1) The document describes a study that uses a cellular automaton model to simulate interactions between customers and service providers over time based on survey data.
2) The model is able to predict the evolution of customer-provider interactions with 73.8% accuracy, offering additional explanatory power beyond linear regression models.
3) Analysis of the model found it was sensitive to initial conditions and groups of individuals with shared opinions, and could help identify problematic customers spreading dissatisfaction.
In context-aware trust evaluation, using ontology tree is a popular approach to represent the relation
between contexts. Usually, similarity between two contexts is computed using these trees. Therefore, the
performance of trust evaluation highly depends on the quality of ontology trees. Fairness or granularity
consistency is one of the major limitations affecting the quality of ontology tree. This limitation refers to
inequality of semantic similarity in the most ontology trees. In other words, semantic similarity of every two
adjacent nodes is unequal in these trees. It deteriorates the performance of contexts similarity computation.
We overcome this limitation by weighting tree edges based on their semantic similarity. Weight of each
edge is computed using Normalized Similarity Score (NSS) method. This method is based on frequencies of
concepts (words) co-occurrences in the pages indexed by search engines. Our experiments represent the
better performance of the proposed approach in comparison with established trust evaluation approaches.
The suggested approach can enhance efficiency of any solution which models semantic relations by
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Enhancing Multi-Aspect Collaborative Filtering for Personalized RecommendationNurfadhlina Mohd Sharef
Khairudin, N., Sharef, N. M., Mustapha, N., Noah, S A. M., (2018), "Enhancing Multi-Aspect Collaborative Filtering for Personalized Recommendation", 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP18), Kota Kinabalu
Detecting Multicollinearity in Regression Analysissajjalp
Multicollinearity occurs when the multiple linear regression analysis includes several variables that are
significantly correlated not only with the dependent variable but also to each other. Multicollinearity makes some of
the significant variables under study to be statistically insignificant. This paper discusses on the three primary
techniques for detecting the multicollinearity using the questionnaire survey data on customer satisfaction. The first
two techniques are the correlation coefficients and the variance inflation factor, while the third method is eigenvalue
method. It is observed that the product attractiveness is more rational cause for the customer satisfaction than other
predictors. Furthermore, advanced regression procedures such as principal components regression, weighted
regression, and ridge regression method can be used to determine the presence of multicollinearity.
In context-aware trust evaluation, using ontology tree is a popular approach to represent the relation
between contexts. Usually, similarity between two contexts is computed using these trees. Therefore, the
performance of trust evaluation highly depends on the quality of ontology trees. Fairness or granularity
consistency is one of the major limitations affecting the quality of ontology tree. This limitation refers to
inequality of semantic similarity in the most ontology trees. In other words, semantic similarity of every two
adjacent nodes is unequal in these trees. It deteriorates the performance of contexts similarity computation.
We overcome this limitation by weighting tree edges based on their semantic similarity. Weight of each
edge is computed using Normalized Similarity Score (NSS) method. This method is based on frequencies of
concepts (words) co-occurrences in the pages indexed by search engines. Our experiments represent the
better performance of the proposed approach in comparison with established trust evaluation approaches.
The suggested approach can enhance efficiency of any solution which models semantic relations by
ontology tree.
Enhancing Multi-Aspect Collaborative Filtering for Personalized RecommendationNurfadhlina Mohd Sharef
Khairudin, N., Sharef, N. M., Mustapha, N., Noah, S A. M., (2018), "Enhancing Multi-Aspect Collaborative Filtering for Personalized Recommendation", 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP18), Kota Kinabalu
Detecting Multicollinearity in Regression Analysissajjalp
Multicollinearity occurs when the multiple linear regression analysis includes several variables that are
significantly correlated not only with the dependent variable but also to each other. Multicollinearity makes some of
the significant variables under study to be statistically insignificant. This paper discusses on the three primary
techniques for detecting the multicollinearity using the questionnaire survey data on customer satisfaction. The first
two techniques are the correlation coefficients and the variance inflation factor, while the third method is eigenvalue
method. It is observed that the product attractiveness is more rational cause for the customer satisfaction than other
predictors. Furthermore, advanced regression procedures such as principal components regression, weighted
regression, and ridge regression method can be used to determine the presence of multicollinearity.
HOW TO GET 41% MORE DIRECT BOOKINGS ON YOUR HOTEL WEBSITEDominik Suter
HOTELIERS,
WHY PAY HIGH COMMISSION FEES TO OTAS WHEN YOU CAN GET MORE DIRECT BOOKINGS?
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No setup fee, No monthly fee, No term commitment, Pay for results only!
