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Project to Enhance ‘Digital Citizenship’ in Qatar
Dr. Osama Halabi an associate professor in Computer Science and Engineering department is one of the leads of a project titled “Future of Digital Citizenship in Qatar: A Socio-Technical Approach”.
The project is a four-year research initiative done by Msheireb Museums and Hamad Bin Khalifa University (HBKU) as a significant step towards promoting responsible digital citizenship and enhancing positive online community engagement in Qatar.
The project is funded by the Qatar Research, Development, and Innovation Council (QRDI). The initiative comprises six sub-projects led by experts in their respective fields, bringing together more than 60 researchers.
The project’s tangible outputs include web interfaces for public analysis of social media, detection of hate speech, discrimination, and propaganda, as well as training materials and ambassadors to educate students on digital literacy, propaganda detection, safety, well-being, and social inclusion.
Dr. Osama Halabi, highlighted the importance of social media literacy and the project’s focus on assessing public attitudes towards social media in Qatar. The project’s findings, which will be primarily presented in Arabic, are expected to provide deeper insights into the variations between Qatar, the Arab world, and Western countries regarding technology and social media acceptance.
Msheireb Museums and HBKU’s commitment to integrating technology with academia and emphasis on sharing research findings with the public through events, exhibitions, and community engagement activities demonstrate their dedication to fostering a more sustainable and inclusive digital environment in Qatar.
This collaborative effort between academia, museums, and the community is a promising endeavor that has the potential to impact digital citizenship and online community engagement in Qatar significantly. The initiative’s focus on responsible digital citizenship and positive online engagement sets a commendable example for other regions and countries.
CSE SDP Contest Day 2024: All Winners
The highly anticipated senior projects’ presentations took place on May 6th at the state-of-the-art new engineering building H07. These remarkable projects, the culmination of tireless efforts by talented students, were subjected to rigorous evaluation by industry examiners. After careful deliberation, outstanding projects from each program, Computer Science (CS) and Computer Engineering (CE), emerged victorious. Exceptional teams of these winners will be chosen to proudly represent our department in the upcoming college contest. We eagerly anticipate the success that awaits our representatives as they compete at the college level, confident in their abilities, to make our department proud once again.
Winning Projects in CSE-SDP23 Contests Day
CE Rank 1
Project title: Q-SAR: Drone Swarm for Disaster Management
Students: Ali Elmancy, Abdalla Ahmed, Assem Alnajjar
Supervisor: Dr. Amr Mohamed
Abstract:
- SAR operations face difficult environments.
- Drones offer faster and more effective SAR missions.
- Design a drone system to enhance SAR missions.
- Radar sensors are used for under-rubble survivor detection.
- Leverage autonomous smart drones.
- Drone assembly and sensor integration.
- Design a wireless charging stations for drones.
- Develop a backend for ground control and monitoring.
CE Rank 1 (equally-ranked)
Project title: Marathon Monitoring System
Students: Aly Okasha, Mohammad Rayyan, Ibrahim koubeisi
Supervisor: Dr. Noora Fetais
Abstract:
The challenges in marathon organization, in particular participant safety and data collection. We’ve evolved a product to track participant positions, detect cheating, identify fainting, and easily transmit data to event administrators.
CE Rank 2
Project title: NABATEQ: Plant Health Monitoring System
Deep Learning Classification Approach
Students: Amro Moursi , Mohamed Tahar, Malek Hamad, Hamad Alansi
Supervisor: Dr. Uvais Qidwai
Abstract:
This project focuses on the development and implementation of an advanced plant health monitoring system. Our approach involves addressing the critical environmental factors essential for preserving plants’ well-being, including temperature, soil moisture, water levels, humidity, as well as the presence of essential nutrients like Nitrogen, Phosphorus, and Potassium.
Central to our methodology is the utilization of computer vision technology with Artificial Intelligence to provide health ranks for the plants under monitoring. By leveraging this data-driven approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.
CE Rank 3
Students: Abeer Madyar , Kawther Ahmed, Leen Alinsari, Razan Abdelgalil
Supervisor: Dr. Mohammed AlSada
CS Rank 1
Project title: Vaultexe/OSS Zero-knowledge Self-hosting Password Manager
Students: Ahmed Ashraf, Husam Snober, Mohammed Saqallah, Walid Ben Ali
Supervisor: Dr. Moutaz Saleh
Abstract:
In this digital age, we rely on passwords to protect our online presence on the web. With so many passwords to memorize, we experience password fatigue and easily fall into the trap of reusing weak passwords across different sites. This is a serious security risk, as a single compromised password can give attackers access to many other accounts. To address this issue, we introduce Vaultexe, an open-source self-hosted zero-knowledge password manager.
