Detection of natural structures and classification of HCI HPR data using robust forward search algorithm

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ISIAKA, Fatima, MWITONDI, Kassim and IBRAHIM, Adamu M. (2016). Detection of

natural structures and classification of HCI-HPR data using robust forward search

algorithm. International Journal of Intelligent Computing and Cybernetics, 9 (1),

23-41.

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Sheffield Hallam University Research Archive

DETECTION OF NATURAL STRUCTURES AND CLASSIFICATION OF HCI-HPR DATA USING ROBUST

FORWARD SEARCH ALGORITHM

Fatima Isiaka∗

Department of Computing, Sheffield Hallam University, Sheffield, S1 1WB,United Kingdom†

[email protected]‡

Kassim S. Mwitondi

Department of Computing, Sheffield Hallam University Sheffield, United Kingdom

Adamu M. Ibrahim

School of Computing, University of Leeds, Leeds United Kingdom

Received:23 August 2015

Revised:2 September 2015

Accepted:20 January 2016

Abstract

Purpose- This paper proposes a forward search algorithm for detecting and identifying natural structures arising in Human-Computer Interaction (HCI) and Human Physiologi-cal Response (HCI) data.

Design/Methodology/Approach The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for de-tecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data was also simulated based on multivariate normal distribution method and used to compare and validate our original data.

Findings- Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data.

Practical implications The authors conducted some of the experiments at individual residence which may affect environmental constraints.

∗_{45 West Point, 58 West Street, S1 4EZ, Sheffield., UK}

†_{Howard Street, Sheffield, S1 1WB.} ‡_{[email protected]..}

Originality/value- Graphical improvement of the forward search algorithm was done with Deducer package in R to present vivid results that indicates detected stress levels in users.

Keywords: Human physiological response; HCI; Categorical data; Maximum likeli-hood estimation; Robust forward search.

Paper type:Research paper

1. Introduction

For continuous data, a very fast robust method is often used for the detection of multiple masked effects (outliers) [Atkinson (1994)]. This type of data often requires regression models frequently used on least squares or least median squares (LMS). For the purpose of detecting outliers and predictions, the estimation uses very precise estimates of the parameters and excessive computations. The main goal here is to use the forward search algorithm for the robust estimation to detect natural structures and the possibilities of fu-ture Human Computer Interaction-Human Physiological Response (HCI-HPR) modelling. The parameters are chosen based on their capabilities for response detection and p-values obtained from the subset selected based on the generalized additive model. The forward search has the potential for computation of parameter estimates. For large datasets, the multi-tasking approach affords a considerable reduction in computational time.

1.1. Motivation

The motivation behind this paper is the need to gain insight into HCI and help to monitor and control stress level, not only directed to interactions with webpage contents, but other visual stimuli. We try to achieve this by investigating how user-generated data be utilised to model and simulate human physiological responses to visual contents of the web. The approach to modeling here is using the forward search algorithm, which is capable of detecting natural structures that might arise in the data.

2. Related work

processing effort on the part of readers. These findings showed that visual attention and engagement are not necessarily linked to user perceptions of experiences, today we struggle to determine the best possible techniques to allow us have a greater understanding of the user’s unconscious minds as they interact with elements of a visual stimulus like a webpage. Physiological measures have evoked enthusiasm regarding this aspect. The primary interest is on mechanisms [Green (1976)][Filipovic et al; (2001)] that tries to answer simple questions on functional phenomena.

Modelling physiological data [Andreassi (2000)] and cognitive state estimate has proved rather complex especially with data scaling and interpreting of results. [Kramer (1990)][Dirican et al; (2011)] describes that there are three types of dimensions that are used in reasoning and anxiety valuation, this includes individual measures, functional mea-sures which are also physiological meamea-sures and recital meamea-sures [Farmer and Brownson (2003)][Cain (2007); Dirican et al; (2011)]. These are also stated with the mental load [Ganglbauer (2009)][Dirican et al; (2011)] and psychological workload/stress assessment. Though their meaning may show differences, they can also be used in the same context [Kalyuga (2008)][Dirican et al; (2011)]. Cognitive load is a conception that is connected to functioning memory in mental workload concept [Kalyuga (2008)][Dirican et al; (2011)], while on the other hand, mental/stress workload has a more challenging and has multi-dimensional concept attached to it. It is either related to task difficulties or potentials, motivation and the emotional state of users [Kramer (1990); Farmer and Brownson (2003); Dirican et al; (2011)].

The physiological measures used for this study are chosen based on their strength and weakness and also on their analytical assessment founded in literature. For Skin Conduc-tance Response (SCR), the phasic changes were considered since its temporal sensitivity is poor, on tonic changes. Mean Skin Temperature (MST) has an impact on diagnosis; as anxiety intensifies, temperature declines as heat transfers to the forms core (central body). Eye movements (Saccade, Fixations in X and Y coordinate) was chosen based on the fact that the eye gaze is an undeviating quantity of reasoning, and it is a good metric for interface assessment and usability testing. The pupil dilation (pupil changes) of the eye correlates with mental workload (stress) and reacts to the expressive valence. Though hard to relate in everyday context since the eye responds to diverse light settings [Ganglbauer (2009)][Kramer (1990)], but useful when apparatus used combines both the eye movement with changes in pupil size. Physiological measures are continuous and time-varying, and when combined with behavioural measures [Insko (2003)], can lead to boundless stages of making a better design decision for user interface design.

3. Methodology

algorithm and detecting of natural structures for categorising the HCI-HPR data with associated affect states.