SalesMasters Meetup presentation deck (as presented on Oct 28th, 2015 at the CampusTLV): Startup Sales 101.
The presentation is about the best practices for Technology Startups looking to begin selling their innovative technology overseas
JOIN our LinkedIn group: http://www.linkedin.com/grp/home?gid=8404006
JOIN our Meetup group: http://www.meetup.com/Sales-Masters/
JOIN our Facebook group: https://www.facebook.com/groups/483102775201392/
Una breve presentación del desarrollo histórico del concepto de la luz, desde los griegos que se interesaron por el proceso de la visión de donde se da un inicio a un par de teorías ondulatorias y corpuscular de luz, en las referencias que están al final se deja un link que explica la concepción dual de la luz.
HOW TO GET 41% MORE DIRECT BOOKINGS ON YOUR HOTEL WEBSITEDominik Suter
HOTELIERS,
WHY PAY HIGH COMMISSION FEES TO OTAS WHEN YOU CAN GET MORE DIRECT BOOKINGS?
GET 41% MORE DIRECT BOOKINGS
No setup fee, No monthly fee, No term commitment, Pay for results only!
SalesMasters Meetup presentation deck (as presented on Oct 28th, 2015 at the CampusTLV): Startup Sales 101.
The presentation is about the best practices for Technology Startups looking to begin selling their innovative technology overseas
JOIN our LinkedIn group: http://www.linkedin.com/grp/home?gid=8404006
JOIN our Meetup group: http://www.meetup.com/Sales-Masters/
JOIN our Facebook group: https://www.facebook.com/groups/483102775201392/
Una breve presentación del desarrollo histórico del concepto de la luz, desde los griegos que se interesaron por el proceso de la visión de donde se da un inicio a un par de teorías ondulatorias y corpuscular de luz, en las referencias que están al final se deja un link que explica la concepción dual de la luz.
LH Ismail (2007). An evaluation of bioclimatic high rise office buildings in a tropical climate: energy consumption and users' satisfaction in selected office buildings in Malaysia. PhD Thesis, University of Liverpool, United Kingdom.
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Not Good Enough but Try Again! Mitigating the Impact of Rejections on New Con...Aleksi Aaltonen
Presentation at the University of Miami on 3 December 2021 on how Stack Overflow improved the retention of new contributors whose initial question is rejected (closed) as substandard. The presentation is based on a paper coauthored with Sunil Wattal.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
SYSTEM IDENTIFICATION AND MODELING FOR INTERACTING AND NON-INTERACTING TANK S...ijistjournal
System identification from the experimental data plays a vital role for model based controller design. Derivation of process model from first principles is often difficult due to its complexity. The first stage in the development of any control and monitoring system is the identification and modeling of the system. Each model is developed within the context of a specific control problem. Thus, the need for a general system identification framework is warranted. The proposed framework should be able to adapt and emphasize different properties based on the control objective and the nature of the behavior of the system. Therefore, system identification has been a valuable tool in identifying the model of the system based on the input and output data for the design of the controller. The present work is concerned with the identification of transfer function models using statistical model identification, process reaction curve method, ARX model, genetic algorithm and modeling using neural network and fuzzy logic for interacting and non interacting tank process. The identification technique and modeling used is prone to parameter change & disturbance. The proposed methods are used for identifying the mathematical model and intelligent model of interacting and non interacting process from the real time experimental data.
Examples of my work in Machine Learning, Deep Learning, Artificial Intelligence and Natural Language Processing using R, Python and Wolfram Mathematica. Also includes Reinforcement Learning, GeoLocation, Face Recognition and Social Networks.
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Examples of my work in Machine Learning, Deep Learning, Artificial Intelligence and Natural Language Processing using R, Python and Wolfram Mathematica. Also includes Reinforcement Learning, GeoLocation, Face Recognition and Social Networks.
Examples of my work in Machine Learning, Deep Learning, Artificial Intelligence and Natural Language Processing using R, Python and Wolfram Mathematica. Also includes Reinforcement Learning, GeoLocation, Face Recognition and Social Networks.
Examples of my work in Machine Learning, Deep Learning, Artificial Intelligence and Natural Language Processing using R, Python and Wolfram Mathematica. Also includes GeoLocation, Face Recognition and Social Networks.
Examples of my work in Machine Learning, Deep Learning, Artificial Intelligence and Natural Language Processing using R, Python and Wolfram Mathematica. Also includes GeoLocation, Face Recognition and Social Networks.