CS Rank 2
Project title: PassGuard
Students: Youssef Aly, Essa Ahmed Kamel Abou Jabal, Mohamed-Dhia Abdaoui, Khalifa Yousuf
Supervisor: Dr. Mohammad Saleh
Abstract:
The importance of a password in today’s world cannot be overstated. Unfortunately, a large number of people falls victim to data breaches because of their reliance on weak passwords, default passwords, reused passwords. It can be explained by a simple reason: it is more convenient for the general public to use predictable passwords and reuse them.
Here comes PassGuard, an offline password manager application, whose sole purpose is to provide password security and user convenience.
CS Rank 3
Project title: ReWisely: a ChatGPT-based comprehensive revision platform for generating user-personalized study materials
Students: Amani Mamiche, Asma Bahabarah, Khadija Khedr, Taqwa Ellabad
Supervisor:Dr. Moutaz Saleh
Abstract:
Our project aims to modernize the creation of revision material by integrating AI into a comprehensive, interactive, customized, and user-friendly web application. It focuses on developing a platform capable of handling vast amounts of data and offering assistance through text summarization, flashcards, question extraction, the Feynman technique, and mind-mapping functionalities.
CSE Senior Students Participate at 11th Agricultural Technologies Conference and Exhibition
A team of CSE senior students have participated in the 11th Agricultural Technologies Conference and Exhibition. The team includes Amro Moursi , Mohamed Tahar, Malek Hamad, and Hamad Alansi, who are working under the supervision of Dr. Uvais Qidwai on their senior project titled “NABATEQ: Plant Health Monitoring System Deep Learning Classification Approach”. The students project is featured at the exhibition until 27/2/2024 as part of Qatar Expo. The event cumulates a number of innovations, products, and companies that have brought a variety of technologies to assist in making the agricultural growth more sustainable, profitable, and viable for food security initiative from Qatar Vision 2030.
The student project focusses on the use of IoT technologies for sensory measurements and utilizes custom-cloud solutions for the implementation of the whole system. Artificial Intelligence has been deployed in providing meaningful feedback to the process controllers related to the plants’ health using optical and IR images.
The group was invited as a special exhibitor by the conference organizers due to its merit and importance in the overall food security efforts.
Wishing the team all the best!
Humanitarian Drone Competition by CSE Students
The CSE department has organized an “Humanitarian Drone Competition” as part of the course project for the “Computer Engineering Practicum” course..
The motivation for this year’s project is to show solidarity with people in Gaza. Unlike typical control circuits used in the drones, the students of the course were asked to design a control circuit for the drone with only passive electronic components and basic IC to control the motors of the drones. In order to achieve this, the students had to go through phases of idea description, schematic design, breadboard prototyping, and a final soldered prototype that has to have at least three functionalities that can assist the humanitarian mission.
The winning drones were judged based on their design, number of functionalities incorporated in the design, A4 poster and a video montage of their work carried out during the project.
The winners in the competition were:
Male Students
- First place: Yusuf Siddiqui, Iheb Zouari, Mohamed Ali, Mahmoud Ali
- Second place: Mubarak AlHajri, Abdulrahman Marwan Aboumadi, Ahmed Miqdad, Deya Aldeen Abdelbaset
- Third place: Muhammad Khan, Abdullah Irhimeh, Laith Nasrallah
Female Students
- First place: Isra Ali, Yoma Mohammed, Maya Attia, Nooralhodaa Elshawadfy
- Second place: Roaa Shady, Noha Elgamal, Amna Al-Ahmed
- Third place: Aisha Al Mannai, Naema Al Abdulla, Hila Al Dosari, Fatima Al Ali
The competition was judged by our faculty members Eng. Amelle Bedair , Eng. Farah E-Qawasma and Eng. Amal Elmasri. The prizes for the winners were awarded by the HoD Prof. Amr Mohamed.
