Biometric similarity Test of The Population of T. (Zaria) bantamensis tjicumpaensis with T. (Zaria) javana as a Form of Phylogeny And Evolutionary Proximity

Biometric aspects in the Turritellidae family need to be studied as an important identification parameter. Zaria, which is one of the sub genera in the Turritellidae family, deserves to be tested in order to determine the feasibility of the biometric aspect as an identification parameter. This paper aims to provide an overview of the benefits of the biometric aspect as an identification parameter. The method used to re-identify the biometric and morphological aspects of the T. (Zaria) bantamensis tjicumpaensis population with T. (Zaria) javana. The results of the identification were carried out by a T-Test on the biometric aspect to see the similarity of the biometric aspects of the two populations. Based on the results of the TTest on the parameters of the Wsut: Wang and Wsut: L ratio, it shows that the two Zaria species are the same. Meanwhile, from the morphological aspect, there is a slight difference in the early growth peripherals.

Introduction
Biometry is one method of identification of fossils, in the form of measurements of certain parts of the body. The biometric aspect as an identification parameter in fossils has not been widely used as a key parameter in determining species (Imbrie, 1956;Pandita et al., 2013). This is because a sufficient amount of sample data is needed to be able to see the pattern of the biometrics. Therefore, research on the role of biometry as an important parameter in fossil identification needs to be carried out continuously.
Zaria, which is a group of fossils from the Turritellidae family, is a sub-genera of Turritella which is interesting to study from its biometric aspect. This is due to the fact that numbers of species of Zaria found on Java Island are morphologically similar, but are separated into different species (Pandita, 2014).
This condition is also shown between T. (Zaria) javana from the location of the Cijarian-Sukabumi river and T. (Zaria) bantamensis from the Cimadur-Bayah area. The two populations are almost similar morphologically. Seeing this problem, it is necessary to study the biometric aspects of the two populations between T. (Zaria) javana and T. (Zaria) bantamensis tjikumpaensis.

Objectives
The purpose of this paper is to provide a morphological and biometric description of the population of Zaria bantamensis tjikumapensis and Zaria javana. The aim is to find out how the biometric aspects provide clarity of the speciiation of the two populations and the opportunities for their phylogeny of closeness. This study is also aimed at reintroducing the biometric parameters used by Pandita et al., (2013).

Materials
The research sample is a collection stored in the Bandung Geology Museum and the ITNY Paleontology Laboratory with a total of 52 samples. The samples came from two locations in West Java Province and Banten Province. The sample T. (Zaria) bantamensis tjicumpaiensis came from the Bayah (BYH) location with coordinates 06 O 54' 09,2"S and 106 O 14' 48,5"E. While the T. (Zaria) javana sample came from the Cijarian River with location coordinates 06 O 59' 31.3 "S and 106 O 38' 01.6" E ( Figure 1).

Methods
The method in this research is to re-identify the morphological and biometric aspects of the two sample populations stored in the ITNY Paleontology Laboratory. The sample coded CJR03 is a population of Zaria javana, and BYH01 is a population of Zaria bantamensis tjikumpaensis. After the identification was carried out, statistical analysis was carried out using linear regression and T-Test on biometric parameters.

Fig 2. Identification Parameters of Zaria
The statistical test conducted is the similarity test using two methods. The first method uses linear regression. The second method uses the Independent T-Test, while the formula used (Hasan, 2004) is as follows: Where: t = value of acceptance if -t(1-1/2)< t < t(1-1/2) x1 & x2 = mean values n1 & n2 = number of population S = is the average standard deviation The formula for the average standard deviation of the two populations: Where: S1 & S2 = standard deviation value of each tested population.

Morphologic Descriptive
The shell that appears is predominantly small, although some are larger than 60 mm. At the beginning of its growth, the main spiral ribs that appear are 4, with a multicostate structure. The shape of the suture is initially channelized and then changes to subcarinate at the 8th or 9th whorl.The three main rib spiral numbers 1, 2 and 3 are counted from the anterior with strong expression starting from the 8th or 9th whorl at the bottom of the room. The shape of the aperture is rounded (Figure 3). Based on the character of the growth line, this species is closer to the Zaria sub-genera.

Morphologic Descriptive
Shell including small to medium sized. In the early rounds made up four primary spirals appears at the beginning teloconch. Whorl shape tends tricostate formed by spiral into 1, 2 and 3 are sorted from the anterior, with a spiral rib to 2 expressed slightly stronger than the other two. A fine spiral located under the posterior sutures as a spiral 4. Sutures at the beginning teleconch shaped channelled.Start whorl to 7 there is a change of sutures and pheriperal form. Forms bicarinate appear in the center of the whorl that is formed by a spiral rib of the anterior second and third. Relative changes to subcarinate sutures. Lateral growth line strongly curved with a single sinus, and basal sinuses also appeared at the bottom. Aperture somewhat elliptical until rounded.

Result
Phenotypically, the two populations have almost the same morphological physical characteristics, there are only differences in the peripheral pattern in early teleconch, where T. (Zaria). javana has a tricostate pattern ( Figure 4B), while T. (Zaria). bantamensis has multicostate ( Figure 3B). Seeing this, it is necessary to test the similarity on biometry whether the two populations are indeed different or the same. Biometric data has been presented in tables 1 and 2. The first study conducted a linear regression pattern analysis to see if there was no relationship between the two species. The Wsut: L and Wsut: Wang ratios show that the two species have parallel and coincided linear lines (Figures 5 and   6). Based on this, the two populations have the same growth pattern.
The second study was analyzed using a two-way difference test known as the T-Test. Data processing using excel program. The acceptance level used ( = 0.05) is a number commonly used in the T-Test. So that the acceptance limit for the total number of the two populations of 52 samples is -2.008 <t0 <2.008 is obtained from the t distribution table (Hasan, 2004). Based on the results of the T-Test test, it shows that the two parameters produce a value of t0 = -0.27736 for the Wsut: L ratio and t0 = 1.406124 for the Wsut:Wang ratio (Table 3). The two values fall into the acceptance range, so that the two populations can be considered to have the same pattern.

Discusion
Seeing the test results of both the linear regression pattern and the T-Test test for both populations can be said to have the same character. The separation of the two as different species needs to be reviewed, considering that from a biometric point of view they both have similarities.
Almost all morphological parameters of the two populations are also the same, with a slight difference in the peripheral pattern at the early teleconch. However, the initial growth pattern by Kotaka, (1959), Marwick, (1957 is one of the factors that differentiate species, so it is difficult to propose that both species are the same. However, by looking at the proximity of the biometric aspects and spiral rib patterns in adult teleconch, the two have a close phylogeny relationship. In terms of geology, these two populations or species come from two different locations. T. (Zaria) javana comes from the Nyalindung Formation which is thought to have formed in the Upper Miocene (Pandita et al., 2013). Meanwhile, Z. bantamensis comes from the Cimanceuri Formation (BYH01) where the formation is estimated to be in the Early Pliocene age (Pandita, 2014).
Combining with the age of appearance of the two species, of which T. (Zaria) javana is older, namely the Upper Miocene, it is certain that T. (Zaria) bantamensis tjicumpaiensis evolved from T. (Z) javana.