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Scientific Market Research with cellular automata model. It's a decision tree + statistical analysis + machine learning + artificial intelligence approach based on my doctorate thesis. Rubens Zimbres
1. RUBENS A. ZIMBRES
PEDRO P.B. DE OLIVEIRA
Dynamics of quality perception in a social
network: A cellular automaton based
model in aesthetics services
Universidade Presbiteriana Mackenzie
São Paulo, SP
Brazil
2. I. INTRODUCTION
Main goal: simulate interactions between
customers and providers and understand their
rationality using cellular automata.
First survey Second survey
Motivation: linear regression models in service
quality literature do not offer a good explanation on
the phenomenom.
Overall perspective: the process in the service
encounter, regarded as a complex system. 2
rule
3. II. COMPLEX SYSTEMS AND ROLE THEORY
Commercial system as a complex system
Clients co-participate in service delivery, influencing
service quality (e.g., self-service restaurant)
Role theory sees service delivery as a theatrical
metaphor, with actors that act following a script (set
of rules according to the situation)
Common behaviours = uniform cellular automata
3
4. III. METHODOLOGICAL PROCEDURES:
QUESTIONAIRE DEVELOPMENT
Qualitative: 6 interviews (3 providers and 3 clients,
in aesthetic services)
Quantitative:
Evaluation of each statement: Likert scale with 5 points:
totally disagree, disagree, neither agree nor disagree,
agree, totally agree
Development of a measurement scale with 74
questions/statements, evaluated by marketing
specialists → 54 were taken
Quantitative pre-test (variance, correlations, factor
analysis and reliability) → 45 final statements
4
5. IV. QUANTITATIVE RESEARCH
First survey: 115 clients and 96 providers
Internal consistency of the questionaire (via
Cronbach's Alpha): 0.937 (clients) and 0.772
(providers) (range from 0 to 1)
Statistical overall quality estimate: given by linear
regression (R2): 0.760 (clients) and 0.873
(providers), significant at 0.000, with Variance
Inflaction Factor < 10, and normal residues
Second survey: 4 months later
36 responses
Cronbach's Alpha: 0.892 (clients) and 0.631
(providers)
5
6. V. PROPOSED MODEL
o Idea: to predict the evolution of clients-providers
interaction between the two subsequent surveys
o Data of the 1st survey = initial parameters
o Interactions between clients and providers
o Data of the 2nd survey = final condition, for
comparison
o Focus of the analysis between the two surveys:
7 (out of the 45), all related to intangible aspects of
service quality, because they are more relevant to
obtain sustainable competitive advantage 6
7. DETAILS OF THE MODEL
o Lattice = 36 subjects and 7 indicators of service
quality (attention, trust, willingness to help, honesty,
concern, responsibility, and adaptation to client’s
needs)
o Row = indicator, column individual
o No multicollinearity = one-dimensional CA
o 7 CAs, same rule = uniform CA
o Facilitates to unveil the rationale embedded in the
transition table.
7
8. QUESTIONAIRE AND CELULAR AUTOMATA
Same respondents
Likert 5 points = CA with 5 states
CA radius = 1 (nearest left- and right-hand
neighbours)
Hence:
→ 125 possible rationales (neighbourhoods)
→ 2.35 1087 possible rules
Search started from the majority rule (herd
behaviour)
8
9. RULE SEARCH
o Initial condition: lattice was ordered according to
the 7th indicator (adaptation to customer needs),
because of its highest variance (higher diversity of
opinions)
Sampling of the space in blocks of 1500 rules,
picking each one of them with uniformly distributed
random steps in the range from 110 to 1084 (with
occasional smaller ranges for closer inspection):
total of 1.8106 rules were evaluated (200hs of
computer time) 9
10. RULE SEARCH
o Direction of the search:
• Target success rate, measured in terms of the best
possible match between the state values of
corresponding cells in the second survey and the
rule outcome (within 20 iterations).
• Trend to best results with 16 timesteps (4 months
between surveys); thus, 20 iterations
• Success rate at least 70%
• Non-cyclic behaviour
• Variance of the lattice ±0.5 when compared with the
second survey 10
11. LIKERT SCALE AND THE CA STATES
5 = Totally agree that attention is very important in service delivery
4 = Agree
3 = Neither agree nor disagree
2 = Disagree
1 = Totally disagree
PPCPPPCCCCCPPPPPPPPPPPPPPPPPCCCCCCCC
Example: Attention
11
Time t=0
Time t=n
12. RULE RATIONALE
If a given customer (C) that perceives actual quality
as good (Likert scale value equal to 5 and cell state
equal to 5) evaluates the service quality offered by
one provider (P) as bad (cell state equal to 1) and
the service quality offered by another provider as
good (cell state equal to 5) this could generate a
regular assessment of quality (cell state equal to 2),
which could interfere with future intentions of the
client to remain with the provider, given the
inconsistency of the attitudes.
