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Study Notes 3rd Year > FRSC 3100 Trends > Flashcards

FRSC 3100 Trends Flashcards

1
Q

AFIS

A

Automated Fingerprint Identification Systems

Uses rolled fingerprints, plain or flat, lower palm, upper palm and writers palm

Contains 4 mil records

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2
Q

How does AFIS interpret data

A

biometric computer system

locates and measures reliable and persistant features within the fingerprint and palm print images i.e. minutiae

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3
Q

Narrow AI

A

Ai designed to do 1 specific task (or narrowly related set of tasks). Virtually all AI today.

Areas of use: AI chatbots, cancer detection, protein analysis, recommendations for shopping/viewing, text-to-speech/speech to text, google maps

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4
Q

Generative AI

A

Ai that creates some kind of ‘new content’.

Areas of use: stable diffusion = Art AI, Chat GPT

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5
Q

Non-Generative AI

A

Speech recognition, Recommended systems, navigation mapping and applications

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6
Q

Large Language Models (LLM)

A

AI program designed to understand, generate, and work with human language on a large scale. Generate coherent and contextually relevant text based on the input they receive. Fed large datasets containing a wide array of text, from which they learn language, patterns, structure, and nuances

Areas of use: translation, summarization, answering questions, creative writing

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7
Q

Natural Language Processing (NLP)

A

focuses on the interaction bw computers and human language. Involves enabling computers to understand, interpret, & respond to human language in a way that is both meaningful/useful.

Areas of use: chatbots and virtual assistants (siri, Alext, etc), “hey google” voice interfaces, customer automation, content categorization in media, email filtering, language translation services.

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8
Q

Application programming Interface (API)

A

protocols that allow different software programs to communicate with each other/AI.
Areas of use:
Text analysis: sentiment analysis, language detection, text summarization, etc.
Image recognition: object detection, factual recognition, etc.
Natural language processing: language translation, text-to-speech, speech-to-text, etc.
Voice interface: language translation, text-to-speech, speech-to-text, etc

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9
Q

Machine Learning

A

use data and algorithms to mimic human learning. Statistical methods to train algorithms to classify or predict and even provide insights into data mining projects.

Text Generation (error-prone, lack of credit)
Image compilation (Copy-right issues, Deepfakes)
Pattern Recognition (AFIS, Facial Recog)

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10
Q

Areas of use of ML

A

Areas of use:
More diverse in application & can be more efficient for simpler tasks/when working w smaller dataset

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11
Q

Deep Learning

A

subset of ML. Uses neural networks to analyze and learn from data. Well suited for processing unstructured data like images and text. ‘Complex’ = have multiple layers that automatically detect and learn hierarchical feature representations.

Areas of use: powerful where learning from vast amounts of data directly is beneficial. Image and speech recognition.

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12
Q

Appearance Based Facial Recognition

A

Scraping: what some AI’s illegally do. Give it an image and it searches the entire internet for anything related.

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13
Q

What are some future trends in forensic science

A

increased use of DNA analysis and genetic profiling

Development of new technologies for trace evidence analysis and interpretation
Integration of artificial intelligence and machine learning into forensic analysis processes

Increased use of virtual and augmented reality for crime scene reconstruction and simulation
Increased emphasis on forensic psychology and behavioral analysis

Greater emphasis on forensic anthropology and human identification

Development of portable and handheld forensic analysis tools for use in the field
Increased use of biometric identification methods, such as facial recognition and fingerprint analysis

Greater collaboration and sharing of forensic data and information among law enforcement agencies and laboratories.

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14
Q

Historical Context: Pre 1900

A

Caesar/ forum
1302 first legal autopsies

1590 microscope invented

1832 James marsh develops first arsenic test

1835 first bullet comparison

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15
Q

Advances in the 1800s

A

Using evidence documents known as questioned documents

The invention of the polarized light microscope, often used today in fiber analysis

Using photography for criminal identification and crime scene documentation

Identifying insect stage development in corpses to determine the time elapsed since death

1888 London Jack the Ripper: first major investigation to follow the processes/practices still in use (post-mortem, scene documentation, interviews, statements, etc) today

1892 fingerprints - the first crime solved by fingerprint analysis and the calculation by Francis Galton that fingerprints had only a 1 in 64 billion chance of being alike.