Conclusion
Both populations of the sub-genus Zaria show the same biometric characteristics. Meanwhile, in the morphological aspect, there is a small difference in early teleconch. However, because the early teleconch by Kotaka (1959) was the determining parameter of the species, the two populations could not be categorized as the same species. This condition implies the need for a ranking test on the identification parameters of Turritella (Zaria) as well.


Individual risk profiling for portable devices using a neural network to process the cognitive reactions and the emotional responses to a multivariate situational risk assessment

In this paper, we are presenting a novel method and system for neuropsychological performance testing that can establish a link between cognition and emotion. It comprises a portable device used to interact with a cloud service which stores user information under username and is logged into by the user through the portable device; the user information is directly captured through the device and is processed by artificial neural network; and this tridimensional information comprises user cognitive reactions, user emotional responses and user chronometrics. The multivariate situational risk assessment is used to evaluate the performance of the subject by capturing the 3 dimensions of each reaction to a series of 30 dichotomous questions describing various situations of daily life and challenging the user's knowledge, values, ethics, and principles. In industrial application, the timing of this assessment will depend on the user's need to obtain a service from a provider such as opening a bank account, getting a mortgage or an insurance policy, authenticating clearance at work or securing online payments.

I. INTRODUCTION
Based on the theory of cognitive neuroscience and experimental neuropsychology, neurolinguistics and computer models, the relationship between the psychological phenomenon of the subject and the brain structure has been established. The investigation techniques based on experimental neuroscience include functional magnetic resonance imaging, positron emission tomography scanner, electroencephalography, and magnetoencephalography. Applied neuroscience has been integrating research results from different fields and on different scales enough to reach a unified descriptive model of the brain functioning and biosocial expression of the personality.
With the development of cognitive neuroscience, Professor Robert Cloninger proposed a unified biosocial theory of personality [1] and a systematic method [2] based on a multidimensional personality questionnaire [3] to assess different facets of the personality: the Temperament and Character Inventory (TCI) [4]. He believes that the method of obtaining accurate neuropsychological performance [5] not only needs to consider behavioral factors, but also needs to consider potential biological and social determinants -and distinguish between the perceptual and the conceptual factors. TCI aims to distinguish the hereditary nature (Temperament) from the acquired nature (Character) of the personality development [6] through an experimental method (Inventory) to obtain the subject's neuropsychological performance. TCI can also be used to identify various personality disorders [7] to examine the extent of personality disorder development and the relationships between personality and depression [8], [9] or between personality and alcohol dependence [10] and between personality and crime [11] and serve as a model using AI to predict patient's relapse [12]. It was also used to approach non-clinical population [13], [14] consumer habits [15] and the mechanism behind choices [16] especially the role of the executive functions.
Traditional TCI-R approach is more about auto-assessment rather than investigation which makes it a relatively biased intervention that obviously misses the emotional assessment and the relationship between cognitive stimulation and emotional response.
In this paper, we are proposing a neuropsychological performance test for the subject that is not only assessing the temperament of the subject but also the correlation between the subject's emotional response to a series of cognitive questions regarding situational risk. The answers to the questions constitute the risk tolerance of the subject while the degree of correlation between the emotional response and the cognitive questions is an indicator of the worthiness of the subject. This motivates us to develop an accurate and efficient machine learning framework for risk profiling and creditworthiness indicator which takes both edges from traditional neuroscience techniques, for example, TCI, and the recent advancement of neuromorphic intelligence design [17] and neural network models especially those dedicated to facial emotion recognition by facial muscles movement analysis [18].
In our artificial neural network model for risk profiling, we are processing simultaneously the emotion response captured by a prominent emotion recognition model, MIMAMO-Net [18], with the cognitive response to a question describing a situational risk also described as a choice between 2 consequential often opposite behaviors when submitted to a situation involving a prescribed level of risk. The subject has to make a Yes/No choice (answer) to a question describing a risky situation and this Q&A is repeated 30 times. It involves 30 situations of various risky nature to cover the daily life topics and interactions of an average adult individual. Each user receives a unique selection of 30 questions from the local database of 1,200 questions. The 3 main dimensions of temperament HA, RD and NS are equally represented and assembled in pairs of binomial nature such as HA/RD, HA/NS, RD/HA, RD/NS, NS/HA and NS/RD and repeatedly proposed during the 30 items long questionnaire. During the questionnaire, the subject facial reaction to the questions or emotional response will be captured continuously and the reaction time between questions and answers will be recorded.
The video capture of the facial gesture of the subject when answering each question is recorded and then fed-in to MIMAMO-Net to generate a arousal-valence score for each question answered.
Our neural network can open a new perspective to the non-invasive field of investigation of the hidden processes of the human brain. Using AI to process simultaneously a double stream of recorded data under time pressure, emotional responses and cognitive reactions, and compare them in real-time has the interest to remove the many biases of current techniques. Because the questionnaire is operated by a mobile application that allows flexibility on the time of the first investigation and enables further use of the results especially in the form of an encrypted QR Code, it may represent a supplementary form of personal identity based on personal attributes to be vectorized by the user for policy-based authentication, login and payment.

A. Situational Risk Tolerance Assessment (SRTA)
Our SRTA is a method and system for neuropsychological performance test inspired by the TCI-R of Cloninger. The SRTA is used to evaluate the reactional performance of the subject to a series of dichotomous questions describing various situations of daily life and challenging the subject values, ethics, principles or virtues. The performance depends on a combination of personal characteristics: the biological characteristics of the subject such as physical health factors, genetic vulnerability, addictive behaviors; the social characteristics such as family environment, close relationships, marital status; and the psychological characteristics such as cooperative skills, social skills, relational skills, self-esteem and mental health. During the industrial application of the SRTA, the time of assessment also depends on the subject's need to obtain a service from a provider such as opening an account in a bank, getting a quote of an insurance premium or an authentication key for a job with clearance or securing online payments.