3.1. Forward Search Algorithm

It is commonly known that objects that are analogous fall in line with each other. Most fundamental theories are built on these concepts. This provides a basis for defining rules that categorize observations as outlying are defined. The method is based on Mahalanobis distances (Algorithm 1) as a measure of the distance between a point and its distribution. The mahalanobis distance is based on a multi-dimensional generalisation of the idea of measuring how many standard deviations away from a pointp, given as

DTm(x) = (x−ω)T S

−1

(x−ω)0.5 (1)

Wherex = (x1, x2, . . . , xN)T set of observations, mean ω = (ω1, ω2, . . . , ωN)T and

co-variance matrix S. To detect anomalies, numerous reproductions of data for calibration of residuals are run on datasets with reliant variable simulated from standard normal distribution and the same design matrix. Each simulation calculates the estimated scale parameter given has:

˜

σm2 =e2[med](βm)

Wheree2_[med] represents the median of the raw residualsσm2. The scale parameter is

regular over the initial points for each model taken, modifying for uncommon approxima-tions of the Sigma that standardises the plots. The modificaapproxima-tions are used for the building of the regularised residuals. The following equations is used to define cut-off point based on first principles.

ti=

eiσ¯m

˜

σmp(1−hi)

∀i∈ Mm

ti=

eiσ¯m

˜

σm(m)p(1 +di)

∀i /∈ Mm

Wherehiis the hatmatrix diagonal of the same distancedi=xTi (X T

X)−1xiwithX has

the explanatorty variable of simulated responseY of a linear model. The forward search moves from fittingmobservations tom+ 1 by choosing them+ 1 observations with the smallest residuals [Atkinson (1994)]. The value of the residual at the starting point serves has the performance for a given model fit.

For our approach, we define the cut-off based on Mahalnobis distance, the following cut-off is used.

Algorithm 1Mahalnobis Squared distances

1: procedure_Mahal(X, index)

2: top:

3: if (!is.matrix(X))then returnfalse

4: Xbar←apply(x[index], 2, mean)

5: loop:

6: if (is.matrix(X))then 7: S←solve(S)

8: Xcent←t(t(X)−Xbar).

9: apply(xcent,1, f unction(X)X%∗%S%∗%X) 10: goto loop.

11: close;

12: gototop.

Given that our input matrix isXwithpparameters and sizen, the natural structures of the dataset are detected in the steps outlined in Algorithm 2. The goal is to largely condense the masking outcome that may arise as a result of outlying existence on the evaluations of means and covariance calculated from the original data. The main idea here is, given certain distances m observations for the valuation of means and covariance, observations at m+ 1 positions to be recycled for the valuation in the next phase are probable tom+ 1 lowest space. Hence, an occurrence can be incorporated in the subgroup used for the approximation for say, value m, this can later be exempted as m increases.

Algorithm 2Robust forward search of matrixX

procedure_Matrix(X)

X =matrix(X) rn = rownames(X) top:

if (is.null(rn))then return false rn←1:nrow(x)

n←length(X[1]) p←length(X[1]) s←1:n

ind = matrix(0, n - p, n): ind1←0

thresh = qisquare((n - 0.5)/n, p): . determine qisquare of parameters index←1 : (p+ 1)

procedure(i in (p+1):n)

ind1←ind1 + 1.

if (i== (p+ 1))D←M ahal(x, index)then: . mahalanohis distances index←order(D).

i←i−1.

index1←sort(index[1 :i]) D←mahal(x, index1) index2←s[D > thresh]

ind[ind1, index2]←ind[ind1, index2] + 1

goto loop.

close;

y←rep(1 : (n−p), rep(n,(n−p))). x←rep(1 :n,(n−p)).

residuals← −c(””,”∗”)[as.vector(t(ind[(n−p) : 1,])) + 1]

printx, y

3.2. Natural Structures with Principle Component Analysis (PCA)

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Baseline

Magnitude

MST

Amplitude MSP

Latency

Gaz eTime

MP eak

PupilChange

MappedFX_MappedFY

FixationD

SaccadeSiz

e

−1.0 −0.5 0.0 0.5 1.0

−1.0 −0.5 0.0 0.5 1.0

PC1 (22.2% explained var.)

PC2 (17.2% e

xplained v

ar

.)

groups

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● Neutral

Relaxed

Stress

(a) PCA of data with stress levels as class labels

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Baseline

Magnitude

MST

Amplitude MSP

Latency

Gaz eTime

MP eak

PupilChange

MappedFX_MappedFY

FixationD

SaccadeSiz

e

−1.0 −0.5 0.0 0.5 1.0

−1.0 −0.5 0.0 0.5 1.0

PC1 (22.2% explained var.)

PC2 (17.2% e

xplained v

ar

.)

groups

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Google search.png

Google suggest1.png

iGoogle1.png

NR.png

NRS.png

Yahoo1.png

(b) PCA of data with websites as class labels

0 1 2 3

0 5 10

pcomp

v

ar

iances

(c) Variance plot showing variations in components

Fig. 1: PCA show variations in components and best features

4. Data Visualisation and Generalised Additive model (GAM)

as the response variable. Considering negatively correlated parameters (Figure 2) such as average peaks (MPeak) with Fixation Duration (FixationD) and Latency. This sort of variables obeys the reversal paradox [Good et al; (1987)][Simpson (1951)] where a trend that appears in different groups of data and disappears or reverses when combined. It can also be related to situations such as the more the eye fixates on a particular object of interest the less the spikes on physiological data or the reverse may be the case.

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10.98968757.8653030312.689

4.2227981654.6406 8.522906977 2.941 3.0976 4.107589286 8.936972477 16.96448454 6.64279069813.5937735821.4220138918.3489849623.39528571

13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.688608247 10.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.82279412 13.3279629616.20722222 5.561442308 10.0256132113.75144737

11.8010891117.6108888915.07005814

9.516169355 7.2925 6.772611947.298716216 8.56738738710.66116667 4.623532934 7.154719101 4.543446602 3.943285714 4.7543888894.703293651 5.2 21 19 6.7 7 11 4.6 8.9 6 23 20 6.60.67 6.9 8.3 24 7.4 8.1 24 26 8 4.1 28 10 31 5.4 58 70 16 10 11 3.7 4.6 9 22 18 13 14 3.6 1321 17

