Mariette Awad

Mariette Awad
American University of Beirut | AUB · Department of Electrical and Computer Engineering

PhD

About

146
Publications
104,554
Reads
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3,277
Citations
Citations since 2017
59 Research Items
3092 Citations
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20172018201920202021202220230200400600800
20172018201920202021202220230200400600800
Introduction
Mariette Awad currently works at the Department of Electrical and Computer Engineering, American University of Beirut. Mariette does research in Artificial Intelligence and Machine Learning.
Additional affiliations
February 2008 - September 2015
American University of Beirut
Position
  • Research Assistant
Description
  • .
January 2000 - December 2007
IBM
Position
  • Wireless Product Engineer

Publications

Publications (146)
Article
Full-text available
Objective The study aims to explore smokers' acceptance of using a conceptual cigarette tracker like a cigarette filter for smoking cessation using the Technology Acceptance Model (TAM). Smokers presenting to the family medicine clinics at a tertiary care center were asked to complete an anonymous questionnaire. Results A total of 45 participants...
Preprint
Full-text available
Current Explainable AI (ExAI) methods, especially in the NLP field, are conducted on various datasets by employing different metrics to evaluate several aspects. The lack of a common evaluation framework is hindering the progress tracking of such methods and their wider adoption. In this work, inspired by offline information retrieval, we propose d...
Preprint
Full-text available
While there has been a recent explosion of work on ExplainableAI ExAI on deep models that operate on imagery and tabular data, textual datasets present new challenges to the ExAI community. Such challenges can be attributed to the lack of input structure in textual data, the use of word embeddings that add to the opacity of the models and the diffi...
Article
Full-text available
Deep neural networks can be used to diagnose and detect plant diseases, helping to avoid the plant health-related crop production losses ranging from 20 to 50% annually. However, the data collection and annotation required to achieve high accuracies can be expensive and sometimes very difficult to obtain in specific use-cases. To this end, this wor...
Chapter
Full-text available
With the pervasive use of Sentiment Analysis (SA) models in financial and social settings, performance is no longer the sole concern for reliable and accountable deployment. SA models are expected to explain their behavior and highlight textual evidence of their predictions. Recently, Explainable AI (ExAI) is enabling the ``third AI wave'' by provi...
Chapter
Beyond Multi-Task or Multi-Agent learning, we develop in this work a multi-agent reinforcement learning algorithm to handle a multi-task environments. Our proposed algorithm, Multi-Task Multi-Agent Deep Deterministic Policy gradient, (MTMA-DDPG) (Code available at https://gitlab.com/awadailab/mtmaddpg), extends its single task counterpart by runnin...
Preprint
Full-text available
In the aftermath of disasters, building damage maps are obtained using change detection to plan rescue operations. Current convolutional neural network approaches do not consider the similarities between neighboring buildings for predicting the damage. We present a novel graph-based building damage detection solution to capture these relationships....
Preprint
Full-text available
Change detection is instrumental to localize damage and understand destruction in disaster informatics. While convolutional neural networks are at the core of recent change detection solutions, we present in this work, BLDNet, a novel graph formulation for building damage change detection and enable learning relationships and representations from b...
Article
The aim of this study was to analyze and compare the effects of different types of digital billboard advertisements (DBAs) on drivers’ performance and attention allocation. Driver distraction is a major threat to driver safety. DBAs are one form of distraction in drivers’ outside environment. There are many different types of DBAs, such as static i...
Article
Motivation: Identifying histone tail modifications using ChIP-seq is commonly used in time-series experiments in development and disease. These assays, however, cover specific time-points leaving intermediate or early stages with missing information. Although several machine learning methods were developed to predict histone marks, none exploited...
Article
Full-text available
There has been an amplified focus on and benefit from the adoption of artificial intelligence (AI) in medical imaging applications. However, deep learning approaches involve training with massive amounts of annotated data in order to guarantee generalization and achieve high accuracies. Gathering and annotating large sets of training images require...
Preprint
Full-text available
Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand and production profiles, with increasing renewable energy integration, as well as the high voltage network technology, constitute a real challenge for human operators wh...
Article
Full-text available
Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and th...
