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Mario A. Aoun
UQAM
Montreal, Canada
 

Mario Aoun received his bachelor’s degree in computer science from Notre Dame University (NDU), Lebanon, in 2003. His final-year project, a location-based service for Windows mobile phones, won the Microsoft Competition for Universities in Lebanon that year. In parallel to his university studies, Mario developed complex database applications ranging from client-server applications to mobile and multi-tier applications.

In 2007, after fulfilling his master’s (MS) degree in computer science from NDU, he was accepted for a PhD scholarship at the University of Twente, with training at Philips Research Center. Unfortunately, he was struck by a neurological disorder called transverse myelitis (TM). TM is an inflammation of a segment of the spinal cord, making him paraplegic; he was forced to cancel the scholarship.

In 2009, he reviewed a book for ACM Computing Reviews (CR), Computational Intelligence: a Compendium, which revived his personal research goals in artificial intelligence and neural networks. Consequently, in 2010, he wrote a research paper based on his MS thesis—“STDP within NDS Neurons”—which he submitted to and presented at the International Symposium on Neural Networks (ISNN 2010) in Shanghai, China.

At the end of 2010, he immigrated to Canada, and is now in his fourth year as a PhD research student in cognitive informatics at the Université du Québec à Montréal (UQAM).

His research interests include chaos theory, chaos control, reservoir computing, computational neuroscience, neurocognition, and the limits of intelligence. He investigates and approaches the exploit of the theory of chaotic nonlinear dynamics in order to emulate cognitive processes, like memory and perception, which are explained and elucidated by the theory of chaotic neurodynamics. The latter favors the role of chaos in the brain as an essential ingredient that makes the brain distinct from any artificial intelligent machine. The impact of this research will help us understand and delineate the limitations of both natural and artificial intelligence. He presented his latest research paper, “Learning algorithm and neurocomputing architecture for NDS neurons, ” in August 2014 at the IEEE International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC 2014) in London, England; he received the Best Paper Award. Also, in the same trip, he was invited to do a seminar at Oxford Brookes University to discuss the potential implication of his research work.

Mario’s review of The universal machine was selected as one of the best CR reviews of 2013.


     

Detecting deception using machine learning with facial expressions and pulse rate
Tsuchiya K., Hatano R., Nishiyama H. Artificial Life and Robotics 28(3): 509-519, 2023.  Type: Article

This is a very important paper because it combines machine learning, facial expressions analysis, and pulse rate in order to detect deception in an interview context. The results are supported by strong machine learning validation settings, applyi...

 

Faking it and breaking it: responsible AI is needed now
Trim M. ACM SIGCAS Computers and Society 51(3): 7-9, 2022.  Type: Article

The paper’s title is catchy and promising. However, readers will find themselves immersed in another trendy cry that criticizes artificial intelligence (AI) evolution while at the same time promoting it, because the paper is just nonsense. I...

 

The language of SQL (3rd ed.)
Rockoff L., Pearson, Hoboken, NJ, 2021. 272 pp.  Type: Book (978-0-137632-69-5)

Structured query language (SQL) allows programmers to interact with a data repository through a relational database management system (RDBMS). The language of SQL is divided into 20 easy chapters. It also includes three appendices about ins...

 

Judgement-proof robots and artificial intelligence: a comparative law and economics approach
Kovac M., Palgrave Macmillan, London, UK, 2020. 184 pp.  Type: Book (978-3-030536-43-5)

Artificial intelligence (AI) research is moving incredibly fast and requires regulation. For instance, a recent study reversed a machine learning model that creates nontoxic drugs and made it create toxic drugs [1]. This is called &...

 

Assessing neural network scene classification from degraded images
Tadros T., Cullen N., Greene M., Cooper E. ACM Transactions on Applied Perception 16(4): 1-20, 2019.  Type: Article, Reviews: (2 of 2)

One of the major problems faced by deep learning techniques--for example, deep convolutional neural networks (CNNs)--is uncontrolled behavior due to glitches in the input. For example, you can modify just a few pixels...

 
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