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16
Q

Advances in the 1900s

A

1900, different human blood types, ABO, are discovered by Karl Landsteiner. allowing crime scene investigators to match blood from a victim to blood at the scene.

1902, the first academic curriculum for forensic science was developed in Switzerland

1905, President Theodore Roosevelt established the FBI, the Federal Bureau of Investigation

Alphonse Bertillon developed an anthropometric system of identifying criminals

Chromatography - Russian botanist Mikhail Tsvet invented column chromatography in 1906

In 1910 first forensic police crime lab was created in Lyon France by Dr. Edmund Locard

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17
Q

Moore’s Law

A

number of transistors on a microprocessor chip and the cost of transistors should increase in two years

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18
Q

Douglas Adams

A

Came up with a set of rules that describes our reactions to technologies:

Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works

Anything that is invented anytime between the time you’re 15 and 35 is new and exciting and revolutionary and you can probably get a career in it

Anything invented after you’re 35 is against the natural order of things

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19
Q

What is a disruptive technology

A

is an innovation that significantly alters how consumers, industries, or businesses operate.

Disruptive technology supersedes an older established process, product, or habit with recognizably superior attributes.

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20
Q

Examples of recent disruptive technologies

A

Recent disruptive technology examples include e-commerce, online news sites, ride-sharing apps, and GPS systems.

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21
Q

Who introduced the idea of disruptive technologies

A

Clayton Christensen

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22
Q

AI is coupled with 3 things

A
  1. Extremely high computing speed
  2. large volume dataset processing
  3. the ability to self-learn
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23
Q

Generative AI

A

focuses on understanding patterns and structure in data and using that to create new data that looks like it.

This includes writing blocks of text, lines of code or creating photorealistic images Predictive Discriminative AI focuses mainly on classification, learning the difference between “things” - cats and dogs, for example.

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24
Q

Levels of artificial intelligence

A

ANI - ARTIFICIAL NARROW INTELLIGENCE

AGI - ARTIFICIAL GENERAL INTELLIGENCE

ASI - ARTIFICIAL SUPER INTELLIGENCE

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25
Q

ANI

A

ANI is ‘narrow’ in that it is specialized to the function for which it has been developed.
Much of the technology running our smartphones, online purchases and social media apps are in fact ANI.

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26
Q

AGi

A

AGI is generally referred to as ‘human-level AI’, because it describes the capacity of a computer that is as smart as a human, a point often referred to as ‘Singularity’.

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27
Q

ASI

A

ASI is the point at which computers possess an intellectual capacity far greater than that of human beings with the capacity for social skills and general knowledge that would increase exponentially over time.

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28
Q

4 parts to a boston dynamics payload

A

spot cam

spot arm

spot core

spot gxp

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29
Q

3 types of image based modelling

A

NeRFs - Neural Radiance Field

SLAM - Simultaneous Location and Mapping

Photogrammetry

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30
Q

Purpose of terrestrial laser scanning

A

Blood-spatter
Ballistics
Height
Line of sight
arson
Accidental reconstruction

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31
Q

4 reports that address issues in the scientific method

A

Campbell Report - 1996
Bernardo
Gaps in investigation
Result: MCM

Kauffman Report 1998
GP Morin
gaps and junk sceince
Result: Recommendations

Gouge Report 2008
Charles SMith
Patholoft
Result: Recommendations

Hart House Report 2013
Report of multidisciplinary discussion
Result Recommendations in standards

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32
Q

OSAC

A

Organization of Scientific Area Commitees

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33
Q

OSAC groupings

A

Biology SAC
Chemistry: Drugs
Chemistry: Trace
Digital/Multimedia
Medicine
Physics
Crime Scene examination

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34
Q

Overall trends in forensic science

A

Studies have shown that certain forensic techniques, such as bite mark analysis and hair microscopy, are unreliable and have led to wrongful convictions.

there have been concerns about bias in interpreting forensic evidence, particularly regarding racial and socioeconomic disparities.

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35
Q

Bias in AI

A

it’s when AI systems or algorithms produce systemically prejudiced results that reflect and perpetuate human biases, due to the quality, objectivity and size of the training data used to train them

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36
Q

Compute

A

refers to the costly energy and time required to train AI models

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37
Q

Context Window

A

The amount of information you can feed into a generative AI model before it produces an output.

