Showcase for Alexander Adam Laurence.

1. ALEXANDER
LAURENCE
Project Showcase

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IoT in Paedriatric Research

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Denoising fMRI

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Cortical Cryptography

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IoT in Paedriatric Research

9. IOT IN PAEDRIATRIC
RESEARCH
• Qualitative research is cumbersome for both the participant and the researcher
• People do not like doing questionaires
• Most of the time, participants must be physically present to complete research psychometrics,
scales, and questions so that researchers recieve their responses
• Online questionnaires exist, but they are equally boring
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10. IOT IN PAEDRIATRIC
RESEARCH
• Worked with the a lab at the UCL Great Ormond Street Institute of Child Health to develop 2
smartphone apps
• Collects responses from patients remotely and securely on my server
• Saves responses to a cloud database (mySQL) through a 256-bit SSL connection
• User data is anonymised and encrypted on the server
• Researchers can collect responses in one safe, secure, and GDPR-compliant location
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11. IOT IN PAEDRIATRIC
RESEARCH
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12. IOT IN PAEDRIATRIC
RESEARCH
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cryptographic architectureweb request architecture

13. IOT IN PAEDRIATRIC
RESEARCH
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14. IOT IN PAEDRIATRIC
RESEARCH
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15. IOT IN PAEDRIATRIC
RESEARCH
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Denoising fMRI

17. DENOISING MEDICAL
IMAGES
• Model for experimenting awake rodents animals in fear conditioning paradigm was proposed
for preclinical research (Brydges et al., 2013).
• Animals were scanned through an MRI whilst conscious during fear expression
• Why conscious? Conducted to better understand memory and emotion by accurately
observing the network level haemodynamic changes
• However, this model is highly susceptible to motion-related artifacts
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18. DENOISING MEDICAL
IMAGES
• Problem 1: Anomalous BOLD Activation
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Stimuli > Baseline Fearful > Neutral
Neutral > Fearful

19. DENOISING MEDICAL
IMAGES
• Problem 2: Non-Uniformity in Signal Intensity
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20. DENOISING MEDICAL
IMAGES
• Problem 3: Gibbs Ringing Artifact
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21. DENOISING MEDICAL
IMAGES
• Framework
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Identify signal
dropout at
specific data
point to train
DL model

22. DENOISING MEDICAL
IMAGES
• Data was too noisy to rely on intensity-analysis alone (combine with observed data)
• Subjective + Objective => Meaninful Training Data
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Visual
Inspection
Voxel Data
subjective objective
training
inference
OK
BAD

23. DENOISING MEDICAL
IMAGES
• Framework
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Identify signal
dropout at
specific data
point to train
DL model
Feed results
into BOLD
analysis
pipeline

24. DENOISING MEDICAL
IMAGES
• Narrowing the focus on specific datapoint range (brain extraction, remove first slice, remove
first 3 volumes)
• Motion Correction
• Slice Time Correction
• Sinebell filtering
• Timepoint repair: Deweighting/Despiking
• Signal Denoising
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25. DENOISING MEDICAL
IMAGES
• Framework
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Identify signal
dropout at
specific data
point to train
DL model
Feed results
into BOLD
analysis
pipeline
Improved
statistical
robustness
Improved
translational
validity
Minimise
impact to
statistical
power

26. DENOISING MEDICAL
IMAGES
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p < 0.05 p < 0.5

27. DENOISING MEDICAL
IMAGES
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28. DENOISING MEDICAL
IMAGES
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Medical Image Understanding and Analysis (MIUA) 2017

29. DENOISING MEDICAL
IMAGES
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Edinburgh (UK)-ChangGung (Taiwan) Research Collaboration

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Cortical Cryptography

31. CORTICAL CRYPTOGRAPHY
• The rubber hose cryptanalysis problem describes most computer security protocols as
inherently susceptible to coercion (via explicit knowledge).
• Although, implicit sensorimotor learning may provide a solution as the consolidation process
can be non-salient to the user.
• This has also been hypothesised to proceed in a bottom-up fashion, such that sequence
knowledge can be identified by enhanced performance on statistically predictable bigrams and
trigrams at test (Bojinov et al, 2012).
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32. CORTICAL CRYPTOGRAPHY
• Embed a payload (target sequence) into a user’s implicit memory
• Tacitly express the sequence (through enhanced motor performance) on cue
• Prevent rubber hose attacks
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33. CORTICAL CRYPTOGRAPHY
• An array of 32 keyboard keys discriminated by left/right hand
• Uniformly distributed and paired through an Euler circuit for optimal motor performance and
enhanced security.
• 3 keys per hand are selected on a pseudorandom basis using a software number generator
• 313,600 possible combinations ((16C3)2), which greatly improves upon Bojinov et al (2012)’s
setup, (4C3)2 = 16.
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34. CORTICAL CRYPTOGRAPHY
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Target Sequence

35. CORTICAL CRYPTOGRAPHY
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Target Sequence
Foil Sequence

36. CORTICAL CRYPTOGRAPHY
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Target Sequence
Foil Sequence
Target Sequence
Target Sequence

37. CORTICAL CRYPTOGRAPHY
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Target Sequence
Foil Sequence
Target Sequence
Target Sequence
Cluster

38. CORTICAL CRYPTOGRAPHY
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Target Sequence
Foil Sequence
Target Sequence
Target Sequence
Target Sequence
Foil Sequence
Target Sequence
Target Sequence

39. CORTICAL CRYPTOGRAPHY
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Run each cluster through the training phase until the participant is unconciously primed

40. CORTICAL CRYPTOGRAPHY
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Target Sequence occurs more frequently than the Foil Sequence
So, users should perform better (typing reaction time) on target sequence when tested

41. CORTICAL CRYPTOGRAPHY
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?
Learning Phase Test Phase
Time
(2s intervals)
Item n

42. CORTICAL CRYPTOGRAPHY
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43. CORTICAL CRYPTOGRAPHY
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44. CORTICAL CRYPTOGRAPHY
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Main point?

45. CORTICAL CRYPTOGRAPHY
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They had no idea what they were typing or what they just learnt

46. CORTICAL CRYPTOGRAPHY
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Therefore this information cannot be breached, stolen, or revealed

47. CORTICAL CRYPTOGRAPHY
47 Source: https://xkcd.com/538/

48. CORTICAL CRYPTOGRAPHY
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Even if someone was aware of the password,
they would not be able to authenticate due to motor performance

49. CORTICAL CRYPTOGRAPHY
• Introduces the idea of “psycho-biometric authentication”
• High-level banking security
• Military research
• Something for the future
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50. CORTICAL CRYPTOGRAPHY
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2016 British Psychological Society Cognitive Section Annual Conference (Barcelona, Spain)

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Cortical CryptographyDenoising fMRIIoT in Paedriatric Research

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CryptographyAIIoT Alexander

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ABOUT ME
• MScR Integrative Neuroscience - Edinburgh
• BSc Cognitive Neuroscience - London
• Research presented in Barcelona, Edinburgh, Oxford,
London, (and at 35,000ft!)
• Recieved scholarship from Facebook Artificial Intelligence
and IBM to study Data Science (Deep Learning with
PyTorch + IoT)
• Started IT consultancy freelance to fund my studies (Clients
include Institute of Child Health at UCL)
• Stayed in Japan (1 month), Taiwan (1 month for research)
www.alexanderlaurence.co.uk