud creates new risks and challenges. The Cloud era is to outsource our services to Cloud Service Provider (CSP). However, we have to develop a strong governance framework to review the service level, to manage risk effectively and to certify that our critical information is secure. In this paper, we develop an innovative governance model. It is based on the theoretical Guo, Z., Song, M. and Song, J governance model for Cloud computing. We distribute Cloud Control Matrix (CCM) on the Guo's model categories. This

Human Microbiome plays a critical role in health and the environment. Colorectal cancer (CRC) is the most common cause of death in many countries, and hence early diagnosis of CRC may help in increasing the survival rate. Tracking changes in the microbiome structure of human gut opens new gates towards the detection and prediction of the risk of CRC. Recently, machine learning became a powerful technique in many bioinformatics fields, one of which is metagenomics. Metagenomics is defined as the study of a collection of microbial genomes isolated directly and sequenced from its natural habitats

National Basketball Association (NBA) All Star Game is a demonstration game played between the selected Western and Eastern conference players. The selection of players for the NBA All Star game purely depends on votes. The fans and coaches vote for the players and decide who is going to make the All Star roster. A player who continues to receive enough votes in following years will play more All Star games. The selection of All Star players in NBA is subjective based on voting and there are no selection criteria that take out the human bias and opinion. Analyzing data from previous sports