The larger the context window, the more information you can add alongside your prompt, giving it new insights or data that it might not have access to through its training or on the open internet.

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38
Q

Data Labelling

A

is the building block of AI upon which AI algorithms and systems are trained and built

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39
Q

Effective Altruism

A

A movement that aims to use research and science to solve the most pressing global problems for the net benefit of humanity

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40
Q

GPU

A

Shorthand for graphics processing units — the tiny server chips that enable AI software to run

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41
Q

Hallucination

A

One of the foremost issues with generative AI models today, where models spit out made-up, false or incorrect answers or facts.

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42
Q

Interference

A

This refers to the actual process of using or powering a trained AI model to generate text, make predictions or identify objects inside photos.

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43
Q

Knowledge Graph

A

A knowledge graph organizes data from multiple sources and across entities pertaining to a given domain or task (like people, places or events) and forges connections between them.

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44
Q

Multimodality

A

When AI systems can simultaneously process audio, visual and language data in combination with and in relation to each other.

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45
Q

RAG

A

Retrieval-augmented generation is a technique for improving the quality of the responses of a generative AI model, making its answers more accurate by retrieving information and facts from external sources

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46
Q

Red Teaming

A

A way in which tech companies stress-test AI systems for vulnerabilities before they are made publicly available

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47
Q

Turing Test

A

Named after the computer scientist Alan Turing, the thought experiment was a way of assessing machine intelligence by measuring whether a computer program could fool a human into believing it was human using a blind on-screen chat.

48
Q

ACE-V

A

Analysis
Comparison
Evaluation
Verification

49
Q

Analysis

A

Look at appearance
Development medium
Pressure

50
Q

Comparison

A

Friction ridge identification

51
Q

Clarity Levels

A

Level 1: ridge flow/ patterns

Level 2: Ridge characteristics

Level 3 details: Pores edge features

52
Q

Verification

A

full verification

Bench notes completed

Signed latent print

Signed memo book

53
Q

Evaluation

A

Two questions must be answered:
Is there agreement between the unknown and known fingerprints
Is there sufficient uniqueness to individualize

54
Q

Standards in Fingerprint analysis

A

SWGFAST document 8:

When friction ridge detail is examined using the ACE-V methodology, examiners’ documentation shall be such that another qualified examiner can determine what was done and interpret the data.

55
Q

Linear ACE-V

A

Linear sequential unmasking

56
Q

Interdependent ACE-V

A

Back and fourth between steps

57
Q

Level 1 Data

A

Patterns
ridge flow

58
Q

Level 2 Data

A

Coarse ridge features
minutuae
Points

59
Q

Level 3 data

A

Poroscopy
Edgescopy
incipient ridges

60
Q

Way to determine value of friction ridge impressions

A

Gyro analysis and annotations

61
Q

Gyro Analysis

A

A fingerprint annotation system that helps examiners and virtually anyone understand the value of friction ridge evidence

System addresses confidence tolerance clarity and weight as assessed by the examiner

62
Q

Colour Confidence levels in Gyro analysis

A

Green- high confidence

Yellow- moderate

Red- low

Orange represent only L2D features that have been observed after you have begun to conduct a comparison

63
Q

Gyro Quantity Values

A

Green and yellow l2d is 1

Red l2d is 0.5

Orange l2d is 0.75

64
Q

CPAP

A

Capture
Process
Analysis
Present

65
Q

laser scanning

A
  1. A laser beam is shone onto a rotating mirror that reflects the beam out towards the area being scanned
  2. Through the rotation of the mirror, the beam is distributed in a vertical arc of ~300 degrees
  3. Simultaneous to this vertical rotation, the system rotates horizontally to cover a range of 360 degrees
  4. The laser beam is reflected back to the scanner by objects and/or surfaces in its path.
  5. The beam is recorded as a series of pulses or points as the distances as well as their relative vertical and horizontal angles are determined, recorded and converted into X-Y-Z values. This data is presented in a graphical format in the form of a point cloud that illustrates the spatial qualities of the objects & surfaces that were scanned.
  6. At the conclusion of a scan, multiple images of the area scanned are recorded so that specific colour values of individual pixels can be attached to the corresponding individual scan points
66
Q

3D Crime Scene documentation in summary

A

UAVs offer aerial perspectives that were once inaccessible, enabling rapid data collection and comprehensive scene analysis.