13 235.1

8.2 11 5.2 20 4.6 7.7 5.9 5.6 22 3.5 5.3 32 47 7.6 4.2 20 51 9.4 25 35 16 51 6.415 61 39 9.6 21 19 9.2 5.4 23 66 27 75 12 22 45 5.2 25 12 4.7 18 36 12 45 7.6 7.8 12 14 8.7 14 5.9 28 7.7 32 8 35 5.4 8.2 10 41 42 16 11 13 43 31 15 65 95 31 29 7.8 6.6

78 15.0054216912.9106060619.5628571414.2038167915.11885246 16.408715616.375 14.00930233

17.1625 14.21307692 18.38125 23.7298165137615 19.52268041 19.11230159 21.29625 21.9533333333333 22.50267857 22.978125 24.2514492815.49722222 17.6514285720.32333333 20.51084337 31.20316456 20.37142857 23.89122807

21.43243243 21.66 29.38253968

21.778125 12.79026786 18.67678571 15.90517241 24.921 18.013125 21.778125 12.82470149 14.60114583 15.90517241 24.921 18.013125 21 8.6 9.9 24 18 16

18 50 12

38 17 10 8.3 6.9 5.5 12 6.4 14 21 72168.7 35 45 38 40 15 9.2 21 4.9

1054 34

20 31 16 559.2 11 25 32 33 4.155 16

12 40 26 34.6731818181818 7.492622951 6.291860465 10.98968757.86530303 12.689 4.222798165 4.64068.522906977 2.941 3.0976 4.107589286 8.936972477 16.964484546.642790698 13.5937735821.4220138918.3489849623.39528571 13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.68860824710.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.8227941213.32796296 16.20722222 5.561442308 10.02561321 13.75144737 11.8010891115.0700581417.61088889

9.516169355 7.2925 6.772611947.298716216 8.56738738710.66116667 4.623532934 7.1547191014.543446602 3.943285714 4.754388889 4.703293651

5.221196.77 11 4.6 8.9 6 2320 6.6 0.67 6.9 8.3 247.4 8.1 24 26 8 4.1 28 10 31 5.4 58 70 16 10 11 3.7 4.6 9 22 18 13 14 3.6 1321 17

135.1 23

8.2 11 5.2 20 4.6 7.7 5.9 5.6 22 3.5 5.3 32 47 7.6 4.2 20 51 9.4 25 35 16 51 6.4 15 61 39 9.6 21 19 9.2 5.4 23 66 27 75 12 22 45 5.2 25 12 4.7 18 36 12 45 7.6 7.8 12 14 8.7 145.9 28 7.7 32 8 35 5.4 8.2 10 41 42 1611 13 43 31 15 65 95 31 29 7.8 6.6 7815.1188524614.2038167915.0054216912.9106060619.56285714 16.408715616.37514.00930233

17.1625 14.21307692 18.38125 23.7298165137615 19.52268041 19.11230159 21.29625 21.9533333333333 22.50267857 22.978125 24.2514492815.49722222 17.65142857 20.32333333 20.51084337 31.20316456 20.37142857 23.8912280721.43243243 21.6629.38253968 21.778125 12.79026786 18.67678571 15.90517241 24.921 18.013125 21.778125 12.82470149 14.6011458315.9051724124.921

18.013125

21 8.69.9 24 18 16 181250 38 1710 8.36.9 5.512 6.4 14 21 72 16358.7

45 38 40 15 9.2 21 4.9 10 54 342031 16 559.2 11 25 32 33 4.1 1655

12 4026 34.6731818181818 7.4926229516.291860465 10.98968757.86530303 12.689 4.222798165 4.64068.522906977 2.941 3.0976 4.107589286 8.936972477 16.96448454 6.642790698 13.5937735821.4220138923.39528571 18.34898496 13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.688608247 10.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.8227941213.32796296 16.20722222 5.561442308 10.0256132113.75144737 11.80108911 17.6108888915.07005814 9.516169355 7.29256.77261194 7.298716216 8.567387387 10.66116667 4.623532934 7.1547191014.543446602 3.943285714 4.7543888894.703293651

5.2 21196.77 11 4.6 8.9 6 2320 6.6 0.67 6.9 8.3 247.4 8.1 24 26 8 4.1 28 10 31 5.4 58 70 16 10 11 3.7 4.6 9 22 18 13 14 3.6 13 21 17 1323 5.1 8.2 11 5.2 20 4.6 7.7 5.9 5.6 22 3.5 5.332 47 7.64.2 20 51 9.4 25 35 16 51 6.4 15 61 39 9.6 21 19 9.2 5.4 23 66 27 75 12 22 45 5.2 25 12 4.7 18 36 12 45 7.6 7.8 12 14 8.7 14 5.9 28 7.7 32 8 35 5.4 8.2 10 41 42 16 11 13 43 31 15 6595 31 297.8 6.6

7815.1188524612.9106060619.5628571414.2038167915.00542169 16.4087156 16.37514.00930233 17.1625 14.21307692 18.38125 23.7298165137615 19.52268041 19.11230159 21.29625 21.9533333333333 22.50267857

22.97812524.2514492815.4972222217.6514285720.32333333 20.51084337 31.20316456 20.37142857 23.8912280721.43243243 21.6629.38253968 21.778125 12.79026786 18.67678571 15.90517241 24.921 18.013125 21.778125 12.82470149 14.6011458315.90517241 24.921 18.013125

21 8.69.9 24 18 16 18 1250

38 1710 8.3 6.9 5.512 6.4 14 21 72 168.735

45 3840

15 9.221

4.9 1034542031

16 55 9.2 11 25 32 33 4.15516

12 40 26 34.6731818181818 7.492622951 6.291860465 10.9896875 7.8653030312.689 4.222798165 4.64068.522906977 2.941 3.0976 4.107589286 8.936972477 16.96448454 6.642790698 13.5937735821.42201389 23.3952857118.34898496 13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.68860824710.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.82279412 13.32796296 16.20722222 5.561442308 10.0256132113.75144737 11.80108911 17.61088889 15.07005814 9.516169355 7.29256.772611947.298716216