B. Neural network model for risk profiling (ANN)
We build our neural network from the standard building blocks of fully connected layers [19], [20], [21], [22]. To put it in a mathematical manner, our data can be expressed as where x 1 is the question type (HA/NS, RD/HA, NS/RD, NS/HA, HA/RD, RD/NS), x 2 is the answer to question (Yes/No), x 3 is the emotional states (valence and arousal) of the subject and N is the number of questions answered (and thus the number of emotional states recorded). The output is a risk profile and take value either HA, RD or NS. Figure 1 is a schematic diagram of the feed forward artificial neural network, also called a multilayer perceptron, of the neuropsychological performance testing system proposed in this paper. It is a supervised, fully connected, feed-forward artificial neural network and uses back-propagation training and generalized delta rule learning [23], [24]. a) Major question types -labeling with cognitive tags for machine learning: The questions are separated in two groups, namely, the major types and the minor types. The major types are related to straightforward and obvious situations or issues in life while the minor types are related to more complex ideas and subjective problems. In our questionnaire, the major type questions appear few times more frequent than the minor types. For example, the following questions are major types: • Do you like gardening? HA/NS • Do you like dancing? RD/HA • Do you seek revenge after being hurt? NS/RD Each question has its specific bi-dimensional properties. The first question: "Do you like gardening?" is a major type of question related to Harm Avoidance (HA) versus Novelty Seeking (NS). When the answer to this question is Yes, it means that the temperament of the test subject or user is in favor of HA. HA refers to harm avoidance also called risk aversion (Averse). To the contrary if the answer is No, the temperament would be in favor of NS. NS refers to novelty seeking also called risk seeking (Taker). A similar approach is applied to a RD/HA question type such as "Do you like dancing?". If the user chooses to answer No, it would in favor of HA which is for risk aversion (Averse); if the user's response is Yes, it would be in favor of RD which refers to Reward Dependence (RD) also called risk dependent (Dependent).
b) Minor question types -labeling with cognitive tags for machine learning: The present invention questionnaire is also using a set of minor type questions in lesser number of occurrences, such as: • Do you feel happy while spending money? NS/HA • Do you keep your problems to yourself? HA/RD • Do you consider right from wrong before taking decisions? RD/NS Alike the major types of question, each minor question has its own bi-dimensional properties referring to temperament characteristics. The exact same processing of information of the major types applies to the minor types.
Each answer to a question is granting one data-point in one of the 3 dimensions/bins and the highest score of the 3 bins where the most data-points are accumulated will determine the first trait of temperament of the user also called primary score of the risk profile. The second bin with the second highest number of data-points will determine the secondary score of the risk profile and the combination of primary and secondary scores will become the user risk profile score.
The user brain processing time in reaction to the situational risk questions of the questionnaire is called latency and the performance test average latency information is the average time of the questionnaire test divided by the total number of questions asked. The range of the test average latency is theoretically between 0.2 and 10 seconds. A reaction time of 0.2 second is considered to be the minimum of reactional time in the human benchmark that doesn't include any reflection or processing time of the question. A maximum processing time of 10 seconds is preset to avoid stalling. The pilot population has showed an average latency between 2 and 7 seconds per question. The result obtained by a test user is sorted and put into a five categories model.
In the five categories model, the test population is divided into extra short (XS), short, median, long, extra long (XL) corresponding to 3 standard deviations. A brain processing time or latency of 0.5 to 1.5 seconds is regarded as extra short; 1.5 to 2.5 seconds is short; 2.5 to 3.5 seconds is median; 3.5 to 4.5 seconds is long; 4.5 to 7 seconds is extra long. The median of population is around 3 seconds. Any result below 2 or above 7 seconds is considered unusual. More details on the mathematical definition of the five categories can be found in section II-C and figure 4. The user can skip a question if he doesn't understand or like it and a new question of similar type is called, this event is called a revalidation. Only 6 revalidations are allowed.
If there are more than 6 revalidations, the whole questionnaire result is deemed untruthful therefore invalid. While the questionnaire is being activated, the camera is starting to capture the video of the survey which is divided in 2 parts: the capture of the facial emotion information immediately after the reception of the question on the screen also called 'reaction time' for which the minimum is approximately 0.2 secondand processed by the mobile AI for facial recognition; then the video capture continues during the reflection time until the response is decided by the test user whether by pushing the Yes or No button on the screen or by recording a vocal answer or both text and voice if voice-movement coordination patterns recognition is activated. These video captures are fed into MIMAMO-Net for emotion recognition, with an output of valence-arousal pair.
The facial expression information or emotional expressions of the test subject are compared with pre-stored biometric information, also called training data. For the matched information, it will determine whether the input is normal and the biometric type is corresponding to the expected emotion of the test subject; or if it deviates, how much is the deviation, and what is the deviation that would be sufficient to disqualify the record. If the mobile AI of the invention disqualifies an emotion to a question, a new question of the similar type will be asked at the end of the 30 questions standard model and will make the questionnaire of 31 questions, 30 standard plus 1 revalidation that will contribute to assess the truthfulness. c) Training process: Backpropagation is the core part of the training and consists of two phases: stimulus propagation and weight update.
The excitation propagation phase consists of two steps: 1. Forward propagation: the training input is sent to the network to obtain the excitation response; 2. Back propagation: the excitation response corresponds to the training input. The target output is evaluated to obtain a response error of the output layer and the hidden layer.
In the weight update phase, update is made for each synapse weight in two steps: 1. Multiply the input stimulus and response error to obtain a gradient of weights; 2. Multiply this gradient by a scale and invert it added to the weight; this ratio (percentage) will affect the speed and effect of the training process, thus becoming a "training factor." The two phases iterate until the network's response to the input reaches a satisfactory predetermined target range. In the below, we will enumerate the training steps with reference to figure 1.
1) Note that the output of each node is given by: wherew is the weight andb is the bias. The output layer is: wherew h andb h are the weight and bias of the last hidden layer, andw 0 andb 0 are the weight and bias of the output layer and the activation function φ is ReLU [25]. 2) Backpropagation is then run to minimize the validation error: where y 0 is the output of the output layer and t is the target output (ground truth). 3) For each node, the back-propagation error term is: We will then update the synaptic weights from a node in layer n to a node in layer n + 1, w =:w + ∆w where ∆w = −yδ h x and y is the learning rate. 5) Lastly, we calculate Mean Squared Errors: We will repeat the forward propagation and back propagation until the number of the epoch limit or early stopping criteria is reached. After training, the model runs on 15% test dataset to calculate precision, accuracy and F score.

C. Individual Worthiness Index (IWI)
After initializing and training the neural network for risk profiling, the output from the neural network, e.g. risk profile, as well as other parameters will enable the delivery of the Individual Worthiness Index (IWI) to the subject.  figure 2 is a table showing the ranges of the scores delivered at the end of processing by the present invention. Notice that 3 types of Metrics are presented and correlated to build a tridimensional 3D profile of the test subject or user. The first part is called Psychometrics and refers to the cognitive part of the performance test based on semantics.
• The primary and secondary scores of the Risk Profile are given by the distribution of the answer's datapoints during the survey in the 3 dimensions/bins. • The Risk Profile has 6 types resulting of 6 combinations of the 3 scores in pairs. • The Truthfulness is determined by the number of revalidation. • The truthfulness range is 83.33-100%. The second part is called Chrono-metrics and refers to the timing part (using a chronometer) of the performance test. It includes the following parameters and corresponding ranges: • The average user latency of a full test is estimated between 2,000 and 7,000 milliseconds. • The Thinking Type has 5 types.
• The Leadership score ranges between 0 and 120 when 6 levels of Education and Job are preset. The third part is called the Biometrics and refers to the result of facial emotion recognition part of the performance test.
• The Biometric Type has 4 types.
• The Confidence score in % is a score delivered by the mobile AI regarding its ability to recognize the facial emotion accurately.
c) The Individual Worthiness Index: Figure 3 are tables dedicated to explanatory analysis using the performance test results of our invention whether combined with Categorical Method or Continuous Method. The test results are divided into three parts as in part (a) of figure 2.
The first part is the Risk Profile (RP) which divided into 6 categories that have been attributed a coefficient according to the social valuation of the risk scores combination (primary + secondary). For example, +1 for Averse Dependent; +2 for Averse Taker; +3 for Dependent Averse; +4 for Dependent Taker; +5 for Taker Averse; +6 for Taker Dependent.
The second part is the Thinking Type (TT) which is divided into 5 categories according to the distribution of the population, variance, and standard deviation, and subject to changes in sub-population and special cohort testing. Assuming that population is normal or quasi normal, the coefficient has been attributed according to the underlying thinking process of the test subject, the length of the brain processing being considered an indicator of the maturity of the brain. As discussed in section II-B, we are deploying a 5 categories model. Mathematically, figure 4 shows how we define the five categories, with 0.5 to 1.5 seconds corresponding to the range of [−3σ + µ, −2σ + µ], 1.5 to 2.5 seconds corresponding to the range of [−2σ + µ, −σ + µ], 2.5 to 3.5 seconds corresponding to the range of [−σ + µ, σ + µ], 3.5 to 4.5 seconds corresponding to the range of [σ + µ, 2σ + µ], and 4.5 to 7 seconds corresponding to the range of [2σ + µ, 3σ + µ] where µ and σ are for mean and standard derivation.
The third part is the Biometric Type (BT) which can be divided into 4 categories assuming that is the average survey type between highest positive (Yes answers) emotion score and lowest negative (No answers) emotion score; or a coefficient can be attributed to the emotion in virtue of the social interest and communication value of showing average emotional reaction. For example in the categorical method: +1 is for Contempt or Disgust; +2 is for Anger or Fear; +3 is for Happiness or Sadness; +4 is for Surprise or Neutral. In the continuous method, a mapping is needed to convert and display the valence-arousal pair into the 4 categories system. A possible method is to divide the valence-arousal 2 dimensional plane into 4 regions representing the 4 categories as shown in right panel of figure 3.
As a result of having these scores calculated, the calculus of the final index (IWI) is greatly simplified by the following formula: Therefore, the index can range in theory between 0 and 120. Maximum is [6 × 5 × 4] assuming T=100% and C=100%. For practical reason, the proposed range in app is set between 20% and 100%.