Laser scanning delivers precise point cloud data, facilitating the creation of detailed 3D models crucial for forensic examinations.

NeRF technology, driven by deep learning algorithms, synthesizes immersive 3D reconstructions from 2D images, providing unparalleled fidelity in crime scene visualization.

Videogrammetry extracts 3D information from video footage, aiding in event reconstruction and spatial analysis.

67
Q

Generation M facts

A

Before Gen Z, it was called Gen M for media consume .

Most children consume over 8 hours of media in a single day

Young people growing up today will see more images then anyone else on the planet has ever seen

68
Q

Attention span

A

The average memory/attention span for the notoriously ill-focused goldfish is 9 seconds

Recent research shows that people now generally lose concentration after eight seconds

This highlights the effects of the digitized lifestyle on the brain

69
Q

Hierarchy of Attention

A

actions→ objects→ pictures→ diagrams → written words→ spoken word

70
Q

HCI

A

Human Computer Interaction

The study of how humans interact with computers

71
Q

Two issues with HCI

A

Bad design

Sense of self

72
Q

Transhumanism

A

the belief or theory that human race can evolve beyond its current physical and mental limitations, especially by means of science and technology

73
Q

Cyborg

A

someone who uses tech to enhance human abilities. Ex; glasses, hearing aids

74
Q

First AI generated movie

A

Salt

75
Q

4 Cognitive Bias

A

Framing effect

Anchoring effect

Recognition and recall

Suggestive evidence

76
Q

Framing effect

A

drawing different conclusions from the same information

Plea Bargaining

Rewording your phrases in order for a certain group to agree

77
Q

Anchoring effect

A

The common human tendency to rely too heavily on the first piece of information presented

Example: the speed limit was 45 miles per hour, the defendant was traveling at 65 mph
When showing evidence, jury will fixate on the first thing they see
Real Estate agents, mattress shopping

78
Q

Recognition and recall

A

Recall is much more difficult than recognition

It is easier to recognize a friend than recall their face

Identity line-up is very common and better than a sketch

People lose 80% of the information by 1 week

79
Q

Suggestive evidence

A

The misinformation effect is a very powerful effect

80
Q

Attention span of a human 20 year ago was 9 seconds, what is it now

A

8 seconds

81
Q

True or false: recognition is better than recall

A

True

82
Q

What significantly alters how consumers, industries or business operate

A

Disruptive technology

83
Q

Source exclusion is reached when in the examiner’s opinion, considering the observed data, the probability that the two impressions came from the same source is considered____

A

unlikely

84
Q

ACE-V

A

Analyis, comparison, evaluation, verification

85
Q

What is genealogy

A

Traces the descendants of one person

86
Q

Two main databases

A

National DNA Databank in Ottawa run by the RCMP

Local DNA database at CFS

87
Q

2 examples of evidentiary challenges

A

Issues regarding secondary & tertiary transfer of DNA.

Increases issues with collection (cross contamination).

88
Q

What is VR

A

A computer-generated artificial Immersive Environment experienced through sensory stimuli (primarily sights and sounds), in which one’s actions may affect what happens in the environment

89
Q

3 types of Fidelity

A

Perceptual fidelity
Feedback fidelity
Emotional fidelity

90
Q

Description of fidelity (3)

A

To the visuals of the actual situation (how realistic are the representations),

To the task (how accurately is the task modelled and presented), and

To the cognitive path (how closely do the mental processes experienced and retained reflect the real-world process)

91
Q

Perceptual fidelity

A

Virtual interactions that closely mimic the physical world activate the same neural pathways in the brain. I.e. “muscle memory” or State-dependent retention

92
Q

Feedback fidelity

A

In VR, learners make decisions just as they would in the real world, and depending on the level of engagement, these decisions can have a direct and immediate positive or negative impact

93
Q

Emotional fidelity

A

VR can invoke a sense of presence that creates real emotional and empathic responses.

These sensory stimulations trigger the brain in much the same way it reacts to actual situations (whether joy, fear or surprise) to release endorphins, serotonin, and/or dopamine.

