Thesis\Treatise and Comprehensive Exam Track: Total Credit Hours Required to Finish the Degree ( 54 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses plus ( 24 ) credit hours for the Thesis and Pass the Comprehensive Exam
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Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
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Theoretical |
Practical |
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| 151818902 | ADVANCED ALGORITHMS | An overview and review of advanced algorithm design and analysis techniques. Topics include algorithms for network flows, data structures (Fibonacci heaps, splay trees, dynamic trees), linear programming (structural results, algorithms), dealing with intractability, approximation algorithms, dealing with large data sets and computational geometry. Students select research problems in algorithms and prepare research papers and make a presentation on that. | 3 | - | 3 |
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| 151818903 | QUANTITATIVE METHODS AND EXPERIMENTAL DESIGN | Integrated treatment of models and practices in experimental information technology. Topics include scientific methods applied to computing, workloads, performance and quality metrics of systems, analytic and simulation models, design of experiments, interpretation and presentation of experimental results, hypothesis testing, and statistical analyses of data. Course Objectives The main objective of this course is to present advance concepts in scientific methods applied to computing and design of experiments and interpretation of experimental results. | 3 | - | 3 |
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| 151818904 | DATA ANALYTICS AND MINING | Data mining and big data analytics is the process of examining data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions. This course presents concepts of data mining, machine learning and big data analytics. Key data mining methods of clustering, classification and pattern mining are illustrated, together with practical tools for their execution. Applications of these tools to a number of datasets are presented. The course has a research component, where students select a research problem, prepare a term paper and present their research results. Course Objectives The main objective of this course is to present the different advance research techniques and concepts in data mining and big data analytics. The course also enables students to conduct research in a problem related to data mining and/or big data analytics. | 3 | - | 3 |
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| 151818905 | ADVANCED SOFTWARE ENGINEERING | Advanced Software Engineering course discusses high-level, up-to-date topics in software engineering including new methods, models, and theories. It includes advanced topics in software engineering, such as fault-tolerant software, software architecture, software patterns, multi-media software and knowledge-based approaches to software engineering. Investigation and application of agile software development practices will be discussed too. The course also includes a number of case studies. Papers from the current literature will be discussed and student participation in a seminar style format may be expected. Course Objectives The main objective of this course is to present advanced techniques and models in Advanced Software Engineering with emphasis on the application of agile software development practices and software architecture. The course also enables students to conduct research in Advanced Software Engineering. | 3 | - | 3 |
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| 151818906 | PHD RESEARCH SEMINAR | This is a seminar course, students select research topics of their interest to start preparing a PhD dissertation proposal. The course emphasizes on ethical issues related to research. | 3 | - | 3 |
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Students must pass ( 15 ) credit hours from any of the following courses
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Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
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Theoretical |
Practical |
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| 151818911 | PARALLEL COMPUTING SYSTEMS | This PhD course is expected to explore parallel computing systems in three areas: parallel system architecture, programming parallel systems, and performance optimization. Course topics will cover up-to-date aspects of parallel computing systems, from hardware to software; like: GPU accelerators, heterogeneous systems, languages and programming environments, message-passing computing, partitioning and synchronization, scheduling and load balancing, algorithms and applications. Course Objectives The objectives of the course are to explore the role of parallel systems in solving complex computational problems, to provide hands-on-experience to build efficient parallel solutions for complex problems using multiple parallel machines, and to provide hands-on-experience in analyzing and evaluating the performance of different parallel systems. | 3 | - | 3 |