D. MIMAMO-NET for video emotion recognition
MIMAMO Net, which is for abbreviation of Micro-Macro Motion Net, is the first to apply phase differences to valencearousal estimation and achieves significant gains. As shown in figure 5, it consists of two streams of convolutional neural networks to model the micro-motion and a recurrent neural network to model the macro-motion. Here the micro-motion and macro-motion are defined as emotion-related facial motions in the micro and macro time scales. The MIMAMO Net samples a sequence of snippets as the inputs and returns the valence-arousal estimations for each snippet, where a snippet includes one RGB image and 13 grayscale images. During inference, the sequence length is 64, which corresponds to about 2 seconds (macro-scale), and the snippet length is 13, which corresponds to about 400 milliseconds (micro-scale).
When the subject is presented with risk related questions of the SRTA, MIMAMO Net can analyze both the instantaneous facial movements in a very short time period and the consistent facial movements in a relatively long time period, which is superior to frame-based emotion recognition techniques. Based on the accurate estimations of valence-arousal scores and other inputs, our neural network can better analyze the risk tolerance and individual worthiness.

III. RELATED WORKS AND FUTURE RESEARCH DIRECTION
There are already many works on understanding the brain mechanisms and establishing a mathematical framework for brain-inspired machine learning systems [17], [26], [27], [28], [29]. But these works are still preliminary and stay at abstract and theoretical level and are not ready for direct real-life applications. However, it does not offer insight on how we should develop an artificial neural network model for the assessment of human characteristics such as Risk Profile, Thinking Type, Truthfulness, Leadership and Individual Worthiness while these are at the center of our model.
Our proposed model is taking a radically different approach by integrating well established neural network architecture [19], [20], [21], [22], state-of-the-art emotion recognition model MIMAMO-Net [18] and insights from neuropsychology [1], [2], [3], [4] to develop a system for the assessment of Individual Worthiness Index (IWI) and other parameters as discussed in section II-C. To the best of the authors' knowledge, this work is the first to propose a feasible approach to assess the risk profile of the test subjects based on how they react to a set of questions related to situational risk in a multidimensional personality questionnaire [3], [4] with the aid of the latest technology in video emotion recognition MIMAMO-Net, and in a completely automated manner.
On the other hand, there are extensive studies on creditworthiness. The studies of econometric for creditworthiness assessment in credit market [30], [31], [32] and the studies of targeting customers with a forecasting of their creditworthiness [33], [34], [35] focus on applying conventional psychology and financial methods to the assessment of creditworthiness such as using Time discounting and FICO score to forecast creditworthiness of customers in [31], without leveraging the latest advances in artificial intelligence. Our proposed model is inspired by traditional neuropsychology but also applies the most recent developments in neuromorphic intelligence to a novel machine learning framework, which is our main contribution. We are the first to design a labeled dataset using cognitive tags of 6 categories (the "question types" discussed in section II-B) to train our machine learning model. in traditional methods, after training, the machine learning model can be applied to data so that new unlabeled data can be presented to the model and a likely label can be estimated or predicted for the unseen new data. In our method, we are using our proprietary labeled dataset inspired by the TCI [4]. Our model was built to establish the relationship between specific neural activities and financial behaviors using personal attributes captured live and real-time and delivering the Risk Tolerance score and Individual Worthiness index of the subject whether in the form of creditworthiness index for borrowing money or trustworthiness index for conduct risk management. There are research areas waiting to be improved, for example, the distribution model for the Thinking Type (TT) could become more accurate after a comprehensive and in-depth study on the brain processing time or average test user latency. One possible suggestion is that a skew normal distribution is more accurate for some cultures or ages. This work intends to define a new direction for future research in Neurofinance using brain-inspired artificial intelligence.
IV. CONCLUSION In this paper, we have reviewed the different aspects of the background theory of human personality testing by the acclaimed TCI of Cloninger and described how that has motivated the construction of a novel neuropsychological performance test leveraging state-of-the-art artificial neural network methods and video emotion recognition model MIMAMO-Net. We have discussed the architecture and details of our proposed risk profiling system, the structure of the ANN, the major and minor question types, the training process, the calculation, range, and assumptions of the parameters including the Individual Worthiness Index and finally the application of MIMAMO-Net. We mentioned that although there are extensive works on understanding the mechanisms of the brain and establishing a mathematical framework for brain-inspired neural network, these models stay at theoretical and abstract level which makes real-life application quasi impossible. We also discussed related works on assessment of creditworthiness in studies addressing econometric and credit market. While they focus on conventional psychology and financial market theories, we intend to implement solutions operating with artificial intelligence and emotion recognition.
The theory presented in this paper is based on two of our patents, namely, "Method for Neuropsychological Performance Test", Frederic Andre Jumelle and Yat Wan Lui, Hong Kong short-term patent grant certificate No. HK30003521A, and "Method and System for Neuropsychological Performance Test", Frederic Andre Jumelle and Yat Wan Lui, international patent application No. PCT/CN2019/095325.