Thanks Voltaat for sponsoring the prizes worth 2,000 QAR. Thanks to Dr. Wadha Labda for her kind support as well to make the awards ceremony in the ICET’23 events. Thanks for the course coordinator Eng. Naveed Nawaz, course instructors Eng. Leila Ghastli and Eng. Tamer Eltaras, and all other faculties for making this competition a successful one.
CSE faculties awarded with Internal Grants (2024, Cycle 7)
Five of our faculty members (Dr. Noora Fetais, Dr. Abdelaziz Bouras, Dr. Mahmoud Barhamgi, Dr.Khaled Shaban, and Dr. Elias Yacoub) and their research teams have just been been awarded QU internal grants 2024 (Cycle 7) in the different categories. Congratulations!
Here are more details about the winning projects:
Award Category: Collaborative grants (CG)
Project title: Practical Adversarial Machine Learning for Network Intrusion Detection Systems
Project team: LPI: Dr. Noora Fetais (QU), PIs: Dr. Khaled Khan (QU), Dr. Armstrong Nhlabatsi (QU), Consultant: Dr. Dan Dongseong Kim (University of Queensland)
Project abstract:
An adversarial example exploits an imperceptible attribute of the input to cause a deep-learning (DL) algorithm to misclassify. Attackers intentionally design these adversarial examples to confuse the model so that it makes a mistake. Some researchers hypothesize that adversarial examples are caused by highly abstract representations, which make the decision function extremely discontinuous. In contrast, others claim that adversarial examples occur due to the locally linear nature of neural networks. As a result of adversarial examples, researchers are developing countermeasures to defend against adversarial attacks against DL-based applications, especially in security-critical domains such as Network Intrusion Detection Systems (NIDS). However, the security of NIDS under adversarial attacks has not been well explored. The aim of this research is to enhance the understanding of practical adversarial attacks on NIDS. Here, a “practical” adversarial attack means the output of the attack is a set of replayable network packets, and adversarial attacks specifically refer to evasion attacks that slightly modify the input to bypass detection. To achieve these goals, this research proposal seeks to:
- Develop an adversarial attack generator tailored for NIDS and present a comprehensive evaluation framework to determine the severity of the attack on the NIDS.
- Propose a defense mechanism to defend against adversarial attacks and formulate another evaluation framework to examine the strength of the defense.
The proposed project is expected to bridge previously identified limitations in adversarial NIDS attacks/defenses. To enable automation and deployment of future attacks/defenses, software toolbox/frameworks will be designed and developed. More importantly, the resulting framework, methods, techniques, and tools of this project are expected to be readily applicable in real world settings.
Award Category: Collaboration Co-Fund (IRCC) Grants
Project title: MESledger: A decentralized intelligent control of production systems based on AI and Blockchain technologies.
Project team: LPI: Dr. Abdelaziz Bouras (QU), PIs: Dr. Khaled Benfriha and Améziane Aoussat (ENSAM Institute of technology, Paris-France). Dr. Abdelhak Belhi (JBJADS, Qatar). Dr. Mahmoud Barhamgi and Dr. Loay Ismael Sabry (QU)
Project Abstract: The project focuses on the development of a new system for the intelligent control of digitalized production systems. Indeed, the old production control systems, based essentially on automats (Programable Logical Controllers), are not adapted to the new manufacturing processes, said digitalized. Such new system will be confronted with a mass of data generated by the IoT layer and the various IT systems required for operation.
It becomes important to think about a new “manufacturing execution system” able to analyze the data, to order dynamically the operations and to take the adequate decisions in front of malfunctions. Moreover, such system will be able to optimize production operations according to priorities, such as cost, energy or production time. To do this, it would be necessary to review the design of a manufacturing operation, known as standard and parametric. Thus, the system should be intelligent enough to configure the values of the parameters according to the priorities.
The industrial stakes are high. Companies will not be able to reach the expected performances without the intelligent exploitation of the generated data. From a scientific point of view, the challenge is to develop intelligent and scalable algorithms that can drive a digital production system with a high level of autonomy.
Project title: Building Transparent, Fair and Privacy-preserving Smart City Applications
Project team: LPI: Dr. Mahmoud Barhamgi (QU), PIs: Dr. Saeed Salem, Dr. Qutaibah m. Malluhi, Dr. Noora Fetais (QU), Dr. Daniela Grigory (Paris-Dauhine University, France), Dr. David Camacho (Madrid University, Spain)
Project abstract: In Qatar, as well as in all advanced countries, Intelligent Cyber-Physical Systems (ICPSs) are increasingly becoming an integral part of people’s life. Their applications are exploited today to optimize many aspects of our daily lives including in healthcare (e.g., remote patient monitoring networks), smart food supply chains, smart road infrastructures (e.g. for efficient real-time regulation of traffic), and smart grids (e.g., for greener energy consumption). All of these applications are collectively called smart city applications.