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| 151818914 | NATURAL LANGUAGE PROCESSING | This course aims at presenting a fairly broad graduate-level introduction to Natural Language Processing (NLP), the study of computing systems that can process, understand, or communicate in human language. The primary focus of the course will be on understanding various NLP tasks, algorithms for effectively solving these problems, and methods for evaluating their performance. There will be a focus on statistical learning algorithms that train on (annotated) text corpora to automatically acquire the knowledge needed to perform the task. The course material will discuss general issues as well as present abstract algorithms. Implemented versions of some of the algorithms will be provided in order to give a feel on how the systems discussed in class “really work”; and allow for extensions and experimentation as part of the course projects. Course Objectives Upon completion of the NLP course, the student shall be able to handle and describe different advance mathematical and formal concepts of NLP and the related algorithms, build different types of corpora for text, images, and speech collections, use the latest off-the-shelf tools for NLP tasks, select and employ different feature types, acquire the skills to design and implement different types of NLP applications, and differentiate between NLP and artificial intelligence (AI) in terms of tasks and algorithms. | 3 | - | 3 |
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| 151818917 | INFORMATION RETRIEVAL | This course expects the Ph.D. student to review background material in the area of Information Retrieval, Extraction, and Management, and to develop/deliver a presentation on a research topic. The course gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. Topics include: Boolean retrieval, term vocabulary and postings lists, dictionaries and tolerant retrieval, index construction and index compression, scoring, term weighting and the vector space model, computing scores in a complete search system, evaluation in information retrieval, relevance feedback and query expansion. Course Objectives The main objective of this course is to present and be able to describe different advance concepts in information retrieval and more advance techniques of multimodal based information systems. The second objective of the course for the student is to understand the underlined problems related to IR and acquire the necessary experience to design, and implement real applications using Information Retrieval systems. | 3 | - | 3 |
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| 151818919 | COMPUTATIONAL BIOLOGY | Computational Biology is a new multidisciplinary field which utilizes computer science, information technology and mathematics to tackle and answer biology problems. This course has been designed for students with computer science and information technology backgrounds who are interested in learning about the huge potential of this field in solving biological problems. In the first part, the students will have a brief introduction about the frequently used biological data models and algorithms. In the second part, a project-based learning approach will be implemented to ensure that students have gained enough hands-on experience in solving biological problems using computational biology algorithms. Course Objectives The objectives of this course are to enable students to appreciate the role of computational biology for solving problems in biology research, to introduce the key biological data analysis algorithms and the current challenges in this field, to provide hands-on experience in using and/or developing computational biology tools and algorithms. | 3 | - | 3 |
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| 151818920 | CLOUD COMPUTING | This course covers advanced (hot) topics in cloud computing, a leading paradigm for cost-effective, scalable, well-managed computing. The topics include: cloud storage systems, content distribution and retrieval, resource availability, programming models, coordination and synchronization, cloud management, cloud security and privacy, and related legal and economical issues. The student is expected to independently review the basics of cloud computing, as well as to deliver a presentation and to write a report on related research topics. Course Objectives The main objective of this course is two-fold: First, to develop the student's understanding of several advanced topics in cloud computing (e.g. describe how cloud storage systems work, describe and discuss security and privacy issues of cloud computing, analyze the performance as well as storage and maintenance overhead of content distribution overlays, develop distributed applications using Hadoop, employ Apache's ZooKeeper to coordinate distributed applications). Second, to enable the student to look for, identify, and discuss research problems related to cloud computing and their state-of-the-art solutions | 3 | - | 3 |
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| 151818921 | CRYPTOGRAPHY | This course present cryptography topics , including: numbers theory and related mathematical tools, cryptography techniques used in information and network security, cryptographic algorithms, primitives and applications, pseudo-random bit generation, digital signature and authentication techniques, key management. Summarize, assess and evaluate some recent research papers. Course Objectives The main objective of this course is to present and describe different advanced concepts of privacy, authenticity and integrity. In details the course presents the knowledge of classical as well as modern symmetric cryptography algorithms and their applications, at the same time, the course has the research view when the candidate assess, evaluate and crypt-analyses some of the recent cryptosystem. | 3 | - | 3 |