Finding the Differences in Capillaries of Taste Buds between Smokers and Non-Smokers Using the Convolutional Neural Networks

the Differences in Capillaries of Taste Buds between Smokers and Non-Smokers Using the Convolutional Networks. Featured Application: The aim of this work is to strengthen patient awareness and willingness to quit smoking by presenting them with the diagnostic results obtained using the capillaroscopy-based deep-learning artiﬁcial intelligence methods. Abstract: Taste function and condition may be a tool that exhibits a rapid deﬁcit to impress the subject with an objectively measured effect of smoking on his/her own body, because smokers exhibit signiﬁcantly lower taste sensitivity than non-smokers. This study proposed a visual method to measure capillaries of taste buds with capillaroscopy and classiﬁed the difference between smokers and non-smokers through convolutional neural networks (CNNs). The dataset was collected from 26 human subjects through the capillaroscopy with the low and high magniﬁcation directly; of which 13 were smokers, and the other 13 were non-smokers. The acquired dataset consisted of 2600 images. The results of gradient-weighted class activation mapping (grad-cam) enabled us to understand the difference in capillaries of taste buds between smokers and non-smokers. Through the results, it was found that CNNs gave us a good performance with 79% accuracy. It was discussed that there was a shortage of extracted features when the conventional methods such as structural similarity index (SSIM) and scale-invariant feature transform (SIFT) were used to classify.

Introduction
A lot of studies have confirmed that smokers exhibit significantly lower taste sensitivity than non-smokers [1]. The taste sensitivity level can be measured by electrogustometric (EGM) thresholds from various parts of the tongue (locus) [2]. After smoking cessation, thresholds of EGM decrease progressively and reach the taste sensitivity range of nonsmokers depending on locus and time. It is known that the recovery in the posterior loci is complete after 9 weeks, and the recovery in the dorsal loci is observed only after 2 months or more. Smoking cessation results in a rapid recovery of taste sensitivity among smokers, with different recovery times [1,3]. Thus, it is considered that the use of taste sensitivity could be explored as a motivation tool for smoking cessation.
The function of capillaries of taste buds is the exchange of material between the blood and tissue cells for gustatory sensitivity [4]. Tobacco users are generally unaware of the effects of tobacco on general health, oral health, etc. [3]. The effect on sensory perception and the demonstration of its deficit to the subject might reveal an actual threat the smoker may wish to avoid. The taste function and the taste condition may be a tool that exhibits a rapid deficit to impress the subject with an objectively measured effect of smoking on his/her own body because smokers exhibit significantly lower taste sensitivity than non-smokers [1,5,6]. From this point, the authors have an interest in whether there is a difference in capillaries of taste buds between smokers and non-smokers or not.
There have been two measuring methods of the taste sensitivity; for the whole mouth and for some mouth regions [2]. The evaluation for the whole mouth can be done with the use of colorless solutions of sweet, bitter, sour, and salt [7]. Then, the simplest regional test is EGM which was introduced in the clinical assessment of taste sensitivity during the 1950s [8]. In addition, contact endoscopy (CE) allows for both in vivo and in situ observations of pathology in the superficial layer of the tongue, nasal mucosa, vocal cords in the larynx-microsurgery and nasopharynx [9,10]. However, these methods are not easy for non-medical practitioners to use in daily life. Furthermore, images from CE are difficult for normal users to understand features related to diseases.
On the other hand, nailfold capillaroscopy is a non-invasive, inexpensive, and reproducible imaging technique to evaluate micro-circulations [11,12]. The capillaries are so abnormally altered that they can be seen with the naked eye, although magnification is usually required. And this method is used for the diagnosis of vascular dysfunction. That is the reason we used the nailfold capillaroscopy as the first testing. In nailfold videocapillaroscopy (NVC) qualitative assessment, scleroderma patterns can distinguish between primary and secondary Raynaud's phenomenon (RP) and represent an essential and reliable parameter for the early, as well as very early, diagnosis of systemic sclerosis (SSc) [13,14]. Thus, the recent introduction of capillaroscopic assessment into the new 2013 American College of Rheumatology (ACR)/European League Against Rheumatism (EU-LAR) classification criteria for SSc reflects its pivotal role in the diagnosis of the disease [15]. However, most studies using capillaroscopy focused on images of nailfold capillaries.
For the nailfold capillary analysis, there have been three approaches: manual, semiautomated, and automated segmentation [16]. The manual method depends on humanrecognizable features and requires experts to perform certain tasks, rendering it impractical for mass and widespread use [17]. The semi-automated method requires initial human intervention to mark the outer and inner parts of each capillary and requires data analysis, which may cause bias and mistakes [12]. The automated method combine a local threshold and the Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm to distinguish nailfold capillaries [18]. However, these methods are contaminated by noise and require post-processing such as morphological operations. Recently, convolutional neural networks (CNNs) have been proposed for semantic segmentation [19][20][21][22]. Although these methods have been effectively applied on medical image segmentation tasks, such as liver, pancreas, MRI, and multiorgan, no CNN has been proposed for capillaries of taste buds. Therefore, this study proposed a visual real-time method to measure capillaries of taste buds with capillaroscopy and classify the difference between smokers and non-smokers through CNNs. After that, this study confirmed which extracted featured points should be used to classify two classes. This paper is organized as follows: In Section 2, related works about details of capillaries microscope and the data choosing for the testing; And we considered the algorithms with handcrafted feature extraction, such as SSIM, SIFT and algorithms without handcrafted feature extraction, such as convolutional neural networks (CNNs). After that, we chose the best method for classifying capillaries images which is our proposed method; Section 3 provides the results of the experiment settings and results; Section 4 provides the discussion; lastly, Section 5 gives the summary and conclusions of this work.

Methods
This section describes the capillaries microscope which is the small and simple device system to capture the capillaries images. Subsequently, we considered when choosing the nailfold capillaries or the blood vessels on tongue and give the reason of our choice. Our proposed method was implemented after testing two methods: the algorithms with handcrafted feature extraction, such as SSIM, SIFT and algorithms without handcrafted feature extraction, such as convolutional neural networks (CNNs) on the applied data and chose the best. Table 1 shows pros and cons of different capillaroscopic devices [15]. Different devices can be used to perform capillaroscopy, as it is an in vivo imaging investigation that consists of a magnified view of the structural aspects of the microcirculation. The commercially available tools range from the wide-field microscope and videocapillaroscope to smartphone devices and are characterized by different portability, magnification, and costs. the USB-connected-typed microscopy is chosen because the necessary magnification for capillaries of taste buds in this study might be larger than 300× and the device is simple enough for non-medical practitioners to use.  Figure 1a shows an overview of experimental environment and system consisted of the microscope with the low and high magnification. Table 2 [23] describes the specification of the experimental system of the microscopy with the low (100×) and high (410×) magnification. They are connected by cables and converter set to convert NTSC analogue images to uncompressed digital images for the real-time displaying on a monitor and saves them as video and still image. The microscope is a GOKO Bscan-Z (GOKO Imaging Devices, Kanagawa, Japan) with a vertical, cylindrical body and compact size. The body weight of the microscope is 150 g when the focus cap is attached; the size of the focus cap is diameter × length = φ45 × 10 mm, and the body weight is 10 g. The diameter of the stand unit is φ = 120, the camera holder is φ = 58, and the height of the stand unit is 72.5 mm. The weight of the stand unit is 250 g. Because of the light body weight, it is easy to carry.