Article
Full-text available
Power networks, responsible for transporting electricity across large geographical regions, are complex infrastructures on which modern life critically depend. Variations in demand and production profiles, with increasing renewable energy integration, as well as the high voltage network technology, constitute a real challenge for human operators wh...
Preprint
Full-text available
In this work, we propose a novel Generative Adversarial Stacked Autoencoder that learns to map facial expressions, with up to plus or minus 60 degrees, to an illumination invariant facial representation of 0 degrees. We accomplish this by using a novel convolutional layer that exploits both local and global spatial information, and a convolutional...
Conference Paper
Have you ever wondered how a song might sound if performed by a different artist? In this work, we propose SCM-GAN, an end-to-end non-parallel song conversion system powered by generative adversarial and transfer learning, which allows users to listen to a selected target singer singing any song. SCM-GAN first separates songs into vocals and instru...
Article
Full-text available
Research has shown that deep neural networks are able to help and assist human workers throughout the industrial sector via different computer vision applications. However, such data-driven learning approaches require a very large number of labeled training images in order to generalize well and achieve high accuracies that meet industry standards....
Article
Full-text available
Bright spots, strong indicators of the existence of hydrocarbon accumulations, have been primarily used by geophysicists in oil and gas exploration. Recently, machine-learning algorithms, adopted to automate bright spot detection, have mainly relied on feature extraction and shallow classification workflows to achieve an 85.4% F1 score at best, on...
Preprint
Full-text available
Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and th...
Preprint
Full-text available
Have you ever wondered how a song might sound if performed by a different artist? In this work, we propose SCM-GAN, an end-to-end non-parallel song conversion system powered by generative adversarial and transfer learning that allows users to listen to a selected target singer singing any song. SCM-GAN first separates songs into vocals and instrume...
Article
Full-text available
The enormous number of articles published daily on the internet, by a diverse array of authors, often offers misleading or unwanted information, rendering activities such as sports betting riskier. As a result, extracting meaningful and reliable information from these sources becomes a time consuming and near impossible task. In this context, label...
Preprint
Full-text available
Bright spots have been the primary approach to identify hydrocarbon bearing formations. Specifically, 3D seismic texture analysis has been employed to identify such locations of interest. However, raw seismic data is large in volume and requires a plethora of prepro-cessing techniques before meaningful information can be extracted. Hence, bright sp...
Article
This letter investigates the incorporation of support vector machines (SVM) to predict the time and location of maximum Wi-Fi coverage for energy harvesting. Such incorporation of machine learning along with radio frequency (RF) energy harvesting systems, improves the proposed rectenna efficiency especially when scavenging wireless routers and acce...
Conference Paper
Medical students undergo exams, called “Objective Structured Clinical Examinations” (OSCEs), to assess their medical competence in clinical tasks. In these OSCEs, a medical student interacts with a standardized patient, asking questions to complete a clinical assessment of the patient’s medical case. In real OSCEs, standardized patients or “Actors”...
Article
Full-text available
First introduced by MountCastle, cortical algorithms (CA) are positioned to outperform artificial neural networks second generations due to their ability to hierarchically store sequences of patterns in an invariant form. Despite their closer resemblance to the human cortex and their hypothetical improved performance, CA adoption as a deep learning...
Article
The emergence of the Internet of things and the widespread deployment of diverse computing systems have led to the formation of heterogeneous multi-agent systems (MAS) to complete a variety of tasks. Motivated to highlight the state of the art on existing MAS while identifying their limitations, remaining challenges, and possible future directions,...
Conference Paper
When natural disasters strike, annotated images and texts flood the Internet, and rescue teams become overwhelmed to prioritize often scarce resources, while relying heavily on human input. In this paper, a novel multi-modal approach is proposed to automate crisis data analysis using machine learning. Our multi-modal two-stage framework relies on c...
Conference Paper
Smartphones have recently seen massive growth in usage and become a repository for many types of personal information. The privacy and security are primary concerns for their usage, where there is a need to provide seamless and continuous authentication systems(CASs) for smartphones. We introduce in this work a proof-of-concept design and a case st...
Preprint
Full-text available
The emergence of the Internet of things and the wide spread deployment of diverse computing systems have led to the formation of heterogeneous multi-agent systems (MAS) to complete a variety of tasks. Motivated to highlight the state of the art on existing MAS while identifying their limitations, remaining challenges and possible future directions,...
