digging deeper into data processing with emphasis on computational and microstructure data_f
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The Socio-Technological Integrator And Innovator
Digging Deeper into Data Processing with Emphasis on Compositional and Microstructure
Data: Machine Learning in Support of Archaeological Analysis
Liza Charalambous [email protected]
The Socio-Technological Integrator And Innovator
Overview
1. Introduction Archaeological Process Data in many forms and types
2. Part I: Compositional Data Pre-Processing Practices Case Study & Experimental Results
3. Part II: Microstructure Data Microstructure Analysis Pattern Recognition for the Characterization of
Microstructure Data
4. Data Analysis Remarks Data Idiosyncrasies
The Socio-Technological Integrator And Innovator
Profile and Background
Real-time Monitoring
Communication systems
Security and Error Protection systems
Research interests and Background
Digital Signal Processing
Artificial Intelligence
Machine Learning
Audio Coding
PhD student in Computer Engineering at University of Cyprus
In cooperation with the KIOS Research Center for Intelligent Systems and Networks
NARNIA ITN ESR08 (starting date 01/11/2011)
Educational Background
BSc in IT and Multimedia Communications (2007-2010), Lancaster University, UK
MSc in Digital Signal Processing and Intelligent Systems (2010-2011), Lancaster University,
UK
The Socio-Technological Integrator And Innovator
Archaeological Data
Then Now
SKETCHES
STRATIGRAPHY LOGS
PETROGRAPHIC ANALYSIS
RELATIONAL DATABASES
DIGITAL REPRESENTATIONS 3D RECONSTRUCTIONS
ELEMENTAL CONCENTRATIONS SPECTRA
The Socio-Technological Integrator And Innovator
Gather Samples/ Artifacts
Technologies
Available Methods
Data Analysis
Form Archaeological Question
Interpretation of Results
Analyze Objectives: What needs to be proved?
Determine and gather the artifacts of interest (based on the previously formed question)
List available technologies for deployment and analyze effectiveness
List available analysis methods compatible to the selected technology
Application of Clustering/ Classification algorithms so as to increase data manageability
ARCHAEOLOGICAL PROCESS:
Steps
The Socio-Technological Integrator And Innovator
“Too much and overly complicated data”
Data analysis in archaeology, is sometimes believed to take the form of: Simple projection of data (a feature against another)
Employment of very simple clustering or other dimensionality reduction methods
Much attention is given when:
Sampling
Data preprocessing
ARCHAEOLOGICAL PROCESS:
Available Methods
belief that good data will speak for themselves
The Socio-Technological Integrator And Innovator
ARCHAEOLOGICAL PROCESS: Technologies
Analysis comes in different forms and shapes The result is usually in the form of:
Peak elemental measurements → as a result of spectrum analysis Pictures or other schematic representations → commonly based on
the sample’s microstructure
Each technology is dictated by its own characteristics, integration of multiple technologies may not always be beneficiary
The Socio-Technological Integrator And Innovator
Overview
1. Introduction Archaeological Process Data in many forms and types
2. Part I: Compositional Data Pre-Processing Practices Case Study & Experimental Results
3. Part II: Microstructure Data Microstructure Analysis Pattern Recognition for the Characterization of
Microstructure Data
4. Data Analysis Remarks Data Idiosyncrasies
The Socio-Technological Integrator And Innovator
Part I: Compositional Data
Cu Mn Mg Ca Ti
K Fe S Cr Al
Compositional data are defined as vectors of proportions strictly positive components
constant sum; a restriction not always maintained
Chemical analysis is not really involved in measuring, but in enumerating, or counting, the number of each type of atoms in a sample
The results are usually given in relative numbers (usually in % or ppm). a) elemental concentrations are frequencies
of nominal or categorical classes (atoms) of a classificatory concept (matter)
b) chemistry is usually interested not in frequencies, but in relative frequencies.
The Socio-Technological Integrator And Innovator
Part I: Pre-Processing Practices
General Belief:
The more precise and accurate the bulk chemical determinations, the better the chance of making more plausible
and refined estimations.
Reproducibility and comparability of results, is commonly assured by adopting one of the following practices: a) Transformation of the relative concentrations into base 10 values
b) Sub-compositional data: the dataset of interest only contains proportions of the components constituting a sample
c) Calculation of averages
d) Elimination of chemical elements dominated by noisy readings or incomplete measuring
The Socio-Technological Integrator And Innovator
Part I: Ceramics Case Study & Experimental Results
Study the impact of pre-processing on datasets obtained from ceramics with the use of NAA
Investigations on the effect of the following parameters: Raw Vs. Log: the transformation of raw data into the equivalent 10-
base logarithm increased data separation (especially for the heterogeneous ceramics)
Sub-compositional data (with the addition of an extra column): has not influenced in any significant way the product of analysis; practice currently deployed in the archaeology domain
Calculation of averages: reduced the variance of clusters between successive runs; particularly useful for the analysis of homogeneous material.