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| 151818922 | COMPUTER ARCHITECTURE | This course focuses on the techniques of quantitative analysis and evaluation of modern computing systems, such as the selection of appropriate benchmarks to reveal and compare the performance of alternative design choices in system design. The emphasis is on the major component subsystems of high performance computers: pipelining, instruction level parallelism, memory hierarchies, input/output, and network-oriented interconnections. Students will undertake a major computing system analysis and design project of their own choosing. Course Objectives This course aims to identify and differentiate among the different techniques of modern computing systems, reveal and compare the performance of alternative design choices in system design, and concentrate on the major component of subsystems of high performance computers (e.g., pipelining, instruction level parallelism, memory hierarchies, input/output, and network-oriented interconnections). | 3 | - | 3 |
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| 151818923 | MATHEMATICAL MODELING | An introduction to the theory and formulation of mathematical modeling with analysis, applications, simulations and computer implementation. Graphical, numerical, differential, difference and geometric techniques will be used. Topics that will be covered are dynamical systems, systems of difference equations, mathematical models, proportionality, geometric similarity, graphical fitting, analytic fitting, least-squares criterion, polynomial models, smoothing, cubic splines, simulation modeling, modeling using graph theory, population growth, prescribing drug usage, numerical approximations, using mathematical software. Course Objectives By the end of the course, students will learn how to model real-world problems by mathematical models. Students will learn how to apply various tools to analyze models including analytic and computational methods. Finally, students will learn how to compare models with observations and how to improve models. | 3 | - | 3 |
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| 151818924 | ADVANCED SOFTWARE TESTING | Students learn to test software effectively. Programmers learn practical ways to design high quality tests during all phases of software development. Students learn the theory behind criteria-based test design and to apply that theory in practice. Topics include test design, test automation, test coverage criteria, and how to test software in cutting-edge software development environments. Course Objectives This course addresses the issue of software testing. The connection between software testing and management of software development will also be discussed. Students will be introduced to research areas and tops in software testing. | 3 | - | 3 |
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| 151818925 | ADVANCED SOFTWARE QUALITY | Software quality improvements derive from the design of software development processes—a quality process creates quality products. The course will cover methods and tools for achieving software quality assurance at various levels of a software system including at the module, subsystem, and system levels, with special emphasis on the processes and activities of quality assurance. State of the art tools and techniques including development process modeling, manual and computer-assisted reviews, and ROI analysis of new processes. In addition, the role of standards, policies, and procedures are discussed, with examples drawn from IEEE, ISO, CMMI, RUP, and other process models and standards. Course Objectives This course addresses the issue of software quality. The connection between software quality and management of software development will also be discussed. Students will be introduced to research areas and tops in software quality. | 3 | - | 3 |
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| 151818930 | LARGE-SCALE INTERNET APPLICATIONS | Advanced Internet-scale systems and applications are geographically distributed, highly available, incrementally scalable, and dynamically configurable. Typical questions that systems researchers are facing today include: How would you build a web service that can handle billions of frantic requests? What systems support do we need for developing applications of large Internet scale? Can we provide dynamic configuration, replication, and migration of Web services? What new techniques will enable Internet systems and applications to better exploit high-speed networks? How should traditional systems issues such as naming, persistence, resource management, performance, and security be provided in a system of Internet scale? How much data can an internet scale system process? What does big data technology mean to a computer scientist? To a data scientist? to a business owner or a scientist. This course provides advanced techniques, and systems issues in advanced Internet application development, and explores new challenges and research issues that are critical for answering the raised questions. Course Objectives One of the important goals of the course is to look beyond the present status of the Internet and conjecture what possible future technologies and applications will evolve. The course will include a significant project component that will typically require Advanced Internet programming. | 3 | - | 3 |
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| 151818931 | INTERNET OF THINGS | Topics includes: IoT hardware platforms, IoT operating systems, Wireless communication technologies for IoT, IP-connected smart objects and networks, Embedded web services, Web of Things, other relevant standardization bodies and protocols, tracking, tracing and positioning, recent evolutions in IoT. Course Objectives The aim of this course is to give insight into the wide variety of platforms, wireless communication technologies and network protocols that are available to realize Internet of Things. | 3 | - | 3 |