8.56738738710.66116667 4.623532934 7.154719101 4.543446602 3.943285714 4.754388889 4.703293651 5.2 21 19 6.7 7 11 4.6 8.9 6 23 20 6.6 0.67 6.9 8.3 24 7.4 8.1 24 26 8 4.1 28 10 31 5.4 58 70 16 10 11 3.7 4.6 9 22 18 13 14 3.6 13 21 17 13235.1 8.2 115.2 20 4.6 7.7 5.9 5.6 22 3.5 5.332 47 7.64.2 20 51 9.4 25 35 16 51 6.415 61 39 9.6 21 19 9.2 5.4 23 66 27 75 12 22 45 5.2 25 12 4.7 18 36 12 45 7.6 7.8 12 14 8.7 14 5.9 28 7.7 32 8 35 5.4 8.2 10 41 42 1611 13 43 3115 65 95 3129 7.8 6.6

7815.0054216912.9106060619.56285714 15.11885246 14.20381679 16.4087156 16.375 14.00930233 17.1625 14.21307692 18.38125 23.7298165137615 19.52268041 19.11230159 21.29625 21.9533333333333 22.50267857

22.97812524.2514492815.49722222 17.65142857 20.32333333

20.5108433731.20316456

20.37142857 23.89122807

21.4324324329.3825396821.66 21.778125 12.79026786 18.67678571 15.90517241 24.921 18.013125 21.778125 12.82470149 14.60114583 15.90517241 24.921 18.013125 218.6 9.9 24 18 16 185012 38 17 10 8.3 6.9 5.5 12 6.4 14 21 72 16 8.735 45 38 40 15 9.2 21 4.9 10543420 31 16 55 9.2 11 25 32 33 4.1 16 55 12 4026 34.6731818181818 7.492622951 6.291860465 10.9896875 7.86530303 12.689 4.222798165 4.6406 8.522906977 2.941 3.0976 4.107589286 8.936972477 16.96448454 6.64279069821.4220138923.3952857118.3489849613.59377358

13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.688608247 10.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.8227941213.32796296 16.20722222 5.561442308 10.02561321 13.75144737 11.80108911 17.6108888915.07005814 9.516169355 7.2925 6.772611947.298716216 8.567387387 10.66116667 4.623532934 7.154719101 4.543446602 3.943285714 4.754388889 4.703293651 5.2 21 19 6.7 7 11 4.6 8.9 6 23 20 6.6 0.67 6.9 8.3 24 7.4 8.1 24 26 8 4.1 28 10 31 5.4 58 70 16 10 11 3.7 4.6 9 22 18 13 14 3.6 13 21 17 13 23 5.1 8.2 11 5.2 20 4.6 7.7 5.9 5.6 22 3.5 5.3 32 47 7.6 4.2 20 51 9.4 25 35 16 51 6.4 15 61 39 9.6 21 19 9.2 5.4 23 66 27 75 12 22 45 5.2 25 12 4.7 18 36 12 45 7.6 7.8 12 14 8.7 14 5.9 28 7.7 32 8 35 5.4 8.2 10 41 42 1611 13 43 31 15 6595 31 297.8 6.6

78 15.1188524614.2038167912.9106060615.0054216919.56285714 16.408715614.0093023316.375

17.1625 14.2130769218.38125 23.7298165137615 19.52268041 19.11230159 21.29625 21.953333333333322.50267857

22.97812515.4972222224.2514492817.6514285720.32333333 20.51084337 31.20316456

20.37142857 23.89122807 21.4324324321.66 29.38253968 21.778125 12.79026786 18.67678571 15.90517241 24.921 18.013125 21.778125 12.82470149 14.6011458315.9051724124.921

18.013125

21 9.98.6 24

18 16 18 5012

38 17 10 8.3 6.9 5.5 12 6.4 14 21 72

16 8.735

45

38 40

15 9.221

4.9

10 34 54 20 31

16 55 9.2 11 25 32 33

4.1 55 16

12 40 26 34.6731818181818 7.492622951 6.291860465 10.9896875 7.86530303 12.689 4.222798165 4.6406 8.522906977 2.941 3.0976 4.107589286 8.936972477 16.96448454 6.64279069813.5937735821.4220138923.39528571 18.34898496 13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.688608247 10.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.82279412 13.3279629616.20722222 5.561442308 10.02561321 13.75144737

11.8010891117.6108888915.07005814

9.516169355 7.2925 6.772611947.298716216 8.56738738710.66116667 4.623532934 7.154719101 4.543446602 3.943285714 4.7543888894.703293651 5.2 21 19 6.77

11 4.6 8.9 6 23 20 6.6 0.67 6.9 8.3 24 7.4 8.1 24 26 8 4.1 28 10 31 5.4 58 70 16 10 11 3.7 4.6 9 22 18 13 14 3.6 13 21 17 13 23 5.1 8.2 11 5.2 20 4.6 7.7 5.9 5.6 22 3.5 5.3 32 47 7.6 4.2 20 51 9.4 25 35 16 51 6.415 61 39 9.6 21 19 9.2 5.4 23 66 27 75 12 22 45 5.2 25 12 4.7 18 36 12 45 7.6 7.8 12 14 8.7 14 5.9 28 7.7 32 8 35 5.4 8.2 10 41 42 16 11 13 43 31 15 65 95 31 29 7.8 6.6 7819.5628571415.1188524614.2038167915.0054216912.91060606 16.4087156 16.37514.00930233 17.1625 14.21307692 18.38125 23.7298165137615 19.52268041 19.11230159 21.29625 21.953333333333322.50267857 22.978125 24.2514492820.3233333317.6514285715.49722222

20.51084337 31.20316456

20.3714285723.8912280721.43243243 21.6629.38253968 21.778125 12.79026786 18.67678571 15.90517241 24.921 18.013125 21.778125 12.82470149 14.6011458315.90517241 24.921 18.013125 21 8.6 9.9 24 18 16 18 1250 38 17 10 8.3 6.9 5.5 12 6.4 14 21 728.716 35 45 38 40 15 9.2 21 4.9 10 54343120