Preprint
Full-text available
When natural disasters strike, annotated images and texts flood the Internet, and rescue teams become overwhelmed to prioritize often scarce resources, while relying heavily on human input. In this paper, a novel multi-modal approach is proposed to automate crisis data analysis using machine learning. Our multi-modal two-stage framework relies on c...
Article
Full-text available
With the “last mile” of the delivery process being the most expensive phase, autonomous package delivery systems are gaining traction as they aim for faster and cheaper delivery of goods to city, urban and rural destinations. This interest is further fueled by the emergence of e-commerce, where many applications can benefit from autonomous package...
Article
Here, we present a biologically inspired visual network (BIVnet) for image processing tasks. The proposed model possesses similarities with its neural counterpart and is trained by a stochastic algorithm which employs a partially observable Markov decision process to execute a reinforcement learning strategy. The network was tested on a collection...
Article
Simultaneous Localization and environment mapping (SLAM) is the core to robotic mapping and navigation as it constructs simultaneously the unknown environment and localizes the agent within. However, in millimeter wave (mmWave) research, SLAM is still at its infancy. In this paper, we introduce MOSAIC a new approach for SLAM in indoor environment b...
Article
Recurrent neural networks (RNN) are a type of artificial neural networks (ANN) that have been successfully applied to many problems in artificial intelligence. However, they are expensive to train since the number of learned weights grows exponentially with the number of hidden neurons. Non-iterative training algorithms have been proposed to reduce...
Article
Full-text available
Intelligent transport systems, efficient electric grids, and sensor networks for data collection and analysis are some examples of the multi-agent systems (MAS) that cooperate to achieve common goals. Decision making is an integral part of intelligent agents and MAS that will allow such systems to accomplish increasingly complex tasks. In this surv...
Conference Paper
Full-text available
Social media has recently become a digital lifeline used to relay information and locate survivors in disaster situations. Currently, officials and volunteers scour social media for any valuable information; however, this approach is implausible as millions of posts are shared by the minute. Our goal is to automate actionable information extraction...
Article
Full-text available
The failure of shallow neural network architectures in replicating human intelligence led the machine learning community to focus on deep learning, to computationally match human intelligence. The wide availability of increasing computing power coupled with the development of more efficient training algorithms have allowed the implementation of dee...
Article
Artificial neural networks (ANN’s) have been used to optimize the performance of a dry reformer with catalyst sintering taken into account. In particular, we study the effects of temperature, pressure and catalyst diameter on the methane and CO2 conversions, as well the H2 to CO ratio and the molar percentage of solid carbon deposited on the cataly...
Article
Full-text available
Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data. LSM were applied to a previously validated EEG dataset where subjects view a battery of emotional f...
Article
Evolutionary artificial neural networks and support vector machines were investigated for the modeling of NOx emissions from gas turbines. For ANN’s, a genetic algorithm (GA) with indirect binary encoding was employed to obtain optimal multi-layer perceptron (MLP)-type architectures. The contribution of this research includes the use of an improved...
Article
In this work, we propose a high performance distributed system that consists of several middleware servers each connected to a number of FPGAs with extended solid state storage that we call reconfigurable active solid state device (RASSD) nodes. A full data communication solution between middleware and RASSD nodes is presented. We use seismic data...
Article
Bright spots have been the primary approach to identify hydrocarbon bearing formations. Specifically, three-dimensional (3-D) seismic texture analysis has been employed to identify such locations of interest. However, raw seismic data are large in volume and require a plethora of preprocessing techniques before meaningful information can be extract...
Article
Full-text available
With the explosive growth of digital music data being stored and easily reachable on the cloud, as well as the increased interest in affective and cognitive computing, identifying composers based on their musical work is an interesting challenge for machine learning and artificial intelligence to explore. Capturing style and recognizing music compo...
Conference Paper
We introduce in this paper two main approaches, Triangulateration (TL) and Angle-Difference-of-Arrival (ADoA) for indoor localization and mapping using single-anchor and millimeter wave (MMW) propagation characteristics. Then, we perform context inference through obstacle localization. To do so, we first include and estimate the positions of virtua...