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| 151818932 | WIRELESS NETWORKS SECURITY | This course covers security and privacy issues in wireless networks. A short overview of cryptography and wireless networking principles is given at the beginning of the course. Attacks and proposed solutions at several layers, authentication, key distribution and key management, secure routing, selfish and malicious behaviors, and secure group communication are analyzed for applicable wireless network types. Topics include: vulnerabilities of wireless networks, security requirements in wireless networks, security in wireless LANs, security in cellular networks, Bluetooth security, Ad hoc and sensor network security, security and privacy in RFID systems, vehicular networks, wireless mesh networks, and satellite networks. Course Objectives The objectives of this course are to provide students with a deep understanding of the general security challenges in wireless networks and gain insight into the security problems facing existing (GSM, UMTS, WiFi, Bluetooth) and upcoming (Mesh, Ad-hoc, Sensor, Mobile, Vehicular, RFID) wireless networks. | 3 | - | 3 |
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| 151818933 | WIRELESS SENSOR NETWORKS | This course deals with the comprehensive knowledge about wireless sensor networks. The use of distributed wireless sensor networks has surged in popularity in recent years with applications ranging from environmental monitoring, transportation, industrial automation, structural health monitoring, to people- and object-tracking in both cooperative and hostile environments. This course is targeted at understanding and obtaining experience with the state-of the-art in such wireless sensor networks which are often composed using relatively inexpensive tiny sensor nodes that have low power consumption, low processing power and bandwidth and equipped with sensing, computation, and wireless communication capabilities. After sensing their local environment, these sensors self-organize to form multi-hop wireless networks capable of relaying their data to a backbone server. Course Objectives The objective of this course is to cover the latest research in the area of Wireless Sensor Networks and all aspects of these unique and important systems, from the hardware and radio architecture through protocols and software to applications. Topics will include sensor network architectures, hardware platforms, physical layer techniques, medium access control, routing, topology control, and quality of service (QoS) management, localization, time synchronization, security, storage, and other advanced topics. Upon completion of this course students should be able to: list various applications of wireless sensor networks, describe the concepts, protocols, and differences underlying the design, implementation, and use of wireless sensor networks, and propose, implement, and evaluate new ideas for solving wireless sensor network design issues. | 3 | - | 3 |
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| 151818934 | MULTI-AGENT SYSTEMS | This course introduces student’s systems with multiple agents that mutually cooperate together in order to perform joint task in order to improve system performance. The course will cover theory for strategic interaction between self-interested agents as well as more altruistic agents working explicitly together in complex distributed environments. Game theory and swarm intelligence will be central parts of the course curriculum. Students will be introduced to the techniques for developing autonomous agents in multi-agent systems. Emphasis on feedback optimization in multiagent reinforcement learning and cooperative coevolutionary algorithms. Mean topics covered in this course are mainly; Autonomous agents, reinforcement learning, evolutionary algorithms, reward shaping, evolutionary game theory, and swarm optimization. Course Objectives The major objectives of this course are to introduce students to the terms of 'agency' and ‘multi-agent system’ and how it is associated with developing intelligent software and the problems associated with designing multi-agent systems, as well as to introduce the decision making frameworks in multi-agent systems. | 3 | - | 3 |
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| 151818935 | DIGITAL IMAGE PROCESSING-ADVANCED TOPICS | 3 | - | 3 |
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| 151818936 | HUMAN-MACHINE INTERFACES? (HMI) | HMI is an interdisciplinary course concerned with the study of the interaction between humans and interactive computing systems including process control systems, handheld devices, human-machine systems etc. Moreover, it discusses the overall HMI design process and how that process relates to system design. In addition to covering topics in engineering and computer sciences such as control systems, automation systems, operating systems, multimedia, image processing, knowledge-based systems etc., the course looks at major cognitive and social phenomena surrounding human use of computers and systems with the goal of understanding their impact and creating guidelines for the design and evaluation of such systems for industrial control and monitoring systems such as distributed control, supervisory control and data acquisition, and stand-alone. Course Objectives The main objectives of the course are to introduce students the concepts and design mechanism of the human machine interface as well as the design methods and to explore the scientific methods and design principles to different kind of human machine interface with emphasis on the identification of the strategies and evaluation standards in satisfying a set of given requirements to user interface design. | 3 | - | 3 |