16 9.2 11 25 32 33 4.1 16 55 12 40 26 34.6731818181818 7.492622951 6.291860465 10.9896875 7.86530303 12.689 4.6406 8.522906977 2.941 3.0976 4.107589286 8.936972477 16.96448454 6.64279069821.4220138913.59377358 23.39528571 18.34898496 13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.688608247 10.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.82279412 13.32796296 16.20722222 5.561442308 10.0256132113.75144737 11.80108911 17.61088889 15.07005814 9.516169355 7.2925 6.772611947.298716216 8.567387387 10.66116667 4.623532934 7.1547191014.543446602 3.943285714 4.7543888894.703293651 5.2 21196.7 7 11 4.6 8.9 6 2320 6.60.67 6.9 8.3 247.4 8.1 24 26 8 4.1 28 10 31 5.4 58 70 16 10 11 3.7 4.6 9 22 18 13 14 3.6 13 21 17 13 23 5.1 8.2 115.2 20 4.6 7.7 5.9 5.6 22 3.5 5.3 32 47 7.64.2 20 51 9.4 25 35 16 51 6.415 61 39 9.6 21 19 9.2 5.4 23 66 27 75 12 22 455.2 25 12 4.7 18 36 12 45 7.6 7.8 12 14 8.7 14 5.9 28 7.7 32 8 35 5.4 8.2 10 41 42 1611 13 43 3115 6595 31 297.8 6.6 78 15.11885246 14.20381679 15.00542169 12.9106060619.56285714 16.4087156 16.37514.00930233 17.1625 14.21307692 18.38125 23.7298165137615 19.52268041 19.11230159 21.29625 21.953333333333322.50267857 22.978125 24.25144928 15.4972222220.3233333317.65142857 20.5108433731.20316456 20.3714285723.89122807 21.43243243 21.6629.38253968 21.778125 12.79026786 18.67678571 15.90517241 24.921 18.013125 21.778125 12.82470149 14.60114583 15.90517241 24.921 18.013125 21 8.6 9.9 24 18 16 18 12 50 38 17 10 8.3 6.9 5.5 12 6.4 14 21 72 16 8.735 45 38 40 159.2 21 4.9 10 54 34 20 31 16 55 9.2 11 25 32 33 4.1 1655 12 40 26 34.6731818181818 7.492622951 6.291860465 10.9896875 7.86530303 12.689 4.222798165 4.64068.522906977 2.941 3.0976 4.107589286 8.936972477 16.964484546.642790698 13.5937735821.4220138923.3952857118.34898496

13.309375 7.346321839 10.25644068 4.799868421 8.254186047 7.984495413 5.688608247 10.25644068 6.168860294 3.215483871 8.8075 17.4295098 7.04259434 12.97605263 16.82279412 13.3279629616.20722222 5.561442308 10.0256132113.75144737 11.80108911 17.61088889 15.07005814 9.516169355 7.29256.772611947.298716216

8.567387387 10.66116667 4.623532934 7.154719101 4.543446602 3.943285714 4.754388889 4.703293651

URL Magnitude Baseline MSP

MappedFX MappedFY MST FixationD

Latency GazeTime PupilChange Amplitude

0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0 0.0 2.5 5.0 7.5 10.0

2 4 6 0 50 100 150 200 0.0 2.5 5.0 7.5 10.0 0 20 40 60 80

200 400 600 800 250 500 750 100020 25 30 35 40 45 0 1000200030004000

0 20 40 60 80 0 300 600 900 12000 2 4 6 0 3 6 9 value

MP

eak

Fig. 2: Scatter plot showing correlation of response variables and predictors

URL Magnitude Baseline MSP

MappedFX MappedFY MST FixationD

Latency GazeTime PupilChange Amplitude

0.0 2.5 5.0 7.5 10.0

0.0 2.5 5.0 7.5 10.0

0.0 2.5 5.0 7.5 10.0

2 4 6 0 50 100 150 200 0.0 2.5 5.0 7.5 10.0 0 20 40 60 80

200 400 600 800 250 500 750 100020 25 30 35 40 45 0 1000200030004000

0 20 40 60 80 0 300 600 900 12000 2 4 6 0 3 6 9

value

MP

eak

Fig. 3: GAM shows parameters that adopts linear fit and un-linear fit

5. Results

For the output of the plots, the cut-off point as mentioned earlier determines the starting point for the subsets based on the least absolute residual value for each given parameter and the variations in the data. The following sections discusses results obtained from a group of dataset.

5.1. Pilot data

page); Google suggest page (have active content), Google search page (contents static) and iGoogle/Yahoo page. For these participants, the amplitude and changes in pupil size have the most impact with p-values (p >0.05).

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ●● ● ● ●●● ● ●● ● ● ● ●●● ● ●● ● ● ●● ●●● ● ●● ● ● ●● ●●● ● ●● ●● ● ●● ● ●●● ● ●● ●● ●● ●● ● ●●● ● ●● ●● ●● ●● ● ● ●●● ● ●● ●● ●● ●● ●● ● ●●● ● ●● ●● ●● ●● ●● ● ●●● ● ● ●● ●● ●● ●●● ●● ● ●●● ● ● ●● ●● ●● ●●● ●● ● ●●● ● ● ●● ●● ●● ●●● ●● ● ●●● ● ● ●● ●● ●● ●●● ●● ● ●●● ● ●● ●●●● ●● ●● ●●● ●● ● ●●● ● ●● ●●● ●● ●● ●●● ●● ● ●●● ● ●● ●●● ●● ●● ●●●●●● ● ●●● ● ●● ●●● ●● ●●● ●●●●●● ● ●●● ● ●● ● ●●● ●● ● ●●● ●●●●●● ● ●●● ● ●●●● ●●● ●● ● ●●● ●●●●●● ● ● ●●● ●● ●●●● ●●● ●● ● ●●● ●●●●●● ● ● ●●● ●●● ●●●● ●●● ●● ● ●●● ●●●●●● ● ● ●●● ●●● ●●●● ●●● ● ●● ●●●●● ●●●●●● ● ● ●●● ●●● ●●●● ●●● ● ●● ●●●●● ●●●●●● ● ● ●●● ●●● ●●●● ●●● ● ●● ●●●●● ● ●●●●●● ● ● ●●● ●●● ●●●● ●●● ● ●● ●●●●●●● ●●●●●● ●● ● ●●● ●●● ●●●● ●●● ● ●● ●●●●●●●●●●●●●● ●● ● ●●● ●●● ●●●● ●●● ● ●● ●●●●●●●●●●●●●● ●●●● ●●● ●●● ●●●● ●●● ● ●● ●●●●●●●●●●●●●● ●●●●●●●● ●●● ●●●● ●●● ● ●● ●●●●●●●●●●●●●● ●●●●●●●●●●●● ●●●● ●●● ● ●● ●●●●●●●●●●●●●● ●●●●●●●●●●●● ●●●● ●●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●● ●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● 0 10 20 30 40