Standardized and Normalized Data: no significant impact on the commonly used analysis methods
The Socio-Technological Integrator And Innovator
Overview
1. Introduction Archaeological Process Data in many forms and types
2. Part I: Compositional Data Pre-Processing Practices Case Study & Experimental Results
3. Part II: Microstructure Data Microstructure Analysis Pattern Recognition for the Characterization of
Microstructure Data 4. Data Analysis Remarks
Data Idiosyncrasies
The Socio-Technological Integrator And Innovator
Part II:
Microstructure Data
Involves the study of silicate and carbonate-based artifacts which may be relatively unmodified from their original
geological parent raw materials
Microstructure analysis is critical in extracting manufacturing
knowledge
Can achieve resolution better than 1nm
Can provide high quality imaging facilities together with quantitative elemental analysis; using an energy dispersive spectrometer
The Socio-Technological Integrator And Innovator
Part II:
Microstructure Data Analysis
Classification by taking into consideration how ceramics are processed Related to the impact on material durability
The nature of the ceramic microstructure, as a function of temperature, can be related to the composition of the clay source exploited
Issues that an archaeological scientist may require to address through SEM: Characterization of origin material Reconstruction of the technology involved in manufacture Influence of the place of manufacture or source of raw materials Changes that have occurred in the object during burial or storage
The Socio-Technological Integrator And Innovator
Part II:
PR for the Characterization of Microstructure Data
Estimation of Annealing
Temperature
Degree of Vitrification
Porosity/ outer-
connection of particles
Microstructure Data
Evaluation of the sophistication of firing process
Knowing the various nuances of materials and processing systems can be overwhelming and confusing
Properties of crystals Average size Orientation/Alignment Coarseness and depth of
primitive elements
Vitrification Stage Identification of crystals
and degree of fusion
Porosity Spread of pores Shape/size
The Socio-Technological Integrator And Innovator
Part II: PR for the Characterization of Microstructural Data
PIXEL POINT & GROUP PROCESSING
Edge related operations Enhancement
Segmentation
Detection
Texture Analysis Co-occurrence matrix: captures numerical
features which can be used to represent, compare, and classify textures.
Auto and cross correlation: can be used to detect repetitive patterns of textures
Estimation of patch similarity: gives the ability to compare image regions
Promoting of unit invariant measures Perforation
Shape Factors
SHAPE FACTORS Aspect Ratio: function of the
largest and the smallest diameters perpendicular to each other
Circularity: a function of the perimeter and the area
Elongation: ratio of minor axis width to major axis length ratio
Compactness: measure of object roundness area to perimeter ratio
Waviness shape factor of the perimeter: often related to fracture toughness of metals and ceramics
The Socio-Technological Integrator And Innovator
Overview
1. Introduction Archaeological Process Data in many forms and types
2. Part I: Compositional Data Pre-Processing Practices Case Study & Experimental Results
3. Part II: Microstructure Data Microstructure Analysis Pattern Recognition for the Characterization of
Microstructure Data
4. Data Analysis Remarks Data Idiosyncrasies
The Socio-Technological Integrator And Innovator
Not all features should be treated equally Artifacts are characterized by primary, secondary and
supplementary elements
All artifacts regardless physical characteristics are treated the same Size, shape, texture, contamination, aperture upon exposure
Preprocessing steps and methodology Preprocessing of the data usually complies to the disciplines of
certain fixed procedures Effectiveness of an analysis method may be greatly influenced
by data preparation routines Important to maintain consistency
Data Analysis Remarks
The Socio-Technological Integrator And Innovator
Problems of Archaeological Data
“THE VALUE OF DATA IS GIVEN BY THE ABILITY TO EXTRACT INFORMATION.”
Scarce and incomplete data
High amounts of uncertainty and subjectivity
Characterised by high degrees of redundancy
Complex interactions between variables
Analysis of findings with the use of different technologies and analysts may result to be inaccurate and imprecise
The Socio-Technological Integrator And Innovator
Barely affected by alteration or deterioration during burial, they generally present the original trading goods, as far as their material properties and composition are concerned. Very helpful in providing classification among ceramic assemblages
Often giving information about their provenance or origin of production
Ceramics of the same production series may reveal a characteristic elemental composition, usually distinct from ceramics from other production places or series. Due to the: Geochemical diversity of raw material sources
The variation in the pottery manufacturing process
DATA IDIOSYNCRASIES:
Ceramics
The Socio-Technological Integrator And Innovator
Prone to corrosion Different raw materials are
corroded at different rates and
degrees
Corrosion is not uniform
Assuming that the sample is
representative is not always trivial
Sampling requires cleaning the outer surface Usually involves removing the outer coat
Issues with licensing
Due to the material’s flexibility, most of metal objects are not flat
Alloys are challenging
DATA IDIOSYNCRASIES:
Metals
The Socio-Technological Integrator And Innovator
Notoriously homogeneous
Very rarely found in large quantities
Highly fragile Their usually thin structure makes artifact analysis a challenge
Artifact in whole form are very rare to find
Contamination over time Their analysis usually requires the use of acidic substances, for
cleaning the extra coating
Sometimes alters some of their characteristics
Against legislation restrictions
DATA IDIOSYNCRASIES:
Glass
The Socio-Technological Integrator And Innovator
“I am enough of an artist to draw freely upon my imagination.
Imagination is more important than knowledge.
Knowledge is limited. Imagination encircles the world.”
Albert Einstein
Thank you for the attention!
Comments and Questions are Welcome!