Experimental Environment and System
The range of the x-axis movement for the stand unit is 10 mm from the left to the right side, and that of the y-axis is 10 mm from the upper to the lower positions, as shown in Figure 1a. Although there is no range of the z-axis, a human subject can regulate his or her finger position. The real-time image of capillaries is projected onto a 14-inch monitor under a range of magnification from low to high magnification [11]. The user can spin the black middle part around to zoom out, zoom in and focus without any change of lens.

Data Acquisition
There is no opened data set to classify the difference of capillary distribution on nailfold and tongue between smokers and non-smokers in the world as we surveyed. Thus, it is necessary to measure the capillaries of the nailfold and tongue surface and make the data set directly. There are two measuring parts which had the contact between the tobacco and the human body while smoking. Figure 1b shows a description to measure capillaries of nailfold and taste bud by using the microscope. According to the measuring part, it is possible to attach or detach the microscope and stand unit.
Twenty-six human subjects (height: 172.2 ± 6.3 cm, weight: 68.3 ± 6.2 kg, Age: 24 ± 9 years old) were employed: 13 smokers in the university with the smoking careers of 5-10 years (class 1), and thirteen non-smokers who were university students without smoking careers (class 2). No subject from either class reported any health problem or a history of neurological disease, drug abuse, alcoholism, and medical constrains that might influence the experimental result. No major difference in body mass index (BMI) was observed between the two classes. Figure 2 shows the location of the 9 recording loci on the surface of the tongue. The nine tongue loci were defined: Tip of the tongue middle (T), right (Tr) and left (Tl) where the density of fungiform papillae is highest, Dorsal right and left (Dr and Dl) where the density of fungiform papillae is lowest, Edge right and left (Er and El) on the foliate papillae, and Posterior right and left (Pr and Pl) just anterior to the circumvallate papillae.
After the authors explained the objectives and procedures of this study, the informed consent was obtained from all subjects. The experimental procedures were performed under the Declaration of Helsinki.

Data Training with Compound Model Scaling
Four different CNNs, with different different architectures (VGG [24], DenseNet [25], ResNet [26], and EfficientNet [27]) were trained on measured dataset. All training is performed using the Python programming language (version 3.8) on a workstation running on Jupyter Notebook with one Nvidia GeForce RTX 2080ti graphic cards (11 GB of RAM).

Model Scaling
All CNN [28] architectures follow the same general design principles of successively applying convolutional layers to the input, periodically downsampling the spatial dimensions while increasing the number of feature maps. While the classic network architectures (LeNet [29], AlexNet [30], and VGG) are comprised simply of stacked convolutional layers, modern architectures (Inception [31], ResNet, ResNeXt [32], DenseNet, and EfficientNet) explore new and innovative ways for constructing convolutional layers in a way which allows for more efficient learning. All these architectures are based on a repeatable unit which is used throughout the network.
For improving the performance, there are a lot of methods to scale up a CNN for different resource constraints. ResNet can be scaled up by regulating network depth which indicates layers, while WideResNet and MobileNet can be scaled by network width which indicates channels. It is also well-recognized that bigger input image size means bigger resolution that helps increasing accuracy with the overhead of more FLOPS (float point operations per second) that is a measure of computer performance. However, there is still the limitation to scale only one of the three dimensions (depth, width, and image resolution). That means that we should use only one scaling factor [27].

Compound Model Scaling
This study tries to use the CNN with compound model scaling, which means to use three scaling factors at the same time, and then evaluate the effect of compound model scaling on classification. The CNN layer i can be expressed as a function: Y i = F i (X i ), where F i is the operator, Y i is output tensor, X i is input tensor, with tensor shape H i , W i , C i 1 , where H i and W i are spatial dimensions and C i is the channel dimension [27,28]. A CNN N can be represented by a list of composed layers: N = F k · · · F 2 F 1 (X 1 ) = j=1·cot ·k F X 1 j , where means the connection between consecutive layers. CNN layers are often partitioned into multiple stages and all layers in each stage share the same architecture. Thus, the CNN can be defined as [27]: where F L i i denotes F i of layer i is repeated L i times in stage i, H i , W i , C i denotes the shape of input tensor X of layer i.
When it is possible that all layers should be scaled with constant ratio, the problem for model scaling can be an optimization problem, which is to maximize the model accuracy for any given resource constraints. This is achieved by the following algorithm: maximize d,w,r Accuracy (N(d, w, r)) where w, d, r are coefficients for scaling network width, depth, and resolution, andF i ,L i , H i ,Ŵ i ,Ĉ i are predefined parameters in baseline network.
The compound scaling method uses a compound coefficient φ to scale network width, depth, and resolution: depth: where α, β, γ are constants that can be determined by a small grid search. From the start with the baseline EfficientNet-B0, the compound scaling method is applied to scale it up with two steps. At first, it performs the search for α, β, γ through Equations (2) and (3) after fix φ = 1. The values for EfficientNet-B0 are α = 1.2, β = 1.1, and γ = 1.15 under the condition of α · β 2 · γ 2 ≈ 2. Then, scale up baseline network with different φ through Equation (3) after fix α, β, γ, to gain EfficientNet-B1 to B7. Figure 3 showed an example of the capillary image of nailfold (a) and taste bud (b) between smokers and non-smokers. The capillary image of the nailfold cannot give us the big difference between smokers and non-smokers. During daily activities such as washing, and doing something with hands, etc., the attached chemicals on the skin surface are eliminated.

Example of Dataset
On the other hand, the size of taste buds on the tongue for smokers seems to be smaller than that for non-smokers, thus, it looks like the condition of capillary distribution is different. The results of measurement as shown in Figure 3b was the dorsal for the middle of tongue (Dr and Dl) as shown in Figure 2.  Figure 4 shows the results of the training and loss for four applied models of CNNs: EfficientNet, VGG16, ResNet50, and DenseNet121. Although the results through VGG16 were not trained well, others showed good performance with nearly 80% accuracy. Our dataset includes 2 classed: smoker and non-smoker with 220 images per one class. We used rate (60/20/20) to split our dataset: 132 images in train set, 44 images in test set and validation set. Because the number of human subjects was small, the function of ImageDataGenerator Class in Keras was applied for increasing the number of images. It is well-known that CNN is only relevant when they are trained with a huge amount of data. In order to make the most of our few training examples, we can augment them via several random transformations. This helps to prevent overfitting and improves the generalization of the model. In Keras, this can be done by the class keras.preprocessing.image. ImageDataGenerator. The total number of images was 2600: 80% was used for the training and validation, and the left 20% was for the testing. We implemented experiment on four CNNs models: VGG, ResNet, DenseNet and EfficientNet with our dataset. And the result was showed on Tables 3 and 4.  Table 3 represents the trainable and non-trainable parameters of four CNN models: EfficientNet-B1, VGG16, Resnet50 and DenseNet121. These results show that the total number of parameters of EfficientNet-B1 is the least of the four models, and is equal to the one fourth of the number of parameters of ResNet and half of VGG16 's. However, EfficientNet-B1 is the most effective, thus EfficientNet-B1 is considered to be the good choice in our data.  Table 4 represents the results of prediction for four different algorithms of CNNs: EfficientNet, VGG16, ResNet50 and DenseNet121. It is shown that the results of prediction for EfficientNet indicates the best performance among four different CNNs. Compared to the result of algorithms with handcrafted feature extraction such as SSIM and SIFT, it was found that most of CNN algorithms without handcrafted feature extraction worked better for the image processing of capillaries, although VGG16 showed the failure.