They collect huge amounts of data about us, process and exploit them to make important decisions that affect our lives both positively or negatively. As these smart systems and applications are expected to grow and stay with us, it is important to ensure they are designed and operationalized in a way to respect the fundamental rights of their users.
In this project, we focus on how three fundamental rights of users including Transparency, Fairness, and Privacy can be ensured in ICPSs. Specifically, we propose models, mechanisms and software tools allowing the users of such systems to monitor how and why a decision affecting them was taken by the system, based on what data as well as how fairly they have been treated by the system compared to their peers or other groups of users. The provided justifications are computed by our solution while respecting the privacy of all users or data subjects impacted by the system. We intend to apply and validate our solution in two specific smart city applications including smart patient monitoring and traffic optimization.
Project title: DeIN: A Drone-based Inspection System for Outdoor Insulators in Qatar.
Project team: LPI: Dr. Khaled Shaban (QU), PIs: Dr. Saeed Salem (QU), Dr. Ayman El-Hag (University of Waterloo)
Project abstract: This research proposal aims to enhance the effectiveness of inspection systems for outdoor, high voltage insulators utilized in the electric power transmission networks of Qatar, the Gulf region, and beyond. The proposed research is significant due to the criticality of outdoor insulators in supporting and insulating overhead power lines (OHPL) and the impact of their poor electrical performance on the resilience of the entire transmission network. Insulators are subjected to harsh environmental factors such as hot and humid weather, heavy dust and sand depositions, and salt buildup, leading to accelerated aging and increased maintenance costs. Insulators are responsible for over 70% of power line outages and require continuous monitoring to prevent premature failures. While traditional inspection methods are expensive, time-intensive, and laborious, aerial-based robots, such as drones, equipped with sensors and advanced algorithms offer a promising solution. However, the data obtained from drones is susceptible to noise and uncertainties, affecting their accuracy and limiting their inspection capabilities. The proposed solution, DeIN, aims to develop a novel drone-based inspection system that combines vision and radiation-based sensors with state-of-the-art representation learning, fusion, and deep learning (DL) algorithms. DeIN offers advantages such as reduced inspection time and cost, increased personnel safety, enhanced precision, repeatability, and improved access to OHPL, eliminating user subjectivity. Moreover, the use of radiation-based sensors provides enhanced inspection capabilities for detecting surface cracks and internal voids, complementing the vision-based sensors. The proposed research contributes to the development of a reliable and efficient inspection system for high voltage insulators in Qatar, the Gulf region, and beyond, ensuring the reliability and security of power transmission infrastructure.
Award Category: CD-IRCC Sustainable Development Goals (SDGs)/Local Projects)
Project title: Wearable Bracelet and Machine Learning for Remote Diagnosis and Pandemic Infection Detection.
Project team: LPI: Prof. Elias Yaacoub (QU), PIs: Dr. Ahmed Badawy and Dr. Khalid Abualsaud (QU)
Project abstract: More than three years into the coronavirus disease 2019 (COVID-19) pandemic, it can be noted that the measures put in place for societies to manage the spread of this disease could have been better. For example, contact tracing mobile applications used to curb the spread of COVID-19 need additional enhancements to allow health care professionals to better understand the proliferation of the disease and to lessen the burden on hospitals and medical centers. In this project, we present an intelligent solution to remotely self-monitor COVID-19 symptoms to help rapidly identify and detect suspected positives. The proposed intelligent solution is based on using a near-field communications (NFC) wristband that collects body temperature, heart rate and SpO2 levels. It is connected to a dedicated mobile application to intelligently draw conclusions from the data (COVID-19 symptoms) it collects. Moreover, the application is trained to analyze cough sounds and detect the probability of infection. Current results show more than 90% of detection accuracy. The work in the project aims to deliver a prototype at TRL 5 or 6, where the bracelet with embedded sensors will be fully functional and the readings are sent to the mobile application, where they can be jointly analyzed with coughing sounds to further enhance performance. The proposed system can be adapted to future pandemics based on respiratory symptoms.