辅导案例-INFO5992
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INFO5992 Understanding IT
Innovations
Ivan Chua
Semester 1, 2020
Assignment II – Commercialisation
Report
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Focus of your assignment
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Assessments
Assessment
name
Team-based Weight Due Weeks Assessed
Outcomes-
Assessed
Assignment I No 15% Week 7 Week 1-3 LO1, LO2, LO3
Mid-Semester
Exam
No 15% Week 8 Week 1-6
LO1, LO2, LO3,
LO4, LO5, LO6,
LO7
Assignment II No 20% Week 13 Week 7-10 LO8
Final Exam No 50% Exam Period Week 1-12 LO1 to LO11
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Commercialisation Report
Assessment II
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Innovation Report – Learning Objectives
– Research into the business model of a company which is at the
commercialization stage
– Undertake critical analysis of a company’s business model and the
– Analyse impact of an emerging technology on a company’s business model
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Overview of Assignment II
– Select an IT company which has an IT product in the market and is being used by paying customers –
this is your “case study”. The company can be in any industry.
– If the company offers more than one product, choose ONE product to focus on for this assignment.
– The company which you choose must be deploying the emerging technology which is assigned to you
in Assignment I.
Key details:
– Individual assignment
– There is a 3,000-word limit for this assignment. (Previously 2,000 – but has been increased to 3,000)
– Due 31 May 2020 at 11:59pm.
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Report Structure
Section
% of
marks
Requirements
Recommended
Word limit
(optional)
Additional
Section 1: Value
Proposition
Canvas & Value
Pyramid
40%
• Apply and evaluate ‘Customer Profile’ from the Value
Proposition Canvas to your chosen case study
(consisting of customer jobs, pains and gains). Provide
supporting evidence.
• Apply and evaluate ‘Value Map’ from the Value
Proposition Canvas to your chosen case study
(consisting of products, pain relievers and gain
creators). Provide supporting evidence.
• Discuss and evaluate the Fit between the Customer
Profile and the Value Map
• Identify and discuss the three most significant value
propositions from the Value Pyramid
1,200 words
You are not required to
identify a complete list of
jobs – after you have
done your research, rank
them, and focus on the
most significant ones
(refer to page 11 of the
lecture week 9)
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Report Structure (Continued)
Section
% of
marks
Requirements
Recommended
Word limit
(optional)
Additional
Section 2:
Business Model
Canvas
40%
• Apply and evaluate all nine building blocks of the
business model canvas – in the same order that is
discussed in the lecture.
• Provide a one-page summary of your business model
canvas – you may download the template here:
https://www.strategyzer.com/canvas/business-
model-canvas
1,200 words
For the ‘Customer
Segment’ and ‘Value
Proposition’ discussions,
you may draw on the
conclusions of your
discussion in Section 1
without repeating the
analysis.
Make sure that you have
a clear structure in your
response (i.e. a heading
for each block, and your
response under the
heading)
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Report Structure (Continued)
Section
% of
marks
Requirements
Recommended
Word limit
(optional)
Additional
Section 3: Impact
of Emerging
Technology on
Business Model
20%
• Identify and discuss the impact of your assigned
emerging technology on the business model canvas
(i.e. which blocks are affected and how?)
• Drawing on your discussion in Assignment I, how would
the future development of the emerging technology
change the business model canvas?
600 words
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Report Template
Section 1.1 Value Proposition Canvas
Customer Profile
Customer jobs
{insert discussion}
Pains
{insert discussion}
Gains
{insert discussion}
Value Map
Product offering
{insert discussion}
Pain Relievers
{insert discussion}
Gain Creators
{insert discussion}
Fit between Customer Profile & Value Map
{insert discussion}
Section 1.2 Value Proposition Pyramid
{insert discussion on value proposition #1}
{insert discussion on value proposition #2}
{insert discussion on value proposition #3}
Section 1 – Value Proposition Canvas & Value Proposition Pyramid
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Report Template
Section 2.1 Business Model Canvas Discussion
Building Block #1 – describe it (e.g. “Customer Segment”)
{insert discussion}
Building Block #2
{insert discussion}
Building Block #3
{insert discussion}

Building Block #7
{insert discussion}
Building Block #8
{insert discussion}
Building Block #9
{insert discussion}
Section 2.2 Business Model Canvas Summary
{insert a summary based on the diagram which can be found here: https://www.strategyzer.com/canvas/business-model-canvas}
Section 2 – Business Model Canvas
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Report Template
Section 4.1 Impact based on current development
{insert discussion}
Section 4.2 Impact based on future development
Section 3 – Impact of Emerging Technology on Business Model
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Report Structure (Cont.)
– Figures (images or diagrams), tables and quotes are typically very effective
in an essay. Please use them, but only if it adds useful information to your
report. If you do, you must reference the source of the information.
– You are encouraged to create your own figures and tables. If you do, show
that you created them (e.g. “created by Firstname Surname for INFO5992”)
– When referring to a figures / tables, make sure appropriate description is
given so that they are understandable – figures / tables contain a lot of
information!
– There is no template – please use a template of your own choice. It is OK
for the text to be either single-spaced or double-spaced.
– Use Harvard or Vancouver referencing style – keep your referencing style
consistent
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Notes for the Report
– Choosing one example company from an Industry:
– If possible, choose an example that are current or from the last three years.
– Try to pick examples that are discussed in a reputable Journal /
Conference articles
– You may choose to use an example from your own company (if you have
permission to use any material needed).
– If in doubt about whether your topic or example are appropriate, check with
the Teaching team
– New examples – innovation is a fast moving topic!
– Do not necessarily accept all that you read at face-value, e.g. from self-published
articles.
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Notes for the Report
– Sources:
– Read widely; read journal articles (eg online through the library), online
magazines and high quality blogs.
– Using reliable scholarly sources – innovation literature
– Wikipedia is highly variable in quality, derivative and typically not a
good source for your essay (except perhaps for gaining a general
understanding before reading more deeply from the literature or high-
quality blogs)
– Company websites are rarely unbiased descriptions of examples
(though may provide some useful information that should be understood
in its context)
– There are tips on library use (and referencing) at
http://www.library.usyd.edu.au/skills/
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Assignment Topics --
Technologies
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Refer to the “Assignment Topic List” on Canvas to find out
which technology you are assigned for Assignment I & II
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The ten technologies
selected for this
assignment are based on
the Gartner Hype Cycle
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Option 1: 3D Sensing Cameras
3D sensing cameras generate 3D images. For the purpose of this assignment, students should focus on the
software aspects and the application of the technology. However, a brief explanation of the hardware is
provided below for context.
There are 3 common ways: stereoscopic imaging, structured light illumination, and flood illumination.
Stereoscopic imaging use two cameras at different angels to capture images. Image-processing software
identifies common features in both images and extract distance information following a triangulation
method. In a structured light camera, the infrared illuminator projects a predetermined pattern onto the
scene, which can be decoded by specific algorithms to extract depth information over the entire image. A
time-of-flight camera (flood illumination) requires a uniform, high-frequency modulated infrared light to
be projected onto the scene. The sensor is synchronised with the illuminator and utilises the light that is
reflected by objects into the scene to determine the distance to these objects.
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Option 2: Graph Analytics
Graph analytics is analytics applied to a graph database. Graph databases are based on a model of
representing individual entities and numerous kinds of relationships that connect those entities. It employs a
graph for representing connectivity, consisting of a collection of vertices (aka nodes or points) that
represent the modelled entities, connected by edges (aka links, connections or relationships) that capture
the way that two entities are related. Unlike the traditional relational database, graph databases places
greater emphasis on the relationships between entities - in which analytics can be applied on the graph
(hence graph analytics) to conduct path, connectivity, community and centrality analysis.
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Option 3: Edge Analytics
Edge analytics refers to data analytics undertaken close to the edge, where things and people generate
or consume that data to save response time and save bandwidth. Examples include on self-driving cars,
satellites and wearable devices.
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Option 4: Earth Observation Software via Low-Earth Orbit
Satellite Systems (also known as "CubeSats")
Low-Earth Satellites, also known as "cubesats", revolve at an altitude between 160 to 2,000 kilometers.
Unlike a traditional satellite, a low-earth satellite is small, located at lower altitudes, and . A constellation
of LEO satellites can provide continuous, global coverage as the satellite move, as well as provide images
of the earth with higher resolution given that they are located at lower altitudes. For the purposes of this
assignment, emphasis should be placed on the software rather than hardware -- meaning "earth
observation" (the software capturing and analyses images) rather than the satellite itself.
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Option 5: Explainable AI (XAI)
Recent years have seen significant advances in the capabilities of artificial intelligence -- being able to
produce highly accurate results (e.g. predictions). However, they are also highly complex if not outright
opague, rendering their workings difficult to interpret. There has been growing discussion about the extent
to which individuals are able to understand how AI works and why a particular decision was reached.
Explainable AI addresses the issues of "black-box models" by making AI interpretable, explainable,
transparent, justifiable and contestable.
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Option 6: Transfer Learning
Traditional data mining and machine learning algorithms make predictions on the future data using
statistical models that are trained on previously collected labelled or unlabelled training data. Most of
them assume that the distributions of the labelled and unlabelled data are the same. Transfer learning, in
contrast, allows the domains, tasks and distributions used in training and testing to be different. In the real
world, we observe many example, we may find that learning to recognise apples might help to recognise
pears. Similarly, learning to play the electronic organ may help facilitiate learning the piano. The study of
transfer learning is motivated by the fact that people can intelligently apply knowledge learned
previously to solve new problems faster or with better solutions. Traditional machine learning techniques
try to learn each task from scratch, while transfer learning techniques try to transfer the knowledge from
some previous tasks to a target task when the latter has fewer high-quality training data.
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Option 7: Emotion AI (Emotion Detection)
Emotion AI is the task of recognising a person's emotional state -- for example, anger, confusion or deceit
both voice and nonvoice channels. The most common analyses the characteristics of the voice signal, with
word use as an additional input, if available.
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Option 8: Virtual Reality (VR) or Augmented Reality (AR)
(Only choose one)
Virtual reality provides a computed-generated 3D environment (including both computer graphics and
360-degree video) that surrounds a user and responds to an individual's actions in a natural way, usually
through immersive head-mounted displays. Gesture recognition or handheld controllers provide hand and
body tracking, and haptic (or touch-sensitive) feedback may be incorporated. Room-based systems
provide a 3D experience while moving around large areas, or they can be used with multiple participants.
Augmented reality (AR) is the real-time use of information in the form of text, graphics, audio and other
virtual enhancements integrated with real-world objects. It is this "real world" element that differentiates
AR from virtual reality. AR integrates and adds value ot the user's interaction with the real world, versus a
simulation.
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Option 9: Digital Twin
A digital twin is a digital representation of a real-world entity or system. The implementation of a digital
twin is an encapsulated software object or model that mirrors a unique physical object, process,
organisation, person or other abstraction. Data from multiple digital twins can be aggregated for a
composite view across a number of real-world entities, such as a power plant or a city, and their related
processes.
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Option 10: Robotics Process Automation (RPA)
Robotic process automation (RPA) is a productivity tool that allows a user to configure one or more scripts
(which some vendors refer to as "bots") to activate specific keystrokes in an automated fashion. The result
is that the bots can be used to mimic oor emulate selected tasks (transaction steps) within an overall
business or IT process. These may include manipulating data, passing data to and from different
applications, triggering responses, or executing transactions. RPA uses a combination of user interface
interaction and descriptor technologies. The scripts can overlay on one or more software applications.
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Submissions
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Submission Notes
– Due at the end of Week 13 on 31 May 2020 (11:59PM)
– The essay must be submitted electronically through Canvas and must be
submitted in PDF format.
– It will go through Turnitin
– The electronic submission must be accompanied by a signed individual
assessment coversheet (either in the same file or in a separate file) available
from:
– http://sydney.edu.au/engineering/it/current_students/postgrad_coursework
/guidelines/assessment-guidlelines.shtml
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Late assessments
– Suppose you hand in work after the deadline
– If you have not been granted special consideration or arrangements
– A penalty of up to 20% of the available marks will be taken, per day
(or part) late
• E.g. your work would have scored 60% and is 1 hour late you get
40%
• E.g. your work would have scored 70% and is 28 hours late you get
30%
– Submit early; you can resubmit if there is time before the deadline
– Each semester, there are always unfortunate cases – if any issues with the
submission, email BEFORE the submission time as a proof
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Finding the right References
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References
– Find journal articles or high-quality online sources on the topic
– News / Magazine / Editorial articles can be used to support your topic, e.g.,
used as an example
– Consultancy reports e.g., HBR, McKinsey are OK, especially as they
introduce newer topics / examples
– If in doubt about quality of reading, please check with your teaching team
– Note: Be careful in how you treat information from companies (such as press
releases, product websites, whitepapers) as they may be biased!)
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References
– University Library
– https://library.sydney.edu.au/
– Google Scholar
– https://scholar.google.com.au/
– Google
– Be careful of identifying reliable sources
– ! Wikipedia – perhaps only for you to read and understand
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Reference Management Software
– Make maintaining references and creating bibliographies easy
– EndNote:
• Free for Uni of Sydney staff and students
• For Windows, Mac
• Plug-in for MS Word
• http://libguides.library.usyd.edu.au/endnote
– Zotero:
• Free, open source
• For Windows, Mac, Linux, …
• Plug-in for Firefox, MS Word, Open Office
• http://www.zotero.org
– Many others:
• http://en.wikipedia.org/wiki/Comparison_of_reference_management_s
oftware
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Other resources
– https://library.sydney.edu.au/help/online-training/elearning/
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Academic dishonesty and
plagiarism
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Academic dishonesty and plagiarism
• Please read the University policy on Academic Honesty carefully:
http://sydney.edu.au/elearning/student/EI/academic_honesty.shtml
• All cases of academic dishonesty and plagiarism will be investigated
• There is a new process and a centralized University system and database
• Three types of offenses:
• Plagiarism – when you copy from another student, website or other source. This
includes copying the whole assignment or only a part of it.
• Academic dishonesty – when you make your work available to another student
to copy (the whole assignment or a part of it). There are other examples of
academic dishonesty.
• Misconduct - when you engage another person to complete your assignment (or
a part of it), for payment or not. This is a very serious matter and the Policy
requires that your case is forwarded to the University Registrar for investigation.
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Penalties
• The penalties are severe and include:
1) a permanent record of academic dishonesty, plagiarism and misconduct in the
University database and on your student file
2) mark deduction, ranging from 0 for the assignment to Fail for the course
3) expulsion from the University and cancelling of your student visa
• Do not confuse legitimate co-operation and cheating! You can discuss the
assignment with another student, this is a legitimate collaboration, but you
cannot complete the assignment together – everyone must write their own
code or report, unless the assignment is group work.
• When there is copying between students, note that both students are
penalised – the student who copies and the student who makes his/her work
available for copying
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Detection
• We will use the similarity detection software TurnItIn and MOSS to compare
your assignments with these of other students (current and previous) and
the Internet
• Turnitin is for text documents: http://www.turnitin.com/en_us/higher-education
• MOSS is for programming code: https://theory.stanford.edu/~aiken/moss/
• These tools are extremely good!
• e.g. MOSS cannot be fooled by changing the names of the variables or
changing the order of the conditions in if-else statements
• Examples of plagiarism in programming code:
• http://www.upenn.edu/academicintegrity/ai_computercode.html
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Student excuses
• All these are cases of plagiarism and academic dishonesty we have seen in
our school
• The student excuses are not acceptable:
• I sat the test and then posted the questions and solutions to my friends whose
test was later in the week. I only wanted to help them understand the concepts
that are examinable.
• I posted parts of my code on my web page (or the group discussion forum)
because my solution was cool (or I wanted to help them). I didn’t expect them to
copy it.
• I tried to do the assignment on my own but I had problems with the extension
part that I couldn’t fix, so I submitted my core part and his extension part. I didn’t
cheat.
• I finished my assignment but my friend had family problems. I felt sorry for her,
so I gave her my assignment as an example. She said she only wanted to have
a look and promised not to copy it.
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Students excuses (2)
• The test has finished but the tutor hasn’t collected the papers yet. I showed my
answer to my friend. I didn’t expect him to copy it.
• He is my best friend. I had no choice but to let him copy my assignment.
• I couldn’t find a partner to work in pairs, so I joined their pair as they are my
friends (when only groups of maximum of 2 students are allowed – illegitimate
collaboration).
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Key message
• Plagiarism and any form of academic dishonesty will be dealt with, and the
penalties are severe
• We use plagiarism detection systems such as MOSS that are extremely
good. If you cheat, the chances you will be caught are very high.
• If someone asks you to see or copy your assignment, or to complete the
assignment instead of them, just say: I can’t do this - we can both be thrown
out of the University. I will not risk my future by doing this.
Be smart and don’t risk your future by engaging in plagiarism and
academic dishonesty!
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