12
1 5 5
2
P C P
13. RATE OF SUCCESS
Rule number
2159062512564987644819455219116893945895
9585281520212287057525638079592376559119
50549124 with 73.80 % of success in predicting
the system evolution
0 5 10 15 20
55
60
65
70
75
Cycles
Accuracy
13
15. EXPLANATORY POWER
CA model offers an additional explanatory power of 8.80
percentage points (73.80%−65.00%). Thus, nonlinear effects
appear to contribute with the corresponding gain.
Sharing only 38.80% in common (intersection) explains
nonlinearity of phenomena, a new perspective of studying
causal relations in management research.
65%
73.80%
73.80%
65%
Linear regression model
Cellular automata model
Linear regression model
Cellular automata model
0% 100%
15
16. EUCLIDEAN DISTANCE
1st survey 2nd survey model outcome
Consensus (think similarly, intimacy, mutual understanding ): role theory
16
20. DISSATISFACTION IN THE NETWORK
Extremely problematic customers should not be in
contact with each other, otherwise they would
spread the dissatisfaction to everyone they have
contact with, customers or providers.
Dynamics of opinions, but also and more
importantly, which individuals create dissatisfaction
in a social network. This can enable managers to
identify problematic customers.
Outbreak of the individuals’ dissatisfaction happens
always in the customer-provider interface.
20
21. TOTAL AVERAGE PER INDICATOR
If based on averages (as usual in the traditional
literature), and not on a cell-by-cell comparison, the
outcome becomes much higher than the model´s
success rate!!
Indicator 1 2 3 4 5 6 7
Phase 2 - Phase 1 (c) 137 136 140 135 132 138 129
Simulation - Phase 1 (d) 130 135 136 134 127 133 126
Difference c-d 7 1 4 1 5 5 3
Accuracy (%) 94,89% 99,26% 97,14% 99,26% 96,21% 96,38% 97,67%
Total per Indicator
21
)
VARIANCE explanation in literature (average)
Cronin e Taylor (1992) R2 max .47 for SERVPERF and .46 for SERVQUAL.
Elliott (1994) R2 max .65 for SERVPERF and .42 for SERVQUAL.
Lee, Lee, Yoo (2000) R2 max .53 for SERVPERF and .35 for SERVQUAL.
26. DESCRIPTIVE STATISTICS OF THE DATA: THE
SURVEYS AND THE MODEL
26
Initial Final Simulation
survey survey result
Mean 3.75 3.65 3.67
Variance 0.16 0.25 0.97
Std deviation 0.25 0.37 0.98
Median 4 4 4
Kurtosis 8 11.82 10.84
Skewness -2.21 -2.72 -3.03
Increase in
perception
-0.10 -0.08
27. LATTICE MEANS (SUM OF INDICATORS) OVER
THE 20 TIME STEPS OF THE CA
Cycles
Construct total
27
cause effect
process
28. OSCILLATORY BEHAVIOUR
First, inconsistencies in mood of customers and
providers, who may perceive the quality of the
relationship differently, according to their emotional
state.
Second, inconsistencies in the delivery of service
by the provider, who may not always offer the same
service quality.
Third, there may be a process of mutual adaptation
over the interactions, and the provider cannot
always meet the expectations of customers and
vice versa.
Intangible aspects of quality and this is probably
due to an affective component, involvement. 28
29. STABILITY
It might be interesting to increase the interval
between longitudinal surveys to 30 time steps, that
is, 30 weeks or 8 months, so as to verify the
emergence of consensus and stability.
0 10 20 30 40
4.0
4.2
4.4
4.6
4.8
5.0
Cycles
Construtmean
29
30. VARIANCES FOUND IN THE FIRST AND THE
SECOND QUANTITATIVE SURVEYS
30
Indicator
Variance
33. CONCLUDING REMARKS
A B B C
Validation:
Role theory metaphor, Statistical analysis, Careful
implementation, and Reliance on real data.
Rationality and behaviours to increase satisfaction.
Management of dissatisfied individuals.
Linear regression: which indicators and their
magnitudes (cause and effect). CA study nonlinearities
in the process.
Cellular automata as a complement to helping
management of human behaviour
Limitation: rule-sample, interpretability of observed
behaviours (cyclic in honesty), initial condition 33
rule rule
34. THANKS!
São Paulo State Foundation for Research
Support: Research grant 2005/04696-3
Mackenzie Research Fund:
Research grant from Edital 2007
Wolfram Research:
Mathematica Academic Grant No. 1149
34National Coordination for the Improvement of
University Level Personel