Results of the Class Activation Map
The Class Activation Map (CAM) helps in the analysis of understanding as to what region of an input image influence the CNN's output prediction. The technique relies on the heat map representation which highlights pixels of the image that triggers the model to associate the image with a particular class. Figure 5 compares the CAM images for three representative human subjects. Images are randomly picked from CNN validation set. The left image indicates the original image, the middle one indicates the results of CAM, and the right indicates the overlapped image with the original and CAM images. The results show that the CNNs tend to predict the nonsmoker class by finding the large capillaries and white-colored taste buds. Additionally, it was also confirmed that the CNN model tended to predict the smoker class with the small capillaries and white-colored taste buds without capillaries [33].

Discussion
We would like to analyze the CNNs model which is the best choice for classifying image data of capillaries. In the case, the algorithms with handcrafted feature extraction get the good result for capillaries images, there is no need to use the CNNs model without handcrafted feature extraction. However, the result of algorithms with handcrafted feature extraction gave us the bad score which showed in Figures 6 and 7. Figure 6 shows the results of the structural similarity index (SSIM) score [34] between two different tongue images for two smokers (a), and between two images for the smoker and non-smoker (b). SSIM score 1.00 represents that two images are same, and the 0.0 of that represents that those are fully different. The SSIM is a perceptual metric that quantifies image quality degradation based on the change in structural information, while also incorporating important perceptual phenomena, including both luminance masking and contrast masking terms. The difference to other methods, such as mean squared error (MSE) or Peak Signal-to-noise ratio (PSNR), is that these approaches estimate absolute errors. Structural information that pixels have strong inter-dependencies especially when they are spatially close. Through the results, it was confirmed that there were some difficulties in classifying the difference of tongue bud between two images of the smoker and the non-smoker through the SSIM score. Figure 7 shows the results of the scale-invariant feature transform (SIFT) score [35] between two different tongue images for two smokers (a), and between two images for the smoker and non-smoker (b). The 100.0% of SIFT score represents that two images are same, and the 0.0% of that represents that those are fully different. SIFT keypoints of targets are first extracted from a set of reference images and stored in a database. A target is recognized in a new image by individually comparing each feature from the new image to this database and finding candidate matching features based on Euclidean distance of their feature vectors. Through the full set of matches, subsets of keypoints that agree on the target and its location, scale, and orientation in the new image are identified to filter out good matches. Through the results, it was confirmed that there were some difficulties in classifying the difference of tongue bud between two images of the smoker and the non-smoker through the SIFT score, because there was the shortage of good match points.
As a result, it was confirmed that there were some difficulties in classifying the difference of tongue bud between two images of the smoker and the non-smoker through the SSIM and SIFT score, because there was the shortage of good match points. It is necessary to have a lot of parameters when the feature from the image of capillaries is extracted. Thus, it is considered that algorithms with handcrafted feature extraction are not good for the image processing of capillaries.

Conclusions
In this study, some CNN models such as EfficientNet, ResNet, and DenseNet enabled us to train the data set of capillaries of taste buds, although two conventional methods such as structural similarity index (SSIM) and scale-invariant feature transform (SIFT) did not work well because of a shortage of extracted featured points. The results of class activation map (CAM) enabled us to understand a difference between smokers and non-smokers because CAM allowed us to know what were extracted featured points through CNNs.
The CNNs model without handcrafted feature extraction, especially EfficientNet with compound model scaling, proved the good performance to detect difference between smokers and non-smokers via tongue capillaries images which captured by the microscope, compared with conventional methods with handcrafted feature extraction. Then, the CAM enabled us to classify a difference in capillaries of taste buds between smokers and nonsmokers. Our system with the bigger data, we can apply in hospital to support doctor in diagnosing disease, and it can be used daily for self-checking health and detecting the abnormal point of capillaries.


Axiomatic Design applied to CRUD matrix in Information Systems

In Information Systems (IS), the search for a good design is still a relevant issue. In IS, two sub-systems coexist, applications and data. To articulate these sub-systems, some authors opt for the CRUD matrix. Similarly, Axiomatic Design (AD) theory studies how functional requirements are related to design parameters using a design matrix. In this paper, the application sub-system corresponds to the functional requirements, the data sub-system to the design parameters, and the CRUD matrix to the design matrix. The goal of the CRUD matrix is to maintain the independence of its items to minimize the information content of the design. Similarly, AD uses the design matrix to define a good design. This work aims to develop a theory to create object-oriented elements based on the CRUD matrix aligned with the business strategy.

Introduction
Axiomatic Design (AD) theory is a powerful systems design methodology based on matrix methods that analyze the pipeline from customer needs to process variables. For a thorough explanation see [11], [12], [13], [14].
Information Systems (IS) can be seen as a simple input/output mechanism that collects, stores, and distributes information. However, new legislation, new entities, and new interactions create new challenges in information management. A significant effort is given to align the business strategy with the technology, i.e., the process in which a business organization uses information technology to achieve business objectives [8].
Despite the high number of methodologies [16] in IS, the search for a good design is still a relevant issue, and the concept of a good design is still a subject of controversy. However, the essential SI elements remain constant. BSP (Business Systems Planning) [7] presented four essential elements for Information Systems Planning, coined as the Iron Cross: organization (people), applications (software), data, and technological systems (hardware).
To articulate the elements, applications, and data, we opt for the CRUD matrix. CRUD, achromic of <create, read, update, delete>, means the four basic operations possible on a data value. In SQL it corresponds to <insert, select, update, delete>. CRUD matrix was developed by IBM [7] and popularized by James Martin [9] in his book 'Managing the Database Environment'. The CRUD matrix is an essential tool to perform diagnostics in software. Unfortunately, there are several practical examples with the CRUD matrix that corresponds to coupled designs. In [1] the authors use the CRUD matrix to know the current state (as-is) and to model the improved future state (to-be). In the current state, the CRUD matrix shows a poor design. After swapping rows and columns, cells are aggregated into macro-cells in a decoupled matrix in the improved state.
The CRUD matrix is still beneficial when integrated with more recent tools, like UML (Unified Modeling Language) [5]. UML in analysis and design allows the fulfillment of the system requirements with object-oriented design and relational databases models.
This work aims to add to CRUD the scientific support given by the Axiomatic Design theory. The design matrix brings value to the project implementation in IS. This work contributes to developing a theory to create object-oriented elements based on the CRUD matrix and aligned with the business strategy.
In a similar approach in enterprise architectures [2], the authors use data as FRs and applications as DPs. In this work, we follow the approach presented in chapter 5, Suh [13], where FRs correspond to applications and DPs to data.
The paper is organized into three additional sections. Section 2 introduces the CRUD matrix in information systems. Section 3 details the proposed CRUD design. Finally, in Section 4, conclusions are drawn.