0 20 40 60

x

y

● *

Fig. 4: Best fit for pilot data

++++++ + + ++++++++++ + + + + + + ++ + ++ ++ +++ ++ + +* ++ + +++ + ++ ++++ + + ++++ ++ ++++++ + + ++++++++++ + + + + + + ++ + ++ ++ +++ ++ + +* ++ + +++ + ++ ++++ + + ++++ ++ ++++++ + + ++++++++++ + + + * + + ++ + ++ ++ +++ ++ + +* ++ + +++ + ++ ++++ + + ++++ ++ ++++++ + + ++++++++++ + + + * + + ++ + ++ ++ +++ ++ + +* ++ + +++ + ++ +++* + + ++++ ++ ++++++ + + ++++++++++ + + + * + + ++ + ++ ++ +++ ++ + +* ++ + +++ + ++ +++* + + ++++ *+ ++++++ + + ++++++++++ + + + * + + ++ + ++ ++ +++ ++ + +* ++ + +++ + *+ +++* + + ++++ *+ ++++++ + + ++++++++++ + + + * + + ++ + ++ ++ +++ ++ + +* *+ + +++ + *+ +++* + + ++++ *+ ++++++ + + ++++++++++ + + + * + + ++ + ++ ++ +++ ++ + +* * * + +++ + *+ ++* * + + ++++ *+ ++++++ + + ++++++++++ + + + * + + ++ + ++ ++ +++ ++ + +* * * + +++ + *+ ++* * + + ++++ *+ ++++++ + + ++++++++++ + + + * + + ++ + *+ ++ +++ ++ + +* * * + +++ + *+ ++* * + + ++++ *+ ++++++ + + ++++++++++ + + + * + + ++ * *+ ++ +++ ++ + +* * * + +++ + *+ ++* * + + ++++ *+ ++++++ + + ++++++++++ + + + * + + ++ * *+ ++ +++ ++ + +* * * + +++ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * + + ++ * *+ ++ +++ *+ + +* * * + +++ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * *+ ++ +++ *+ + +* * * + +++ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * *+ +* +++ *+ + +* * * + +++ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * *+ +* *++ *+ + +* * * + +++ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * *+ +* *++ *+ + +* * * + +*+ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * * * +* *++ *+ + +* * * + +*+ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * * * +* *++ *+ + +* * * + +*+ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * * * +* *++ *+ + +* * * + +*+ + *+ ++* * + + ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * * * +* *++ *+ + +* * * + +*+ + * * ++* * * * ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * * * +* *++ *+ + +* * * + +*+ + * * +++* * * ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * * * +* *++ *+ + +* * * + +*+ + * * +++* * * ++++ * * ++++++ + + ++++++++++ + + + * * + ++ * * * * * *++ *+ + +* * * + +*+ + * * +++* * * ++++ * * ++++++ + + ++++++++++ + + * * * + ++ * * * * * *++ *+ + +* * * + +*+ + * * +*+* * * ++++ * * ++++++ + + ++++++++++ * + * * * + ++ * * * * * *++ *+ + +* * * + +*+ + * * * *+* * * ++++ * * ++++++ + + ++++++++++ * + * * * + ++ * * * * * *++ *+ * +* * * + * *+ + * * * *+* * * ++++ * * ++++++ + + ++++++++++ * + * * * + ++ * * * * * *++ *+ * +* * * + * * * + * * * *+* * * ++++ * * ++++++ + + ++++++++++ * + * * * + ++ * * * * * *++ *+ * +* * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * + ++ * * * * * *++ *+ * +* * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * + ++ * * * * * *++ *+ * +* * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * + *+ * * * * * *++ *+ * +* * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * * *+ * * * * * *+* *+ * +* * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * *+* *+ * +* * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * *+* * * * +* * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * *+* * * * * * * * + * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * *+* * * * * * * * * * * * + * * * *+* * * ++*+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * *+* * * * * * * * * * * * + * * * *+* * * +* *+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * * * * * * * * * * * * * * * + * * * *+* * * +* *+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * * * * * * * * * * * * * * * + * * * * * * * * +* *+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * * * * * * * * * * * * * * * + * * * * * * * * * * *+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *+ * * ++++++ + + ++++++++++ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ++++++ + + +++++++++* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ++++++ + + ++++++++* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ++++++ + + +++++++* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * ++++++ + + ++++++* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Neutral Relaxed Stress GoogleSug1 GoogleSearch1

Yahoo1 _iGoogle1 NR1 NRS1

GoogleSug2

GoogleSearch2

Yahoo2 _iGoogle2 NR2 NRS2

GoogleSug3

GoogleSearch3

Yahoo3 _iGoogle3 NR3 NRS3

GoogleSug4

GoogleSearch4

Yahoo4 _iGoogle4 NR4 NRS4

GoogleSug5

GoogleSearch5

Yahoo5 _iGoogle5 NR5 NRS5

GoogleSug6

GoogleSearch6

Yahoo6 _iGoogle6 NR6 NRS6

GoogleSug7

GoogleSearch7

Yahoo7 _iGoogle7 NR7 NRS7

GoogleSug8

GoogleSearch8

Yahoo8 _iGoogle8 NR8 NRS8

GoogleSug9

GoogleSearch9

Yahoo9 _iGoogle9 NR9 NRS9

GoogleSug10

GoogleSearch10

Yahoo10 _iGoogle10 NR10 NRS10

URL Stress Le vel residuals a a * +

5.2. Older Student data

The dataset for older student contains 60 observations and 15 explanatory variables just like the pilot study. This part of reflecting of society more accurately by including older participants. It is known that being young, technically conversant and extremely cultivated is not signifying demographic certainties. In research, it is more feasible to include older participants to compare the differences in interaction and model evaluation from other groups. The result shows best fit for running the algorithm on the dataset.