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| 151818937 | ARTIFICIAL INTELLIGENCE | The course is designed to prepare students to carry out in-depth research work, typically a thesis in an academic context or a research engineer's work in a professional context. At the end of the course, each student will have deepened a research sector in the field of Artificial Intelligence and will have consolidated the basic knowledge necessary for any Artificial Intelligence research. The course of Artificial Intelligence at doctoral level is thus organized according to the inverted class approach. The principle is the preparation of the courses by the students from the material made available. The teacher will discuss and propose the following main fields to students: • Knowledge engineering in the web era (information search, recommendations, social networks,...), • Collective intelligence (multi-agent) • Artificial intelligence for human learning • Learning Approaches for Artificial Agents • Artificial Intelligence and Data Sciences • Artificial intelligence and robotics • Artificial intelligence and connected objects • Other student-initiated topics. Course objectives Learning research in any field requires not only workload but also time to mature. The module requires a semester to be successfully completed by the students. Pedagogical appointments are important at the beginning and end of the semester, while progress monitoring is carried out remotely during the period of the research on the topic chosen by the students. The tentative timetable could be as follows: • 1 month of intensive training and situation studies. It concludes with a mini-course conducted by each student on the subject of their choice (inverted class). • 2 months of study with coaching and distance monitoring. • 1 month of intensive preparation for the presentation of the research work carried out. At the end of this period, a scientific seminar is organised as an international conference to present the work. | 3 | - | 3 |
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| 151818938 | INFORMATION DESIGN | Digital information is generated in hybrid spaces that are composed of traditional "tangible" physical elements and "intangible" elements. This course is based on three observations: ? Digital technology, as a social and professional fact, gives rise to "innovative" strategies to accompany conceptual and perceptual changes in information design. ? Human interactions are increasingly meditated. ? Information (by its perception and its uses) is "still" related to the human presence. Thus the information design has to be centered on the Human who acts in a hybrid space. To introduce and develop the multidisciplinary character of Information Design, we propose a user-centered collaborative design approach. Given the increased complexity of design in a hybrid and globalized (borderless) space, we introduce a highly experimental and constructive methodological approach of the research-action type (project driven approach). Course Objectives This course addresses the issue of digital information, its uses, its socio-professional status as well as its evolutions. The connection between information design and management of information systems will also be discussed. This will lead us to revisit the issue of Information Design in its complexity and growing importance especially when it is becomes part of online, interactive and open information system (open data system). | 3 | - | 3 |
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| 151818939 | SMART SYSTEMS INTEGRATION AND SIMULATION | The course aims to provide a fundamental review for the complex adaptive systems theory followed by presenting the different types of techniques used in the smart systems from the perspectives of adaptation such as the growing and increasingly strategic field of Smart Systems Integration. Multi Agent systems will have a special part in this course. The course will guide the students to use new methods and tools for the integration and simulation for the complex and adaptive smart objects in smart environment. Some approaches will be described, and explicit examples will be given. Smart components and systems of systems concepts will be the considered as a special constraint for this course. The structure of multi-sensors and multi-actuator systems will be described in the second stage of this course. Smart Systems will be presented as combine data processing with sensing, actuating and communication and how the student be able to analyse complex situations, and how his system will take self-decisions and be predictive. The course will describe the relation between the research and the industry with different examples related to real problem such as water, weather, transportation, pollution and security. Course Outcomes The student will be guided to build his own simulation process using some proper simulators. Their simulation models will be evaluated. The students also will be able to create the co design environment that is consisted of different system of systems. A long report will be delivered by the end of the course. This course will target the students who are willing to specialize in hardware/software modelling, component integration and simulation under different positions, researchers in dynamic systems, self-decision systems, etc. | 3 | - | 3 |
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| 151818940 | SPECIAL TOPICS | The objective of this course is to cover the latest contemporary issues and areas that are not covered in other courses, with an emphasis on research trends. | 3 | - | 3 |
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| 151818942 | THESIS | 1 | - | 1 |
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