CRUD Matrix
Enterprise Architecture is usually defined by the three-layers model [8]: business/process architecture, information system architecture, and technological architecture, as shown in Figure 1. In Enterprise Architecture, many synonyms depend on the layer, methodology, framework, or tool used, so it is crucial to find a synthesis with a reduced number of names.
In Cavique et al. [3], only three essential elements were chosen: the actors, the activities, and the data. Some of the synonyms are as follows: (i) actor is a synonym of lines-of-responsibility, (ii) activities is about applications, tasks, uses-cases, or operational-processes, (iii) data is a synonym of classes or informational entities. In the following examples, we will use actors (α, β), activities (A, B, C), and data (X, Y, Z, W). This version of Enterprise Architecture with three layers includes: -Business / Processes Architecture: where processes are made up of activities (A, B, C) and managed by human actors (α, β); -Information System Architecture: with two different software groups, the data (X, Y, Z, W) and the applications (A, B, C); -Technological Infrastructure Architecture: consists of hardware components and essential software (operating systems and database management systems).
The CRUD matrix plays a vital role in the Information System Architecture by combining data between applications (A, B, C) and data (X, Y, Z, W). In a relational data model approach, data (X, Y, Z, W) correspond to tables of a database and the applications (A, B, C) to operations that maintain the data sets.
To develop straightforward projects, the authors Pereira and Sousa [10] and Vasconcelos [17] presented a set of three heuristic rules that guarantee the alignment of the information system. The heuristic rules can be summarized as follows: #1 Each processing activity of the business process is automated by a single application; #2 The data items must support the processes activities of the business processes; #3 Each data item is managed (CUD) by a single application in the CRUD matrix.
In this work, rule #3 emphasizes developing a more managed CRUD matrix since data items updated by different applications are harder to manage. To ensure rule #3, Figure 2 shows the CRUD matrix with CRUD counters that validate possible inconsistencies [4]. Counter 1211 indicates that there are 1 Create, 2 Reads, 1 Update, and 1 Delete. There must be a single application that performs Create, Update and Delete, (CUD), and there may be multiple applications with the Read operator.
Preferably, the CRUD counters should be 1N11, i.e., a unique Create, Update and Delete and multiple Read operators. As previously stated, Read operations correspond to the SQL Select and usually involve many tables to obtain new information. The multiple Reads are allowed and do not produce concurrence; therefore, they will not be considered in the rest of our analysis.
The CRUD approach intends to obtain a close view of the system to avoid coupled sub-systems by finding a matrix with the main diagonal filled with the least number of elements.
With the three-layer model, Enterprise Architecture presents similarities with Axiomatic Design, which uses three mapping zones. Firstly, Axiomatic Design studies the transformation of the customer attributes into functional requirements, which corresponds to the transformation of activities into applications. Then, Axiomatic Design maps the functional requirements with a set of design parameters using the design matrix, corresponding to the CRUD matrix matching, between applications and data.

Proposed CRUD design
This section focuses on how Axiomatic Design can improve and better justify the CRUD structure in information systems projects. After clarifying the design matrix, the transformation to objected-oriented design is shown. Finally, we discuss the similarities between AD and IS rules.  [15]. In the design matrix, the cells with operators CRUD are replaced by an X. Such as the axioms of AD, the goal of the CRUD matrix is to maintain the independence of the functional requirements, which usually correspond to minimize the information content of the design.
When there are more DPs than FRs, the design is either a redundant design or a coupled design, as stated by Theorem 3 [11]. Gonçalves-Coelho et al. [6] claim that redundancy designs belong to particular kinds of designs and propose seven theorems. Figure 2 shows a redundant design, as application C operates on tables Z and W. According to AD, the design equation is by Equation 1: However, an application makes a change on a table and then on another table. Therefore, the redundant design of application C and data (Z, W) is equivalent to two different states of the same FR, that can be fulfilled by DP_Z or DP_W. Equation 2 shows the mentioned redundant design: shows the first state of operation on Equation 3.
And the other state by Equation 4.
Application C can split in C1 when addressing table Z and C2 when addressing table W. It makes all applications belong to the same level of decomposing. Therefore, the design matrix can turn into a square matrix by the decomposition of FR-C as shown in Figure 3, using a square sub-matrix, where FR-C is decomposed into FR-C1 and FR-C2.  Figure 3. Decomposition of FR-C

Object-oriented design
Object-oriented (OO) techniques are some of the most potent paradigms widely used in computer science in software design. An object is a bundle of data associated with a set of operations that act on that data. The object-oriented paradigm supports four crucial features: abstraction, encapsulation, inheritance, and modularity.
Object-oriented techniques encapsulate data and the operations that manipulate that data in a specific object. OO paradigm allows a higher level of abstraction, focusing only on those characteristics available by the object's interface and omitting the details of data and operations. The system's modularity is also achieved by decomposing each problem into loosely coupled intra-objects so that internal changes to one object do not affect another one.
In this work, the data items can be enriched with the OO approach. Four objects {A.X, B.Y, C1.Z, C2.W} can be found in the design matrix. Each cross ('X') in the design matrix [A], in Figure 3, corresponds to an object. Figure 4 shows the object {A.X} that associates the data DF-X with the operations/application FR-A.
Object A.X data DP-X operations application FR-A The data schema presents in Figure 1 can be enriched with the functional requirements. The result is presented in Figure 5, where relations link the four objects. The object-oriented design supported by AD reinforces the OO paradigm to incorporate the advantages of modularity and reuse, providing savings in the software project development.

Discussion of the two approaches
Since there are similarities between IS and AD, some relationships between the axioms of Axiomatic Design and the heuristic rules in Information Systems are detailed in Table 1. Using AD theorems, a new rule, number 4, is proposed. Rule #1, a single application that automates each process activity, also corresponds to the mapping of Customer domain (process activities) and Functional domain (applications).
Rule #2, the data items must support the process activities of the business processes, or in order words, data items must support the applications.
Rule #3, each data item is managed by a single application in the CRUD matrix, as already seen, almost corresponds to the design matrix. In the transformation, two additional issues should be mentioned.
On the one hand, AD imposes a square design matrix, according to Theorem 4 [11]. Theorem 4 states that the number of DPs is equal to the number of FRs in an ideal design, so the CRUD should be square. To overcome this issue, we add rule #4, where the number of applications is equal to the number of data items.
On the other hand, using the CRUD counters, the CRUD matrix only allows uncoupled designs, showing decoupled matrices are not adequate for IS. This statement agrees with Theorem Soft 1 [13] that uncoupled software or hardware systems can be operated without precise knowledge of the design elements (i.e., modules) if the design is uncoupled.
Heuristic rules #1 and #2 ensure the vertical alignment with the business processes, and rule #3 ensure the horizontal alignment between applications and data.

Conclusions
Business strategy alignment with Information Systems and Information Technology is a longstanding problem because of the increasing complexity of the designs. In Information Systems (IS), the horizontal alignment of applications and data items is possible with the CRUD matrix.
However, regarding IS, the concept of a good design is still a subject of controversy. Adopting Axiomatic Design theory validates what a good design can be in information systems. AD helps to justify and simplify the design of the CRUD matrix of IS projects. We propose to adopt rule #4 that makes the number of applications equal to the number of data items, to create object-oriented modules.