Article
Full-text available
Many studies have been performed to classify large-sized text documents using different classifiers, ranging from simple distance classifiers such as K-Nearest-Neighbor (KNN) to more advanced classifiers such as Support Vector Machines. Traditional approaches fail when a short text is encountered due to sparsity resulting from a limited number of w...
Article
Full-text available
The cochlea is an indispensable preliminary processing stage in auditory perception that employs mechanical frequency-tuning and electrical transduction of incoming sound waves. Cochlear mechanical responses are shown to exhibit active nonlinear spatiotemporal response dynamics (e.g., otoacoustic emission). To model such phenomena, it is often nece...
Article
Full-text available
The availability of location information has become a key factor in today’s communications systems allowing location based services. In outdoor scenarios, the mobile terminal position is obtained with high accuracy thanks to the Global Positioning System (GPS) or to the standalone cellular systems. However, the main problem of GPS and cellular syst...
Conference Paper
Different algorithms have been applied to solve the shared resource allocation problem in the cloud environment, some of which are based on game theory with the aim to maximize the provider's profit while ensuring good customer experience. However, the traditional methods do not account for the users' rationality. In addition to being selfish by na...
Conference Paper
Power gating is often used to reduce dynamic power consumption in microelectronics systems. There exist several methodologies for the implementation of power gating with varying effect on performance. In this work, we propose a new power gating utilization methodology that is based on controlling the instruction scheduler from a game theoretical pe...
Chapter
Different intelligent techniques have been proposed to solve the problem of downlink resource allocation in orthogonal frequency division multiple access (OFDMA)-based networks. These include mathematical optimization, game theory and heuristic algorithms. In an attempt to improve the performance of traditional genetic algorithm (GA) and its heuris...
Article
We propose a high performance distributed system that consists of several middleware servers (MWS) each connected to a number of FPGAs with extended solid state storage that we call reconfigurable active solid state device (RASSD) nodes. A MWS manages a group of RASSD nodes and bridges the connection between a client and the RASSD nodes within a co...
Preprint
Full-text available
We propose a high performance distributed system that consists of several middleware servers (MWS) each connected to a number of FPGAs with extended solid state storage that we call reconfigurable active solid state device (RASSD) nodes. A MWS manages a group of RASSD nodes and bridges the connection between a client and the RASSD nodes within a co...
Article
Due to globalization trends and the increasing competition between ports, the maritime policy for container shipments has witnessed a change in operations that resulted in less reliance on direct freight flows and higher transshipment operations. Motivated to investigate a soft intelligent decision-making approach using game theory in the context o...
Conference Paper
Full-text available
Searching for texts inside a full comic strip may be exhaustive, and can be simplified by restricting the scope of the search to single panels, and better yet to within individual speech balloon. In this paper, a novel approach is devised where a tracking algorithm is employed for panel extraction, and speech balloons are identified using ‘Roberts’...
Conference Paper
Full-text available
The emergence of the big data problem has pushed the machine learning research community to develop unsupervised, distributed and computationally efficient learning algorithms to benefit from this data. Extreme learning machines (ELM) have gained popularity as a neuron based architecture with fast training time and good generalization. In this work...
Conference Paper
Full-text available
In the big data era, the need for fast robust machine learning techniques is rapidly increasing. Deep network architectures such as cortical algorithms are challenged by big data problems which result in lengthy and complex training. In this paper, we present a distributed cortical algorithm implementation for the unsupervised learning of big data...
Patent
Full-text available
In one embodiment, the invention is a method and apparatus for secure and reliable computing. One embodiment of an end-to-end security system for protecting a computing system includes a processor interface coupled to at least one of an application processor and an accelerator of the computing system, for receiving requests from the at least one of...
Book
Full-text available
Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have co-evolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. The book explore current developments in the deep learning techni...
Conference Paper
Full-text available
Marine Seismic data analysis involves data acquisition, data processing, and data analysis, each of which has a plethora of methods that were extensively expanded and researched. This paper presents an overview of the different procedures and methods for marine seismic data acquisition, interpretation and analysis. It showcases the complexity of th...
Chapter
With the increase in global energy awareness, smart grids improve the efficiency and peak leveling of power systems. Demand side management is the controlling scheme in such grids and it aims to optimize several characteristics using an interactive dynamic pricing scheme. In this paper we propose a game theoretic approach to the demand side managem...