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0 20 40 60

Observations

Number of obser

vations used f

or estimation

residuals

● *

Fig. 6: Best fit for older student data

+++++++++++++++++ + +++++++++++ + ++ + ++++++ + ++ + + + + + +++ + + +++++++ + ++++++++++*++++++ + +++++++++++ + ++ + ++++++ + ++ + + + + + +++ + + +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ + + + + + +++ + + +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ + + + + + +++ + + +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ + * + + + +++ + + +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + + + +++ + + +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + + + *++ + + +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + + + *++ + * +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + + + *++ + * +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + + + * * * + * +++++++ + ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + + + * * * + * +++++++ * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + * + * * * + * +++++++ * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + * + * * * * * +++++++ * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + * + * * * * * ++++*++ * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + * + * * * * * ++++*+* * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * + * * * * * * * ++++*+* * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * * * * * * * * * ++++* * * * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * * * * * * * * * ++++* * * * ++++++++++*++++++ + +++++++*+++ + ++ + ++++++ * ++ * * * * * * * * * * +++* * * * * ++++++++++*++++++ + +++++++*+++ + ++ + +*++++ * ++ * * * * * * * * * * +++* * * * * ++++++++++*++++++ + +++++++*+++ + ++ + +*++++ * ++ * * * * * * * * * * +*+* * * * * ++++++++++*++++++ + +++++++*+++ + ++ + +*++++ * ++ * * * * * * * * * * * *+* * * * * ++++++++++*++++++ + +++++++*+++ + ++ + +*++++ * ++ * * * * * * * * * * * * * * * * * * ++++++++++*++++++ * +++++++*+++ + ++ + +*++++ * ++ * * * * * * * * * * * * * * * * * * ++++++++++*++++++ * +++++++*+++ + *+ + +*++++ * ++ * * * * * * * * * * * * * * * * * * ++++++++++*++++++ * +++++++*+++ + *+ + +*++++ * *+ * * * * * * * * * * * * * * * * * * +++++++++++++++++ * +++++++*+++ + *+ + +*++++ * *+ * * * * * * * * * * * * * * * * * * +++++++++++++++++ * +++*+++*+++ + *+ + +*++++ * * * * * * * * * * * * * * * * * * * * * +++++++++++++++++ * +++*+++*+++ + * * + +*++++ * * * * * * * * * * * * * * * * * * * * * +++++++++++++++++ * +++*+++*++* + * * + +*++*+ * * * * * * * * * * * * * * * * * * * * * +++++++++++++++++ * *++*+++*++* + * * + +*++*+ * * * * * * * * * * * * * * * * * * * * * +++++++++++++++++ * *++*+++*++* + * * * +*++*+ * * * * * * * * * * * * * * * * * * * * * +++++++++++++++++ * *++*+++*++* + * * * +*++*+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++++* * *++* *++*++* + * * * +*++*+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++++* * *++* *++*++* + * * * +*++*+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++++* * *++* *++*++* + * * * +* *+*+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++++* * *++* *+* *++* + * * * +* *+*+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++++* * *++* *+* *++* + * * * +* * * *+ * * * * * * * * * * * * * * * * * * * * * +++++++++++++++* * * *++* *+* *++* + * * * +* * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * *++* *+* *++* + * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * *++* *+* *++* + * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * *++* *+* * *+* * * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * *+* * *+* * *+* * * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * *+* * *+* * * * * * * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * * * * * *+* * * * * * * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * * * * * * * * * * * * * * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * ++++++++++++++* * * * * * * * * * * * * * * * * * * * * * * *+ * * * * * * * * * * * * * * * * * * * * * Neutral Relaxed Stress GoogleSug11 GoogleSearch11

Yahoo11 _iGoogle11 NR11 NRS11

GoogleSug12

GoogleSearch12

Yahoo12 _iGoogle12 NR12 NRS12

GoogleSug13

GoogleSearch13

Yahoo13 _iGoogle13 NR13 NRS13

GoogleSug14

GoogleSearch14

Yahoo14 _iGoogle14 NR14 NRS14

GoogleSug15

GoogleSearch15

Yahoo15 _iGoogle15 NR15 NRS15

Google1Sug16

GoogleSearch16

Yahoo16 _iGoogle16 NR16 NRS16

GoogleSug17

GoogleSearch17

Yahoo17 _iGoogle17 NR17 NRS17

GoogleSug18

GoogleSearch18

Yahoo18 _iGoogle18 NR18 NRS18

GoogleSug19

GoogleSearch19

Yahoo19 _iGoogle19 NR19 NRS19

GoogleSug20

GoogleSearch20

Yahoo20 _iGoogle20 NR20 NRS20

URL Stress Le vel residuals a a * +

Fig. 7: Detected categorical class for older student data

appear only on all blank area of space of the fit in both models and before starting points of Figure 6. The starting points are noted to be instances 11 and 15 with performance (-0.9 and 1.5). Comparing this with R-square value (r2= 0.83and p= 1.663e−14) it seem to be more feasible for this set of data. For this model, the magnitude of response and intercept seem to be of high significance. The class value for the high-stress level is also dominating, and maximizing contents that appear to contribute to relaxed should be of high priority. Associating this to pilot data, it gives an impression to be a good model proposition for prediction.

