CSYS5020: Semester 1, 2024

University home

Library

Current students

Staff intranet

Find an event

Give

Current students

Units CSYS5020 Semester 1 2024 [Normal evening]

Unit outline_

CSYS5020: Interdependent Civil Systems

Semester 1, 2024 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Our modern day civil infrastructure includes transport networks, telecommunications, power systems, financial infrastructure and emergency services, all of which are growing more and more interconnected. Moreover, the behaviour of the modern infrastructure is not dependent only upon the behaviour of its parts: complex civil systems (such as modern power grids), communication and transport systems, megaprojects, social and eco-systems, generate rich interactions among the individual components with interdependencies across systems. This interdependent behaviour brings about significant new challenges associated with the design and management of complex systems. Cascading power failures, traffic disruptions, epidemic outbreaks, chronic diseases, financial market crashes, and ecosystem collapses are typical manifestations of these challenges, affecting the stability of modern society and civil infrastructure. This unit will develop an understanding of how interdependent systems perform under stress, how to improve resilience and how best to mitigate the effects of various kinds of component failure or human error, by more accurate analysis of interdependent cascades of failures across system boundaries. The studied topics will include dynamical analysis of complex interdependent networks, local and global measures of network structure and evolution, cascading failures, as well as predictive measures of catastrophic failure in complex adaptive systems, and the tools that enable planning for resilient infrastructure. This unit will equip future professionals with sufficient expertise and technical know-how for the design of efficient prevention and intervention policies, and robust crisis forecasting and management. This unit will equip future professionals with sufficient expertise and technical know-how for the design of efficient prevention and intervention policies, and robust crisis forecasting and management.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Mahendra Piraveenan, mahendrarajah.piraveenan@sydney.edu.au
Lecturer(s)	Mahendra Piraveenan, mahendrarajah.piraveenan@sydney.edu.au
Tutor(s)	Upul Senanayake, upul.senanayake@sydney.edu.au

Assessment

The census date for this unit availability is 2 April 2024

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Assignment 1 computing assignment	30%	Week 06 Due date: 29 Mar 2024 at 23:59 Closing date: 29 Mar 2024	n/a
Outcomes assessed:
Assignment	Assignment 2 calculation assignment	30%	Week 09 Due date: 26 Apr 2024 at 23:59 Closing date: 26 Apr 2024	n/a
Outcomes assessed:
Assignment	Assignment 3 calculation and computation assignment	40%	Week 13 Due date: 24 May 2024 at 23:59 Closing date: 24 May 2024	n/a
Outcomes assessed:

Assessment summary

The assignments will require you to integrate information from lectures and practicals to create a concise written argument

Detailed information for each assessment can be found on Canvas

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to interdependent civil systems	Lecture (2 hr)
Week 02	The world of networks	Lecture and tutorial (3 hr)
Week 02	Topological analysis of complex networks: part 1	Lecture and tutorial (3 hr)
Week 04	Topological analysis of complex networks: part 2	Lecture and tutorial (3 hr)
Week 05	Cascading failures	Lecture and tutorial (3 hr)
Week 06	Measuring network robustness and resilience	Lecture and tutorial (3 hr)
Week 07	Spectral methods	Lecture and tutorial (3 hr)
Week 08	Percolation in networks	Lecture and tutorial (3 hr)
Week 09	Game theory in networked systems	Lecture and tutorial (3 hr)
Week 10	Modelling epidemic spread in communities	Lecture and tutorial (3 hr)
Week 11	Modelling networked financial systems	Lecture and tutorial (3 hr)
Week 12	Modelling socio-ecological systems	Lecture and tutorial (3 hr)
Week 13	Modelling power and transport systems	Lecture and tutorial (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

S. N. Dorogovtsev (Author), J.F.F. Mendes (Author), Evolution of Networks: From Biological Nets to the Internet and WWW (Physics) . Oxford University Press, 2003. 978-0198515906.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. use elementary and intermediate programming skills to analyse, understand and simulate the dynamics of complex systems
LO2. apply topological analysis to a particular complex system to critically understand its structure
LO3. efficiently use existing software tools (e.g., Cytoscape, Pajek ) in complex network analysis and evaluating topological resilience
LO4. develop scientific programming skills which can be applied in network analysis
LO5. develop understanding of the nature and dynamics of interdependent systems
LO6. understand and apply elementary game theory in networked systems to simulate their dynamics and cognitive decision making of the participating entities
LO7. understand, and successfully use in analysis, the concepts of percolation, cascading failures, robustness, resilience, and related concepts within the context of interdependent systems
LO8. design basic network structures that satisfy structural and functional criteria within given domains and contexts, and are resilient to random failures, and various types of targeted attacks and disturbances.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No significant changes have been made since this unit was last offered

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect