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COM6012 Assignment

创建日期：2024-05-08 14:32:09

所属分类：计算机computer science

标签： COM6012

Assignment

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COM6012 Assignment

Please, carefully read the assignment brief before starting to complete the assignment
Assignment Brief
How and what to submit
A. Create a .zip file containing the following:
1) ASPart1_report.pdf: A report in PDF containing answers to ALL questions. The
report should be concise. You may include appendices/references for additional
information but marking will focus on the main body of the report.
2) Code, script, and output files: All files used to generate the answers for individual
questions above, except the data. These files should be named properly starting
with the question number: e.g., your python code as QP1_xxx.py, your script for
HPC as QP1_HPC.sh, and your output files on HPC such as QP1_output.txt or
QP1_figB.jpg. The results should be generated from the HPC, not your local
machine.
B. Upload a .zip file to Blackboard (BB) before the deadline that contains the files in A (you
can group them in a folder called Part1) and any other files requested for the solution of
Part 2 of the Assignment (Dr Lu will be in charge of releasing this part).
C. NO DATA UPLOAD: Please do not upload the data files used. We have a copy already.
Instead, please use a relative file path in your code (data files under folder ‘Data’), as
in the lab sheets so that we can run your code smoothly.
D. Code and output. 1) Use PySpark as covered in the lecture and lab sessions to
complete the tasks; 2) Submit your PySpark job to HPC with qsub to obtain the output.
Assessment Criteria (Scope: Sessions 3-6; Total marks: 20)
1. Being able to use pipelines, cross-validators and a different range of supervised
learning methods for large datasets
2. Being able to analyse and put in place a suitable course of action to address a large
scale data analytics challenge
Late submissions: We follow the Department's guidelines about late submissions, i.e., “If
you submit work to be marked after the deadline you will incur a deduction of 5% of the mark
each working day the work is late after the deadline, up to a maximum of 5 working days” but
NO late submission will be marked after the maximum of 5 working days because we
will release a solution by then. Please see this link.
Use of unfair means: "Any form of unfair means is treated as a serious academic offence
and action may be taken under the Discipline Regulations." (from the MSc Handbook).
Please carefully read this link on what constitutes Unfair Means if not sure.
Please, only use interactive HPC when you work with small data to test that your algorithms
are working fine. If you use rse-com6012 in interactive HPC, the performance for the whole
group of students will be better if you only use up to four cores and up to 10G per core. When
you want to produce your results for the assignment and/or want to request access to more
cores and more memory, PLEASE USE BATCH HPC. This will be mandatory. We will
monitor the time your jobs are taking to run and will automatically “qdel” the job if it is taken
much more than expected. We want to promote good code practices (e.g. memory
usage) so, please, once more, make sure that what you run on HPC has already been
tested enough for a smaller dataset. It is OK to attempt to produce results several times in
HPC, but please, be mindful that extensive running jobs will affect the access of other users
to the pool of resources. Review Lab 4 where examples of good practices were
described.
__________________________________________________________________________
Scalable supervised learning to study the Vulnerability of
Resource-Constraint Internet of Things
In part 1 of the assignment, you will explore the use of scalable supervised classification
algorithms to discover vulnerabilities in certain type of Internet of Things (IoT) devices. The
dataset was generated as part of the paper “A Machine Learning-Based Security Vulnerability
Study on XOR PUFs for Resource-Constraint Internet of Things” by Ahmad O. Aseeri; Yu
Zhuang; Mohammed Saeed Alkatheiri published in the 2018 IEEE International Congress on
Internet of Things (July 2018).
Quoting from the paper,
“Physical unclonable functions (PUFs) utilize variations in integrated circuits to produce
responses unique for individual PUFs, and hence are not reproducible by manufacturers as
well as by attackers even if they have obtained the exact PUF circuit design, possessing
great potential for implementing secure mechanisms. Implementable with very simplistic
circuits with extremely low energy and other resource requirements, PUFs are particularly
promising for delivering high security for resource-constraint IoT devices”
“In a nutshell, a PUF is an input-output mapping in the form where theΥ: 0, 1{ } → 0, 1{ }
input is -bit binary vector (called challenge) and the output is -bit binary vector (called
response). A PUF circuit can receive different possible -bit challenge vectors, each of2
which produces an -bit output. Silicon PUFs can generate unique challenge-response pairs
(CRPs) for different integrated circuit instances”
“While physically unreproducible, PUFs are reported to be “mathematically clonable” by
machine learning-based modeling methods which can accurately predict the responses of
PUFs. Mathematical clonability allows attackers to develop malicious software to
impersonate the identity of trusted PUF-embedded devices by producing the same
responses PUFs would give.”
The dataset that you will use is from the UCI repository and can be downloaded from this
link. The downloaded file is a .zip file. The uncompressed file includes two folders, namely,
5xor_128bit and 6xor_64bit. You will work with the files inside the folder 5xor_128bit. There
are two .csv files inside 5xor_128bit: train_5xor_128dim.csv and test_5xor_128dim.csv. Each
file contains a table with 129 columns, where the last column is the label (1 or -1) and the first
128 columns are the features. Each row corresponds to a CRP, with the features
representing the challenge and the label representing the (binary) response. When it comes
to testing your algorithms, you will use the exact same train and test sets.
You will apply Random Forests, Logistic Regression (including regularisation) and (shallow)
Neural networks over a subset of the dataset and over the full dataset. As performance
measures use classification accuracy.
1. Working with a subset of the larger dataset. Use pipelines and cross-validation to find the
best configuration of parameters for each model [8 marks]
a. For finding the best configuration of parameters, use 1% of the data chosen
randomly from the training set [2 marks]
b. Use a sensible grid for the parameters (for example, at least the three most relevant
parameters with at least three options for each parameter) for each predictive
model:
i. Pipeline and cross-validation for random forests [2 marks]
ii. Pipeline and cross-validation for logistic regression [2 marks]
iii. Pipeline and cross-validation for neural networks [2 marks]
Please, use the batch mode to work on this. Although the dataset is not as large, the
batch mode allows queueing jobs and for the cluster to better allocate resources.
2. Working with the larger dataset. Once you have found the best parameter configurations
for each algorithm in the smaller subset of the data, use the full dataset to train on the
whole training data and compare the performance of the three algorithms on the test data
[8 marks]
a. Use the best parameters found for each model in the smaller dataset of the
previous step, for the models used in this step. You need to pass these parameters
programmatically. Do not hard-code them. [2 marks]
b. Provide accuracy, area under the curve, training time and testing time when using
five CORES and ten CORES for each of the predictive models using
train_5xor_128dim.csv for training and test_5xor_128dim.csv for testing.
i. Five and ten cores results for random forests [2 marks]
ii. Five and ten cores results for logistic regression [2 marks]
iii. Five and ten cores results for neural networks [2 marks]
Remember to use the batch mode to work on this.
3. Discuss at least four observations (e.g., anything interesting), with two to three sentences
for each observation. If you need to, you can run additional experiments that help you to
provide these observations [4 marks] (one mark per observation)
Do not try to upload the dataset to BB when returning your work. It is around 200 Mb
(compressed) and around 2.34Gb (uncompressed).
COMMENTS: 1) An old, but very powerful engineering principle says: divide and conquer. If
you are unable to analyse your datasets out of the box, you can always start with a smaller
one, and build your way from it. 2) Use wget to download the data file directly in HPC. You
can navigate to /data/your_username/ScalableML/Data/ in HPC and once there type
You can copy the link to the zip file after wget. To uncompress the .zip file, use unzip
FAQ
- How long does it take to run the whole assignment?
Using a minimum of three hyperparameters and a minimum of three options for each
hyperparameter in Step 1, running the whole assignment in Batch mode should take
less than 2 hours when using 10 cores.

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关键词一关键词二关键词三关键词四关键词五和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 EMESTER1 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