94
Q

Goal of immersive environment

A

Agency along with the Immersive Environment generates Presence which in turn encourages Engagement.
Engagement when combined with Fidelity determines the Success of the training process.

Thus, a fully engaged student in a high-fidelity environment is more likely to demonstrate a successful learning outcome.

95
Q

SoD

A

Suspension of Disbelief - believing what is being presented is for the moment real

The first steps in immersion and the formation of Presence involve Suspension of Disbelief

96
Q

Persistance in Immersive Environment

A

to develop a deeper investment in the virtual world, participants need to feel it is persistent and consistent.

This in turn creates trust in the system; builds community to encourage social norms, and fosters co-creativity

97
Q

The Narrative or Storyline in Immersive Environment

A

Immersion is significantly enhanced by a compelling and comprehensive narrative.

It contextualizes and helps makes sense of the activity and tasks being encountered.

98
Q

Three primary decisions to make when choosing a system

A

Content: Off-the-Shelf or In-House/Custom
Degrees of Freedom: 3 DoF or 6 DoF
Guidance: Implicit or Explicit (self-discovery or directed learning)

At all stages, whether planning, selecting, deploying or evaluating, remember prioritize the pedagogy over the technology

99
Q

3 degrees of freedom

A

VR headsets with 3DoF only have rotational control (yaw, pitch, roll).

Ex. subject on a tour bus who can look around but can not interact with the environment or dictate where to go.

Best suited when students are in an observer’s role and do not require freedom of movement or interaction with the environment.

100
Q

6 degrees of freedom

A

VR headsets with 6DoF have both rotational and translation control (vertical, lateral, forward/back).

Subjects using a 6DoF system have control over interacting with the scenario (picking up, moving around) and can use haptic technology (touch, aroma etc.) to enhance the experience

101
Q

Proteus Effect

A

studies have shown participants who experience strong embodiment to avatars possessing enhanced positive traits (i.e. strength) may take on and exhibit those characteristics (cycling performance/endurance)

102
Q

5 W’s of VR training

A

Who – would benefit
What – does it excel in
Where – can it be provided
When – should it be provided &
Why

103
Q

VR advantage stats

A

Surgeons who were trained on VR were 29% faster and made 6x fewer errors

83% of VR-trained residents could successfully perform a new procedure whereas o% of traditionally trained students could do the same

104
Q

Downfalls of VR

A

Costs
Vendor and selling issues
Health related issues
Technical issues

105
Q

7 rules of the metaverse

A

Rule #1. There is only one Metaverse.
Rule #2: The Metaverse is for everyone.
Rule #3: Nobody controls the Metaverse.
Rule #4: The Metaverse is open.
Rule #5: The Metaverse is hardware-independent.
Rule #6: The Metaverse is a Network.
Rule #7: The Metaverse is the Internet.

106
Q

DICE

A

Dangerous
Impossible
Counter productive
Expensive

107
Q

Examples of dice

A

Dangerous - high risk situations (Active shooter)

Impossible - Time travel

Counter productive - Complex activities requiring repetitive action cycles to experience

Expensive - offsite travel

108
Q

What was meta formerly owned by

A

Facebook

109
Q

Metaverse simply put

A

Simply put, the Metaverse is the internet where you can go into it (VR) or it can come out to you (AR).

110
Q

Building block of the metaverse

A

Metaverse platform
XR (Extended reality)
AI (Artificial intelligence)
Blockchain

111
Q

Metaverse platform

A

a standardized platform that allows participants to seamlessly move between various experiences in a 3D context.

112
Q

XR

A

Extended reality

3D, Avatars, Communication, VR/AR/MR, Volumetric Capture, Haptics, Scent, Spatial Audio, Game Engines.

113
Q

AI

A

Artificial intelligence

Computer Vision, Natural Language Processing (NLP), Simultaneous Location And Mapping (SLAM), Automated Content Creation, Suggestion Algorithms.

114
Q

Block chain

A

Non-Fungible Tokens (NFT’s), Cryptocurrency (BTC, HBAR, ETH), Smart Contracts (ETH, HBAR, Distributed Autonomous Organizations (DAO’s), Digital Land, Decentralized Finance (DeFi).

115
Q

UAV

A

Unmanned Aerial Vehicle

116
Q
A

Study Notes 3rd Year (11 decks)

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