5.3. Workers Group

Specifically we considered highly engaged workers, to determine if behaviour, tools and practices that drive their engagement can influence and improve model performance. We can use this to define prediction of users interaction in both areas of interests and expression attributes towards web pages. To determine the level of model performance, based on workplace, we run the algorithm on the data collected for this group. Below is the best fit for the model. Highly stressed users are still the motivating class factor for this set of interaction, which could also relate to the amount of work added to their cognitive process during office hours as compared to pilot data.

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0 10 20 30 40

0 20 40 60

Observations

Number of obser

vations used f

or estimation

residuals

● *

* + +++++++ + + + ++ + +++++++++++++ + ++ + + +++ + ++++ + +++ + ++ ++++++++++* * * + +++++++ + + + ++ + +++++++++++++ + *+ + + +++ + ++++ + +++ + ++ ++++++++++* * * + +++++++ + + + ++ + +++++++++++++ + *+ + + +++ + ++++ + +++ + ++ ++++++++++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + +++ + ++ ++++++++++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + +++ + ++ ++++++++++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + +++ + ++ ++++++++++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + +++ + ++ *+++++*+++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + +++ + ++ *+++++*+++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + ++* + ++ *+++++*+++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + ++* + ++ *+*+++*+++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + *+* + ++ *+*+++*+++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * + ++ *+*+++*+++* * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * + ++ *+*+++*++* * * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * + ++ *+*+++*++* * * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * + * * *+*+++*++* * * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * * * * *+*+++*++* * * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * * * * *+*+++*+* * * * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * * * * * * *+++*+* * * * * + +++++++ + + + ++ + *++++++++++++ + *+ + + +++ + ++++ + * * * * * * * * *+++*+* * * * * + +++++++ + + + ++ + *++++++++++++ * *+ + + +++ + ++++ + * * * * * * * * *+++*+* * * * * + +++++++ + + + ++ + *++++++++++++ * *+ + + +++ + ++++ + * * * * * * * * *+++*+* * * * * + +++++++ + + + ++ + *+++++*+* *+++ * *+ + + +++ + ++++ + * * * * * * * * *+++*+* * * * * + +++++++ + + + ++ + *+++++*+* *+++ * *+ + + +++ + ++++ + * * * * * * * * *+++*+* * * * * + +++++++ + + + ++ + *+++++*+* *+++ * *+ + + +++ + ++++ + * * * * * * * * *+++*+* * * * * + +++++++ + + + ++ + *+++++* * * *++* * *+ + + +++ + ++++ + * * * * * * * * * * *+*+* * * * * + +++++++ + + + ++ + *+++++* * * *+* * * *+ + + +++ + ++++ + * * * * * * * * * * *+*+* * * * * + +++++++ + + + ++ + *+++++* * * * * * * * *+ + + +++ + ++++ + * * * * * * * * * * *+*+* * * * * + +++++++ + + + ++ + *+*+++* * * * * * * * *+ + + +++ + ++++ + * * * * * * * * * * *+*+* * * * * + +++++++ + + + ++ + *+*+++* * * * * * * * *+ + + +++ + ++++ + * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * *+* * * * * * * * *+ + + +++ + ++++ + * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * *+* * * * * * * * *+ + + +++ + ++++ + * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * *+* * * * * * * * * * + + +++ * ++++ + * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * *+* * * * * * * * * * + * +++ * ++++ + * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * *+* * * * * * * * * * * * +++ * ++++ + * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * *+* * * * * * * * * * * * +++ * ++++ * * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * * * * * * * * * * * * * * * +++ * ++++ * * * * * * * * * * * *+*+* * * * + + +++++++ + + + ++ + *+* * * * * * * * * * * * * * * * +++ * ++++ * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ + *+* * * * * * * * * * * * * * * * ++* * ++++ * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * *+* * * * * * * * * * * * * * * * ++* * ++++ * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * *+* * * * * * * * * * * * * * * * ++* * +++* * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * *+* * * * * * * * * * * * * * * * *+* * +++* * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * *+* * * * * * * * * * * * * * * * *+* * +*+* * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * *+* * * * * * * * * * * * * * * * * * * * +*+* * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * *+* * * * * * * * * * * * * * * * * * * * +* * * * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * *+* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *+* * * * + + +++++++ + + + ++ * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * Neutral Relaxed Stress GoogleSug21 GoogleSearch21

Yahoo21 _iGoogle21 NR21 NRS21

GoogleSug22

GoogleSearch22

Yahoo22 _iGoogle22 NR22 NRS22

GoogleSug23

GoogleSearch23

Yahoo23 _iGoogle23 NR23 NRS23

GoogleSug24

GoogleSearch24

Yahoo24 _iGoogle24 NR24 NRS24

GoogleSug25

GoogleSearch25

Yahoo25 _iGoogle25 NR25 NRS25

GoogleSug26

GoogleSearch26

Yahoo26 _iGoogle26 NR26 NRS26

GoogleSug27

GoogleSearch27

Yahoo27 _iGoogle27 NR27 NRS27

GoogleSug28

GoogleSearch28

Yahoo28 _iGoogle28 NRS28 NR28

GoogleSug29

GoogleSearch29

Yahoo29 _iGoogle29 NR29 NRS29

GoogleSug30

GoogleSearch30

Yahoo30 _iGoogle30 NR30 NRS30

URL Stress Le vel residuals a a * +

Fig. 9: Detected categorical class for Workers group data

The starting point for this is the first instance for this group and observation 15 on the iGoogle page. A residual value and performance of 0.06 and 0.10 compared to R-squared of (r2= 0.9826and p= 2.2e−16), is enhanced from previous datasets and also for advancing subsequent models. The model is practicable in terms of prediction of future occurrences such as events and behaviour relating to interactions with the web contents. Mostly where users are likely to look at and what will optimize marking strategy and boost sales based on the heuristic analysis.

5.4. Regular Users Group