Dec. 2015 - Mar. 2019
Ph.D. Researcher,
Université Grenoble Alpes
Developed the Dr-BIP framework for modeling self-configuring systems, relying on a model-based and architectural-based approach to prescribe systems
Rim El Ballouli, is a fresh doctorate graduate from the Université Grenoble Alpes, France. Her Ph.D. tackled the modeling of self-configuration in systems with dynamic architectures. Her thesis was under the supervision of prof. Saddek Bensalem and in collaboration with Dr. Marius Bozga and turing award winner Prof. Joseph Sifakis.
Prioir to that, she received her B.Sc. and M.Sc. in Computer Science from the American University of Beirut. In her Master's thesis she proposed a framework relying on machine learning techniques for credibility classification of arabic tweets. Her work was in collaboration with OMA research group and under the supervision of prof. Wassim El-Hajj.
Ph.D. Researcher,
Université Grenoble Alpes
Developed the Dr-BIP framework for modeling self-configuring systems, relying on a model-based and architectural-based approach to prescribe systems
Full Stack Web Developer, Neelwafurat
Designed and developed a mobile friendly version of the company site, developed an autocomplete webservice, and coded an html email generator
Instructor,
American University of Beirut
Taught technical course material and underlying theory for the courses: introduction to programming, and computers and programming
M.Sc. Researcher,
American University of Beirut
Developed a framework for credibility classification of Arabic content on Twitter The classifier outperformed the state of the art approach with an increase of 14% in F-measure
Teaching Assistant,
American University of Beirut
Taught technical course material and underlying theory for the courses: introduction to programming, computers and programming, programming languages, web programming, and discrete algorithms
Intern,
Oger Systems
Proposed and developed a new design for the design of the company's landing page website
Human Resources Assistant,
American University of Beirut
Consulted students with job applications, interview preparation, and cv writing
Modern systems are pressured to adapt in response to their constantly changing environment to remain useful. Self-adaptive systems are able to modify at runtime their behavior and/or structure in response to their perception of the environment, the system itself, and their requirements. The focus of this work is on realizing self-configuration, a key and essential property of self-adaptive systems. Self-configuration is the capability of reconfiguring automatically and dynamically in response to changes. This may include installing, integrating, removing and composing/decomposing system elements. We introduce the Dr-BIP framework, an extension of the BIP framework for modeling self-configuring systems that relies on a model-based and component & connector approach to prescribe systems.
A Dr-BIP system model is a runtime model which captures the running system at three different levels of abstraction namely behavior, configuration, and configuration variants. To capture the three levels of abstraction, we introduce motifs as primary structures to prescribe a self-configuring Dr-BIP system. A motif defines a set of components that evolve according to interaction and reconfiguration rules. A system is composed of multiple motifs that possibly share components and evolve together. Interaction rules dictate how components composing the system can interact and reconfiguration rules dictate how the system configuration can evolve over time. Finally, we show that the proposed framework is both minimal and expressive by modeling four different self-configuring systems. Last but not least, we propose a modeling language to codify the framework concepts and provision an interpreter implementation.
Data generated on Twitter has become a rich source for various data mining tasks. Those data analysis tasks that are dependent on the tweet semantics, such as sentiment analysis, emotion mining, and rumor detection among others, suffer considerably if the tweet is not credible, not real, or spam. We perform an extensive analysis on credibility of Arabic content on Twitter. We also build a classification model (CAT) to automatically predict the credibility of a given Arabic tweet. Of particular originality is the inclusion of features extracted directly or indirectly from the author’s profile and timeline. To train and test CAT, we annotated for credibility a data set of 9, 000 Arabic tweets that are topic independent. CAT achieved consistent improvements in predicting the credibility of the tweets when compared to several baselines and when compared to the state-of-the-art approach with an improvement of 21% in weighted average F-measure. We also conducted experiments to highlight the importance of the userbased features as opposed to the contentbased features. We conclude our work with a feature reduction experiment that highlights the best indicative features of credibility.
Citations: Google scholar, Research gate